Abstract:
A method for processing a natural language input to a computerized system. The method parses the input to identify a query portion and a communication portion of the input. The system then determines an answer to the query portion, including identifying communication parameters from the communication portion. Upon determining the answer, the system prepares an answer to the communication and transmits that answer. If the answer requires information from a remote source, the system creates a subsidiary query to obtain that information and then submits the subsidiary query to the remote source. A response to the query is used to compose the answer to the query from the answer to the subsidiary query. If the system concludes that the query portion does not require information from a remote source, analyzing and answering the query locally.

Description:
TECHNICAL FIELD 
     Broadly, the presently disclosed embodiments relate to speech recognition systems, and more particularly to speech recognition systems capable of analyzing and processing natural language inputs. 
     BACKGROUND 
     It is axiomatic that computers do what one commands them to do, not what one wants them to do. In the early days of computer science, programmers were required to expressly command every change of state that occurred during execution of the program, in language directly understandable by the machine. In those days, interactive user interfaces did not exist; users simply provided data for processing and received results. The development of high-level programming, in which commands were expressed in more or less descriptive terms, proceeded in parallel with the introduction of interactive systems where users communicated with the computer during operation of the program. 
     At first, user interaction was highly constrained. Uses were allowed to choose among varieties of options, but the system presented the allowable choices. Introduction of intuitive user input devices, such as the mouse and the touchpad provided greater user flexibility, but the user was still limited to inputs that the computer could easily recognize. 
     Speech recognition systems offer the possibility of free-form user interaction with a computer system, but the complexity of human language has proved a formidable obstacle to the implementation of true natural language commands. The common experience of attempting to deal with speech recognition systems used by various dial-up customer service operations easily demonstrates the distance yet to be traversed before achieving true natural language control of computer systems. Existing technology available to the art can provide systems capable of accepting a number of vocal commands from a user, but those commands must be carefully defined, and for optimum success, training may be required before a computer system can consistently recognize commands. 
     Ordinary natural language often makes use of compound thoughts and commands. Among the aspects of human speech that present major obstacles to computers is the tendency to combine thoughts and ideas in ways that machines simply cannot understand but pose no problem for fellow humans to sort out. When a teacher says to a student, “Make sure you tell everyone who is absent today about the homework assignment,” the meaning of the statement depends on context in a number of ways. First, there is a requirement that the student know what the homework assignment is. If the student does not know the assignment, the statement includes an unstated command to find out that information. The same holds true for the identities of everyone absent from class that day—if that information is not known, it must be determined. Once those two subsidiary queries are satisfied, the resultant information is not communicated back to the teacher, as would be the case with a conventional query, but rather the results of the first query (the homework assignment) is forwarded to the persons who were absent from class that day (results of the second query) by constructing and issuing additional commands. 
     Natural language statements such as the one set out above present no problems of understanding to a human listener. A conventional computer system, on the other hand, even a system capable of dealing with natural language inputs, would not be able to process that statement or others like it. Thus, the art stands in need of a method for handling natural language inputs that combine communication and queries. 
     SUMMARY 
     An aspect of the present disclosure is a method for processing a natural language input to a computerized system. The method begins by parsing the input to identify a query portion and a communication portion of the input. The system then determines an answer to the query portion, including identifying communication parameters from the communication portion. Upon determining the answer, the system prepares an answer to the communication and transmits that answer. 
     Another aspect of the present disclosure analyzes whether the query portion requires information from a source remote from the system. Upon concluding that the query portion does require information from a remote source, a communication channel is opened from the source to a remote source where the query will be researched. For that purpose, the system creates a subsidiary query to obtain the information required and then submits the subsidiary query to the remote source. The system then receives an answer to the subsidiary query from the remote source. That information is used to compose the answer to the query from the answer to the subsidiary query. If the system concludes that the query portion does not require information from a remote source, the query is analyzed and answered locally. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flowchart illustrating an exemplary embodiment of a method for combining a query in a communication command in a natural language computer system, according to the present disclosure. 
         FIG. 2  is a flowchart illustrating a detailed process within the method of  FIG. 1 . 
     
    
    
     DETAILED DESCRIPTION 
     The following detailed description is made with reference to the figures. Preferred embodiments are described to illustrate the disclosure, not to limit its scope, which is defined by the claims. Those of ordinary skill in the art will recognize a number of equivalent variations in the description that follows. 
     DEFINITIONS 
     The term “natural language understanding computer system” broadly includes computer systems that accept spoken inputs from users, where those inputs are structured in normal spoken language, and are capable of responding to such inputs in an appropriately intelligent way. Here, “computer system” should be understood in its broadest context, including large server-based systems, desktop systems, and mobile systems of all types. The term “query” indicates an input to a computer system from a user asking for information in response to the input. The term “command” in the context of a computer system indicates an input designed to cause the computer system to perform an action, rather than return information. The term “destination,” in the context of the recipient of a communication, should also be understood broadly, indicating a single recipient, multiple recipients, or predetermined list of recipients (“distribution lists”). In general, a recipient may be any person or device capable of receiving or configured to receive, an electronic transmission. Examples of recipients are both a person and that person&#39;s voice mailbox, or any storage or communication device capable of storing audio and the attached meta-data. Those skilled in the art will understand that the definitions set out above do not limit the scope of the disclosure. 
