System

Abstract

An object of a system according to an embodiment is to propose an optimal action by utilizing user's regret and reflection.SOLUTION: A system includes a regret collection part, a reflection collection part, a generation AI, and an action suggestion part. The regret collection unit collects regrets from the user. The reflection collection part collects reflection from a user. The generative AI learns the information collected by the regret collector and the self-reflection collector. The action suggestion unit suggests an optimal action based on the AI learned by the generative learning data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to effectively utilize users' regrets and reflections to suggest optimal actions.

[0005] The system according to the embodiment aims to suggest optimal actions by utilizing the regrets and reflections of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a regret collection unit, a reflection collection unit, a generation AI, and an action suggestion unit. The regret collection unit collects regrets from users. The reflection collection unit collects reflections from users. The generation AI learns data collected by the regret collection unit and the reflection collection unit. The action suggestion unit suggests optimal actions based on the data learned by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal actions by utilizing the regrets and reflections of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The decision support system according to an embodiment of the present invention analyzes daily regrets and reflections, and the generative AI learns and suggests optimal actions. This makes it possible for the decision support system to prevent mistakes in daily life and work, and visualize optimal actions when faced with difficult decisions.

[0029] A decision support system according to an embodiment includes a regret collection unit, a reflection collection unit, a generation AI, and an action suggestion unit. The regret collection unit collects regrets from users. For example, a user may input a regret such as, "I should have spoken in the meeting today, but I kept quiet." The regret collection unit can also collect regrets about past actions taken by users. For example, a user may input a regret such as, "I should have acted earlier." The reflection collection unit collects reflections from users. For example, a user may input a reflection such as, "I should have prepared more next time." The reflection collection unit can also collect improvements to the user's actions. For example, a user may input a reflection such as, "I should have taken more time to plan next time." The generation AI learns data collected by the regret collection unit and the reflection collection unit. For example, the generation AI uses a text generation AI (e.g., LLM) to learn patterns of regret and reflection. The generation AI can also learn regret and reflection data using a multimodal generation AI. The generation AI builds a model to suggest optimal actions based on the user's regret and reflection data. For example, the generative AI suggests the optimal action in a similar situation based on past data. The action suggestion unit suggests the optimal action based on data learned by the generative AI. For example, the action suggestion unit provides specific advice such as, "At the next meeting, prepare what you will say in advance and speak with confidence." The action suggestion unit can also suggest the optimal action when a user is struggling to make a decision. For example, the action suggestion unit provides specific advice such as, "If the project is behind schedule, review the task priorities and work with your team members to accelerate progress." In this way, the decision support system according to the embodiment can collect the user's regrets and reflections, and the generative AI can learn from them to suggest the optimal action. For example, a user can prevent mistakes in their daily life or work based on the advice provided by the generative AI. Furthermore, when a user is struggling to make a decision, the user can refer to the advice provided by the generative AI to select the optimal action.

[0030] When collecting a user's reflections, the reflection collection unit simultaneously collects behavioral history and environmental data, allowing for detailed analysis. For example, when a user inputs a reflection, the reflection collection unit simultaneously collects behavioral history. For example, if a user inputs "I should have spoken in the meeting today, but I kept quiet," the reflection collection unit records the behavioral history of that day (the time and location of the meeting). The reflection collection unit can also simultaneously collect environmental data. For example, when a user inputs a reflection, the unit records the weather and surrounding conditions of that day. This allows for the user's behavioral history and environmental data to be simultaneously collected, allowing for detailed analysis.

[0031] Generative AI can provide balanced advice by referring to the user's past successful experiences and positive events. For example, when analyzing data on regrets and reflections, generative AI refers to the user's past successful experiences. For example, if a user inputs, "I should have spoken in the meeting today, but I kept quiet," it will provide advice based on past examples of successful speech. Generative AI can also refer to the user's positive events. For example, if a user inputs a positive event such as, "The project was successful," it will provide advice based on that data. This allows it to provide balanced advice by referring to the user's past successful experiences and positive events.

