System

Abstract

An object of a system according to an embodiment is to provide multifaceted advice on personal worries.SOLUTION: A system includes a AI selection unit, a trouble analysis unit, and an advice generation unit. The AI selection unit receives a trouble of a user. The trouble analysis section analyzes the trouble received by the AI selection section. The advice generation section generates advice based on a result of the analysis performed by the trouble analysis section.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] With conventional technology, it has been difficult to get appropriate advice when seeking advice on personal concerns.

[0005] The system according to the embodiment aims to provide multifaceted advice for personal concerns. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI selection unit, a problem analysis unit, and an advice generation unit. The AI ​​selection unit receives a user's problem. The problem analysis unit analyzes the problem received by the AI ​​selection unit. The advice generation unit generates advice based on the results of the analysis by the problem analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide multifaceted advice on personal concerns. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A consultation system according to an embodiment of the present invention is a system in which a user consults with an AI about personal concerns. This system uses multiple AIs with different personalities, ages, genders, nationalities, races, etc., to provide advice from various angles and perspectives. This enables the consultation system to analyze the user's concerns from multiple angles and provide appropriate advice.

[0029] A consultation system according to an embodiment includes an AI selection unit, a concern analysis unit, and an advice generation unit. The AI ​​selection unit accepts a user's concern. For example, the user can input the concern in text format. The AI ​​selection unit can also select an optimal AI based on the user's past consultation history. The concern analysis unit analyzes the concern accepted by the AI ​​selection unit. For example, the generation AI analyzes the content of the concern using a text generation AI (e.g., LLM). The generation AI can also perform emotion analysis to understand the user's emotional state. The advice generation unit generates advice based on the results of the analysis by the concern analysis unit. For example, the generation AI generates advice for the user's concern from multiple perspectives. The generation AI can also customize the advice based on user feedback. This allows the consultation system according to an embodiment to analyze a user's concern from multiple angles and provide appropriate advice. For example, the system analyzes a concern input by a user and provides advice from multiple AIs. Customizing the advice based on user feedback provides more appropriate support.

[0030] The AI ​​selection unit dynamically generates the personality of each AI based on the user's past consultation history, enabling the provision of more personalized advice. For example, the generation AI in the AI ​​selection unit analyzes the user's past consultation history and dynamically generates the personality of each AI based on that data. For example, the AI ​​selection unit generates an AI with an optimal personality based on the content of the user's past consultations and their responses. The AI ​​selection unit can also adjust the AI's personality based on user feedback. This allows the system to provide more appropriate advice based on the user's past consultation history.

[0031] The AI ​​selection unit can select AIs that specialize in different fields and provide advice from a specialized perspective. For example, the AI ​​selection unit allows each AI to specialize in a different field and provide specialized advice on the user's concerns. For example, an AI specializing in psychology can provide advice on mental health. Also, an AI specializing in law can provide legal advice. This makes it possible to provide advice from a specialized perspective.

[0032] The AI ​​selection unit can select AIs that speak different languages, making it possible to accommodate international users. For example, the AI ​​selection unit can set each AI to speak a different language, making it possible to accommodate international users. For example, an AI that can speak multiple languages, such as English, French, and Chinese, can be introduced. It can also provide advice in the language selected by the user. This makes it possible to accommodate international users.

[0033] The AI ​​selection unit can select AIs with different historical backgrounds and provide advice from a timeless perspective. For example, the AI ​​selection unit can set each AI to have a different historical background and provide the user with advice from a timeless perspective. For example, an AI modeled after a historical figure from the past can provide advice from a historical perspective. Also, an AI modeled after a fictional person from the future can provide advice from a future perspective. This makes it possible to provide advice from a timeless perspective.

[0034] The concern analysis unit can accept user input in the form of voice or images and perform multimodal analysis. For example, the concern analysis unit allows users to input their concerns not only as text but also as voice or images. For example, it can analyze voice input and convert it into text to understand the concerns. It can also analyze image input and understand the concerns based on visual information. This makes it possible to handle a variety of input formats, including voice and images.

