System

Abstract

An object of a system according to an embodiment is to enable a user to learn appropriate words and actions in a specific situation.SOLUTION: A system includes a registration unit, a simulation unit, and a feedback unit. The registration unit registers a position or a situation of a user. The simulation unit performs a simulation based on the data registered by the registration unit. The feedback unit feeds back a result obtained by the simulation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult for users to learn appropriate words and actions in specific situations.

[0005] The system according to the embodiment aims to help users learn appropriate words and actions in specific situations. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a simulation unit, and a feedback unit. The registration unit registers a user's position or situation. The simulation unit performs a simulation based on the data registered by the registration unit. The feedback unit feeds back the results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to learn appropriate words and actions in a particular situation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A system according to an embodiment of the present invention enables users to naturally produce witty remarks. This system registers the user's position and situation, and a virtual AI teacher performs a simulation. The system then provides feedback on how the other person responded to the user's comments and how the user should have responded. For example, when the user inputs a specific situation, such as a conversation at work or with friends, the virtual AI teacher analyzes the information and performs a simulation. The virtual AI teacher then analyzes the other person's reaction to the user's comments and provides feedback on whether there was a better way to express them. This gradually enables the user to be more considerate and considerate of others, and to naturally produce witty remarks and take actions. This allows the system to naturally produce "just a few words" that make those around them happy, depending on the situation, without the user even being aware of it. For example, the system enables users to use witty remarks and take actions in a variety of situations, such as conversations at work or with friends.

[0029] The system according to the embodiment includes a registration unit, a simulation unit, and a feedback unit. The registration unit registers the user's position or situation. For example, specific situations such as conversations at work or with friends can be input. The simulation unit performs a simulation based on the data registered by the registration unit. For example, the simulation unit uses natural language processing technology to analyze how the other person felt about words spoken by the user. The feedback unit provides feedback on the results obtained by the simulation unit. For example, the feedback unit provides feedback on whether there was a better way to say something spoken by the user. This allows the system according to the embodiment to allow the user to naturally use clever words and actions.

[0030] The simulation unit can use natural language processing technology to analyze how the other person thinks about the words uttered by the user. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the simulation unit uses morphological analysis to break down the user's utterances and uses grammatical analysis to analyze the structure of the sentence. The simulation unit can also use semantic analysis to understand the meaning of the utterances and analyze how the other person thinks about them. This allows for more appropriate feedback by analyzing the other person's reaction to the user's utterances.

[0031] The feedback unit can provide feedback on whether there is a better way to express what the user has said. Criteria for a better way to express gratitude include, for example, politeness, clarity, and conveying emotion. For example, when the user says "thank you," the feedback unit can provide feedback on how to express gratitude more specifically. For example, the feedback unit can suggest specific words of gratitude such as "Thank you, you helped me a lot." The feedback unit can also provide feedback to help the user choose more appropriate words for what the user has said. This provides feedback to help the user choose more appropriate words.

[0032] The registration unit can register specific situations such as conversations at work or conversations with friends. Specific situations include, for example, conversations at work, conversations with friends, and conversations at home. For example, when a user registers a conversation at work, the registration unit can input detailed situations such as a conversation with a boss, a conversation with a colleague, or a conversation with a subordinate. Furthermore, when registering a conversation with a friend, the registration unit can also input specific situations such as an everyday conversation with a friend, a consultation with a friend, or a plan to hang out with a friend. This allows the user to register specific situations, enabling more realistic simulations.

[0033] The feedback unit can provide feedback on specific ways to express gratitude in response to words spoken by the user. Specific ways to express gratitude include, for example, the choice of words, facial expressions, gestures, and the like. For example, when the user says "thank you," the feedback unit can provide feedback on more specific ways to express gratitude. For example, the feedback unit can suggest specific words of gratitude such as "Thank you, you helped me a lot." The feedback unit can also suggest facial expressions and gestures to convey gratitude in response to the user's statement. This allows the user to learn specific ways to express gratitude.

[0034] The registration unit can analyze the user's past situation registration history and select the optimal registration method. Criteria for the optimal registration method include, for example, the user's past behavioral patterns and frequency of use. The registration unit can, for example, automatically display situations that the user has frequently registered in the past as candidates. The registration unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the registration unit can predict and suggest situations to be used in specific time periods based on the user's past registration history. This allows the optimal registration method to be provided based on the past history.

