Patent Publication Number: US-10762306-B2

Title: Computing system with a cross-locale natural language searching mechanism and method of operation thereof

Description:
TECHNICAL FIELD 
     An embodiment of the present invention relates generally to a computing system, and more particularly to a computing system with a cross-locale natural language searching mechanism. 
     BACKGROUND 
     Modern consumer and industrial electronics, especially devices such as cellular phones, smart phones, tablet computers, vehicle integrated computing and communication systems, portable digital assistants, and combination devices, are providing increasing levels of functionality to support modern life including communication services. Research and development in the existing technologies can take a myriad of different directions. 
     Location based applications offer a typical portfolio of services including: navigation, map lookup and display, and local business and point of interest search. These services are naturally tied together in that they are all location-based and assume that these services operate within a locale shared between a user and real-world objects of reference. That is to say that if the user is physically within the United States, then applications tend to assume that because the primary language within the United States is U.S. English, the user will want to navigate to locations named and expressed in U.S. English, ask to view maps of U.S. locations displayed with labels written in U.S. English, hear feedback spoken or displayed in U.S. English, and search for businesses and points of interest using names and concepts drawn from U.S. English. However, this assumption does not hold for everyone, for example, in the United States who is learning U.S. English or does not speak U.S. English at all. Thus, a need still remains to translate search terms from a user&#39;s native language into semantically equivalent search terms in a language associated with the data locale and insert this translation step between the search acceptance and result display stages of the search request processing. 
     Solutions to these problems have been long sought but prior developments have not taught or suggested any solutions and, thus, solutions to these problems have long eluded those skilled in the art. 
     SUMMARY 
     An embodiment of the present invention provides a computing system comprising: a control unit configured to: receive an input request for a point of interest; determine a first linguistic context for the input request based on one or more input request characteristics, a user profile, a location, and a first connotation database; translate the input request to a second linguistic context based on a translation flag and a second connotation database, wherein the second connotation database is mapped to the first connotation database; and a user interface, coupled to the control unit, configured to display a translation result for the input request based on the first linguistic context or the second linguistic context. 
     An embodiment of the present invention provides a computing system comprising: a first control unit configured to: receive an input request for a point of interest; determine a first linguistic context for the input request based on one or more input request characteristics, a user profile, a location, and a first connotation database; a communication unit, coupled to the first control unit, configured to: send a transmission of the input request to a second control unit based on a translation flag; receive a translation result for the input request based on a translation of the input request to a second linguistic context by the second control unit; the second control unit, coupled to the communication unit, configured to translate the input request to the second linguistic context based on the translation flag and a second connotation database, wherein the second connotation database is mapped to the first connotation database; and a user interface, coupled to the first control unit, configured to display a translation result for the input request based on the first linguistic context or the second linguistic context. 
     An embodiment of the present invention provides a method of operating a computing system comprising: receiving an input request for a point of interest; determining a first linguistic context for the input request based on one or more input request characteristics, a user profile, a location, and a first connotation database translating the input request to a second linguistic context based on a translation flag and a second connotation database, wherein the second connotation database is mapped to the first connotation database; and displaying a translation result for the input request based on the first linguistic context or the second linguistic context. 
     An embodiment of the present invention provides a non-transitory computer readable medium including instructions for operating a computing system comprising: receiving an input request for a point of interest; determining a first linguistic context for the input request based on one or more input request characteristics, a user profile, a location, and a first connotation database; translating the input request to a second linguistic context based on a translation flag and a second connotation database, wherein the second connotation database is mapped to the first connotation database; and displaying a translation result for the input request based on the first linguistic context or the second linguistic context. 
     Certain embodiments of the invention have other steps or elements in addition to or in place of those mentioned above. The steps or elements will become apparent to those skilled in the art from a reading of the following detailed description when taken with reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a computing system with a cross-locale natural language searching mechanism in an embodiment of the present invention. 
         FIG. 2  is an exemplary block diagram of the components of the computing system. 
         FIG. 3  is an example of a display interface of the computing system. 
         FIG. 4  is an exemplary control flow of the computing system. 
         FIG. 5  is an exemplary representation of a cognitive synonym space for the computing system. 
         FIG. 6  is a flow chart of a method of operation of the computing system in a further embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The following embodiments are described in sufficient detail to enable those skilled in the art to make and use the invention. It is to be understood that other embodiments would be evident based on the present disclosure, and that system, process, or mechanical changes may be made without departing from the scope of an embodiment of the present invention. 
     In the following description, numerous specific details are given to provide a thorough understanding of the invention. However, it will be apparent that the invention may be practiced without these specific details. In order to avoid obscuring an embodiment of the present invention, some well-known circuits, system configurations, and process steps are not disclosed in detail. 
     The drawings showing embodiments of the system are semi-diagrammatic, and not to scale and, particularly, some of the dimensions are for the clarity of presentation and are shown exaggerated in the drawing figures. Similarly, although the views in the drawings for ease of description generally show similar orientations, this depiction in the figures is arbitrary for the most part. Generally, the invention can be operated in any orientation. 
     The term “vehicle” referred to herein can include cars, self-driving cars, trains, buses, bicycles, boats, motorcycles, airplanes, helicopters, or any other mode of transport, or a combination thereof in an embodiment of the present invention in accordance with the context in which the term is used. 
     The term “module” or “unit” referred to herein can include software, hardware, or a combination thereof in an embodiment of the present invention in accordance with the context in which the term is used. For example, the software can be machine code, firmware, embedded code, and application software. Also for example, the hardware can be circuitry, processor, computer, integrated circuit, integrated circuit cores, a pressure sensor, an inertial sensor, a microelectromechanical system (MEMS), passive devices, or a combination thereof. Further, if a module is written in the system claims section below, the modules are deemed to include hardware circuitry for the purposes and the scope of system claims. 
     The modules in the following description of the embodiments can be coupled to one other as described or as shown. The coupling can be direct or indirect without or with, respectively, intervening items between coupled items. The coupling can be by physical contact or by communication between items. 
     Referring now to  FIG. 1 , therein is shown a computing system  100  with a cross-locale natural language searching mechanism in a first embodiment of the present invention. The computing system  100  includes a first device  102 , such as a client or a server, connected to a second device  106 , such as a client or server. The first device  102  can communicate with the second device  106  with a communication path  104 , such as a wireless or wired network. 
     For example, the first device  102  can be of any of a variety of devices, such as a smart phone, cellular phone, personal digital assistant, tablet computer, a notebook computer, laptop computer, desktop computer, or a vehicle integrated communication system. The first device  102  can couple, either directly or indirectly, to the communication path  104  to communicate with the second device  106  or can be a stand-alone device. The first device  102  can be incorporated in a vehicle. 
     The second device  106  can be any of a variety of centralized or decentralized computing devices. For example, the second device  106  can be a laptop computer, a desktop computer, grid-computing resources, a virtualized computer resource, cloud computing resources, routers, switches, peer-to-peer distributed computing devices, a server, or a combination thereof. The second device  106  can be centralized in a single room, distributed across different rooms, distributed across different geographical locations, embedded within a telecommunications network. The second device  106  can couple with the communication path  104  to communicate with the first device  102 . The second device  106  can be incorporated in a vehicle. 
     For illustrative purposes, the computing system  100  is shown with the first device  102  as a client device, although it is understood that the computing system  100  can have the first device  102  as a different type of device. For example, the first device  102  can be a server. Also for illustrative purposes, the computing system  100  is shown with the second device  106  as a server, although it is understood that the computing system  100  can have the second device  106  as a different type of device. For example, the second device  106  can be a client device. 
