System

Abstract

An object of a system according to an embodiment is to propose an optimal expression on the basis of characteristics of a partner and a context of communication.SOLUTION: A system includes a characteristic analysis part, a context analysis part, and an expression proposal part. The characteristic analysis unit analyzes a characteristic of the other party. The context analysis unit analyzes the context of the communication on the basis of the characteristics of the other party analyzed by the characteristic analysis unit. The expression proposal unit proposes an expression optimized based on the context of the communication analyzed by the context analysis 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 do not adequately suggest optimal expressions based on the context of communication and the characteristics of the other party, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal expressions based on the characteristics of the other party and the context of the communication. [Means for solving the problem]

[0006] The system according to the embodiment includes a characteristic analysis unit, a context analysis unit, and an expression suggestion unit. The characteristic analysis unit analyzes the characteristics of the other party. The context analysis unit analyzes the context of communication based on the characteristics of the other party analyzed by the characteristic analysis unit. The expression suggestion unit suggests an optimized expression based on the context of communication analyzed by the context analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most appropriate expression based on the characteristics of the other person and the context of the communication. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​assistant system according to an embodiment of the present invention is a system that, when a user chats or inputs a message, provides optimized expressions and suggestions based on the characteristics of the other party and the context of communication, allowing the user to send an effective message that is appropriate for the other party.

[0029] An AI assistant system according to an embodiment includes a characteristic analysis unit, a context analysis unit, and an expression suggestion unit. The characteristic analysis unit analyzes the characteristics of the other party. For example, the characteristic analysis unit determines what expression is appropriate based on information such as the other party's age, gender, occupation, and hobbies. The characteristic analysis unit analyzes the characteristics of the other party using a generation AI (e.g., a text generation AI or a multimodal generation AI). The context analysis unit analyzes the context of communication based on the characteristics of the other party analyzed by the characteristic analysis unit. For example, the context analysis unit determines what expression is appropriate based on past message exchanges and the current flow of conversation. The context analysis unit analyzes the context of communication using the generation AI. The expression suggestion unit suggests an optimized expression based on the context of communication analyzed by the context analysis unit. For example, when a user inputs "thank you," the expression suggestion unit uses the generation AI to suggest a more polite expression such as "I really appreciate it." The expression suggestion unit uses the generation AI to suggest an optimized expression. As a result, the AI ​​assistant system according to the embodiment can provide optimized expressions and suggestions based on the characteristics of the other party and the context of communication when a user chats or enters a message.

[0030] The characteristic analysis unit can extract more detailed characteristics by analyzing the other party's past online activities and social media posts. For example, the characteristic analysis unit can analyze the other party's past social media posts to identify their interests. For example, it can analyze the topics the other party frequently posts on and the hashtags they use to extract specific areas of interest. The characteristic analysis unit needs to clarify the specific content and scope of online activities. For example, it can analyze social media posts, blog updates, forum participation, etc. The characteristic analysis unit needs to clarify the specific type of social media posts and the analysis method. For example, it can analyze text posts, image posts, comments, etc. This makes it possible to extract more detailed characteristics by analyzing the other party's past online activities and social media posts.

[0031] The feature analysis unit is able to suggest regional expressions by taking into account the language, dialect, and slang used by the other party. For example, the feature analysis unit analyzes the language and dialect used by the other party and suggests regional expressions. For example, it selects appropriate expressions by taking into account dialects such as Kansai dialect and Tohoku dialect. The feature analysis unit needs to clarify the specific type and range of language. For example, it analyzes English, Japanese, Spanish, etc. The feature analysis unit needs to clarify the specific type and range of dialect. For example, it analyzes Kansai dialect and New York accent. The feature analysis unit needs to clarify the specific type and range of slang. For example, it analyzes youth slang and internet slang. This makes it possible to suggest regional expressions by taking into account the language, dialect, and slang used by the other party.

