EP2691949A2 - Compréhension conversationnelle basée sur l'emplacement - Google Patents

Compréhension conversationnelle basée sur l'emplacement

Info

Publication number
EP2691949A2
EP2691949A2 EP20120763866 EP12763866A EP2691949A2 EP 2691949 A2 EP2691949 A2 EP 2691949A2 EP 20120763866 EP20120763866 EP 20120763866 EP 12763866 A EP12763866 A EP 12763866A EP 2691949 A2 EP2691949 A2 EP 2691949A2
Authority
EP
European Patent Office
Prior art keywords
query
location
environmental context
user
speech
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
EP20120763866
Other languages
German (de)
English (en)
Other versions
EP2691949A4 (fr
Inventor
Larry Paul Heck
Madhusudan Chinthakunta
David Mitby
Lisa Stifelman
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Microsoft Technology Licensing LLC
Original Assignee
Microsoft Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US13/076,862 external-priority patent/US9760566B2/en
Priority claimed from US13/077,431 external-priority patent/US10642934B2/en
Priority claimed from US13/077,368 external-priority patent/US9298287B2/en
Priority claimed from US13/077,303 external-priority patent/US9858343B2/en
Priority claimed from US13/077,233 external-priority patent/US20120253789A1/en
Priority claimed from US13/077,396 external-priority patent/US9842168B2/en
Priority claimed from US13/077,455 external-priority patent/US9244984B2/en
Application filed by Microsoft Corp filed Critical Microsoft Corp
Publication of EP2691949A2 publication Critical patent/EP2691949A2/fr
Publication of EP2691949A4 publication Critical patent/EP2691949A4/fr
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3329—Natural language query formulation or dialogue systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying
    • G06F16/9032—Query formulation
    • G06F16/90332—Natural language query formulation or dialogue systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/951—Indexing; Web crawling techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/26—Speech to text systems