     For brevity of description in the embodiments discussed below, the first device  102  will be described as a client device and the second device  106  will be described as a server device. The embodiments of the present invention, however, are not limited to this selection for the type of devices. The selection is an example of an embodiment of the present invention. 
     Also for illustrative purposes, the computing system  100  is shown with the second device  106  and the first device  102  as end points of the communication path  104 , although it is understood that the computing system  100  can have a different partition between the first device  102 , the second device  106 , and the communication path  104 . For example, the first device  102 , the second device  106 , or a combination thereof can also function as part of the communication path  104 . 
     The communication path  104  can span and represent a variety of networks and network topologies. For example, the communication path  104  can include wireless communication, wired communication, optical communication, ultrasonic communication, or the combination thereof. For example, satellite communication, cellular communication, Bluetooth, Infrared Data Association standard (IrDA), wireless fidelity (WiFi), and worldwide interoperability for microwave access (WiMAX) are examples of wireless communication that can be included in the communication path  104 . Cable, Ethernet, digital subscriber line (DSL), fiber optic lines, fiber to the home (FTTH), and plain old telephone service (POTS) are examples of wired communication that can be included in the communication path  104 . Further, the communication path  104  can traverse a number of network topologies and distances. For example, the communication path  104  can include direct connection, personal area network (PAN), local area network (LAN), metropolitan area network (MAN), wide area network (WAN), or a combination thereof. 
     Referring now to  FIG. 2 , therein is shown an exemplary block diagram of the components of the computing system  100 . The first device  102  can send information in a first device transmission  222  over the communication path  104  to the second device  106 . The second device  106  can send information in a second device transmission  224  over the communication path  104  to the first device  102 . The first device transmission  222  and the second device transmission  224  can be sent over one or more communication channels  248 . A communication channel  248  refers either to a physical transmission medium such as a wire, or to a logical connection over a multiplexed medium such as a radio channel. 
     For illustrative purposes, the computing system  100  is shown with the first device  102  as a client device, although it is understood that the computing system  100  can have the first device  102  as a different type of device. For example, the first device  102  can be a server. 
     Also for illustrative purposes, the computing system  100  is shown with the second device  106  as a server, although it is understood that the computing system  100  can have the second device  106  as a different type of device. For example, the second device  106  can be a client device. 
     For brevity of description in this embodiment of the present invention, the first device  102  will be described as a client device and the second device  106  will be described as a server device. The embodiment of the present invention is not limited to this selection for the type of devices. The selection is an example of an embodiment of the present invention. 
     The first device  102  can include a first control unit  210 , a first storage unit  216 , a first communication unit  202 , a first user interface  254 , and a first location unit  214 . The first control unit  210  can include a first control interface  212 . The first control unit  210  can execute a first software  220  to provide the intelligence of the computing system  100 . The first control unit  210  can be implemented in a number of different ways. For example, the first control unit  210  can be a processor, an application specific integrated circuit (ASIC), an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), or a combination thereof. 
     The first control interface  212  can be used for communication between the first control unit  210  and other functional units in the first device  102 . The first control interface  212  can also be used for communication that is external to the first device  102 . The first control interface  212  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the first device  102 . 
     The first control interface  212  can be implemented in different ways and can include different implementations depending on which functional units or external units are being interfaced with the first control interface  212 . For example, the first control interface  212  can be implemented with a pressure sensor, an inertial sensor, a microelectromechanical system (MEMS), optical circuitry, waveguides, wireless circuitry, wireline circuitry, application programming interface, or a combination thereof. 
     The first storage unit  216  can store the first software  220 . For illustrative purposes, the first storage unit  216  is shown as a single element, although it is understood that the first storage unit  216  can be a distribution of storage elements. Also for illustrative purposes, the computing system  100  is shown with the first storage unit  216  as a single hierarchy storage system, although it is understood that the computing system  100  can have the first storage unit  216  in a different configuration. For example, the first storage unit  216  can be formed with different storage technologies forming a memory hierarchal system including different levels of caching, main memory, rotating media, or off-line storage. 
     The first storage unit  216  can be a volatile memory, a nonvolatile memory, an internal memory, an external memory, or a combination thereof. For example, the first storage unit  216  can be a nonvolatile storage such as non-volatile random access memory (NVRAM), Flash memory, disk storage, or a volatile storage such as static random access memory (SRAM). 
     The first storage unit  216  can include a first storage interface  218 . The first storage interface  218  can be used for communication between the first storage unit  216  and other functional units in the first device  102 . The first storage interface  218  can also be used for communication that is external to the first device  102 . The first storage interface  218  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the first device  102 . 
     The first storage interface  218  can include different implementations depending on which functional units or external units are being interfaced with the first storage unit  216 . The first storage interface  218  can be implemented with technologies and techniques similar to the implementation of the first control interface  212 . 
     The first communication unit  202  can enable external communication to and from the first device  102 . For example, the first communication unit  202  can permit the first device  102  to communicate with the second device  106  of  FIG. 1 , an attachment, such as a peripheral device or a computer desktop, and the communication path  104 . 
     The first communication unit  202  can also function as a communication hub allowing the first device  102  to function as part of the communication path  104  and not limited to be an end point or terminal unit to the communication path  104 . The first communication unit  202  can include active and passive components, such as microelectronics or an antenna, for interaction with the communication path  104 . 
     The first communication unit  202  can include a first communication interface  208 . The first communication interface  208  can be used for communication between the first communication unit  202  and other functional units in the first device  102 . The first communication interface  208  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the first device  102 . 
     The first communication interface  208  can include different implementations depending on which functional units are being interfaced with the first communication unit  202 . The first communication interface  208  can be implemented with technologies and techniques similar to the implementation of the first control interface  212 . 
     The first communication unit  202  can couple with the communication path  104  to send information to the second device  106  in the first device transmission  222 . The second device  106  can receive information in a second communication unit  226  from the first device transmission  222  of the communication path  104 . 
     The first control unit  210  can operate the first user interface  254  to present information generated by the computing system  100 . The first user interface  254 , in one embodiment, allows a user of the computing system  100  to interface with the first device  102 . The first user interface  254  can include an input device and an output device. Examples of the input device of the first user interface  254  can include a keypad, a touchpad, soft-keys, a keyboard, a microphone, sensors for receiving remote signals, or any combination thereof to provide data and communication inputs. Examples of the output device can include a first display interface  206  and a first audio interface  204 . 
     The first control unit  210  can operate the first user interface  254  to present information generated by the computing system  100 . The first control unit  210  can also execute the first software  220  for the other functions of the computing system  100 . The first control unit  210  can further execute the first software  220  for interaction with the communication path  104  via the first communication unit  202 . 
     The first display interface  206  can be any graphical user interface such as a display, a projector, a video screen, or any combination thereof. The first audio interface  204  can include sensors, speakers, microphones, headphones, subwoofers, surround sound components, transducers, or any combination thereof. The first display interface  206  and the first audio interface  204  allow a user of the computing system  100  to interact with the computing system  100 . 
     The first location unit  214  can generate location information, current heading, and current speed of the first device  102 , as examples. The first location unit  214  can be implemented in many ways. For example, the first location unit  214  can function as at least a part of a global positioning system (GPS) and can include components, such as a GPS receiver, an inertial navigation system, a cellular-tower location system, a pressure location system, or any combination thereof. 
     The first location unit  214  can include a first location interface  250 . The first location interface  250  can be used for communication between the first location unit  214  and other functional units in the first device  102 . The first location interface  250  can also be used for communication that is external to the first device  102 . The first location interface  250  can be implemented with technologies and techniques similar to the implementation of the first control interface  212 . 