     Overview 
     Broadly, the present disclosure relates to systems and methods for combining a query and a communication command as a single input in a computers system capable of processing natural language inputs. Generally, the system parses the input to separate the query from the communication command. The query is then processed to determine the answer. The communication command is processed to identify and prepare any required communication parameters, and the answer is then incorporated into the required communication format. The answer is then transmitted to the required one or more destinations. 
     Exemplary Embodiments 
       FIG. 1  is a block diagram of an embodiment of a method  100  for processing a natural language input that contains both a query and a communication command. The process can be envisioned as three sets of actions. First, the input is received and prepared for further processing. Then processing then occurs, with separate actions aimed at determining an answer to the query and preparing communication parameters for communication. Finally, the required communication channel is employed to transmit the answer to the query to the required destination. 
     The method  100  begins by receiving a natural language communication. In keeping with the definition set out above, a natural language communication is understood to include human speech, delivered by a suitable means to the computer system at step  102 . It can be easily understood that a wide variety of delivery means can be employed to present the human speech to the computer system. Most directly, a human being can speak into a receiving device, such as a microphone, which is directly connected to the computer system. For example, contemporary laptop computers generally include an integral microphone, and a user could speak into that microphone to present natural language input. Alternatively, the natural language input could be delivered from a remote location. For example, a user could speak into a mobile device, such as a smartphone, and that device could forward the natural language input to a host system, resident on a server, for example. The server could work through the method  100 , and any response could be directed as required, with an acknowledgment sent to the original user at her smartphone. Alternatively, input could be provided to a computer system from a recording device. In that manner, a user could record notes or other material on a recording device, which could be integral with a mobile device, such as a tablet computer. The recorded material could be input to a more powerful computing device later, and the method of the present disclosure could be employed then. Those of skill in the art will imagine the variety of scenarios that are possible within these parameters. 
     The computer system then identifies a portion of the natural language input as containing a combined query/communication command. It should be noted that traditional keyword-based speech recognition systems may operate by separately performing the operations of identifying specific keywords, then parsing the rest of the utterance accordingly, while more sophisticated systems may combine those operations. Either approach may be taken in implementing the present disclosure. A more sophisticated system for parsing and interpretation would combine recognition of the need for analysis with the analysis itself (steps  104  and  106 ). Such a system is disclosed in U.S. patent application Ser. No. 13/842,735, entitled “An Integrated Programming Framework for Speech and Text Understanding with Meaning Parsing,” filed Mar. 15, 2013, which application is hereby incorporated into the present application in its entirety, for all purposes. In other embodiments, the operations of recognizing the need for analysis and perform the analysis proceed separately. In such a system, recognition takes place at step  104  during the normal course of analyzing the input to identify specific types of content. For example, natural language input is continually analyzed to determine whether a given portion of the input requires further action by the system. Typically, the system will be scanning to determine whether the input contains either a query or a command. 
     Keyword-based systems recognize commands from the use of particular trigger words, and the system executes a particular command when encountering such keywords, without regard to any semantic analysis. Thus, if such a system encounters the word “stop”, it will respond by ceasing work, even when that word occurs in the phrase “keep going until you reach the stop sign.” A simplified embodiment of the present disclosure could indicate the presence of a combined query/communication by the use of a keyword or similar identifier. In such systems, the user could say, for example, “query communication” or a similar word or phrase. The system could recognize the keyword as satisfying the requirements of step  104 . Such measures can be employed, but they do not achieve the full effectiveness of the present disclosure. Rather, the present disclosure aims at allowing a user simply to speak naturally. That requirement presents the necessity for a sophisticated processing system, but it allows a user to proceed based upon nothing more than good speech habits. 
     Having identified the presence of a combined query/communication command, step  106  separates the two portions of that combination by parsing the language content. Here, such parsing requires that the system identify and separate the query portion of the input from the communication command portion. A number of semantic analysis techniques can be employed to accomplish this end, and such techniques are known to those of skill in the art. 
     After parsing the content of the input, processing splits into two paths, which proceed independently and in parallel. At step  110 , the system determines the answer to the query contained in the natural language input. The actions involved in that step are set out in some detail in connection with  FIG. 2 . Simultaneously, at step  112 , the system identifies the required communication parameters. Required communication parameters will depend upon a particular situation, including the identification of the device being employed. Some parameters will be inherent to the device itself, and that information can be retrieved from system storage. Other information can be contained within the input, with the possibility of employing a default parameter in the absence of direct input. 
     An important parameter to be identified from the input is the destination of the required communication. If the input includes, “Tell Natalie the current exchange rate of the euro,” the system can immediately identify that the intended recipient is “Natalie”. The information required to reach the intended recipient can then be determined by looking at the speaker&#39;s contact list, combined with a log of recent calls. From that information, the system can infer that the speaker is referring to his wife, for example. If the system does not find enough information to establish the recipient&#39;s identity with a fair degree of confidence, it can ask the speaker for additional input. Alternatively, the system can be configured to include keywords for frequent communication recipients. Using that feature, a user can quickly adding nicknames to already identified contact listings, so that the vast majority of intended recipients are identified by the system using the same nickname as the user employs in ordinary conversation. 
