System

Abstract

An object of a system according to an embodiment is to analyze a chat log, generate a natural conversation, and propose a partner with good compatibility.SOLUTION: A system according to an embodiment includes a chat log upload unit, an analysis unit, a speech generation unit, and a matching unit. The chat log uploading unit uploads a chat log of a user. The analysis unit analyzes the chat log uploaded by the chat log upload unit. The conversation generation unit generates a conversation on the basis of a result analyzed by the analysis unit. The matching unit proposes a partner having good compatibility on the basis of a result analyzed by the 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 have had the problem of not fully utilizing chat logs to generate natural conversations and suggest compatible partners.

[0005] The system according to the embodiment aims to analyze chat logs, generate natural conversations, and suggest compatible partners. [Means for solving the problem]

[0006] The system according to the embodiment includes a chat log upload unit, an analysis unit, a conversation generation unit, and a matching unit. The chat log upload unit uploads the user's chat log. The analysis unit analyzes the chat log uploaded by the chat log upload unit. The conversation generation unit generates a conversation based on the analysis result by the analysis unit. The matching unit suggests compatible partners based on the analysis result by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze chat logs, generate natural conversations, and suggest compatible partners. [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) The Talk Matchmaker according to an embodiment of the present invention is a system that automatically generates conversations using chat logs from a social networking service (LINE (registered trademark)). This system allows users to upload their own chat logs, and a generation AI analyzes the data to recreate conversations with other users. This allows the Talk Matchmaker to automatically generate conversations based on past chat history, saving time and effort while enabling natural communication. The system also includes an automatic matching function that can automatically suggest conversations with compatible users.

[0029] A talk matchmaker according to an embodiment includes a chat log uploading unit, an analysis unit, a conversation generation unit, and a matching unit. The chat log uploading unit uploads a user's chat log. For example, a user can upload a chat log from a social networking service (LINE (registered trademark)) to the service. The analysis unit analyzes the chat log uploaded by the chat log uploading unit. For example, a generation AI analyzes the user's chat log and extracts conversation patterns and characteristics. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's frequently used phrases, topics, and the other person's reactions. The conversation generation unit generates a conversation based on the results of the analysis by the analysis unit. For example, the generation AI generates natural responses by referring to the other person's reactions from past conversations. The generation AI can also generate conversation content using a multimodal generation AI. The matching unit suggests compatible partners based on the results of the analysis by the analysis unit. For example, the generation AI finds partners with common hobbies and interests, or partners with a similar conversational tempo, and suggests them to the user. As a result, the talk matchmaker according to the embodiment can analyze the user's chat log, generate natural conversations, and suggest compatible partners, thereby realizing efficient and natural communication.

[0030] The analysis unit can identify conversation patterns for each time period based on the time period and frequency of user comments. For example, when analyzing a chat log, the analysis unit considers the time period of user comments and identifies conversation patterns for each time period. For example, the analysis unit analyzes the content and frequency of user comments for each time period: morning, afternoon, and evening. The analysis unit also analyzes the frequency of user comments and identifies conversation patterns for specific time periods. For example, if a user frequently comments during a specific time period, the analysis unit extracts the conversation pattern for that time period. The analysis unit also considers the time period and frequency of user comments and identifies conversation patterns for each time period when analyzing a chat log. For example, the analysis unit analyzes the tendency of users to talk about specific topics during specific time periods. This makes it possible to generate more appropriate conversations by considering the time period and frequency of user comments.

[0031] The analysis unit can track changes in a user's writing style and language usage and generate a conversation that corresponds to the change in writing style. For example, when analyzing a chat log, the analysis unit tracks changes in a user's writing style and language usage and generates a conversation that corresponds to the change in writing style. For example, if a user changes from a formal to a casual writing style, the analysis unit generates a conversation that corresponds to the change in writing style. The analysis unit also analyzes changes in a user's writing style and language usage and generates a conversation that corresponds to the change in writing style. For example, if a user uses a different writing style with a specific person, the analysis unit generates a conversation that corresponds to that writing style. The analysis unit also tracks changes in a user's writing style and language usage and generates a conversation that corresponds to the change in writing style when analyzing a chat log. For example, if a user uses a different writing style for a specific topic, the analysis unit generates a conversation that corresponds to that writing style. This makes it possible to generate a conversation that corresponds to changes in a user's writing style and language usage.

