System

Abstract

An object of the system according to the embodiment is to provide information customized for an individual and reflecting the latest information.SOLUTION: A system includes a conversation log acquisition unit, a database generation unit, and an information provision unit. The conversation log acquisition unit acquires a conversation log of an individual. The database generation unit generates a structured database based on the reliable information. The information providing unit provides information using the structured database generated by the database generating unit.SELECTED DRAWING: Figure 1

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] Previous technology had the problem that the generating AI could not reflect the latest information, making it difficult to customize for individuals.

[0005] The system according to the embodiment aims to provide information that is customized for individuals and reflects the latest information. [Means for solving the problem]

[0006] A system according to an embodiment includes a conversation log acquisition unit, a database generation unit, and an information provision unit. The conversation log acquisition unit acquires individual conversation logs. The database generation unit generates a structured database based on reliable information. The information provision unit provides information using the structured database generated by the database generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide information that is customized for individuals and reflects the latest information. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) A large-scale language model according to an embodiment of the present invention is a system that utilizes a structured database of personal conversation logs and reliable information, has memory, is customized for individuals, reflects the latest information, and provides highly accurate information. As a result, the large-scale language model can provide responses that have memory, are customized for individuals, reflect the latest information, and provide highly accurate information based on the personal conversation logs and reliable information.

[0029] A large-scale language model according to an embodiment includes a conversation log acquisition unit, a database generation unit, and an information providing unit. The conversation log acquisition unit acquires individual conversation logs. For example, the conversation log acquisition unit collects conversation logs in text format. The conversation log acquisition unit can also collect conversation logs in audio format. The conversation log acquisition unit stores the collected conversation logs as digital data. For example, the conversation log acquisition unit converts user utterances into text data in real time and stores the text data. Audio-format conversation logs can be converted into text data using speech recognition technology. The database generation unit generates a structured database based on reliable information. For example, the database generation unit generates a structured database based on official data. The database generation unit can also generate a structured database based on data from authenticated information sources. The database generation unit can also structure the collected information as a relational database. For example, the database generation unit stores official data in a relational database and retrieves information using queries. Data from authenticated information sources is stored in a NoSQL database and retrieves information using flexible queries. The information providing unit provides information using the structured database generated by the database generation unit. For example, the information providing unit obtains the latest information from a structured database and provides it to the user. The information providing unit can also provide information customized for individuals from the structured database. The information providing unit also causes the generation AI to generate a response based on the information obtained from the structured database. For example, the information providing unit obtains the latest news articles and provides them to the user. The personalized information is generated based on the user's past conversation logs. The generation AI generates a response to the user's question based on the information obtained from the structured database. As a result, the large-scale language model according to the embodiment can provide a response that has memory, is customized for individuals, reflects the latest information, and is highly accurate based on personal conversation logs and reliable information. For example, related information is provided based on what the user has said in the past. The latest news and trends are provided based on reliable information.Generate personalized responses to provide information tailored to the user's specific needs.

[0030] The database generation unit can integrate a user's past search history or browsing history to provide more personalized information. The database generation unit, for example, integrates a user's past search history or browsing history into a database to build a system that provides personalized information. For example, related information is provided based on keywords that the user has searched for in the past. The database generation unit also analyzes the search history or browsing history to develop an algorithm that identifies the user's interests. For example, information related to a specific topic is preferentially displayed. The database generation unit also builds a system that provides reliable information based on the user's past behavioral data. For example, information about sites that the user frequently visits is preferentially displayed. This makes it possible to provide more personalized information based on the user's past behavioral data.

[0031] The database generation unit can extract specific keywords or phrases from the conversation log and develop an algorithm that predicts a user's interests based on the extracted keywords. The database generation unit, for example, develops an algorithm that extracts specific keywords and phrases from the conversation log and builds a system that predicts a user's interests. For example, interests are identified based on frequently used keywords. The database generation unit also analyzes the frequency of appearance of keywords and phrases to develop an algorithm that predicts a user's interests. For example, if there are many keywords related to a specific topic, it determines that the user is interested in that topic. The database generation unit also analyzes the conversation log and builds a system that predicts a user's interests. For example, if a specific phrase is frequently used, information related to that phrase is provided. This makes it possible to predict a user's interests and provide more appropriate information.

