system

Abstract

The system according to the embodiment aims to provide personalized content based on the user's preferences and usage history. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives input in natural language from a user. The analysis unit analyzes the information received by the reception unit and analyzes the user's preferences and usage history. The provision unit provides personalized content based on the information obtained by the analysis unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately provided personalized content based on users' preferences and usage history, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized content based on the user's preferences and usage history. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives input in natural language from a user. The analysis unit analyzes the information received by the reception unit and analyzes the user's preferences and usage history. The provision unit provides personalized content based on the information obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized content based on the user's preferences and usage history. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The dialogue-centric platform of an embodiment of the present invention utilizes advances in generative AI and voice technology to provide users with everyday content in an interactive format. This system allows users to ask questions or make requests in natural language, and the dialogue AI responds by providing appropriate content. For example, content such as news, weather forecasts, music, and movies is provided interactively. Users ask questions or make requests in natural language, and the dialogue AI responds by providing appropriate content. The dialogue AI then learns the user's preferences and usage history to provide personalized content. For example, if a user prefers a particular genre of movies, the platform recommends new movies in that genre. It also suggests related content based on the content the user has previously viewed. Furthermore, the platform provides familiar content with an excellent UX to prevent users from being confused by a new UI. For example, by providing content that users previously accessed via traditional methods in an interactive format, users can experience the benefits and appeal of dialogue AI. This mechanism allows users to accept dialogue AI as a part of their daily lives. Users can access a variety of content through the dialogue AI and enjoy personalized experiences. Furthermore, the dialogue AI's learning capabilities enable it to provide content tailored to users' preferences, providing a more satisfying experience. This allows the conversation-centric platform to provide personalized content based on users' preferences and usage history.

[0029] The dialogue-centric platform according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives natural language input from a user. The natural language input from the user includes, but is not limited to, text input and voice input. For example, the reception unit receives a question or request input from the user in text format. The reception unit can also receive a question or request input from the user in voice format. For example, the reception unit converts the user's voice input into text data using voice recognition technology. The analysis unit analyzes the information received by the reception unit and learns the user's preferences and usage history. For example, the analysis unit analyzes the user's preferences using a machine learning algorithm. The analysis unit can also analyze the user's usage history using statistical analysis. For example, the analysis unit analyzes the user's preferences based on the user's past selection history and evaluation data. The provision unit provides personalized content based on the information obtained by the analysis unit. For example, the provision unit provides personalized content using a recommendation algorithm based on the user's preferences. The providing unit may also provide related content based on the user's usage history. For example, the providing unit may recommend related content based on content previously viewed by the user. This allows the dialogue-centric platform according to the embodiment to provide personalized content based on the user's preferences and usage history.

[0030] The reception unit can receive questions or requests in natural language from the user. For example, the reception unit can receive questions or requests input by the user in text format. The reception unit can also receive questions or requests input by the user in voice format. For example, the reception unit can convert the user's voice input into text data using voice recognition technology. This allows the reception unit to receive questions or requests in natural language from the user.

[0031] The analysis unit can learn the user's preferences and usage history and generate information for providing personalized content. The analysis unit can analyze the user's preferences using, for example, a machine learning algorithm. The analysis unit can also analyze the user's usage history using statistical analysis. For example, the analysis unit can analyze the user's preferences based on the user's past selection history and evaluation data. In this way, the analysis unit can learn the user's preferences and usage history and generate information for providing personalized content.

[0032] The providing unit can provide personalized content to the user. For example, the providing unit provides personalized content using a recommendation algorithm based on the user's preferences. The providing unit can also provide related content based on the user's usage history. For example, the providing unit recommends related content based on content that the user has viewed in the past. This allows the providing unit to provide personalized content to the user.

[0033] The providing unit can provide the content that the user has accessed in a conventional manner so that the user will not be confused by the new UI. The providing unit, for example, provides the content that the user has accessed in a conventional manner in an interactive format. For example, the providing unit provides the content to the user based on a conventional UI design or operation procedure. In this way, the providing unit can provide the content that the user has accessed in a conventional manner so that the user will not be confused by the new UI.