Definitions

  • Location-based conversational understanding may provide a mechanism for leveraging environmental contexts to improve query execution and results.
  • Conventional speech recognition programs do not have techniques for leveraging information (e.g., speech utterances, geographical data, acoustic environments of certain locations, typical queries made from a particular location) from one user to another to improve the quality and accuracy of new queries from new and/or existing users.
  • speech- to-text conversions must be made without the benefit of using similar, potentially related queries to aid in understanding.
  • Speech-to-text conversion may comprise converting a spoken phrase into a text phrase that may be processed by a computing system.
  • Acoustic modeling and/or language modeling may be used in modern statistic- based speech recognition algorithms.
  • Hidden Markov models are widely used in many conventional systems. HMMs may comprise statistical models that may output a sequence of symbols or quantities. HMMs may be used in speech recognition because a speech signal may be viewed as a piecewise stationary signal or a short-time stationary signal. In a short-time (e.g., 10 milliseconds), speech may be approximated as a stationary process. Speech may thus be thought of as a Markov model for many stochastic purposes.
  • Location-based conversational understanding may be provided.
  • an environmental context associated with the query may be generated.
  • the query may be interpreted according to the environmental context.
  • the interpreted query may be executed and at at least one result associated with the query may be provided to the user.
  • FIG. 1 is a block diagram of an operating environment
  • FIG. 2 is a flow chart of a method for providing location-based
  • FIG. 3 is a block diagram of a system including a computing device.
  • Location-based conversational understanding may be provided.
  • a speech-to-text system may be provided that correlates information from multiple users in order to improve the accuracy of the conversion and the results of queries included in the converted statement.
  • a personal assistant program may receive speech-based queries from user(s) at multiple locations. Each query may be analyzed for acoustic and/or environmental characteristics, and such characteristics may be stored and associated with the location from which the query was received. For example, a query received from a user at a subway station may detect the presence of acoustic echoes off of tile walls and/or the background
  • the location may be defined, for example by a global positioning system (GPS) location of the user, an area code associated with the user, a ZIP code associated with the user, and/or a proximity of the user to a landmark (e.g., a train station, a stadium, a museum, an office building, etc.).
  • GPS global positioning system
  • Processing the query may comprise adapting the query according to an acoustic model.
  • the acoustic model may comprise a background sound known to be present at a particular location. Applying the acoustic model may allow the query to be converted more accurately by ignoring irrelevant sounds.
  • the acoustic model may also allow for altering the display of any results associated with the query. For example, in a particularly loud environment, results may be displayed on a screen rather than via audio.
  • the environmental context may further be associated with an
  • the understanding model may comprise a Hidden Markov Model (HMM).
  • HMM Hidden Markov Model
  • the environmental context may further be associated with a semantic model to aid in executing the query.
  • the semantic model may comprise an ontology.
  • the subject of the query may be used to improve the results for future queries. For example, if users at the subway station query "when is the next one?", the personal assistant program may determine, over the course of several queries, that the user wants to know when the next train will arrive. This may be accomplished by asking for clarification of the query from a first user, and storing the clarification for use in the future. For another example, if one user queries "when is the next one?" and another user queries "when is the next train?", the program may correlate these queries and make the assumption that both users are requesting the same information.
  • FIG. 1 is a block diagram of an operating environment 100 for providing location-based conversational understanding.
  • Operating environment 100 may comprise a spoken dialog system (SDS) 110 comprising a personal assistant program 112, a speech- to-text converter 1 14, and a context database 116.
  • SDS spoken dialog system
  • Personal assistant program 1 12 may receive queries over a network 120 from a first plurality of users 130(A)-(C) at a first location 140 and/or a second plurality of users 150(A)-(C) at a second location 160.
  • Context database 116 may be operative to store context data associated with queries received from users such as first plurality of users 130(A)-(C) and/or second plurality of users 150(A)-(C).
  • the context data may comprise acoustic and/or environmental characteristics and query context information such as query subjects, time/date of the query, user details, and/or the location from which the query was made.
  • network 120 may comprise, for example, a private data network (e.g., an intranet), a cellular data network, and/or a public network such as the Internet.
  • the agent may be associated with a spoken dialog system (SDS).
  • SDS spoken dialog system
  • the primary component that drives the SDS may comprise a dialog manager: this component manages the dialog-based conversation with the user.
  • the dialog manager may determine the intention of the user through a combination of multiple sources of input, such as speech recognition and natural language understanding component outputs, context from the prior dialog turns, user context, and/or results returned from a knowledge base (e.g., search engine). After determining the intention, the dialog manager may take an action, such as displaying the final results to the user and/or continuing in a dialog with the user to satisfy their intent.
  • the spoken dialog system may comprise a plurality of conversational understanding models, such as acoustic models associated with locations and/or a spoken language understanding model for processing speech-based inputs.
  • FIG. 2 is a flow chart setting forth the general stages involved in a method
  • Method 200 may be implemented using a computing device 300 as described in more detail below with respect to FIG. 3. Ways to implement the stages of method 200 will be described in greater detail below.