[0035] The registration unit can filter situations based on the user's current living situation and areas of interest when registering the situations. Filtering criteria include, for example, the user's living situation and areas of interest. For example, when the user inputs their current living situation, the registration unit can preferentially display related situations. The registration unit can also suggest related situations based on the user's areas of interest. Furthermore, the registration unit can filter out unnecessary situations taking into account the user's living situation and areas of interest. This allows situations to be provided that match the user's living situation and areas of interest.

[0036] When registering a situation, the registration unit can select the optimal registration means depending on the user's input method. Criteria for the optimal registration means include, for example, voice input, text input, image input, etc. For example, when the user registers a situation by voice, the registration unit automatically converts it into text using voice recognition technology. Furthermore, when the user registers a situation by text, the registration unit can provide an input completion function to enable quick registration. Furthermore, when the user registers a situation by image, the registration unit can automatically recognize the situation using image analysis technology. This provides the optimal registration means depending on the user's input method.

[0037] When registering a situation, the registration unit can prioritize registering a highly relevant situation by taking into consideration the user's geographical location information. Geographical location information includes, for example, GPS data, location information services, etc. For example, when the user is in a specific location, the registration unit can prioritize suggesting a situation related to that location. The registration unit can also prioritize suggesting a situation related to a location close to the user's current location. Furthermore, the registration unit can also suggest a relevant situation by referring to the user's past location information. In this way, situations based on the user's geographical location information are provided.

[0038] When registering a situation, the registration unit can analyze the user's social media activity and register related situations. Social media activity includes, for example, the content of posts, the number of likes, comments, etc. The registration unit can, for example, suggest situations related to places where the user has checked in on social media. The registration unit can also analyze the content of the user's posts on social media and suggest related situations. Furthermore, the registration unit can suggest related situations based on the activity of the user's friends on social media. In this way, situations based on the user's social media activity are provided.

[0039] The registration unit can customize the registration method by reflecting the user's past feedback when registering a situation. The customization criteria include, for example, the user's past feedback, usage history, etc. The registration unit can suggest the optimal registration method based on, for example, the user's past feedback. The registration unit can also preferentially suggest a specific registration method based on the user's past feedback. Furthermore, the registration unit can customize the registration interface by reflecting the user's feedback. This provides the optimal registration method based on the user's past feedback.

[0040] During the simulation, the simulation unit can adjust the level of detail of the simulation based on the importance of the situation. Criteria for the level of detail of the simulation include, for example, the importance of the situation, user requests, etc. The simulation unit can provide a detailed simulation, for example, for an important situation. The simulation unit can also provide a simplified simulation for a situation with low importance. Furthermore, the simulation unit can dynamically adjust the level of detail of the simulation according to the importance of the situation. This allows for the provision of an optimal simulation according to the importance of the situation.

[0041] During the simulation, the simulation unit can apply different simulation algorithms depending on the category of the situation. Simulation algorithms include, for example, the Monte Carlo method and agent-based simulation. For example, in the case of a conversation at work, the simulation unit can apply a simulation algorithm specialized for business scenes. In addition, in the case of a conversation with friends, the simulation unit can also apply a simulation algorithm specialized for casual scenes. Furthermore, in the case of a conversation with family, the simulation unit can apply a simulation algorithm specialized for domestic scenes. This provides an optimal simulation according to the category of the situation.

[0042] During the simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results. Methods for improving the accuracy of the simulation include, for example, using past data and improving the algorithm. For example, the simulation unit improves the accuracy of the simulation based on the results of simulations performed by the user in the past. The simulation unit can also extract specific patterns from the user's past simulation results to improve the accuracy. Furthermore, the simulation unit can reflect user feedback and improve the simulation algorithm. This improves the accuracy of the simulation based on the results of past simulations.

[0043] During simulation, the simulation unit can determine the priority of simulations based on the time of occurrence of a situation. Criteria for determining the time of occurrence of a situation include, for example, past data, a prediction model, etc. The simulation unit, for example, prioritizes simulation of a situation that will occur most recently. The simulation unit can also prioritize simulation of a situation that has frequently occurred in the past. Furthermore, the simulation unit can also prioritize simulation of a situation that a user uses during a specific time period. This provides an optimal simulation priority according to the time of occurrence of a situation.

