System

Abstract

An object of a system according to an embodiment is to propose an appropriate settlement message among people involved in a relationship due to a fight or misunderstanding.SOLUTION: A system according to an embodiment includes a role and emotion analysis unit and a settlement message suggestion unit. The role and emotion analysis unit analyzes both roles and emotions based on the information provided by the user. A settlement message proposal part proposes a message of settlement by proper word selection and timing on the basis of a result analyzed by the analysis part of the position and the feeling.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 techniques have had the problem of making it difficult to find an appropriate message of reconciliation between people whose relationships have become strained due to quarrels or misunderstandings.

[0005] The system according to the embodiment aims to propose appropriate reconciliation messages between people whose relationships have become strained due to quarrels or misunderstandings. [Means for solving the problem]

[0006] The system according to the embodiment includes a position and emotion analysis unit and a reconciliation message proposal unit. The position and emotion analysis unit analyzes the positions and emotions of both parties based on information provided by the user. The reconciliation message proposal unit proposes a reconciliation message with appropriate wording and timing based on the results of the analysis by the position and emotion analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest appropriate reconciliation messages between people whose relationships have been strained due to quarrels or misunderstandings. [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 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) The PeaceMaker AI system according to an embodiment of the present invention is a system that brings peace between people whose relationships have become strained due to quarrels or misunderstandings. This system analyzes the positions and feelings of both parties in detail and suggests a reconciliation message with appropriate words and timing. In this way, the PeaceMaker AI system can bring peace between people whose relationships have become strained.

[0029] The PeaceMaker AI system according to the embodiment includes a position and emotion analysis unit and a reconciliation message suggestion unit. The position and emotion analysis unit analyzes the positions and emotions of both parties based on information provided by the user. For example, when a user inputs the cause of a fight and the other party's reaction, the generation AI analyzes the information and understands the emotions and positions of both parties. The generation AI performs emotion analysis and relationship evaluation using a text generation AI (e.g., LLM) or a multimodal generation AI. The reconciliation message suggestion unit suggests a reconciliation message with appropriate wording and timing based on the results of the analysis by the position and emotion analysis unit. For example, it suggests a message such as, "I understand how you feel. I felt the same way." The generation AI does this by inputting prompts to generate the most effective reconciliation message based on the user's input information. This allows the PeaceMaker AI system to help repair relationships.

[0030] The position and emotion analysis unit analyzes a user's past communication history and identifies specific patterns and triggers, enabling more accurate emotion analysis. For example, the position and emotion analysis unit analyzes a user's past message history and identifies triggers that cause specific words or phrases to change emotions. For example, if a user tends to become emotional when a specific word is used, the system can advise the user to avoid that word. In this way, analyzing past communication history enables more accurate emotion analysis.

[0031] The position and emotion analysis unit can analyze the user's tone of voice and facial expressions in real time and instantly reflect changes in emotion. The position and emotion analysis unit can, for example, analyze the user's tone of voice in real time and build a system that instantly reflects changes in emotion. For example, if emotions tend to rise when the tone of voice becomes higher, advice can be given to avoid that tone. In this way, by analyzing the user's tone of voice and facial expressions in real time, changes in emotion can be instantly reflected.

[0032] The position and emotion analysis unit can perform multimodal emotion analysis by including not only the text entered by the user but also image and audio data as the analysis target. For example, the position and emotion analysis unit can build a system that includes image and audio data as the analysis target in addition to the text entered by the user. For example, image analysis technology can be used to analyze facial expressions and detect changes in emotions. This makes multimodal emotion analysis possible by including text, image, and audio data as the analysis target.

[0033] The position and emotion analysis unit can perform emotion analysis that takes into account cultural backgrounds and language-specific nuances in order to accommodate users from different cultures and linguistic regions. The position and emotion analysis unit builds a system that performs emotion analysis that takes into account cultural backgrounds and language-specific nuances in order to accommodate users from different cultures and linguistic regions, for example. For example, it analyzes words that have positive meanings in a particular culture. This makes it possible to perform emotion analysis that takes into account cultural backgrounds and language-specific nuances in order to accommodate users from different cultures and linguistic regions.

