Patent Publication Number: US-2022230075-A1

Title: Curating knowledge of a knowledge database

Description:
CROSS REFERENCE TO RELATED PATENTS 
     The present U.S. Utility Patent Application claims priority pursuant to 35 U.S.C. § 120 as a continuation in part of U.S. Utility application Ser. No. 16/598,142, entitled “REMEDYING DEFECTIVE KNOWLEDGE OF A KNOWLEDGE DATABASE” filed Oct. 10, 2019, issuing Mar. 29, 2022 as U.S. Pat. No. 11,288,583, which claims priority pursuant to  35  U.S.C. § 119(e) to U.S. Provisional Application No. 62/752,399, entitled “IDENTIFYING BEST PRACTICES BASED ON PLANNING AND OUTCOME CONTENT,” filed Oct. 30, 2018, all of which are hereby incorporated herein by reference in their entirety and made part of the present U.S. Utility Patent Application for all purposes. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT—NOT APPLICABLE 
     INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC—NOT APPLICABLE 
     BACKGROUND OF THE INVENTION 
     Technical Field of the Invention 
     This invention relates to computing systems and more particularly to generating data representations of data and analyzing the data utilizing the data representations. 
     Description of Related Art 
     It is known that data is stored in information systems, such as files containing text. It is often difficult to produce useful information from this stored data due to many factors. The factors include the volume of available data, accuracy of the data, and variances in how text is interpreted to express knowledge. For example, many languages and regional dialects utilize the same or similar words to represent different concepts. 
     Computers are known to utilize pattern recognition techniques and apply statistical reasoning to process text to express an interpretation in an attempt to overcome ambiguities inherent in words. One pattern recognition technique includes matching a word pattern of a query to a word pattern of the stored data to find an explicit textual answer. Another pattern recognition technique classifies words into major grammatical types such as functional words, nouns, adjectives, verbs and adverbs. Grammar based techniques then utilize these grammatical types to study how words should be distributed within a string of words to form a properly constructed grammatical sentence where each word is forced to support a grammatical operation without necessarily identifying what the word is actually trying to describe. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S) 
         FIG. 1  is a schematic block diagram of an embodiment of a computing system in accordance with the present invention; 
         FIG. 2  is a schematic block diagram of an embodiment of various servers of a computing system in accordance with the present invention; 
         FIG. 3  is a schematic block diagram of an embodiment of various devices of a computing system in accordance with the present invention; 
         FIGS. 4A and 4B  are schematic block diagrams of another embodiment of a computing system in accordance with the present invention; 
         FIG. 4C  is a logic diagram of an embodiment of a method for interpreting content to produce a response to a query within a computing system in accordance with the present invention; 
         FIG. 5A  is a schematic block diagram of an embodiment of a collections module of a computing system in accordance with the present invention; 
         FIG. 5B  is a logic diagram of an embodiment of a method for obtaining content within a computing system in accordance with the present invention; 
         FIG. 5C  is a schematic block diagram of an embodiment of a query module of a computing system in accordance with the present invention; 
         FIG. 5D  is a logic diagram of an embodiment of a method for providing a response to a query within a computing system in accordance with the present invention; 
         FIG. 5E  is a schematic block diagram of an embodiment of an identigen entigen intelligence (IEI) module of a computing system in accordance with the present invention; 
         FIG. 5F  is a logic diagram of an embodiment of a method for analyzing content within a computing system in accordance with the present invention; 
         FIG. 6A  is a schematic block diagram of an embodiment of an element identification module and an interpretation module of a computing system in accordance with the present invention; 
         FIG. 6B  is a logic diagram of an embodiment of a method for interpreting information within a computing system in accordance with the present invention; 
         FIG. 6C  is a schematic block diagram of an embodiment of an answer resolution module of a computing system in accordance with the present invention; 
         FIG. 6D  is a logic diagram of an embodiment of a method for producing an answer within a computing system in accordance with the present invention; 
         FIG. 7A  is an information flow diagram for interpreting information within a computing system in accordance with the present invention; 
         FIG. 7B  is a relationship block diagram illustrating an embodiment of relationships between things and representations of things within a computing system in accordance with the present invention; 
         FIG. 7C  is a diagram of an embodiment of a synonym words table within a computing system in accordance with the present invention; 
         FIG. 7D  is a diagram of an embodiment of a polysemous words table within a computing system in accordance with the present invention; 
         FIG. 7E  is a diagram of an embodiment of transforming words into groupings within a computing system in accordance with the present invention; 
         FIG. 8A  is a data flow diagram for accumulating knowledge within a computing system in accordance with the present invention; 
         FIG. 8B  is a diagram of an embodiment of a groupings table within a computing system in accordance with the present invention; 
         FIG. 8C  is a data flow diagram for answering questions utilizing accumulated knowledge within a computing system in accordance with the present invention; 
         FIG. 8D  is a data flow diagram for answering questions utilizing interference within a computing system in accordance with the present invention; 
         FIG. 8E  is a relationship block diagram illustrating another embodiment of relationships between things and representations of things within a computing system in accordance with the present invention; 
         FIGS. 8F and 8G  are schematic block diagrams of another embodiment of a computing system in accordance with the present invention; 
         FIG. 8H  is a logic diagram of an embodiment of a method for processing content to produce knowledge within a computing system in accordance with the present invention; 
         FIGS. 8J and 8K  are schematic block diagrams another embodiment of a computing system in accordance with the present invention; 
         FIG. 8L  is a logic diagram of an embodiment of a method for generating a query response to a query within a computing system in accordance with the present invention; 
         FIG. 9A  is a schematic block diagram of another embodiment of a computing system in accordance with the present invention; 
         FIG. 9B  is a logic diagram of an embodiment of a method for identifying best practices within a computing system in accordance with the present invention; 
         FIG. 10A  is a schematic block diagram of another embodiment of a computing system in accordance with the present invention; 
         FIG. 10B  is a logic diagram of an embodiment of a method for providing a query dashboard within a computing system in accordance with the present invention; 
         FIG. 11A  is a schematic block diagram of another embodiment of a computing system in accordance with the present invention; 
         FIG. 11B  is a logic diagram of an embodiment of a method for processing a suspended query within a computing system in accordance with the present invention; 
         FIGS. 12A-12D  are schematic block diagrams of another embodiment of a computing system illustrating an embodiment of a method for collecting content to remedy potentially incomplete and/or incorrect knowledge within a computing system in accordance with the present invention; 
         FIG. 13A  is a schematic block diagram of another embodiment of a computing system in accordance with the present invention; 
         FIG. 13B  is a logic diagram of an embodiment of a method for curating new knowledge within a computing system in accordance with the present invention; 
         FIGS. 13C-13F  are schematic block diagrams of another embodiment of a computing system illustrating an embodiment of a method for curating knowledge within a computing system in accordance with the present invention; 
         FIGS. 14A and 14B  are schematic block diagrams of another embodiment of a computing system in accordance with the present invention; 
         FIG. 14C  is a logic diagram of an embodiment of a method for processing content to produce knowledge utilizing a confidence level within a computing system in accordance with the present invention; 
         FIGS. 15A and 15B  are schematic block diagrams another embodiment of a computing system in accordance with the present invention; 
         FIG. 15C  is a logic diagram of an embodiment of a method for generating a query response to a query utilizing groupings within a knowledge base within a computing system in accordance with the present invention; 
         FIG. 16A  is a schematic block diagram of another embodiment of a computing system in accordance with the present invention; 
         FIG. 16B  is a logic diagram of an embodiment of a method for generating a query response to a query utilizing a confidence level within a computing system in accordance with the present invention; 
         FIGS. 17A and 17B  are schematic block diagrams of another embodiment of a computing system in accordance with the present invention; 
         FIG. 17C  is a logic diagram of an embodiment of a method for processing content to produce knowledge utilizing a certainty level within a computing system in accordance with the present invention; 
         FIGS. 18A and 18B  are schematic block diagrams of another embodiment of a computing system in accordance with the present invention; 
         FIG. 18C  is a logic diagram of an embodiment of a method for processing content to produce knowledge within a computing system in accordance with the present invention; 
         FIGS. 19A and 19B  are schematic block diagrams another embodiment of a computing system in accordance with the present invention; and  FIG. 19C  is a logic diagram of an embodiment of a method for generating a query response to a query within a computing system in accordance with the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG. 1  is a schematic block diagram of an embodiment of a computing system  10  that includes a plurality of user devices  12 - 1  through  12 -N, a plurality of wireless user devices  14 - 1  through  14 -N, a plurality of content sources  16 - 1  through  16 -N, a plurality of transactional servers  18 - 1  through  18 -N, a plurality of artificial intelligence (AI) servers  20 - 1  through  20 -N, and a core network  24 . The core network  24  includes at least one of the Internet, a public radio access network (RAN), and any private network. Hereafter, the computing system  10  may be interchangeably referred to as a data network, a data communication network, a system, a communication system, and a data communication system. Hereafter, the user device and the wireless user device may be interchangeably referred to as user devices, and each of the transactional servers and the AI servers may be interchangeably referred to as servers. 
     Each user device, wireless user device, transactional server, and AI server includes a computing device that includes a computing core. In general, a computing device is any electronic device that can communicate data, process data, and/or store data. A further generality of a computing device is that it includes one or more of a central processing unit (CPU), a memory system, a sensor (e.g., internal or external), user input/output interfaces, peripheral device interfaces, communication elements, and an interconnecting bus structure. 
     As further specific examples, each of the computing devices may be a portable computing device and/or a fixed computing device. A portable computing device may be an embedded controller, a smart sensor, a smart pill, a social networking device, a gaming device, a cell phone, a smart phone, a robot, a personal digital assistant, a digital music player, a digital video player, a laptop computer, a handheld computer, a tablet, a video game controller, an engine controller, a vehicular controller, an aircraft controller, a maritime vessel controller, and/or any other portable device that includes a computing core. A fixed computing device may be security camera, a sensor device, a household appliance, a machine, a robot, an embedded controller, a personal computer (PC), a computer server, a cable set-top box, a satellite receiver, a television set, a printer, a fax machine, home entertainment equipment, a camera controller, a video game console, a critical infrastructure controller, and/or any type of home or office computing equipment that includes a computing core. An embodiment of the various servers is discussed in greater detail with reference to  FIG. 2 . An embodiment of the various devices is discussed in greater detail with reference to  FIG. 3 . 
     Each of the content sources  16 - 1  through  16 -N includes any source of content, where the content includes one or more of data files, a data stream, a tech stream, a text file, an audio stream, an audio file, a video stream, a video file, etc. Examples of the content sources include a weather service, a multi-language online dictionary, a fact server, a big data storage system, the Internet, social media systems, an email server, a news server, a schedule server, a traffic monitor, a security camera system, audio monitoring equipment, an information server, a service provider, a data aggregator, and airline traffic server, a shipping and logistics server, a banking server, a financial transaction server, etc. Alternatively, or in addition to, one or more of the various user devices may provide content. For example, a wireless user device may provide content (e.g., issued as a content message) when the wireless user device is able to capture data (e.g., text input, sensor input, etc.). 
     Generally, an embodiment of this invention presents solutions where the computing system  10  supports the generation and utilization of knowledge extracted from content. For example, the AI servers  20 - 1  through  20 -N ingest content from the content sources  16 - 1  through  16 -N by receiving, via the core network  24  content messages  28 - 1  through  28 -N as AI messages  32 - 1  through  32 -N, extract the knowledge from the ingested content, and interact with the various user devices to utilize the extracted knowledge by facilitating the issuing, via the core network  24 , user messages  22 - 1  through  22 -N to the user devices  12 - 1  through  12 -N and wireless signals  26 - 1  through  26 -N to the wireless user devices  14 - 1  through  14 -N. 
     Each content message  28 - 1  through  28 -N includes a content request (e.g., requesting content related to a topic, content type, content timing, one or more domains, etc.) or a content response, where the content response includes real-time or static content such as one or more of dictionary information, facts, non-facts, weather information, sensor data, news information, blog information, social media content, user daily activity schedules, traffic conditions, community event schedules, school schedules, user schedules airline records, shipping records, logistics records, banking records, census information, global financial history information, etc. Each AI message  32 - 1  through  32 -N includes one or more of content messages, user messages (e.g., a query request, a query response that includes an answer to a query request), and transaction messages (e.g., transaction information, requests and responses related to transactions). Each user message  22 - 1  through  22 -N includes one or more of a query request, a query response, a trigger request, a trigger response, a content collection, control information, software information, configuration information, security information, routing information, addressing information, presence information, analytics information, protocol information, all types of media, sensor data, statistical data, user data, error messages, etc. 
     When utilizing a wireless signal capability of the core network  24 , each of the wireless user devices  14 - 1  through  14 -N encodes/decodes data and/or information messages (e.g., user messages such as user messages  22 - 1  through  22 -N) in accordance with one or more wireless standards for local wireless data signals (e.g., Wi-Fi, Bluetooth, ZigBee) and/or for wide area wireless data signals (e.g., 2G, 3G, 4G, 5G, satellite, point-to-point, etc.) to produce wireless signals  26 - 1  through  26 -N. Having encoded/decoded the data and/or information messages, the wireless user devices  14 - 1  through  14 -N and/receive the wireless signals to/from the wireless capability of the core network  24 . 
     As another example of the generation and utilization of knowledge, the transactional servers  18 - 1  through  18 -N communicate, via the core network  24 , transaction messages  30 - 1  through  30 -N as further AI messages  32 - 1  through  32 -N to facilitate ingesting of transactional type content (e.g., real-time crypto currency transaction information) and to facilitate handling of utilization of the knowledge by one or more of the transactional servers (e.g., for a transactional function) in addition to the utilization of the knowledge by the various user devices. Each transaction message  30 - 1  through  30 -N includes one or more of a query request, a query response, a trigger request, a trigger response, a content message, and transactional information, where the transactional information may include one or more of consumer purchasing history, crypto currency ledgers, stock market trade information, other investment transaction information, etc. 
     In another specific example of operation of the generation and utilization of knowledge extracted from the content, the user device  12 - 1  issues a user message  22 - 1  to the AI server  20 - 1 , where the user message  22 - 1  includes a query request and where the query request includes a question related to a first domain of knowledge. The issuing includes generating the user message  22 - 1  based on the query request (e.g., the question), selecting the AI server  20 - 1  based on the first domain of knowledge, and sending, via the core network  24 , the user message  22 - 1  as a further AI message  32 - 1  to the AI server  20 - 1 . Having received the AI message  32 - 1 , the AI server  20 - 1  analyzes the question within the first domain, generates further knowledge, generates a preliminary answer, generates a quality level indicator of the preliminary answer, and determines to gather further content when the quality level indicator is below a minimum quality threshold level. 
     When gathering the further content, the AI server  20 - 1  issues, via the core network  24 , a still further AI message  32 - 1  as a further content message  28 - 1  to the content source  16 - 1 , where the content message  28 - 1  includes a content request for more content associated with the first domain of knowledge and in particular the question. Alternatively, or in addition to, the AI server  20 - 1  issues the content request to another AI server to facilitate a response within a domain associated with the other AI server. Further alternatively, or in addition to, the AI server  20 - 1  issues the content request to one or more of the various user devices to facilitate a response from a subject matter expert. 
     Having received the content message  28 - 1 , the contents or  16 - 1  issues, via the core network  24 , a still further content message  28 - 1  to the AI server  20 - 1  as a yet further AI message  32 - 1 , where the still further content message  28 - 1  includes requested content. The AI server  20 - 1  processes the received content to generate further knowledge. Having generated the further knowledge, the AI server  20 - 1  re-analyzes the question, generates still further knowledge, generates another preliminary answer, generates another quality level indicator of the other preliminary answer, and determines to issue a query response to the user device  12 - 1  when the quality level indicator is above the minimum quality threshold level. When issuing the query response, the AI server  20 - 1  generates an AI message  32 - 1  that includes another user message  22 - 1 , where the other user message  22 - 1  includes the other preliminary answer as a query response including the answer to the question. Having generated the AI message  32 - 1 , the AI server  20 - 1  sends, via the core network  24 , the AI message  32 - 1  as the user message  22 - 1  to the user device  12 - 1  thus providing the answer to the original question of the query request. 
       FIG. 2  is a schematic block diagram of an embodiment of the AI servers  20 - 1  through  20 -N and the transactional servers  18 - 1  through  18 -N of the computing system  10  of  FIG. 1 . The servers include a computing core  52 , one or more visual output devices  74  (e.g., video graphics display, touchscreen, LED, etc.), one or more user input devices  76  (e.g., keypad, keyboard, touchscreen, voice to text, a push button, a microphone, a card reader, a door position switch, a biometric input device, etc.), one or more audio output devices  78  (e.g., speaker(s), headphone jack, a motor, etc.), and one or more visual input devices  80  (e.g., a still image camera, a video camera, photocell, etc.). 
     The servers further include one or more universal serial bus (USB) devices (USB devices  1 -U), one or more peripheral devices (e.g., peripheral devices  1 -P), one or more memory devices (e.g., one or more flash memory devices  92 , one or more hard drive (HD) memories  94 , and one or more solid state (SS) memory devices  96 , and/or cloud memory  98 ). The servers further include one or more wireless location modems  84  (e.g., global positioning satellite (GPS), Wi-Fi, angle of arrival, time difference of arrival, signal strength, dedicated wireless location, etc.), one or more wireless communication modems  86 - 1  through  86 -N (e.g., a cellular network transceiver, a wireless data network transceiver, a Wi-Fi transceiver, a Bluetooth transceiver, a 315 MHz transceiver, a zig bee transceiver, a 60 GHz transceiver, etc.), a telco interface  102  (e.g., to interface to a public switched telephone network), and a wired local area network (LAN)  88  (e.g., optical, electrical), and a wired wide area network (WAN)  90  (e.g., optical, electrical). 
     The computing core  52  includes a video graphics module  54 , one or more processing modules  50 - 1  through  50 -N (e.g., which may include one or more secure co-processors), a memory controller  56  and one or more main memories  58 - 1  through  58 -N (e.g., RAM serving as local memory). The computing core  52  further includes one or more input/output (I/O) device interfaces  62 , an input/output (I/O) controller  60 , a peripheral interface  64 , one or more USB interfaces  66 , one or more network interfaces  72 , one or more memory interfaces  70 , and/or one or more peripheral device interfaces  68 . 
     The processing modules may be a single processing device or a plurality of processing devices where the processing device may further be referred to as one or more of a “processing circuit”, a “processor”, and/or a “processing unit”. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. 
     The processing module, module, processing circuit, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, and/or processing unit. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module, module, processing circuit, and/or processing unit includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). 
     Further note that if the processing module, module, processing circuit, and/or processing unit implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element may store, and the processing module, module, processing circuit, and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture. 
     Each of the interfaces  62 ,  66 ,  68 ,  70 , and  72  includes a combination of hardware (e.g., connectors, wiring, etc.) and may further include operational instructions stored on memory (e.g., driver software) that are executed by one or more of the processing modules  50 - 1  through  50 -N and/or a processing circuit within the interface. Each of the interfaces couples to one or more components of the servers. For example, one of the IO device interfaces  62  couples to an audio output device  78 . As another example, one of the memory interfaces  70  couples to flash memory  92  and another one of the memory interfaces  70  couples to cloud memory  98  (e.g., an on-line storage system and/or on-line backup system). In other embodiments, the servers may include more or less devices and modules than shown in this example embodiment of the servers. 
       FIG. 3  is a schematic block diagram of an embodiment of the various devices of the computing system  10  of  FIG. 1 , including the user devices  12 - 1  through  12 -N and the wireless user devices  14 - 1  through  14 -N. The various devices include the visual output device  74  of  FIG. 2 , the user input device  76  of  FIG. 2 , the audio output device  78  of  FIG. 2 , the visual input device  80  of  FIG. 2 , and one or more sensors  82 . 
     The sensor may be implemented internally and/or externally to the device. Example sensors includes a still camera, a video camera, servo motors associated with a camera, a position detector, a smoke detector, a gas detector, a motion sensor, an accelerometer, velocity detector, a compass, a gyro, a temperature sensor, a pressure sensor, an altitude sensor, a humidity detector, a moisture detector, an imaging sensor, and a biometric sensor. Further examples of the sensor include an infrared sensor, an audio sensor, an ultrasonic sensor, a proximity detector, a magnetic field detector, a biomaterial detector, a radiation detector, a weight detector, a density detector, a chemical analysis detector, a fluid flow volume sensor, a DNA reader, a wind speed sensor, a wind direction sensor, and an object detection sensor. 
     Further examples of the sensor include an object identifier sensor, a motion recognition detector, a battery level detector, a room temperature sensor, a sound detector, a smoke detector, an intrusion detector, a motion detector, a door position sensor, a window position sensor, and a sunlight detector. Still further sensor examples include medical category sensors including: a pulse rate monitor, a heart rhythm monitor, a breathing detector, a blood pressure monitor, a blood glucose level detector, blood type, an electrocardiogram sensor, a body mass detector, an imaging sensor, a microphone, body temperature, etc. 
     The various devices further include the computing core  52  of  FIG. 2 , the one or more universal serial bus (USB) devices (USB devices  1 -U) of  FIG. 2 , the one or more peripheral devices (e.g., peripheral devices  1 -P) of  FIG. 2 , and the one or more memories of  FIG. 2  (e.g., flash memories  92 , HD memories  94 , SS memories  96 , and/or cloud memories  98 ). The various devices further include the one or more wireless location modems  84  of  FIG. 2 , the one or more wireless communication modems  86 - 1  through  86 -N of  FIG. 2 , the telco interface  102  of  FIG. 2 , the wired local area network (LAN)  88  of  FIG. 2 , and the wired wide area network (WAN)  90  of  FIG. 2 . In other embodiments, the various devices may include more or less internal devices and modules than shown in this example embodiment of the various devices. 
       FIGS. 4A and 4B  are schematic block diagrams of another embodiment of a computing system that includes one or more of the user device  12 - 1  of  FIG. 1 , the wireless user device  14 - 1  of  FIG. 1 , the content source  16 - 1  of  FIG. 1 , the transactional server  18 - 1  of  FIG. 1 , the user device  12 - 2  of  FIG. 1 , and the AI server  20 - 1  of  FIG. 1 . The AI server  20 - 1  includes the processing module  50 - 1  (e.g., associated with the servers) of  FIG. 2 , where the processing module  50 - 1  includes a collections module  120 , an identigen entigen intelligence (IEI) module  122 , and a query module  124 . Alternatively, the collections module  120 , the IEI module  122 , and the query module  124  may be implemented by the processing module  50 - 1  (e.g., associated with the various user devices) of  FIG. 3 . The computing system functions to interpret content to produce a response to a query. 