[0044] During the simulation, the simulation unit can adjust the order of the simulation based on the relevance of the situations. Criteria for the relevance of the situations include, for example, the content of the situation, the user's interests, etc. The simulation unit, for example, prioritizes simulating highly relevant situations. The simulation unit can also postpone less relevant situations. Furthermore, the simulation unit can dynamically adjust the order of the simulation based on the relevance of the situations. This provides an optimal order of the simulation based on the relevance of the situations.

[0045] During the simulation, the simulation unit can adjust the use of technical terms in the simulation according to the user's level of expertise. Criteria for the use of technical terms include, for example, the user's level of expertise and the content of the situation. For example, if the user has technical expertise, the simulation unit can provide a simulation that makes extensive use of technical terms. Also, if the user does not have technical expertise, the simulation unit can provide a simulation that explains things in simple terms. Furthermore, the simulation unit can dynamically adjust the use of technical terms according to the user's level of expertise. This allows for the provision of an optimal simulation according to the user's level of expertise.

[0046] When providing feedback, the feedback unit can analyze the user's past comment history and select the optimal feedback method. Criteria for the optimal feedback method include, for example, the user's past comment history and the content of the feedback. The feedback unit can provide optimal feedback, for example, based on the user's past comments. The feedback unit can also extract specific patterns from the user's past comment history and provide feedback. Furthermore, the feedback unit can customize the content of the feedback by referring to the user's past comment history. This allows the optimal feedback method to be provided based on the user's past comment history.

[0047] When providing feedback, the feedback unit can customize the means of feedback based on the user's current living situation. The means of feedback can include, for example, text, audio, visual, etc. For example, the feedback unit can provide short and to-the-point feedback when the user is busy. Alternatively, the feedback unit can provide detailed feedback when the user is relaxed. Furthermore, the feedback unit can customize the means of feedback (text, audio, etc.) according to the user's living situation. This allows the optimal means of feedback to be provided according to the user's living situation.

[0048] The feedback unit can improve the feedback method by reflecting the user's feedback when providing feedback. Criteria for the feedback method include, for example, the content of the user's feedback and the frequency of feedback. The feedback unit can improve the feedback method, for example, based on feedback provided by the user in the past. The feedback unit can also extract specific improvements from the user's feedback and optimize the feedback method. Furthermore, the feedback unit can customize the content and means of feedback by reflecting the user's feedback. This provides an optimal feedback method based on the user's feedback.

[0049] The feedback unit may select an optimal feedback method by taking into consideration the user's geographical location information when providing feedback. Examples of geographical location information include GPS data, location information services, and the like. For example, if the user is in a specific location, the feedback unit may provide feedback related to the location. The feedback unit may also provide feedback related to locations close to the user's current location. Furthermore, the feedback unit may provide relevant feedback by referring to the user's past location information. This provides an optimal feedback method based on the user's geographical location information.

[0050] When providing feedback, the feedback unit may analyze the user's social media activity and suggest a means of providing feedback. Social media activity includes, for example, the content of posts, the number of likes, and comments. The feedback unit may provide feedback related to, for example, places where the user has checked in on social media. The feedback unit may also analyze the content of the user's posts on social media and provide related feedback. Furthermore, the feedback unit may provide related feedback by referring to the activities of the user's friends on social media. This allows the optimal means of feedback to be provided based on the user's social media activity.

[0051] The feedback unit can customize the feedback method by reflecting the user's past feedback when providing feedback. Customization criteria include, for example, the user's past feedback, usage history, etc. The feedback unit can, for example, suggest an optimal feedback method based on the user's past feedback. The feedback unit can also preferentially suggest a specific feedback method based on the user's past feedback. Furthermore, the feedback unit can customize the feedback interface by reflecting the user's feedback. This provides an optimal feedback method based on the user's past feedback.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The system can analyze a user's past speech history and provide optimal feedback. For example, it can analyze what the user has said in similar situations in the past and provide feedback based on the results. It can also provide more effective feedback by referring to feedback the user has received in the past. Furthermore, it can extract specific patterns from the user's past speech history and provide feedback based on those patterns. This allows the system to provide optimal feedback based on the user's past speech history.

[0054] The system can provide feedback taking into account the user's current life situation. For example, if the user is busy, the system can provide short and to-the-point feedback. If the user is relaxed, the system can provide detailed feedback. Furthermore, the system can customize the means of feedback (text, voice, etc.) depending on the user's life situation. This allows the system to provide optimal feedback according to the user's life situation.