[0034] The reconciliation message proposal unit can create a database of past successful cases and propose an optimal reconciliation message based on similar cases. The reconciliation message proposal unit, for example, creates a database of past successful cases and builds a system that proposes an optimal reconciliation message based on similar cases. For example, a new message is generated based on a reconciliation message that was successful in the past. In this way, the success rate of reconciliation is increased by proposing an optimal reconciliation message based on past successful cases.

[0035] The reconciliation message suggestion unit can customize the tone and content of the message to suit the individual communication style and preferences of the user. The reconciliation message suggestion unit, for example, builds a system that customizes the tone and content of the message to suit the individual communication style and preferences of the user. For example, the tone of the message is adjusted according to the user's preferences. In this way, the effectiveness of reconciliation is enhanced by customizing the message to suit the individual communication style and preferences of the user.

[0036] The settlement message proposal unit can automatically translate the settlement message into multiple languages, thereby supporting international users. The settlement message proposal unit, for example, builds a system that automatically translates the settlement message into multiple languages, thereby supporting international users. For example, it translates into English, French, Chinese, etc. In this way, by automatically translating the settlement message into multiple languages, it can support international users.

[0037] The reconciliation message proposal unit can also propose the reconciliation message as a visual or audio message, adopting an approach that appeals to the visual or audio senses. The reconciliation message proposal unit, for example, builds a system that proposes the reconciliation message as a visual or audio message, adopting an approach that appeals to the visual or audio senses. For example, it generates a message using images or audio. This makes it possible to propose the reconciliation message as a visual or audio message, thereby enabling an approach that appeals to the visual or audio senses.

[0038] The communication strategy customization unit can analyze the user's past behavioral data and identify the most effective communication strategy. The communication strategy customization unit, for example, analyzes the user's past behavioral data and builds a system that identifies the most effective communication strategy. For example, it proposes an optimal strategy based on past success stories. In this way, the most effective communication strategy can be identified by analyzing the user's past behavioral data.

[0039] The communication strategy customization unit can propose a customized approach that takes into account the user's personality traits and psychological state. The communication strategy customization unit, for example, constructs a system that proposes a customized approach that takes into account the user's personality traits and psychological state. For example, a gentle approach is proposed to an introverted user. This enables more effective communication by proposing a customized approach that takes into account the user's personality traits and psychological state.

[0040] The communication strategy customization unit can provide customized communication strategies for different industries and uses. The communication strategy customization unit builds a system that provides customized communication strategies for different industries and uses. For example, it proposes different approaches for business and private situations. This enables more effective communication by providing customized communication strategies for different industries and uses.

[0041] The communication strategy customization unit can visualize the communication strategy to enable the user to intuitively understand it. The communication strategy customization unit, for example, builds a system that visualizes the communication strategy to enable the user to intuitively understand it. For example, the strategy is displayed using a flowchart or a mind map. In this way, by visualizing the communication strategy, the user can intuitively understand it.

[0042] The user advice-based action unit can analyze the user's behavior history and develop an algorithm for providing the most effective advice. The user advice-based action unit, for example, analyzes the user's behavior history and develops an algorithm for providing the most effective advice. For example, optimal advice is generated based on past success stories. In this way, by analyzing the user's behavior history and developing an algorithm for providing the most effective advice, more effective advice becomes possible.

[0043] The user advice-based action unit can collect feedback on the user's actions and improve the accuracy of advice. The user advice-based action unit, for example, collects feedback on the user's actions and builds a system that improves the accuracy of advice based on the results. For example, the advice is adjusted based on the user feedback. In this way, by collecting feedback on the user's actions and improving the accuracy of advice, more effective advice becomes possible.

[0044] The action unit based on the user's advice can visualize the user's action, allowing the user to intuitively grasp the progress. The action unit based on the user's advice, for example, builds a system that visualizes the user's action, allowing the user to intuitively grasp the progress. For example, the progress of the action is displayed using a graph or chart. This allows the user's action to be visualized, allowing the user to intuitively grasp the progress, thereby enabling more effective action.

