System

Abstract

An object of a system according to an embodiment is to generate an optimal answer by integrating shared information on the Web and original information of individual users.SOLUTION: A system according to an embodiment includes an information collection unit, an information integration unit, and an answer generation unit. The information collection unit collects shared information on the Web. The information integration unit integrates original information of each user in addition to the information collected by the information collection unit. The answer generation unit generates an answer based on the information integrated by the information integration 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] Conventional technologies provide answers based only on common information on the Web, making it difficult to find the most appropriate answer for each individual user.

[0005] The system according to the embodiment aims to generate optimal answers by integrating shared information on the Web with original information from individual users. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an information integration unit, and an answer generation unit. The information collection unit collects information on a common web. The information integration unit integrates original information for each user in addition to the information collected by the information collection unit. The answer generation unit generates an answer based on the information integrated by the information integration unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate optimal answers by integrating common information on the Web with original information of individual users. [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 MyAI system according to an embodiment of the present invention is a system that derives answers by integrating personal original information in addition to shared information on the web. This enables the MyAI system to support users' daily lives and provide more personalized answers.

[0029] The MyAI system according to the embodiment includes an information collection unit, an information integration unit, and an answer generation unit. The information collection unit collects common web information. For example, it collects information from news sites, social networking sites, blogs, and so on. The information collection unit can also collect information from the user's device. For example, it collects smartphone application usage history and location information. The information integration unit integrates the information collected by the information collection unit with original information for each individual user. For example, it incorporates personal information such as the user's schedule, preferences, and past activity history. The information integration unit can also integrate the user's health data and purchase history. For example, it incorporates health data such as heart rate, step count, and sleep data to provide advice based on the user's health condition. The answer generation unit generates an answer based on the information integrated by the information integration unit. For example, if a user asks, "What events do you recommend this weekend?", the answer generation unit recommends the most appropriate event based on the user's past event participation history and preferences. If a user asks, "What should I do to prepare for my next meeting?", the answer generation unit suggests specific preparations based on the user's schedule and the content of the meeting. As a result, the MyAI system according to the embodiment can support the user's daily life and provide more personalized answers. For example, the system can help the user efficiently manage their schedule and receive personalized information in their busy daily lives, reducing stress. Furthermore, by covering areas where AI is weak, the system can provide more reliable support.

[0030] The information integration unit can incorporate at least one of the user's personal information, such as the user's schedule, preferences, and past behavioral history. For example, the information integration unit incorporates the user's schedule and sets reminders based on the user's plans. The information integration unit also incorporates the user's preferences and provides personalized information based on the user's preferences. For example, it recommends movies or music in the user's favorite genre. The information integration unit also incorporates the user's past behavioral history and analyzes the user's behavioral patterns. For example, it predicts the user's next actions and makes optimal suggestions based on the user's history of places visited and events attended in the past. In this way, by incorporating the user's personal information, it is possible to provide more personalized answers.

[0031] The information integration unit can integrate the user's health data and provide advice based on the user's health condition. For example, the information integration unit analyzes the user's heart rate and sleep patterns to provide exercise and diet advice based on the user's health condition. For example, it may suggest walking if the user is not getting enough exercise. The information integration unit may also suggest relaxation methods for stress management based on the user's health data. For example, it may recommend videos of deep breathing or yoga. The information integration unit may also set reminders to encourage regular health checks or visits to a medical institution based on the user's health condition. For example, it may recommend a doctor's appointment if blood pressure is high. This makes it possible to provide advice based on the user's health condition.

[0032] The information integration unit can analyze the user's purchasing history and recommend products based on consumption trends. For example, the information integration unit analyzes the user's past purchasing history and recommends related products. For example, it can suggest new products from the same brand as products previously purchased. The information integration unit also recommends products suited to seasons and events based on the user's consumption trends. For example, it can suggest gift items during the Christmas season. The information integration unit also preferentially displays products from specific categories based on the user's purchasing history. For example, it can list frequently purchased foods and daily necessities. This makes it possible to recommend products based on the user's consumption trends.

[0033] The information integration unit can recommend related online communities and events based on the user's hobbies and interests. For example, the information integration unit recommends related online forums and SNS groups based on the user's hobbies and interests. For example, a movie fan community is introduced to a user who loves movies. The information integration unit also recommends related events and workshops based on the user's interests. For example, information on cooking classes is provided to a user whose hobby is cooking. The information integration unit also provides related news and articles based on the user's hobbies. For example, the latest sports news is delivered to a user who loves sports. This makes it possible to recommend related online communities and events based on the user's hobbies and interests.