[0055] The system can provide feedback taking into account the user's geographic location. For example, if the user is in a specific location, the system can provide feedback related to that location. It can also provide feedback related to locations close to the user's current location. It can also provide relevant feedback based on the user's past location information. This allows the system to provide optimal feedback based on the user's geographic location.

[0056] The system can analyze the user's social media activity and provide feedback. For example, the system can provide feedback related to the places the user has checked in on social media. The system can also analyze the content of the user's social media posts and provide relevant feedback. Furthermore, the system can provide relevant feedback based on the activities of the user's friends on social media. This allows the system to provide optimal feedback based on the user's social media activity.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The registration unit registers the user's position or situation. For example, a specific situation such as a conversation at work or with a friend can be entered. Step 2: The simulation unit performs a simulation based on the data registered by the registration unit. For example, the simulation unit uses natural language processing technology to analyze how the other person reacts to the words spoken by the user. Step 3: The feedback unit feeds back the results obtained by the simulation unit. For example, the feedback unit feeds back whether there was a better way to say something that the user said.

[0059] (Example 2) A system according to an embodiment of the present invention enables users to naturally produce witty remarks. This system registers the user's position and situation, and a virtual AI teacher performs a simulation. The system then provides feedback on how the other person responded to the user's comments and how the user should have responded. For example, when the user inputs a specific situation, such as a conversation at work or with friends, the virtual AI teacher analyzes the information and performs a simulation. The virtual AI teacher then analyzes the other person's reaction to the user's comments and provides feedback on whether there was a better way to express them. This gradually enables the user to be more considerate and considerate of others, and to naturally produce witty remarks and take actions. This allows the system to naturally produce "just a few words" that make those around them happy, depending on the situation, without the user even being aware of it. For example, the system enables users to use witty remarks and take actions in a variety of situations, such as conversations at work or with friends.

[0060] The system according to the embodiment includes a registration unit, a simulation unit, and a feedback unit. The registration unit registers the user's position or situation. For example, specific situations such as conversations at work or with friends can be input. The simulation unit performs a simulation based on the data registered by the registration unit. For example, the simulation unit uses natural language processing technology to analyze how the other person felt about words spoken by the user. The feedback unit provides feedback on the results obtained by the simulation unit. For example, the feedback unit provides feedback on whether there was a better way to say something spoken by the user. This allows the system according to the embodiment to allow the user to naturally use clever words and actions.

[0061] The simulation unit can use natural language processing technology to analyze how the other person thinks about the words uttered by the user. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. For example, the simulation unit uses morphological analysis to break down the user's utterances and uses grammatical analysis to analyze the structure of the sentence. The simulation unit can also use semantic analysis to understand the meaning of the utterances and analyze how the other person thinks about them. This allows for more appropriate feedback by analyzing the other person's reaction to the user's utterances.

[0062] The feedback unit can provide feedback on whether there is a better way to express what the user has said. Criteria for a better way to express gratitude include, for example, politeness, clarity, and conveying emotion. For example, when the user says "thank you," the feedback unit can provide feedback on how to express gratitude more specifically. For example, the feedback unit can suggest specific words of gratitude such as "Thank you, you helped me a lot." The feedback unit can also provide feedback to help the user choose more appropriate words for what the user has said. This provides feedback to help the user choose more appropriate words.

[0063] The registration unit can register specific situations such as conversations at work or conversations with friends. Specific situations include, for example, conversations at work, conversations with friends, and conversations at home. For example, when a user registers a conversation at work, the registration unit can input detailed situations such as a conversation with a boss, a conversation with a colleague, or a conversation with a subordinate. Furthermore, when registering a conversation with a friend, the registration unit can also input specific situations such as an everyday conversation with a friend, a consultation with a friend, or a plan to hang out with a friend. This allows the user to register specific situations, enabling more realistic simulations.

[0064] The feedback unit can provide feedback on specific ways to express gratitude in response to words spoken by the user. Specific ways to express gratitude include, for example, the choice of words, facial expressions, gestures, and the like. For example, when the user says "thank you," the feedback unit can provide feedback on more specific ways to express gratitude. For example, the feedback unit can suggest specific words of gratitude such as "Thank you, you helped me a lot." The feedback unit can also suggest facial expressions and gestures to convey gratitude in response to the user's statement. This allows the user to learn specific ways to express gratitude.