       FIG. 4A  illustrates an example of the interpreting of the content to produce the response to the query where the collections module  120  interprets (e.g., based on an interpretation approach such as rules) at least one of a collections request  132  from the query module  124  and a collections request within collections information  130  from the IEI module  122  to produce content request information (e.g., potential sources, content descriptors of desired content). Alternatively, or in addition to, the collections module  120  may facilitate gathering further content based on a plurality of collection requests from a plurality of devices of the computing system  10  of  FIG. 1 . 
     The collections request  132  is utilized to facilitate collection of content, where the content may be received in a real-time fashion once or at desired intervals, or in a static fashion from previous discrete time frames. For instance, the query module  124  issues the collections request  132  to facilitate collection of content as a background activity to support a long-term query (e.g., how many domestic airline flights over the next seven days include travelers between the age of 18 and 35 years old). The collections request  132  may include one or more of a requester identifier (ID), a content type (e.g., language, dialect, media type, topic, etc.), a content source indicator, security credentials (e.g., an authorization level, a password, a user ID, parameters utilized for encryption, etc.), a desired content quality level, trigger information (e.g., parameters under which to collect content based on a pre-event, an event (i.e., content quality level reaches a threshold to cause the trigger, trueness), or a timeframe), a desired format, and a desired timing associated with the content. 
     Having interpreted the collections request  132 , the collections module  120  selects a source of content based on the content request information. The selecting includes one or more of identifying one or more potential sources based on the content request information, selecting the source of content from the potential sources utilizing a selection approach (e.g., favorable history, a favorable security level, favorable accessibility, favorable cost, favorable performance, etc.). For example, the collections module  120  selects the content source  16 - 1  when the content source  16 - 1  is known to provide a favorable content quality level for a domain associated with the collections request  132 . 
     Having selected the source of content, the collections module  120  issues a content request  126  to the selected source of content. The issuing includes generating the content request  126  based on the content request information for the selected source of content and sending the content request  126  to the selected source of content. The content request  126  may include one or more of a content type indicator, a requester ID, security credentials for content access, and any other information associated with the collections request  132 . For example, the collections module  120  sends the content request  126 , via the core network  24  of  FIG. 1 , to the content source  16 - 1 . Alternatively, or in addition to, the collections module  120  may send a similar content request  126  to one or more of the user device  12 - 1 , the wireless user device  14 - 1 , and the transactional server  18 - 1  to facilitate collecting of further content. 
     In response to the content request  126 , the collections module  120  receives one or more content responses  128 . The content response  128  includes one or more of content associated with the content source, a content source identifier, security credential processing information, and any other information pertaining to the desired content. Having received the content response  128 , the collections module  120  interprets the received content response  128  to produce collections information  130 , where the collections information  130  further includes a collections response from the collections module  120  to the IEI module  122 . 
     The collections response includes one or more of transformed content (e.g., completed sentences and paragraphs), timing information associated with the content, a content source ID, and a content quality level. Having generated the collections response of the collections information  130 , the collections module  120  sends the collections information  130  to the IEI module  122 . Having received the collections information  130  from the collections module  120 , the IEI module  122  interprets the further content of the content response to generate further knowledge, where the further knowledge is stored in a memory associated with the IEI module  122  to facilitate subsequent answering of questions posed in received queries. 
       FIG. 4B  further illustrates the example of the interpreting of the content to produce the response to the query where, the query module  124  interprets a received query request  136  from a requester to produce an interpretation of the query request. For example, the query module  124  receives the query request  136  from the user device  12 - 2 , and/or from one or more of the wireless user device  14 - 2  and the transactional server  18 - 2 . The query request  136  includes one or more of an identifier (ID) associated with the request (e.g., requester ID, ID of an entity to send a response to), a question, question constraints (e.g., within a timeframe, within a geographic area, within a domain of knowledge, etc.), and content associated with the question (e.g., which may be analyzed for new knowledge itself). 
     The interpreting of the query request  136  includes determining whether to issue a request to the IEI module  122  (e.g., a question, perhaps with content) and/or to issue a request to the collections module  120  (e.g., for further background content). For example, the query module  124  produces the interpretation of the query request to indicate to send the request directly to the IEI module  122  when the question is associated with a simple non-time varying function answer (e.g., question: “how many hydrogen atoms does a molecule of water have?”). 
     Having interpreted the query request  136 , the query module  124  issues at least one of an IEI request as query information  138  to the IEI module  122  (e.g., when receiving a simple new query request) and a collections request  132  to the collections module  120  (e.g., based on two or more query requests  136  requiring more substantive content gathering). The IEI request of the query information  138  includes one or more of an identifier (ID) of the query module  124 , an ID of the requester (e.g., the user device  12 - 2 ), a question (e.g., with regards to content for analysis, with regards to knowledge minded by the AI server from general content), one or more constraints (e.g., assumptions, restrictions, etc.) associated with the question, content for analysis of the question, and timing information (e.g., a date range for relevance of the question). 
     Having received the query information  138  that includes the IEI request from the query module  124 , the IEI module  122  determines whether a satisfactory response can be generated based on currently available knowledge, including that of the query request  136 . The determining includes indicating that the satisfactory response cannot be generated when an estimated quality level of an answer falls below a minimum quality threshold level. When the satisfactory response cannot be generated, the IEI module  122  facilitates collecting more content. The facilitating includes issuing a collections request to the collections module  120  of the AI server  20 - 1  and/or to another server or user device, and interpreting a subsequent collections response  134  of collections information  130  that includes further content to produce further knowledge to enable a more favorable answer. 
     When the IEI module  122  indicates that the satisfactory response can be generated, the IEI module  122  issues an IEI response as query information  138  to the query module  124 . The IEI response includes one or more of one or more answers, timing relevance of the one or more answers, an estimated quality level of each answer, and one or more assumptions associated with the answer. The issuing includes generating the IEI response based on the collections response  134  of the collections information  130  and the IEI request, and sending the IEI response as the query information  138  to the query module  124 . Alternatively, or in addition to, at least some of the further content collected by the collections module  120  is utilized to generate a collections response  134  issued by the collections module  120  to the query module  124 . The collections response  134  includes one or more of further content, a content availability indicator (e.g., when, where, required credentials, etc.), a content freshness indicator (e.g., timestamps, predicted time availability), content source identifiers, and a content quality level. 
     Having received the query information  138  from the IEI module  122 , the query module  124  issues a query response  140  to the requester based on the IEI response and/or the collections response  134  directly from the collections module  120 , where the collection module  120  generates the collections response  134  based on collected content and the collections request  132 . The query response  140  includes one or more of an answer, answer timing, an answer quality level, and answer assumptions. 
       FIG. 4C  is a logic diagram of an embodiment of a method for interpreting content to produce a response to a query within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-3, 4A-4B , and also  FIG. 4C . The method includes step  150  where a collections module of a processing module of one or more computing devices (e.g., of one or more servers) interprets a collections request to produce content request information. The interpreting may include one or more of identifying a desired content source, identifying a content type, identifying a content domain, and identifying content timing requirements. 
     The method continues at step  152  where the collections module selects a source of content based on the content request information. For example, the collections module identifies one or more potential sources based on the content request information and selects the source of content from the potential sources utilizing a selection approach (e.g., based on one or more of favorable history, a favorable security level, favorable accessibility, favorable cost, favorable performance, etc.). The method continues at step  154  where the collections module issues a content request to the selected source of content. The issuing includes generating a content request based on the content request information for the selected source of content and sending the content request to the selected source of content. 
     The method continues at step  156  where the collections module issues collections information to an identigen entigen intelligence (IEI) module based on a received content response, where the LEI module extracts further knowledge from newly obtained content from the one or more received content responses. For example, the collections module generates the collections information based on newly obtained content from the one or more received content responses of the selected source of content. 
     The method continues at step  158  where a query module interprets a received query request from a requester to produce an interpretation of the query request. The interpreting may include determining whether to issue a request to the LEI module (e.g., a question) or to issue a request to the collections module to gather further background content. The method continues at step  160  where the query module issues a further collections request. For example, when receiving a new query request, the query module generates a request for the LEI module. As another example, when receiving a plurality of query requests for similar questions, the query module generates a request for the collections module to gather further background content. 
     The method continues at step  162  where the IEI module determines whether a satisfactory query response can be generated when receiving the request from the query module. For example, the IEI module indicates that the satisfactory query response cannot be generated when an estimated quality level of an answer is below a minimum answer quality threshold level. The method branches to step  166  when the IEI module determines that the satisfactory query response can be generated. The method continues to step  164  when the IEI module determines that the satisfactory query response cannot be generated. When the satisfactory query response cannot be generated, the method continues at step  164  where the IEI module facilitates collecting more content. The method loops back to step  150 . 
     When the satisfactory query response can be generated, the method continues at step  166  where the IEI module issues an IEI response to the query module. The issuing includes generating the IEI response based on the collections response and the IEI request, and sending the IEI response to the query module. The method continues at step  168  where the query module issues a query response to the requester. For example, the query module generates the query response based on the IEI response and/or a collections response from the collections module and sends the query response to the requester, where the collections module generates the collections response based on collected content and the collections request. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIG. 5A  is a schematic block diagram of an embodiment of the collections module  120  of  FIG. 4A  that includes a content acquisition module  180 , a content selection module  182 , a source selection module  184 , a content security module  186 , an acquisition timing module  188 , a content transformation module  190 , and a content quality module  192 . Generally, an embodiment of this invention presents solutions where the collections module  120  supports collecting content. 
     In an example of operation of the collecting of the content, the content acquisition module  180  receives a collections request  132  from a requester. The content acquisition module  180  obtains content selection information  194  based on the collections request  132 . The content selection information  194  includes one or more of content requirements, a desired content type indicator, a desired content source identifier, a content type indicator, a candidate source identifier (ID), and a content profile (e.g., a template of typical parameters of the content). For example, the content acquisition module  180  receives the content selection information  194  from the content selection module  182 , where the content selection module  182  generates the content selection information  194  based on a content selection information request from the content acquisition module  180  and where the content acquisition module  180  generates the content selection information request based on the collections request  132 . 
     The content acquisition module  180  obtains source selection information  196  based on the collections request  132 . The source selection information  196  includes one or more of candidate source identifiers, a content profile, selected sources, source priority levels, and recommended source access timing. For example, the content acquisition module  180  receives the source selection information  196  from the source selection module  184 , where the source selection module  184  generates the source selection information  196  based on a source selection information request from the content acquisition module  180  and where the content acquisition module  180  generates the source selection information request based on the collections request  132 . 
     The content acquisition module  180  obtains acquisition timing information  200  based on the collections request  132 . The acquisition timing information  200  includes one or more of recommended source access timing, confirmed source access timing, source access testing results, estimated velocity of content update&#39;s, content precious, timestamps, predicted time availability, required content acquisition triggers, content acquisition trigger detection indicators, and a duplicative indicator with a pending content request. For example, the content acquisition module  180  receives the acquisition timing information  200  from the acquisition timing module  188 , where the acquisition timing module  188  generates the acquisition timing information  200  based on an acquisition timing information request from the content acquisition module  180  and where the content acquisition module  180  generates the acquisition timing information request based on the collections request  132 . 
     Having obtained the content selection information  194 , the source selection information  196 , and the acquisition timing information  200 , the content acquisition module  180  issues a content request  126  to a content source utilizing security information  198  from the content security module  186 , where the content acquisition module  180  generates the content request  126  in accordance with the content selection information  194 , the source selection information  196 , and the acquisition timing information  200 . The security information  198  includes one or more of source priority requirements, requester security information, available security procedures, and security credentials for trust and/or encryption. For example, the content acquisition module  180  generates the content request  126  to request a particular content type in accordance with the content selection information  194  and to include security parameters of the security information  198 , initiates sending of the content request  126  in accordance with the acquisition timing information  200 , and sends the content request  126  to a particular targeted content source in accordance with the source selection information  196 . 
     In response to receiving a content response  128 , the content acquisition module  180  determines the quality level of received content extracted from the content response  128 . For example, the content acquisition module  180  receives content quality information  204  from the content quality module  192 , where the content quality module  192  generates the quality level of the received content based on receiving a content quality request from the content acquisition module  180  and where the content acquisition module  180  generates the content quality request based on content extracted from the content response  128 . The content quality information includes one or more of a content reliability threshold range, a content accuracy threshold range, a desired content quality level, a predicted content quality level, and a predicted level of trust. 
     When the quality level is below a minimum desired quality threshold level, the content acquisition module  180  facilitates acquisition of further content. The facilitating includes issuing another content request  126  to a same content source and/or to another content source to receive and interpret further received content. When the quality level is above the minimum desired quality threshold level, the content acquisition module  180  issues a collections response  134  to the requester. The issuing includes processing the content in accordance with a transformation approach to produce transformed content, generating the collections response  134  to include the transformed content, and sending the collections response  134  to the requester. The processing of the content to produce the transformed content includes receiving content transformation information  202  from the content transformation module  190 , where the content transformation module  190  transforms the content in accordance with the transformation approach to produce the transformed content. The content transformation information includes a desired format, available formats, recommended formatting, the received content, transformation instructions, and the transformed content.  FIG. 5B  is a logic diagram of an embodiment of a method for obtaining content within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-3, 4A-4C, 5A , and also  FIG. 5B . The method includes step  210  where a processing module of one or more processing modules of one or more computing devices of the computing system receives a collections request from the requester. The method continues at step  212  where the processing module determines content selection information. The determining includes interpreting the collections request to identify requirements of the content. 
     The method continues at step  214  where the processing module determines source selection information. The determining includes interpreting the collections request to identify and select one or more sources for the content to be collected. The method continues at step  216  where the processing module determines acquisition timing information. The determining includes interpreting the collections request to identify timing requirements for the acquisition of the content from the one or more sources. The method continues at step  218  where the processing module issues a content request utilizing security information and in accordance with one or more of the content selection information, the source selection information, and the acquisition timing information. For example, the processing module issues the content request to the one or more sources for the content in accordance with the content requirements, where the sending of the request is in accordance with the acquisition timing information. 
     The method continues at step  220  where the processing module determines a content quality level for received content area the determining includes receiving the content from the one or more sources, obtaining content quality information for the received content based on a quality analysis of the received content. The method branches to step  224  when the content quality level is favorable and the method continues to step  222  when the quality level is unfavorable. For example, the processing module determines that the content quality level is favorable when the content quality level is equal to or above a minimum quality threshold level and determines that the content quality level is unfavorable when the content quality level is less than the minimum quality threshold level. 
     When the content quality level is unfavorable, the method continues at step  222  where the processing module facilitates acquisition and further content. For example, the processing module issues further content requests and receives further content for analysis. When the content quality level is favorable, the method continues at step  224  where the processing module issues a collections response to the requester. The issuing includes generating the collections response and sending the collections response to the requester. The generating of the collections response may include transforming the received content into transformed content in accordance with a transformation approach (e.g., reformatting, interpreting absolute meaning and translating into another language in accordance with the absolute meaning, etc.). 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIG. 5C  is a schematic block diagram of an embodiment of the query module  124  of  FIG. 4A  that includes an answer acquisition module  230 , a content requirements module  232  a source requirements module  234 , a content security module  236 , an answer timing module  238 , an answer transformation module  240 , and an answer quality module  242 . Generally, an embodiment of this invention presents solutions where the query module  124  supports responding to a query. 
     In an example of operation of the responding to the query, the answer acquisition module  230  receives a query request  136  from a requester. The answer acquisition module  230  obtains content requirements information  248  based on the query request  136 . The content requirements information  248  includes one or more of content parameters, a desired content type, a desired content source if any, a content type if any, candidate source identifiers, a content profile, and a question of the query request  136 . For example, the answer acquisition module  230  receives the content requirements information  248  from the content requirements module  232 , where the content requirements module  232  generates the content requirements information  248  based on a content requirements information request from the answer acquisition module  230  and where the answer acquisition module  230  generates the content requirements information request based on the query request  136 . 
     The answer acquisition module  230  obtains source requirements information  250  based on the query request  136 . The source requirements information  250  includes one or more of candidate source identifiers, a content profile, a desired source parameter, recommended source parameters, source priority levels, and recommended source access timing. For example, the answer acquisition module  230  receives the source requirements information  250  from the source requirements module  234 , where the source requirements module  234  generates the source requirements information  250  based on a source requirements information request from the answer acquisition module  230  and where the answer acquisition module  230  generates the source requirements information request based on the query request  136 . 
     The answer acquisition module  230  obtains answer timing information  254  based on the query request  136 . The answer timing information  254  includes one or more of requested answer timing, confirmed answer timing, source access testing results, estimated velocity of content updates, content freshness, timestamps, predicted time available, requested content acquisition trigger, and a content acquisition trigger detected indicator. For example, the answer acquisition module  230  receives the answer timing information  254  from the answer timing module  238 , where the answer timing module  238  generates the answer timing information  254  based on an answer timing information request from the answer acquisition module  230  and where the answer acquisition module  230  generates the answer timing information request based on the query request  136 . 
     Having obtained the content requirements information  248 , the source requirements information  250 , and the answer timing information  254 , the answer acquisition module  230  determines whether to issue an ID request  244  and/or a collections request  132  based on one or more of the content requirements information  248 , the source requirements information  250 , and the answer timing information  254 . For example, the answer acquisition module  230  selects the ID request  244  when an immediate answer to a simple query request  136  is required and is expected to have a favorable quality level. As another example, the answer acquisition module  230  selects the collections request  132  when a longer-term answer is required as indicated by the answer timing information to before and/or when the query request  136  has an unfavorable quality level. 
     When issuing the IEI request  244 , the answer acquisition module  230  generates the IEI request  244  in accordance with security information  252  received from the content security module  236  and based on one or more of the content requirements information  248 , the source requirements information  250 , and the answer timing information  254 . Having generated the IEI request  244 , the answer acquisition module  230  sends the IEI request  244  to at least one IEI module. 
     When issuing the collections request  132 , the answer acquisition module  230  generates the collections request  132  in accordance with the security information  252  received from the content security module  236  and based on one or more of the content requirements information  248 , the source requirements information  250 , and the answer timing information  254 . Having generated the collections request  132 , the answer acquisition module  230  sends the collections request  132  to at least one collections module. Alternatively, the answer acquisition module  230  facilitate sending of the collections request  132  to one or more various user devices (e.g., to access a subject matter expert). 
     The answer acquisition module  230  determines a quality level of a received answer extracted from a collections response  134  and/or an IEI response  246 . For example, the answer acquisition module  230  extracts the quality level of the received answer from answer quality information  258  received from the answer quality module  242  in response to an answer quality request from the answer acquisition module  230 . When the quality level is unfavorable, the answer acquisition module  230  facilitates obtaining a further answer. The facilitation includes issuing at least one of a further IEI request  244  and a further collections request  132  to generate a further answer for further quality testing. When the quality level is favorable, the answer acquisition module  230  issues a query response  140  to the requester. The issuing includes generating the query response  140  based on answer transformation information  256  received from the answer transformation module  240 , where the answer transformation module  240  generates the answer transformation information  256  to include a transformed answer based on receiving the answer from the answer acquisition module  230 . The answer transformation information  250   6 A further include the question, a desired format of the answer, available formats, recommended formatting, received IEI responses, transformation instructions, and transformed IEI responses into an answer. 
       FIG. 5D  is a logic diagram of an embodiment of a method for providing a response to a query within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-3, 4A-4C, 5C , and also  FIG. 5D . The method includes step  270  where a processing module of one or more processing modules of one or more computing devices of the computing system receives a query request (e.g., a question) from a requester. The method continues at step  272  where the processing module determines content requirements information. The determining includes interpreting the query request to produce the content requirements. The method continues at step  274  where the processing module determines source requirements information. The determining includes interpreting the query request to produce the source requirements. The method continues at step  276  where the processing module determines answer timing information. The determining includes interpreting the query request to produce the answer timing information. 
     The method continues at step  278  the processing module determines whether to issue an IEI request and/or a collections request. For example, the determining includes selecting the IEI request when the answer timing information indicates that a simple one-time answer is appropriate. As another example, the processing module selects the collections request when the answer timing information indicates that the answer is associated with a series of events over an event time frame. When issuing the IEI request, the method continues at step  280  where the processing module issues the IEI request to an IEI module. The issuing includes generating the IEI request in accordance with security information and based on one or more of the content requirements information, the source requirements information, and the answer timing information. 
     When issuing the collections request, the method continues at step  282  where the processing module issues the collections request to a collections module. The issuing includes generating the collections request in accordance with the security information and based on one or more of the content requirements information, the source requirements information, and the answer timing information. Alternatively, the processing module issues both the IEI request and the collections request when a satisfactory partial answer may be provided based on a corresponding LEI response and a further more generalized and specific answer may be provided based on a corresponding collections response and associated further IEI response. 
     The method continues at step  284  where the processing module determines a quality level of a received answer. The determining includes extracting the answer from the collections response and/or the IEI response and interpreting the answer in accordance with one or more of the content requirements information, the source requirements information, the answer timing information, and the query request to produce the quality level. The method branches to step  288  when the quality level is favorable and the method continues to step  286  when the quality level is unfavorable. For example, the processing module indicates that the quality level is favorable when the quality level is equal to or greater than a minimum answer quality threshold level. As another example, the processing module indicates that the quality level is unfavorable when the quality level is less than the minimum answer quality threshold level. 
     When the quality level is unfavorable, the method continues at step  286  where the processing module obtains a further answer. The obtaining includes at least one of issuing a further IEI request and a further collections request to facilitate obtaining of a further answer for further answer quality level testing as the method loops back to step  270 . When the quality level is favorable, the method continues at step  288  where the processing module issues a query response to the requester. The issuing includes transforming the answer into a transformed answer in accordance with an answer transformation approach (e.g., formatting, further interpretations of the virtual question in light of the answer and further knowledge) and sending the transformed answer to the requester as the query response. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIG. 5E  is a schematic block diagram of an embodiment of the identigen entigen intelligence (IEI) module  122  of  FIG. 4A  that includes a content ingestion module  300 , an element identification module  302 , and interpretation module  304 , and answer resolution module  306 , and an IEI control module  308 . Generally, an embodiment of this invention presents solutions where the IEI module  122  supports interpreting content to produce knowledge that may be utilized to answer questions. 
     In an example of operation of the producing and utilizing of the knowledge, the content ingestion module  300  generates formatted content  314  based on question content  312  and/or source content  310 , where the IEI module  122  receives an IEI request  244  that includes the question content  312  and the IEI module  122  receives a collections response  134  that includes the source content  310 . The source content  310  includes content from a source extracted from the collections response  134 . The question content  312  includes content extracted from the IEI request  244  (e.g., content paired with a question). The content ingestion module  300  generates the formatted content  314  in accordance with a formatting approach (e.g., creating proper sentences from words of the content). The formatted content  314  includes modified content that is compatible with subsequent element identification (e.g., complete sentences, combinations of words and interpreted sounds and/or inflection cues with temporal associations of words). 