[0034] The information integration unit can integrate information about the user's family and friends and provide information based on social relationships. For example, the information integration unit can remind the user of their family and friends' birthdays and anniversaries and suggest gifts and messages. For example, it can provide gift ideas for a friend's birthday. The information integration unit can also analyze the user's social network and suggest events and activities with friends who share common interests. For example, it can introduce events that can be attended with friends who share common hobbies. The information integration unit can also provide relevant information and advice based on the health status and lifestyle of the user's family and friends. For example, it can share information useful for managing the health of family members. This makes it possible to integrate information about the user's family and friends and provide information based on social relationships.

[0035] The information collection unit can utilize the user's location information to suggest optimal routes and means of transportation in real time. The information collection unit, for example, proposes the optimal route based on the user's current location and destination. For example, it provides an alternative route to avoid traffic congestion. The information collection unit also proposes the optimal means of transportation based on the user's location information. For example, it suggests public transportation timetables or the use of taxis. The information collection unit also utilizes the user's location information to recommend nearby facilities and services. For example, it introduces nearby restaurants and cafes. In this way, the information collection unit can propose optimal routes and means of transportation in real time by utilizing the user's location information.

[0036] The information collection unit can analyze the user's device usage patterns and suggest the optimal timing for using applications. The information collection unit, for example, analyzes the user's device usage patterns and suggests the optimal timing for using applications. For example, it recommends a learning app for times when the user can concentrate best. The information collection unit also optimizes the timing for using applications based on the user's device usage history. For example, it suggests a relaxation app for times when the user wants to relax. The information collection unit also analyzes the user's device usage patterns and optimizes the frequency of application use. For example, it prioritizes the display of apps that the user uses frequently. This makes it possible to analyze the user's device usage patterns and suggest the optimal timing for using applications.

[0037] The information integration unit can facilitate information sharing between a user's devices and provide a seamless experience. For example, the information integration unit builds a system that facilitates information sharing between a user's smartphone and other devices. For example, it allows notes created on a smartphone to be viewed on a PC or tablet. The information integration unit also automates data synchronization between a user's devices and provides a seamless experience. For example, it automatically uploads photos taken on a smartphone to the cloud so that they can be viewed on other devices. The information integration unit also strengthens the collaboration of applications between a user's devices and achieves seamless operation. For example, it allows a task started on a smartphone to be continued on a PC. This facilitates information sharing between a user's devices and provides a seamless experience.

[0038] The information collection unit can manage the user's smartphone usage time and support digital detox. For example, the information collection unit can monitor the user's smartphone usage time and set alerts to prevent excessive usage. For example, it can send a notification urging the user to take a break if usage continues for more than a certain period of time. The information collection unit can also analyze the user's smartphone usage patterns and provide advice for digital detox. For example, it can suggest specific methods for reducing usage time. The information collection unit can also add a function to manage the user's smartphone usage time and set digital detox goals. For example, it can provide a setting to limit daily usage time. This can help manage the user's smartphone usage time and support digital detox.

[0039] The answer generation unit can analyze the user's past question history and provide highly accurate answers. For example, the answer generation unit analyzes the user's past question history and provides answers based on related information. For example, if a similar question has been asked in the past, it refers to the answer to that question. The answer generation unit also provides personalized answers based on the user's question history. For example, it provides information according to the user's preferences and interests. The answer generation unit also analyzes the user's past question history and automatically generates answers to frequently asked questions. For example, it prepares templates for frequently asked questions. This makes it possible to analyze the user's past question history and provide more accurate answers.

[0040] The answer generation unit can provide the optimal answer based on the user's real-time situation. The answer generation unit provides the optimal answer based on, for example, the user's current activity or location. For example, if the user is out, the answer generation unit introduces nearby facilities and services. The answer generation unit also analyzes the user's real-time situation and provides advice according to the situation. For example, if the user is in a meeting, the answer generation unit provides information that is useful for progressing the meeting. The answer generation unit also suggests the optimal action based on the user's current situation. For example, if the user is exercising, the answer generation unit provides advice on appropriate exercise methods and points to be careful about. This makes it possible to provide the optimal answer based on the user's real-time situation.