     The second device  106  can be optimized for implementing an embodiment of the present invention in a multiple device embodiment with the first device  102 . The second device  106  can provide additional or higher performance processing power compared to the first device  102 . The second device  106  can include a second control unit  238 , a second storage unit  240 , a second communication unit  226 , a second user interface  228 , and a second location unit  246 . 
     The second control unit  238  can include a second control interface  236 . The second control unit  238  can execute a second software  244  to provide the intelligence of the computing system  100 . The second software  244  can also operate independently or in conjunction with the first software  220 . The second control unit  238  can provide additional performance compared to the first control unit  210 . 
     The second control unit  238  can be implemented in a number of different ways. For example, the second control unit  238  can be a processor, an application specific integrated circuit (ASIC), an embedded processor, a microprocessor, a hardware control logic, a hardware finite state machine (FSM), a digital signal processor (DSP), or a combination thereof. 
     The second control interface  236  can be used for communication between the second control unit  238  and other functional units in the second device  106 . The second control interface  236  can also be used for communication that is external to the second device  106 . The second control interface  236  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the second device  106 . 
     The second control interface  236  can be implemented in different ways and can include different implementations depending on which functional units or external units are being interfaced with the second control interface  236 . For example, the second control interface  236  can be implemented with a pressure sensor, an inertial sensor, a microelectromechanical system (MEMS), optical circuitry, waveguides, wireless circuitry, wireline circuitry, application programming interface, or a combination thereof. 
     The second storage unit  240  can store the second software  244 . The second storage unit  240  can be sized to provide the additional storage capacity to supplement the first storage unit  216 . For illustrative purposes, the second storage unit  240  is shown as a single element, although it is understood that the second storage unit  240  can be a distribution of storage elements. Also for illustrative purposes, the computing system  100  is shown with the second storage unit  240  as a single hierarchy storage system, although it is understood that the computing system  100  can have the second storage unit  240  in a different configuration. For example, the second storage unit  240  can be formed with different storage technologies forming a memory hierarchal system including different levels of caching, main memory, rotating media, or off-line storage. 
     The second storage unit  240  can be a volatile memory, a nonvolatile memory, an internal memory, an external memory, or a combination thereof. For example, the second storage unit  240  can be a nonvolatile storage such as non-volatile random access memory (NVRAM), Flash memory, disk storage, or a volatile storage such as static random access memory (SRAM). 
     The second storage unit  240  can include a second storage interface  242 . The second storage interface  242  can be used for communication between the second storage unit  240  and other functional units in the second device  106 . The second storage interface  242  can also be used for communication that is external to the second device  106 . The second storage interface  242  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the second device  106 . 
     The second storage interface  242  can include different implementations depending on which functional units or external units are being interfaced with the second storage unit  240 . The second storage interface  242  can be implemented with technologies and techniques similar to the implementation of the second control interface  236 . 
     The second communication unit  226  can enable external communication to and from the second device  106 . For example, the second communication unit  226  can permit the second device  106  to communicate with the first device  102  of  FIG. 1 , an attachment, such as a peripheral device or a computer desktop, and the communication path  104 . 
     The second communication unit  226  can also function as a communication hub allowing the second device  106  to function as part of the communication path  104  and not limited to be an end point or terminal unit to the communication path  104 . The second communication unit  226  can include active and passive components, such as microelectronics or an antenna, for interaction with the communication path  104 . 
     The second communication unit  226  can include a second communication interface  230 . The second communication interface  230  can be used for communication between the second communication unit  226  and other functional units in the second device  106 . The second communication interface  230  can receive information from the other functional units or from external sources, or can transmit information to the other functional units or to external destinations. The external sources and the external destinations refer to sources and destinations external to the second device  106 . 
     The second communication interface  230  can include different implementations depending on which functional units are being interfaced with the second communication unit  226 . The second communication interface  230  can be implemented with technologies and techniques similar to the implementation of the second control interface  236 . 
     The second communication unit  226  can couple with the communication path  104  to send information to the first device  102  in the second device transmission  224 . The first device  102  can receive information in the first communication unit  202  from the second device transmission  224  of the communication path  104   
     The second control unit  238  can operate the second user interface  228  to present information generated by the computing system  100 . The second user interface  228 , in one embodiment, allows a user of the computing system  100  to interface with the second device  106 . The second user interface  228  can include an input device and an output device. Examples of the input device of the second user interface  228  can include a keypad, a touchpad, soft-keys, a keyboard, a microphone, sensors for receiving remote signals, or any combination thereof to provide data and communication inputs. Examples of the output device can include a second display interface  234  and a second audio interface  232 . 
     The second control unit  238  can operate the second user interface  228  to present information generated by the computing system  100 . The second control unit  238  can also execute the second software  244  for the other functions of the computing system  100 . The second control unit  238  can further execute the second software  244  for interaction with the communication path  104  via the second communication unit  226 . 
     The second display interface  234  can be any graphical user interface such as a display, a projector, a video screen, or any combination thereof. The second audio interface  232  can include sensors, speakers, microphones, headphones, subwoofers, surround sound components, transducers, or any combination thereof. The second display interface  234  and the second audio interface  232  allow a user of the computing system  100  to interact with the computing system  100 . 
     The second location unit  246  can generate location information, current heading, and current speed of the second device  106 , as examples. The second location unit  246  can be implemented in many ways. For example, the second location unit  246  can function as at least a part of a global positioning system (GPS) and can include components, such as a GPS receiver, an inertial navigation system, a cellular-tower location system, a pressure location system, or any combination thereof. 
     The second location unit  246  can include a second location interface  252 . The second location interface  252  can be used for communication between the second location unit  246  and other functional units in the second device  106 . The second location interface  252  can also be used for communication that is external to the second device  106 . The second location interface  252  can be implemented with technologies and techniques similar to the implementation of the second control interface  236 . 
     Functionality of the computing system  100  can be provided by the first control unit  210 , the second control unit  238 , or a combination thereof. For illustrative purposes, the second device  106  is shown with the partition having the second user interface  228 , the second storage unit  240 , the second control unit  238 , a second location unit  246 , and the second communication unit  226 , although it is understood that the second device  106  can have a different partition. For example, the second software  244  can be partitioned differently such that some or all of its function can be in the second control unit  238  and the second communication unit  226 . Also, the second device  106  can include other functional units not shown in  FIG. 2  for clarity. 
     The first device  102  can have a similar or different partition as the second device  106 . The functional units in the first device  102  can work individually and independently of the other functional units. The first device  102  can work individually and independently from the second device  106  and the communication path  104 . The functional units in the second device  106  can work individually and independently of the other functional units. The second device  106  can work individually and independently from the first device  102  and the communication path  104 . 
     For illustrative purposes, the computing system  100  is described by operation of the first device  102  and the second device  106 . It is understood that the first device  102  and the second device  106  can operate any of the modules, units, and functions of the computing system  100 . 
     Referring now to  FIG. 3 , therein is shown an example of the first user interface  254  of the first device  102  or the second user interface  228  of the second device  106  of  FIG. 1 . For brevity of description in this embodiment, reference to the first user interface  254  will be made, however, the descriptions with respect to the first user interface  254  can be similarly applicable to the second user interface  228 . 
     In one embodiment, the first user interface  254  includes the first display interface  206 . The first display interface  206  can enable an input request  306  for a point of interest  310  using a search box  302 . The input request  306  can include any manner of inputting a search request for the point of interest  310 . The input request  306  can be any way by which a system user  304  can search for the point of interest  310 , including but not limited to, searching using alpha-numeric characters, symbols, voice commands, gestures, or a combination thereof. The search box  302  can enable searching for the point of interest  310  using any search engine techniques, such as those used in a desktop search engine or a web search engine. Details of the search mechanism for the point of interest  310  will be discussed in greater detail below. 