[0034] The reception unit can analyze the user's past question history and select an appropriate reception method. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest questions that will be asked in a specific time period based on the user's past question history. This allows the reception unit to analyze the user's past question history and select an appropriate reception method.

[0035] The reception unit can filter questions and requests based on the user's current areas of interest when receiving the questions and requests. For example, the reception unit preferentially receives questions related to topics that the user has recently been interested in. Furthermore, if the user is interested in content of a specific genre, the reception unit can also preferentially receive questions related to that genre. Furthermore, the reception unit can filter and receive related questions based on keywords recently searched by the user. This allows the reception unit to filter questions and requests based on the user's current areas of interest.

[0036] When receiving a question or request, the reception unit can prioritize receiving highly relevant information in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving information related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving questions related to information around the user's home. This allows the reception unit to prioritize receiving highly relevant information in consideration of the user's geographical location information.

[0037] The reception unit can analyze the user's social media activity and receive related information when receiving a question or request. For example, the reception unit can preferentially receive related questions based on information shared by the user on social media. The reception unit can also preferentially receive information related to accounts the user follows on social media. The reception unit can also preferentially receive related questions based on posts the user has "liked" on social media. This allows the reception unit to analyze the user's social media activity and receive related information.

[0038] During analysis, the analysis unit can optimize the analysis algorithm by referring to the user's past preferences and usage history. The analysis unit adjusts the analysis algorithm based on, for example, content that the user has enjoyed viewing in the past. The analysis unit can also extract specific patterns from the user's past usage history and optimize the analysis algorithm. The analysis unit can also customize the analysis results to match the user's preferences. This allows the analysis unit to optimize the analysis algorithm by referring to the user's past preferences and usage history.

[0039] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's current areas of interest. For example, the analysis unit prioritizes analysis of data related to topics in which the user has recently taken an interest. Furthermore, if the user is interested in content of a particular genre, the analysis unit can also focus on analyzing data related to that genre. Furthermore, the analysis unit can analyze related data based on keywords recently searched by the user. This allows the analysis unit to improve the accuracy of the analysis based on the user's current areas of interest.

[0040] The analysis unit can perform the analysis taking into account the user's geographical location information. For example, if the user is in a specific area, the analysis unit prioritizes analyzing data related to that area. Furthermore, if the user is traveling, the analysis unit can also analyze data related to the travel destination. Furthermore, if the user is at home, the analysis unit can also focus on analyzing data around the user's home. This allows the analysis unit to perform the analysis taking into account the user's geographical location information.

[0041] During the analysis, the analysis unit can analyze the user's social media activities and analyze related information. For example, the analysis unit analyzes related data based on information shared by the user on social media. The analysis unit can also analyze data related to accounts the user follows on social media. The analysis unit can also analyze related data based on posts the user has "liked" on social media. This allows the analysis unit to analyze the user's social media activities and analyze related information.

[0042] The providing unit can select optimal content by referring to the user's past usage history when providing content. For example, the providing unit provides related content based on content that the user has viewed and liked in the past. The providing unit can also recommend new content in a specific genre based on the user's past usage history. The providing unit can also analyze the user's past usage history and provide the most interesting content. This allows the providing unit to select optimal content by referring to the user's past usage history.

[0043] The providing unit may adjust the level of detail of the content based on the user's current field of interest when providing the content. For example, the providing unit may provide detailed content related to a topic that the user has recently been interested in. Furthermore, if the user has shown interest in content of a specific genre, the providing unit may provide detailed content related to the genre. Furthermore, the providing unit may provide detailed related content based on keywords recently searched by the user. This allows the providing unit to adjust the level of detail of the content based on the user's current field of interest.