[0041] The answer generation unit can provide professional advice based on the user's occupation and expertise. The answer generation unit provides relevant professional advice based on the user's occupation and expertise, for example. For example, it provides the latest medical information to medical professionals. The answer generation unit also recommends information and tools that are useful for the user's work depending on the user's occupation. For example, it introduces the latest technology trends and development tools to engineers. The answer generation unit also takes the user's expertise into consideration and provides detailed answers to professional questions. For example, it provides legal grounds for questions about the law. This makes it possible to provide professional advice based on the user's occupation and expertise.

[0042] The answer generation unit can provide answers customized according to the user's cultural background and language. The answer generation unit, for example, takes into account the user's cultural background and provides answers that are culturally sensitive. For example, it provides information about customs and manners in a particular culture. The answer generation unit also customizes answers according to the user's language. For example, if the user's native language is English, it provides answers in English. The answer generation unit also recommends appropriate content and services based on the user's cultural background and language. For example, it introduces events and news related to a particular culture. This makes it possible to provide answers customized according to the user's cultural background and language.

[0043] The information integration unit can integrate multiple information sources and provide multifaceted solutions to complex problems. For example, the information integration unit collects and integrates data from multiple information sources to provide solutions to complex problems. For example, it may propose optimal treatments based on medical information and expert opinions. The information integration unit also provides information from different perspectives in response to user questions. For example, it may present multiple solutions to technical problems. The information integration unit also integrates multiple information sources to provide comprehensive solutions. For example, it may integrate information on business strategies and propose optimal strategies. In this way, it is possible to integrate multiple information sources and provide multifaceted solutions to complex problems.

[0044] The information integration unit can analyze the user's past behavioral patterns and provide predictive advice. The information integration unit, for example, analyzes the user's past behavioral patterns and predicts future behavior. For example, it suggests the next plan based on past schedules. The information integration unit also provides predictive advice based on the user's behavioral patterns. For example, it predicts and recommends the next purchase based on past purchasing history. The information integration unit also analyzes the user's past behavioral data and provides advice for future behavior. For example, it suggests the next exercise plan based on past exercise data. In this way, it is possible to analyze the user's past behavioral patterns and provide predictive advice.

[0045] The answer generation unit can continuously improve the accuracy of the AI's answers based on user feedback. For example, the answer generation unit collects feedback from users and improves the accuracy of the AI's answers based on that data. For example, it adjusts the answer algorithm based on user ratings. The answer generation unit also analyzes user feedback and identifies areas for improvement in the AI's answers. For example, it corrects frequently pointed out problems. The answer generation unit also reflects user feedback in real time and continuously improves the accuracy of the AI's answers. For example, it immediately corrects the content of the answers based on user opinions. This allows the accuracy of the AI's answers to be continuously improved based on user feedback.

[0046] The answer generation unit can provide a customized response according to the user's communication style. The answer generation unit, for example, analyzes the user's communication style and provides a customized response according to that. For example, if the user prefers short messages, it provides a concise answer. The answer generation unit also selects an appropriate tone and language based on the user's communication style. For example, it uses polite language for a user who prefers a formal style. The answer generation unit also takes the user's communication style into consideration and suggests the optimal communication method. For example, if the user prefers visual information, it uses diagrams and graphs. This makes it possible to provide a customized response according to the user's communication style.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The MyAI system can also analyze a user's device usage patterns and suggest the optimal timing for using applications. For example, it can recommend a learning app for the time when the user is most focused, or a relaxation app for the time when the user wants to relax. This allows the system to analyze a user's device usage patterns and suggest the optimal timing for using applications.

[0049] The MyAI system can also utilize the user's location information to provide a function that suggests the optimal route and transportation method in real time. For example, it can suggest the optimal route based on the user's current location and destination. It can also provide alternative routes to avoid traffic congestion. This allows the system to suggest the optimal route and transportation method in real time by utilizing the user's location information.

[0050] The MyAI system can also analyze a user's purchasing history and provide a function to recommend products based on consumption trends. For example, it can suggest new products from the same brand as previously purchased products. It can also recommend products suited to the season or events. This allows it to recommend products based on the user's consumption trends.

[0051] The MyAI system can also be equipped with a function to recommend related online communities and events based on a user's hobbies and interests. For example, a user who loves movies can be introduced to a movie fan community. A user who enjoys cooking can be provided with information about cooking classes. This allows the system to recommend related online communities and events based on the user's hobbies and interests.