     The point of interest  310  is a physical location that a system user  304  finds useful or interesting, or that a system user  304  or others might consider be particularly affiliating with or tied to a geographic area. For example, a point of interest  310  may be a store, a landmark, an office building or site, a park, an address, a point on a map, or another attraction that is popular among people of a geographic area. 
     Continuing with the example, the input request  306  can be input into the search box  302  using one or more languages. For example, the input request  306  can be input into the search box  302  in a system user&#39;s language  314 . The system user&#39;s language  314  is a language understood by, spoken by, or native to the system user  304 . As an example, the system user&#39;s language  314  can be designated by the system user  304 . Also as an example, the system user&#39;s language  314  can be assigned automatically by the computing system  100  based on one or more characteristics of the input request  306 . The one or more characteristics of the input request  306  refers to a linguistic property of the input request  306  and can include, for example, a syntax of the input request  306 , language characters associated with the input request  306 , a dialect designation of the input request  306 , a sentence structure of the input request  306 , a grammar of the input request  306 , a linguistic pattern of the input request  306 , phonemes of the input request  306 , or any combination thereof. 
     The input request  306  can be in the same language or a different language as the system user&#39;s language  314  or the language associated with the system user&#39;s  304  current location  308 . For example, if the system user&#39;s language  314  is assigned or designated to be “Mexican Spanish,” the input request  306  can be input into the search box  302  using “Mexican Spanish” syntax, words, or phrases, despite the system user&#39;s  304  current location  308  being in, for example, Toronto, Canada, where the primary language is “Canadian English,” and in which the points of interest  310  are typically designated in “Canadian English.” In another example, if the system user&#39;s language  314  is assigned or designated to be “Mexican Spanish,” the input request  306  can be input into the search box  302  using “Brazilian Portuguese” despite the system user&#39;s  304  current location  308  being in, for example, Toronto, Canada. The system user&#39;s language  314  can be associated with a profile associated with the system user  304 . The system user&#39;s language  314  designation can be stored in the first storage unit  216 , the second storage unit  240 , or a combination thereof. 
     Continuing with the example, the input request  306  can be received by the computing system  100  in order for the computing system  100  to search for the point of interest  310  corresponding to the input request  306 , either on the first device  102 , the second device  106 , or a combination thereof. Once received, the computing system  100  can search for the point of interest  310  corresponding to the input request  306  and return a translation result  318  to the first device  102 , the second device  106 , or a combination thereof, such that the translation result  318  is to be displayed on the first display interface  206 , the second display interface  234 , or a combination thereof. The translation result  318  refers to a value or a result returned based on the search for the point of interest  310 , in which the input request  306  is matched to the closes relevant point or points of interest  310  based on the translation between the language of the input request  306  to the language of the system user&#39;s  304  current location  308 . The first control unit  210 , the second control unit  238 , or a combination thereof can enable the search for the point of interest  310 . 
     In one embodiment, the computing system  100  can search for the point of interest  310  on the second device  106  and return the translation result  318  to the first device  102 . The first device  102  can display the translation result  318  on the first display interface  206 . In another embodiment, the computing system  100  can search for the point of interest  310  on the first device  102  and return the translation result  318  to be displayed on the first display interface  206 . 
     In another embodiment, the first audio interface  204 , in conjunction with the first display interface  206 , or by itself, can enable input of the input request  306  using audio commands. Audio commands are instructions given to the computing system  100  using an audio input, such as voice, or other acoustic, mechanical, or electrical frequencies corresponding to audible sound waves. For example, audio commands can be received through one or more sensors, microphones, transducers, or a combination thereof using the first audio interface  204 . Similarly, the audio commands can be received in a similar manner through the second audio interface  232  of the second device  106 . The audio commands can instruct the computing system  100  to search for the point of interest  310  in the same manner as described above. 
     Continuing with the example, in one embodiment, the computing system  100  can have a feedback mechanism allowing the system user  304  to give a feedback value  312  to the computing system  100  based on the translation result  318 . The feedback value  312  can represent the quality of the translation result  318  returned by the computing system  100 . For example, in one embodiment, if the input request  306  is “Gasolinera” and the computing system  100  returns a translation result  318  showing points of interest  310  as “gas stations,” the system user  304  can give a feedback value  312  indicating the translation result  318  was satisfactory. Alternatively, if the input request is “Gasolinera” and the computing system  100  returns a translation result  318  showing the point of interest  310  as “restaurants,” the system user  304  can give a feedback value  312  indicating the translation result  318  was unsatisfactory. 
     The feedback value  312  can also be provided implicitly. For example, in one embodiment, when the translation result  318  is returned, the system user  304  can navigate to the translation result  318  and doing nothing else. By doing so, the computing system  100  will know that the system user  304  was satisfied with the translation result  318 . 
     The feedback value  312  can take a variety of forms. For example, the feedback value can take the form of any ranking system, including but not limited to, a numeric ranking system, a sliding scale ranking system, a binary input, a “good/bad” ranking, or a combination thereof. The feedback value  312  can be stored in the first storage unit  216 , the second storage unit  240 , or a combination thereof. The feedback value  312  can be used by the computing system  100  to refine a future translation result of a future input request for the input request  306 . For example, in one embodiment, the feedback value  312  can be used to train the computing system  100  as a part of a machine learning algorithm or deep learning algorithm, using a supervised or an unsupervised machine learning mechanism, where the feedback value  312  can be used to teach the computing system  100  to learn patterns and representations for the input request  306  so that a future input request for the input request  306  yields more accurate and relevant results and returns better translation results  318 . 
     Referring now to  FIG. 4 , therein is shown an exemplary control flow  400  of the computing system  100 . The computing system  100  can include a receiver module  402 , a profile module  406 , an input characterization module  410 , a location module  414 , a map module  404 , a determination module  418 , a first connotation database  426 , a second connotation database  432 , a translation module  438 , a display module  444 , a feedback module  446 , and a storage module  450 . 
     In one embodiment, the receiver module  402  can be coupled to the profile module  406  and the location module  414 . The location module  414  can be coupled to the map module  404 , the input characterization module  410 , the translation module  438 , and the determination module  418 . The profile module  406  can be coupled to the determination module  418  and the input characterization module  410 . The input characterization module  410  can be coupled to the determination module  418 . The determination module  418  can be coupled to the first connotation database  426 , the translation module  438 , and the display module  444 . The translation module  438  can be coupled to the second connotation database  432  and the display module  444 . The display module  444  can be coupled to the feedback module  446 . The feedback module  446  can be coupled to the storage module  450 . The storage module can be coupled to the first connotation database  426  and the second connotation database  432 . The first connotation database  426  can be coupled to the second connotation database  432 . 
     The first connotation database  426  and the second connotation database  432  are lexical databases of structured sets of terms and phrases of one or more languages that are categorized as sets of cognitive synonyms expressing a distinct concept. Terms and phrases are cognitively synonymous with another word if they refer to the same thing independently of context. The first connotation database  426  and the second connotation database  432  can have the sets of cognitive synonyms categorized based on a machine learning algorithm or deep learning algorithm, using a supervised or an unsupervised machine learning mechanism. The first connotation database  426  and the second connotation database  432  can have the sets of cognitive synonyms grouped in a variety of ways. For example, the sets of cognitive synonyms can be grouped based on word associations, real world relationships and sub-relationships, a specific cultural context of words or phrases, location information, a time and date information, a hypernym (“is-a”) relationship, a meronym (“part-whole”) relationship, a sisternym (“like-a”) relationship, or a combination thereof. 