[0032] The chat log upload unit analyzes multimedia data including voice messages and images, and can generate richer conversations. For example, when uploading a chat log, the chat log upload unit analyzes voice messages and extracts text data from the voice data. For example, it converts voice messages into text using voice recognition technology and uses the text to generate conversations. The chat log upload unit also analyzes image data and generates conversations based on the content of the images. For example, it analyzes the content of images using image recognition technology and generates conversations based on the content. The chat log upload unit also analyzes voice messages and image data and generates conversations based on the multimedia data. For example, it analyzes the content of voice messages and the content of images and generates conversations based on the content. In this way, analyzing voice messages and images makes it possible to generate richer conversations.

[0033] The chat log uploading unit can also analyze chat logs from different SNS platforms and compare conversation patterns across multiple platforms. For example, the chat log uploading unit analyzes chat logs from different SNS platforms and compares conversation patterns across each platform. For example, it analyzes chat logs from Facebook (registered trademark) and Twitter (registered trademark) and compares the content and frequency of user comments. The chat log uploading unit also analyzes chat logs from multiple SNS platforms and identifies common conversation patterns. For example, it analyzes common topics and phrases across different platforms. The chat log uploading unit also analyzes chat logs from different SNS platforms and compares conversation patterns across each platform. For example, it analyzes the user's speaking style and writing style on each platform. This makes it possible to compare a wider range of conversation patterns by analyzing chat logs from different SNS platforms.

[0034] The conversation generation unit can understand the context of the user's past statements in detail and generate advanced replies that correspond to the context. For example, when automatically generating a conversation, the conversation generation unit analyzes the context of the user's past statements and generates advanced replies that correspond to the context. For example, it suggests related topics based on what the user has said in the past. The conversation generation unit also deeply understands the context of the user's past statements and generates replies that correspond to the context. For example, it provides appropriate advice based on what the user has said in the past. The conversation generation unit also analyzes the context of the user's past statements and generates advanced replies that correspond to the context. For example, it provides related information based on what the user has said in the past. In this way, by deeply understanding the context of the user's past statements, more appropriate replies can be generated and natural communication can be achieved.

[0035] The conversation generation unit can analyze the intention of a user's utterance and generate an appropriate reply according to the intention. The conversation generation unit, for example, during automatic conversation generation, analyzes the intention of a user's utterance and generates an appropriate reply according to the intention. For example, if a user asks a question, it generates a specific answer to the question. The conversation generation unit also analyzes the intention of a user's utterance and generates an appropriate reply according to the intention. For example, if a user makes a suggestion, it generates opinions for or against the suggestion. The conversation generation unit also analyzes the intention of a user's utterance during automatic conversation generation and generates an appropriate reply according to the intention. For example, if a user expresses gratitude, it generates a reply to that gratitude. In this way, by analyzing the intention of a user's utterance, more appropriate replies can be generated, achieving natural communication.

[0036] The conversation generation unit enables conversation generation in different languages, thereby supporting international communication. The conversation generation unit, for example, enables conversation generation in different languages ​​during automatic conversation generation. For example, conversations are generated in multiple languages, such as English, Japanese, and French. The conversation generation unit also enables conversation generation in different languages, thereby supporting international communication. For example, it enables a user to have a natural conversation with a partner who speaks a different language. The conversation generation unit also enables conversation generation in different languages ​​during automatic conversation generation, thereby supporting international communication. For example, it enables a user to communicate smoothly with a partner who speaks a different language. This supports international communication by enabling conversation generation in different languages.