[0044] The providing unit can provide optimal content by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the providing unit can provide content related to that area. Furthermore, when the user is traveling, the providing unit can also provide content related to the travel destination. Furthermore, when the user is at home, the providing unit can also provide content related to information about the area around the user's home. This allows the providing unit to provide optimal content by taking into consideration the user's geographical location information.

[0045] At the time of providing, the providing unit can analyze the user's social media activity and provide related content. The providing unit can provide related content based on, for example, information shared by the user on social media. The providing unit can also provide content related to accounts the user follows on social media. The providing unit can also provide related content based on posts the user has "liked" on social media. This allows the providing unit to analyze the user's social media activity and provide related content.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The analysis unit can analyze the user's health data in addition to the user's preferences and usage history. For example, the analysis unit can analyze data obtained from the user's fitness tracker or smartwatch to provide content based on the user's health condition. For example, if the user has just exercised, the analysis unit can provide relaxing music or meditation content. If the user is sleep-deprived, the analysis unit can recommend relaxing movies or short videos. This allows the analysis unit to provide personalized content based on the user's health condition.

[0048] The providing unit can suggest events and activities that the user may be interested in based on the user's preferences and usage history. For example, if the user likes movies of a particular genre, information on film festivals and screenings related to that genre can be provided. If the user likes music, information on nearby concerts and live events can be provided. Furthermore, if the user is interested in sports, information on local sporting events and matches can be provided. This allows the providing unit to suggest events and activities based on the user's preferences.

[0049] The analysis unit can analyze the user's purchasing history in addition to the user's preferences and usage history. For example, it can provide relevant content based on products and services the user has purchased in the past. For example, if the user has purchased a product from a specific brand, it can provide news and new product information related to that brand. Also, if the user has purchased books in a specific genre, it can provide reviews and recommendations of books related to that genre. This allows the analysis unit to provide personalized content based on the user's purchasing history.

[0050] The reception unit can customize the interface for the user based on the user's preferences and usage history. For example, if the user has a preference for a particular color or design, the reception unit can provide an interface that matches that preference. Also, if the user frequently uses a particular function, the reception unit can display that function preferentially. Furthermore, if the user has a preference for a particular operating procedure, the reception unit can provide an interface based on that procedure. This allows the reception unit to customize the interface based on the user's preferences.

[0051] The providing unit can diversify the method of providing content to a user based on the user's preferences and usage history. For example, if the user prefers visual content, videos and images can be provided as the main content. If the user prefers auditory content, audio and music can be provided as the main content. Furthermore, if the user prefers text-based content, articles and blogs can be provided as the main content. This allows the providing unit to diversify the method of providing content based on the user's preferences.

[0052] The analysis unit can analyze the user's social media activity in addition to the user's preferences and usage history. For example, it can provide relevant content based on the information the user has shared on social media and the accounts the user follows. For example, if the user frequently shares posts about a particular topic, it can provide news and articles related to that topic. Also, if the user follows a particular influencer, it can provide content related to that influencer. This allows the analysis unit to provide personalized content based on the user's social media activity.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit receives natural language input from the user. The natural language input from the user includes text input and voice input. For example, the reception unit receives a question or request input from the user in text format. Alternatively, the reception unit can use voice recognition technology to convert the user's voice input into text data. Step 2: The analysis unit analyzes the information received by the reception unit and learns the user's preferences and usage history. For example, the analysis unit uses a machine learning algorithm to analyze the user's preferences and statistical analysis to analyze the user's usage history. The analysis unit analyzes the user's preferences based on the user's past selection history and evaluation data. Step 3: The provider provides personalized content based on the information obtained by the analyzer. For example, the provider provides personalized content using a recommendation algorithm based on the user's preferences, and provides related content based on the user's usage history. The provider recommends related content based on content the user has viewed in the past.