[0035] The problem analysis unit can compare the problem entered by the user with similar problems of other users and provide advice based on past success stories. For example, the problem analysis unit can compare the problem entered by the user with a database and provide advice based on success stories of other users with similar problems. For example, it can present past cases in which similar problems were solved. It can also propose specific solutions based on success stories. This makes it possible to provide advice based on past success stories.

[0036] The worry analysis unit analyzes the user's worries in association with their lifestyle habits and behavioral patterns, and can provide more specific advice. The worry analysis unit, for example, analyzes the user's lifestyle habits and behavioral patterns, and builds a system that understands the content of the worries based on that data. For example, it analyzes the user's sleeping patterns and eating habits. It can also analyze the user's behavioral patterns and provide specific advice. This makes it possible to provide more specific advice based on the user's lifestyle habits and behavioral patterns.

[0037] The advice generation unit customizes advice based on the user's past reactions, making it possible to provide more effective advice. For example, the advice generation unit builds a system that customizes advice provided by each AI based on the user's past reaction data. For example, it provides advice that has been effective in the past with priority. It can also adjust advice based on user feedback. This makes it possible to provide more effective advice based on the user's past reactions.

[0038] The advice generation unit can adjust advice to suit the user's cultural background and values. The advice generation unit, for example, builds a system that adjusts the advice provided by each AI based on the user's cultural background and values. For example, it provides advice that is appropriate for the user's culture. It can also customize advice based on the user's values. This makes it possible to adjust advice to suit the user's cultural background and values.

[0039] The advice generation unit can provide advice provided by each AI in different media to suit the user's preferences. The advice generation unit builds a system that provides advice provided by each AI in different media, such as text, audio, or video. For example, the advice generation unit provides advice in a media format selected by the user. It can also adjust the media format based on user feedback. This makes it possible to provide advice in different media formats to suit the user's preferences.

[0040] The advice generation unit can customize advice to suit the user's living environment. For example, the advice generation unit builds a system that customizes the advice provided by each AI based on the user's living environment. For example, it can provide advice that is suitable for a home environment. It can also provide advice that is suitable for a work environment or a school environment. This makes it possible to customize advice to suit the user's living environment.

[0041] The advice generation unit can analyze user feedback in real time and instantly adjust the content of advice. The advice generation unit, for example, builds a system that analyzes user feedback in real time and instantly adjusts the content of advice based on the results. For example, advice that the user has rated as "helpful" can be strengthened. Also, advice that the user has rated as "not helpful" can be improved. This makes it possible to instantly adjust the content of advice based on user feedback.

[0042] The advice generation unit learns advice patterns based on the user's past consultation history and can provide more appropriate advice. The advice generation unit, for example, analyzes the user's past consultation history and builds a system that learns advice patterns based on that data. For example, advice that has been effective in the past can be given priority. The advice pattern can also be adjusted based on user feedback. This makes it possible to provide more appropriate advice based on the user's past consultation history.

[0043] The advice generation unit can compare the user's feedback with that of other users and find common areas for improvement. The advice generation unit, for example, stores the user's feedback in a database and builds a system for comparing it with the feedback of other users. For example, the advice generation unit can identify common areas for improvement and adjust the advice. It can also improve the quality of the advice based on the results of analyzing the feedback. In this way, the quality of the advice can be improved by finding common areas for improvement.

[0044] The advice generation unit can customize advice based on the user's lifestyle habits and behavioral patterns. The advice generation unit, for example, analyzes the user's lifestyle habits and behavioral patterns and builds a system that customizes advice based on that data. For example, the advice generation unit analyzes the user's sleeping patterns and eating habits. It can also analyze the user's behavioral patterns and provide specific advice. This makes it possible to provide more specific advice based on the user's lifestyle habits and behavioral patterns.

[0045] The system uses blockchain technology to manage user data and prevent data tampering or leakage. For example, the system uses blockchain technology to manage user data and build a system that prevents data tampering or leakage. For example, data consistency and transparency are ensured. Blockchain technology can also be used to improve data reliability. This makes it possible to manage user data using blockchain technology and prevent data tampering or leakage.

[0046] The system manages user data in a distributed storage system, thereby enhancing security. For example, the system manages user data in a distributed storage system, thereby enhancing security. For example, data is stored in a distributed manner across multiple servers. Furthermore, the use of a distributed storage system can also improve data availability. This allows user data to be managed in a distributed storage system, thereby enhancing security.