[0052] The MyAI system can also integrate information about a user's family and friends and provide information based on their social relationships. For example, it can remind users of their family and friends' birthdays and anniversaries and suggest gifts and messages. It can also recommend events that users can attend with friends who share common hobbies. This allows the system to integrate information about a user's family and friends and provide information based on their social relationships.

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

[0054] Step 1: The information collection unit collects common information on the web. For example, it collects information from news sites, social media, blogs, etc. The information collection unit can also collect information from the user's device. For example, it may collect smartphone application usage history and location information. Step 2: The information integration unit integrates the information collected by the information collection unit with original information for each individual user. For example, it incorporates personal information such as the user's schedule, preferences, and past behavioral history. The information integration unit can also integrate the user's health data and purchase history. For example, it incorporates health data such as heart rate, number of steps, and sleep data, and provides advice based on the user's health condition. Step 3: The answer generation unit generates an answer based on the information integrated by the information integration unit. For example, if a user asks, "What events do you recommend this weekend?", the answer generation unit will recommend the most suitable event, taking into account the user's past event participation history and preferences. Similarly, if a user asks, "What should I do to prepare for my next meeting?", the answer generation unit will suggest specific preparations based on the user's schedule and the content of the meeting.

[0055] (Example 2) The MyAI system according to an embodiment of the present invention is a system that derives answers by integrating personal original information in addition to shared information on the web. This enables the MyAI system to support users' daily lives and provide more personalized answers.

[0056] The MyAI system according to the embodiment includes an information collection unit, an information integration unit, and an answer generation unit. The information collection unit collects common web information. For example, it collects information from news sites, social networking sites, blogs, and so on. The information collection unit can also collect information from the user's device. For example, it collects smartphone application usage history and location information. The information integration unit integrates the information collected by the information collection unit with original information for each individual user. For example, it incorporates personal information such as the user's schedule, preferences, and past activity history. The information integration unit can also integrate the user's health data and purchase history. For example, it incorporates health data such as heart rate, step count, and sleep data to provide advice based on the user's health condition. The answer generation unit generates an answer based on the information integrated by the information integration unit. For example, if a user asks, "What events do you recommend this weekend?", the answer generation unit recommends the most appropriate event based on the user's past event participation history and preferences. If a user asks, "What should I do to prepare for my next meeting?", the answer generation unit suggests specific preparations based on the user's schedule and the content of the meeting. As a result, the MyAI system according to the embodiment can support the user's daily life and provide more personalized answers. For example, the system can help the user efficiently manage their schedule and receive personalized information in their busy daily lives, reducing stress. Furthermore, by covering areas where AI is weak, the system can provide more reliable support.

[0057] The information integration unit can incorporate at least one of the user's personal information, such as the user's schedule, preferences, and past behavioral history. For example, the information integration unit incorporates the user's schedule and sets reminders based on the user's plans. The information integration unit also incorporates the user's preferences and provides personalized information based on the user's preferences. For example, it recommends movies or music in the user's favorite genre. The information integration unit also incorporates the user's past behavioral history and analyzes the user's behavioral patterns. For example, it predicts the user's next actions and makes optimal suggestions based on the user's history of places visited and events attended in the past. In this way, by incorporating the user's personal information, it is possible to provide more personalized answers.

[0058] The answer generation unit can estimate the user's emotions and provide personalized information based on those emotions. For example, the answer generation unit captures the user's facial expression with a camera and analyzes the emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression. The answer generation unit also records the user's voice and estimates the emotions using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The answer generation unit also analyzes the user's text input and estimates the emotions using text analysis technology. For example, it analyzes the content and wording of the sentence and calculates an emotion score. This makes it possible to provide personalized information based on the user's emotions. For example, if the user is feeling stressed, relaxing content can be recommended. If the user is feeling happy, positive news or entertainment can be provided to further enhance the emotions. If the user is feeling anxious, information to give a sense of security can be provided.

[0059] The information integration unit can integrate the user's health data and provide advice based on the user's health condition. For example, the information integration unit analyzes the user's heart rate and sleep patterns to provide exercise and diet advice based on the user's health condition. For example, it may suggest walking if the user is not getting enough exercise. The information integration unit may also suggest relaxation methods for stress management based on the user's health data. For example, it may recommend videos of deep breathing or yoga. The information integration unit may also set reminders to encourage regular health checks or visits to a medical institution based on the user's health condition. For example, it may recommend a doctor's appointment if blood pressure is high. This makes it possible to provide advice based on the user's health condition.