[0045] The user advice-based action unit can provide action guidelines according to different scenarios, allowing the user to adapt to a variety of situations. The user advice-based action unit, for example, provides action guidelines according to different scenarios, building a system that allows the user to adapt to a variety of situations. For example, different action guidelines are proposed for the workplace and the home. This allows the user to adapt to a variety of situations by providing action guidelines according to different scenarios.

[0046] The continuous relationship improvement support unit can develop an algorithm that periodically monitors the user's relationship status and provides advice as needed. The continuous relationship improvement support unit, for example, develops an algorithm that periodically monitors the user's relationship status and provides advice as needed. For example, advice is provided before the relationship deteriorates. In this way, by developing an algorithm that periodically monitors the user's relationship status and provides advice as needed, more effective relationship improvement is possible.

[0047] The continuous relationship improvement support unit can create a database of the user's progress in improving relationships and identify patterns for long-term relationship improvement. The continuous relationship improvement support unit, for example, creates a system that creates a database of the user's progress in improving relationships and identifies patterns for long-term relationship improvement. For example, it extracts patterns of successful relationship improvement. This enables more effective relationship improvement by creating a database of the user's progress in improving relationships and identifying patterns for long-term relationship improvement.

[0048] The support unit for continuous relationship improvement can visualize the continuous support, allowing the user to intuitively grasp the progress. The support unit for continuous relationship improvement, for example, builds a system that visualizes the continuous support, allowing the user to intuitively grasp the progress. For example, the progress of support is displayed using graphs and charts. This enables more effective relationship improvement by visualizing the continuous support and allowing the user to intuitively grasp the progress.

[0049] The continuous relationship improvement support unit can provide customized support according to different relationships (friends, family, workplace, etc.). The continuous relationship improvement support unit, for example, builds a system that provides customized support according to different relationships. For example, different support is proposed for friendships and family relationships. This allows for more effective relationship improvement by providing customized support according to different relationships.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The PeaceMaker AI system can also be equipped with a health management module that monitors the user's health status. For example, it can analyze the user's stress level and sleep patterns and provide advice based on the user's health status. This allows the system to propose more appropriate reconciliation messages taking into account the user's health status. The health management module can also monitor the user's heart rate and blood pressure and suggest ways to relax before stress builds up. It can also analyze the user's diet and exercise habits and provide advice to promote a healthy lifestyle.

[0052] The PeaceMaker AI system can also be equipped with a hobby analysis module that analyzes users' hobbies and interests. For example, it can collect information about a user's favorite music, movies, sports, and other interests, and suggest messages that promote reconciliation with people who share the same hobbies. This allows for closer relationships to be built through shared hobbies and interests. The hobby analysis module can also collect information about hobbies from a user's social media account and reflect this information in reconciliation messages. It can also elicit positive emotions by helping users discover new hobbies.

[0053] The PeaceMaker AI system can also include a training component to improve users' communication skills. For example, it can provide online courses and workshops for users to learn effective communication techniques. This allows users to acquire better communication skills and repair relationships more effectively. The training component can analyze users' communication styles and provide individually customized training programs. It can also provide a simulation function that allows users to practice in real-life communication scenarios.

[0054] The PeaceMaker AI system can also include a learning analysis module that analyzes a user's learning style. For example, it can analyze how a user most effectively learns information and suggest appropriate learning methods. This can help users more effectively acquire new skills and knowledge, and help repair relationships. The learning analysis module can also analyze a user's past learning history and performance to provide an individually customized learning plan. It can also suggest learning resources related to the user's areas of interest.