[0047] The system can further protect user data by not only anonymizing the data but also masking parts of the data. For example, the system can build a system that protects privacy by anonymizing user data and then masking parts of the data. For example, personally identifiable information can be masked. Also, data can be prevented from being re-identified by randomizing parts of the data. This makes it possible to further strengthen privacy protection by anonymizing user data and then masking parts of the data.

[0048] The system can reduce the risk of long-term data leakage by periodically deleting user data. The system, for example, builds a system that reduces the risk of long-term data leakage by periodically deleting user data. For example, data that has been there for a certain period of time is automatically deleted. It is also possible to provide an option for users to manually delete data. In this way, the system can reduce the risk of long-term data leakage by periodically deleting user data.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The consultation system may further include a sharing unit that allows users to anonymously share their concerns with other users and receive feedback from the community. For example, when a user inputs a concern, the sharing unit anonymizes the concern and makes it available to other users. The system may also collect advice and sympathetic comments from other users and provide them to the user. This allows users to obtain advice from other people's perspectives and experiences.

[0051] The consultation system may further include an action plan generation unit that provides a specific action plan to resolve the user's concerns. For example, if the user consults about work stress, the action plan generation unit may suggest specific steps for stress management. Also, if the user has concerns about interpersonal relationships, the action plan generation unit may provide practice methods for improving communication skills. This allows the user to resolve their concerns with a specific action plan.

[0052] The consultation system can also include a resource introduction section that introduces resources and experts to resolve the user's concerns. For example, if the user has legal concerns, the resource introduction section can introduce a reliable lawyer. Also, if the user has health concerns, the resource introduction section can introduce a specialized doctor or counselor. This allows the user to receive support from an appropriate expert.

[0053] The consultation system can also include a self-development module that provides self-development content to help users solve their problems. For example, if a user has a problem related to personal growth, the self-development module can recommend related books or online courses. It can also provide videos or podcasts to motivate users. This allows users to use self-development resources to solve their problems.

[0054] The consultation system may further include a group session unit that provides group sessions to help users solve their problems. For example, if a user feels lonely, the group session unit provides an opportunity to connect online with other users who share the same problem. Through group sessions, users can empathize with and support each other. This helps users feel less lonely and allows them to receive support from a community.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The AI ​​selection unit accepts the user's concerns. For example, the user can input their concerns in text format. The AI ​​selection unit can also select the most suitable AI based on the user's past consultation history. Step 2: The concern analysis unit analyzes the concerns received by the AI ​​selection unit. For example, the generation AI analyzes the content of the concerns using a text generation AI (e.g., LLM). The generation AI can also perform emotion analysis to understand the user's emotional state. Step 3: The advice generator generates advice based on the results of the analysis by the concern analyzer. For example, the generator AI generates advice from multiple perspectives on the user's concerns. The generator AI can also customize the advice based on user feedback.

[0057] (Example 2) A consultation system according to an embodiment of the present invention is a system in which a user consults with an AI about personal concerns. This system uses multiple AIs with different personalities, ages, genders, nationalities, races, etc., to provide advice from various angles and perspectives. This enables the consultation system to analyze the user's concerns from multiple angles and provide appropriate advice.

[0058] A consultation system according to an embodiment includes an AI selection unit, a concern analysis unit, and an advice generation unit. The AI ​​selection unit accepts a user's concern. For example, the user can input the concern in text format. The AI ​​selection unit can also select an optimal AI based on the user's past consultation history. The concern analysis unit analyzes the concern accepted by the AI ​​selection unit. For example, the generation AI analyzes the content of the concern using a text generation AI (e.g., LLM). The generation AI can also perform emotion analysis to understand the user's emotional state. The advice generation unit generates advice based on the results of the analysis by the concern analysis unit. For example, the generation AI generates advice for the user's concern from multiple perspectives. The generation AI can also customize the advice based on user feedback. This allows the consultation system according to an embodiment to analyze a user's concern from multiple angles and provide appropriate advice. For example, the system analyzes a concern input by a user and provides advice from multiple AIs. Customizing the advice based on user feedback provides more appropriate support.