     The element identification module  302  processes the formatted content  314  based on element rules  318  and an element list  332  to produce identified element information  340 . Rules  316  includes the element rules  318  (e.g., match, partial match, language translation, etc.). Lists  330  includes the element list  332  (e.g., element ID, element context ID, element usage ID, words, characters, symbols etc.). The IEI control module  308  may provide the rules  316  and the lists  330  by accessing stored data  360  from a memory associated with the IEI module  122 . Generally, an embodiment of this invention presents solutions where the stored data  360  may further include one or more of a descriptive dictionary, categories, representations of element sets, element list, sequence data, pending questions, pending request, recognized elements, unrecognized elements, errors, etc. 
     The identified element information  340  includes one or more of identifiers of elements identified in the formatted content  314 , may include ordering and/or sequencing and grouping information. For example, the element identification module  302  compares elements of the formatted content  314  to known elements of the element list  332  to produce identifiers of the known elements as the identified element information  340  in accordance with the element rules  318 . Alternatively, the element identification module  302  outputs un-identified element information  342  to the IEI control module  308 , where the un-identified element information  342  includes temporary identifiers for elements not identifiable from the formatted content  314  when compared to the element list  332 . 
     The interpretation module  304  processes the identified element information  340  in accordance with interpretation rules  320  (e.g., potentially valid permutations of various combinations of identified elements), question information  346  (e.g., a question extracted from the IEI request  244  which may be paired with content associated with the question), and a groupings list  334  (e.g., representations of associated groups of representations of things, a set of element identifiers, valid element usage IDs in accordance with similar, an element context, permutations of sets of identifiers for possible interpretations of a sentence or other) to produce interpreted information  344 . The interpreted information  344  includes potentially valid interpretations of combinations of identified elements. Generally, an embodiment of this invention presents solutions where the interpretation module  304  supports producing the interpreted information  344  by considering permutations of the identified element information  340  in accordance with the interpretation rules  320  and the groupings list  334 . 
     The answer resolution module  306  processes the interpreted information  344  based on answer rules  322  (e.g., guidance to extract a desired answer), the question information  346 , and inferred question information  352  (e.g., posed by the IEI control module or analysis of general collections of content or refinement of a stated question from a request) to produce preliminary answers  354  and an answer quality level  356 . The answer generally lies in the interpreted information  344  as both new content received and knowledge based on groupings list  334  generated based on previously received content. The preliminary answers  354  includes an answer to a stated or inferred question that subject further refinement. The answer quality level  356  includes a determination of a quality level of the preliminary answers  354  based on the answer rules  322 . The inferred question information  352  may further be associated with time information  348 , where the time information includes one or more of current real-time, a time reference associated with entity submitting a request, and a time reference of a collections response. 
     When the IEI control module  308  determines that the answer quality level  356  is below an answer quality threshold level, the IEI control module  308  facilitates collecting of further content (e.g., by issuing a collections request  132  and receiving corresponding collections responses  134  for analysis). When the answer quality level  356  compares favorably to the answer quality threshold level, the IEI control module  308  issues an IEI response  246  based on the preliminary answers  354 . When receiving training information  358 , the IEI control module  308  facilitates updating of one or more of the lists  330  and the rules  316  and stores the updated list  330  and the updated rules  316  in the memories as updated stored data  360 . 
       FIG. 5F  is a logic diagram of an embodiment of a method for analyzing content within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-3, 4A-4C, 5E , and also  FIG. 5F . The method includes step  370  where a processing module of one or more processing modules of one or more computing devices of the computing system facilitates updating of one or more rules and lists based on one or more of received training information and received content. For example, the processing module updates rules with received rules to produce updated rules and updates element lists with received elements to produce updated element lists. As another example, the processing module interprets the received content to identify a new word for at least temporary inclusion in the updated element list. 
     The method continues at step  372  where the processing module transforms at least some of the received content into formatted content. For example, the processing module processes the received content in accordance with a transformation approach to produce the formatted content, where the formatted content supports compatibility with subsequent element identification (e.g., typical sentence structures of groups of words). 
     The method continues at step  374  where the processing module processes the formatted content based on the rules and the lists to produce identified element information and/or an identified element information. For example, the processing module compares the formatted content to element lists to identify a match producing identifiers for identified elements or new identifiers for unidentified elements when there is no match. 
     The method continues at step  376  with a processing module processes the identified element information based on rules, the lists, and question information to produce interpreted information. For example, the processing module compares the identified element information to associated groups of representations of things to generate potentially valid interpretations of combinations of identified elements. 
     The method continues at step  378  where the processing module processes the interpreted information based on the rules, the question information, and inferred question information to produce preliminary answers. For example, the processing module matches the interpreted information to one or more answers (e.g., embedded knowledge based on a fact base built from previously received content) with highest correctness likelihood levels that is subject to further refinement. The method continues at step  380  where the processing module generates an answer quality level based on the preliminary answers, the rules, and the inferred question information. For example, the processing module predicts the answer correctness likelihood level based on the rules, the inferred question information, and the question information. The method branches to step  384  when the answer quality level is favorable and the method continues to step  382  when the answer quality level is unfavorable. For example, the generating of the answer quality level further includes the processing module indicating that the answer quality level is favorable when the answer quality level is greater than or equal to a minimum answer quality threshold level. As another example, the generating of the answer quality level further includes the processing module indicating that the answer quality level is unfavorable when the answer quality level is less than the minimum answer quality threshold level. 
     When the answer quality level is unfavorable, the method continues at step  382  where the processing module facilitates gathering clarifying information. For example, the processing module issues a collections request to facilitate receiving further content and or request question clarification from a question requester. When the answer quality level is favorable, the method continues at step  384  where the processing module issues a response that includes one or more answers based on the preliminary answers and/or further updated preliminary answers based on gathering further content. For example, the processing module generates a response that includes one or more answers and the answer quality level and issues the response to the requester. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIG. 6A  is a schematic block diagram of an embodiment of the element identification module  302  of  FIG. 5A  and the interpretation module  304  of  FIG. 5A . The element identification module  302  includes an element matching module  400  and an element grouping module  402 . The interpretation module  304  includes a grouping matching module  404  and a grouping interpretation module  406 . Generally, an embodiment of this invention presents solutions where the element identification module  302  supports identifying potentially valid permutations of groupings of elements while the interpretation module  304  interprets the potentially valid permutations of groupings of elements to produce interpreted information that includes the most likely of groupings based on a question. 
     In an example of operation of the identifying of the potentially valid permutations of groupings of elements, when matching elements of the formatted content  314 , the element matching module  400  generates matched elements  412  (e.g., identifiers of elements contained in the formatted content  314 ) based on the element list  332 . For example, the element matching module  400  matches a received element to an element of the element list  332  and outputs the matched elements  412  to include an identifier of the matched element. When finding elements that are unidentified, the element matching module  400  outputs un-recognized words information  408  (e.g., words not in the element list  332 , may temporarily add) as part of un-identified element information  342 . For example, the element matching module  400  indicates that a match cannot be made between a received element of the formatted content  314 , generates the unrecognized words info  408  to include the received element and/or a temporary identifier, and issues and updated element list  414  that includes the temporary identifier and the corresponding unidentified received element. The element grouping module  402  analyzes the matched elements  412  in accordance with element rules  318  to produce grouping error information  410  (e.g., incorrect sentence structure indicators) when a structural error is detected. The element grouping module  402  produces identified element information  340  when favorable structure is associated with the matched elements in accordance with the element rules  318 . The identified element information  340  may further include grouping information of the plurality of permutations of groups of elements (e.g., several possible interpretations), where the grouping information includes one or more groups of words forming an associated set and/or super-group set of two or more subsets when subsets share a common core element. 
     In an example of operation of the interpreting of the potentially valid permutations of groupings of elements to produce the interpreted information, the grouping matching module  404  analyzes the identified element information  340  in accordance with a groupings list  334  to produce validated groupings information  416 . For example, the grouping matching module  404  compares a grouping aspect of the identified element information  340  (e.g., for each permutation of groups of elements of possible interpretations), generates the validated groupings information  416  to include identification of valid permutations aligned with the groupings list  334 . Alternatively, or in addition to, the grouping matching module  404  generates an updated groupings list  418  when determining a new valid grouping (e.g., has favorable structure and interpreted meaning) that is to be added to the groupings list  334 . 
     The grouping interpretation module  406  interprets the validated groupings information  416  based on the question information  346  and in accordance with the interpretation rules  320  to produce interpreted information  344  (e.g., most likely interpretations, next most likely interpretations, etc.). For example, the grouping interpretation module  406  obtains context, obtains favorable historical interpretations, processes the validated groupings based on interpretation rules  320 , where each interpretation is associated with a correctness likelihood level. 
       FIG. 6B  is a logic diagram of an embodiment of a method for interpreting information within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-3, 4A-4C, 5E-5F, 6A , and also  FIG. 6B . The method includes step  430  where a processing module of one or more processing modules of one or more computing devices of the computing system analyzes formatted content. For example, the processing module attempt to match a received element of the formatted content to one or more elements of an elements list. When there is no match, the method branches to step  434  and when there is a match, the method continues to step  432 . When there is a match, the method continues at step  432  where the processing module outputs matched elements (e.g., to include the matched element and/or an identifier of the matched element). When there is no match, the method continues at step  434  where the processing module outputs unrecognized words (e.g., elements and/or a temporary identifier for the unmatched element). 
     The method continues at step  436  where the processing module analyzes matched elements. For example, the processing module attempt to match a detected structure of the matched elements (e.g., chained elements as in a received sequence) to favorable structures in accordance with element rules. The method branches to step  440  when the analysis is unfavorable and the method continues to step  438  when the analysis is favorable. When the analysis is favorable matching a detected structure to the favorable structure of the element rules, the method continues at step  438  where the processing module outputs identified element information (e.g., an identifier of the favorable structure, identifiers of each of the detected elements). When the analysis is unfavorable matching a detected structure to the favorable structure of the element rules, the method continues at step  440  where the processing module outputs grouping error information (e.g., a representation of the incorrect structure, identifiers of the elements of the incorrect structure, a temporary new identifier of the incorrect structure). 
     The method continues at step  442  where the processing module analyzes the identified element information to produce validated groupings information. For example, the processing module compares a grouping aspect of the identified element information and generates the validated groupings information to include identification of valid permutations that align with the groupings list. Alternatively, or in addition to, the processing module generates an updated groupings list when determining a new valid grouping. 
     The method continues at step  444  where the processing module interprets the validated groupings information to produce interpreted information. For example, the processing module obtains one or more of context and historical interpretations and processes the validated groupings based on interpretation rules to generate the interpreted information, where each interpretation is associated with a correctness likelihood level (e.g., a quality level). 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIG. 6C  is a schematic block diagram of an embodiment of the answer resolution module  306  of  FIG. 5A  that includes an interim answer module  460 , and answer prioritization module  462 , and a preliminary answer quality module  464 . Generally, an embodiment of this invention presents solutions where the answer resolution module  306  supports producing an answer for interpreted information  344 . 
     In an example of operation of the providing of the answer, the interim answer module  460  analyzes the interpreted information  344  based on question information  346  and inferred question information  352  to produce interim answers  466  (e.g., answers to stated and/or inferred questions without regard to rules that is subject to further refinement). The answer prioritization module  462  analyzes the interim answers  466  based on answer rules  322  to produce preliminary answer  354 . For example, the answer prioritization module  462  identifies all possible answers from the interim answers  466  that conform to the answer rules  322 . 
     The preliminary answer quality module  464  analyzes the preliminary answers  354  in accordance with the question information  346 , the inferred question information  352 , and the answer rules  322  to produce an answer quality level  356 . For example, for each of the preliminary answers  354 , the preliminary answer quality module  464  may compare a fit of the preliminary answer  354  to a corresponding previous answer and question quality level, calculate the answer quality level  356  based on a level of conformance to the answer rules  322 , calculate the answer quality level  356  based on alignment with the inferred question information  352 , and determine the answer quality level  356  based on an interpreted correlation with the question information  346 . 
       FIG. 6D  is a logic diagram of an embodiment of a method for producing an answer within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-3, 4A-4C, 5E-5F, 6C , and also  FIG. 6D . The method includes step  480  where a processing module of one or more processing modules of one or more computing devices of the computing system analyzes received interpreted information based on question information and inferred question information to produce one or more interim answers. For example, the processing module generates potential answers based on patterns consistent with previously produced knowledge and likelihood of correctness. 
     The method continues at step  482  where the processing module analyzes the one or more interim answers based on answer rules to produce preliminary answers. For example, the processing module identifies all possible answers from the interim answers that conform to the answer rules. The method continues at step  484  where the processing module analyzes the preliminary answers in accordance with the question information, the inferred question information, and the answer rules to produce an answer quality level. For example, for each of the elementary answers, the processing module may compare a fit of the preliminary answer to a corresponding previous answer-and-answer quality level, calculate the answer quality level based on performance to the answer rules, calculate answer quality level based on alignment with the inferred question information, and determine the answer quality level based on interpreted correlation with the question information. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIG. 7A  is an information flow diagram for interpreting information within a computing system, where sets of entigens  504  are interpreted from sets of identigens  502  which are interpreted from sentences of words  500 . Such identigen entigen intelligence (IEI) processing of the words (e.g., to IEI process) includes producing one or more of interim knowledge, a preliminary answer, and an answer quality level. For example, the IEI processing includes identifying permutations of identigens of a phrase of a sentence (e.g., interpreting human expressions to produce identigen groupings for each word of ingested content), reducing the permutations of identigens (e.g., utilizing rules to eliminate unfavorable permutations), mapping the reduced permutations of identigens to at least one set of entigens (e.g., most likely identigens become the entigens) to produce the interim knowledge, processing the knowledge in accordance with a knowledge database (e.g., comparing the set of entigens to the knowledge database) to produce a preliminary answer, and generating the answer quality level based on the preliminary answer for a corresponding domain. 
     Human expressions are utilized to portray facts and fiction about the real world. The real-world includes items, actions, and attributes. The human expressions include textual words, textual symbols, images, and other sensorial information (e.g., sounds). It is known that many words, within a given language, can mean different things based on groupings and orderings of the words. For example, the sentences of words  500  can include many different forms of sentences that mean vastly different things even when the words are very similar. 
     The present invention presents solutions where the computing system  10  supports producing a computer-based representation of a truest meaning possible of the human expressions given the way that multitudes of human expressions relate to these meanings. As a first step of the flow diagram to transition from human representations of things to a most precise computer representation of the things, the computer identifies the words, phrases, sentences, etc. from the human expressions to produce the sets of identigens  502 . Each identigen includes an identifier of their meaning and an identifier of an instance for each possible language, culture, etc. For example, the words car and automobile share a common meaning identifier but have different instance identifiers since they are different words and are spelled differently. As another example, the word duck is associated both with a bird and an action to elude even though they are spelled the same. In this example the bird duck has a different meaning than the elude duck and as such each has a different meaning identifier of the corresponding identigens. 
     As a second step of the flow diagram to transition from human representations of things to the most precise computer representation of the things, the computer extracts meaning from groupings of the identified words, phrases, sentences, etc. to produce the sets of entigens  504 . Each entigen includes an identifier of a single conceivable and perceivable thing in space and time (e.g., independent of language and other aspects of the human expressions). For example, the words car and automobile are different instances of the same meaning and point to a common shared entigen. As another example, the word duck for the bird meaning has an associated unique entigen that is different than the entigen for the word duck for the elude meaning. 
     As a third step of the flow diagram to transition from human expressions of things to the most precise computer representation of the things, the computer reasons facts from the extracted meanings. For example, the computer maintains a fact-based of the valid meanings from the valid groupings or sets of entigens so as to support subsequent inferences, deductions, rationalizations of posed questions to produce answers that are aligned with a most factual view. As time goes on, and as an entigen has been identified, it can encounter an experience transformations in time, space, attributes, actions, and words which are used to identify it without creating contradictions or ever losing its identity. 
       FIG. 7B  is a relationship block diagram illustrating an embodiment of relationships between things  510  and representations of things  512  within a computing system. The things  510  includes conceivable and perceivable things including actions  522 , items  524 , and attributes  526 . The representation of things  512  includes representations of things used by humans  514  and representation of things used by of computing devices  516  of embodiments of the present invention. The things  510  relates to the representations of things used by humans  514  where the invention presents solutions where the computing system  10  supports mapping the representations of things used by humans  514  to the representations of things used by computing devices  516 , where the representations of things used by computing devices  516  map back to the things  510 . 
     The representations of things used by humans  514  includes textual words  528 , textual symbols  530 , images (e.g., non-textual)  532 , and other sensorial information  534  (e.g., sounds, sensor data, electrical fields, voice inflections, emotion representations, facial expressions, whistles, etc.). The representations of things used by computing devices  516  includes identigens  518  and entigens  520 . The representations of things used by humans  514  maps to the identigens  518  and the identigens  518  map to the entigens  520 . The entigens  520  uniquely maps back to the things  510  in space and time, a truest meaning the computer is looking for to create knowledge and answer questions based on the knowledge. 
     To accommodate the mapping of the representations of things used by humans  514  to the identigens  518 , the identigens  518  is partitioned into actenyms  544  (e.g., actions), itenyms  546  (e.g., items), attrenyms  548  (e.g., attributes), and functionals  550  (e.g., that join and/or describe). Each of the actenyms  544 , itenyms  546 , and attrenyms  548  may be further classified into singulatums  552  (e.g., identify one unique entigen) and pluratums  554  (e.g., identify a plurality of entigens that have similarities). 
     Each identigen  518  is associated with an identigens identifier (IDN)  536 . The IDN  536  includes a meaning identifier (ID)  538  portion, an instance ID  540  portion, and a type ID  542  portion. The meaning ID  538  includes an identifier of common meaning. The instance ID  540  includes an identifier of a particular word and language. The type ID  542  includes one or more identifiers for actenyms, itenyms, attrenyms, singulatums, pluratums, a time reference, and any other reference to describe the IDN  536 . The mapping of the representations of things used by humans  514  to the identigens  518  by the computing system of the present invention includes determining the identigens  518  in accordance with logic and instructions for forming groupings of words. 
     Generally, an embodiment of this invention presents solutions where the identigens  518  map to the entigens  520 . Multiple identigens may map to a common unique entigen. The mapping of the identigens  518  to the entigens  520  by the computing system of the present invention includes determining entigens in accordance with logic and instructions for forming groupings of identigens. 
       FIG. 7C  is a diagram of an embodiment of a synonym words table  570  within a computing system, where the synonym words table  570  includes multiple fields including textual words  572 , identigens  518 , and entigens  520 . The identigens  518  includes fields for the meaning identifier (ID)  538  and the instance ID  540 . The computing system of the present invention may utilize the synonym words table  570  to map textual words  572  to identigens  518  and map the identigens  518  to entigens  520 . For example, the words car, automobile, auto, bil (Swedish), carro (Spanish), and bil (Danish) all share a common meaning but are different instances (e.g., different words and languages). The words map to a common meaning ID but to individual unique instant identifiers. Each of the different identigens map to a common entigen since they describe the same thing. 
       FIG. 7D  is a diagram of an embodiment of a polysemous words table  576  within a computing system, where the polysemous words table  576  includes multiple fields including textual words  572 , identigens  518 , and entigens  520 . The identigens  518  includes fields for the meaning identifier (ID)  538  and the instance ID  540 . The computing system of the present invention may utilize the polysemous words table  576  to map textual words  572  to identigens  518  and map the identigens  518  to entigens  520 . For example, the word duck maps to four different identigens since the word duck has four associated different meanings (e.g., bird, fabric, to submerge, to elude) and instances. Each of the identigens represent different things and hence map to four different entigens. 
       FIG. 7E  is a diagram of an embodiment of transforming words into groupings within a computing system that includes a words table  580 , a groupings of words section to validate permutations of groupings, and a groupings table  584  to capture the valid groupings. The words table  580  includes multiple fields including textual words  572 , identigens  518 , and entigens  520 . The identigens  518  includes fields for the meaning identifier (ID)  538 , the instance ID  540 , and the type ID  542 . The computing system of the present invention may utilize the words table  580  to map textual words  572  to identigens  518  and map the identigens  518  to entigens  520 . For example, the word pilot may refer to a flyer and the action to fly. Each meaning has a different identigen and different entigen. 
     The computing system the present invention may apply rules to the fields of the words table  580  to validate various groupings of words. Those that are invalid are denoted with a “X” while those that are valid are associated with a check mark. For example, the grouping “pilot Tom” is invalid when the word pilot refers to flying and Tom refers to a person. The identigen combinations for the flying pilot and the person Tom are denoted as invalid by the rules. As another example, the grouping “pilot Tom” is valid when the word pilot refers to a flyer and Tom refers to the person. The identigen combinations for the flyer pilot and the person Tom are denoted as valid by the rules. 
     The groupings table  584  includes multiple fields including grouping ID  586 , word strings  588 , identigens  518 , and entigens  520 . The computing system of the present invention may produce the groupings table  584  as a stored fact base for valid and/or invalid groupings of words identified by their corresponding identigens. For example, the valid grouping “pilot Tom” referring to flyer Tom the person is represented with a grouping identifier of 3001 and identity and identifiers 150.001 and 457.001. The entigen field  520  may indicate associated entigens that correspond to the identigens. For example, entigen e717 corresponds to the flyer pilot meaning and entigen e61 corresponds to the time the person meaning. Alternatively, or in addition to, the entigen field  520  may be populated with a single entigen identifier (ENI). 
     The word strings field  588  may include any number of words in a string. Different ordering of the same words can produce multiple different strings and even different meanings and hence entigens. More broadly, each entry (e.g., role) of the groupings table  584  may refer to groupings of words, two or more word strings, an idiom, just identigens, just entigens, and/or any combination of the preceding elements. Each entry has a unique grouping identifier. An idiom may have a unique grouping ID and include identifiers of original word identigens and replacing identigens associated with the meaning of the idiom not just the meaning of the original words. Valid groupings may still have ambiguity on their own and may need more strings and/or context to select a best fit when interpreting a truest meaning of the grouping. 
       FIG. 8A  is a data flow diagram for accumulating knowledge within a computing system, where a computing device, at a time=t 0 , ingests and processes facts  598  at a step  590  based on rules  316  and fact base information  600  to produce groupings  602  for storage in a fact base  592  (e.g., words, phrases, word groupings, identigens, entigens, quality levels). The facts  598  may include information from books, archive data, Central intelligence agency (CIA) world fact book, trusted content, etc. The ingesting may include filtering to organize and promote better valid groupings detection (e.g., considering similar domains together). The groupings  602  includes one or more of groupings identifiers, identigen identifiers, entigen identifiers, and estimated fit quality levels. The processing step  590  may include identifying identigens from words of the facts  598  in accordance with the rules  316  and the fact base info  600  and identifying groupings utilizing identigens in accordance with rules  316  and fact base info  600 . 