[0032] The context analysis unit can automatically extract topics and themes from a conversation and provide information related to those topics. For example, the context analysis unit automatically extracts the topic of the current conversation and provides information related to that topic. For example, if the topic is travel, it will suggest information about travel destinations and recommended spots. The context analysis unit needs to clarify the specific type of topic and the extraction method. For example, it may extract the theme of the conversation or the focus of the topic. The context analysis unit needs to clarify the specific type of theme and the extraction method. For example, it may extract the subject of the discussion or the focus of the topic. This allows the topic or theme of a conversation to be automatically extracted and information related to that topic to be provided.

[0033] The context analysis unit can analyze past message exchanges and determine the context based on the frequency of specific phrases and keywords. The context analysis unit, for example, analyzes past message exchanges and identifies frequently used phrases and keywords. For example, if a specific keyword is used frequently, it makes suggestions that take that context into consideration. The context analysis unit needs to clarify the specific type of phrase and the extraction method. For example, it extracts commonly used phrases, set phrases, etc. The context analysis unit needs to clarify the specific type of keyword and the extraction method. For example, it extracts frequently used words, important phrases, etc. This makes it possible to analyze past message exchanges and determine the context based on the frequency of specific phrases and keywords.

[0034] The expression suggestion unit can learn the user's past message style and suggest expressions that match the user's personality. The expression suggestion unit, for example, learns the user's past message style and suggests expressions that match that style. For example, it takes into account phrases and expressions that the user frequently uses. The expression suggestion unit needs to clarify the specific type of message style and the learning method. For example, it can learn formal, casual, humorous, etc. The expression suggestion unit needs to clarify the specific elements of personality and the evaluation method. For example, it evaluates the use of language, characteristics of expressions, etc. This makes it possible to learn the user's past message style and suggest expressions that match the user's personality.

[0035] The expression suggestion unit can evaluate the effectiveness of the proposed expressions and improve the proposed content based on user feedback. The expression suggestion unit, for example, builds a system that evaluates the effectiveness of the proposed expressions and improves the proposed content based on user feedback. For example, it adjusts the proposal based on the user's evaluation score. The expression suggestion unit needs to clarify specific evaluation criteria and methods for effectiveness. For example, it evaluates the user's reaction, how the message is received, etc. The expression suggestion unit needs to clarify specific types of feedback and collection methods. For example, it collects user comments, evaluation scores, etc. This allows it to evaluate the effectiveness of the proposed expressions and improve the proposed content based on user feedback.

[0036] The expression suggestion unit can suggest expressions that correspond to different languages ​​and cultural spheres, thereby supporting international communication. The expression suggestion unit, for example, builds a system that suggests expressions that correspond to different languages. For example, it makes suggestions that correspond to multiple languages, such as English, French, and Chinese. The expression suggestion unit needs to clarify the specific type of language and how it will be handled. For example, it provides multilingual support and translation functions. The expression suggestion unit needs to clarify the specific type of cultural sphere and how it will be handled. For example, it takes into account differences in cultural background and customs. This makes it possible to support international communication by suggesting expressions that correspond to different languages ​​and cultural spheres.

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

[0038] The AI ​​assistant system may also include a health analysis unit that monitors the user's health condition. The health analysis unit analyzes the user's health data (e.g., heart rate, sleep patterns, amount of exercise, etc.) and suggests appropriate messages based on the user's health condition. For example, if the user is feeling stressed, it may provide advice on how to relax. The health analysis unit must clarify the specific type of health data and the analysis method. For example, it may use data obtained from a wearable device. This allows it to suggest appropriate messages based on the user's health condition.

[0039] The AI ​​assistant system can also include a schedule management unit that manages the user's schedule. The schedule management unit analyzes the user's calendar and planner and suggests appropriate messages based on important events and tasks. For example, the user can receive a reminder before a meeting. The schedule management unit must clarify the specific type of schedule data and the analysis method. For example, it can use data from Google Calendar or Outlook. This allows it to suggest appropriate messages based on the user's schedule.

[0040] The AI ​​assistant system can also be equipped with a purchasing analysis unit that analyzes the user's purchasing history. The purchasing analysis unit analyzes the user's past purchasing history and suggests appropriate products and services based on the user's preferences. For example, it can suggest related products based on products the user has previously purchased. The purchasing analysis unit must clarify the specific type of purchasing history and the analysis method. For example, it can use data from online shopping sites. This allows it to suggest appropriate products and services based on the user's purchasing history.