[0032] The regret collection unit and the reflection collection unit enable not only text input but also voice input and image input, allowing multimodal data collection. The regret collection unit and the reflection collection unit, for example, allow a user to collect regrets and reflections by voice input. For example, if a user voice-inputs, "I should have spoken in the meeting today, but I kept quiet," the voice data is analyzed and the regret content is converted into text. The regret collection unit and the reflection collection unit can also enable image input. For example, if a user inputs regrets and reflections as images, the image data is analyzed and the regret content is converted into text. This allows not only text input but also voice input and image input, allowing multimodal data collection.

[0033] The regret collection unit and the reflection collection unit can collect regrets and reflections from users of different cultures and regions, and generate advice that takes cultural backgrounds into consideration. The regret collection unit and the reflection collection unit, for example, collect regrets and reflections from users of different cultures and regions. For example, advice that takes cultural backgrounds into consideration is provided based on regret data collected from users in Japan and the United States. The regret collection unit and the reflection collection unit can also generate advice that takes cultural backgrounds into consideration. For example, if a user inputs, "I should have spoken in the meeting today, but I kept quiet," advice that takes cultural backgrounds into consideration is provided. In this way, regrets and reflections from users of different cultures and regions can be collected, and advice that takes cultural backgrounds into consideration can be generated.

[0034] The action suggestion unit tracks the effects of the proposed actions and collects the results of the user's actual actions as feedback, which can be reflected in the learning data of the generation AI. The action suggestion unit, for example, tracks the effects of the proposed actions and collects the results of the user's actual actions as feedback. For example, after the user acts in accordance with the suggestion, the results are recorded. The action suggestion unit can also reflect the collected feedback in the learning data of the generation AI. For example, based on the results of the user's actions, the generation AI learns data to improve the accuracy of its suggestions. This allows the effects of the proposed actions to be tracked and the results of the user's actual actions to be collected as feedback, which can be reflected in the learning data of the generation AI.

[0035] The action suggestion unit can provide the proposed action not only in text format but also in visual or video format, and present it in a form that is easy for the user to intuitively understand. The action suggestion unit, for example, provides the proposed action in visual format. For example, the steps of the action can be shown in diagrams, so that the user can intuitively understand. The action suggestion unit can also provide the proposed action in video format. For example, the steps of the action can be shown in video, so that the user can visually understand. In this way, the proposed action can be provided not only in text format but also in visual or video format, and presented in a form that is easy for the user to intuitively understand.

[0036] The action suggestion unit can generate optimal action proposals specialized for different industries or occupations and provide specialized advice. The action suggestion unit generates optimal action proposals specialized for different industries or occupations, for example. For example, proposals for the medical industry and proposals for the IT industry are provided separately. The action suggestion unit can also provide specialized advice. For example, proposals for engineers and proposals for marketing personnel are provided separately. This makes it possible to generate optimal action proposals specialized for different industries or occupations and provide specialized advice.

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

[0038] The decision support system can also collect the user's health data and suggest actions based on the user's health condition. For example, it can collect the user's sleep data and suggest that the user prioritize rest if they are sleep deprived. It can also collect the user's exercise data and recommend moderate exercise if they are not getting enough exercise. It can also collect the user's dietary data and suggest dietary improvements if their nutritional balance is unbalanced. This makes it possible to suggest optimal actions based on the user's health condition.

[0039] When collecting a user's reflections, the reflection collection unit simultaneously collects behavioral history and environmental data, allowing for detailed analysis. For example, when a user inputs a reflection, the unit simultaneously collects behavioral history. For example, if a user inputs, "I should have spoken in the meeting today, but I kept quiet," the unit records the behavioral history of that day (the time and location of the meeting). The reflection collection unit can also simultaneously collect environmental data. For example, when a user inputs a reflection, the unit records the weather and surrounding conditions of that day. This allows the unit to simultaneously collect a user's behavioral history and environmental data, allowing for detailed analysis.

[0040] Generative AI can provide balanced advice by referring to the user's past successful experiences and positive events. For example, when analyzing data on regrets and reflections, it refers to the user's past successful experiences. For example, if a user inputs, "I should have spoken in the meeting today, but I kept quiet," it will provide advice based on past examples of successful speech. Generative AI can also refer to the user's positive events. For example, if a user inputs a positive event such as, "The project was successful," it will provide advice based on that data. This allows it to provide balanced advice by referring to the user's past successful experiences and positive events.