[0055] The PeaceMaker AI system can also be equipped with a time management component to support users in managing their time. For example, it can suggest a schedule that allows users to use their time efficiently. This reduces stress and allows users to focus on the reconciliation process. The time management component can analyze the user's schedule and tasks and suggest an optimal schedule. It can also set reminders to ensure users do not forget important tasks. It can also provide time management techniques to improve users' productivity.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The position and emotion analysis unit analyzes the positions and emotions of both parties based on information provided by the user. For example, when a user inputs the cause of a fight and the other party's reaction, the generation AI analyzes that information and understands the emotions and positions of both parties. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to analyze emotions and evaluate relationships. Step 2: The reconciliation message suggestion unit proposes a reconciliation message with appropriate wording and timing based on the results of the analysis by the position and emotion analysis unit. For example, it suggests a message such as, "I understand how you feel. I felt the same way." The generation AI inputs prompts to generate the most effective reconciliation message based on the user's input information.

[0058] (Example 2) The PeaceMaker AI system according to an embodiment of the present invention is a system that brings peace between people whose relationships have become strained due to quarrels or misunderstandings. This system analyzes the positions and feelings of both parties in detail and suggests a reconciliation message with appropriate words and timing. In this way, the PeaceMaker AI system can bring peace between people whose relationships have become strained.

[0059] The PeaceMaker AI system according to the embodiment includes a position and emotion analysis unit and a reconciliation message suggestion unit. The position and emotion analysis unit analyzes the positions and emotions of both parties based on information provided by the user. For example, when a user inputs the cause of a fight and the other party's reaction, the generation AI analyzes the information and understands the emotions and positions of both parties. The generation AI performs emotion analysis and relationship evaluation using a text generation AI (e.g., LLM) or a multimodal generation AI. The reconciliation message suggestion unit suggests a reconciliation message with appropriate wording and timing based on the results of the analysis by the position and emotion analysis unit. For example, it suggests a message such as, "I understand how you feel. I felt the same way." The generation AI does this by inputting prompts to generate the most effective reconciliation message based on the user's input information. This allows the PeaceMaker AI system to help repair relationships.

[0060] The position and emotion analysis unit analyzes a user's past communication history and identifies specific patterns and triggers, enabling more accurate emotion analysis. For example, the position and emotion analysis unit analyzes a user's past message history and identifies triggers that cause specific words or phrases to change emotions. For example, if a user tends to become emotional when a specific word is used, the system can advise the user to avoid that word. In this way, analyzing past communication history enables more accurate emotion analysis.

[0061] The position and emotion analysis unit can analyze the user's tone of voice and facial expressions in real time and instantly reflect changes in emotion. The position and emotion analysis unit can, for example, analyze the user's tone of voice in real time and build a system that instantly reflects changes in emotion. For example, if emotions tend to rise when the tone of voice becomes higher, advice can be given to avoid that tone. In this way, by analyzing the user's tone of voice and facial expressions in real time, changes in emotion can be instantly reflected.

[0062] The position and emotion analysis unit uses the emotion estimation function to analyze in detail the intensity and type of a user's emotion, and can provide real-time feedback according to changes in emotion. The position and emotion analysis unit, for example, uses the emotion estimation function to build a system that analyzes in detail the intensity and type of a user's emotion. For example, feedback is provided in real time when the intensity of an emotion is high. In this way, by using the emotion estimation function, the intensity and type of a user's emotion can be analyzed in detail, and real-time feedback can be provided.

[0063] The position and emotion analysis unit can perform multimodal emotion analysis by including not only the text entered by the user but also image and audio data as the analysis target. For example, the position and emotion analysis unit can build a system that includes image and audio data as the analysis target in addition to the text entered by the user. For example, image analysis technology can be used to analyze facial expressions and detect changes in emotions. This makes multimodal emotion analysis possible by including text, image, and audio data as the analysis target.

[0064] The position and emotion analysis unit can perform emotion analysis that takes into account cultural backgrounds and language-specific nuances in order to accommodate users from different cultures and linguistic regions. The position and emotion analysis unit builds a system that performs emotion analysis that takes into account cultural backgrounds and language-specific nuances in order to accommodate users from different cultures and linguistic regions, for example. For example, it analyzes words that have positive meanings in a particular culture. This makes it possible to perform emotion analysis that takes into account cultural backgrounds and language-specific nuances in order to accommodate users from different cultures and linguistic regions.