[0032] The information providing unit can combine the conversation log with reliable information to monitor the user's health condition or lifestyle habits and provide health advice. The information providing unit, for example, combines the conversation log with reliable information to build a system that monitors the user's health condition. For example, it analyzes conversation logs related to diet and exercise and provides health advice. The information providing unit also develops an algorithm that identifies the user's lifestyle habits based on the conversation log and provides health advice based on the reliable information. For example, it analyzes conversation logs related to sleep patterns and makes suggestions for improvement. The information providing unit also integrates the conversation log with reliable information to build a system that monitors the user's health condition in real time. For example, it analyzes conversation logs related to stress levels and suggests relaxation methods. This makes it possible to monitor the user's health condition and lifestyle habits and provide appropriate health advice.

[0033] The information providing unit can use the structured database to build a product recommendation system based on a user's purchasing history or preferences. The information providing unit, for example, uses the structured database to analyze a user's purchasing history and preferences and build a product recommendation system. For example, related products are recommended based on past purchasing history. The information providing unit also develops an algorithm that recommends optimal products to a user based on purchasing history and preference data. For example, preferences for specific brands or categories are analyzed and related products are displayed. The information providing unit also utilizes the structured database to build a personalized product recommendation system based on a user's purchasing history and preferences. For example, a product list tailored to the user's preferences is provided. This makes it possible to recommend products based on the user's purchasing history and preferences.

[0034] The information providing unit can generate more personalized responses by including a user's past conversation logs in the training data for the language model. The information providing unit, for example, builds a system for generating personalized responses by including a user's past conversation logs in the training data for the language model. For example, a response is generated based on the user's past utterances. The information providing unit also incorporates the past conversation logs into the training data to develop an algorithm that generates responses according to the user's specific needs and preferences. For example, a response related to a topic that the user previously discussed is generated. The information providing unit also develops a language model that generates more personalized responses by including the user's past conversation logs in the training data. For example, a response is generated based on the user's preferences and interests. This makes it possible to generate more personalized responses based on the user's past conversation logs.

[0035] The information providing unit provides the language model with customization options according to the user's specific needs and preferences, thereby improving the accuracy of responses. The information providing unit, for example, builds a system that provides the language model with customization options according to the user's specific needs and preferences. For example, it prioritizes generating responses related to topics that interest the user. The information providing unit also develops an algorithm that provides customization options and generates responses according to the user's needs and preferences. For example, if the user prefers a specific style of response, it generates a response that matches that style. The information providing unit also develops a language model that provides customization options according to the user's specific needs and preferences, thereby improving the accuracy of responses. For example, it generates a response that matches the user's preferred wording and tone. This makes it possible to improve the accuracy of responses by providing customization options according to the user's specific needs and preferences.

[0036] The information providing unit can provide personalized responses to global users by adapting the language model to different languages ​​or cultural areas. The information providing unit, for example, develops an algorithm for adapting the language model to different languages ​​or cultural areas, and builds a system for providing personalized responses to global users. For example, the information providing unit generates responses that support multiple languages. Furthermore, in order to support different cultural areas, the information providing unit trains the language model to learn culturally specific expressions and customs. For example, it generates responses that reflect greetings and etiquette in a specific culture. Furthermore, the information providing unit develops a system for adapting the language model to multiple languages ​​in order to provide personalized responses to global users. For example, it generates responses that are tailored to the user's native language. In this way, by adapting to different languages ​​and cultural areas, it is possible to provide personalized responses to global users.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The database generation unit can integrate a user's past purchase history and browsing history to predict the user's consumption behavior. For example, it can predict the next product that the user is likely to purchase based on data on products purchased in the past and websites visited. The database generation unit can also analyze a user's purchase history and browsing history to develop an algorithm that identifies the user's consumption pattern. For example, it can prioritize displaying products related to a particular season or event. The database generation unit can also build a system that provides reliable information based on the user's past behavioral data. For example, it can prioritize displaying information from sites that the user frequently visits.