[0055] (Example 2) The dialogue-centric platform of an embodiment of the present invention utilizes advances in generative AI and voice technology to provide users with everyday content in an interactive format. This system allows users to ask questions or make requests in natural language, and the dialogue AI responds by providing appropriate content. For example, content such as news, weather forecasts, music, and movies is provided interactively. Users ask questions or make requests in natural language, and the dialogue AI responds by providing appropriate content. The dialogue AI then learns the user's preferences and usage history to provide personalized content. For example, if a user prefers a particular genre of movies, the platform recommends new movies in that genre. It also suggests related content based on the content the user has previously viewed. Furthermore, the platform provides familiar content with an excellent UX to prevent users from being confused by a new UI. For example, by providing content that users previously accessed via traditional methods in an interactive format, users can experience the benefits and appeal of dialogue AI. This mechanism allows users to accept dialogue AI as a part of their daily lives. Users can access a variety of content through the dialogue AI and enjoy personalized experiences. Furthermore, the dialogue AI's learning capabilities enable it to provide content tailored to users' preferences, providing a more satisfying experience. This allows the conversation-centric platform to provide personalized content based on users' preferences and usage history.

[0056] The dialogue-centric platform according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives natural language input from a user. The natural language input from the user includes, but is not limited to, text input and voice input. For example, the reception unit receives a question or request input from the user in text format. The reception unit can also receive a question or request input from the user in voice format. For example, the reception unit converts the user's voice input into text data using voice recognition technology. The analysis unit analyzes the information received by the reception unit and learns the user's preferences and usage history. For example, the analysis unit analyzes the user's preferences using a machine learning algorithm. The analysis unit can also analyze the user's usage history using statistical analysis. For example, the analysis unit analyzes the user's preferences based on the user's past selection history and evaluation data. The provision unit provides personalized content based on the information obtained by the analysis unit. For example, the provision unit provides personalized content using a recommendation algorithm based on the user's preferences. The providing unit may also provide related content based on the user's usage history. For example, the providing unit may recommend related content based on content previously viewed by the user. This allows the dialogue-centric platform according to the embodiment to provide personalized content based on the user's preferences and usage history.

[0057] The reception unit can receive questions or requests in natural language from the user. For example, the reception unit can receive questions or requests input by the user in text format. The reception unit can also receive questions or requests input by the user in voice format. For example, the reception unit can convert the user's voice input into text data using voice recognition technology. This allows the reception unit to receive questions or requests in natural language from the user.

[0058] The analysis unit can learn the user's preferences and usage history and generate information for providing personalized content. The analysis unit can analyze the user's preferences using, for example, a machine learning algorithm. The analysis unit can also analyze the user's usage history using statistical analysis. For example, the analysis unit can analyze the user's preferences based on the user's past selection history and evaluation data. In this way, the analysis unit can learn the user's preferences and usage history and generate information for providing personalized content.

[0059] The providing unit can provide personalized content to the user. For example, the providing unit provides personalized content using a recommendation algorithm based on the user's preferences. The providing unit can also provide related content based on the user's usage history. For example, the providing unit recommends related content based on content that the user has viewed in the past. This allows the providing unit to provide personalized content to the user.

[0060] The providing unit can provide the content that the user has accessed in a conventional manner so that the user will not be confused by the new UI. The providing unit, for example, provides the content that the user has accessed in a conventional manner in an interactive format. For example, the providing unit provides the content to the user based on a conventional UI design or operation procedure. In this way, the providing unit can provide the content that the user has accessed in a conventional manner so that the user will not be confused by the new UI.

[0061] The reception unit can estimate the user's emotions and adjust the method of accepting questions and requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to quickly accept questions and requests. This allows the reception unit to adjust the method of accepting questions and requests based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0062] The reception unit can analyze the user's past question history and select an appropriate reception method. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest questions that will be asked in a specific time period based on the user's past question history. This allows the reception unit to analyze the user's past question history and select an appropriate reception method.

[0063] The reception unit can filter questions and requests based on the user's current areas of interest when receiving the questions and requests. For example, the reception unit preferentially receives questions related to topics that the user has recently been interested in. Furthermore, if the user is interested in content of a specific genre, the reception unit can also preferentially receive questions related to that genre. Furthermore, the reception unit can filter and receive related questions based on keywords recently searched by the user. This allows the reception unit to filter questions and requests based on the user's current areas of interest.