     The hypernym relationship describes relationships between cognitive synonyms in which one term has a broader meaning and that more specific words fall under or a superordinate to that term (i.e., “is-a”). For example, the term “color” is a hypernym of red. The meronym relationship describes relationships between cognitive synonyms in which one term denotes part of something which is used to refer to the whole of it (i.e., “part-whole”). For example, the term “faces” when used to mean people in the phrase “I see several familiar faces present” is a meronym of “people.” A sisternym relationship describes relationships between cognitive synonyms in which one term refers to a concept that is similar to the concept expressed by another term but it not identical (i.e., “like-a”). For example, the term “coffee shop” is a sisternym of “cafe.” 
     In one embodiment, for example, the first connotation database  426  and the second connotation database  432  can group the terms “restaurant,” “gas station,” and “cafe” together because in some locals, for example in the United States, each of a “restaurant,” “gas station,” and “cafe” are places where a person can obtain food, and therefore an input request  306  for “Food” should return the translation result  318  showing restaurants, gas stations, and cafes as relevant points of interest  310  where a system user  304  can get food. In another embodiment, the phrases “pet store,” “zoo,” and “farm” can be grouped together because these are places where animals can be seen. 
     In one embodiment, the receiver module  402  can enable the receiving of the input request  306  for a point of interest  310 , as described with respect to  FIG. 3 . The receiver module  402  can pass control of the input request  306  to the profile module  406 , the location module  414 , or a combination thereof. 
     Continuing with the example, if control is passed to the profile module  406 , the profile module  406  can analyze the input request  306  against a user profile  408 , using the input characterization module  410  to determine whether the input request  306  can be matched to an assigned or designated system user&#39;s language  314  that is assigned or designated in a user profile  408 . The user profile  408  is a description or a representation of the system user  304 . The user profile  408  can include user identification as information utilized for identifying the system user  304 . For example, the user identification can include a name, a government-issued identification information, an account name or identification, a contact information, physical features or traits of the system user  304 , voice recognition meta-data of the system user  304 , system user  304  preferences, assigned or designated system user&#39;s languages  314 , or a combination thereof. 
     If the input characterization module  410  finds a match, the profile module  406 , the input characterization module  410 , or a combination thereof can pass one or more indicators to the determination module  418  indicating that the first connotation database  426  to be accessed when determining the first linguistic context  420  should be one with cognitive synonym sets assigned and associated with the system user&#39;s language  314 . 
     The one or more indicators refer to an associated symbolic name, which contains some known or unknown quantity of information referred to as a value. For example, in one embodiment, if the system user&#39;s language  314  is designated as “Mexican Spanish” and the input characterization module  410  determines that the input request  306  is also input in “Mexican Spanish” by analyzing the syntax, words, phrases, or a combination thereof of the input request  306 , then a match is found and the profile module  406 , the input characterization module  410 , or a combination thereof can pass a symbolic name such as “MX_SP” to the determination module  418  indicating that the first connotation database  426  to be accessed should contain cognitive synonym sets for the “Mexican Spanish” language. 
     Continuing with the example, in one embodiment, the receiver module  402  can pass control of the input request  306  to the location module  414 , either by itself or in conjunction with passing control of the input request  306  to the profile module  406 . The location module  414  can use the first location unit  214 , the second location unit  246 , or a combination thereof to obtain the system user&#39;s  304  current location  308 . The location module  414  can analyze the input request  306  against the system user&#39;s  304  current location  308 , using the input characterization module  410  to determine whether the language of the input request  306  can be matched to the language associated with the locale of the system user&#39;s  304  current location  308 . If a match is found, the location module  414 , the input characterization module  410 , or a combination thereof can pass one or more indicators to the determination module  418  indicating that the first connotation database  426  to be accessed when determining the first linguistic context  420  should be one with cognitive synonym sets assigned and associated with the system user&#39;s  304  current location  308 . For example, in one embodiment, if the input request  306  is determined to be input in “Mexican Spanish,” and the location module  414  determines that the system user&#39;s  304  current location  308  is in Mexico City, Mexico, the location module  414 , the input characterization module  410 , or a combination thereof can pass a symbolic name such as “MX_SP” to the determination module  418  indicating that the first connotation database  426  to be accessed should contain cognitive synonym sets for the “Mexican Spanish” language. 
     In one embodiment, the location module  414  can obtain the system user&#39;s  304  current location  308  using the first location unit  214 , the second location unit  246 , or a combination thereof in conjunction with a map module  404  holding map information of a variety of countries, regions, states, counties, cities, neighborhoods, blocks or a combination thereof. The map module  404  can also contain information regarding points of interest  310  or can interface with a further database containing information regarding points of interest  310 . 
     The input characterization module  410  can allow the profile module  406 , the location module  414 , or a combination thereof to identify the language of the input request  306  using one or more input request characteristics  412 . The input characterization module  410  can do so by analyzing one or more input request characteristics  412  of the input request  306  in order to categorize, determine, or otherwise identify the language of the input request  306 . For example, in one embodiment, the input characterization module  410  can analyze the syntax of the input request  306  and compare the syntax of the input request  306  to the syntax for a set of known languages to determine the language of the input request  306 . In another embodiment, the input characterization module  410  can analyze the language characters associated with the input request  306  using, for example, an optical character recognition (OCR) technique, and compare the characters of the input request  306  to a set of known characters for known languages to determine the language of the input request  306 . In another embodiment, where the input request  306  is given through a voice command through the first audio interface  204 , the second audio interface  232 , or a combination thereof, the input characterization module  410  can analyze the voice command by comparing a sound or an audible signal to a set of known sounds, dialects, phonetic tones, or a combination thereof of known languages to determine the language of the input request  306 . Analyzing the voice command can be done using any number of techniques including but not limited to those used in speech recognition system based on Hidden Markov Models (HMM), dynamic time warping (DTW) based speech recognition, neural networks, end-to-end automatic speech recognition, or a combination thereof. In another embodiment, the input characterization module  410  can analyze a sentence structure of the input request  306 , a grammar of the input request  306 , a linguistic pattern of the input request  306 , or any combination thereof using text recognition techniques to determine the language of the input request  306 . 
     In another embodiment, if no system user language  314  is assigned or designated in the user profile  408 , the input characterization module  410  can analyze the input request  306  using the techniques described above in order to recognize the input request  306  language without reference to the user profile  408 . In one embodiment, once the input characterization module  410  is able to determine the language of the input request  306 , the input characterization module  410  can assign or designate the language to the user profile  408 . 
     Continuing with the example, once control is passed to the determination module  418 , the determination module  418  can determine the first linguistic context  420  for the input request  306 . The first linguistic context  420  indicates how the meaning of the input request  306  is understood by a system user  304  or by a person or persons who speak the system user&#39;s language  314 . For example, in one embodiment, if the input request  306  is “Gasolinera,” the first linguistic context  420  allows the computing system  100  to determine that the system user  304  would like to search for a “gas station” despite, for example, the system user  304  being in an area that does not have “Mexican Spanish” as the primary language of the locale, for example in Canada, and where the points of interest  310  are not designated in “Mexican Spanish.” Similarly, if the input request  306  is “Happy Hour” the first linguistic context  420  allows the computing system  100  to determine that the system user  304  likely wants to look for businesses or establishments serving alcoholic beverages within a certain time period. The first linguistic context  420  enables the system user  304  to search for the points of interest  310  using the input request  306  without the system user  304  needing to know or understand the language associated with the system user&#39;s  304  current location  308 . Details of the first linguistic context  420  will be discussed below. The second linguistic context  440  provides similar functionality as the first linguistic context  420 . Details of the second linguistic context  440  will be discussed below 
     The determination module  418  can determine the first linguistic context  420  using the first connotation database  426  and the one or more indicators passed to the determination module  418  by the profile module  406 , the input characterization module  410 , the location module  414 , or a combination thereof. In one embodiment, the first linguistic context  420  can be determined based on the language of the input request  306  matching the language associated with the locale of the system user&#39;s  304  current location  308 . For example, if the language of input request  306  matches the language associated with the system user&#39;s  304  current location  308 , the determination module  418  can access a first connotation database  426  associated with the matching language. For example, if the language of the input request  306  is “Mexican Spanish” and the current location  308  is Mexico City, Mexico, the first connotation database  426  to be accessed can be associated with and contain cognitive synonym sets for the “Mexican Spanish” language. 