[0039] The information providing unit can work with a sensor device to monitor the user's health condition and provide health advice. For example, it can analyze heart rate and sleep data obtained from a wearable device to evaluate the user's health condition. The information providing unit can also collect data on the user's diet and exercise and develop an algorithm to provide health advice. For example, it can provide advice on adjusting nutritional balance and exercise volume based on the contents of meals and frequency of exercise. The information providing unit can also build a system that monitors the user's health condition in real time and issues an alert if an abnormality is detected. For example, it can encourage the user to visit a medical institution if the heart rate is abnormally high.

[0040] The information providing unit can build a product recommendation system based on a user's purchase history and preferences. For example, it can recommend related products based on past purchase history. The information providing unit can also develop an algorithm that recommends optimal products to a user based on purchase history and preference data. For example, it can analyze preferences for specific brands or categories and display related products. The information providing unit can also utilize a structured database to build a personalized product recommendation system based on a user's purchase history and preferences. For example, it can provide a product list tailored to the user's preferences.

[0041] The information providing unit can build a system that generates personalized responses by including a user's past conversation logs in the training data. For example, responses can be generated based on the user's past utterances. The information providing unit can also incorporate the past conversation logs into the training data to develop an algorithm that generates responses tailored to the user's specific needs and preferences. For example, responses can be generated related to topics the user previously discussed. The information providing unit can also develop a language model that generates more personalized responses by including a user's past conversation logs in the training data. For example, responses can be generated based on the user's preferences and interests.

[0042] The information providing unit can develop algorithms to adapt the language model to different languages ​​and cultural spheres, and build a system that provides personalized responses to global users. For example, it generates responses that support multiple languages. The information providing unit can also train the language model to learn culturally specific expressions and customs to adapt to different cultural spheres. For example, it generates responses that reflect greetings and etiquette in a specific culture. The information providing unit can also develop a system that adapts the language model to multiple languages ​​to provide personalized responses to global users. For example, it generates responses that are tailored to the user's native language.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The conversation log acquisition unit acquires an individual's conversation log. For example, the conversation log acquisition unit collects text-format conversation logs and audio-format conversation logs and stores them as digital data. Audio-format conversation logs are converted into text data using voice recognition technology. Step 2: The database generation unit generates a structured database based on reliable information, for example, data from official or authenticated sources, and structures it as a relational or NoSQL database. Step 3: The information provision unit provides information using the structured database generated by the database generation unit. For example, it provides the latest information or information customized for individuals, and the generation AI generates a response based on the information obtained from the structured database.

[0045] (Example 2) A large-scale language model according to an embodiment of the present invention is a system that utilizes a structured database of personal conversation logs and reliable information, has memory, is customized for individuals, reflects the latest information, and provides highly accurate information. As a result, the large-scale language model can provide responses that have memory, are customized for individuals, reflect the latest information, and provide highly accurate information based on the personal conversation logs and reliable information.