     Subsequent to ingestion and processing of the facts  598  to establish the fact base  592 , at a time=t 1 +, the computing device ingests and processes new content  604  at a step  594  in accordance with the rules  316  and the fact base information  600  to produce preliminary grouping  606 . The new content may include updated content (e.g., timewise) from periodicals, newsfeeds, social media, etc. The preliminary grouping  606  includes one or more of preliminary groupings identifiers, preliminary identigen identifiers, preliminary entigen identifiers, estimated fit quality levels, and representations of unidentified words. 
     The computing device validates the preliminary groupings  606  at a step  596  based on the rules  316  and the fact base info  600  to produce updated fact base info  608  for storage in the fact base  592 . The validating includes one or more of reasoning a fit of existing fact base info  600  with the new preliminary grouping  606 , discarding preliminary groupings, updating just time frame information associated with an entry of the existing fact base info  600  (e.g., to validate knowledge for the present), creating new entigens, and creating a median entigen to summarize portions of knowledge within a median indicator as a quality level indicator (e.g., suggestive not certain). 
     Storage of the updated fact base information  608  captures patterns that develop by themselves instead of searching for patterns as in prior art artificial intelligence systems. Growth of the fact base  592  enables subsequent reasoning to create new knowledge including deduction, induction, inference, and inferential sentiment (e.g., a chain of sentiment sentences). Examples of sentiments includes emotion, beliefs, convictions, feelings, judgments, notions, opinions, and views. 
       FIG. 8B  is a diagram of an embodiment of a groupings table  620  within a computing system. The groupings table  620  includes multiple fields including grouping ID  586 , word strings  588 , an IF string  622  and a THEN string  624 . Each of the fields for the IF string  622  and the THEN string  624  includes fields for an identigen (IDN) string  626 , and an entigen (ENI) string  628 . The computing system of the present invention may produce the groupings table  620  as a stored fact base to enable IF THEN based inference to generate a new knowledge inference  630 . 
     As a specific example, grouping  5493  points out the logic of IF someone has a tumor, THEN someone is sick and the grouping  5494  points of the logic that IF someone is sick, THEN someone is sad. As a result of utilizing inference, the new knowledge inference  630  may produce grouping  5495  where IF someone has a tumor, THEN someone is possibly sad (e.g., or is sad). 
       FIG. 8C  is a data flow diagram for answering questions utilizing accumulated knowledge within a computing system, where a computing device ingests and processes question information  346  at a step  640  based on rules  316  and fact base info  600  from a fact base  592  to produce preliminary grouping  606 . The ingesting and processing questions step  640  includes identifying identigens from words of a question in accordance with the rules  316  and the fact base information  600  and may also include identifying groupings from the identified identigens in accordance with the rules  316  and the fact base information  600 . 
     The computing device validates the preliminary grouping  606  at a step  596  based on the rules  316  and the fact base information  600  to produce identified element information  340 . For example, the computing device reasons fit of existing fact base information with new preliminary groupings  606  to produce the identified element information  340  associated with highest quality levels. The computing device interprets a question of the identified element information  340  at a step  642  based on the rules  316  and the fact base information  600 . The interpreting of the question may include separating new content from the question and reducing the question based on the fact base information  600  and the new content. 
     The computing device produces preliminary answers  354  from the interpreted information  344  at a resolve answer step  644  based on the rules  316  and the fact base information  600 . For example, the computing device compares the interpreted information  344  two the fact base information  600  to produce the preliminary answers  354  with highest quality levels utilizing one or more of deduction, induction, inferencing, and applying inferential sentiments logic. Alternatively, or in addition to, the computing device may save new knowledge identified from the question information  346  to update the fact base  592 . 
       FIG. 8D  is a data flow diagram for answering questions utilizing interference within a computing system that includes a groupings table  648  and the resolve answer step  644  of  FIG. 8C . The groupings table  648  includes multiple fields including fields for a grouping (GRP) identifier (ID)  586 , word strings  588 , an identigen (IDN) string  626 , and an entigen (ENI)  628 . The groupings table  648  may be utilized to build a fact base to enable resolving a future question into an answer. For example, the grouping  8356  notes knowledge that Michael sleeps eight hours and grouping  8357  notes that Michael usually starts to sleep at 11 PM. 
     In a first question example that includes a question “Michael sleeping?”, the resolve answer step  644  analyzes the question from the interpreted information  344  in accordance with the fact base information  600 , the rules  316 , and a real-time indicator that the current time is 1 AM to produce a preliminary answer of “possibly YES” when inferring that Michael is probably sleeping at 1 AM when Michael usually starts sleeping at 11 PM and Michael usually sleeps for a duration of eight hours. 
     In a second question example that includes the question “Michael sleeping?”, the resolve answer step  644  analyzes the question from the interpreted information  344  in accordance with the fact base information  600 , the rules  316 , and a real-time indicator that the current time is now 11 AM to produce a preliminary answer of “possibly NO” when inferring that Michael is probably not sleeping at 11 AM when Michael usually starts sleeping at 11 PM and Michael usually sleeps for a duration of eight hours. 
       FIG. 8E  is a relationship block diagram illustrating another embodiment of relationships between things and representations of things within a computing system. While things in the real world are described with words, it is often the case that a particular word has multiple meanings in isolation. Interpreting the meaning of the particular word may hinge on analyzing how the word is utilized in a phrase, a sentence, multiple sentences, paragraphs, and even whole documents or more. Describing and stratifying the use of words, word types, and possible meanings help in interpreting a true meaning. 
     Humans utilize textual words  528  to represent things in the real world. Quite often a particular word has multiple instances of different grammatical use when part of a phrase of one or more sentences. The grammatical use  649  of words includes the nouns and the verbs, and also includes adverbs, adjectives, pronouns, conjunctions, prepositions, determiners, exclamations, etc. 
     As an example of multiple grammatical use, the word “bat” in the English language can be utilized as a noun or a verb. For instance, when utilized as a noun, the word “bat” may apply to a baseball bat or may apply to a flying “bat.” As another instance, when utilized as a verb, the word “bat” may apply to the action of hitting or batting an object, i.e., “bat the ball.” 
     To stratify word types by use, the words are associated with a word type (e.g., type identifier  542 ). The word types include objects (e.g., items  524 ), characteristics (e.g., attributes  526 ), actions  522 , and the functionals  550  for joining other words and describing words. For example, when the word “bat” is utilized as a noun, the word is describing the object of either the baseball bat or the flying bat. As another example, when the word “bat” is utilized as a verb, the word is describing the action of hitting. 
     To determine possible meanings, the words, by word type, are mapped to associative meanings (e.g., identigens  518 ). For each possible associative meaning, the word type is documented with the meaning and further with an identifier (ID) of the instance (e.g., an identigen identifier). 
     For the example of the word “bat” when utilized as a noun for the baseball bat, a first identigen identifier  536 - 1  includes a type ID  542 - 1  associated with the object  524 , an instance ID  540 - 1  associated with the first identigen identifier (e.g., unique for the baseball bat), and a meaning ID  538 - 1  associated with the baseball bat. For the example of the word “bat” when utilized as a noun for the flying bat, a second identigen identifier  536 - 2  includes a type ID  542 - 1  associated with the object  524 , an instance ID  540 - 2  associated with the second identigen identifier (e.g., unique for the flying bat), and a meaning ID  538 - 2  associated with the flying bat. For the example of the word “bat” when utilized as a verb for the bat that hits, a third identigen identifier  536 - 2  includes a type ID  542 - 2  associated with the actions  522 , an instance ID  540 - 3  associated with the third identigen identifier (e.g., unique for the bat that hits), and a meaning ID  538 - 3  associated with the bat that hits. 
     With the word described by a type and possible associative meanings, a combination of full grammatical use of the word within the phrase etc., application of rules, and utilization of an ever-growing knowledge database that represents knowledge by linked entigens, the absolute meaning (e.g., entigen  520 ) of the word is represented as a unique entigen. For example, a first entigen el represents the absolute meaning of a baseball bat (e.g., a generic baseball bat not a particular baseball bat that belongs to anyone), a second entigen e 2  represents the absolute meaning of the flying bat (e.g., a generic flying bat not a particular flying bat), and a third entigen e 3  represents the absolute meaning of the verb bat (e.g., to hit). 
     An embodiment of methods to ingest text to produce absolute meanings for storage in a knowledge database are discussed in greater detail with reference to  FIGS. 8F-H . Those embodiments further discuss the discerning of the grammatical use, the use of the rules, and the utilization of the knowledge database to definitively interpret the absolute meaning of a string of words. 
     Another embodiment of methods to respond to a query to produce an answer based on knowledge stored in the knowledge database are discussed in greater detail with reference to  FIGS. 8J-L . Those embodiments further discuss the discerning of the grammatical use, the use of the rules, and the utilization of the knowledge database to interpret the query. The query interpretation is utilized to extract the answer from the knowledge database to facilitate forming the query response. 
       FIGS. 8F and 8G  are schematic block diagrams of another embodiment of a computing system that includes the content ingestion module  300  of  FIG. 5E , the element identification module  302  of  FIG. 5E , the interpretation module  304  of  FIG. 5E , the IEI control module  308  of  FIG. 5E , and the SS memory  96  of  FIG. 2 . Generally, an embodiment of this invention provides presents solutions where the computing system  10  supports processing content to produce knowledge for storage in a knowledge database. 
     The processing of the content to produce the knowledge includes a series of steps. For example, a first step includes identifying words of an ingested phrase to produce tokenized words. As depicted in  FIG. 8F , a specific example of the first step includes the content ingestion module  300  comparing words of source content  310  to dictionary entries to produce formatted content  314  that includes identifiers of known words. Alternatively, when a comparison is unfavorable, the temporary identifier may be assigned to an unknown word. For instance, the content ingestion module  300  produces identifiers associated with the words “the”, “black”, “bat”, “eats”, and “fruit” when the ingested phrase includes “The black bat eats fruit”, and generates the formatted content  314  to include the identifiers of the words. 
     A second step of the processing of the content to produce the knowledge includes, for each tokenized word, identifying one or more identigens that correspond the tokenized word, where each identigen describes one of an object, a characteristic, and an action. As depicted in  FIG. 8F , a specific example of the second step includes the element identification module  302  performing a look up of identigen identifiers, utilizing an element list  332  and in accordance with element rules  318 , of the one or more identigens associated with each tokenized word of the formatted content  314  to produce identified element information  340 . 
     A unique identifier is associated with each of the potential object, the characteristic, and the action (OCA) associated with the tokenized word (e.g. sequential identigens). For instance, the element identification module  302  identifies a functional symbol for “the”, identifies a single identigen for “black”, identifies two identigens for “bat” (e.g., baseball bat and flying bat), identifies a single identigen for “eats”, and identifies a single identigen for “fruit.” When at least one tokenized word is associated with multiple identigens, two or more permutations of sequential combinations of identigens for each tokenized word result. For example, when “bat” is associated with two identigens, two permutations of sequential combinations of identigens result for the ingested phrase. 
     A third step of the processing of the content to produce the knowledge includes, for each permutation of sequential combinations of identigens, generating a corresponding equation package (i.e., candidate interpretation), where the equation package includes a sequential linking of pairs of identigens (e.g., relationships), where each sequential linking pairs a preceding identigen to a next identigen, and where an equation element describes a relationship between paired identigens (OCAs) such as describes, acts on, is a, belongs to, did, did to, etc. Multiple OCAs occur for a common word when the word has multiple potential meanings (e.g., a baseball bat, a flying bat). 
     As depicted in  FIG. 8F , a specific example of the third step includes the interpretation module  304 , for each permutation of identigens of each tokenized word of the identified element information  340 , the interpretation module  304  generates, in accordance with interpretation rules  320  and a groupings list  334 , an equation package to include one or more of the identifiers of the tokenized words, a list of identifiers of the identigens of the equation package, a list of pairing identifiers for sequential pairs of identigens, and a quality metric associated with each sequential pair of identigens (e.g., likelihood of a proper interpretation). For instance, the interpretation module  304  produces a first equation package that includes a first identigen pairing of a black bat (e.g., flying bat with a higher quality metric level), the second pairing of bat eats (e.g., the flying bat eats, with a higher quality metric level), and a third pairing of eats fruit, and the interpretation module  304  produces a second equation package that includes a first pairing of a black bat (e.g., baseball bat, with a neutral quality metric level), the second pairing of bat eats (e.g., the baseball bat eats, with a lower quality metric level), and a third pairing of eats fruit. 
     A fourth step of the processing of the content to produce the knowledge includes selecting a surviving equation package associated with a most favorable confidence level. As depicted in  FIG. 8F , a specific example of the fourth step includes the interpretation module  304  applying interpretation rules  320  (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce a number of permutations of the sequential combinations of identigens to produce interpreted information  344  that includes identification of at least one equation package as a surviving interpretation SI (e.g., higher quality metric level). 
     Non-surviving equation packages are eliminated that compare unfavorably to pairing rules and/or are associated with an unfavorable quality metric levels to produce a non-surviving interpretation NSI  2  (e.g., lower quality metric level), where an overall quality metric level may be assigned to each equation package based on quality metric levels of each pairing, such that a higher quality metric level of an equation package indicates a higher probability of a most favorable interpretation. For instance, the interpretation module  304  eliminates the equation package that includes the second pairing indicating that the “baseball bat eats” which is inconsistent with a desired quality metric level of one or more of the groupings list  334  and the interpretation rules  320  and selects the equation package associated with the “flying bat eats” which is favorably consistent with the one or more of the quality metric levels of the groupings list  334  and the interpretation rules  320 . 
     A fifth step of the processing of the content to produce the knowledge utilizing the confidence level includes integrating knowledge of the surviving equation package into a knowledge database. For example, integrating at least a portion of the reduced OCA combinations into a graphical database to produce updated knowledge. As another example, the portion of the reduced OCA combinations may be translated into rows and columns entries when utilizing a rows and columns database rather than a graphical database. When utilizing the rows and columns approach for the knowledge database, subsequent access to the knowledge database may utilize structured query language (SQL) queries. 
     As depicted in  FIG. 8G , a specific example of the fifth step includes the ID control module  308  recovering fact base information  600  from SS memory  96  to identify a portion of the knowledge database for potential modification utilizing the OCAs of the surviving interpretation SI  1  (i.e., compare a pattern of relationships between the OCAs of the surviving interpretation SI  1  from the interpreted information  344  to relationships of OCAs of the portion of the knowledge database including potentially new quality metric levels). 
     The fifth step further includes determining modifications (e.g., additions, subtractions, further clarifications required when information is complex, etc.) to the portion of the knowledge database based on the new quality metric levels. For instance, the ID control module  308  causes adding the element “black” as a “describes” relationship of an existing bat OCA and adding the element “fruit” as an eats “does to” relationship to implement the modifications to the portion of the fact base information  600  to produce updated fact base information  608  for storage in the SS memory  96 . 
       FIG. 8H  is a logic diagram of an embodiment of a method for processing content to produce knowledge for storage within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-8E, 8F , and also  FIG. 8G . The method includes step  650  where a processing module of one or more processing modules of one or more computing devices of the computing system identifies words of an ingested phrase to produce tokenized words. The identified includes comparing words to known words of dictionary entries to produce identifiers of known words. 
     For each tokenized word, the method continues at step  651  where the processing module identifies one or more identigens that corresponds to the tokenized word, where each identigen describes one of an object, a characteristic, and an action (e.g., OCA). The identifying includes performing a lookup of identifiers of the one or more identigens associated with each tokenized word, where he different identifiers associated with each of the potential object, the characteristic, and the action associated with the tokenized word. 
     The method continues at step  652  where the processing module, for each permutation of sequential combinations of identigens, generates a plurality of equation elements to form a corresponding equation package, where each equation element describes a relationship between sequentially linked pairs of identigens, where each sequential linking pairs a preceding identigen to a next identigen. For example, for each permutation of identigens of each tokenized word, the processing module generates the equation package to include a plurality of equation elements, where each equation element describes the relationship (e.g., describes, acts on, is a, belongs to, did, did too, etc.) between sequentially adjacent identigens of a plurality of sequential combinations of identigens. Each equation element may be further associated with a quality metric to evaluate a favorability level of an interpretation in light of the sequence of identigens of the equation package. 
     The method continues at step  653  where the processing module selects a surviving equation package associated with most favorable interpretation. For example, the processing module applies rules (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens), to reduce the number of permutations of the sequential combinations of identigens to identify at least one equation package, where non-surviving equation packages are eliminated the compare unfavorably to pairing rules and/or are associated with an unfavorable quality metric levels to produce a non-surviving interpretation, where an overall quality metric level is assigned to each equation package based on quality metric levels of each pairing, such that a higher quality metric level indicates an equation package with a higher probability of favorability of correctness. 
     The method continues at step  654  where the processing module integrates knowledge of the surviving equation package into a knowledge database. For example, the processing module integrates at least a portion of the reduced OCA combinations into a graphical database to produce updated knowledge. The integrating may include recovering fact base information from storage of the knowledge database to identify a portion of the knowledge database for potential modifications utilizing the OCAs of the surviving equation package (i.e., compare a pattern of relationships between the OCAs of the surviving equation package to relationships of the OCAs of the portion of the knowledge database including potentially new quality metric levels). The integrating further includes determining modifications (e.g., additions, subtractions, further clarifications required when complex information is presented, etc.) to produce the updated knowledge database that is based on fit of acceptable quality metric levels, and implementing the modifications to the portion of the fact base information to produce the updated fact base information for storage in the portion of the knowledge database. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS. 8J and 8K  are schematic block diagrams of another embodiment of a computing system that includes the content ingestion module  300  of  FIG. 5E , the element identification module  302  of  FIG. 5E , the interpretation module  304  of  FIG. 5E , the answer resolution module  306  of  FIG. 5E , and the SS memory  96  of  FIG. 2 . Generally, an embodiment of this invention provides solutions where the computing system  10  supports for generating a query response to a query utilizing a knowledge database. 
     The generating of the query response to the query includes a series of steps. For example, a first step includes identifying words of an ingested query to produce tokenized words. As depicted in  FIG. 8J , a specific example of the first step includes the content ingestion module  300  comparing words of query info  138  to dictionary entries to produce formatted content  314  that includes identifiers of known words. For instance, the content ingestion module  300  produces identifiers for each word of the query “what black animal flies and eats fruit and insects?” 
     A second step of the generating of the query response to the query includes, for each tokenized word, identifying one or more identigens that correspond the tokenized word, where each identigen describes one of an object, a characteristic, and an action (OCA). As depicted in  FIG. 8J , a specific example of the second step includes the element identification module  302  performing a look up of identifiers, utilizing an element list  332  and in accordance with element rules  318 , of the one or more identigens associated with each tokenized word of the formatted content  314  to produce identified element information  340 . A unique identifier is associated with each of the potential object, the characteristic, and the action associated with a particular tokenized word. For instance, the element identification module  302  produces a single identigen identifier for each of the black color, an animal, flies, eats, fruit, and insects. 
     A third step of the generating of the query response to the query includes, for each permutation of sequential combinations of identigens, generating a corresponding equation package (i.e., candidate interpretation). The equation package includes a sequential linking of pairs of identigens, where each sequential linking pairs a preceding identigen to a next identigen. An equation element describes a relationship between paired identigens (OCAs) such as describes, acts on, is a, belongs to, did, did to, etc. 
     As depicted in  FIG. 8J , a specific example of the third step includes the interpretation module  304 , for each permutation of identigens of each tokenized word of the identified element information  340 , generating the equation packages in accordance with interpretation rules  320  and a groupings list  334  to produce a series of equation elements that include pairings of identigens. For instance, the interpretation module  304  generates a first pairing to describe a black animal, a second pairing to describe an animal that flies, a third pairing to describe flies and eats, a fourth pairing to describe eats fruit, and a fifth pairing to describe eats fruit and insects. 
     A fourth step of the generating the query response to the query includes selecting a surviving equation package associated with a most favorable interpretation. As depicted in  FIG. 8J , a specific example of the fourth step includes the interpretation module  304  applying the interpretation rules  320  (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce the number of permutations of the sequential combinations of identigens to produce interpreted information  344 . The interpreted information  344  includes identification of at least one equation package as a surviving interpretation SI  10 , where non-surviving equation packages, if any, are eliminated that compare unfavorably to pairing rules to produce a non-surviving interpretation. 
     A fifth step of the generating the query response to the query includes utilizing a knowledge database, generating a query response to the surviving equation package of the query, where the surviving equation package of the query is transformed to produce query knowledge for comparison to a portion of the knowledge database. An answer is extracted from the portion of the knowledge database to produce the query response. 
     As depicted in  FIG. 8K , a specific example of the fifth step includes the answer resolution module  306  interpreting the surviving interpretation SI  10  of the interpreted information  344  in accordance with answer rules  322  to produce query knowledge QK  10  (i.e., a graphical representation of knowledge when the knowledge database utilizes a graphical database). For example, the answer resolution module  306  accesses fact base information  600  from the SS memory  96  to identify the portion of the knowledge database associated with a favorable comparison of the query knowledge QK  10  (e.g., by comparing attributes of the query knowledge QK  10  to attributes of the fact base information  600 ), and generates preliminary answers  354  that includes the answer to the query. For instance, the answer is “bat” when the associated OCAs of bat, such as black, eats fruit, eats insects, is an animal, and flies, aligns with OCAs of the query knowledge. 
       FIG. 8L  is a logic diagram of an embodiment of a method for generating a query response to a query utilizing knowledge within a knowledge database within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-8D, 8J , and also  FIG. 8K . The method includes step  655  where a processing module of one or more processing modules of one or more computing devices of the computing system identifies words of an ingested query to produce tokenized words. For example, the processing module compares words to known words of dictionary entries to produce identifiers of known words. 
     For each tokenized word, the method continues at step  656  where the processing module identifies one or more identigens that correspond to the tokenized word, where each identigen describes one of an object, a characteristic, and an action. For example, the processing module performs a lookup of identifiers of the one or more identigens associated with each tokenized word, where different identifiers associated with each permutation of a potential object, characteristic, and action associated with the tokenized word. 
     For each permutation of sequential combinations of identigens, the method continues at step  657  where the processing module generates a plurality of equation elements to form a corresponding equation package, where each equation element describes a relationship between sequentially linked pairs of identigens. Each sequential linking pairs a preceding identigen to a next identigen. For example, for each permutation of identigens of each tokenized word, the processing module includes all other permutations of all other tokenized words to generate the equation packages. Each equation package includes a plurality of equation elements describing the relationships between sequentially adjacent identigens of a plurality of sequential combinations of identigens. 
     The method continues at step  658  where the processing module selects a surviving equation package associated with a most favorable interpretation. For example, the processing module applies rules (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce the number of permutations of the sequential combinations of identigens to identify at least one equation package. Non-surviving equation packages are eliminated the compare unfavorably to pairing rules. 