  • Method 200 may begin at starting block 205 and proceed to stage 210 where computing device 300 may receive a speech-based query from a user at a location.
  • user 130(A) may send a query to SDS 110 via a device such as a cellular telephone.
  • method 200 may advance to stage 215 where computing device 300 may determine whether an environmental context associated with the location exists in the memory storage.
  • SDS 110 may identify the location from which the query was received (e.g., first location 140) and determine whether an environmental context associated with that location is present in context database 1 16.
  • method 200 may advance to stage 220 where computing device 300 may identify at least one acoustic interference in the speech-based query.
  • SDS 110 may analyze the audio of the query and identify background noise such as that associated with a large crowd around user 130(A) and/or of a passing train.
  • Method 200 may then advance to stage 225 where computing device 300 may identify at least one subject associated with the speech-based query. For example, if the query comprises "When is the next arrival?", SDS 110 may identify train schedules as the subject of the query when the user is at a train station.
  • Method 200 may then advance to stage 230 where computing device 300 may create a new environmental context associated with the location for storing in the memory storage.
  • SDS 110 may store the identified acoustic interference and query subject in context database 1 16 as being associated with the user's location.
  • method 200 may advance to stage 235 where computing device 300 may load the environmental context associated with the location.
  • SDS 1 10 may load an environmental context as described above from context database 116.
  • method 200 may then advance to stage 240 where computing device 300 may convert the speech-based query to a text-based query according to the environmental context.
  • computing device 300 may convert the speech-based query to a text-based query by applying a filter for removing the at least one acoustic interference associated with the
  • Method 200 may then advance to stage 245 where computing device 300 may execute the text-based query according to the environmental context.
  • SDS 1 10 may execute the query (e.g., "When is the next arrival?") within a search domain (e.g., train schedules) associated with the at least one subject associated with the environmental context.
  • search domain e.g., train schedules
  • Method 200 may then advance to stage 250 where computing device 300 may provide at least one result of the executed text-based query to the user.
  • SDS 110 may transmit a result to a device associated with user 130(A) (e.g., a cellular phone) for display.
  • Method 200 may then end at stage 255.
  • An embodiment consistent with the invention may comprise a system for providing location-based conversational understanding.
  • the system may comprise a memory storage and a processing unit coupled to the memory storage.
  • the processing unit may be operative to receive a query from a user, generate an environmental context associated with the query, interpret the query according to the environmental context, execute the interpreted query, and provide at least one result of the query to the user.
  • the query may comprise, for example, a spoken query that the processing unit may be operative to convert into computer-readable text.
  • the speech-to-text conversion may utilize a Hidden Markov Model algorithm comprising statistical weightings for various likely words associated with an
  • the processing unit may be operative to increase a statistical weighting of at least one predicted word according to at least one previous query received from the location, for example, and store that statistical weighting as part of the environmental context.
  • the environmental context may comprise an acoustic model associated with a location from which the query was received.
  • the processing unit may be operative to adapt the query according to at least one background sound out of the speech-based query according to the acoustic model.
  • the background sound e.g., a train whistle
  • the background sound may be detected, measured for pitch, amplitude, and other acoustic characteristics.
  • the query may be adapted to ignore such sounds, and the sound may be computed and stored for application to future queries from that location.
  • the processing unit may be further operative to receive a second speech- based query from a second user and adapt the query to same the background sound out according to the updated acoustic model.
  • the processing unit may be further operative to aggregate the environmental contexts associated with a plurality of queries from a plurality of users and store the aggregated environmental contexts associated with the location.
  • Another embodiment consistent with the invention may comprise a system for providing location-based conversational understanding.
  • the system may comprise a memory storage and a processing unit coupled to the memory storage.
  • the processing unit may be operative to receive a speech-based query from a user at a location, load an environmental context associated with the location, convert the speech-based query to text according to the environmental context, execute the converted query according to the environmental context, and provide at least one result associated with the executed query to the user.
  • the environmental context may comprise, for example, a time of at least one previous query, a date of at least one previous query, a subject of at least one previous query, a semantic model comprising an ontology, an understanding model, and an acoustic model of the location.
  • the processing unit may be operative to adapt the query according to a known acoustic interference associated with the location.
  • the processing unit may be further operative to store a plurality of environmental contexts associated with a plurality of locations aggregated according to a plurality of queries received from a plurality of users.
  • the processing unit may be further operative to receive a correction to the converted text from the user and update the environmental context according to the correction.
  • the processing unit may be further operative to receive a second speech-based query from the user at a second location, load a second environmental context associated with the second location, convert the second speech-based query to text according to the second environmental context, execute the converted query according to the second
  • Yet another embodiment consistent with the invention may comprise a system for providing a context-aware environment.
  • the system may comprise a memory storage and a processing unit coupled to the memory storage.