[0046] A large-scale language model according to an embodiment includes a conversation log acquisition unit, a database generation unit, and an information providing unit. The conversation log acquisition unit acquires individual conversation logs. For example, the conversation log acquisition unit collects conversation logs in text format. The conversation log acquisition unit can also collect conversation logs in audio format. The conversation log acquisition unit stores the collected conversation logs as digital data. For example, the conversation log acquisition unit converts user utterances into text data in real time and stores the text data. Audio-format conversation logs can be converted into text data using speech recognition technology. The database generation unit generates a structured database based on reliable information. For example, the database generation unit generates a structured database based on official data. The database generation unit can also generate a structured database based on data from authenticated information sources. The database generation unit can also structure the collected information as a relational database. For example, the database generation unit stores official data in a relational database and retrieves information using queries. Data from authenticated information sources is stored in a NoSQL database and retrieves information using flexible queries. The information providing unit provides information using the structured database generated by the database generation unit. For example, the information providing unit obtains the latest information from a structured database and provides it to the user. The information providing unit can also provide information customized for individuals from the structured database. The information providing unit also causes the generation AI to generate a response based on the information obtained from the structured database. For example, the information providing unit obtains the latest news articles and provides them to the user. The personalized information is generated based on the user's past conversation logs. The generation AI generates a response to the user's question based on the information obtained from the structured database. As a result, the large-scale language model according to the embodiment can provide a response that has memory, is customized for individuals, reflects the latest information, and is highly accurate based on personal conversation logs and reliable information. For example, related information is provided based on what the user has said in the past. The latest news and trends are provided based on reliable information.Generate personalized responses to provide information tailored to the user's specific needs.

[0047] The conversation log acquisition unit can track changes in a user's emotions in real time and generate responses according to those emotions. The conversation log acquisition unit, for example, analyzes an individual's conversation log and builds a system that tracks changes in a user's emotions in real time. For example, it analyzes the content and tone of the conversation to detect changes in emotions. The conversation log acquisition unit also develops an emotion model based on the conversation log to generate responses according to changes in emotions. For example, if a user is feeling stressed, it generates a response that relaxes the user. The conversation log acquisition unit also builds a system that tracks changes in emotions in real time and generates responses based on that data. For example, if a user's emotions are positive, it provides words of encouragement. This makes it possible to provide a more personalized experience by generating responses according to changes in the user's emotions.

[0048] The database generation unit can integrate a user's past search history or browsing history to provide more personalized information. The database generation unit, for example, integrates a user's past search history or browsing history into a database to build a system that provides personalized information. For example, related information is provided based on keywords that the user has searched for in the past. The database generation unit also analyzes the search history or browsing history to develop an algorithm that identifies the user's interests. For example, information related to a specific topic is preferentially displayed. The database generation unit also builds a system that provides reliable information based on the user's past behavioral data. For example, information about sites that the user frequently visits is preferentially displayed. This makes it possible to provide more personalized information based on the user's past behavioral data.

[0049] The database generation unit can extract specific keywords or phrases from the conversation log and develop an algorithm that predicts a user's interests based on the extracted keywords. The database generation unit, for example, develops an algorithm that extracts specific keywords and phrases from the conversation log and builds a system that predicts a user's interests. For example, interests are identified based on frequently used keywords. The database generation unit also analyzes the frequency of appearance of keywords and phrases to develop an algorithm that predicts a user's interests. For example, if there are many keywords related to a specific topic, it determines that the user is interested in that topic. The database generation unit also analyzes the conversation log and builds a system that predicts a user's interests. For example, if a specific phrase is frequently used, information related to that phrase is provided. This makes it possible to predict a user's interests and provide more appropriate information.

[0050] The information providing unit can combine the conversation log with reliable information to monitor the user's health condition or lifestyle habits and provide health advice. The information providing unit, for example, combines the conversation log with reliable information to build a system that monitors the user's health condition. For example, it analyzes conversation logs related to diet and exercise and provides health advice. The information providing unit also develops an algorithm that identifies the user's lifestyle habits based on the conversation log and provides health advice based on the reliable information. For example, it analyzes conversation logs related to sleep patterns and makes suggestions for improvement. The information providing unit also integrates the conversation log with reliable information to build a system that monitors the user's health condition in real time. For example, it analyzes conversation logs related to stress levels and suggests relaxation methods. This makes it possible to monitor the user's health condition and lifestyle habits and provide appropriate health advice.

[0051] The information providing unit can use the structured database to build a product recommendation system based on a user's purchasing history or preferences. The information providing unit, for example, uses the structured database to analyze a user's purchasing history and preferences and build a product recommendation system. For example, related products are recommended based on past purchasing history. The information providing unit also develops an algorithm that recommends optimal products to a user based on purchasing history and preference data. For example, preferences for specific brands or categories are analyzed and related products are displayed. The information providing unit also utilizes the structured database to build a personalized product recommendation system based on a user's purchasing history and preferences. For example, a product list tailored to the user's preferences is provided. This makes it possible to recommend products based on the user's purchasing history and preferences.