     The method continues at step  659  where the processing module generates a query response to the surviving equation package, where the surviving equation package is transformed to produce query knowledge for locating the portion of a knowledge database that includes an answer to the query. As an example of generating the query response, the processing module interprets the surviving the equation package in accordance with answer rules to produce the query knowledge (e.g., a graphical representation of knowledge when the knowledge database utilizes a graphical database format). 
     The processing module accesses fact base information from the knowledge database to identify the portion of the knowledge database associated with a favorable comparison of the query knowledge (e.g., favorable comparison of attributes of the query knowledge to the portion of the knowledge database, aligning favorably comparing entigens without conflicting entigens). The processing module extracts an answer from the portion of the knowledge database to produce the query response. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIG. 9A  is a schematic block diagram of another embodiment of a computing system that includes planning and outcome content sources  660 , the AI server  20 - 1  of  FIG. 1 , and the user device  12 - 1  of  FIG. 1 . The planning and outcome content sources  660  includes the content sources  16 - 1  through  16 -N of  FIG. 1 . The AI server  20 - 1  includes the processing module  50 - 1  of  FIG. 2  and the SS memory  96  of  FIG. 2 . The processing module  50 - 1  includes the collections module  120  of  FIG. 4A , the IEI module  122  of  FIG. 4A , and the query module  124  of  FIG. 4A . Generally, an embodiment of this invention presents solutions where the computing system  10  supports identifying best practices. 
     The identifying of the best practices includes a series of steps. For example, a first step includes facilitating gathering planning and outcome content based on one or more planning best practices requests. Planning content may include one or more of estimated outcomes, reports, future forecasts, action plans, etc. and outcome content may include one or more of closing reports, actual results, action plans successes, action plan failures, etc. The planning best practices requests may include one or more of a plan identifier (ID), outcome ID, planning and outcome content sources ID, supplemental outcome information, minimum threshold of quality requirement, type of management function, etc. 
     As a specific example of the first step to facilitate the gathering of the planning and outcome content, the IEI module  122  receives an LEI request  244  from the query module  124 . The query module  124  generates the IEI request  244  based on receiving a planning best practices request  662  from the user device  12 - 1 . The planning best practices request  662  includes the one or more planning best practice requests. 
     The IEI module  122  determines whether to gather incremental planning and outcome content based on a maturity level of associated knowledge of a knowledge base (e.g., fact base information  600  from SS memory  96 ) and indicates to gather more when the maturity level is low, i.e., little knowledge associated with the one or more requests. When gathering more, the IEI module  122  identifies one or more content sources of the planning and outcome content sources  660  based on the one or more planning best practice requests (e.g., sources associated with planning and/or outcome content associated with the request). 
     The IEI module  122  issues a collections request  132  to the collections module  120 , receives a collections response  134  from the collections module  120 , where the collections response  134  includes the incremental planning and outcome content. The collections module  120  issues one or more planning and outcome content requests  664  to the identified content sources of the planning and outcome content sources  660 , receives planning and outcome content responses  666  from the planning and outcome content sources  660 , and generates the collections response  134  based on the received planning and outcome content responses  666 . 
     A second step of the identifying of the best practices includes IEI processing the gathered planning and outcome content to produce incremental knowledge associated with the one or more planning best practices requests. As a specific example of the second step, the IEI module  122  IEI processes the incremental planning and outcome content to produce the incremental knowledge and facilitates storage of the incremental knowledge as fact base information  600  in the SS memory  96 . 
     A third step of the identifying of the best practices includes locating knowledge to respond to a particular planning best practices request of the one or more planning best practices requests. As a specific example of the third step, the IEI module  122  IEI processes the particular planning best practices request to generate request knowledge. The IEI module  122  compares the request knowledge to knowledge of the knowledge base to locate a portion of the knowledge base associated with the particular planning best practices request (i.e., compare a graphical database representation of the request to portions of the knowledge base to locate the portion) and indicates the locating of the knowledge. 
     A fourth step of the identifying of the best practices includes generating a planning best practices response with regards to the particular planning best practices request. As a specific example of the fourth step, the IEI module  122  utilizes the located portion of the knowledge base to produce the planning best practices response  668 . The planning best practices response  668  includes multiple facets of planning and results including one or more of planning quality metrics for plans, desired planning practices associated with favorable outcomes (e.g., practices correlated to desired outcomes), and undesired planning practices associated with unfavorable outcomes (e.g., biases, lapses, gaps, mismatches, incorrect assumptions, etc.). 
     Having produced the planning best practices response  668 , the IEI module  122  generates an IEI response  246  that includes the planning best practices response  668 . The IEI module  122  sends the IEI response  246  to the query module  124 . The query module  124  sends the planning best practices response  668  to the user device  12 - 1 . 
       FIG. 9B  is a logic diagram of an embodiment of a method for identifying best practices within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-8D , and also  FIG. 9A . The method includes step  680  where a processing module of one or more processing modules of one or more computing devices of the computing system facilitates gathering planning and outcome content based on one or more planning best practices requests. 
     The facilitating includes receiving the one or more planning best practices request and determining whether to gather incremental planning and outcome content based on a maturity level of the associated knowledge of the knowledge base (e.g., indicate together more when the maturity level is low, i.e., little knowledge associated with the one or more requests). When gathering more, the processing module identifies one or more content sources of planning and outcome content sources based on the one or more planning best practice requests (e.g., sources associated with planning and/or outcome content associated with the request). The processing module causes issuing of one or more planning and outcome content requests to the identified one or more content sources and receives the incremental planning and outcome content in response to the one or more planning and outcome content request. 
     The method continues at step  682  where the processing module IEI processes the gathered planning and outcome content to produce incremental knowledge associated with the one or more planning best practices requests. For example, the processing module IEI processes the incremental planning and content outcome to produce incremental knowledge and facilitates storage of the incremental knowledge in the knowledge base. 
     The method continues at step  684  where the processing module locates knowledge to respond to a particular planning best practices request of the one or more planning best practices requests. For example, the locating includes IEI processing the particular planning best practices request to generate request knowledge. The locating further includes comparing the request knowledge to knowledge of the knowledge base to locate a portion of the knowledge base associated with the particular planning best practices request. For instance, compare a graphical database representation of the request to portions of the knowledge base to locate the portion of the knowledge base. 
     The method continues at step  686  where the processing module generates a planning best practices response with regards to the particular planning best practices request. For example, the generating includes utilizing the located portion of the knowledge base to produce the planning best practices response. The response includes multiple facets of planning and results including one or more of planning quality metrics for plans, desired planning practices associated with favorable outcomes (i.e., practices correlated to desired outcomes), and undesired planning practices associated with unfavorable outcomes (i.e., biases, lapses, gaps, mismatches, incorrect assumptions, etc.). In an embodiment, the processing module sends the planning best practices response to a requesting entity. Alternatively, or in addition to, the processing module ingests the planning best practices response as new content to produce further incremental knowledge for integration with the knowledge base to produce an updated knowledge base. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIG. 10A  is a schematic block diagram of another embodiment of a computing system that includes open query content sources  700 , the AI server  20 - 1  of  FIG. 1 , and the user device  12 - 1  of  FIG. 1 . The open query content sources  700  includes the content sources  16 - 1  through  16 -N of  FIG. 1 . The AI server  20 - 1  includes the processing module  50 - 1  of  FIG. 2  and the SS memory  96  of  FIG. 2 . The processing module  50 - 1  includes the collections module  120  of  FIG. 4A , the IEI module  122  of  FIG. 4A , and the query module  124  of  FIG. 4A . Generally, an embodiment of this invention presents solutions where the computing system  10  supports providing a query dashboard. 
     The providing of the query dashboard includes a series of steps. For example, a first step includes facilitating gathering incremental content associated with one or more open queries to a knowledge base. As a specific example of the first step, the IEI module  122  receives one or more IEI requests  244  from the query module  124 . The query module  124  generates each of the one or more IEI requests  244  based on queries extracted from one or more query requests  136  from the user device  12 - 1  and identifies one or more content sources  16 - 1  through  16 -N of the open query content sources  700  associated with desired open query content associated with one or more of the queries. For instance, IEI process a query to produce query knowledge, compare the query knowledge to fact base information  600  from the SS memory  96  that includes stored knowledge, indicate to gather the Incremental content when the comparison is unfavorable. 
     The query module  124  issues a collections request  132  to the collections module  120 , where the collections request  132  includes the identity of the one or more content sources  16 - 1  through  16 -N and content identifier information. The collections module  120  issues one or more open query content request  704  to the identified content sources of the open query content sources  700  and receives a collections response  134  from the collections module  120 . The collections response  134  includes the incremental content. The collections module  120  extracts the incremental content from one or more open query content responses  706  received from the open query content sources  700 . 
     A second step of the providing of the query dashboard includes processing the incremental content to update the knowledge base with incremental knowledge. As a specific example of the second step, the IEI module  122  IEI processes the incremental content to produce the incremental knowledge (i.e. for each word of the incremental content, identify a group of identigens, for each pairwise grouping of sequential identigens, identify entigens of each of the group of identigens in accordance with rules to produce a sequence of entigens). The IEI module  122  integrates one or more portions of the sequence of entigens with entigen representations of the knowledge base to produce the updated knowledge base. 
     A third step of the providing of the query dashboard includes, for a first open query, generating query dashboard information. As a specific example of the third step, the IEI module  122  analyzes a status of the open query to include one or more of the query, previous interim query responses, a final query response, a quality metric associated with a response, an estimated time to next response, one or more content descriptors associated with content required but not yet received to facilitate a response, a quality level of the open query (i.e., maturity level of an interim response), and a suggested shift in an open query to facilitate producing a favorable response (i.e., suggested rewording of a question). 
     A fourth step of the providing of the query dashboard includes outputting, to a requesting entity associated with the first open query, one or more of the query dashboard information and a query response to the first open query. As a specific example of the fourth step, when the maturity level of the current response to the first open query is unfavorable, the IEI module  122  issues an IEI response  246  to the query module  124 . The IEI response  246  includes the query dashboard information. 
     The query module  124  issues a query dashboard response  708  to the user device  12 - 1  associated with the first open query, otherwise, when the maturity level of the current response to the first open query is favorable, the IEI module  122  issues the IEI response  246  to the query module  124 . The IEI response  246  includes a query response (i.e., a final answer). The query module  124  issues a query response  140  to the user device  12 - 1 . The query response  140  includes the query response. 
       FIG. 10B  is a logic diagram of an embodiment of a method for providing a query dashboard within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-8D , and also  FIG. 10A . The method includes step  730  where a processing module of one or more processing modules of one or more computing devices of the computing system facilitates gathering incremental content associated with one or more open queries to a knowledge base. 
     The facilitating includes one or more of receiving queries and identifying one or more content sources of open query content sources associated with desired open query content associated with one or more of the queries (i.e., IEI process a query to produce query knowledge, compare the query knowledge to fact base information of the knowledge base, indicate together the incremental content when the comparison is unfavorable). The facilitating further includes causing the issuing of one or more open query content requests to the identified content sources of the open query content sources, and receiving one or more open query content responses that includes the incremental content. 
     The method continues at step  732  where the processing module processes the incremental content to update the knowledge base with incremental knowledge. For example, the processing module IEI processes the incremental content to produce the incremental knowledge (i.e., for each word of the incremental content, identify a group of identigens, for each pairwise grouping of sequential identigens, identify entigens of each of the group of identigens in accordance with rules to produce a sequence of entigens). The processing module integrates one or more portions of the sequence of entigens with entigen representations of the knowledge base to produce the updated knowledge base. 
     The method continues at step  734  where, for a first open query, the processing module generates query dashboard information. For example, the processing module analyzes a status of the first open query to include one or more of the query, previous interim query responses, a final query response, and a quality metric associated with a response. The first open query may further include an estimated time to next responses, one or more content descriptors associated with content required but not yet received to facilitate a response, a quality level of the open query (i.e., a maturity level of an interim response), and a suggested shift in an open query to facilitate producing the favorable response (i.e., suggested rewording of a question). 
     The method continues at step  736  where the processing module outputs, to a requesting entity associated with the first open query, one or more of the query dashboard information and the query response to the first open query. For example, when the maturity level of the current response to the first open query is unfavorable, the processing module issues a query dashboard response to the requesting entity associated with the first open query, otherwise, when the maturity level of the current response to the first open query is favorable, the processing module issues a query response (i.e., a final answer) to the requesting entity. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIG. 11A  is a schematic block diagram of another embodiment of a computing system that includes new content sources  750 , the AI server  20 - 1  of  FIG. 1 , and the user device  12 - 1  of  FIG. 1 . The new content sources  750  includes the content sources  16 - 1  through  16 -N of  FIG. 1 . The AI server  20 - 1  includes the processing module  50 - 1  of  FIG. 2  and the SS memory  96  of  FIG. 2 . The processing module  50 - 1  includes the collections module  120  of  FIG. 4A , the IEI module  122  of  FIG. 4A , and the query module  124  of  FIG. 4A . Generally, an embodiment of this invention presents solutions where the computing system  10  supports processing a suspended query. 
     The processing of the suspended query includes a series of steps. For example, a first step includes determining to suspend an open query when a maturity level of a portion of a knowledge base associated with the open query is unfavorable. As a specific example of the first step, the IEI module  122  receives an IEI request  244  from the query module  124 . The query module  124  receives an initial query request  752  from the user device  12 - 1  and facilitates acquisition of knowledge that would provide a basis of a query response to the open query of the initial query request  752  (e.g., identify stored knowledge of the knowledge base from fact base information  600  of the fact base  592  of the SS memory  96  and may gather content associated with the open query for generation of the incremental knowledge to update the knowledge base). 
     When a knowledge acquisition time frame expires, the ID module  122  indicates to suspend the open query when the maturity level of the acquired knowledge of the knowledge base that would provide the basis of the query response to the open query is less than a low maturity threshold level (i.e., not enough knowledge has been acquired at the end of the timeframe). The ID module  122  generates query metadata  751  for storage in the SS memory  96 . The query metadata  751  includes one or more of an ID of the suspended query, timeframe of query suspension, the query, ID of previous content source(s), desired incremental content, desired response quality level, content trigger, query response recipient(s), etc. 
     A second step of the processing of the suspended query includes determining to reactivate the suspended query when detecting an enabling condition to support generating a favorable query response. The enabling condition includes at least one of detecting that the maturity level of the portion of the knowledge base associated with the open query is favorable and detecting that new content is now available to facilitate updating of the knowledge base such that the maturity level of the portion of the knowledge base is favorable. As a specific example of the second step, the ID module  122 , when updating the knowledge base, favorably compares newly acquired content to a content trigger of the query metadata  751 , retrieved from the SS memory  96 , where the content trigger is associated with the suspended query, and indicates to reactivate upon the favorable comparison when improving the maturity level of the portion of the knowledge base associated with the query. 
     Alternatively, or in addition to, ID module  122  receives a collections response  134  from the collections module  120  that includes new content that compares favorably to the content trigger. The collections module  120  receives new content responses  756  from the new content sources  750  in response to new content requests  754  issued to the new content sources  750  by the collections module  120  based on receiving a collections request  132  from the ID module  122 . 
     The IEI module  122  issues the collections request  132  based on extraction of a descriptor of the desired incremental content from the query metadata  751  of the suspended query. The ID module  122  IEI processes the incremental content to update the knowledge base (i.e., update the fact base  592 ), and where the ID module  122  indicates to reactivate when the updated knowledge base may be utilized to produce the favorable query response. 
     A third step of the processing of the suspended query includes, when reactivating the suspended query, facilitate generating the favorable query response based on an updated knowledge base. As a specific example of the third step, the ID module  122  processes the query to produce query knowledge, compares the query knowledge to the updated knowledge base, and extracts query response information to produce the favorable query response. 
     A fourth step of the processing of the suspended query includes outputting the favorable query response to at least one query response recipient. As a specific example of the fourth step, the IEI module  122  identifies, based on the query metadata  751 , an identifier of at least one query response recipient, and issues an IEI response  246  to the query module  124 . The IEI response  246  includes the favorable query response and the identifier of the at least one query response recipient (i.e., the user device  12 - 1 ). The query module  124  issues a delayed query response  758  to the user device  12 - 1 . The delayed query response  758  includes the favorable query response. 
       FIG. 11B  is a logic diagram of an embodiment of a method for processing a suspended query within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-8D , and also  FIG. 11A . The method includes step  760  where a processing module of one or more processing modules of one or more computing devices of the computing system determines to suspend an open query based on the maturity level of a portion of a knowledge base associated with the open query. For example, the processing module receives an initial query request and facilitates acquisition of knowledge that would provide a basis of a query response to the open query of the initial query response (e.g., identify stored knowledge of the knowledge base and may gather content associated with the open query for generation of the incremental knowledge to update the knowledge base). 
     When a knowledge acquisition time frame expires, the processing module indicates to suspend the open query when the maturity level of the acquired knowledge of the knowledge base that would provide the basis of the query response to the open query is less than a low maturity threshold level (i.e., not enough knowledge has been acquired at the end of the timeframe). The processing module generates query metadata for storage. The query metadata includes one or more of an identifier (ID) of the suspended query, timeframe of query suspension, the query, ID of previous content source(s), desired incremental content, desired response quality level, content trigger, query response recipient(s), etc. 
     The method continues at step  762  where the processing module determines to reactivate the suspended query when detecting an enabling condition to support generating the favorable query response. For example, when updating the knowledge base, the processing module favorably compares newly acquired content to a content trigger of the query metadata. The content trigger is associated with the suspended query. The processing module indicates to reactivate upon the favorable comparison when improving the maturity level of the portion of the knowledge base associated with the query. 
     Alternatively, or in addition to, the processing module receives new content that compares favorably to the content trigger, where new content responses are received from new content sources in response to new content requests issued to the new content sources, where the new content requests are based on extraction of a descriptor of the desired incremental content from the query metadata of the suspended query. The processing module ID processes the incremental content to update the knowledge, where the processing module indicates to reactivate the suspended query when the updated knowledge base may be utilized to produce the favorable query response. 
     When reactivating the suspended query, the method continues at step  764  where the processing module facilitates generating the favorable query response based on the updated knowledge base. The facilitating includes processing the query to produce query knowledge, comparing the query knowledge to the updated knowledge base, and extracting query response information to produce the favorable query response. 
     The method continues at step  766  where the processing module outputs the favorable query response to at least one query response recipient. For example, the processing module identifies, based on the query metadata, an identifier of at least one query response recipient, issues the favorable query response, based on the identifier of the at least one query response recipient, to the at least one query response recipient. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS. 12A-12D  are schematic block diagrams of another embodiment of a computing system illustrating an embodiment of a method for collecting content to remedy potentially incomplete and/or incorrect knowledge. The computing system includes the content ingestion module  300  of  FIG. 5E , the element identification module  302  of  FIG. 5E , the interpretation module  304  of  FIG. 5E , the answer resolution module  306  of  FIG. 5E , a corrective content source  780 , and a knowledge database  782 . In an embodiment, the corrective content source  780  is implemented utilizing one or more of the content sources  16 - 1  through  16 -N of  FIG. 1 . In an embodiment, the knowledge database  782  is implemented utilizing the fact base  592  of  FIG. 8A . 
       FIG. 12A  illustrates an example of the method of the collecting content to remedy the potentially incomplete and/or incorrect knowledge where the interpretation module  304  detects a defective entigen group  784 . In an embodiment, the defective entigen group  784  is generated based on new content received from the element identification module  302 . In another embodiment, the defective entigen group  784  is recovered from the knowledge database  782 . 
     The knowledge database  782  includes the defective entigen group  784  and a multitude of other entigen groups associated with a variety of topics. The defective entigen group  784  includes a plurality of entigens and one or more entigen relationships between at least some of the plurality of entigens. The defective entigen group  784  represents knowledge of a topic of the variety of topics. Examples of the entigen relationships includes “describes”, “acts on”, “is a”, “belongs to”, “did”, and “did to.” 
     The detecting of the defective entigen group  784  includes a variety of approaches. A first approach includes determining that a number of entigens of the defective entigen group  784  compares unfavorably (e.g., too few, too many, difference greater than a difference threshold) to a number of other entigens of another entigen group associated with other knowledge of another topic. The knowledge database  782  further includes the other entigen group. For example, the interpretation module  304  receives entigen information  786  from the knowledge database  782  that includes an entigen group associated with bats and another entigen group associated with birds. The interpretation module  304  determines that the bat entigen group has far fewer (e.g., more than the difference threshold number) entigens than the bird entigen group. 
     A second approach to detect the defective entigen group  784  includes identifying an incorrect entigen of the defective entigen group  784 . For example, the interpretation module  304  detects an entigen that does not belong to the defective entigen group  784  (e.g., logically inconsistent with other entigens of the defective entigen group  784 ). 
     A third approach to detect the defective entigen group  784  includes identifying an incorrect entigen relationship between first and second entigens of the defective entigen group  784 . For example, the interpretation module  304  detects an error in a listed entigen relationship between entigens of the defective entigen group  784  (e.g., logically inconsistent with other entigen relationships of the defective entigen group  784 ). 
       FIG. 12B  further illustrates the example of the method of the collecting content to remedy the potentially incomplete and/or incorrect knowledge where the interpretation module  304  obtains corrective content  790  for the topic based on the defective entigen group  784 . The obtaining includes a series of steps. A first step includes identifying a defect of the defective entigen group. The defect includes one or more of too few entigens, too many entigens, and incorrect entigen, and an incorrect entigen relationship. For example, the interpretation module  304  identifies the defect as too few entigens associated with the “bat” entigen when the other entigen group associated with birds has many more entigens. 
     Having identified the defect, a second step includes the interpretation module  304  identifying a content aspect based on the defect. The content aspect includes at least one of related entigens, entigen types (e.g., object, characteristic, action), and related entigen relationships. For example, the interpretation module  304  identifies the content aspect as content associated with one or more of bats, flying bats, bats that are mammals, and what bats eat. 
     Having identified the content aspect, a third step includes the interpretation module  304  selecting a content source based on the content aspect. The selecting includes one or more of accessing a list of content sources associated with various content aspects, identifying a content source associated with the defective entigen group  784 , and identifying content sources associated with the content aspect. For example, the interpretation module  304  selects the corrective content source  780  when the corrective content source  780  is known to include content associated with bats. 
     Having selected the content source, a fourth step includes the interpretation module  304  obtaining the corrective content from the content source based on the content aspect. For example, the interpretation module  304  issues a content request  788  to the corrective content source  780 , where the content request  788  specifies content associated with bats. In response to the content request  788 , the corrective content source  780  sends the corrective content  790  to the content ingestion module  300  for further processing. 