[0041] The regret collection unit and the reflection collection unit can accept not only text input but also voice input and image input, allowing for multimodal data collection. For example, a user can collect regrets and reflections through voice input. For example, if a user voice-inputs, "I should have spoken in the meeting today, but I kept quiet," the voice data is analyzed and the regret content is converted into text. The regret collection unit and the reflection collection unit can also accept image input. For example, if a user inputs regrets and reflections through images, the image data is analyzed and the regret content is converted into text. This allows not only text input but also voice input and image input, allowing for multimodal data collection.

[0042] The regret collection unit and the reflection collection unit can collect regrets and reflections from users of different cultures and regions, and generate advice that takes cultural backgrounds into consideration. For example, regrets and reflections are collected from users of different cultures and regions. For example, advice that takes cultural backgrounds into consideration is provided based on regret data collected from users in Japan and the United States. The regret collection unit and the reflection collection unit can also generate advice that takes cultural backgrounds into consideration. For example, if a user inputs, "I should have spoken in the meeting today, but I kept quiet," advice that takes cultural backgrounds into consideration is provided. In this way, regrets and reflections can be collected from users of different cultures and regions, and advice that takes cultural backgrounds into consideration can be generated.

[0043] The action suggestion unit can track the effects of the proposed actions, collect the results of the user's actual actions as feedback, and reflect these in the learning data of the generation AI. For example, the action suggestion unit can track the effects of the proposed actions, and collect the results of the user's actual actions as feedback. For example, after the user acts in accordance with the suggestion, the results are recorded. The action suggestion unit can also reflect the collected feedback in the learning data of the generation AI. For example, based on the results of the user's actions, the generation AI learns data to improve the accuracy of its suggestions. This allows the effect of the proposed actions to be tracked, and the results of the user's actual actions to be collected as feedback, and reflected in the learning data of the generation AI.

[0044] The action suggestion unit can provide the suggested action not only in text format but also in visual or video format, and present it in a form that is easy for the user to intuitively understand. For example, the suggested action can be provided in a visual format. For example, the steps of the action can be shown in diagrams, so that the user can intuitively understand. The action suggestion unit can also provide the suggested action in video format. For example, the steps of the action can be shown in video, so that the user can visually understand. In this way, the suggested action can be provided not only in text format but also in visual or video format, and presented in a form that is easy for the user to intuitively understand.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The regret collector collects regrets from the user. For example, the user inputs a regret such as "I should have spoken in the meeting today, but I kept quiet." It can also collect regrets about actions the user has taken in the past. For example, the user inputs a regret such as "I should have acted sooner." Step 2: The reflection collection unit collects reflections from the user. For example, the user may input a reflection such as "I should have prepared more next time." It can also collect points for improvement in the user's actions. For example, the user may input a reflection such as "I should have taken more time to plan next time." Step 3: The generation AI learns from the data collected by the regret collection unit and the reflection collection unit. For example, the generation AI uses a text generation AI (e.g., LLM) to learn patterns of regret and reflection. The generation AI can also use a multimodal generation AI to learn the regret and reflection data. Furthermore, the generation AI builds a model to suggest optimal actions based on the user's regret and reflection data. Step 4: The action suggestion module suggests optimal actions based on the data learned by the generative AI. For example, the action suggestion module provides specific advice such as, "At the next meeting, prepare what you'll say in advance and speak with confidence." The action suggestion module can also suggest optimal actions when the user is unsure of what to do. For example, it provides specific advice such as, "If the project is behind schedule, review task priorities and work with team members to accelerate progress."

[0047] (Example 2) The decision support system according to an embodiment of the present invention analyzes daily regrets and reflections, and the generative AI learns and suggests optimal actions. This makes it possible for the decision support system to prevent mistakes in daily life and work, and visualize optimal actions when faced with difficult decisions.