[0052] The information providing unit can use the emotion estimation function to automatically recommend music or video content according to the user's emotions. The information providing unit, for example, uses the emotion estimation function to build a system that automatically recommends music or video content according to the user's emotions. For example, if the user feels like relaxing, it recommends relaxing music. The information providing unit also analyzes the user's emotions in real time and develops an algorithm that recommends music or video content based on the results. For example, if the user feels sad, it recommends uplifting videos. The information providing unit also builds a system that automatically recommends music or video content according to the user's emotions based on the emotion estimation data. For example, if the user is excited, it recommends calming music. This makes it possible to provide a more personalized experience by automatically recommending music or video content according to the user's emotions.

[0053] The information providing unit can feed back the user's emotion estimation result and generate a response according to the emotion. The information providing unit, for example, feeds back the user's emotion estimation result to a large-scale language model and adds a function to generate a response according to the emotion. For example, if the user is angry, a calm response is generated. The information providing unit also develops an algorithm that enables the large-scale language model to generate a response according to the user's emotion based on the emotion estimation result. For example, if the user is happy, a sympathetic response is generated. The information providing unit also constructs a system that feeds back the user's emotion estimation data and enables the large-scale language model to generate a response according to the emotion. For example, if the user is sad, a comforting response is generated. This makes it possible to provide a more personalized experience by generating a response according to the user's emotion.

[0054] The information providing unit can generate more personalized responses by including a user's past conversation logs in the training data for the language model. The information providing unit, for example, builds a system for generating personalized responses by including a user's past conversation logs in the training data for the language model. For example, a response is generated based on the user's past utterances. The information providing unit also incorporates the past conversation logs into the training data to develop an algorithm that generates responses according to the user's specific needs and preferences. For example, a response related to a topic that the user previously discussed is generated. The information providing unit also develops a language model that generates more personalized responses by including the user's past conversation logs in the training data. For example, a response is generated based on the user's preferences and interests. This makes it possible to generate more personalized responses based on the user's past conversation logs.

[0055] The information providing unit provides the language model with customization options according to the user's specific needs and preferences, thereby improving the accuracy of responses. The information providing unit, for example, builds a system that provides the language model with customization options according to the user's specific needs and preferences. For example, it prioritizes generating responses related to topics that interest the user. The information providing unit also develops an algorithm that provides customization options and generates responses according to the user's needs and preferences. For example, if the user prefers a specific style of response, it generates a response that matches that style. The information providing unit also develops a language model that provides customization options according to the user's specific needs and preferences, thereby improving the accuracy of responses. For example, it generates a response that matches the user's preferred wording and tone. This makes it possible to improve the accuracy of responses by providing customization options according to the user's specific needs and preferences.

[0056] The information providing unit can use a large-scale language model to perform storytelling or story generation according to the user's emotions. The information providing unit, for example, uses a large-scale language model to build a system that performs storytelling according to the user's emotions. For example, if the user is feeling sad, an encouraging story is generated. The information providing unit also develops an algorithm that generates a story according to the user's emotions based on the emotion estimation data. For example, an adventure story is generated if the user is excited. The information providing unit also utilizes a large-scale language model to develop a system that performs storytelling or story generation according to the user's emotions. For example, a soothing story is generated if the user feels like relaxing. In this way, storytelling or story generation according to the user's emotions can be performed to provide a more personalized experience.