     Continuing with the example, where the language of the input request  306  matches the language associated with the system user&#39;s  304  current location  308  and the first connotation database  426  is determined, the first linguistic context  420  can be determined based on a mapping of the input request  306  to the words, phrases, categories, or a combination thereof contained in the first connotation database  426  that are similar to, identical to, related to or otherwise associated with the words, phrases, categories, associated with the input request  306 . The determination module  418  can assign the mapping by applying an identity transform or a data mapping of the input request  306  to the first connotation database  426 . 
     The identity transform or data mapping refer to a data transformation that copies a source data from a first connotation database  426 , the second connotation database  432 , or a combination thereof to the determination module  418 , the translation module  438 , the first connotation database  426 , the second connotation database  432 , or a combination thereof. 
     For example, if the input request  306  is for “Gasolinera,” the determination module  418  can access the first connotation database  426  associated with the “Mexican Spanish” language and search for words, terms, phrases, categories, or a combination thereof categorized in the first connotation database  426  and matching or associated with “Gasolinera.” Once found, the first connotation database  426  can return a first result  422  based on or containing the matching words, terms, phrases, categories, or a combination thereof to the determination module  418  based on the identity transform or the data mapping. The determination module  418  can assign the first linguistic context  420  as the first result  422  and search for the point of interest  310  based on the first linguistic context  420 . The determination module  418  can search for the point of interest  310  using the location module  414  and the map module  404  based on the first linguistic context  420  and return a translation result  318  associated with the search. 
     Continuing with the example, in another embodiment, if the one or more indicators passed by the profile module  406 , the input characterization module  410 , the location module  414 , or a combination thereof indicate that the language of the input request  306  does not match the language associated with the locale of the system user&#39;s  304  current location  308 , the determination module  418  can determine that the first connotation database  426  to be accessed should be associated with the language associated with the input request  306 . For example, in one embodiment, if the language of the input request  306  is in “Mexican Spanish” and the current location  308  is Toronto, Canada, the determination module  418  can determine that the first connotation database  426  to be accessed should be one associated with and contain cognitive synonym sets for the “Mexican Spanish” language. The determination module can also set a flag  424  indicating that the language of the input request  306  and language associated with the locale of the system user&#39;s  304  current location  308  do not match and that further processing of the input request  306  is required by the translation module  438 . Details regarding the further processing will be discussed below. The flag  424  refers to a software or hardware mark, variable, condition, or a combination thereof that signals a particular condition or status. 
     Continuing with the example, in one embodiment, if no further processing is required, the determination module  418  can pass control to the display module  444  to display the translation result  318  on the first display interface  206 , the second display interface  234 , or a combination thereof. 
     In another embodiment, if further processing is required, the determination module  418  can pass control to the translation module  438 . The translation module  438  can determine a second linguistic context  440  for the input request  306 . The second linguistic context  440  provides similar functionality as the first linguistic context  420 . The translation module  438  can determine the second linguistic context  440  in the same manner in which the first linguistic context  420  is determined, except using a second connotation database  432  associated with the language associated with the locale of system user&#39;s  304  current location  308 . For example, in one embodiment, if the system user&#39;s  304  current location  308  is New York City, U.S.A, the second connotation database  432  to be accessed is one associated with and containing cognitive synonym sets for the “U.S. English” language. Once the first connotation database  426  and the second connotation database  432  are determined, the second linguistic context  440  can be determined by the translation module  438  as a mapping of the words, phrases, categories, or a combination thereof associated with the first linguistic context  420  mapped to the equivalent words, phrases, categories, or a combination thereof in the second connotation database  432 . 
     The translation module  438  can assign the mapping by applying an identity transform or a data mapping similar to that described above with respect to first connotation database  426 , determination module  418 , and the input request  306  when a matching language is found. Details regarding the mapping will be discussed below. 
     Continuing with the example, once the mapping of the first linguistic context  420  to the second connotation database  432  is done, the second connotation database  432  can return a second result  442  to the translation module  438  based on the mapping. The translation module  438  can assign the second linguistic context  440  as the second result  442  and determine that the search for the point of interest  310  associated with the input request  306  should include a search for the words, phrases, categories, or a combination thereof associated with the second linguistic context  440 . The translation module  438  can search for the point of interest  310  using the location module  414  and the map module  404  based on the second linguistic context  440  and return a translation result  318  associated with the search. Once the translation result  318  is returned the translation module  438  can pass control to the display module  444  to display the translation result  318  on the first display interface  206 , the second display interface  234 , or a combination thereof. 
     Continuing with the example, in one embodiment, once the translation result  318  is displayed on the first display interface  206 , the second display interface  234  or a combination thereof, control can pass to the feedback module  446  to allow the feedback value  312  to be received by the computing system  100  in manner described in  FIG. 3 . The feedback module  446  can enable the feedback value  312  to be received using the first display interface  206 , the second display interface  234 , the first audio interface  204 , the second audio interface  232 , or a combination thereof. For example, in one embodiment, the feedback value  312  can be received by the computing system  100  through an entry from the first display interface  206 , the second display interface  234 , or a combination thereof. In another embodiment, the feedback module  446  enable the feedback value  312  to be received by the computing system  100  through a voice command through the first audio interface  204 , the second audio interface  232 , or a combination thereof. In another embodiment, the feedback module  446  can interface with a further database to provide the feedback value  312 . 
     Once received, in one embodiment, the feedback module  446  can pass the feedback value  312  to the storage module  450 . The storage module  450  can enable the storage of the feedback value  312  in the first storage unit  216 , the second storage unit  240 , or a combination thereof. The feedback value  312  can be used by the computing system  100  to refine a future translation result of a future input request for the input request  306  in a manner described in  FIG. 3 . For example, in one embodiment, the storage module  450  can interface with the first connotation database  426 , the second connotation database  432 , or a combination thereof to modify the sets of cognitive synonyms in the first connotation database  426 , the second connotation database  432 , or a combination thereof based on the feedback value  312  to provide better results for a future input request. The computing system  100  can achieve this modification by for example, rearranging, re-categorizing, reclassifying, or otherwise changing the word, phrase, term, or category associations in the first connotation database  426 , the second connotation database  432 , or a combination thereof to provide more relevant mappings or data transformations for a future input request for the input request  306 . 
     For example, in one embodiment, if the input request  306  is for “Gasolinera” and the translation result  318  displayed is for a “restaurant,” the feedback value  312  can indicate an unsatisfactory result. This unsatisfactory result can be passed to the first connotation database  426 , the second connotation database  432 , or a combination thereof such that the sets of cognitive synonyms in the first connotation database  426 , the second connotation database  432 , or a combination thereof can be rearranged, re-categorized, reclassified, or otherwise changed to disassociate “restaurants” from the term “Gasolinera” to provide more relevant mappings or data transformations for a future input request for the input request  306 . 