[0065] The registration unit can estimate the user's emotion and adjust the situation registration method based on the estimated user emotion. Methods for estimating the user's emotion include, for example, facial expression recognition, voice analysis, and text analysis. For example, if the user is nervous, the registration unit can provide a simple and intuitive interface and minimize the registration procedure. Furthermore, if the user is relaxed, the registration unit can provide detailed input options and suggest a customizable registration method. Furthermore, if the user is in a hurry, the registration unit can prioritize voice input and enable quick situation registration. This provides an optimal situation registration method according to the user's emotion.

[0066] The registration unit can analyze the user's past situation registration history and select the optimal registration method. Criteria for the optimal registration method include, for example, the user's past behavioral patterns and frequency of use. The registration unit can, for example, automatically display situations that the user has frequently registered in the past as candidates. The registration unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the registration unit can predict and suggest situations to be used in specific time periods based on the user's past registration history. This allows the optimal registration method to be provided based on the past history.

[0067] The registration unit can filter situations based on the user's current living situation and areas of interest when registering the situations. Filtering criteria include, for example, the user's living situation and areas of interest. For example, when the user inputs their current living situation, the registration unit can preferentially display related situations. The registration unit can also suggest related situations based on the user's areas of interest. Furthermore, the registration unit can filter out unnecessary situations taking into account the user's living situation and areas of interest. This allows situations to be provided that match the user's living situation and areas of interest.

[0068] When registering a situation, the registration unit can select the optimal registration means depending on the user's input method. Criteria for the optimal registration means include, for example, voice input, text input, image input, etc. For example, when the user registers a situation by voice, the registration unit automatically converts it into text using voice recognition technology. Furthermore, when the user registers a situation by text, the registration unit can provide an input completion function to enable quick registration. Furthermore, when the user registers a situation by image, the registration unit can automatically recognize the situation using image analysis technology. This provides the optimal registration means depending on the user's input method.

[0069] The registration unit can estimate the user's emotions and determine the priority of situations to be registered based on the estimated user's emotions. Criteria for prioritizing situations include, for example, the user's emotions and the importance of the situations. For example, if the user is feeling stressed, the registration unit can preferentially suggest relaxing situations. Furthermore, if the user is having fun, the registration unit can also preferentially suggest fun situations. Furthermore, if the user is tired, the registration unit can preferentially suggest easy and less stressful situations. This provides a priority order of situations according to the user's emotions.

[0070] When registering a situation, the registration unit can prioritize registering a highly relevant situation by taking into consideration the user's geographical location information. Geographical location information includes, for example, GPS data, location information services, etc. For example, when the user is in a specific location, the registration unit can prioritize suggesting a situation related to that location. The registration unit can also prioritize suggesting a situation related to a location close to the user's current location. Furthermore, the registration unit can also suggest a relevant situation by referring to the user's past location information. In this way, situations based on the user's geographical location information are provided.

[0071] When registering a situation, the registration unit can analyze the user's social media activity and register related situations. Social media activity includes, for example, the content of posts, the number of likes, comments, etc. The registration unit can, for example, suggest situations related to places where the user has checked in on social media. The registration unit can also analyze the content of the user's posts on social media and suggest related situations. Furthermore, the registration unit can suggest related situations based on the activity of the user's friends on social media. In this way, situations based on the user's social media activity are provided.

[0072] The registration unit can customize the registration method by reflecting the user's past feedback when registering a situation. The customization criteria include, for example, the user's past feedback, usage history, etc. The registration unit can suggest the optimal registration method based on, for example, the user's past feedback. The registration unit can also preferentially suggest a specific registration method based on the user's past feedback. Furthermore, the registration unit can customize the registration interface by reflecting the user's feedback. This provides the optimal registration method based on the user's past feedback.

[0073] The simulation unit can estimate the user's emotions and adjust the way the simulation is presented based on the estimated user's emotions. The simulation presentation methods include, for example, text format, audio format, and visual format. For example, if the user is nervous, the simulation unit can provide a simple, highly visible presentation method. Furthermore, if the user is relaxed, the simulation unit can also provide a presentation method that includes detailed information. Furthermore, if the user is in a hurry, the simulation unit can also provide a presentation method that focuses on the main points. This allows the optimal way of presenting the simulation according to the user's emotions to be presented.