[0057] The information providing unit can provide personalized responses to global users by adapting the language model to different languages ​​or cultural areas. The information providing unit, for example, develops an algorithm for adapting the language model to different languages ​​or cultural areas, and builds a system for providing personalized responses to global users. For example, the information providing unit generates responses that support multiple languages. Furthermore, in order to support different cultural areas, the information providing unit trains the language model to learn culturally specific expressions and customs. For example, it generates responses that reflect greetings and etiquette in a specific culture. Furthermore, the information providing unit develops a system for adapting the language model to multiple languages ​​in order to provide personalized responses to global users. For example, it generates responses that are tailored to the user's native language. In this way, by adapting to different languages ​​and cultural areas, it is possible to provide personalized responses to global users.

[0058] The information providing unit can use the emotion estimation function to automatically generate educational content or a learning program according to the user's emotions. For example, the information providing unit uses the emotion estimation function to build a system that automatically generates educational content according to the user's emotions. For example, the information providing unit provides a learning program related to a topic that the user is interested in. The information providing unit also analyzes the user's emotions in real time and develops an algorithm that generates educational content based on the results. For example, when the user is concentrating, it provides content with a high level of difficulty. The information providing unit also builds a system that automatically generates a learning program according to the user's emotions based on the emotion estimation data. For example, when the user is tired, it provides learning content that helps them relax. This makes it possible to provide a more effective learning experience by automatically generating educational content or a learning program according to the user's emotions.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The conversation log acquisition unit can analyze the user's speech rate and volume to estimate the user's level of tension and stress. For example, if the user speaks at a fast rate and loud volume, it can determine that the user is tense and generate a response to help the user relax. The conversation log acquisition unit can also analyze the intervals and rhythm of the user's speech to estimate the user's emotional state. For example, if the intervals between speeches are short and the rhythm is irregular, it can determine that the user is feeling stressed and provide advice to reduce stress. Furthermore, the conversation log acquisition unit can analyze the tone and pitch of the user's voice to track changes in emotions in real time. For example, if the voice tone is low and the pitch is unstable, it can determine that the user is feeling anxious and generate a response to reassure the user.

[0061] The database generation unit can integrate a user's past purchase history and browsing history to predict the user's consumption behavior. For example, it can predict the next product that the user is likely to purchase based on data on products purchased in the past and websites visited. The database generation unit can also analyze a user's purchase history and browsing history to develop an algorithm that identifies the user's consumption pattern. For example, it can prioritize displaying products related to a particular season or event. The database generation unit can also build a system that provides reliable information based on the user's past behavioral data. For example, it can prioritize displaying information from sites that the user frequently visits.

[0062] The information providing unit can work with a sensor device to monitor the user's health condition and provide health advice. For example, it can analyze heart rate and sleep data obtained from a wearable device to evaluate the user's health condition. The information providing unit can also collect data on the user's diet and exercise and develop an algorithm to provide health advice. For example, it can provide advice on adjusting nutritional balance and exercise volume based on the contents of meals and frequency of exercise. The information providing unit can also build a system that monitors the user's health condition in real time and issues an alert if an abnormality is detected. For example, it can encourage the user to visit a medical institution if the heart rate is abnormally high.

[0063] The information providing unit can provide feedback according to the user's emotions based on the emotion estimation results. For example, if the user is feeling down, it can send an encouraging message. The information providing unit can also develop an algorithm that suggests activities to improve the user's emotional state based on the emotion estimation results. For example, if the user is feeling stressed, it can suggest a relaxing activity. The information providing unit can also build a system that provides feedback according to the user's emotions based on the emotion estimation data. For example, if the user is happy, it can send a message to share that emotion.

[0064] The information providing unit can recommend music and video content according to the user's emotion based on the emotion estimation results. For example, if the user feels like relaxing, it can recommend relaxing music. The information providing unit can also analyze the user's emotions in real time and develop an algorithm that recommends music and video content based on the results. For example, if the user feels sad, it can recommend uplifting videos. The information providing unit can also build a system that automatically recommends music and video content according to the user's emotion based on the emotion estimation data. For example, if the user is excited, it can recommend calming music.