       FIG. 12C  further illustrates the example of the method of the collecting content to remedy the potentially incomplete and/or incorrect knowledge where the interpretation module generates a corrective entigen group  800  based on the corrective content  790 . The generating of the corrective entigen group  800  includes a series of steps. A first step includes the content ingestion module  300  receiving and parsing the corrective content  790  to produce phrase words  792  that includes a plurality of words. For example, when the corrective content  790  includes “black bat eats fruit”, the content ingestion module  300  produces the phrase words  792  to include “black”, “bat”, “eats”, and “fruit.” 
     Having received the phrase words  792 , a second step of the generating the corrective entigen group  800  includes the element identification module  302  identifying a set of identigens for each word of the corrective content  790  to produce a plurality of sets of identigens (e.g., hereafter interchangeably referred to as sets of identigens  796 ). A set of identigens of the plurality of sets of identigens represents one or more different meanings of a word of the corrective content  790 . 
     As an example of the identifying the sets of identigens  796 , the element identification module  302  accesses the knowledge database  782  utilizing the phrase words  792  to recover identigen information  794 . The identigen information  794  includes, for each word, a set of associated identigens. A set of identigens of the sets of identigens  796  includes one or more different meanings of a word of the corrective content  790 . For instance, identigens of a first word of the corrective content  790  includes one or more different meanings of the first word. As a particular instance, meanings of the word “black” includes an identigen no. 1 “dark-skin people”, an identigen no. 2 for “black color”, and another identigen no. 3 for “to make black.” 
     Having received the sets of identigens  796 , a third step of the generating the corrective entigen group  800  includes the interpretation module  304  identifying one valid identigen of each set of identigens of the plurality of sets of identigens by applying identigen rules  798  to the plurality of sets of identigens to produce the corrective entigen group  800 . The corrective entigen group  800  represents a most likely meaning of the corrective content  790 . 
     As an example of the identifying the one valid identigens of each set of identigens, the interpretation module accesses the knowledge database  782  to recover the identigen rules  798 . The identigens rules  798  includes, for each adjacent pair of identigens of each of the sets of identigens  796 , a rule to indicate validity (e.g., valid, invalid). Having recovered the identigen rules  798 , the interpretation module  304  applies the identigen rules  798  to the sets of identigens  796  to produce the corrective entigen group  800 . In an instance of producing the corrective entigen group  800 , the interpretation module  304  determines that the identigen rules  798  indicates that a 2-5 identigen pairing is valid, a 5-8 identigen pairing is valid, and an 8-9 identigen pairing is valid to produce the corrective entigen group  800 . The corrective entigen group  800  includes entigens 2, 5, 8, and 9 representing the most likely meaning of the corrective content  790  “black bat eats fruit.” 
       FIG. 12D  further illustrates the example of the method of the collecting content to remedy the potentially incomplete and/or incorrect knowledge where the answer resolution module  306  updates the defective entigen group  784  utilizing the corrective entigen group  800  to produce a curated entigen group  802 . The updating includes a variety of approaches. 
     A first approach of the variety of approaches includes the answer resolution module  306  replacing an incorrect entigen of the defective entigen group  784  with a correct entigen of the corrective entigen group  800 . For example, the answer resolution module  306  replaces the “flies” entigen (e.g., incorrect entigen) with a “crawls” entigen (e.g., corrective entigen) when bats are known to crawl instead of fly. 
     A second approach of the variety of approaches includes the answer resolution module  306  updating an incorrect entigen relationship between first and second entigens of the defective entigen group  784  with a correct entigen relationship between the first and second entigens of the corrective entigen group  800 . For example, the answer resolution module  306  replaces a relationship between the “eats” and “insects” entigens of the defective entigen group  784  when that relationship indicates “is a” with a relationship that indicates “does to” from the corrective entigen group  800 . 
     A third approach of the variety of approaches includes the answer resolution module  306  augmenting the defective entigen group  784  utilizing the corrective entigen group  800  to produce the curated entigen group  802 . For example, the answer resolution module  306  attaches the “black” entigen of the corrective entigen group  800  to the “bat” entigen of the defective entigen group  784  and attaches the “fruit” entigen of the corrective entigen group  800  to the “eats” entigen of the defective entigen group  784  to produce the curated entigen group  802 . The overall method described above may repeat until a favorable entigen group associated with bats is produced. 
     Having produced the curated entigen group  802 , the method further includes the answer resolution module  306  storing the curated entigen group  802  in the knowledge database  782 . The storing includes one of replacing the defective entigen group  784  of the knowledge database  782  with the curated entigen group  802  or augmenting the defective entigen group  784  of the knowledge database  782  with the curated entigen group  802 . 
     The method described above in conjunction with any module can alternatively be performed by any modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIG. 13A  is a schematic block diagram of another embodiment of a computing system that includes unknown trust content sources  820  and the AI server  20 - 1  of  FIG. 1 . The unknown trust content sources  820  includes the content sources  16 - 1  through  16 -N of  FIG. 1 . The AI server  20 - 1  includes the processing module  50 - 1  of  FIG. 2  and the SS memory  96  of  FIG. 2 . The processing module  50 - 1  includes the collections module  120  of  FIG. 4A  and the IEI module  122  of  FIG. 4A . the SS memory  96  includes curated knowledge  822  and non-curated knowledge  824  Generally, an embodiment of this invention presents solutions where the computing system  10  supports curating of new knowledge. 
     The curating of the new knowledge includes a series of steps. For example, a first step includes IEI processing new content to produce incremental knowledge. As a specific example of the first step, the IEI module  122  issues a collections request  132  to the collections module  120 , where the collections module  120  issues one or more new content request  826  to content sources  16 - 1  through  16 -N of the unknown trust content sources  820 . The collections module  120  receives one or more new content responses  828  from the unknown trust content sources  820 , where the new content responses  828  includes the new content (e.g., content that has not been curated such that a correctness level is unknown, etc.). 
     The collections module  120  issues a collections response  134  to the IEI module  122 , where the collections response  134  includes the new content. The IEI module  122  IEI processes the new content to produce the incremental knowledge. 
     A second step of the curating of the new knowledge includes, when the incremental knowledge is non-redundant with regards to a knowledge base, determining whether the incremental knowledge is true (e.g., verifiable), false (e.g., verified as false), or unknown (e.g., unverifiable). As a specific example of the second step, the IEI module  122  indicates that the incremental knowledge is non-redundant when a comparison of the incremental knowledge with knowledge of the knowledge base (e.g., fact base information  600  from the SS memory  96 ) is unfavorable (e.g., not the same). The IEI module  122  applies one or more verification tests to the incremental knowledge, indicates whether true, false, or unknown, where the verification tests may further include comparing to another knowledge base, inferring based on current knowledge of the knowledge base, and processing a response from a trusted content and/or knowledge source. 
     A third step of the curating of the new knowledge includes, when the incremental knowledge is true, integrating the incremental knowledge with the knowledge base as curated knowledge. As a specific example of the third step, the IEI module  122  modifies a portion of curated knowledge  822  to include the incremental knowledge when the incremental knowledge is true (e.g., recover curated knowledge  822  from the SS memory  96 , integrate the incremental knowledge with the curated knowledge  822  to produce updated curated knowledge  822  for storage as fact base information  600  in the SS memory  96 ). 
     A fourth step of the curating of the new knowledge includes, when the incremental knowledge is unknown, determining whether the incremental knowledge conflicts with the knowledge base. As a specific example of the fourth step, the IEI module  122  compares a portion of the knowledge base to the incremental knowledge and indicates conflict when the comparison is unfavorable (e.g., conflicting facts etc.)). For instance, the IEI module  122  compares fact base information  600  recovered from the SS memory  96  to the incremental knowledge and indicates the conflict when the comparison indicates a contradiction. 
     A fifth step of the curating of the new knowledge includes, when the incremental knowledge does not conflict with the knowledge base, integrating the incremental knowledge with the knowledge base as curated knowledge. As a specific example of the first step, the IEI module  122  modifies a portion of curated knowledge  822  to include the incremental knowledge. 
     A sixth step of the curating of the new knowledge includes, when the incremental knowledge conflicts with the knowledge base, integrating the incremental knowledge with the knowledge base as non-curated knowledge. As a specific example of the sixth step, the IEI module  122  modifies a portion of non-curated knowledge  824  to include the incremental knowledge. For instance, the IEI module  122  recovers non-curated knowledge  824  from the SS memory  96 , integrates the incremental knowledge with the non-curated knowledge  824  to produce updated non-curated knowledge  824  for storage as fact base information  600  in the SS memory  96 . 
       FIG. 13B  is a logic diagram of an embodiment of a method for curating new knowledge within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-8D , and also  FIG. 13A . The method includes step  850  where a processing module of one or more processing modules of one or more computing devices of the computing system IEI processes new content to produce incremental knowledge, where the incremental knowledge is represented as relationships between entigens. For example, the processing module obtains the new content from one or more unknown trust content sources and IEI processes the new content to produce incremental knowledge (e.g., of unknown trust). 
     When the incremental knowledge is non-redundant with regards to a knowledge base, the method continues at step  852  where the processing module determines whether the incremental knowledge is true, false, or unknown. For example, the processing module indicates that the incremental knowledge is non-redundant when a comparison of the incremental knowledge with knowledge of the knowledge base is unfavorable (e.g., not the same), applies one or more verification tests to the incremental knowledge and indicates whether true, false, or unknown. The verification tests include one or more of comparing to another knowledge base, inferring based on current knowledge of the knowledge base, and processing the response from a trusted content and/or knowledge source. 
     When the incremental knowledge is true, the method continues at step  854  where the processing module integrates the incremental knowledge with the knowledge base as curated knowledge. For example, the processing module modifies a portion of curated knowledge of the knowledge base to include the incremental knowledge. 
     When the incremental knowledge is unknown, the method continues at step  856  where the processing module determines whether the incremental knowledge conflicts with the knowledge base. For example, the processing module compares a portion of the knowledge base to the incremental knowledge and indicates the conflict when the comparison is unfavorable (e.g., conflicting facts, etc.). 
     When the incremental knowledge does not conflict with the knowledge base, the method continues at step  858  where the processing module integrates the incremental knowledge with the knowledge base as curated knowledge. For example, the processing module modifies a portion of curated knowledge of the knowledge base to include the incremental knowledge. 
     When the incremental knowledge conflicts with the knowledge base, the method continues at step  860  where the processing module integrates the incremental knowledge with the knowledge base as non-curated knowledge. For example, the processing module modifies a portion of non-curated knowledge of the knowledge base to include the incremental knowledge. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS. 13C-13F  are schematic block diagrams of another embodiment of a computing system illustrating an embodiment of a method for curating knowledge. The computing system includes the content ingestion module  300  of  FIG. 5E , the element identification module  302  of  FIG. 5E , the interpretation module  304  of  FIG. 5E , the answer resolution module  306  of  FIG. 5E , the user device  12 - 1  of  FIG. 1 , and the knowledge database  782  of  FIG. 12A . In another embodiment, the user device  12 - 1  is replaced by one or more of the content sources  16 - 1  through  16 -N of  FIG. 1 . In an embodiment, the knowledge database  782  is implemented utilizing the fact base  592  of  FIG. 8A . 
       FIG. 13C  illustrates an example method operation of the curating of the knowledge, where, a first step includes the interpretation module  304  detecting an incomplete entigen group  830 . In an embodiment, the incomplete entigen group  830  is generated based on new content received from the element identification module  302 . In another embodiment, the incomplete entigen group  830  is recovered from the knowledge database  782 . In the example, the knowledge database  782  includes the incomplete entigen group. The incomplete entigen group includes a plurality of entigens and one or more entigen relationships between at least some of the plurality of entigens. The incomplete entigen group represents at least some knowledge of a topic. 
     The detecting the incomplete entigen group includes a variety of approaches. A first approach includes determining that a number of entigens of the incomplete entigen group compares is less than a minimum number of entigens threshold number. For example, the interpretation module  304  interprets entigen information  786  from the knowledge database  782  to determine that the number of entigens is only 6 for an entigen group about a bat and the minimum number of entigens threshold number is 7. 
     A second approach includes determining that the incomplete entigen group does not contain an expected yet missing entigen of an expected category. For example, the interpretation module  304  determines that a “what bats eat” category is missing an entigen to specify one or more examples of what bats eat (e.g., a value as an example). The interpretation module  304  produces the incomplete entigen group  830  to include entigens for bat, flies, mammal, animal, and eats identifying a value entigen as missing to specify what bats eat. 
     A third approach includes determining that the incomplete entigen group does not contain an expected yet missing entigen relationship between first and second entigens of the incomplete entigen group. For example, the interpretation module  304  determines that there is no relationship from what bats eat entigen and another entigen other than the bat entigen. 
       FIG. 13D  further illustrates the example method operation of the curating of the knowledge, where, having detected the incomplete entigen group, a second step includes the interpretation module  304  issuing, via a user interface, an additive content query based on the incomplete entigen group (e.g., to obtain content related to what bats eat). For example, the interpretation module  304  generates a content request  788  associated with requesting what bats eat and sends the content request  788  to the user device  12 - 1 . In an alternative embodiment, the interpretation module  304  sends the content request  788  to the content source  16 - 1  of  FIG. 1 . 
     Having issued the additive content query, a third step of the example method of operation includes the content ingestion module  300  obtaining, via the user interface, additive content  832  for the topic based on the incomplete entigen group  830 . The issuing the query and the obtaining the additive content  832  for the topic based on the incomplete entigen group includes a series of sub-steps. A first sub-step includes identifying at least one of a missing entigen and a missing entigen relationship of the incomplete entigen group as previously discussed. 
     A second sub-step includes identifying a content aspect based on the at least one of the missing entigen and the missing entigen relationship. For example, the interpretation module  304  identifies “what bats eat” as the content aspect when the eats entigen is dangling as depicted. 
     A third sub-step includes selecting a content source based on the content aspect. For example, the interpretation module  304  selects the user device  12 - 1  when the user device  12 - 1  is utilized to acquire additive content from time to time (e.g., a subject matter expert is available). As another example, the interpretation module  304  selects the content source  16 - 1  when the content source  16 - 1  is known to be rich with content associated with bats including what bats eat. 
     A fourth sub-step includes generating the additive content query based on the content aspect. For example, the interpretation module  304  generates the content request  788  to include a query associated with what bats eat. 
     A fifth sub-step includes the interpretation module  304  outputting, via the user interface, the additive content query to the user device  12 - 1  and/or the content source  16 - 1  in accordance with the selecting of the content source. A sixth sub-step includes the content ingestion module  300  receiving, via the user interface, the additive content  832  from the content source based on the content aspect in response to the additive content query. For example, the content ingestion module  300  receives the additive content  832  “black bat eats fruit.” 
       FIG. 13E  further illustrates the example method operation of the curating of the knowledge, where, having obtained the additive content, a fourth step includes the element identification module  302  determining a set of identigens for each word of a plurality of phrase words  792  of the additive content a  32  to produce a plurality of sets of identigens  796 . Each identigen of the set of identigens  796  includes a meaning identifier, an instance identifier, and a time reference. Each meaning identifier associated with a particular set of identigens represents a different meaning of one or more different meanings of a corresponding word of the plurality of phrase words  792  of the additive content  832 . 
     A first set of identigens of the plurality of sets of identigens is produced for a first word of the plurality of words of the additive content. For example, the content ingestion module  300  parses words of the additive content  832  to produce the phrase words  792  (e.g., black, bat, eats, fruit) as previously discussed. The element identification module  302  utilizes identigen information  794  from the knowledge database  782  to identify the sets of identigens for each of the words as previously discussed. 
     Having produced the sets of identigens  796 , a fifth step of the example method of operation includes the interpretation module  304  interpreting, in accordance with identigen (pairing) rules  798  of the knowledge database  782 , the plurality of sets of identigens  796  to determine a most likely meaning interpretation of the additive content a  32  and produce an additive entigen group  834  including one or more entigens. The additive entigen group  834  represents the most likely meaning interpretation of the additive content  832 . Each entigen of the additive entigen group corresponds to a selected identigen of the set identigens having a selected meaning of the one or more different meanings of each word of the plurality of words. Each entigen of the additive entigen group represents a single conceivable and perceivable thing in space and time that is independent of language and corresponds to a time reference of the selected identigen associated with the additive entigen group. The selected identigen favorably pairs with at least one corresponding sequentially adjacent identigen of another set of identigens of the plurality of sets of identigens based on the identigen (pairing) rules  798 . For example, the interpretation module  304  interprets identigen rules  798  recovered from the knowledge database  782  with regards to the sets of identigens  796  to produce the additive entigen group  834  linking entigens 2, 5, 8, 9 for the most likely meanings of the words black bat eats fruit as previously discussed. 
       FIG. 13F  further illustrates the example method operation of the curating of the knowledge where, having produced the additive entigen group  834 , a sixth step includes the answer resolution module  306  determining whether the additive entigen group  834  conflicts with the incomplete entigen group  830 . The determining whether the additive entigen group conflicts with the incomplete entigen group includes a variety of approaches. A first approach includes identifying a conflict between a first attribute value entigen of the additive entigen group and a second attribute value entigen of the incomplete entigen group for a common attribute category. For example, the answer resolution module  306  would identify the conflict if the incomplete entigen group already included in entigen that is the opposite of fruit when the additive entigen group brings the fruit entigen to this creation process. 
     A second approach includes determining that a second attribute value entigen of the additive entigen group has a value that has not been verified. For example, the answer resolution module  306  determines that the entigen value of fruit for what bats eat has never been verified by any other available knowledge associated with the knowledge database  782 . For instance, the answer resolution module  306  indicates the conflict since the fruit value has never been verified. As such, the answer resolution module  306  assigns an un-curated status to the entigen for fruit at this point in time. Subsequent verification may result in changing a status to curated. 
     Having determined whether the additive entigen group conflicts with the incomplete entigen group, when the additive entigen group conflicts with the incomplete entigen group a seventh step of the example method of operation includes updating the incomplete entigen group utilizing the additive entigen group to produce an updated entigen group with an un-curated status. For example, the answer resolution module  306  adds the missing entigen 9 for fruit to the incomplete entigen group  830  to produce the updated entigen group  836  that includes linked entigens for the meanings of black, bat, eats, fruit and bats fly, and bats are mammals instances of animals. 
     The seventh step further includes the answer resolution module  306  outputting, via the user interface, a representation of the updated entigen group  838  with an indication of the un-curated status. For example, the answer resolution module  306  converts the updated entigen group  836  into plaintext (e.g., flying mammal animal bats are black and eat fruit) to produce the representation of the updated entigen group  838  and sends the representation to the user device  12 - 1 . In another embodiment, the answer resolution module  306  sends the representation to the content source  16 - 1 . 
     Having produced the updated entigen group  836 , the example of operation includes the answer resolution module  306  storing the updated entigen group  836  in the knowledge database  782  along with the un-curated status for the fruit entigen. 
     As another example, when the additive entigen group does not conflict with the incomplete entigen group (e.g., the fruit entigen is verified by another source or somewhere else in the knowledge database), the answer resolution module  306  updates the incomplete entigen group utilizing the additive entigen group to produce the updated entigen group with a curated status. Having produced the updated entigen group, the answer resolution module  306  outputs, via the user interface, the representation of the updated entigen group, this time with an indication of the curated status 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS. 14A and 14B  are schematic block diagrams of another embodiment of a computing system that includes the content ingestion module  300  of  FIG. 5E , the element identification module  302  of  FIG. 5E , the interpretation module  304  of  FIG. 5E , the IEI control module  308  of  FIG. 5E , and the SS memory  96  of  FIG. 2 . Generally, an embodiment of this invention presents solutions where the computing system  10  supports processing content to produce knowledge utilizing a confidence level. 
     The processing of the content to produce the knowledge utilizing the confidence level includes a series of steps. For example, a first step includes identifying words of an ingested phrase to produce tokenized words. As depicted in  FIG. 14A , a specific example of the first step includes the content ingestion module  300  comparing words of source content  310  to dictionary entries to produce formatted content  314  that includes identifiers of known words. For instance, the content ingestion module  300  identifies words “the”, “black”, “bat”, “eats”, and “fruit” when the ingested phrase includes “The black bat eats fruit.” 
     A second step of the processing of the content to produce the knowledge utilizing the confidence level includes, for each tokenized word, identifying one or more identigens that correspond the tokenized word, where each identigen describes one of an object, a characteristic, and an action. As depicted in  FIG. 14A , a specific example of the second step includes the element identification module  302  performing a look up of identigen identifiers, utilizing an element list  332  and in accordance with element rules  318 , of the one or more identigens associated with each tokenized word of the formatted content  314  to produce identified element information  340 . 
     A unique identifier is associated with each of the potential object, the characteristic, and action (OCA) associated with a particular tokenized word. For instance, the element identification module  302  identifies a functional symbol for “the”, identifies a single identigen for “black”, identifies two identigens for “bat” (e.g., baseball bat and flying bat), identifies a single identigen for “eats”, and identifies a single identigen for “fruit.” 
     A third step of the processing of the content to produce the knowledge utilizing the confidence level includes, for each permutation of sequential combinations of identigens, generating a corresponding equation package (i.e., candidate interpretation), where the equation package includes a sequential linking of pairs of identigens. Each sequential linking pairs a preceding identigen to a next identigen. An equation element describes a relationship between paired identigens (OCAs) such as describes, acts on, is a, belongs to, did, did to, etc. 
     Multiple OCAs occur for a common word when the word has multiple meanings (e.g., a baseball bat, a flying bat). As depicted in  FIG. 14A , a specific example of the third step includes the interpretation module  304 , for each permutation of identigens of each tokenized word of the identified element information  340 , including with all other permutations of all other tokenized words to generate the equation packages in accordance with interpretation rules  320  and a groupings list  334 . For instance, the interpretation module  304  produces a first equation package that includes a first pairing of a black bat (e.g., flying bat), the second pairing of bat eats (e.g., the flying bat eats), and a third pairing of eats fruit, and the interpretation module  304  produces a second equation package that includes a first pairing of a black bat (e.g., baseball bat), the second pairing of bat eats (e.g., the baseball bat eats), and a third pairing of eats fruit. 
     A fourth step of the processing of the content to produce the knowledge utilizing the confidence level includes selecting a surviving equation package associated with a most favorable confidence level. As depicted in  FIG. 14A , a specific example of the fourth step includes the interpretation module  304  applying interpretation rules  320  (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce the number of permutations of the sequential combinations of identigens to produce interpreted information  344  that includes identification of at least one equation package as a surviving interpretation  880  (e.g., higher confidence level). 
     Non-surviving equation packages are eliminated that compare unfavorably to pairing rules and/or are associated with an unfavorable confidence level to produce a non-surviving interpretation  882  (e.g., lower confidence level), where a confidence level is assigned to each equation package such that a higher confidence level indicates that the equation package includes equation elements that are substantially the same as in other equation packages (i.e., more consistency). For instance, the interpretation module  304  eliminates the equation package that includes the second pairing indicating that the “baseball bat eats” which is inconsistent with one or more of the groupings list  334  and the interpretation rules  320  and selects the equation package associated with the “flying bat eats” which is favorably consistent with the one or more of the groupings list  334  and the interpretation rules  320 . 