     Another required communication parameter is the communication channel. In this instance, some semantic analysis may be required to identify the proper channel. For example, the input set out above contains the verb “tell”. A computer device cannot literally tell a person something, but it can determine a preferred channel for such a person. Here, a user could have a preset preference, so that system memory includes a setting directing that communications to the user&#39;s spouse should be sent via voice, while another setting could indicate that messages to children be sent via SMS or by a particular text messaging system. After the system has been in use for a period of time, the system can associate particular natural language phrases with identified communication channels. For example, the system could store previously identified combination of verbs associated with particular recipients, so that it would interpret “tell Natalie” as “send a voice message via telephone to my wife Natalie.” 
     Other combinations of recipient and communications channel can be directly derived from the input, preset, or inferred from past behavior. Communication channels can be chosen from among available systems, such as email, SMS, voice communication, and the like. Recipients can be identified as individuals, distribution lists, or positions. All of these can be determined by the system by quick investigation of a contact list, such as the contact list maintained by a particular communication system, such as Outlook email, or a list maintained on a particular device, such as the contact list maintained by a particular iPhone. Specialized contact lists can be accessed by importing them. One may import a company contact list, by using a special import routine, for example. These and other specialized communication parameters may be set up by the user. Over time, the system can develop enough information so that it can infer communication parameters by consulting past user behavior. 
     Similarly, communication channels can be selected from those available at the time this application is filed, or from among communication channels developed hereafter. Such channels can be identified generically, so that when a user says, “email Frank Overton,” the system understands that it should use the default email provider available to the system. As with recipients, the system can gain experience over time. The system can employ such an experience base to understand that a user command, “send a text message to . . . ” signifies the user&#39;s intent to communicate using a specialized text message service, such as the FaceTime messaging system available on Apple devices, or the Skype SMS system. Other systems will undoubtedly be added hereafter, either in conventional computing devices or newly introduced devices such as such as Google Glass. 
     Once the system has determine an answer to the user&#39;s query and has identified all the required communication parameters, the system can proceed to prepare the resulting information for communication, at step  114 . There, the system begins by preparing the fixed information required for a message, such as the message header and the like, requiring employment of the identifying communication parameters. To that shell, the system adds body information obtained from the query. In the simple message set out above, incorporating the query about the current exchange rate of the euro, the system may determine that the euro has been trending upward over the last week, having gained $0.03, and that the exchange rate increase by another penny in the day&#39;s trading. Thus, the system could construct the message along the lines of, “Euro continues upward trend, currently trading at $1.42.” Finally, at step  116 , the system transmits the required communication, employing the desired communication method. For oral messages, the system could be configured to construct messages using stock phrases compiled by the user. In that instance, the system could have a number of prepared phrases, such as “continues upward trend,” “currently trading at,” and the like already recorded, and the system could pick and choose from among these phrases to construct a suitable message. Text messages and the like would be somewhat easier to construct, but the system could similarly flesh out the user command by building a message based upon typical salutation, heading, and closing, gleaned from previous messages. 
       FIG. 2  details the method  200  involved in determining an answer to the user query, set out at step  110  in  FIG. 1 . This method begins by determining whether a remote query is actually required, at step  202 . The question here is whether the information sufficient to answer the user&#39;s query is available at the local device, eliminating the need for any communication. That analysis requires examination of the data available on the local system, in relation to the types of queries that can be answered there. A number of ways to solve this problem are available to those in the art, such as building a database or lookup table identifying topics that can be addressed locally. If required information is not available locally, the system will need to determine where the information can be located. Typically, a single device will be available to address such queries, and most often that device will be based in a server accessible to the user&#39;s device. One such one capability of the server can be to forward queries to more appropriate solution locations, as well as to receive answers and forward them to the user. The structure and employment of such systems are known to those in the art and will not be addressed further here. For present purposes, it suffices to say that the system begins by opening a communication channel to the location where the query can be handled, at step  204 . 
     Then, at step  206 , system generates a subsidiary query. That query is formatted as required by the query handler, which may be different from the form in which the query is received by the system. As noted above, natural language queries tend to be somewhat elliptical in construction, and they can contain implied or assumed facts. The query handler cannot handle queries in that format, and thus implied information must be inserted into the query structure. 
     The subsidiary query is then submitted to the query handler, in step  208 . This step employs the open communication channel established at step  204 . This portion of the operation proceeds exactly as if the system had been directed to find out the particular information involved. Thus, no further explanation of this step is required. 
     Upon receiving an answer to the query, at step  210 , the system proceeds to structure a response to the user&#39;s original query, at step  212 . Once the answer is formulated, that information is combined with the communication parameters, in step  114  ( FIG. 1 ). 
     The specification has described a method and system for providing real-time assistance to a traveler. Those of skill in the art will perceive a number of variations possible with the system and method set out above. These and other variations are possible within the scope of the claimed invention, which scope is defined solely by the claims set out below.