[0060] The information integration unit can analyze the user's purchasing history and recommend products based on consumption trends. For example, the information integration unit analyzes the user's past purchasing history and recommends related products. For example, it can suggest new products from the same brand as products previously purchased. The information integration unit also recommends products suited to seasons and events based on the user's consumption trends. For example, it can suggest gift items during the Christmas season. The information integration unit also preferentially displays products from specific categories based on the user's purchasing history. For example, it can list frequently purchased foods and daily necessities. This makes it possible to recommend products based on the user's consumption trends.

[0061] The information integration unit can recommend related online communities and events based on the user's hobbies and interests. For example, the information integration unit recommends related online forums and SNS groups based on the user's hobbies and interests. For example, a movie fan community is introduced to a user who loves movies. The information integration unit also recommends related events and workshops based on the user's interests. For example, information on cooking classes is provided to a user whose hobby is cooking. The information integration unit also provides related news and articles based on the user's hobbies. For example, the latest sports news is delivered to a user who loves sports. This makes it possible to recommend related online communities and events based on the user's hobbies and interests.

[0062] The information integration unit can integrate information about the user's family and friends and provide information based on social relationships. For example, the information integration unit can remind the user of their family and friends' birthdays and anniversaries and suggest gifts and messages. For example, it can provide gift ideas for a friend's birthday. The information integration unit can also analyze the user's social network and suggest events and activities with friends who share common interests. For example, it can introduce events that can be attended with friends who share common hobbies. The information integration unit can also provide relevant information and advice based on the health status and lifestyle of the user's family and friends. For example, it can share information useful for managing the health of family members. This makes it possible to integrate information about the user's family and friends and provide information based on social relationships.

[0063] The answer generation unit can recommend music and movies according to the user's emotions. For example, the answer generation unit analyzes the user's emotional state and recommends music that matches that emotion. For example, if the user wants to relax, it suggests relaxation music. The answer generation unit also recommends movies and dramas based on the user's emotions. For example, if the user is feeling sad, it recommends an inspiring movie. The answer generation unit also provides entertainment content to enhance the user's emotions based on the user's emotion estimation data. For example, if the user is feeling happy, it recommends a comedy movie. This makes it possible to recommend music and movies according to the user's emotions.

[0064] The answer generation unit can use the emotion estimation function to customize reminders and notifications according to the user's emotions. For example, if the user is tired, the answer generation unit sets a reminder to take a break to relax. For example, it suggests a short meditation or stretching session. If the user is feeling stressed, the answer generation unit sends a notification suggesting an activity to relieve stress. For example, it reminds the user to take a walk or take a deep breath. If the user is feeling joyful, the answer generation unit suggests positive messages or activities to further enhance those emotions. For example, it reminds the user to spend time interacting with friends or engaging in hobbies. This makes it possible to customize reminders and notifications according to the user's emotions.

[0065] The information collection unit can utilize the user's location information to suggest optimal routes and means of transportation in real time. The information collection unit, for example, proposes the optimal route based on the user's current location and destination. For example, it provides an alternative route to avoid traffic congestion. The information collection unit also proposes the optimal means of transportation based on the user's location information. For example, it suggests public transportation timetables or the use of taxis. The information collection unit also utilizes the user's location information to recommend nearby facilities and services. For example, it introduces nearby restaurants and cafes. In this way, the information collection unit can propose optimal routes and means of transportation in real time by utilizing the user's location information.

[0066] The information collection unit can analyze the user's device usage patterns and suggest the optimal timing for using applications. The information collection unit, for example, analyzes the user's device usage patterns and suggests the optimal timing for using applications. For example, it recommends a learning app for times when the user can concentrate best. The information collection unit also optimizes the timing for using applications based on the user's device usage history. For example, it suggests a relaxation app for times when the user wants to relax. The information collection unit also analyzes the user's device usage patterns and optimizes the frequency of application use. For example, it prioritizes the display of apps that the user uses frequently. This makes it possible to analyze the user's device usage patterns and suggest the optimal timing for using applications.