  • the processing unit may be operative to receive a speech-based query from a user at a location and determine whether an environmental context associated with the location exists in the memory storage.
  • the processing unit may be operative to identify at least one acoustic interference in the speech-based query, identify at least one subject associated with the speech-based query, and create a new environmental context associated with the location for storing in the memory storage.
  • the processing unit may be operative to load the environmental context.
  • the processing unit may then be operative to convert the speech-based query to a text-based query according to the environmental context, wherein being operative to convert the speech-based query to a text-based query according to the environmental context comprises being operative to apply a filter for removing the at least one acoustic interference associated with the environmental context, execute the text-based query according to the environmental context, wherein being operative to execute the text-based query according to the environmental context comprises being operative to execute the query wherein the at least one acoustic interference is associated with an acoustic model and wherein the at least one identified subject is associated with a semantic model associated with the environmental context, and provide at least one result of the executed text-based query to the user.
  • FIG. 3 is a block diagram of a system including computing device 300.
  • the aforementioned memory storage and processing unit may be implemented in a computing device, such as computing device 300 of FIG. 3. Any suitable combination of hardware, software, or firmware may be used to implement the memory storage and processing unit.
  • the memory storage and processing unit may be implemented with computing device 300 or any of other computing devices 318, in combination with computing device 300.
  • the aforementioned system, device, and processors are examples and other systems, devices, and processors may comprise the aforementioned memory storage and processing unit, consistent with embodiments of the invention.
  • computing device 300 may comprise an operating environment for system 100 as described above. System 100 may operate in other environments and is not limited to computing device 300.
  • a system consistent with an embodiment of the invention may include a computing device, such as computing device 300.
  • computing device 300 may include at least one processing unit 302 and a system memory 304.
  • system memory 304 may comprise, but is not limited to, volatile (e.g., random access memory (RAM)), non- volatile (e.g., read-only memory (ROM)), flash memory, or any combination.
  • System memory 304 may include operating system 305, one or more programming modules 306, and may include personal assistant program 1 12.
  • Operating system 305 for example, may be suitable for controlling computing device 300's operation.
  • embodiments of the invention may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in FIG. 3 by those components within a dashed line 308.
  • Computing device 300 may have additional features or functionality.
  • computing device 300 may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape.
  • additional storage is illustrated in FIG. 3 by a removable storage 309 and a non-removable storage 310.
  • Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data.
  • System memory 304, removable storage 309, and non-removable storage 310 are all computer storage media examples (i.e., memory storage.)
  • Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 300. Any such computer storage media may be part of device 300.
  • Computing device 300 may also have input device(s) 312 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, etc.
  • Output device(s) 314 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.
  • Computing device 300 may also contain a communication connection 316 that may allow device 300 to communicate with other computing devices 318, such as over a network in a distributed computing environment, for example, an intranet or the Internet.
  • Communication connection 316 is one example of communication media.
  • Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media.
  • modulated data signal may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal.
  • communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.
  • RF radio frequency
  • computer readable media as used herein may include both storage media and communication media.
  • program modules and data files may be stored in system memory 304, including operating system 305.
  • programming modules 306 e.g., personal assistant program 1 12
  • processing unit 302 may perform processes including, for example, one or more of method 200's stages as described above.
  • processing unit 302 may perform other processes.
  • Other programming modules that may be used in accordance with
  • embodiments of the present invention may include electronic mail and contacts applications, word processing applications, spreadsheet applications, database
  • program modules may include routines, programs, components, data structures, and other types of structures that may perform particular tasks or that may implement particular abstract data types.
  • embodiments of the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor- based or programmable consumer electronics, minicomputers, mainframe computers, and the like.
  • Embodiments of the invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.
  • program modules may be located in both local and remote memory storage devices.
  • embodiments of the invention may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors.
  • Embodiments of the invention may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies.
  • embodiments of the invention may be practiced within a general purpose computer or in any other circuits or systems.
  • Embodiments of the invention may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media.
  • the computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process.
  • the computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process.
  • the present invention may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.).
  • embodiments of the present invention may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system.
  • a computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • the computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific computer-readable medium examples (a non-exhaustive list), the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM).
  • RAM random access memory
  • ROM read-only memory
  • EPROM or Flash memory erasable programmable read-only memory
  • CD-ROM portable compact disc read-only memory
  • the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