[0048] A decision support system according to an embodiment includes a regret collection unit, a reflection collection unit, a generation AI, and an action suggestion unit. The regret collection unit collects regrets from users. For example, a user may input a regret such as, "I should have spoken in the meeting today, but I kept quiet." The regret collection unit can also collect regrets about past actions taken by users. For example, a user may input a regret such as, "I should have acted earlier." The reflection collection unit collects reflections from users. For example, a user may input a reflection such as, "I should have prepared more next time." The reflection collection unit can also collect improvements to the user's actions. For example, a user may input a reflection such as, "I should have taken more time to plan next time." The generation AI learns data collected by the regret collection unit and the reflection collection unit. For example, the generation AI uses a text generation AI (e.g., LLM) to learn patterns of regret and reflection. The generation AI can also learn regret and reflection data using a multimodal generation AI. The generation AI builds a model to suggest optimal actions based on the user's regret and reflection data. For example, the generative AI suggests the optimal action in a similar situation based on past data. The action suggestion unit suggests the optimal action based on data learned by the generative AI. For example, the action suggestion unit provides specific advice such as, "At the next meeting, prepare what you will say in advance and speak with confidence." The action suggestion unit can also suggest the optimal action when a user is struggling to make a decision. For example, the action suggestion unit provides specific advice such as, "If the project is behind schedule, review the task priorities and work with your team members to accelerate progress." In this way, the decision support system according to the embodiment can collect the user's regrets and reflections, and the generative AI can learn from them to suggest the optimal action. For example, a user can prevent mistakes in their daily life or work based on the advice provided by the generative AI. Furthermore, when a user is struggling to make a decision, the user can refer to the advice provided by the generative AI to select the optimal action.

[0049] When collecting a user's regrets, the regret collection unit uses the emotion estimation function to analyze the intensity and type of emotion and track changes in emotion. For example, when a user inputs regrets, the regret collection unit uses the emotion estimation function to analyze the intensity and type of emotion in real time. For example, if a user inputs "I should have spoken in the meeting today, but I kept quiet," the regret collection unit quantifies how strong the regret is. The regret collection unit can also track changes in the user's emotions. For example, it tracks how the emotion changes after the user inputs regret. This makes it possible to analyze the intensity and type of the user's emotions and track changes in emotion.

[0050] When collecting a user's reflections, the reflection collection unit simultaneously collects behavioral history and environmental data, allowing for detailed analysis. For example, when a user inputs a reflection, the reflection collection unit simultaneously collects behavioral history. For example, if a user inputs "I should have spoken in the meeting today, but I kept quiet," the reflection collection unit records the behavioral history of that day (the time and location of the meeting). The reflection collection unit can also simultaneously collect environmental data. For example, when a user inputs a reflection, the unit records the weather and surrounding conditions of that day. This allows for the user's behavioral history and environmental data to be simultaneously collected, allowing for detailed analysis.

[0051] Generative AI can provide balanced advice by referring to the user's past successful experiences and positive events. For example, when analyzing data on regrets and reflections, generative AI refers to the user's past successful experiences. For example, if a user inputs, "I should have spoken in the meeting today, but I kept quiet," it will provide advice based on past examples of successful speech. Generative AI can also refer to the user's positive events. For example, if a user inputs a positive event such as, "The project was successful," it will provide advice based on that data. This allows it to provide balanced advice by referring to the user's past successful experiences and positive events.

[0052] The regret collection unit and the reflection collection unit enable not only text input but also voice input and image input, allowing multimodal data collection. The regret collection unit and the reflection collection unit, for example, allow a user to collect regrets and reflections by voice input. For example, if a user voice-inputs, "I should have spoken in the meeting today, but I kept quiet," the voice data is analyzed and the regret content is converted into text. The regret collection unit and the reflection collection unit can also enable image input. For example, if a user inputs regrets and reflections as images, the image data is analyzed and the regret content is converted into text. This allows not only text input but also voice input and image input, allowing multimodal data collection.

[0053] The regret collection unit and the reflection collection unit can collect regrets and reflections from users of different cultures and regions, and generate advice that takes cultural backgrounds into consideration. The regret collection unit and the reflection collection unit, for example, collect regrets and reflections from users of different cultures and regions. For example, advice that takes cultural backgrounds into consideration is provided based on regret data collected from users in Japan and the United States. The regret collection unit and the reflection collection unit can also generate advice that takes cultural backgrounds into consideration. For example, if a user inputs, "I should have spoken in the meeting today, but I kept quiet," advice that takes cultural backgrounds into consideration is provided. In this way, regrets and reflections from users of different cultures and regions can be collected, and advice that takes cultural backgrounds into consideration can be generated.