[0065] The position and emotion analysis unit can use the emotion estimation function to estimate the emotion a user is feeling when entering text in real time and make suggestions to elicit positive emotions. The position and emotion analysis unit, for example, uses the emotion estimation function to build a system that estimates the emotion a user is feeling when entering text in real time. For example, it makes positive suggestions when negative emotions are detected. This allows the system to estimate the emotion a user is feeling when entering text in real time and make suggestions to elicit positive emotions, thereby promoting better communication.

[0066] The reconciliation message proposal unit can create a database of past successful cases and propose an optimal reconciliation message based on similar cases. The reconciliation message proposal unit, for example, creates a database of past successful cases and builds a system that proposes an optimal reconciliation message based on similar cases. For example, a new message is generated based on a reconciliation message that was successful in the past. In this way, the success rate of reconciliation is increased by proposing an optimal reconciliation message based on past successful cases.

[0067] The reconciliation message suggestion unit can customize the tone and content of the message to suit the individual communication style and preferences of the user. The reconciliation message suggestion unit, for example, builds a system that customizes the tone and content of the message to suit the individual communication style and preferences of the user. For example, the tone of the message is adjusted according to the user's preferences. In this way, the effectiveness of reconciliation is enhanced by customizing the message to suit the individual communication style and preferences of the user.

[0068] The reconciliation message proposing unit can use the emotion estimation function to propose a reconciliation message at optimal timing according to the user's emotional state. The reconciliation message proposing unit, for example, uses the emotion estimation function to construct a system that proposes a reconciliation message at optimal timing according to the user's emotional state. For example, the message is sent when the user's emotions have calmed down. This increases the success rate of reconciliation by proposing a reconciliation message at optimal timing according to the user's emotional state.

[0069] The settlement message proposal unit can automatically translate the settlement message into multiple languages, thereby supporting international users. The settlement message proposal unit, for example, builds a system that automatically translates the settlement message into multiple languages, thereby supporting international users. For example, it translates into English, French, Chinese, etc. In this way, by automatically translating the settlement message into multiple languages, it can support international users.

[0070] The reconciliation message proposal unit can also propose the reconciliation message as a visual or audio message, adopting an approach that appeals to the visual or audio senses. The reconciliation message proposal unit, for example, builds a system that proposes the reconciliation message as a visual or audio message, adopting an approach that appeals to the visual or audio senses. For example, it generates a message using images or audio. This makes it possible to propose the reconciliation message as a visual or audio message, thereby enabling an approach that appeals to the visual or audio senses.

[0071] The reconciliation message proposing unit can use the emotion estimation function to predict the other party's emotional reaction to the reconciliation message and select an optimal message. The reconciliation message proposing unit, for example, uses the emotion estimation function to build a system that predicts the other party's emotional reaction to the reconciliation message. For example, the unit selects an optimal message based on the other party's emotional reaction. In this way, by predicting the other party's emotional reaction to the reconciliation message and selecting an optimal message, the success rate of reconciliation is increased.

[0072] The communication strategy customization unit can analyze the user's past behavioral data and identify the most effective communication strategy. The communication strategy customization unit, for example, analyzes the user's past behavioral data and builds a system that identifies the most effective communication strategy. For example, it proposes an optimal strategy based on past success stories. In this way, the most effective communication strategy can be identified by analyzing the user's past behavioral data.

[0073] The communication strategy customization unit can propose a customized approach that takes into account the user's personality traits and psychological state. The communication strategy customization unit, for example, constructs a system that proposes a customized approach that takes into account the user's personality traits and psychological state. For example, a gentle approach is proposed to an introverted user. This enables more effective communication by proposing a customized approach that takes into account the user's personality traits and psychological state.

[0074] The communication strategy customization unit can use the emotion estimation function to propose an optimal communication strategy in real time according to the user's emotional state. The communication strategy customization unit, for example, uses the emotion estimation function to build a system that proposes an optimal communication strategy in real time according to the user's emotional state. For example, it suggests a way to calm down before emotions get too high. This allows for more effective communication by proposing an optimal communication strategy in real time according to the user's emotional state.