     In one embodiment, the rearranging, re-categorizing, or reclassification can be based on the feedback module  446  monitoring the system user&#39;s  304  actions subsequent receiving the unsatisfactory feedback  446 . For example, in one embodiment, the feedback module  446  can monitor one or more of the system user&#39;s  304  actions using one or more components of the computing system  100 , for example sensors, microphones, transducers, the first location unit  214 , the second location unit  246 , or any combination thereof. For example, in one embodiment, the feedback module  444  can monitor the system user&#39;s  304  navigation path to determine where the system user&#39;s  304  destination ends during a trip in which the system user  304  searches for an input request  306 , such that the address of the destination and the address of the translation result  318  can be compared to determine relationships and attributes similar or different between the two such that further categorizations or cognitive synonymous relationships can be generated based on those relationships and attributes. For example, if the input request  306  is for “Food” and a “gas station” is not returned as a translation result  318  in a locale where gas stations also serve food, the feedback module  446  can monitor the system user&#39;s  304  navigation path and if the system user  304  ends up at a gas station, can determine that a “gas station” should be categorized under a search for “food” in the future when the input request  306  is for “Food” in the particular locale. The first connotation database  426 , the second connotation database  432 , or a combination thereof can then be updated with the updated categorization, relationship, or attribute for future input requests associated with “Food” in that locale. 
     The computing system  100  has been described with module functions or order as an example. The computing system  100  can partition the modules differently or order the modules differently. For example, the first software  220  of  FIG. 2  of the first device  102  can include the modules for the computing system  100 . As a specific example, the first software  220  can include the receiver module  402 , the profile module  406 , the input characterization module  410 , the location module  414 , the map module  404 , the determination module  418 , the first connotation database  426 , the second connotation database  432 , the translation module  438 , the display module  444 , the feedback module  446 , and the storage module  450 , and associated sub-modules included therein. 
     The first control unit  210  of  FIG. 2  can execute the first software  220  to operate the modules. For example, the first control unit  210  can implement the receiver module  402 , the profile module  406 , the input characterization module  410 , the location module  414 , the map module  404 , the determination module  418 , the first connotation database  426 , the second connotation database  432 , the translation module  438 , the display module  444 , the feedback module  446 , and the storage module  450 , and associated sub-modules included therein. 
     In another example of module partitions, the second software  244  of  FIG. 2  of the second device  106  can include the modules for the computing system  100 . As a specific example, the second software  244  can include the receiver module  402 , the profile module  406 , the input characterization module  410 , the location module  414 , the map module  404 , the determination module  418 , the first connotation database  426 , the second connotation database  432 , the translation module  438 , the display module  444 , the feedback module  446 , and the storage module  450 , and associated sub-modules included therein. 
     The second control unit  238  of  FIG. 2  can execute the second software  244  to operate the modules. For example, the second control unit  238  can implement the receiver module  402 , the profile module  406 , the input characterization module  410 , the location module  414 , the map module  404 , the determination module  418 , the first connotation database  426 , the second connotation database  432 , the translation module  438 , the display module  444 , the feedback module  446 , and the storage module  450 , and associated sub-modules included therein. 
     The computing system  100  has been described with module functions or order as an example. The computing system  100  can partition the modules differently or order the modules differently. 
     The modules described in this application can be implemented as instructions stored on a non-transitory computer readable medium to be executed by a first control unit  210 , the second control unit  238 , or a combination thereof. The non-transitory computer medium can include the first storage unit  216 , the second storage unit  240 , or a combination thereof. The non-transitory computer readable medium can include non-volatile memory, such as a hard disk drive, non-volatile random access memory (NVRAM), solid-state storage device (SSD), compact disk (CD), digital video disk (DVD), or universal serial bus (USB) flash memory devices. The non-transitory computer readable medium can be integrated as a part of the computing system  100  or installed as a removable portion of the computing system  100 . 
     Referring now to  FIG. 5 , therein is shown an exemplary representation of a cognitive synonym space  500  for the computing system  100 . Specifically,  FIG. 5  shows the cognitive synonym space  500  for the first connotation database  426 , the second connotation database  432 , or a combination thereof. The cognitive synonym space  500  represents how words, terms, phrases, categories, or a combination thereof in the first connotation database  426 , the second connotation database  432 , or a combination thereof are grouped and mapped to one another as cognitive synonym sets.  FIG. 5  represents only one embodiment for a cognitive synonym space  500 . The example used in  FIG. 5  is for the input request  306  “Meal.” Cognitive synonym spaces  500  for other input requests  306  can be represented similarly. 
     Continuing with the example, in one embodiment, assuming the input request  306  is for “Meal,” the cognitive synonym space  500  can include one or more measures that allow the determination module  418 , the translation module  438 , the first connotation database  426 , the second connotation database  432 , or a combination thereof to perform the identity transform or the data mapping for the input request  306 , such that the first linguistic context  420 , the second linguistic context  440 , or a combination thereof can be determined across one or more languages. The one or more measures can be represented as one or more variables, meta-data, parameters, or a combination thereof in the first connotation database  426 , the second connotation database  432 , or a combination thereof. 
     For example, for the input request  306  “Meal,” the cognitive synonym space  500  can include one or more variables, meta-data, parameters, or a combination thereof that allows the determination module  418 , the translation module  438 , the first connotation database  426 , the second connotation database  432 , or a combination thereof to map the input request  306  to words, terms, phrases, or categories associated with “Meal” in the first connotation database  426 , the second connotation database  432 , or a combination thereof. For the input request  306  “Meal,” these can include, for example, a weight measure  506 , a probability measure  502 , and a time measure  504 . Other variables, meta-data, or parameters can be used and the aforementioned are exemplary, and are set forth for brevity of discussion and to better explain the example discussed in  FIG. 5 . 
     Continuing with the example, for the input request  306  “Meal,” the weight measure  506  can refer to a degree, a quantity, a measure, or a combination thereof of the input request  306 . For example, a meal can contain a degree representing a “heaviness” of the meal which is associated with how much food is typically eaten during a meal. For example, meals can be categorized from “Very Light” representing that very little food is typically eaten during that meal, to “Very Heavy” representing that a large quantity of food is typically eaten during that meal. Examples of “Very Light” meals can include snacks or breakfasts. Examples of “Very Heavy” meals can include dinners, lunches, or feasts. The weight measure  506  can vary amongst different connotation databases associated with different languages. For example, connotation databases associated with languages in which the culture associated with that language considers breakfast a “Very Heavy” meal can have the weight measure  506  categorized differently than connotation databases associated with languages in which the culture associated with that language considers breakfast as a “Very Light” meal. 
     The time measure  504  can refer to a time or date associated with the input request  306 . For example, for the input request  306  “Meal,” the time measure  504  can include times or dates when the meal is typically eaten. For example, dinners can be categorized as typically being eaten in the evenings between 5:00 pm-8:00 pm and breakfasts can be categorized as typically eaten before noon. In another embodiment, the input request  306  “Christmas Dinner” can be categorized as typically being eaten on or around December 25 of the calendar year in a connotation database associated with “U.S. English.” The time measure  504  can vary amongst different connotation databases associated with different languages similar to how the weight measure  506  can differ amongst different connotation databases. 
     The probability measure  502  can refer to the probability that a certain attribute of the input request  306  will be present. Continuing with the example, in one embodiment, for the input request  306  “Meal,” the probability measure  502  can represent the probability that the meal will contain, for example, sweets. Other probability measures  502  can be used such as those indicating the probability that the meal will contain, for example, “meat” or a “dessert.” In one embodiment, the probability measure  502  can be based on a numerical value range. In one embodiment, the numerical value range range can include a range from 0.0-1.0 indicating how probable it is that the attribute of the input request  306  will be present, with 0.0 representing zero to little probability and 1.0 representing an absolute certainty or a high probability. In another embodiment, the probability measure  502  can be represented using categorizations, for example, “Highly Probable,” “Probable,” “Not Probable.” For example, in the current example where the probability of a meal containing sweets is considered, for meals such as dinner or lunch, the probability measure  502  can be a lower probability while for meals such as snacks the probability measure can be a higher probability. The probability measure  502  can vary amongst different connotation databases associated with different languages similar to how the weight measure  506  can differ amongst different connotation databases. 