[0054] The regret collection unit and the remorse collection unit can use the emotion estimation function to analyze the emotions of the user when entering regrets or remorse in real time and provide feedback to elicit positive emotions. For example, when a user enters regrets or remorse, the regret collection unit and the remorse collection unit use the emotion estimation function to analyze the emotions in real time. For example, when a user enters "I should have spoken in the meeting today, but I kept quiet," the emotion is analyzed in real time. The regret collection unit and the remorse collection unit can also provide feedback to elicit positive emotions. For example, when a user enters regrets or remorse, advice is provided to change the emotion to a positive one. In this way, the emotions of the user when entering regrets or remorse can be analyzed in real time and feedback to elicit positive emotions can be provided.

[0055] The action suggestion unit monitors the user's emotional reactions in real time to the optimal actions proposed by the generation AI, and can prioritize suggestions that are emotionally easy to accept. The action suggestion unit, for example, monitors the user's emotional reactions in real time to the optimal actions proposed by the generation AI. For example, it analyzes the user's facial expressions and voice when they accept a suggestion, and prioritizes suggestions that are emotionally easy to accept. The action suggestion unit can also adjust the content of the suggestion based on the user's emotional reactions. For example, if the user has a negative reaction to a suggestion, it provides feedback to improve the suggestion. This makes it possible to monitor the user's emotional reactions in real time to the optimal actions proposed by the generation AI, and prioritize suggestions that are emotionally easy to accept.

[0056] The action suggestion unit tracks the effects of the proposed actions and collects the results of the user's actual actions as feedback, which can be reflected in the learning data of the generation AI. The action suggestion unit, for example, tracks the effects of the proposed actions and collects the results of the user's actual actions as feedback. For example, after the user acts in accordance with the suggestion, the results are recorded. The action suggestion unit can also reflect the collected feedback in the learning data of the generation AI. For example, based on the results of the user's actions, the generation AI learns data to improve the accuracy of its suggestions. This allows the effects of the proposed actions to be tracked and the results of the user's actual actions to be collected as feedback, which can be reflected in the learning data of the generation AI.

[0057] The action suggestion unit can provide the proposed action not only in text format but also in visual or video format, and present it in a form that is easy for the user to intuitively understand. The action suggestion unit, for example, provides the proposed action in visual format. For example, the steps of the action can be shown in diagrams, so that the user can intuitively understand. The action suggestion unit can also provide the proposed action in video format. For example, the steps of the action can be shown in video, so that the user can visually understand. In this way, the proposed action can be provided not only in text format but also in visual or video format, and presented in a form that is easy for the user to intuitively understand.

[0058] The action suggestion unit can generate optimal action proposals specialized for different industries or occupations and provide specialized advice. The action suggestion unit generates optimal action proposals specialized for different industries or occupations, for example. For example, proposals for the medical industry and proposals for the IT industry are provided separately. The action suggestion unit can also provide specialized advice. For example, proposals for engineers and proposals for marketing personnel are provided separately. This makes it possible to generate optimal action proposals specialized for different industries or occupations and provide specialized advice.

[0059] The action suggestion unit can use the emotion estimation function to analyze the user's emotional reaction to the proposed action and provide additional advice to elicit positive emotions. The action suggestion unit, for example, uses the emotion estimation function to analyze the user's emotional reaction to the proposed action in real time. For example, if the user has a positive reaction to the suggestion, the action suggestion unit can provide additional advice based on that reaction. The action suggestion unit can also adjust the content of the suggestion based on the user's emotional reaction. For example, if the user has a negative reaction to the suggestion, the action suggestion unit can provide feedback to improve the suggestion. In this way, the emotion estimation function can be used to analyze the user's emotional reaction to the proposed action and provide additional advice to elicit positive emotions.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The decision support system can also collect the user's health data and suggest actions based on the user's health condition. For example, it can collect the user's sleep data and suggest that the user prioritize rest if they are sleep deprived. It can also collect the user's exercise data and recommend moderate exercise if they are not getting enough exercise. It can also collect the user's dietary data and suggest dietary improvements if their nutritional balance is unbalanced. This makes it possible to suggest optimal actions based on the user's health condition.