[0074] During the simulation, the simulation unit can adjust the level of detail of the simulation based on the importance of the situation. Criteria for the level of detail of the simulation include, for example, the importance of the situation, user requests, etc. The simulation unit can provide a detailed simulation, for example, for an important situation. The simulation unit can also provide a simplified simulation for a situation with low importance. Furthermore, the simulation unit can dynamically adjust the level of detail of the simulation according to the importance of the situation. This allows for the provision of an optimal simulation according to the importance of the situation.

[0075] During the simulation, the simulation unit can apply different simulation algorithms depending on the category of the situation. Simulation algorithms include, for example, the Monte Carlo method and agent-based simulation. For example, in the case of a conversation at work, the simulation unit can apply a simulation algorithm specialized for business scenes. In addition, in the case of a conversation with friends, the simulation unit can also apply a simulation algorithm specialized for casual scenes. Furthermore, in the case of a conversation with family, the simulation unit can apply a simulation algorithm specialized for domestic scenes. This provides an optimal simulation according to the category of the situation.

[0076] During the simulation, the simulation unit can improve the accuracy of the simulation by referring to the user's past simulation results. Methods for improving the accuracy of the simulation include, for example, using past data and improving the algorithm. For example, the simulation unit improves the accuracy of the simulation based on the results of simulations performed by the user in the past. The simulation unit can also extract specific patterns from the user's past simulation results to improve the accuracy. Furthermore, the simulation unit can reflect user feedback and improve the simulation algorithm. This improves the accuracy of the simulation based on the results of past simulations.

[0077] The simulation unit can estimate the user's emotions and adjust the length of the simulation based on the estimated user's emotions. Criteria for the length of the simulation include, for example, the user's emotions, the complexity of the situation, etc. For example, if the user is in a hurry, the simulation unit can provide a short, concise simulation. If the user is relaxed, the simulation unit can also provide a longer simulation with detailed explanations. Furthermore, if the user is excited, the simulation unit can also provide a simulation with visually stimulating effects. In this way, the optimal length of the simulation can be provided according to the user's emotions.

[0078] During simulation, the simulation unit can determine the priority of simulations based on the time of occurrence of a situation. Criteria for determining the time of occurrence of a situation include, for example, past data, a prediction model, etc. The simulation unit, for example, prioritizes simulation of a situation that will occur most recently. The simulation unit can also prioritize simulation of a situation that has frequently occurred in the past. Furthermore, the simulation unit can also prioritize simulation of a situation that a user uses during a specific time period. This provides an optimal simulation priority according to the time of occurrence of a situation.

[0079] During the simulation, the simulation unit can adjust the order of the simulation based on the relevance of the situations. Criteria for the relevance of the situations include, for example, the content of the situation, the user's interests, etc. The simulation unit, for example, prioritizes simulating highly relevant situations. The simulation unit can also postpone less relevant situations. Furthermore, the simulation unit can dynamically adjust the order of the simulation based on the relevance of the situations. This provides an optimal order of the simulation based on the relevance of the situations.

[0080] During the simulation, the simulation unit can adjust the use of technical terms in the simulation according to the user's level of expertise. Criteria for the use of technical terms include, for example, the user's level of expertise and the content of the situation. For example, if the user has technical expertise, the simulation unit can provide a simulation that makes extensive use of technical terms. Also, if the user does not have technical expertise, the simulation unit can provide a simulation that explains things in simple terms. Furthermore, the simulation unit can dynamically adjust the use of technical terms according to the user's level of expertise. This allows for the provision of an optimal simulation according to the user's level of expertise.

[0081] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated user's emotions. Feedback methods include, for example, text format, audio format, visual format, etc. For example, the feedback unit can provide gentle feedback when the user is nervous. The feedback unit can also provide detailed feedback when the user is relaxed. Furthermore, the feedback unit can provide concise and to-the-point feedback when the user is in a hurry. This allows for an optimal feedback method to be provided according to the user's emotions.

[0082] When providing feedback, the feedback unit can analyze the user's past comment history and select the optimal feedback method. Criteria for the optimal feedback method include, for example, the user's past comment history and the content of the feedback. The feedback unit can provide optimal feedback, for example, based on the user's past comments. The feedback unit can also extract specific patterns from the user's past comment history and provide feedback. Furthermore, the feedback unit can customize the content of the feedback by referring to the user's past comment history. This allows the optimal feedback method to be provided based on the user's past comment history.