[0041] The AI ​​assistant system can also be equipped with a learning analysis unit that analyzes the user's learning history. The learning analysis unit analyzes the user's past learning history and suggests appropriate learning content based on the user's learning style and progress. For example, it may suggest what the user should learn next based on what they have learned in the past. The learning analysis unit must clarify the specific type of learning history and the analysis method. For example, it may use data from an online learning platform. This allows it to suggest appropriate learning content based on the user's learning history.

[0042] The AI ​​assistant system can also include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit analyzes the user's past activities and interests and suggests appropriate activities and events based on the user's hobbies. For example, it may suggest related events based on events the user has previously participated in. The hobby analysis unit must clarify the specific types of hobbies and interests and the analysis method. For example, it may use social media data. This allows it to suggest appropriate activities and events based on the user's hobbies and interests.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The characteristic analysis unit analyzes the characteristics of the other person. For example, it determines what expression is appropriate based on information such as the other person's age, gender, occupation, and hobbies. The characteristic analysis unit uses generative AI (for example, text generation AI or multimodal generation AI) to analyze the other person's characteristics. Step 2: The context analysis unit analyzes the context of the communication based on the characteristics of the other party analyzed by the characteristic analysis unit. For example, it determines what expressions are appropriate based on past message exchanges and the current flow of the conversation. The context analysis unit uses generative AI to analyze the context of the communication. Step 3: The expression suggestion unit proposes optimized expressions based on the communication context analyzed by the context analysis unit. For example, if a user enters "Thank you," the generation AI suggests a more polite expression such as "I'm really grateful." The expression suggestion unit proposes optimized expressions using the generation AI.

[0045] (Example 2) The AI ​​assistant system according to an embodiment of the present invention is a system that, when a user chats or inputs a message, provides optimized expressions and suggestions based on the characteristics of the other party and the context of communication, allowing the user to send an effective message that is appropriate for the other party.

[0046] An AI assistant system according to an embodiment includes a characteristic analysis unit, a context analysis unit, and an expression suggestion unit. The characteristic analysis unit analyzes the characteristics of the other party. For example, the characteristic analysis unit determines what expression is appropriate based on information such as the other party's age, gender, occupation, and hobbies. The characteristic analysis unit analyzes the characteristics of the other party using a generation AI (e.g., a text generation AI or a multimodal generation AI). The context analysis unit analyzes the context of communication based on the characteristics of the other party analyzed by the characteristic analysis unit. For example, the context analysis unit determines what expression is appropriate based on past message exchanges and the current flow of conversation. The context analysis unit analyzes the context of communication using the generation AI. The expression suggestion unit suggests an optimized expression based on the context of communication analyzed by the context analysis unit. For example, when a user inputs "thank you," the expression suggestion unit uses the generation AI to suggest a more polite expression such as "I really appreciate it." The expression suggestion unit uses the generation AI to suggest an optimized expression. As a result, the AI ​​assistant system according to the embodiment can provide optimized expressions and suggestions based on the characteristics of the other party and the context of communication when a user chats or enters a message.

[0047] The characteristic analysis unit can extract more detailed characteristics by analyzing the other party's past online activities and social media posts. For example, the characteristic analysis unit can analyze the other party's past social media posts to identify their interests. For example, it can analyze the topics the other party frequently posts on and the hashtags they use to extract specific areas of interest. The characteristic analysis unit needs to clarify the specific content and scope of online activities. For example, it can analyze social media posts, blog updates, forum participation, etc. The characteristic analysis unit needs to clarify the specific type of social media posts and the analysis method. For example, it can analyze text posts, image posts, comments, etc. This makes it possible to extract more detailed characteristics by analyzing the other party's past online activities and social media posts.