[0075] The communication strategy customization unit can provide customized communication strategies for different industries and uses. The communication strategy customization unit builds a system that provides customized communication strategies for different industries and uses. For example, it proposes different approaches for business and private situations. This enables more effective communication by providing customized communication strategies for different industries and uses.

[0076] The communication strategy customization unit can visualize the communication strategy to enable the user to intuitively understand it. The communication strategy customization unit, for example, builds a system that visualizes the communication strategy to enable the user to intuitively understand it. For example, the strategy is displayed using a flowchart or a mind map. In this way, by visualizing the communication strategy, the user can intuitively understand it.

[0077] The communication strategy customization unit can use the emotion estimation function to monitor the user's emotional reactions in real time and continuously adjust the optimal strategy. The communication strategy customization unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional reactions in real time and continuously adjusts the optimal strategy. For example, the strategy is adjusted every time the emotion changes. In this way, more effective communication is possible by monitoring the user's emotional reactions in real time and continuously adjusting the optimal strategy.

[0078] The user advice-based action unit can analyze the user's behavior history and develop an algorithm for providing the most effective advice. The user advice-based action unit, for example, analyzes the user's behavior history and develops an algorithm for providing the most effective advice. For example, optimal advice is generated based on past success stories. In this way, by analyzing the user's behavior history and developing an algorithm for providing the most effective advice, more effective advice becomes possible.

[0079] The user advice-based action unit can collect feedback on the user's actions and improve the accuracy of advice. The user advice-based action unit, for example, collects feedback on the user's actions and builds a system that improves the accuracy of advice based on the results. For example, the advice is adjusted based on the user feedback. In this way, by collecting feedback on the user's actions and improving the accuracy of advice, more effective advice becomes possible.

[0080] The user advice-based action unit can use the emotion estimation function to provide specific action guidelines in real time according to the user's emotional state. The user advice-based action unit, for example, uses the emotion estimation function to build a system that provides specific action guidelines in real time according to the user's emotional state. For example, it suggests ways to calm down before emotions become heightened. This allows for more effective action by providing specific action guidelines in real time according to the user's emotional state.

[0081] The action unit based on the user's advice can visualize the user's action, allowing the user to intuitively grasp the progress. The action unit based on the user's advice, for example, builds a system that visualizes the user's action, allowing the user to intuitively grasp the progress. For example, the progress of the action is displayed using a graph or chart. This allows the user's action to be visualized, allowing the user to intuitively grasp the progress, thereby enabling more effective action.

[0082] The user advice-based action unit can provide action guidelines according to different scenarios, allowing the user to adapt to a variety of situations. The user advice-based action unit, for example, provides action guidelines according to different scenarios, building a system that allows the user to adapt to a variety of situations. For example, different action guidelines are proposed for the workplace and the home. This allows the user to adapt to a variety of situations by providing action guidelines according to different scenarios.

[0083] The user advice-based action unit can use the emotion estimation function to monitor the emotional reactions to the user's actions and continuously provide optimal guidelines for action. The user advice-based action unit, for example, uses the emotion estimation function to monitor the emotional reactions to the user's actions and builds a system that continuously provides optimal guidelines for action. For example, it suggests ways to calm down before emotions become heightened. In this way, more effective actions can be taken by monitoring the emotional reactions to the user's actions and continuously providing optimal guidelines for action.

[0084] The continuous relationship improvement support unit can develop an algorithm that periodically monitors the user's relationship status and provides advice as needed. The continuous relationship improvement support unit, for example, develops an algorithm that periodically monitors the user's relationship status and provides advice as needed. For example, advice is provided before the relationship deteriorates. In this way, by developing an algorithm that periodically monitors the user's relationship status and provides advice as needed, more effective relationship improvement is possible.