     Continuing with the example, in one embodiment, each word, term, phrase, category, or combination thereof contained a connotation database can be mapped to a point  508  in the cognitive synonym space  500  based on the one or more measures for each word, term, phrase, or category. In the current example, because the cognitive synonym space  500  consists of three points of measure which are the weight measure  506 , the probability measure  502 , and the time measure  504 , the cognitive synonym space can form a three-dimensional space. In one embodiment, the three-dimensional space can be represented with a multi-axis graph representing an (X,Y,Z) axis. The words, terms, phrases, categories, or a combination thereof can be set to a point  508  in the three-dimensional space by connecting the one or more measures along each of their values on each axis. As a result, words, terms, phrases, categories, or a combination thereof with similar values for their one or more measures can be grouped together in clusters within the cognitive synonym space  500 . The clusters can form the bases for word, phrase, term, or category groupings. The computing system  100  can use the words, terms, phrases, categories, or a combination thereof to determine the first linguistic context  420 , the second linguistic context  440 , or a combination thereof and to generate and return the first result  422 , the second result  442 , or a combination thereof. 
     For example, in one of the embodiments previously mentioned with respect to  FIG. 4 , if the input request  306  matches the language associated with the system user&#39;s  304  current location  308 , the first connotation database  426  is determined and the first linguistic context  420  can be determined based on a mapping of the input request  306  to the words, phrases, categories, or a combination thereof contained in the first connotation database  426  that are similar to, identical to, related to, grouped closely to, or otherwise associated with the words, phrases, categories, associated with the input request  306 . Once mapped, the first connotation database  426  can assign the mapped result as the first result  422 . For example, assuming the input request  306  is for “Dinner” and the system user&#39;s  304  current location  308  is New York City, USA. The first connotation database  426  is determined to be one associated with “U.S. English.” The computing system  100  can then look for words, phrases, or categories related to “Dinner” and return mapped words, phrases, or categories as the first result  422 . In the aforementioned example, the mapping of the words, phrases, or categories can be done one-to-one and a direct lookup of words, phrases, terms, or categories of the first connotation database  426  can be done because the language of the input request  306  is the same as the language associated with the first connotation database  426  and therefore the same words or phrases can be searched for. 
     In another embodiment previously mentioned with respect to  FIG. 4 , if the language of the input request  306  does not match with the system user&#39;s  304  current location  308 , the computing system  100  will need to map the first linguistic context  420  associated with the first connotation database  426  to the second linguistic context  440  associated with the second connotation database  432  such that the translation module  438  can search for the input request  306  using the equivalent terms and in the same context between the first connotation database  426  and the second connotation database  432 . For example, if the input request  306  is for “Dinner” and the system user&#39;s  304  current location  308  is in Mexico City, Mexico, then the first connotation database can be associated with “U.S. English” while the second connotation database  432  can be associated with “Mexican Spanish.” Thus, the term “Dinner” in the first connotation database  426  must be mapped to the equivalent term in the second connotation database  432  which is “Cena.” In one embodiment, the mapping can be done using a distance calculation between words, terms, phrases, or categories in the first connotation database  426  and the second connotation database  432 . In one embodiment the distance calculation can be done by taking the point  508  associated with the word, term, phrase, or category in the first connotation database  426  and finding the equivalent point  508  in the second connotation database  432 . The computing system  100  can then find words, terms, phrases, or categories, in the second connotation database close to or nearby the point in the second connotation database  432 . The computing system  100  can then measure the distances  510  between the equivalent point  508  in the second connotation database  432  and the various words, terms, phrases, or categories. The smaller the distances  510  between the equivalent point  508  and the words, terms, phrases, or categories in the second connotation database  432 , the more likely the word, term, phrase, or category in the second connotation database  432  is related to or equivalent to the word, term, phrase, or category of the first connotation database  426 . In this embodiment and example, the assumption is that the one or more measures are the same amongst the one or more connotation databases such that points  508  can be mapped equivalently. 
     An example of the distance calculations is as follows. As shown in  FIG. 5 , which depicts the aforementioned embodiment, the input request  306  can be “Dinner,” which is associated with the first connotation database  426 . The second connotation database  432  can be associated with the “Mexican Spanish” language and can contain the terms “Cena,” which is the equivalent for “Dinner” and the word “Desayuno” which is the equivalent for “Breakfast.” The distance “d1” is the distance between the equivalent point  508  for “Dinner” in the second connotation database  432  and the word “Cena” which has similar or equivalent measures as that for “Dinner.” The distance “d2” is the distance between the equivalent point  508  for “Dinner” in the second connotation database  432  and the word “Desayuno” which has different measures as that for “Dinner.” Because “d1” is smaller and thus closer to the equivalent point  508  than “d2,” the second connotation database will choose the word “Cena” as a mapping to the word “Dinner” and return “Cena” as the second result  442 . 
     While the aforementioned example and embodiment, indicates a single word mapping, the benefits of the invention can be readily realized when searching for more complex phrases or words that do not have equivalents amongst languages. In such situations, words, phrases, terms, or categories with no equivalents can still be mapped based on the one or more measures and the mapping techniques described herein. 
     It has been discovered that the mapping techniques described herein provides increased usability and accessibility for searching for points of interest  310  in an area where the system user&#39;s language  314  is different than the language of the system user&#39;s  304  current location  308 . The computing system  100  with the first connotation database  426 , the second connotation database  432 , the first linguistic context  420 , and the second linguistic context  432  can provide increase in recognition of natural language or speech patterns such that better translations can be made across languages. 
     It has been discovered that the computing system  100  with the first connotation database  426 , the second connotation database  432 , the first linguistic context  420 , and the second linguistic context  432  using the mapping techniques disclosed herein can provide increased probability of identifying points of interest  310  relevant to the current situation or condition of the system user  304  without the system user  304  knowing the language associated with the system user&#39;s  304  current location  308 . As a result, the computer system  100  can lessen the probability that a system user  304  will not find a desired point of interest  310  when they are in a geographic area in which the language of the locate is different from the system user&#39;s language  314  or the language of the input request  306 . 
     Referring now to  FIG. 6 , therein is shown a flow chart of a method  600  of operation of a computing system in a further embodiment of the present invention. The method  600  includes: receiving  602  an input request  306  for a point of interest  310 ; determining  604  a first linguistic context  420  for the input request  306  based on one or more input request characteristics  412 , a user profile  408 , a location  308 , and a first connotation database  426 ; translating  608  the input request  306  to a second linguistic context  440  based on a translation flag  424  and a second connotation database  432 , wherein the second connotation database  432  is mapped to the first connotation database  426 ; and displaying  610  a translation result  318  for the input request  306  based on the first linguistic context  420  or the second linguistic context  440 . 
     The resulting method, process, apparatus, device, product, and system is cost-effective, highly versatile, and accurate, and can be implemented by adapting known components for ready, efficient, and economical manufacturing, application, and utilization. Another important aspect of an embodiment of the present invention is that it valuably supports and services the historical trend of reducing costs, simplifying systems, and increasing performance. 
     These and other valuable aspects of the embodiments of the present invention consequently further the state of the technology to at least the next level. While the invention has been described in conjunction with a specific best mode, it is to be understood that many alternatives, modifications, and variations will be apparent to those skilled in the art in light of the descriptions herein. Accordingly, it is intended to embrace all such alternatives, modifications, and variations that fall within the scope of the included claims. All matters set forth herein or shown in the accompanying drawings are to be interpreted in an illustrative and non-limiting sense.