[0048] The feature analysis unit is able to suggest regional expressions by taking into account the language, dialect, and slang used by the other party. For example, the feature analysis unit analyzes the language and dialect used by the other party and suggests regional expressions. For example, it selects appropriate expressions by taking into account dialects such as Kansai dialect and Tohoku dialect. The feature analysis unit needs to clarify the specific type and range of language. For example, it analyzes English, Japanese, Spanish, etc. The feature analysis unit needs to clarify the specific type and range of dialect. For example, it analyzes Kansai dialect and New York accent. The feature analysis unit needs to clarify the specific type and range of slang. For example, it analyzes youth slang and internet slang. This makes it possible to suggest regional expressions by taking into account the language, dialect, and slang used by the other party.

[0049] The characteristic analysis unit can use the emotion estimation function to analyze the emotional tendencies of the other party from their past messages and suggest expressions that match those emotions. For example, the characteristic analysis unit analyzes the other party's past messages and identifies their emotional tendencies. For example, if there are a lot of positive emotions, it suggests cheerful expressions. The characteristic analysis unit needs to clarify the specific technology and method of the emotion estimation function. For example, it uses natural language processing, machine learning algorithms, etc. The characteristic analysis unit needs to clarify the specific analysis method and criteria for emotional tendencies. For example, it analyzes positive, negative, neutral, etc. As a result, the emotion estimation function can be used to suggest expressions that match the other party's emotions.

[0050] The context analysis unit can automatically extract topics and themes from a conversation and provide information related to those topics. For example, the context analysis unit automatically extracts the topic of the current conversation and provides information related to that topic. For example, if the topic is travel, it will suggest information about travel destinations and recommended spots. The context analysis unit needs to clarify the specific type of topic and the extraction method. For example, it may extract the theme of the conversation or the focus of the topic. The context analysis unit needs to clarify the specific type of theme and the extraction method. For example, it may extract the subject of the discussion or the focus of the topic. This allows the topic or theme of a conversation to be automatically extracted and information related to that topic to be provided.

[0051] The context analysis unit can analyze past message exchanges and determine the context based on the frequency of specific phrases and keywords. The context analysis unit, for example, analyzes past message exchanges and identifies frequently used phrases and keywords. For example, if a specific keyword is used frequently, it makes suggestions that take that context into consideration. The context analysis unit needs to clarify the specific type of phrase and the extraction method. For example, it extracts commonly used phrases, set phrases, etc. The context analysis unit needs to clarify the specific type of keyword and the extraction method. For example, it extracts frequently used words, important phrases, etc. This makes it possible to analyze past message exchanges and determine the context based on the frequency of specific phrases and keywords.

[0052] The context analysis unit can use the emotion estimation function to analyze the emotional flow of the current conversation and suggest expressions that match that emotion. The context analysis unit, for example, analyzes the emotional flow of the current conversation and suggests expressions that match that emotion. For example, if the conversation is proceeding with positive emotions, it suggests upbeat expressions. The context analysis unit needs to clarify the specific analysis method and criteria for the emotional flow. For example, it analyzes the tone of the conversation, changes in emotion, etc. In this way, by using the emotion estimation function, it can suggest expressions that match the emotional flow of the current conversation.

[0053] The expression suggestion unit can learn the user's past message style and suggest expressions that match the user's personality. The expression suggestion unit, for example, learns the user's past message style and suggests expressions that match that style. For example, it takes into account phrases and expressions that the user frequently uses. The expression suggestion unit needs to clarify the specific type of message style and the learning method. For example, it can learn formal, casual, humorous, etc. The expression suggestion unit needs to clarify the specific elements of personality and the evaluation method. For example, it evaluates the use of language, characteristics of expressions, etc. This makes it possible to learn the user's past message style and suggest expressions that match the user's personality.

[0054] The expression suggestion unit can evaluate the effectiveness of the proposed expressions and improve the proposed content based on user feedback. The expression suggestion unit, for example, builds a system that evaluates the effectiveness of the proposed expressions and improves the proposed content based on user feedback. For example, it adjusts the proposal based on the user's evaluation score. The expression suggestion unit needs to clarify specific evaluation criteria and methods for effectiveness. For example, it evaluates the user's reaction, how the message is received, etc. The expression suggestion unit needs to clarify specific types of feedback and collection methods. For example, it collects user comments, evaluation scores, etc. This allows it to evaluate the effectiveness of the proposed expressions and improve the proposed content based on user feedback.