Abstract

L'invention concerne une compréhension conversationnelle basée sur l'emplacement. Dès réception d'une demande d'un utilisateur, un contexte environnemental associé à la demande peut être généré. La demande peut être interprétée en fonction du contexte environnemental. La demande interprétée peut être exécutée et au moins un résultat associé à la demande peut être fourni à l'utilisateur.
EP12763866.6A 2011-03-31 2012-03-27 Compréhension conversationnelle basée sur l'emplacement Ceased EP2691949A4 (fr)

Applications Claiming Priority (8)

Application Number Priority Date Filing Date Title
US13/076,862 US9760566B2 (en) 2011-03-31 2011-03-31 Augmented conversational understanding agent to identify conversation context between two humans and taking an agent action thereof
US13/077,431 US10642934B2 (en) 2011-03-31 2011-03-31 Augmented conversational understanding architecture
US13/077,368 US9298287B2 (en) 2011-03-31 2011-03-31 Combined activation for natural user interface systems
US13/077,303 US9858343B2 (en) 2011-03-31 2011-03-31 Personalization of queries, conversations, and searches
US13/077,233 US20120253789A1 (en) 2011-03-31 2011-03-31 Conversational Dialog Learning and Correction
US13/077,396 US9842168B2 (en) 2011-03-31 2011-03-31 Task driven user intents
US13/077,455 US9244984B2 (en) 2011-03-31 2011-03-31 Location based conversational understanding
PCT/US2012/030730 WO2012135210A2 (fr) 2011-03-31 2012-03-27 Compréhension conversationnelle basée sur l'emplacement

Publications (2)

Publication Number Publication Date
EP2691949A2 true EP2691949A2 (fr) 2014-02-05
EP2691949A4 EP2691949A4 (fr) 2015-06-10

Family

ID=46931884

Family Applications (6)

Application Number Title Priority Date Filing Date
EP12765896.1A Withdrawn EP2691877A4 (fr) 2011-03-31 2012-03-27 Apprentissage et correction d'un dialogue conversationnel
EP12764494.6A Ceased EP2691870A4 (fr) 2011-03-31 2012-03-27 Intentions d'utilisateurs orientées sur les tâches
EP12763913.6A Ceased EP2691885A4 (fr) 2011-03-31 2012-03-27 Architecture de compréhension conversationnelle augmentée
EP12763866.6A Ceased EP2691949A4 (fr) 2011-03-31 2012-03-27 Compréhension conversationnelle basée sur l'emplacement
EP12765100.8A Ceased EP2691876A4 (fr) 2011-03-31 2012-03-30 Personnalisation de requêtes, de conversations et de recherches
EP12764853.3A Ceased EP2691875A4 (fr) 2011-03-31 2012-03-30 Agent augmenté de compréhension de la conversation

Family Applications Before (3)

Application Number Title Priority Date Filing Date
EP12765896.1A Withdrawn EP2691877A4 (fr) 2011-03-31 2012-03-27 Apprentissage et correction d'un dialogue conversationnel
EP12764494.6A Ceased EP2691870A4 (fr) 2011-03-31 2012-03-27 Intentions d'utilisateurs orientées sur les tâches
EP12763913.6A Ceased EP2691885A4 (fr) 2011-03-31 2012-03-27 Architecture de compréhension conversationnelle augmentée

Family Applications After (2)

Application Number Title Priority Date Filing Date
EP12765100.8A Ceased EP2691876A4 (fr) 2011-03-31 2012-03-30 Personnalisation de requêtes, de conversations et de recherches
EP12764853.3A Ceased EP2691875A4 (fr) 2011-03-31 2012-03-30 Agent augmenté de compréhension de la conversation

Country Status (5)

Country Link
EP (6) EP2691877A4 (fr)
JP (4) JP2014512046A (fr)
KR (3) KR20140014200A (fr)
CN (8) CN102737096B (fr)
WO (7) WO2012135210A2 (fr)

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EP2691877A4 (fr) 2015-06-24
WO2012135229A3 (fr) 2012-12-27
WO2012135783A2 (fr) 2012-10-04
EP2691876A4 (fr) 2015-06-10
WO2012135218A2 (fr) 2012-10-04
JP2014515853A (ja) 2014-07-03
CN102737104A (zh) 2012-10-17
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CN102750271B (zh) 2017-10-17
WO2012135226A1 (fr) 2012-10-04
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CN102737101A (zh) 2012-10-17
CN102750311B (zh) 2018-07-20
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CN102737099A (zh) 2012-10-17
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CN102737099B (zh) 2017-12-19
JP6105552B2 (ja) 2017-03-29
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