[0067] The information integration unit can facilitate information sharing between a user's devices and provide a seamless experience. For example, the information integration unit builds a system that facilitates information sharing between a user's smartphone and other devices. For example, it allows notes created on a smartphone to be viewed on a PC or tablet. The information integration unit also automates data synchronization between a user's devices and provides a seamless experience. For example, it automatically uploads photos taken on a smartphone to the cloud so that they can be viewed on other devices. The information integration unit also strengthens the collaboration of applications between a user's devices and achieves seamless operation. For example, it allows a task started on a smartphone to be continued on a PC. This facilitates information sharing between a user's devices and provides a seamless experience.

[0068] The information collection unit can manage the user's smartphone usage time and support digital detox. For example, the information collection unit can monitor the user's smartphone usage time and set alerts to prevent excessive usage. For example, it can send a notification urging the user to take a break if usage continues for more than a certain period of time. The information collection unit can also analyze the user's smartphone usage patterns and provide advice for digital detox. For example, it can suggest specific methods for reducing usage time. The information collection unit can also add a function to manage the user's smartphone usage time and set digital detox goals. For example, it can provide a setting to limit daily usage time. This can help manage the user's smartphone usage time and support digital detox.

[0069] The answer generation unit can use the emotion estimation function to recommend digital content according to the user's emotion. The answer generation unit, for example, analyzes the user's emotional state and recommends digital content that matches that emotion. For example, if the user wants to relax, it suggests relaxation music. The answer generation unit also recommends movies and dramas based on the user's emotion. For example, if the user is feeling sad, it recommends an inspiring movie. The answer generation unit also provides entertainment content to enhance the user's emotion based on the user's emotion estimation data. For example, if the user is feeling happy, it recommends a comedy movie. This makes it possible to recommend digital content according to the user's emotion.

[0070] The answer generation unit can use the emotion estimation function to provide an answer based on the user's emotions. For example, if the user is feeling anxious, the answer generation unit generates an answer that gives the user a sense of security. For example, it provides an answer from a reliable source to the user's question. Furthermore, if the user is feeling happy, the answer generation unit provides a positive answer to further enhance that emotion. For example, it sends a message praising the user's successful experience. Furthermore, if the user is feeling stressed, the answer generation unit provides advice for relieving stress. For example, it introduces relaxation methods and stress management techniques. In this way, it is possible to provide an answer based on the user's emotions.

[0071] The answer generation unit can analyze the user's past question history and provide highly accurate answers. For example, the answer generation unit analyzes the user's past question history and provides answers based on related information. For example, if a similar question has been asked in the past, it refers to the answer to that question. The answer generation unit also provides personalized answers based on the user's question history. For example, it provides information according to the user's preferences and interests. The answer generation unit also analyzes the user's past question history and automatically generates answers to frequently asked questions. For example, it prepares templates for frequently asked questions. This makes it possible to analyze the user's past question history and provide more accurate answers.

[0072] The answer generation unit can provide the optimal answer based on the user's real-time situation. The answer generation unit provides the optimal answer based on, for example, the user's current activity or location. For example, if the user is out, the answer generation unit introduces nearby facilities and services. The answer generation unit also analyzes the user's real-time situation and provides advice according to the situation. For example, if the user is in a meeting, the answer generation unit provides information that is useful for progressing the meeting. The answer generation unit also suggests the optimal action based on the user's current situation. For example, if the user is exercising, the answer generation unit provides advice on appropriate exercise methods and points to be careful about. This makes it possible to provide the optimal answer based on the user's real-time situation.

[0073] The answer generation unit can provide professional advice based on the user's occupation and expertise. The answer generation unit provides relevant professional advice based on the user's occupation and expertise, for example. For example, it provides the latest medical information to medical professionals. The answer generation unit also recommends information and tools that are useful for the user's work depending on the user's occupation. For example, it introduces the latest technology trends and development tools to engineers. The answer generation unit also takes the user's expertise into consideration and provides detailed answers to professional questions. For example, it provides legal grounds for questions about the law. This makes it possible to provide professional advice based on the user's occupation and expertise.

[0074] The answer generation unit can provide answers customized according to the user's cultural background and language. The answer generation unit, for example, takes into account the user's cultural background and provides answers that are culturally sensitive. For example, it provides information about customs and manners in a particular culture. The answer generation unit also customizes answers according to the user's language. For example, if the user's native language is English, it provides answers in English. The answer generation unit also recommends appropriate content and services based on the user's cultural background and language. For example, it introduces events and news related to a particular culture. This makes it possible to provide answers customized according to the user's cultural background and language.