[0055] The expression suggestion unit uses the emotion estimation function to suggest expressions that match the user's emotional state, thereby making the user's emotions more positive. The expression suggestion unit, for example, uses the emotion estimation function to suggest expressions that match the user's emotional state. For example, if the user is feeling down, the expression suggestion unit suggests encouraging expressions. The expression suggestion unit needs to clarify specific evaluation criteria and analysis methods for the emotional state. For example, stress levels, happiness levels, etc. are evaluated. In this way, the emotion estimation function can be used to suggest expressions that match the user's emotional state, thereby making the user's emotions more positive.

[0056] The expression suggestion unit can suggest expressions that correspond to different languages ​​and cultural spheres, thereby supporting international communication. The expression suggestion unit, for example, builds a system that suggests expressions that correspond to different languages. For example, it makes suggestions that correspond to multiple languages, such as English, French, and Chinese. The expression suggestion unit needs to clarify the specific type of language and how it will be handled. For example, it provides multilingual support and translation functions. The expression suggestion unit needs to clarify the specific type of cultural sphere and how it will be handled. For example, it takes into account differences in cultural background and customs. This makes it possible to support international communication by suggesting expressions that correspond to different languages ​​and cultural spheres.

[0057] The expression suggestion unit can use the emotion estimation function to predict the emotional impact that a proposed expression will have on the other person, and suggest the expression that will have the most positive impact. The expression suggestion unit, for example, uses the emotion estimation function to predict the emotional impact that a proposed expression will have on the other person. For example, it evaluates whether the proposed expression will bring joy to the other person. The expression suggestion unit needs to clarify specific evaluation criteria and prediction methods for the emotional impact. For example, it evaluates positive impact, negative impact, etc. In this way, by using the emotion estimation function, it is possible to predict the emotional impact that a proposed expression will have on the other person, and suggest the expression that will have the most positive impact.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The AI ​​assistant system may also include a health analysis unit that monitors the user's health condition. The health analysis unit analyzes the user's health data (e.g., heart rate, sleep patterns, amount of exercise, etc.) and suggests appropriate messages based on the user's health condition. For example, if the user is feeling stressed, it may provide advice on how to relax. The health analysis unit must clarify the specific type of health data and the analysis method. For example, it may use data obtained from a wearable device. This allows it to suggest appropriate messages based on the user's health condition.

[0060] The AI ​​assistant system can also include a schedule management unit that manages the user's schedule. The schedule management unit analyzes the user's calendar and planner and suggests appropriate messages based on important events and tasks. For example, the user can receive a reminder before a meeting. The schedule management unit must clarify the specific type of schedule data and the analysis method. For example, it can use data from Google Calendar or Outlook. This allows it to suggest appropriate messages based on the user's schedule.

[0061] The AI ​​assistant system can also be equipped with a purchasing analysis unit that analyzes the user's purchasing history. The purchasing analysis unit analyzes the user's past purchasing history and suggests appropriate products and services based on the user's preferences. For example, it can suggest related products based on products the user has previously purchased. The purchasing analysis unit must clarify the specific type of purchasing history and the analysis method. For example, it can use data from online shopping sites. This allows it to suggest appropriate products and services based on the user's purchasing history.

[0062] The AI ​​assistant system can also be equipped with a learning analysis unit that analyzes the user's learning history. The learning analysis unit analyzes the user's past learning history and suggests appropriate learning content based on the user's learning style and progress. For example, it may suggest what the user should learn next based on what they have learned in the past. The learning analysis unit must clarify the specific type of learning history and the analysis method. For example, it may use data from an online learning platform. This allows it to suggest appropriate learning content based on the user's learning history.

[0063] The AI ​​assistant system can also include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit analyzes the user's past activities and interests and suggests appropriate activities and events based on the user's hobbies. For example, it may suggest related events based on events the user has previously participated in. The hobby analysis unit must clarify the specific types of hobbies and interests and the analysis method. For example, it may use social media data. This allows it to suggest appropriate activities and events based on the user's hobbies and interests.