[0064] The reception unit can estimate the user's emotions and determine the priority of questions and requests to be received based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize urgent questions. Furthermore, if the user is relaxed, the reception unit can prioritize detailed questions. Furthermore, if the user is in a hurry, the reception unit can prioritize questions that require a quick response. This allows the reception unit to prioritize questions and requests based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] When receiving a question or request, the reception unit can prioritize receiving highly relevant information in consideration of the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving information related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving questions related to information around the user's home. This allows the reception unit to prioritize receiving highly relevant information in consideration of the user's geographical location information.

[0066] The reception unit can analyze the user's social media activity and receive related information when receiving a question or request. For example, the reception unit can preferentially receive related questions based on information shared by the user on social media. The reception unit can also preferentially receive information related to accounts the user follows on social media. The reception unit can also preferentially receive related questions based on posts the user has "liked" on social media. This allows the reception unit to analyze the user's social media activity and receive related information.

[0067] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. If the user is in a hurry, the analysis unit can perform a quick analysis and provide information that focuses on the main points. If the user is excited, the analysis unit can provide visually stimulating analysis results. This allows the analysis unit to adjust the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] During analysis, the analysis unit can optimize the analysis algorithm by referring to the user's past preferences and usage history. The analysis unit adjusts the analysis algorithm based on, for example, content that the user has enjoyed viewing in the past. The analysis unit can also extract specific patterns from the user's past usage history and optimize the analysis algorithm. The analysis unit can also customize the analysis results to match the user's preferences. This allows the analysis unit to optimize the analysis algorithm by referring to the user's past preferences and usage history.

[0069] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's current areas of interest. For example, the analysis unit prioritizes analysis of data related to topics in which the user has recently taken an interest. Furthermore, if the user is interested in content of a particular genre, the analysis unit can also focus on analyzing data related to that genre. Furthermore, the analysis unit can analyze related data based on keywords recently searched by the user. This allows the analysis unit to improve the accuracy of the analysis based on the user's current areas of interest.

[0070] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method including detailed information. If the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This allows the analysis unit to adjust the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0071] The analysis unit can perform the analysis taking into account the user's geographical location information. For example, if the user is in a specific area, the analysis unit prioritizes analyzing data related to that area. Furthermore, if the user is traveling, the analysis unit can also analyze data related to the travel destination. Furthermore, if the user is at home, the analysis unit can also focus on analyzing data around the user's home. This allows the analysis unit to perform the analysis taking into account the user's geographical location information.

[0072] During the analysis, the analysis unit can analyze the user's social media activities and analyze related information. For example, the analysis unit analyzes related data based on information shared by the user on social media. The analysis unit can also analyze data related to accounts the user follows on social media. The analysis unit can also analyze related data based on posts the user has "liked" on social media. This allows the analysis unit to analyze the user's social media activities and analyze related information.

[0073] The providing unit can estimate the user's emotions and adjust the way in which content is presented based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide content in a calm tone. If the user is relaxed, the providing unit can also provide content in a bright tone. If the user is in a hurry, the providing unit can also provide quick and concise content. This allows the providing unit to adjust the way in which content is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0074] The providing unit can select optimal content by referring to the user's past usage history when providing content. For example, the providing unit provides related content based on content that the user has viewed and liked in the past. The providing unit can also recommend new content in a specific genre based on the user's past usage history. The providing unit can also analyze the user's past usage history and provide the most interesting content. This allows the providing unit to select optimal content by referring to the user's past usage history.

[0075] The providing unit may adjust the level of detail of the content based on the user's current field of interest when providing the content. For example, the providing unit may provide detailed content related to a topic that the user has recently been interested in. Furthermore, if the user has shown interest in content of a specific genre, the providing unit may provide detailed content related to the genre. Furthermore, the providing unit may provide detailed related content based on keywords recently searched by the user. This allows the providing unit to adjust the level of detail of the content based on the user's current field of interest.