[0062] When collecting a user's regrets, the regret collection unit uses the emotion estimation function to analyze the intensity and type of emotion and track changes in emotion. For example, when a user inputs regrets, the generation AI uses the emotion estimation function to analyze the intensity and type of emotion in real time. For example, if a user inputs, "I should have spoken in the meeting today, but I kept quiet," the generation AI can quantify how strong the regret is. The regret collection unit can also track changes in the user's emotions. For example, it tracks how emotions change after the user inputs regrets. This makes it possible to analyze the intensity and type of the user's emotions and track changes in emotions.

[0063] When collecting a user's reflections, the reflection collection unit simultaneously collects behavioral history and environmental data, allowing for detailed analysis. For example, when a user inputs a reflection, the unit simultaneously collects behavioral history. For example, if a user inputs, "I should have spoken in the meeting today, but I kept quiet," the unit records the behavioral history of that day (the time and location of the meeting). The reflection collection unit can also simultaneously collect environmental data. For example, when a user inputs a reflection, the unit records the weather and surrounding conditions of that day. This allows the unit to simultaneously collect a user's behavioral history and environmental data, allowing for detailed analysis.

[0064] Generative AI can provide balanced advice by referring to the user's past successful experiences and positive events. For example, when analyzing data on regrets and reflections, it refers to the user's past successful experiences. For example, if a user inputs, "I should have spoken in the meeting today, but I kept quiet," it will provide advice based on past examples of successful speech. Generative AI can also refer to the user's positive events. For example, if a user inputs a positive event such as, "The project was successful," it will provide advice based on that data. This allows it to provide balanced advice by referring to the user's past successful experiences and positive events.

[0065] The regret collection unit and the reflection collection unit can accept not only text input but also voice input and image input, allowing for multimodal data collection. For example, a user can collect regrets and reflections through voice input. For example, if a user voice-inputs, "I should have spoken in the meeting today, but I kept quiet," the voice data is analyzed and the regret content is converted into text. The regret collection unit and the reflection collection unit can also accept image input. For example, if a user inputs regrets and reflections through images, the image data is analyzed and the regret content is converted into text. This allows not only text input but also voice input and image input, allowing for multimodal data collection.

[0066] The regret collection unit and the reflection collection unit can collect regrets and reflections from users of different cultures and regions, and generate advice that takes cultural backgrounds into consideration. For example, regrets and reflections are collected from users of different cultures and regions. For example, advice that takes cultural backgrounds into consideration is provided based on regret data collected from users in Japan and the United States. The regret collection unit and the reflection collection unit can also generate advice that takes cultural backgrounds into consideration. For example, if a user inputs, "I should have spoken in the meeting today, but I kept quiet," advice that takes cultural backgrounds into consideration is provided. In this way, regrets and reflections can be collected from users of different cultures and regions, and advice that takes cultural backgrounds into consideration can be generated.

[0067] The regret collection unit and the remorse collection unit can use the emotion estimation function to analyze the emotions of the user when entering regrets or remorse in real time, and provide feedback to elicit positive emotions. For example, when a user enters regrets or remorse, the emotion estimation function is used to analyze the emotions in real time. For example, when a user enters "I should have spoken in the meeting today, but I kept quiet," the emotion is analyzed in real time. The regret collection unit and the remorse collection unit can also provide feedback to elicit positive emotions. For example, when a user enters regrets or remorse, advice is provided to change the emotion to a positive one. In this way, the emotions of the user when entering regrets or remorse can be analyzed in real time, and feedback to elicit positive emotions can be provided.

[0068] The action suggestion unit can monitor the user's emotional reactions in real time to the optimal actions proposed by the generation AI and prioritize suggestions that are emotionally easy to accept. For example, it can monitor the user's emotional reactions in real time to the optimal actions proposed by the generation AI. For example, it can analyze the user's facial expressions and voice when they accept a suggestion and prioritize suggestions that are emotionally easy to accept. The action suggestion unit can also adjust the content of the suggestion based on the user's emotional reactions. For example, if the user has a negative reaction to a suggestion, it can provide feedback to improve the suggestion. This makes it possible to monitor the user's emotional reactions in real time to the optimal actions proposed by the generation AI and prioritize suggestions that are emotionally easy to accept.