[0075] The answer generation unit can use the emotion estimation function to provide feedback according to the user's emotion and improve the quality of the answer. The answer generation unit, for example, analyzes the user's emotional state and provides feedback according to that emotion. For example, if the user is feeling anxious, it sends a message that gives a sense of security. The answer generation unit also provides feedback to improve the quality of the answer based on the user's emotion estimation data. For example, it provides advice to elicit positive emotions. The answer generation unit also provides feedback according to the user's emotion in real time and continuously improves the quality of the answer. For example, it adjusts the content of the answer according to changes in the user's emotion. This makes it possible to provide feedback according to the user's emotion and improve the quality of the answer.

[0076] The answer generation unit can use the emotion estimation function to respond based on the user's emotions. For example, if the user is sad, the answer generation unit will send an encouraging message, for example, by providing positive words or an encouraging story. Furthermore, if the user is angry, the answer generation unit will provide advice to help the user stay calm, for example, by suggesting deep breathing or relaxation techniques. Furthermore, if the user is happy, the answer generation unit will provide positive feedback to further enhance those emotions, for example, by sending a message praising a successful experience. This makes it possible to respond based on the user's emotions.

[0077] The information integration unit can integrate multiple information sources and provide multifaceted solutions to complex problems. For example, the information integration unit collects and integrates data from multiple information sources to provide solutions to complex problems. For example, it may propose optimal treatments based on medical information and expert opinions. The information integration unit also provides information from different perspectives in response to user questions. For example, it may present multiple solutions to technical problems. The information integration unit also integrates multiple information sources to provide comprehensive solutions. For example, it may integrate information on business strategies and propose optimal strategies. In this way, it is possible to integrate multiple information sources and provide multifaceted solutions to complex problems.

[0078] The information integration unit can analyze the user's past behavioral patterns and provide predictive advice. The information integration unit, for example, analyzes the user's past behavioral patterns and predicts future behavior. For example, it suggests the next plan based on past schedules. The information integration unit also provides predictive advice based on the user's behavioral patterns. For example, it predicts and recommends the next purchase based on past purchasing history. The information integration unit also analyzes the user's past behavioral data and provides advice for future behavior. For example, it suggests the next exercise plan based on past exercise data. In this way, it is possible to analyze the user's past behavioral patterns and provide predictive advice.

[0079] The answer generation unit can continuously improve the accuracy of the AI's answers based on user feedback. For example, the answer generation unit collects feedback from users and improves the accuracy of the AI's answers based on that data. For example, it adjusts the answer algorithm based on user ratings. The answer generation unit also analyzes user feedback and identifies areas for improvement in the AI's answers. For example, it corrects frequently pointed out problems. The answer generation unit also reflects user feedback in real time and continuously improves the accuracy of the AI's answers. For example, it immediately corrects the content of the answers based on user opinions. This allows the accuracy of the AI's answers to be continuously improved based on user feedback.

[0080] The answer generation unit can provide a customized response according to the user's communication style. The answer generation unit, for example, analyzes the user's communication style and provides a customized response according to that. For example, if the user prefers short messages, it provides a concise answer. The answer generation unit also selects an appropriate tone and language based on the user's communication style. For example, it uses polite language for a user who prefers a formal style. The answer generation unit also takes the user's communication style into consideration and suggests the optimal communication method. For example, if the user prefers visual information, it uses diagrams and graphs. This makes it possible to provide a customized response according to the user's communication style.

[0081] The answer generation unit uses the emotion estimation function to provide support according to the user's emotions, thereby improving the reliability of the AI. The answer generation unit, for example, analyzes the user's emotional state and provides support according to those emotions. For example, if the user is feeling anxious, it sends a message that gives a sense of security. The answer generation unit also improves the quality of the AI's answers based on the user's emotion estimation data. For example, it provides advice to elicit positive emotions. The answer generation unit also provides support according to the user's emotions in real time, thereby improving the reliability of the AI. For example, it adjusts the content of the answer according to changes in the user's emotions. This makes it possible to provide support according to the user's emotions and improve the reliability of the AI.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The MyAI system can also estimate the user's emotions and evaluate their stress level based on the estimated emotions. For example, if the user is feeling high stress, it can suggest relaxation methods and activities to relieve stress. If the user's stress level is low, it can provide positive feedback and activities to increase motivation. This can provide appropriate support according to the user's emotions and improve the user's quality of life.