[0085] The continuous relationship improvement support unit can create a database of the user's progress in improving relationships and identify patterns for long-term relationship improvement. The continuous relationship improvement support unit, for example, creates a system that creates a database of the user's progress in improving relationships and identifies patterns for long-term relationship improvement. For example, it extracts patterns of successful relationship improvement. This enables more effective relationship improvement by creating a database of the user's progress in improving relationships and identifying patterns for long-term relationship improvement.

[0086] The continuous relationship improvement support unit can use the emotion estimation function to provide continuous support in real time according to the user's emotional state. The continuous relationship improvement support unit, for example, uses the emotion estimation function to build a system that provides continuous support in real time according to the user's emotional state. For example, it suggests ways to calm down before emotions become heightened. This allows for more effective relationship improvement by providing continuous support in real time according to the user's emotional state.

[0087] The support unit for continuous relationship improvement can visualize the continuous support, allowing the user to intuitively grasp the progress. The support unit for continuous relationship improvement, for example, builds a system that visualizes the continuous support, allowing the user to intuitively grasp the progress. For example, the progress of support is displayed using graphs and charts. This enables more effective relationship improvement by visualizing the continuous support and allowing the user to intuitively grasp the progress.

[0088] The continuous relationship improvement support unit can provide customized support according to different relationships (friends, family, workplace, etc.). The continuous relationship improvement support unit, for example, builds a system that provides customized support according to different relationships. For example, different support is proposed for friendships and family relationships. This allows for more effective relationship improvement by providing customized support according to different relationships.

[0089] The continuous relationship improvement support unit can use the emotion estimation function to monitor the user's emotional reactions to the relationship situation and continuously provide optimal support. The continuous relationship improvement support unit, for example, uses the emotion estimation function to monitor the user's emotional reactions to the relationship situation and builds a system that continuously provides optimal support. For example, it suggests ways to calm down before emotions become heightened. In this way, more effective relationship improvement becomes possible by monitoring the user's emotional reactions to the relationship situation and continuously providing optimal support.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The PeaceMaker AI system can also be equipped with a health management module that monitors the user's health status. For example, it can analyze the user's stress level and sleep patterns and provide advice based on the user's health status. This allows the system to propose more appropriate reconciliation messages taking into account the user's health status. The health management module can also monitor the user's heart rate and blood pressure and suggest ways to relax before stress builds up. It can also analyze the user's diet and exercise habits and provide advice to promote a healthy lifestyle.

[0092] The PeaceMaker AI system can also be equipped with a hobby analysis module that analyzes users' hobbies and interests. For example, it can collect information about a user's favorite music, movies, sports, and other interests, and suggest messages that promote reconciliation with people who share the same hobbies. This allows for closer relationships to be built through shared hobbies and interests. The hobby analysis module can also collect information about hobbies from a user's social media account and reflect this information in reconciliation messages. It can also elicit positive emotions by helping users discover new hobbies.

[0093] The PeaceMaker AI system can also include a training component to improve users' communication skills. For example, it can provide online courses and workshops for users to learn effective communication techniques. This allows users to acquire better communication skills and repair relationships more effectively. The training component can analyze users' communication styles and provide individually customized training programs. It can also provide a simulation function that allows users to practice in real-life communication scenarios.

[0094] The PeaceMaker AI system can also include a relaxation suggestion unit that estimates the user's emotions and suggests relaxation methods based on the estimated emotions. For example, if the user is feeling stressed, it can suggest deep breathing, meditation, or relaxation music. This can stabilize the user's emotions and smoothly advance the reconciliation process. The relaxation suggestion unit can also monitor the user's emotional state in real time and suggest relaxation methods at appropriate times. It is also possible to customize relaxation methods according to the user's preferences.

[0095] The PeaceMaker AI system can also include an exercise suggestion module that estimates the user's emotions and suggests appropriate exercises based on the estimated emotions. For example, if the user is feeling anxious, the system can suggest yoga, stretching, or light exercise. This can refresh the user's emotions and support the reconciliation process. The exercise suggestion module can also monitor the user's emotional state in real time and suggest exercises at appropriate times. It can also customize exercises based on the user's fitness level and preferences.