[0059] The AI ​​selection unit dynamically generates the personality of each AI based on the user's past consultation history, enabling the provision of more personalized advice. For example, the generation AI in the AI ​​selection unit analyzes the user's past consultation history and dynamically generates the personality of each AI based on that data. For example, the AI ​​selection unit generates an AI with an optimal personality based on the content of the user's past consultations and their responses. The AI ​​selection unit can also adjust the AI's personality based on user feedback. This allows the system to provide more appropriate advice based on the user's past consultation history.

[0060] The AI ​​selection unit can select AIs that specialize in different fields and provide advice from a specialized perspective. For example, the AI ​​selection unit allows each AI to specialize in a different field and provide specialized advice on the user's concerns. For example, an AI specializing in psychology can provide advice on mental health. Also, an AI specializing in law can provide legal advice. This makes it possible to provide advice from a specialized perspective.

[0061] The AI ​​selection unit can use the emotion estimation function to select the most appropriate AI according to the user's emotional state and provide advice. For example, the AI ​​selection unit can use the emotion estimation function to analyze the user's emotional state in real time and select the most appropriate AI based on the results. For example, if the user is sad, it can select an AI with a gentle tone. Alternatively, if the user is angry, it can select an AI with a calm tone. This makes it possible to provide the most appropriate advice according to the user's emotional state.

[0062] The AI ​​selection unit can select AIs that speak different languages, making it possible to accommodate international users. For example, the AI ​​selection unit can set each AI to speak a different language, making it possible to accommodate international users. For example, an AI that can speak multiple languages, such as English, French, and Chinese, can be introduced. It can also provide advice in the language selected by the user. This makes it possible to accommodate international users.

[0063] The AI ​​selection unit can select AIs with different historical backgrounds and provide advice from a timeless perspective. For example, the AI ​​selection unit can set each AI to have a different historical background and provide the user with advice from a timeless perspective. For example, an AI modeled after a historical figure from the past can provide advice from a historical perspective. Also, an AI modeled after a fictional person from the future can provide advice from a future perspective. This makes it possible to provide advice from a timeless perspective.

[0064] The AI ​​selection unit can use the emotion estimation function to select the AI ​​that the user can most easily empathize with and provide advice. For example, the AI ​​selection unit uses the emotion estimation function to analyze the user's emotional state in real time and select the AI ​​that the user can most easily empathize with based on the results. For example, if the user is sad, it can select an AI that speaks in a gentle tone. On the other hand, if the user is angry, it can select an AI that speaks in a calm tone. This allows the AI ​​selection unit to provide advice that the user can most easily empathize with.

[0065] The concern analysis unit can accept user input in the form of voice or images and perform multimodal analysis. For example, the concern analysis unit allows users to input their concerns not only as text but also as voice or images. For example, it can analyze voice input and convert it into text to understand the concerns. It can also analyze image input and understand the concerns based on visual information. This makes it possible to handle a variety of input formats, including voice and images.

[0066] The worry analysis unit can also analyze the user's facial expression or tone of voice to gain a deeper understanding. The worry analysis unit, for example, analyzes the user's facial expression to build a system that understands the user's emotional state. For example, a camera can be used to analyze the user's facial expression in real time to calculate an emotion score. The user's tone of voice can also be analyzed to understand the user's emotional state. For example, a voice waveform can be analyzed to calculate an emotion score. In this way, a deeper understanding can be gained by analyzing the user's facial expression and tone of voice.

[0067] The worry analysis unit can use the emotion estimation function to analyze the user's emotional state in real time and provide appropriate advice. The worry analysis unit can, for example, use the emotion estimation function to analyze the user's emotional state in real time and provide appropriate advice based on the results. For example, if the user is sad, it can provide comforting words. Also, if the user is angry, it can provide calm advice. In this way, it is possible to analyze the user's emotional state in real time and provide appropriate advice.

[0068] The problem analysis unit can compare the problem entered by the user with similar problems of other users and provide advice based on past success stories. For example, the problem analysis unit can compare the problem entered by the user with a database and provide advice based on success stories of other users with similar problems. For example, it can present past cases in which similar problems were solved. It can also propose specific solutions based on success stories. This makes it possible to provide advice based on past success stories.