[0064] The determination unit can also estimate the user's emotions and suggest music suitable for the user based on the estimated user emotions. For example, if the user is feeling stressed, it can suggest relaxing music. The determination unit must clarify the specific technology and method of the emotion estimation function. For example, natural language processing or machine learning algorithms can be used. This allows it to suggest appropriate music based on the user's emotions.

[0065] The determination unit can also estimate the user's emotions and suggest movies and dramas that are suitable for the user based on the estimated user emotions. For example, if the user is feeling sad, it can suggest comedy movies that will brighten the mood. The determination unit needs to clarify the specific technology and method of the emotion estimation function. For example, natural language processing or machine learning algorithms can be used. This allows it to suggest appropriate movies and dramas based on the user's emotions.

[0066] The determination unit can also estimate the user's emotions and suggest appropriate exercises based on the estimated user emotions. For example, if the user is feeling energetic, it can suggest high-intensity exercises. The determination unit must clarify the specific technology and method for its emotion estimation function. For example, it can use natural language processing or machine learning algorithms. This allows it to suggest appropriate exercises based on the user's emotions.

[0067] The determination unit can also estimate the user's emotions and suggest appropriate reading content to the user based on the estimated user emotions. For example, if the user wants to relax, it can suggest a relaxing novel. The determination unit must clarify the specific technology and method of the emotion estimation function. For example, natural language processing or machine learning algorithms can be used. This allows it to suggest appropriate reading content based on the user's emotions.

[0068] The determination unit can also estimate the user's emotions and suggest suitable travel destinations to the user based on the estimated user emotions. For example, if the user is adventurous, it can suggest active travel destinations. The determination unit must clarify the specific technology and method of the emotion estimation function. For example, natural language processing or machine learning algorithms can be used. This allows it to suggest suitable travel destinations based on the user's emotions.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The characteristic analysis unit analyzes the characteristics of the other person. For example, it determines what expression is appropriate based on information such as the other person's age, gender, occupation, and hobbies. The characteristic analysis unit uses generative AI (for example, text generation AI or multimodal generation AI) to analyze the other person's characteristics. Step 2: The context analysis unit analyzes the context of the communication based on the characteristics of the other party analyzed by the characteristic analysis unit. For example, it determines what expressions are appropriate based on past message exchanges and the current flow of the conversation. The context analysis unit uses generative AI to analyze the context of the communication. Step 3: The expression suggestion unit proposes optimized expressions based on the communication context analyzed by the context analysis unit. For example, if a user enters "Thank you," the generation AI suggests a more polite expression such as "I'm really grateful." The expression suggestion unit proposes optimized expressions using the generation AI.

[0071] 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.

[0072] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0073] 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.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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).

[0080] 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.

[0081] 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.

[0082] 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.

[0083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The 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.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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."

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0138] 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 characteristic analysis unit that analyzes the characteristics of the opponent; a context analysis unit that analyzes the context of communication based on the characteristics of the other party analyzed by the characteristic analysis unit; an expression suggestion unit that suggests an optimized expression based on the context of the communication analyzed by the context analysis unit. A system characterized by:

2. The characteristic analysis unit Analyze the other person's past online activity and social media posts to extract more detailed characteristics 2. The system of claim 1.

3. The context analysis unit Automatically extract conversation topics and themes and provide information related to those topics 2. The system of claim 1.

4. The expression suggestion unit Learns the user's past messaging style and suggests expressions that match the user's personality 2. The system of claim 1.

5. The characteristic analysis unit Using emotion estimation, the app analyzes the other person's emotional tendencies from their past messages and suggests expressions that match those emotions.

2. The system of claim 1.

6. The context analysis unit Using emotion estimation, the system analyzes the emotional flow of the current conversation and suggests expressions that match that emotion.

2. The system of claim 1.

7. The expression suggestion unit Using emotion estimation function, we suggest expressions that match the user's emotional state, making the user feel positive.

2. The system of claim 1.

8. The expression suggestion unit Using emotion estimation, the system predicts the emotional impact of the proposed expression on the other person and suggests the expression that will have the most positive impact.

2. The system of claim 1.

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