[0076] The providing unit can estimate the user's emotions and determine the priority of content to be provided based on the estimated user's emotions. For example, if the user is nervous, the providing unit can prioritize providing relaxing content. Furthermore, if the user is relaxed, the providing unit can prioritize providing interesting content. Furthermore, if the user is in a hurry, the providing unit can prioritize providing content that can be consumed quickly. This allows the providing unit to determine the priority of content to be provided based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The providing unit can provide optimal content by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the providing unit can provide content related to that area. Furthermore, when the user is traveling, the providing unit can also provide content related to the travel destination. Furthermore, when the user is at home, the providing unit can also provide content related to information about the area around the user's home. This allows the providing unit to provide optimal content by taking into consideration the user's geographical location information.

[0078] At the time of providing, the providing unit can analyze the user's social media activity and provide related content. The providing unit can provide related content based on, for example, information shared by the user on social media. The providing unit can also provide content related to accounts the user follows on social media. The providing unit can also provide related content based on posts the user has "liked" on social media. This allows the providing unit to analyze the user's social media activity and provide related content. === Hard Collateral 1-1 === Each of the multiple elements including the above-described reception unit, analysis unit, and provision unit may be realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit may be realized by the reception device 38 of the smart device 14 and receive text or voice input from the user. The analysis unit may be realized by the specific processing unit 290 of the data processing device 12 and analyze the user's preferences and usage history. The provision unit may be realized by the output device 40 of the smart device 14 and provide personalized content to the user. The analysis unit and provision unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a user's voice input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences and usage history. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides personalized content to the user. The analysis unit and the provision unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives voice input from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences and usage history. The provision unit is realized by the speaker 240 of the headset type terminal 314 and provides personalized content to the user. The analysis unit and the provision unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's preferences and usage history. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides personalized content to the user. The analysis unit and the provision unit may be realized, for example, by the specific processing unit 290 of the data processing device 12.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The analysis unit can analyze the user's health data in addition to the user's preferences and usage history. For example, the analysis unit can analyze data obtained from the user's fitness tracker or smartwatch to provide content based on the user's health condition. For example, if the user has just exercised, the analysis unit can provide relaxing music or meditation content. If the user is sleep-deprived, the analysis unit can recommend relaxing movies or short videos. This allows the analysis unit to provide personalized content based on the user's health condition.

[0081] The providing unit can suggest events and activities that the user may be interested in based on the user's preferences and usage history. For example, if the user likes movies of a particular genre, information on film festivals and screenings related to that genre can be provided. If the user likes music, information on nearby concerts and live events can be provided. Furthermore, if the user is interested in sports, information on local sporting events and matches can be provided. This allows the providing unit to suggest events and activities based on the user's preferences.

[0082] The reception unit can estimate the user's emotions and adjust the tone and style of its response to the user based on the estimated user's emotions. For example, if the user is feeling stressed, it can respond in a gentle tone and choose words that will help the user relax. If the user is excited, it can respond in an energetic tone and share the user's excitement. Furthermore, if the user is sad, it can respond in a comforting tone and empathize with the user's feelings. This allows the reception unit to adjust the tone and style of its response based on the user's emotions.

[0083] The analysis unit can analyze the user's purchasing history in addition to the user's preferences and usage history. For example, it can provide relevant content based on products and services the user has purchased in the past. For example, if the user has purchased a product from a specific brand, it can provide news and new product information related to that brand. Also, if the user has purchased books in a specific genre, it can provide reviews and recommendations of books related to that genre. This allows the analysis unit to provide personalized content based on the user's purchasing history.

[0084] The providing unit can estimate the user's emotions and adjust the frequency of content provision to the user based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of content provision can be reduced to allow the user to relax. Also, if the user is excited, the frequency of content provision can be increased to maintain the user's excitement. Furthermore, if the user is sad, comforting content can be frequently provided to soothe the user's feelings. In this way, the providing unit can adjust the frequency of content provision based on the user's emotions.