[0069] The worry analysis unit analyzes the user's worries in association with their lifestyle habits and behavioral patterns, and can provide more specific advice. The worry analysis unit, for example, analyzes the user's lifestyle habits and behavioral patterns, and builds a system that understands the content of the worries based on that data. For example, it analyzes the user's sleeping patterns and eating habits. It can also analyze the user's behavioral patterns and provide specific advice. This makes it possible to provide more specific advice based on the user's lifestyle habits and behavioral patterns.

[0070] The concern analysis unit can use the emotion estimation function to track changes in the user's emotions and provide long-term support. For example, the concern analysis unit uses the emotion estimation function to track changes in the user's emotions in real time and build a system that provides long-term support based on that data. For example, the concern analysis unit can perform periodic emotion checks to monitor the user's emotional state. It can also provide appropriate advice in response to changes in emotions. This makes it possible to track changes in the user's emotions and provide long-term support.

[0071] The advice generation unit customizes advice based on the user's past reactions, making it possible to provide more effective advice. For example, the advice generation unit builds a system that customizes advice provided by each AI based on the user's past reaction data. For example, it provides advice that has been effective in the past with priority. It can also adjust advice based on user feedback. This makes it possible to provide more effective advice based on the user's past reactions.

[0072] The advice generation unit can adjust advice to suit the user's cultural background and values. The advice generation unit, for example, builds a system that adjusts the advice provided by each AI based on the user's cultural background and values. For example, it provides advice that is appropriate for the user's culture. It can also customize advice based on the user's values. This makes it possible to adjust advice to suit the user's cultural background and values.

[0073] The advice generation unit can provide advice provided by each AI in different media to suit the user's preferences. The advice generation unit builds a system that provides advice provided by each AI in different media, such as text, audio, or video. For example, the advice generation unit provides advice in a media format selected by the user. It can also adjust the media format based on user feedback. This makes it possible to provide advice in different media formats to suit the user's preferences.

[0074] The advice generation unit can customize advice to suit the user's living environment. For example, the advice generation unit builds a system that customizes the advice provided by each AI based on the user's living environment. For example, it can provide advice that is suitable for a home environment. It can also provide advice that is suitable for a work environment or a school environment. This makes it possible to customize advice to suit the user's living environment.

[0075] The advice generation unit can use the emotion estimation function to provide advice in a format that is most acceptable to the user. For example, the advice generation unit uses the emotion estimation function to analyze the user's emotional state in real time, and builds a system that provides advice in a format that is most acceptable to the user based on the results. For example, the optimal format is selected based on the user's emotion score. The advice format can also be adjusted based on user feedback. This makes it possible to provide advice in a format that is most acceptable to the user.

[0076] The advice generation unit can analyze user feedback in real time and instantly adjust the content of advice. The advice generation unit, for example, builds a system that analyzes user feedback in real time and instantly adjusts the content of advice based on the results. For example, advice that the user has rated as "helpful" can be strengthened. Also, advice that the user has rated as "not helpful" can be improved. This makes it possible to instantly adjust the content of advice based on user feedback.

[0077] The advice generation unit learns advice patterns based on the user's past consultation history and can provide more appropriate advice. The advice generation unit, for example, analyzes the user's past consultation history and builds a system that learns advice patterns based on that data. For example, advice that has been effective in the past can be given priority. The advice pattern can also be adjusted based on user feedback. This makes it possible to provide more appropriate advice based on the user's past consultation history.

[0078] The advice generation unit can use the emotion estimation function to collect feedback according to the user's emotional state and customize advice. The advice generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and builds a system that collects feedback based on the results. For example, if the user is sad, it can provide comforting words. Also, if the user is angry, it can provide calm advice. In this way, it is possible to collect feedback according to the user's emotional state and customize advice.

[0079] The advice generation unit can compare the user's feedback with that of other users and find common areas for improvement. The advice generation unit, for example, stores the user's feedback in a database and builds a system for comparing it with the feedback of other users. For example, the advice generation unit can identify common areas for improvement and adjust the advice. It can also improve the quality of the advice based on the results of analyzing the feedback. In this way, the quality of the advice can be improved by finding common areas for improvement.