[0065] The information providing unit can build a product recommendation system based on a user's purchase history and preferences. For example, it can recommend related products based on past purchase history. The information providing unit can also develop an algorithm that recommends optimal products to a user based on purchase history and preference data. For example, it can analyze preferences for specific brands or categories and display related products. The information providing unit can also utilize a structured database to build a personalized product recommendation system based on a user's purchase history and preferences. For example, it can provide a product list tailored to the user's preferences.

[0066] The information providing unit can automatically generate educational content and learning programs according to the user's emotions based on the emotion estimation results. For example, it can provide learning programs related to topics that interest the user. The information providing unit can also develop an algorithm that analyzes the user's emotions in real time and generates educational content based on the results. For example, it can provide content with a high level of difficulty when the user is concentrating. The information providing unit can also build a system that automatically generates learning programs according to the user's emotions based on the emotion estimation data. For example, it can provide learning content that helps the user relax when the user is tired.

[0067] The information providing unit can build a system that generates personalized responses by including a user's past conversation logs in the training data. For example, responses can be generated based on the user's past utterances. The information providing unit can also incorporate the past conversation logs into the training data to develop an algorithm that generates responses tailored to the user's specific needs and preferences. For example, responses can be generated related to topics the user previously discussed. The information providing unit can also develop a language model that generates more personalized responses by including a user's past conversation logs in the training data. For example, responses can be generated based on the user's preferences and interests.

[0068] The information providing unit can develop algorithms to adapt the language model to different languages ​​and cultural spheres, and build a system that provides personalized responses to global users. For example, it generates responses that support multiple languages. The information providing unit can also train the language model to learn culturally specific expressions and customs to adapt to different cultural spheres. For example, it generates responses that reflect greetings and etiquette in a specific culture. The information providing unit can also develop a system that adapts the language model to multiple languages ​​to provide personalized responses to global users. For example, it generates responses that are tailored to the user's native language.

[0069] The information providing unit can perform storytelling or story generation according to the user's emotions based on the user's emotion estimation results. For example, if the user is feeling sad, it generates an encouraging story. The information providing unit can also develop an algorithm that generates a story according to the user's emotions based on the emotion estimation data. For example, if the user is excited, it generates an adventure story. The information providing unit can also develop a system that uses a large-scale language model to perform storytelling or story generation according to the user's emotions. For example, if the user feels like relaxing, it generates a soothing story.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The conversation log acquisition unit acquires an individual's conversation log. For example, the conversation log acquisition unit collects text-format conversation logs and audio-format conversation logs and stores them as digital data. Audio-format conversation logs are converted into text data using voice recognition technology. Step 2: The database generation unit generates a structured database based on reliable information, for example, data from official or authenticated sources, and structures it as a relational or NoSQL database. Step 3: The information provision unit provides information using the structured database generated by the database generation unit. For example, it provides the latest information or information customized for individuals, and the generation AI generates a response based on the information obtained from the structured database.

[0072] 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.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0085] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0086] 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.

[0087] 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.

[0088] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0098] 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.

[0099] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] 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.

[0102] 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.

[0103] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, the 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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, in order to avoid confusion and to 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.

[0138] 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. [Explanation of symbols]

[0139] 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 conversation log acquisition unit that acquires an individual's conversation log; a database generation unit that generates a structured database based on reliable information; an information providing unit that provides information using the structured database generated by the database generating unit; A system characterized by:

2. The conversation log acquisition unit Tracking changes in a user's emotions in real time and generating responses based on those emotions The system of claim 1 .

3. The database generation unit Integrate your past search or browsing history to provide more personalized information 2. The system of claim 1.

4. The information providing unit The conversation log and the trusted information are combined to monitor the user's health condition or lifestyle habits and provide health advice.

2. The system of claim 1.

5. The information providing unit Automatically recommending music or video content according to user emotions 2. The system of claim 1.

6. The information providing unit Feedback the user's emotion estimation results and generate responses according to the emotion. The system of claim 1 .

7. The information providing unit Storytelling or narrative generation based on user emotions using large-scale language models 2. The system of claim 1.

8. The information providing unit Automatically generate educational content or learning programs according to user emotions 2. The system of claim 1.

Images