[0084] The MyAI system can also estimate the user's emotions and provide feedback appropriate to the user's emotional state based on the estimated emotions. For example, if the user is feeling anxious, it can send a message that provides reassurance. If the user is feeling happy, it can provide positive feedback to further enhance that emotion. In this way, it can provide feedback appropriate to the user's emotions and support the user's emotional state.

[0085] The MyAI system can also estimate a user's emotions and customize reminders and notifications based on the user's emotional state. For example, if a user is tired, it can set a reminder to take a break to relax. If a user is stressed, it can send a notification suggesting an activity to relieve stress. This allows the system to customize reminders and notifications based on the user's emotions, improving the user's quality of life.

[0086] The MyAI system can also estimate the user's emotions and, based on the estimated emotions, recommend music and movies that fit the user's emotional state. For example, if the user wants to relax, it can suggest relaxing music. Or, if the user is feeling sad, it can recommend an inspiring movie. This allows the system to recommend music and movies that fit the user's emotions and support their emotional state.

[0087] The MyAI system can also estimate the user's emotions and recommend digital content based on the estimated emotions. For example, if the user wants to relax, the system can suggest relaxing music. If the user is feeling sad, the system can recommend an inspiring movie. This allows the system to recommend digital content based on the user's emotions and support the user's emotional state.

[0088] The MyAI system can also analyze a user's device usage patterns and suggest the optimal timing for using applications. For example, it can recommend a learning app for the time when the user is most focused, or a relaxation app for the time when the user wants to relax. This allows the system to analyze a user's device usage patterns and suggest the optimal timing for using applications.

[0089] The MyAI system can also utilize the user's location information to provide a function that suggests the optimal route and transportation method in real time. For example, it can suggest the optimal route based on the user's current location and destination. It can also provide alternative routes to avoid traffic congestion. This allows the system to suggest the optimal route and transportation method in real time by utilizing the user's location information.

[0090] The MyAI system can also analyze a user's purchasing history and provide a function to recommend products based on consumption trends. For example, it can suggest new products from the same brand as previously purchased products. It can also recommend products suited to the season or events. This allows it to recommend products based on the user's consumption trends.

[0091] The MyAI system can also be equipped with a function to recommend related online communities and events based on a user's hobbies and interests. For example, a user who loves movies can be introduced to a movie fan community. A user who enjoys cooking can be provided with information about cooking classes. This allows the system to recommend related online communities and events based on the user's hobbies and interests.

[0092] The MyAI system can also integrate information about a user's family and friends and provide information based on their social relationships. For example, it can remind users of their family and friends' birthdays and anniversaries and suggest gifts and messages. It can also recommend events that users can attend with friends who share common hobbies. This allows the system to integrate information about a user's family and friends and provide information based on their social relationships.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The information collection unit collects common information on the web. For example, it collects information from news sites, social media, blogs, etc. The information collection unit can also collect information from the user's device. For example, it may collect smartphone application usage history and location information. Step 2: The information integration unit integrates the information collected by the information collection unit with original information for each individual user. For example, it incorporates personal information such as the user's schedule, preferences, and past behavioral history. The information integration unit can also integrate the user's health data and purchase history. For example, it incorporates health data such as heart rate, number of steps, and sleep data, and provides advice based on the user's health condition. Step 3: The answer generation unit generates an answer based on the information integrated by the information integration unit. For example, if a user asks, "What events do you recommend this weekend?", the answer generation unit will recommend the most suitable event, taking into account the user's past event participation history and preferences. Similarly, if a user asks, "What should I do to prepare for my next meeting?", the answer generation unit will suggest specific preparations based on the user's schedule and the content of the meeting.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] In the headset type terminal 314, 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 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 specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

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

[0129] 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.

[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 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 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 processing similar to that of the specific 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 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.

[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 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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. an information gathering unit that collects common web information; an information integration unit that integrates original information of each user in addition to the information collected by the information collection unit; an answer generation unit that generates an answer based on the information integrated by the information integration unit; A system characterized by:

2. The information integration unit Capture personal information about the user, including at least one of the user's schedule, preferences, and past behavior history.

2. The system of claim 1.

3. The answer generation unit Inferring user emotions and providing personalized information based on those emotions 2. The system of claim 1.

4. The information integration unit Integrates user health data and provides advice based on health status 2. The system of claim 1.

5. The information integration unit Analyzes user purchasing history and recommends products based on consumption trends 2. The system of claim 1.

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