[0083] When providing feedback, the feedback unit can customize the means of feedback based on the user's current living situation. The means of feedback can include, for example, text, audio, visual, etc. For example, the feedback unit can provide short and to-the-point feedback when the user is busy. Alternatively, the feedback unit can provide detailed feedback when the user is relaxed. Furthermore, the feedback unit can customize the means of feedback (text, audio, etc.) according to the user's living situation. This allows the optimal means of feedback to be provided according to the user's living situation.

[0084] The feedback unit can improve the feedback method by reflecting the user's feedback when providing feedback. Criteria for the feedback method include, for example, the content of the user's feedback and the frequency of feedback. The feedback unit can improve the feedback method, for example, based on feedback provided by the user in the past. The feedback unit can also extract specific improvements from the user's feedback and optimize the feedback method. Furthermore, the feedback unit can customize the content and means of feedback by reflecting the user's feedback. This provides an optimal feedback method based on the user's feedback.

[0085] The feedback unit can estimate the user's emotion and determine the priority of feedback based on the estimated user's emotion. Criteria for determining the priority of feedback include, for example, the user's emotion and the importance of the feedback. For example, if the user is feeling stressed, the feedback unit can prioritize providing relaxing feedback. Also, if the user is having fun, the feedback unit can prioritize providing enjoyable feedback. Furthermore, if the user is tired, the feedback unit can prioritize providing simple and less burdensome feedback. In this way, optimal feedback priority is provided according to the user's emotion.

[0086] The feedback unit may select an optimal feedback method by taking into consideration the user's geographical location information when providing feedback. Examples of geographical location information include GPS data, location information services, and the like. For example, if the user is in a specific location, the feedback unit may provide feedback related to the location. The feedback unit may also provide feedback related to locations close to the user's current location. Furthermore, the feedback unit may provide relevant feedback by referring to the user's past location information. This provides an optimal feedback method based on the user's geographical location information.

[0087] When providing feedback, the feedback unit may analyze the user's social media activity and suggest a means of providing feedback. Social media activity includes, for example, the content of posts, the number of likes, and comments. The feedback unit may provide feedback related to, for example, places where the user has checked in on social media. The feedback unit may also analyze the content of the user's posts on social media and provide related feedback. Furthermore, the feedback unit may provide related feedback by referring to the activities of the user's friends on social media. This allows the optimal means of feedback to be provided based on the user's social media activity.

[0088] The feedback unit can customize the feedback method by reflecting the user's past feedback when providing feedback. Customization criteria include, for example, the user's past feedback, usage history, etc. The feedback unit can, for example, suggest an optimal feedback method based on the user's past feedback. The feedback unit can also preferentially suggest a specific feedback method based on the user's past feedback. Furthermore, the feedback unit can customize the feedback interface by reflecting the user's feedback. This provides an optimal feedback method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned registration unit, simulation unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart device 14 and can input the user's position and situation. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's utterances using natural language processing technology. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback to the user. This allows the user to naturally use clever words and actions. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned registration unit, simulation unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214 and can input the user's position and situation. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's utterances using natural language processing technology. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback to the user. This allows the user to naturally use clever words and actions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned registration unit, simulation unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the headset type terminal 314 and can input the user's position and situation. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's utterances using natural language processing technology. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback to the user. This allows the user to naturally use clever words and actions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned registration unit, simulation unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414 and can input the user's position and situation. The simulation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's utterances using natural language processing technology. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides feedback to the user. This allows the user to naturally use clever words and actions.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The system can analyze the tone and speed of a user's voice to infer their emotions. For example, if a user speaks quickly, it can infer that they are nervous and provide advice to relax. If a user speaks slowly, it can infer that they are relaxed and provide detailed feedback. Furthermore, if the user's voice tone is high-pitched, it can infer that they are excited and provide advice to calm down. This allows the system to provide optimal feedback based on the user's tone and speed.

[0091] The system can analyze a user's past speech history and provide optimal feedback. For example, it can analyze what the user has said in similar situations in the past and provide feedback based on the results. It can also provide more effective feedback by referring to feedback the user has received in the past. Furthermore, it can extract specific patterns from the user's past speech history and provide feedback based on those patterns. This allows the system to provide optimal feedback based on the user's past speech history.