[0080] The advice generation unit can customize advice based on the user's lifestyle habits and behavioral patterns. The advice generation unit, for example, analyzes the user's lifestyle habits and behavioral patterns and builds a system that customizes advice based on that data. For example, the advice generation unit analyzes the user's sleeping patterns and eating habits. It can also analyze the user's behavioral patterns and provide specific advice. This makes it possible to provide more specific advice based on the user's lifestyle habits and behavioral patterns.

[0081] The advice generation unit can use the emotion estimation function to track changes in the user's emotions and provide long-term support. The advice generation unit, for example, uses the emotion estimation function to track changes in the user's emotions in real time and builds a system that provides long-term support based on that data. For example, the advice generation unit can perform periodic emotion checks to monitor the user's emotional state. It can also provide appropriate advice in response to changes in emotions. This makes it possible to track changes in the user's emotions and provide long-term support.

[0082] The system uses blockchain technology to manage user data and prevent data tampering or leakage. For example, the system uses blockchain technology to manage user data and build a system that prevents data tampering or leakage. For example, data consistency and transparency are ensured. Blockchain technology can also be used to improve data reliability. This makes it possible to manage user data using blockchain technology and prevent data tampering or leakage.

[0083] The system manages user data in a distributed storage system, thereby enhancing security. For example, the system manages user data in a distributed storage system, thereby enhancing security. For example, data is stored in a distributed manner across multiple servers. Furthermore, the use of a distributed storage system can also improve data availability. This allows user data to be managed in a distributed storage system, thereby enhancing security.

[0084] The system can adjust the level of privacy protection according to the emotional state of the user using the emotion estimation function. For example, the system uses the emotion estimation function to analyze the emotional state of the user in real time and build a system that adjusts the level of privacy protection based on the results. For example, if the user feels anxious, the system can strengthen privacy protection. On the other hand, if the user feels relieved, the system can relax the level of privacy protection. In this way, the system can adjust the level of privacy protection according to the emotional state of the user.

[0085] The system can further protect user data by not only anonymizing the data but also masking parts of the data. For example, the system can build a system that protects privacy by anonymizing user data and then masking parts of the data. For example, personally identifiable information can be masked. Also, data can be prevented from being re-identified by randomizing parts of the data. This makes it possible to further strengthen privacy protection by anonymizing user data and then masking parts of the data.

[0086] The system can reduce the risk of long-term data leakage by periodically deleting user data. The system, for example, builds a system that reduces the risk of long-term data leakage by periodically deleting user data. For example, data that has been there for a certain period of time is automatically deleted. It is also possible to provide an option for users to manually delete data. In this way, the system can reduce the risk of long-term data leakage by periodically deleting user data.

[0087] The system can use the emotion estimation function to select the privacy protection method that gives the user the most peace of mind. For example, the system uses the emotion estimation function to analyze the user's emotional state in real time, and builds a system that selects the privacy protection method that gives the user the most peace of mind based on the results. For example, if the user feels anxious, the system can strengthen privacy protection. Also, if the user feels comfortable, the level of privacy protection can be relaxed. In this way, the system can select the privacy protection method that gives the user the most peace of mind.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The consultation system may further include a sharing unit that allows users to anonymously share their concerns with other users and receive feedback from the community. For example, when a user inputs a concern, the sharing unit anonymizes the concern and makes it available to other users. The system may also collect advice and sympathetic comments from other users and provide them to the user. This allows users to obtain advice from other people's perspectives and experiences.

[0090] The consultation system may further include an action plan generation unit that provides a specific action plan to resolve the user's concerns. For example, if the user consults about work stress, the action plan generation unit may suggest specific steps for stress management. Also, if the user has concerns about interpersonal relationships, the action plan generation unit may provide practice methods for improving communication skills. This allows the user to resolve their concerns with a specific action plan.

[0091] The consultation system can also include a resource introduction section that introduces resources and experts to resolve the user's concerns. For example, if the user has legal concerns, the resource introduction section can introduce a reliable lawyer. Also, if the user has health concerns, the resource introduction section can introduce a specialized doctor or counselor. This allows the user to receive support from an appropriate expert.