[0096] The PeaceMaker AI system can also be equipped with a meal suggestion unit that estimates the user's emotions and suggests appropriate meals based on the estimated emotions. For example, if the user is feeling stressed, it can suggest recipes using ingredients that have a relaxing effect. This can stabilize the user's emotions and support the reconciliation process. The meal suggestion unit can also monitor the user's emotional state in real time and suggest meals at the appropriate time. It can also customize meals based on the user's food preferences and allergy information.

[0097] The PeaceMaker AI system can also include an entertainment suggestion unit that estimates the user's emotions and suggests appropriate entertainment based on the estimated emotions. For example, if the user is feeling sad, it can suggest movies or music that will uplift the mood. This can make the user's emotions more positive and support the reconciliation process. The entertainment suggestion unit can also monitor the user's emotional state in real time and suggest entertainment at the appropriate time. It can also customize entertainment according to the user's preferences.

[0098] The PeaceMaker AI system can also be equipped with a rest suggestion unit that estimates the user's emotions and suggests appropriate rest methods based on the estimated emotions. For example, if the user feels tired, it can suggest a short nap or a relaxing environment. This can refresh the user's emotions and support the reconciliation process. The rest suggestion unit can also monitor the user's emotional state in real time and suggest rest methods at appropriate times. It is also possible to customize rest methods based on the user's lifestyle and preferences.

[0099] The PeaceMaker AI system can also include a learning analysis module that analyzes a user's learning style. For example, it can analyze how a user most effectively learns information and suggest appropriate learning methods. This can help users more effectively acquire new skills and knowledge, and help repair relationships. The learning analysis module can also analyze a user's past learning history and performance to provide an individually customized learning plan. It can also suggest learning resources related to the user's areas of interest.

[0100] The PeaceMaker AI system can also be equipped with a time management component to support users in managing their time. For example, it can suggest a schedule that allows users to use their time efficiently. This reduces stress and allows users to focus on the reconciliation process. The time management component can analyze the user's schedule and tasks and suggest an optimal schedule. It can also set reminders to ensure users do not forget important tasks. It can also provide time management techniques to improve users' productivity.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The position and emotion analysis unit analyzes the positions and emotions of both parties based on information provided by the user. For example, when a user inputs the cause of a fight and the other party's reaction, the generation AI analyzes that information and understands the emotions and positions of both parties. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to analyze emotions and evaluate relationships. Step 2: The reconciliation message suggestion unit proposes a reconciliation message with appropriate wording and timing based on the results of the analysis by the position and emotion analysis unit. For example, it suggests a message such as, "I understand how you feel. I felt the same way." The generation AI inputs prompts to generate the most effective reconciliation message based on the user's input information.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the 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.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a position and emotion analysis unit that analyzes the positions and emotions of both parties based on information provided by the user; and a reconciliation message suggestion unit that suggests a reconciliation message with appropriate wording and timing based on the results of the analysis by the position and emotion analysis unit. A system characterized by:

2. The position and emotion analysis unit Multimodal sentiment analysis is performed by analyzing not only the text entered by the user but also images and audio data.

2. The system of claim 1.

3. The reconciliation message proposal unit Compiling a database of past success stories and proposing optimal settlement messages based on similar cases 2. The system of claim 1.

4. The customization section of the communication strategy is Analyzing the user's past behavioral data to identify the most effective communication strategy 2. The system of claim 1.

5. The user advice based action unit Analyzing the user's behavioral history and developing an algorithm to provide the most effective advice 2. The system of claim 1.

6. The Support Department for Continuous Relationship Improvement Develop an algorithm that regularly monitors the user's relationship status and provides advice as needed.

2. The system of claim 1.

7. The position and emotion analysis unit Using emotion estimation function, the intensity and type of the user's emotion are analyzed in detail, and real-time feedback is provided according to changes in the emotion.

2. The system of claim 1.

8. The reconciliation message proposal unit Using an emotion estimation function, a reconciliation message is proposed at an optimal timing according to the user's emotional state.

2. The system of claim 1.

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