[0085] The reception unit can customize the interface for the user based on the user's preferences and usage history. For example, if the user has a preference for a particular color or design, the reception unit can provide an interface that matches that preference. Also, if the user frequently uses a particular function, the reception unit can display that function preferentially. Furthermore, if the user has a preference for a particular operating procedure, the reception unit can provide an interface based on that procedure. This allows the reception unit to customize the interface based on the user's preferences.

[0086] The analysis unit can estimate the user's emotions and adjust the method of notifying the analysis results based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be notified to allow the user to understand them in depth. If the user is in a hurry, concise analysis results that focus on the main points can be notified. Furthermore, if the user is excited, visually stimulating analysis results can be notified. This allows the analysis unit to adjust the method of notifying the analysis results based on the user's emotions.

[0087] The providing unit can diversify the method of providing content to a user based on the user's preferences and usage history. For example, if the user prefers visual content, videos and images can be provided as the main content. If the user prefers auditory content, audio and music can be provided as the main content. Furthermore, if the user prefers text-based content, articles and blogs can be provided as the main content. This allows the providing unit to diversify the method of providing content based on the user's preferences.

[0088] The reception unit can estimate the user's emotions and adjust the method of providing feedback to the user based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide feedback in kind words to soothe the user's feelings. If the user is excited, the reception unit can provide feedback in energetic words to share the user's excitement. If the user is sad, the reception unit can provide feedback in comforting words to empathize with the user's feelings. This allows the reception unit to adjust the method of providing feedback based on the user's emotions.

[0089] The analysis unit can analyze the user's social media activity in addition to the user's preferences and usage history. For example, it can provide relevant content based on the information the user has shared on social media and the accounts the user follows. For example, if the user frequently shares posts about a particular topic, it can provide news and articles related to that topic. Also, if the user follows a particular influencer, it can provide content related to that influencer. This allows the analysis unit to provide personalized content based on the user's social media activity.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The reception unit receives natural language input from the user. The natural language input from the user includes text input and voice input. For example, the reception unit receives a question or request input from the user in text format. Alternatively, the reception unit can use voice recognition technology to convert the user's voice input into text data. Step 2: The analysis unit analyzes the information received by the reception unit and learns the user's preferences and usage history. For example, the analysis unit uses a machine learning algorithm to analyze the user's preferences and statistical analysis to analyze the user's usage history. The analysis unit analyzes the user's preferences based on the user's past selection history and evaluation data. Step 3: The provider provides personalized content based on the information obtained by the analyzer. For example, the provider provides personalized content using a recommendation algorithm based on the user's preferences, and provides related content based on the user's usage history. The provider recommends related content based on content the user has viewed in the past.

[0092] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0094] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0136] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0146] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0147] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0148] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0149] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0150] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0151] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0152] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0153] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0154] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0155] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0156] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0157] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0158] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0159] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0160] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0163] [Explanation of symbols]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input in natural language from a user; an analysis unit that analyzes the information received by the reception unit and analyzes the user's preferences and usage history; and a providing unit that provides personalized content based on the information obtained by the analyzing unit. A system characterized by:

2. The reception unit Accept natural language questions or requests from users 2. The system of claim 1.

3. The analysis unit Learns user preferences and usage history and generates information to provide personalized content 2. The system of claim 1.

4. The providing unit Providing personalized content to users 2. The system of claim 1.

5. The providing unit Provide users with the content they previously accessed so they don't get confused by the new UI 2. The system of claim 1.

6. The reception unit Estimate user emotions and adjust how questions and requests are received based on the estimated user emotions 2. The system of claim 1.

7. The reception unit Analyze the user's past question history and select the appropriate reception method 2. The system of claim 1.

8. The reception unit Filtering questions and requests based on the user's current interests 2. The system of claim 1.

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