[0069] The action suggestion unit can track the effects of the proposed actions, collect the results of the user's actual actions as feedback, and reflect these in the learning data of the generation AI. For example, the action suggestion unit can track the effects of the proposed actions, and collect the results of the user's actual actions as feedback. For example, after the user acts in accordance with the suggestion, the results are recorded. The action suggestion unit can also reflect the collected feedback in the learning data of the generation AI. For example, based on the results of the user's actions, the generation AI learns data to improve the accuracy of its suggestions. This allows the effect of the proposed actions to be tracked, and the results of the user's actual actions to be collected as feedback, and reflected in the learning data of the generation AI.

[0070] The action suggestion unit can provide the suggested action not only in text format but also in visual or video format, and present it in a form that is easy for the user to intuitively understand. For example, the suggested action can be provided in a visual format. For example, the steps of the action can be shown in diagrams, so that the user can intuitively understand. The action suggestion unit can also provide the suggested action in video format. For example, the steps of the action can be shown in video, so that the user can visually understand. In this way, the suggested action can be provided not only in text format but also in visual or video format, and presented in a form that is easy for the user to intuitively understand.

[0071] The action suggestion unit can use the emotion estimation function to analyze the user's emotional reaction to the proposed action and provide additional advice to elicit positive emotions. For example, the emotion estimation function is used to analyze the user's emotional reaction to the proposed action in real time. For example, if the user has a positive reaction to the suggestion, additional advice is provided based on that reaction. The action suggestion unit can also adjust the content of the suggestion based on the user's emotional reaction. For example, if the user has a negative reaction to the suggestion, feedback is provided to improve the suggestion. In this way, the emotion estimation function can be used to analyze the user's emotional reaction to the proposed action and provide additional advice to elicit positive emotions.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The regret collector collects regrets from the user. For example, the user inputs a regret such as "I should have spoken in the meeting today, but I kept quiet." It can also collect regrets about actions the user has taken in the past. For example, the user inputs a regret such as "I should have acted sooner." Step 2: The reflection collection unit collects reflections from the user. For example, the user may input a reflection such as "I should have prepared more next time." It can also collect points for improvement in the user's actions. For example, the user may input a reflection such as "I should have taken more time to plan next time." Step 3: The generation AI learns from the data collected by the regret collection unit and the reflection collection unit. For example, the generation AI uses a text generation AI (e.g., LLM) to learn patterns of regret and reflection. The generation AI can also use a multimodal generation AI to learn the regret and reflection data. Furthermore, the generation AI builds a model to suggest optimal actions based on the user's regret and reflection data. Step 4: The action suggestion module suggests optimal actions based on the data learned by the generative AI. For example, the action suggestion module provides specific advice such as, "At the next meeting, prepare what you'll say in advance and speak with confidence." The action suggestion module can also suggest optimal actions when the user is unsure of what to do. For example, it provides specific advice such as, "If the project is behind schedule, review task priorities and work with team members to accelerate progress."

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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 AI 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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 AI 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 AI 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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 regret collection unit that collects regrets from users; a reflection collection unit that collects reflections from users; A generation AI that learns data collected by the regret collection unit and the reflection collection unit; and an action suggestion unit that suggests optimal actions based on the data learned by the generation AI. A system characterized by:

2. The regret collector: When collecting users' regrets, an emotion estimation function is used to analyze the intensity and type of emotions and track changes in said emotions.

2. The system of claim 1.

3. The reflection collection unit When collecting user reflections, behavioral history and environmental data are simultaneously collected and analyzed in detail.

2. The system of claim 1.

4. The generated AI is Referencing users' past successes and positive experiences to provide balanced advice 2. The system of claim 1.

5. The regret collecting unit and the reflection collecting unit are It allows for multimodal data collection, enabling not only text input but also voice and image input.

2. The system of claim 1.

6. The regret collecting unit and the reflection collecting unit are Collect regrets and reflections from users in different cultures and regions, and generate advice that takes cultural background into account 2. The system of claim 1.

7. The action suggestion unit The AI ​​then monitors the user's emotional response in real time to the optimal actions it proposes, prioritizing suggestions that are emotionally acceptable.

2. The system of claim 1.

8. The action suggestion unit Using emotion estimation, we analyze the user's emotional response to the proposed action and provide additional advice to elicit positive emotions.

2. The system of claim 1.

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