[0092] The consultation system can also include a self-development module that provides self-development content to help users solve their problems. For example, if a user has a problem related to personal growth, the self-development module can recommend related books or online courses. It can also provide videos or podcasts to motivate users. This allows users to use self-development resources to solve their problems.

[0093] The consultation system may further include a group session unit that provides group sessions to help users solve their problems. For example, if a user feels lonely, the group session unit provides an opportunity to connect online with other users who share the same problem. Through group sessions, users can empathize with and support each other. This helps users feel less lonely and allows them to receive support from a community.

[0094] The consultation system may further include an emotion monitoring unit that monitors the user's emotional state in real time and adjusts advice according to changes in emotion. For example, if the user shows a change in emotion during the consultation, the emotion monitoring unit detects that change and adjusts the content and tone of the advice. In addition, if the user is in a specific emotional state, the system may provide advice appropriate to that state. This makes it possible to provide flexible support according to the user's emotional state.

[0095] The consultation system can also include a relaxation provider that analyzes the user's emotional state and provides relaxation techniques based on the results. For example, if the user is feeling stressed, the relaxation provider can provide deep breathing or meditation guidance. If the user is feeling anxious, the system can also suggest relaxation music or aromatherapy. This allows the user to utilize relaxation techniques appropriate to their emotional state to achieve peace of mind.

[0096] The consultation system may further include a positive feedback unit that analyzes the user's emotional state and provides positive feedback based on the results. For example, if the user has low self-esteem, the positive feedback unit may highlight the user's strengths and successes. Also, if the user is feeling down, the positive feedback unit may provide encouraging words and positive messages. This allows the user to improve their self-esteem and feel more positive.

[0097] The consultation system may further include an exercise suggestion unit that analyzes the user's emotional state and suggests appropriate exercises based on the results. For example, if the user is feeling stressed, the exercise suggestion unit may suggest yoga or stretching, which are effective for relieving stress. Also, if the user feels low in energy, the exercise suggestion unit may suggest light aerobic exercise or walking. This allows the user to maintain their physical and mental health through exercises that correspond to their emotional state.

[0098] The consultation system may further include a dietary advice unit that analyzes the user's emotional state and provides appropriate dietary advice based on the results. For example, if the user is feeling stressed, the dietary advice unit may suggest ingredients and recipes that are effective in reducing stress. Also, if the user feels that they are lacking in energy, the system may provide a nutritionally balanced meal plan. This allows the user to receive dietary advice tailored to their emotional state.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The AI ​​selection unit accepts the user's concerns. For example, the user can input their concerns in text format. The AI ​​selection unit can also select the most suitable AI based on the user's past consultation history. Step 2: The concern analysis unit analyzes the concerns received by the AI ​​selection unit. For example, the generation AI analyzes the content of the concerns using a text generation AI (e.g., LLM). The generation AI can also perform emotion analysis to understand the user's emotional state. Step 3: The advice generator generates advice based on the results of the analysis by the concern analyzer. For example, the generator AI generates advice from multiple perspectives on the user's concerns. The generator AI can also customize the advice based on user feedback.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 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 AI selection department that accepts user concerns, a worry analysis unit that analyzes the worries accepted by the AI ​​selection unit; an advice generation unit that generates advice based on the results of the analysis by the problem analysis unit; A system characterized by:

2. The AI ​​selection unit Select the most appropriate AI based on the user's emotional state and provide advice 2. The system of claim 1.

3. The concern analysis unit Accepts user input via voice or image and performs multimodal analysis 2. The system of claim 1.

4. The advice generation unit Customize advice based on the user's past responses to provide more effective advice 2. The system of claim 1.

5. The system comprises: Adjusting the level of privacy protection according to the emotional state of the user 2. The system of claim 1.

6. The concern analysis unit The user's facial expression or tone of voice may also be analyzed to gain a deeper understanding.

2. The system of claim 1.

7. The advice generation unit Providing advice according to the user's emotional state and enhancing emotional support 2. The system of claim 1.

8. The system comprises: The user selects the privacy protection method that gives them the most peace of mind.

2. The system of claim 1.

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