[0092] The system can provide feedback taking into account the user's current life situation. For example, if the user is busy, the system can provide short and to-the-point feedback. If the user is relaxed, the system can provide detailed feedback. Furthermore, the system can customize the means of feedback (text, voice, etc.) depending on the user's life situation. This allows the system to provide optimal feedback according to the user's life situation.

[0093] The system can provide feedback taking into account the user's geographic location. For example, if the user is in a specific location, the system can provide feedback related to that location. It can also provide feedback related to locations close to the user's current location. It can also provide relevant feedback based on the user's past location information. This allows the system to provide optimal feedback based on the user's geographic location.

[0094] The system can analyze the user's social media activity and provide feedback. For example, the system can provide feedback related to the places the user has checked in on social media. The system can also analyze the content of the user's social media posts and provide relevant feedback. Furthermore, the system can provide relevant feedback based on the activities of the user's friends on social media. This allows the system to provide optimal feedback based on the user's social media activity.

[0095] The system can estimate the user's emotions and prioritize feedback based on the estimated emotions. For example, if the user is feeling stressed, it can prioritize providing relaxing feedback. If the user is having fun, it can prioritize providing enjoyable feedback. Furthermore, if the user is tired, it can prioritize providing simple and less burdensome feedback. In this way, it can provide optimal feedback priorities according to the user's emotions.

[0096] The system can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, if the user is nervous, the system can provide gentle feedback. If the user is relaxed, the system can provide detailed feedback. If the user is in a hurry, the system can provide concise and to-the-point feedback. This allows the system to provide the optimal feedback method according to the user's emotions.

[0097] The system can estimate the user's emotions and adjust the way the simulation is presented based on the estimated emotions. For example, if the user is nervous, a simple, highly visible presentation can be provided. If the user is relaxed, a presentation that includes detailed information can be provided. Furthermore, if the user is in a hurry, a presentation that focuses on the main points can be provided. In this way, the system can provide the optimal way of presenting the simulation according to the user's emotions.

[0098] The system can estimate the user's emotions and adjust the length of the simulation based on the estimated emotions. For example, if the user is in a hurry, the system can provide a short, to-the-point simulation. If the user is relaxed, the system can provide a longer simulation with detailed explanations. If the user is excited, the system can provide a simulation with visually stimulating effects. This allows the system to provide the optimal length of the simulation according to the user's emotions.

[0099] The system can estimate the user's emotions and adjust the situation registration method based on the estimated emotions. For example, if the user is nervous, it can provide a simple and intuitive interface to minimize the registration steps. Alternatively, if the user is relaxed, it can provide detailed input options and suggest a customizable registration method. Furthermore, if the user is in a hurry, it can prioritize voice input to quickly register the situation. This allows the system to provide the optimal situation registration method according to the user's emotions.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The registration unit registers the user's position or situation. For example, a specific situation such as a conversation at work or with a friend can be entered. Step 2: The simulation unit performs a simulation based on the data registered by the registration unit. For example, the simulation unit uses natural language processing technology to analyze how the other person reacts to the words spoken by the user. Step 3: The feedback unit feeds back the results obtained by the simulation unit. For example, the feedback unit feeds back whether there was a better way to say something that the user said.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 registration unit for registering a user's position or situation; a simulation unit that performs a simulation based on the data registered by the registration unit; a feedback unit that feeds back the results obtained by the simulation unit; Equipped with A system characterized by:

2. The simulation unit Using natural language processing technology, we analyze how the other person feels about the words spoken by the user.

2. The system of claim 1.

3. The feedback unit Provide feedback on what the user said to see if there was a better way to say it.

2. The system of claim 1.

4. The registration unit Register specific situations, such as conversations at work or with friends 2. The system of claim 1.

5. The feedback unit Give feedback on how to express your gratitude in a specific way for the user's comments 2. The system of claim 1.

6. The registration unit Estimate the user's emotions and adjust the method of registering situations based on the estimated user emotions.

2. The system of claim 1.

7. The registration unit Analyze the user's past situation registration history and select the optimal registration method 2. The system of claim 1.

8. The registration unit When registering situations, filtering is performed based on the user's current life situation and areas of interest.

2. The system of claim 1.

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