     A fifth step of the processing of the content to produce the knowledge utilizing the confidence level includes integrating knowledge of the surviving equation package into a knowledge base. For example, integrating at least a portion of the reduced OCA combinations into a graphical database to produce updated knowledge. As another example, the portion of the reduced OCA combinations may be translated into rows and columns entries when utilizing a rows and columns database rather than a graphical database. 
     As depicted in  FIG. 14B , a specific example of the fifth step includes the ID control module  308  recovering fact base information  600  from SS memory  96  to identify a portion of the knowledge base for potential modification utilizing the OCAs of the surviving equation package (i.e., compare a pattern of relationships between the OCAs of the surviving equation package from the interpreted information  344  to relationships of OCAs of the portion of the knowledge base including new confidence levels). The steps further includes the IEI control module  308  determining modifications (e.g., additions, subtractions, further clarifications required when information is complex, etc.) to the portion of the knowledge base based on the new confidence levels (i.e., add the element “black” as a “describes” relationship of an existing bat OCA and add the element “fruit” as a eats “does to” relationship), and implement the modifications to the portion of the fact base information  600  to produce updated fact base information  608  for storage in the SS memory  96 . 
       FIG. 14C  is a logic diagram of an embodiment of a method for processing content to produce knowledge utilizing a confidence level within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-8D, 14A , and also  FIG. 14B . The method includes step  900  where a processing module of one or more processing modules of one or more computing devices of the computing system identifies words of an ingested phrase to produce tokenized words. The identified includes comparing words to known words of dictionary entries to produce identifiers of known words. 
     For each tokenized word, the method continues at step  902  where the processing module identifies one or more identigens that corresponds to the tokenized word, where each identigen describes one of an object, a characteristic, and an action. The identifying includes performing a lookup of identifiers of the one or more identigens associated with each tokenized word, where the different identifiers associated with each of the potential object, the characteristic, and the action associated with the tokenized word. 
     For each permutation of sequential combinations of identigens, the method continues at step  904  where the processing module generates a corresponding equation package, where the equation package includes a sequential linking of pairs of identigens. Each sequential linking pairs a preceding identigen to a next identigen. An equation element describes a relationship and confidence level between paired identigens. For example, for each permutation of identigens of each tokenized word, the processing module includes with all other permutations of all other tokenized words to generate the equation packages. A confidence level is assigned to each equation package such that a higher confidence level indicates that the equation package is associated with equation elements that are substantially the same as in other equation packages (i.e., more consistency). 
     The method continues at step  906  where the processing module selects a surviving equation package associated with a most favorable equation element confidence level. For example, the processing module applies rules (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce the number of permutations of the sequential combinations of identigens to identify at least one equation package. Non-surviving equation packages are eliminated that compare unfavorably to pairing rules and/or are associated with an unfavorable confidence level. 
     The method continues at step  908  where the processing module integrates knowledge of the surviving equation package into a knowledge base. For example, the processing module integrates at least a portion of the reduced OCA combinations into a graphical database to produce updated knowledge. For instance, the processing module recovers fact base information from storage of the knowledge base to identify a portion of the knowledge base for potential modification utilizing the OCAs of the surviving equation package (i.e., compare a pattern of relationships between the OCAs of the surviving equation package to relationships of OCAs of the portion of the knowledge base). The processing module determines modifications (e.g., additions, subtractions, further clarifications required when information complex, etc.) to the portion of the knowledge base and implements the modifications to the portion of the fact base information to produce updated fact base information for storage in the portion of the knowledge base. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS. 15A and 15B  are schematic block diagrams of another embodiment of a computing system that includes the content ingestion module  300  of  FIG. 5E , the element identification module  302  of  FIG. 5E , the interpretation module  304  of  FIG. 5E , the answer resolution module  306  of  FIG. 5E , and the SS memory  96  of  FIG. 2 . Generally, an embodiment of this invention presents solutions where the computing system  10  supports for generating a query response to a query. 
     The generating of the query response to the query includes a series of steps. For example, a first step includes identifying words of an ingested query to produce tokenized words. As depicted in  FIG. 15A , a specific example of the first step includes the content ingestion module  300  comparing words of query info  138  to dictionary entries to produce formatted content  314  that includes identifiers of known words. For instance, the content ingestion module  300  produces identifiers for each word of the query “what black animal flies and eats fruit and insects?” 
     A second step of the generating of the query response to the query includes, for each tokenized word, identifying one or more identigens that correspond the tokenized word, where each identigen describes one of an object, a characteristic, and an action. As depicted in  FIG. 15A , a specific example of the second step includes the element identification module  302  performing a look up of identifiers, utilizing an element list  332  and in accordance with element rules  318 , of the one or more identigens associated with each tokenized word of the formatted content  314  to produce identified element information  340 . A unique identifier is associated with each of the potential object, the characteristic, and action associated with a particular tokenized word. For instance, the element identification module  302  produces a single identigen identifier for each of the black color, an animal, flies, eats, fruit, and insects. 
     A third step of the generating of the query response to the query includes, for each permutation of sequential combinations of identigens, generating a corresponding equation package (i.e., candidate interpretation), where the equation package includes a sequential linking of pairs of identigens. Each sequential linking pairs a preceding identigen to a next identigen. An equation an element describes a relationship between paired identigens (OCAs) such as describes, acts on, is a, belongs to, did, did to, etc. 
     As depicted in  FIG. 15A , a specific example of the third step includes the interpretation module  304 , for each permutation of identigens of each tokenized word of the identified element information  340 , generating the equation packages in accordance with interpretation rules  320  and a groupings list  334  to produce a series of equation elements that include pairings of identigens. For instance, the interpretation module  304  generates a first pairing to describe a black animal, a second pairing to describe an animal that flies, a third pairing to describe flies and eats, a fourth pairing to describe eats fruit, and a fifth pairing to describe eats fruit and insects. 
     A fourth step of the generating the query response to the query includes selecting a surviving equation package associated with a most favorable interpretation. As depicted in  FIG. 15A , a specific example of the fourth step includes the interpretation module  304  applying the interpretation rules  320  (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce the number of permutations of the sequential combinations of identigens to produce interpreted information  344 . 
     The interpreted information  344  includes identification of at least one equation package as a surviving interpretation  920 . Non-surviving equation packages, if any, are eliminated that compare unfavorably to pairing rules to produce a non-surviving interpretation. 
     A fifth step of the generating the query response to the query includes utilizing a knowledge base, generating a query response to the surviving equation package of the query, where attributes of the surviving equation package are compared to attributes of one or more portions of the knowledge base to identify a corresponding portion of the knowledge base associated with a favorable comparison. The comparison includes at least one of directly comparing attributes to find a favorable match and comparing the attributes of the surviving equation package to major attributes and/or reader attribute comparisons of one or more groups of entigens associated with the one or more portions of the knowledge base. 
     As depicted in  FIG. 15B , a specific example of the fifth step includes the answer resolution module  306  interpreting the surviving interpretation  920  of the interpreted information  344  in accordance with answer rules  322  to generate the attributes of the surviving equation package, accessing fact base information  600  from the SS memory  96  to identify the one or more portions of the knowledge base associated with likely favorable comparisons of the attributes of the surviving equation package to attributes of the groups of entigens, and selecting one of the entigen groups based on comparing the attributes of the entigen group with the attributes of the surviving equation package (i.e., align favorable comparison entigens without conflicting entigens). 
     The fifth step further includes comparing differences of entigen group metadata  930  to quickly identify the entigen group with a most favorable comparison (i.e., while a black animal that eats fruits and insects, a panther does not fly, but a bat flies and eats fruit and insects). The fifth step further includes generating preliminary answers  354  to include a query response to the query (i.e., a bat is a black animal that flies and eats fruit and insects is associated with entigen group  2 )). 
       FIG. 15C  is a logic diagram of an embodiment of a method for generating a query response to a query utilizing groupings within a knowledge base within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-8D, 15A , and also  FIG. 15B . The method includes step  940  where a processing module of one or more processing modules of one or more computing devices of the computing system identifies words of an ingested query to produce tokenized words. For example, the processing module compares words to known words of dictionary entries to produce identifiers of known words. 
     For each tokenized word, the method continues at step  942  where the processing module identifies one or more identigens that correspond to the tokenized word, where each identigen describes one of an object, a characteristic, and an action. For example, the processing module performs a lookup of identifiers of the one or more identigens associated with each tokenized word, where different identifiers associated with each permutation of a potential object, characteristic, and action associated with the tokenized word. 
     For each permutation of sequential combinations of identigens, the method continues at step  944  where the processing module generates a corresponding equation package, where the equation package includes a sequential linking of pairs of identigens, where each sequential linking pairs a preceding identigen to a next identigen. An equation element describes a relationship between paired identigens. For example, for each permutation of identigens of each tokenized word, the processing module includes, with all other permutations of all other tokenized words, to generate the equation packages. 
     The method continues at step  946  where the processing module selects a surviving equation package associated with a most favorable interpretation. For example, the processing module applies rules (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce the number of permutations of the sequential combinations of identigens to identify at least one equation package. Non-surviving equation packages are eliminated the compare unfavorably to pairing rules. 
     The method continues at step  948  where the processing module, utilizing entigen group access to a knowledge base, generates a query response to the surviving equation package (e.g., the query). Attributes of the surviving equation package are compared to attributes of one or more portions of the knowledge base to identify a corresponding portion of the knowledge base associated with a favorable comparison. The comparison includes at least one of directly comparing attributes to find a favorable match and comparing the attributes of the surviving equation package to major attributes and/or reader attribute comparisons of one or more groups of entigens associated with the one or more portions of the knowledge base. 
     As an example of generating the query response, the processing module interprets the surviving the equation package in accordance with answer rules to generate the attributes of the surviving equation package, accesses fact base information of the knowledge base to identify the one or more portions of the knowledge base associated with likely favorable comparisons of the attributes of the surviving equation package to attributes of the groups of entigens, and selects one of the entigen groups based on comparing the attributes of the entigen group with the attributes of the surviving equation package (i.e., align favorable comparison entigens without conflicting entigens). The example further includes comparing differences of entigen group metadata to quickly identify the entigen group with a most favorable comparison, and generates preliminary answers to include a query response to the query. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to diverse content sources  960   
       FIG. 16A  is a schematic block diagram of another embodiment of a computing system that includes diverse content sources  960 , the AI server  20 - 1  of  FIG. 1 , and the user device  12 - 1  of  FIG. 1 . The diverse content sources  960  includes the content sources  16 - 1  through  16 -N of  FIG. 1 . The AI server  20 - 1  includes the processing module  50 - 1  of  FIG. 2  and the SS memory  96  of  FIG. 2 . The processing module  50 - 1  includes the collections module  120  of  FIG. 4A , the IEI module  122  of  FIG. 4A , and the query module  124  of  FIG. 4A . Generally, an embodiment of this invention presents solutions where the computing system  10  supports generating a query response to a query utilizing a confidence level. 
     The generating of the query response to the query utilizing the confidence level includes a series of steps. For example, the first step includes determining an approach to generating a query response to a query, where the query response is generated utilizing knowledge contained in two or more knowledge bases. Diverse content is ingested to produce knowledge stored in the two for more knowledge bases. 
     As a specific example of the first step, based on one or more of a predetermination, historical quality levels of query responses, and extracting guidance from the query, where when extracting the guidance from the query, the LEI module  122  receives an LEI request  244  from the query module  124 . The LEI module  122  extracts the approach from a query of the IEI request  244 , where the query module  124  receives a qualified query request  962  from the user device  12 - 1 . The qualified query request  962  includes one or more of a desired minimum confidence level and identifier of a query response selection approach (i.e., a highest confidence level, a blended response, etc.,). 
     When the diverse content is ingested, further steps includes the IEI module  122  issuing a collections request  132  to the collections module  120 , where the collections module  120  issues one or more diverse content requests  964  to content sources  16 - 1  through  16 -N of the diverse content sources  960 . The collections module  120  issues a collections response  134  to the IEI module  122  based on received diverse content responses  966  that include the diverse content. 
     The IEI module  122  extracts the diverse content from the collections response  134 . The IEI module  122  IEI processes the source content to produce incremental knowledge for storage as fact base information  600 - 1  and/or  600 - 2  in one or more of the SS memories  96 - 1  and  96 - 2  (i.e., mostly a first type of knowledge stored in a first memory and mostly a second type of knowledge stored in a second memory where the first type of knowledge and a second type of knowledge may overlap). 
     A second step of the generating of the query response to the query utilizing the confidence level includes generating, for each knowledge base of the two or more knowledge bases, a corresponding query response utilizing the approach to generating the query response. As a specific example of the second step, for each knowledge base, the IEI module  122  IEI processes the query from the qualified query request  962  to produce query knowledge. The IEI module  122  accesses a portion of the knowledge base corresponding to the query knowledge to produce a corresponding query response (i.e., the IEI module  122  recovers fact base information  600 - 1  from the SS memory  96 - 1  for a first knowledge base for locating of the first portion of the first knowledge base that corresponds favorably to the query knowledge, etc.). 
     A third step of generating the query response to the query utilizing the confidence level includes generating, for each query response, a corresponding confidence level. As a specific example of the third step, the IEI module  122  compares each query response to each other query response to generate comparisons and indicates a higher confidence level for a particular query response when a comparison of the particular query response to other query responses are more favorable (e.g., more similar responses). 
     A fourth step of the generating the query response to the query utilizing the confidence level includes generating a qualified query response utilizing the query responses and based on the approach to generating the query response and based on the corresponding confidence levels. As a specific example of the fourth step, the IEI module  122  generates the qualified query response in accordance with the approach (i.e., selecting a query response with the highest confidence level, combining query responses when confidence levels are favorable and subtle differences exists in two or more query responses of the combination, etc.). The IEI module  122  issues an IEI response  246  to the query module  124 , where the IEI response  246  includes the qualified query response  968 . The query module  124  sends the qualified query response  968  to the user device  12 - 1 . 
       FIG. 16B  is a logic diagram of an embodiment of a method for generating a query response to a query utilizing a confidence level within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-8D , and also  FIG. 16A . The method includes step  980  where a processing module of one or more processing modules of one or more computing devices of the computing system determines an approach to generating a query response to a query. 
     The query response is generated utilizing knowledge contained in two or more knowledge bases. Diverse content may be ingested to produce knowledge stored in the two or more knowledge bases. For example, the processing module, based on one or more of a predetermination, historical quality levels of query responses, and extracting guidance from the query, where when extracting the guidance from the query, receives a qualified query request. The processing module extracts the approach, where the qualified query request includes one or more of the query, a desired minimum confidence level, an identifier of a query response selection approach (i.e., a highest confidence level, a blended response, etc.,). 
     When the diverse content is ingested, the processing module causes issuing of one or more diverse content requests to content sources of diverse content sources and receives diverse content responses that include the diverse content. The processing module extracts the diverse content from diverse content responses and IEI processes the diverse content to produce incremental knowledge for storage as fact base information in storage of the two or more knowledge bases (i.e., mostly a first type of knowledge stored in a first portion of the storage and mostly a second type of knowledge stored in a second portion of the storage where the first type of knowledge and a second type of knowledge may overlap). 
     For each knowledge base of the two or more knowledge bases, the method continues at step  982  where the processing module generates, utilizing the approach, a corresponding query response. For example, for each knowledge base, the processing module ID processes the query from the qualified query request to produce query knowledge and accesses a portion of the knowledge base corresponding to the query knowledge to produce a corresponding query response (i.e., processing module recovers fact base information from a first portion of storage of a first knowledge base for locating of the first portion of the first knowledge base that corresponds favorably to the query knowledge, etc.). 
     For each query response, the method continues at step  984  where the processing module determines a corresponding confidence level. For example, the processing module compares each query response to each other query response to generate comparisons and indicates a higher confidence level for a particular query response when a comparison of the particular query response to other query responses are more favorable (e.g., more similar responses). 
     The method continues at step  986  for the processing module generates a qualified query response utilizing at least some of the query responses based on the approach and the confidence levels. For example, the processing module generates the qualified query response in accordance with the approach (i.e., selecting a query response with a highest confidence level, combining query responses one confidence levels are favorable and subtle differences exist in two or more query responses of the combinations, etc.), and issues the qualified query response to a query response recipient. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS. 17A and 17B  are schematic block diagrams of another embodiment of a computing system that includes the content ingestion module  300  of  FIG. 5E , the element identification module  302  of  FIG. 5E , the interpretation module  304  of  FIG. 5E , the IEI control module  308  of  FIG. 5E , and the SS memory  96  of  FIG. 2 . Generally, an embodiment of this invention presents solutions where the computing system  10  supports processing content to produce knowledge utilizing a certainty level. 
     The processing of the content to produce the knowledge utilizing the certainty level includes a series of steps. For example, a first step includes identifying words of an ingested phrase to produce tokenized words. As depicted in  FIG. 17A , a specific example of the first step includes the content ingestion module  300  comparing words of source content  310  to dictionary entries to produce formatted content  314  that includes identifiers of known words. For instance, the content ingestion module  300  identifies words “the”, “black”, “bat”, “eats”, and “fruit” when the ingested phrase includes “The black bat eats fruit.” 
     A second step of the processing of the content to produce the knowledge utilizing the certainty level includes, for each tokenized word, identifying one or more identigens that correspond the tokenized word, where each identigen describes one of an object, a characteristic, and an action. As depicted in  FIG. 17A , a specific example of the second step includes the element identification module  302  performing a look up of identigen identifiers, utilizing an element list  332  and in accordance with element rules  318 , of the one or more identigens associated with each tokenized word of the formatted content  314  to produce identified element information  340 . 
     A unique identifier is associated with each of the potential object, the characteristic, and action (OCA) associated with a particular tokenized word. For instance, the element identification module  302  identifies a functional symbol for “the”, identifies a single identigen for “black”, identifies two identigens for “bat” (e.g., baseball bat and flying bat), identifies a single identigen for “eats”, and identifies a single identigen for “fruit.” 
     A third step of the processing of the content to produce the knowledge utilizing the certainty level includes, for each permutation of sequential combinations of identigens, generating a corresponding equation package (i.e., candidate interpretation). The equation package includes a sequential linking of pairs of identigens (e.g., relationships, probability level of accuracy of the relationship to provide the certainty level of the relationship). 
     Each sequential linking pairs a preceding identigen to a next identigen. An equation element describes a relationship between paired identigens (OCAs) such as describes, acts on, is a, belongs to, did, did to, etc. Multiple OCAs occur for a common word when the word has multiple meanings (e.g., a baseball bat, a flying bat). 
     As depicted in  FIG. 17A , a specific example of the third step includes the interpretation module  304 , for each permutation of identigens of each tokenized word of the identified element information  340 , including with all other permutations of all other tokenized words to generate the equation packages. The packages include pairings certainty levels, in accordance with interpretation rules  320 , and a groupings list  334 . For instance, the interpretation module  304  produces a first equation package that includes a first pairing of a black bat (e.g., flying bat with a higher pairing certainty level), the second pairing of bat eats (e.g., the flying bat eats, with a higher pairing certainty level), and a third pairing of eats fruit. The interpretation module  304  produces a second equation package that includes a first pairing of a black bat (e.g., baseball bat, with a neutral pairing certainty level), the second pairing of bat eats (e.g., the baseball bat eats, with a lower pairing certainty level), and a third pairing of eats fruit. 
     A fourth step of the processing of the content to produce the knowledge utilizing the certainty level includes selecting a surviving equation package associated with a most favorable confidence level. As depicted in  FIG. 17A , a specific example of the fourth step includes the interpretation module  304  applying interpretation rules  320  (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce the number of permutations of the sequential combinations of identigens to produce interpreted information  344 . 
     The interpreted information  344  includes identification of at least one equation package as a surviving interpretation  1000  (e.g., higher pairings certainty level), where non-surviving equation packages are eliminated that compare unfavorably to pairing rules and/or are associated with an unfavorable pairings certainty levels to produce a non-surviving interpretation  1002  (e.g., lower pairings certainty level). An overall pairings certainty level is assigned to each equation package based on pairing certainty levels of each pairing, such that a higher pairing certainty level indicates that equation package with a higher probability of correctness. For instance, the interpretation module  304  eliminates the equation package that includes the second pairing indicating that the “baseball bat eats” which is inconsistent with a pairings certainty level of one or more of the groupings list  334  and the interpretation rules  320  and selects the equation package associated with the “flying bat eats” which is favorably consistent with the one or more of the pairing certainty level of the groupings list  334  and the interpretation rules  320 . 
     A fifth step of the processing of the content to produce the knowledge utilizing the confidence level includes integrating knowledge of the surviving equation package into a knowledge base. For example, integrating at least a portion of the reduced OCA combinations into a graphical database to produce updated knowledge. As another example, the portion of the reduced OCA combinations may be translated into rows and columns entries when utilizing a rows and columns database rather than a graphical database. 
     As depicted in  FIG. 17B , a specific example of the fifth step includes the ID control module  308  recovering fact base information  600  from SS memory  96  to identify a portion of the knowledge base for potential modification utilizing the OCAs of the surviving interpretation  1000  (i.e., compare a pattern of relationships between the OCAs of the surviving interpretation  1000  from the interpreted information  344  to relationships of OCAs of the portion of the knowledge base including potentially new pairings certainty levels). The fifth step further includes determining modifications (e.g., additions, subtractions, further clarifications required when information is complex, etc.) to the portion of the knowledge base based on the new pairings certainty levels. For instance, the ID control module  308  causes adding the element “black” as a “describes” relationship of an existing bat OCA and adding the element “fruit” as a eats “does to” relationship to implement the modifications to the portion of the fact base information  600  to produce updated fact base information  608  for storage in the SS memory  96 . 
       FIG. 17C  is a logic diagram of an embodiment of a method for processing content to produce knowledge utilizing a certainty level within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-8D, 17A , and also  FIG. 17B . The method includes step  1010  where a processing module of one or more processing modules of one or more computing devices of the computing system identifies words of an ingested phrase to produce tokenized words. The identifying includes comparing words to known words of dictionary entries to produce identifiers of known words. 
     For each tokenized word, the method continues at step  1012  where the processing module identifies one or more identigens that corresponds to the tokenized word, where each identigen describes one of an object, a characteristic, and an action. The identifying includes performing a lookup of identifiers of the one or more identigens associated with each tokenized word, where the different identifiers associated with each of the potential object, the characteristic, and the action associated with the tokenized word. 
     The method continues at step  1014  where the processing module, for each permutation of sequential combinations of identigens, generates a corresponding equation package. The equation package includes a sequential linking of pairs of identigens, where each sequential linking pairs a preceding identigen to a next identigen, and where an equation element describes a relationship and certainty level between paired identigens. For example, for each permutation of identigens of each tokenized word, the processing module includes, with all other permutations of all other tokenized words to generate the equation packages, where the equation elements include probability of correctness based on certainty levels of pairings of identigens. 
     The method continues at step  1016  where the processing module selects a surviving equation package associated with most favorable equation element certainty levels. For example, the processing module applies rules (i.e., inference, pragmatic engine) utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens, to reduce the number of permutations of the sequential combinations of identigens. This identifies at least one equation package, where non-surviving equation packages are eliminated the compare favorably to pairing rules and/or are associated with an unfavorable pairings certainty level to produce a non-surviving interpretation. An overall certainty level is assigned to each equation package based on certainty levels of each pairing, such that a higher certainty level indicates an equation package with a higher probability of correctness. 
     The method continues at step  1018  where the processing module integrates knowledge of the surviving equation package into a knowledge base. For example, the processing module integrates at least a portion of the reduced OCA combinations into a graphical database to produce updated knowledge. The integrating may include recovering fact base information from storage of the knowledge base to identify a portion of the knowledge base for potential modifications utilizing the OCs of the surviving equation package (i.e., compare a pattern of relationships between the OCs of the surviving equation package to relationships of the OCs of the portion of the knowledge base including potentially new pairing certainty levels). 
     The integrating further includes determining modifications (e.g., additions, subtractions, further clarifications required when complex information is presented, etc.) to produce the knowledge base that is based on fit of acceptable pairing certainty levels. The integrating further includes implementing the modifications to the portion of the fact base information to produce the updated fact base information for storage in the portion of the knowledge base. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS. 18A and 18B  are schematic block diagrams of another embodiment of a computing system that includes the content ingestion module  300  of  FIG. 5E , the element identification module  302  of  FIG. 5E , the interpretation module  304  of  FIG. 5E , the IEI control module  308  of  FIG. 5E , and the SS memory  96  of  FIG. 2 . Generally, an embodiment of this invention presents solutions where the computing system  10  supports processing content to produce knowledge. 
     The processing of the content to produce the knowledge includes a series of steps. For example, a first step includes identifying words of an ingested phrase to produce tokenized words. As depicted in  FIG. 18A , a specific example of the first step includes the content ingestion module  300  comparing words of source content  310  to dictionary entries to produce formatted content  314  that includes identifiers of known words. Alternatively, when a comparison is unfavorable, the temporary identifier may be assigned to a unknown word. For instance, the content ingestion module  300  produces identifiers associated with the words “the”, “black”, “bat”, “eats”, and “fruit” when the ingested phrase includes “The black bat eats fruit”, and generates the formatted content  314  to include the identifiers of the words. 
     A second step of the processing of the content to produce the knowledge includes, for each tokenized word, identifying one or more identigens that correspond the tokenized word. Each identigen describes one of an object, a characteristic, and an action. As depicted in  FIG. 18A , a specific example of the second step includes the element identification module  302  performing a look up of identigen identifiers, utilizing an element list  332  and in accordance with element rules  318 , of the one or more identigens associated with each tokenized word of the formatted content  314  to produce identified element information  340 . 
     A unique identifier is associated with each of the potential object, the characteristic, and the action (OCA) associated with the tokenized word. For instance, the element identification module  302  identifies a functional symbol for “the”, identifies a single identigen for “black”, identifies two identigens for “bat” (e.g., baseball bat and flying bat), identifies a single identigen for “eats”, and identifies a single identigen for “fruit.” When at least one tokenized word is associated with multiple identigens, two or more permutations of sequential combinations of identigens for each tokenized word result. For example, when “bat” is associated with two identigens, two permutations of sequential combinations of identigens result for the ingested phrase. 
     A third step of the processing of the content to produce the knowledge includes, for each permutation of sequential combinations of identigens, generating a corresponding equation package (i.e., candidate interpretation). The equation package includes a sequential linking of pairs of identigens (e.g., relationships), where each sequential linking pairs a preceding identigen to a next identigen. An equation element describes a relationship between paired identigens (OCAs) such as describes, acts on, is a, belongs to, did, did to, etc. Multiple OCAs occur for a common word when the word has multiple potential meanings (e.g., a baseball bat, a flying bat). 
     As depicted in  FIG. 18A , a specific example of the third step includes the interpretation module  304 , for each permutation of identigens of each tokenized word of the identified element information  340 , generating, in accordance with interpretation rules  320  and a groupings list  334 , the equation package. The package includes one or more of the identifiers of the tokenized words, a list of identifiers of the identigens of the equation package, a list of pairing identifiers for sequential pairs of identigens, and a quality metric associated with each sequential pair of identigens (e.g., likelihood of a proper interpretation). 
     For instance, the interpretation module  304  produces a first equation package that includes a first identigen pairing of a black bat (e.g., flying bat with a higher quality metric level), the second pairing of bat eats (e.g., the flying bat eats, with a higher quality metric level), and a third pairing of eats fruit. The interpretation module  304  produces a second equation package that includes a first pairing of a black bat (e.g., baseball bat, with a neutral quality metric level), the second pairing of bat eats (e.g., the baseball bat eats, with a lower quality metric level), and a third pairing of eats fruit. 
     A fourth step of the processing of the content to produce the knowledge includes selecting a surviving equation package associated with a most favorable confidence level. As depicted in  FIG. 18A , a specific example of the fourth step includes the interpretation module  304  applying interpretation rules  320  (i.e., inference, pragmatic engine) utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens to reduce a number of permutations of the sequential combinations of identigens to produce interpreted information  344 . 
     The interpreted information  344  includes identification of at least one equation package as a surviving interpretation  1030  (e.g., higher quality metric level). Non-surviving equation packages are eliminated that compare unfavorably to pairing rules and/or are associated with an unfavorable quality metric levels to produce a non-surviving interpretation  1032  (e.g., lower quality metric level). 
     In an embodiment, an overall quality metric level is assigned to each equation package based on quality metric levels of each pairing, such that a higher quality metric level of an equation package indicates a higher probability of a most favorable interpretation. For instance, the interpretation module  304  eliminates the equation package that includes the second pairing indicating that the “baseball bat eats” which is inconsistent with a desired quality metric level of one or more of the groupings list  334  and the interpretation rules  320  and selects the equation package associated with the “flying bat eats” which is favorably consistent with the one or more of the quality metric levels of the groupings list  334  and the interpretation rules  320 . 
     A fifth step of the processing of the content to produce the knowledge utilizing the confidence level includes integrating knowledge of the surviving equation package into a knowledge base. For example, integrating at least a portion of the reduced OCA combinations into a graphical database to produce updated knowledge. As another example, the portion of the reduced OCA combinations may be translated into rows and columns entries when utilizing a rows and columns database rather than a graphical database. When utilizing the rows and columns approach for the knowledge base, subsequent access to the knowledge base may utilize structured query language (SQL) queries. 
     As depicted in  FIG. 18B , a specific example of the fifth step includes the ID control module  308  recovering fact base information  600  from SS memory  96  to identify a portion of the knowledge base for potential modification utilizing the OCAs of the surviving interpretation  1030  (i.e., compare a pattern of relationships between the OCAs of the surviving interpretation  1030  from the interpreted information  344  to relationships of OCAs of the portion of the knowledge base including potentially new quality metric levels). The fifth step further includes determining modifications (e.g., additions, subtractions, further clarifications required when information is complex, etc.) to the portion of the knowledge base based on the new quality metric levels. For instance, the ID control module  308  causes adding the element “black” as a “describes” relationship of an existing bat OCA and adding the element “fruit” as a eats “does to” relationship to implement the modifications to the portion of the fact base information  600  to produce updated fact base information  608  for storage in the SS memory  96 . 
       FIG. 18C  is a logic diagram of an embodiment of a method for processing content to produce knowledge within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-8D, 18A , and also  FIG. 18B . The method includes step  1040  where a processing module of one or more processing modules of one or more computing devices of the computing system identifies words of an ingested phrase to produce tokenized words. The identified includes comparing words to known words of dictionary entries to produce identifiers of known words. 
     For each tokenized word, the method continues at step  1042  where the processing module identifies one or more identigens that corresponds to the tokenized word, where each identigen describes one of an object, a characteristic, and an action (e.g., OCA). The identifying includes performing a lookup of identifiers of the one or more identigens associated with each tokenized word, where the different identifiers associated with each of the potential object, the characteristic, and the action associated with the tokenized word. 
     The method continues at step  1044  where the processing module, for each permutation of sequential combinations of identigens, generates a plurality of equation elements to form a corresponding equation package. Each equation element describes a relationship between sequentially linked pairs of identigens, where each sequential linking pairs a preceding identigen to a next identigen. For example, for each permutation of identigens of each tokenized word, the processing module generates the equation package to include a plurality of equation elements. 
     Each equation element describes the relationship (e.g., describes, acts on, is a, belongs to, did, did too, etc.) between sequentially adjacent identigens of a plurality of sequential combinations of identigens. Each equation element may be further associated with a quality metric to evaluate a favorability level of an interpretation in light of the sequence of identigens of the equation package. 
     The method continues at step  1046  where the processing module selects a surviving equation package associated with most favorable interpretation. For example, the processing module applies rules (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens), to reduce the number of permutations of the sequential combinations of identigens to identify at least one equation package. 
     Non-surviving equation packages are eliminated the compare unfavorably to pairing rules and/or are associated with an unfavorable quality metric levels to produce a non-surviving interpretation. An overall quality metric level is assigned to each equation package based on quality metric levels of each pairing, such that a higher quality metric level indicates an equation package with a higher probability of favorability of correctness. 
     The method continues at step  1048  where the processing module integrates knowledge of the surviving equation package into a knowledge base. For example, the processing module integrates at least a portion of the reduced OCA combinations into a graphical database to produce updated knowledge. The integrating may include recovering fact base information from storage of the knowledge base to identify a portion of the knowledge base for potential modifications utilizing the OCAs of the surviving equation package (i.e., compare a pattern of relationships between the OCAs of the surviving equation package to relationships of the OCAs of the portion of the knowledge base including potentially new quality metric levels). 
     The integrating further includes determining modifications (e.g., additions, subtractions, further clarifications required when complex information is presented, etc.) to produce the updated knowledge base that is based on fit of acceptable quality metric levels. The integrating further includes implementing the modifications to the portion of the fact base information to produce the updated fact base information for storage in the portion of the knowledge base. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS. 19A and 19B  are schematic block diagrams of another embodiment of a computing system that includes that includes the content ingestion module  300  of  FIG. 5E , the element identification module  302  of  FIG. 5E , the interpretation module  304  of  FIG. 5E , the answer resolution module  306  of  FIG. 5E , and the SS memory  96  of  FIG. 2 . Generally, an embodiment of this invention presents solutions where the computing system  10  supports for generating a query response to a query. 
     The generating of the query response to the query includes a series of steps. For example, a first step includes identifying words of an ingested query to produce tokenized words. As depicted in  FIG. 19A , a specific example of the first step includes the content ingestion module  300  comparing words of query info  138  to dictionary entries to produce formatted content  314  that includes identifiers of known words. For instance, the content ingestion module  300  produces identifiers for each word of the query “what black animal flies and eats fruit and insects?” 
     A second step of the generating of the query response to the query includes, for each tokenized word, identifying one or more identigens that correspond the tokenized word, where each identigen describes one of an object, a characteristic, and an action (OCA). As depicted in  FIG. 19A , a specific example of the second step includes the element identification module  302  performing a look up of identifiers, utilizing an element list  332  and in accordance with element rules  318 , of the one or more identigens associated with each tokenized word of the formatted content  314  to produce identified element information  340 . A unique identifier is associated with each of the potential object, the characteristic, and the action associated with a particular tokenized word. For instance, the element identification module  302  produces a single identigen identifier for each of the black color, an animal, flies, eats, fruit, and insects. 
     A third step of the generating of the query response to the query includes, for each permutation of sequential combinations of identigens, generating a corresponding equation package (i.e., candidate interpretation), where the equation package includes a sequential linking of pairs of identigens. Each sequential linking pairs a preceding identigen to a next identigen. An equation element describes a relationship between paired identigens (OCAs) such as describes, acts on, is a, belongs to, did, did to, etc. 
     As depicted in  FIG. 19A , a specific example of the third step includes the interpretation module  304 , for each permutation of identigens of each tokenized word of the identified element information  340 , generating the equation packages in accordance with interpretation rules  320  and a groupings list  334  to produce a series of equation elements that include pairings of identigens. For instance, the interpretation module  304  generates a first pairing to describe a black animal, a second pairing to describe an animal that flies, a third pairing to describe flies and eats, a fourth pairing to describe eats fruit, and a fifth pairing to describe eats fruit and insects. 
     A fourth step of the generating the query response to the query includes selecting a surviving equation package associated with a most favorable interpretation. As depicted in  FIG. 19A , a specific example of the fourth step includes the interpretation module  304  applying the interpretation rules  320  (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce the number of permutations of the sequential combinations of identigens to produce interpreted information  344  that includes identification of at least one equation package as a surviving interpretation  1060 . Non-surviving equation packages, if any, are eliminated that compare unfavorably to pairing rules to produce a non-surviving interpretation. 
     A fifth step of the generating the query response to the query includes utilizing a knowledge base, generating a query response to the surviving equation package of the query. The surviving equation package of the query is transformed to produce query knowledge for comparison to a portion of the knowledge base, and where an answer is extracted from the portion of the knowledge base to produce the query response. 
     As depicted in  FIG. 19B , a specific example of the fifth step includes the answer resolution module  306  interpreting the surviving interpretation  1060  of the interpreted information  344  in accordance with answer rules  322  to produce query knowledge  1070  (i.e., a graphical representation of knowledge when the knowledge base utilizes a graphical database). The fifth step further includes accessing fact base information  600  from the SS memory  96  to identify the portion of the knowledge base associated with a favorable comparison of the query knowledge (e.g., by comparing attributes of the query knowledge  1072  attributes of the fact base information  600 ). The fifth step further includes generating preliminary answers  354  that includes the answer to the query. For instance, the answer is bat when the associated OCAs of bat, such as black, eats fruit, eats insects, is an animal, and flies, aligns with OCAs of the query knowledge. 
       FIG. 19C  is a logic diagram of an embodiment of a method for generating a query response to a query utilizing groupings within a knowledge base within a computing system. In particular, a method is presented for use in conjunction with one or more functions and features described in conjunction with  FIGS. 1-8D, 19A , and also  FIG. 19B . The method includes step  1080  where a processing module of one or more processing modules of one or more computing devices of the computing system identifies words of an ingested query to produce tokenized words. For example, the processing module compares words to known words of dictionary entries to produce identifiers of known words. 
     For each tokenized word, the method continues at step  1082  where the processing module identifies one or more identigens that correspond to the tokenized word, where each identigen describes one of an object, a characteristic, and an action. For example, the processing module performs a lookup of identifiers of the one or more identigens associated with each tokenized word, where different identifiers associated with each permutation of a potential object, characteristic, and action associated with the tokenized word. 
     For each permutation of sequential combinations of identigens, the method continues at step  1084  where the processing module generates a plurality of equation elements to form a corresponding equation package. Each equation element describes a relationship between sequentially linked pairs of identigens. Each sequential linking pairs a preceding identigen to a next identigen. For example, for each permutation of identigens of each tokenized word, the processing module includes, with all other permutations of all other tokenized words, to generate the equation packages, where each equation package includes a plurality of equation elements describing the relationships between sequentially adjacent identigens of a plurality of sequential combinations of identigens. 
     The method continues at step  1086  where the processing module selects a surviving equation package associated with a most favorable interpretation. For example, the processing module applies rules (i.e., inference, pragmatic engine, utilizing the identifiers of the identigens to match against known valid combinations of identifiers of entigens) to reduce the number of permutations of the sequential combinations of identigens to identify at least one equation package. Non-surviving equation packages are eliminated the compare unfavorably to pairing rules. 
     The method continues at step  1088  where the processing module generates a query response to the surviving equation package, where the surviving equation package is transformed to produce query knowledge for locating the portion of a knowledge base that includes an answer. As an example of generating the query response, the processing module interprets the surviving the equation package in accordance with answer rules to produce the query knowledge (e.g., a graphical representation of knowledge when the knowledge base utilizes a graphical database format). 
     The processing module accesses fact base information from the knowledge base to identify the portion of the knowledge base associated with a favorable comparison of the query knowledge (e.g., favorable comparison of attributes of the query knowledge to the portion of the knowledge base, aligning favorably comparing entigens without conflicting entigens). The processing module extracts an answer from the portion of the knowledge base to produce the query response. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG. 1  or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element etc.) that stores operational instructions can, when executed by one or more processing modules of one or more computing devices (e.g., one or more servers, one or more user devices) of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
     It is noted that terminologies as may be used herein such as bit stream, stream, signal sequence, etc. (or their equivalents) have been used interchangeably to describe digital information whose content corresponds to any of a number of desired types (e.g., data, video, speech, text, graphics, audio, etc. any of which may generally be referred to as ‘data’). 
     As may be used herein, the terms “substantially” and “approximately” provides an industry-accepted tolerance for its corresponding term and/or relativity between items. For some industries, an industry-accepted tolerance is less than one percent and, for other industries, the industry-accepted tolerance is  10  percent or more. Other examples of industry-accepted tolerance range from less than one percent to fifty percent. Industry-accepted tolerances correspond to, but are not limited to, component values, integrated circuit process variations, temperature variations, rise and fall times, thermal noise, dimensions, signaling errors, dropped packets, temperatures, pressures, material compositions, and/or performance metrics. Within an industry, tolerance variances of accepted tolerances may be more or less than a percentage level (e.g., dimension tolerance of less than +/−1%). Some relativity between items may range from a difference of less than a percentage level to a few percent. Other relativity between items may range from a difference of a few percent to magnitude of differences. 
     As may also be used herein, the term(s) “configured to”, “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, and/or a module) where, for an example of indirect coupling, the intervening item does not modify the information of a signal but may adjust its current level, voltage level, and/or power level. As may further be used herein, inferred coupling (i.e., where one element is coupled to another element by inference) includes direct and indirect coupling between two items in the same manner as “coupled to”. 
     As may even further be used herein, the term “configured to”, “operable to”, “coupled to”, or “operably coupled to” indicates that an item includes one or more of power connections, input(s), output(s), etc., to perform, when activated, one or more its corresponding functions and may further include inferred coupling to one or more other items. As may still further be used herein, the term “associated with”, includes direct and/or indirect coupling of separate items and/or one item being embedded within another item. 
     As may be used herein, the term “compares favorably”, indicates that a comparison between two or more items, signals, etc., provides a desired relationship. For example, when the desired relationship is that signal  1  has a greater magnitude than signal  2 , a favorable comparison may be achieved when the magnitude of signal  1  is greater than that of signal  2  or when the magnitude of signal  2  is less than that of signal  1 . As may be used herein, the term “compares unfavorably”, indicates that a comparison between two or more items, signals, etc., fails to provide the desired relationship. 
     As may be used herein, one or more claims may include, in a specific form of this generic form, the phrase “at least one of a, b, and c” or of this generic form “at least one of a, b, or c”, with more or less elements than “a”, “b”, and “c”. In either phrasing, the phrases are to be interpreted identically. In particular, “at least one of a, b, and c” is equivalent to “at least one of a, b, or c” and shall mean a, b, and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and “b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”. 
     As may also be used herein, the terms “processing module”, “processing circuit”, “processor”, “processing circuitry”, and/or “processing unit” may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. The processing module, module, processing circuit, processing circuitry, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, processing circuitry, and/or processing unit. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module, module, processing circuit, processing circuitry, and/or processing unit includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that if the processing module, module, processing circuit, processing circuitry and/or processing unit implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element may store, and the processing module, module, processing circuit, processing circuitry and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture. 
     One or more embodiments have been described above with the aid of method steps illustrating the performance of specified functions and relationships thereof. The boundaries and sequence of these functional building blocks and method steps have been arbitrarily defined herein for convenience of description. Alternate boundaries and sequences can be defined so long as the specified functions and relationships are appropriately performed. Any such alternate boundaries or sequences are thus within the scope and spirit of the claims. Further, the boundaries of these functional building blocks have been arbitrarily defined for convenience of description. Alternate boundaries could be defined as long as the certain significant functions are appropriately performed. Similarly, flow diagram blocks may also have been arbitrarily defined herein to illustrate certain significant functionality. 
     To the extent used, the flow diagram block boundaries and sequence could have been defined otherwise and still perform the certain significant functionality. Such alternate definitions of both functional building blocks and flow diagram blocks and sequences are thus within the scope and spirit of the claims. One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof. 
     In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with one or more other routines. In addition, a flow diagram may include an “end” and/or “continue” indication. The “end” and/or “continue” indications reflect that the steps presented can end as described and shown or optionally be incorporated in or otherwise used in conjunction with one or more other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained. 
     The one or more embodiments are used herein to illustrate one or more aspects, one or more features, one or more concepts, and/or one or more examples. A physical embodiment of an apparatus, an article of manufacture, a machine, and/or of a process may include one or more of the aspects, features, concepts, examples, etc. described with reference to one or more of the embodiments discussed herein. Further, from figure to figure, the embodiments may incorporate the same or similarly named functions, steps, modules, etc. that may use the same or different reference numbers and, as such, the functions, steps, modules, etc. may be the same or similar functions, steps, modules, etc. or different ones. 
     Unless specifically stated to the contra, signals to, from, and/or between elements in a figure of any of the figures presented herein may be analog or digital, continuous time or discrete time, and single-ended or differential. For instance, if a signal path is shown as a single-ended path, it also represents a differential signal path. Similarly, if a signal path is shown as a differential path, it also represents a single-ended signal path. While one or more particular architectures are described herein, other architectures can likewise be implemented that use one or more data buses not expressly shown, direct connectivity between elements, and/or indirect coupling between other elements as recognized by one of average skill in the art. 
     The term “module” is used in the description of one or more of the embodiments. A module implements one or more functions via a device such as a processor or other processing device or other hardware that may include or operate in association with a memory that stores operational instructions. A module may operate independently and/or in conjunction with software and/or firmware. As also used herein, a module may contain one or more sub-modules, each of which may be one or more modules. 
     As may further be used herein, a computer readable memory includes one or more memory elements. A memory element may be a separate memory device, multiple memory devices, or a set of memory locations within a memory device. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. The memory device may be in a form a solid-state memory, a hard drive memory, cloud memory, thumb drive, server memory, computing device memory, and/or other physical medium for storing digital information. 
     While particular combinations of various functions and features of the one or more embodiments have been expressly described herein, other combinations of these features and functions are likewise possible. The present disclosure is not limited by the particular examples disclosed herein and expressly incorporates these other combinations.