[0037] The conversation generation unit can predict a future conversation scenario based on the user's past conversation history and generate a reply based on the prediction. The conversation generation unit, for example, analyzes the user's past conversation history and predicts a future conversation scenario when automatically generating a conversation. For example, it predicts a topic that the user is likely to talk about next based on content that the user has spoken in the past. The conversation generation unit also predicts a future conversation scenario based on the user's past conversation history and generates a reply based on the prediction. For example, it generates a reply to a topic that the user is likely to talk about next. The conversation generation unit also analyzes the user's past conversation history and predicts a future conversation scenario when automatically generating a conversation. For example, it predicts a topic that the user is likely to talk about next and generates a reply to that topic. In this way, a future conversation scenario is predicted based on the user's past conversation history and a more appropriate reply is generated.

[0038] The matching unit can analyze the user's past conversation history in detail and identify compatible partners based on the conversation tempo and style. For example, during automatic matching, the matching unit analyzes the user's past conversation history in detail and identifies compatible partners based on the conversation tempo and style. For example, if the user speaks at a fast tempo, the matching unit suggests partners who speak at the same tempo. The matching unit also analyzes the user's past conversation history and identifies compatible partners based on the conversation style. For example, if the user uses a formal writing style, the matching unit suggests partners who use the same writing style. The matching unit also analyzes the user's past conversation history in detail during automatic matching and identifies compatible partners based on the conversation tempo and style. For example, if the user uses a casual writing style, the matching unit suggests partners who use the same writing style. In this way, by analyzing the user's past conversation history in detail, compatible partners based on the conversation tempo and style are identified.

[0039] The matching unit can analyze the user's hobbies and interests in detail and suggest partners who share the same hobbies and interests. For example, during automatic matching, the matching unit analyzes the user's hobbies and interests in detail and suggests partners who share the same hobbies and interests. For example, if the user is interested in music, it suggests partners who share the same hobbies. The matching unit also analyzes the user's hobbies and interests in detail and suggests partners who share the same hobbies and interests. For example, if the user is interested in sports, it suggests partners who share the same hobbies. The matching unit also analyzes the user's hobbies and interests in detail during automatic matching and suggests partners who share the same hobbies and interests. For example, if the user is interested in movies, it suggests partners who share the same hobbies. In this way, by analyzing the user's hobbies and interests in detail, it suggests partners who share the same hobbies and interests.

[0040] The matching unit enables matching between users of different SNS platforms, thereby providing opportunities to meet a wider range of potential partners. The matching unit enables matching between users of different SNS platforms, for example, during automatic matching. For example, it matches users of Facebook (registered trademark) and Twitter (registered trademark). The matching unit also enables matching between users of different SNS platforms, thereby providing opportunities to meet a wider range of potential partners. For example, it matches users of Instagram (registered trademark) and LinkedIn. The matching unit also enables matching between users of different SNS platforms, during automatic matching, thereby providing opportunities to meet a wider range of potential partners. For example, it matches users of LINE (registered trademark) and WhatsApp. This enables matching between users of different SNS platforms, thereby providing opportunities to meet a wider range of potential partners.

[0041] The matching unit can preferentially suggest nearby partners based on the user's geographical location information. For example, during automatic matching, the matching unit takes into account the user's geographical location information and preferentially suggests nearby partners. For example, partners who live in the same city or area are suggested. The matching unit also analyzes the user's geographical location information and preferentially suggests nearby partners. For example, partners are suggested based on the area where the user lives. The matching unit also takes into account the user's geographical location information and preferentially suggests nearby partners during automatic matching. For example, partners are suggested based on the area where the user commutes to. In this way, nearby partners are preferentially suggested by taking into account the user's geographical location information.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The analysis unit can identify conversation patterns for each time period based on the time period and frequency of user comments. For example, when analyzing a chat log, the analysis unit considers the time period of user comments and identifies conversation patterns for each time period. For example, the analysis unit analyzes the content and frequency of user comments for each time period: morning, afternoon, and evening. The analysis unit also analyzes the frequency of user comments and identifies conversation patterns for specific time periods. For example, if a user frequently comments during a specific time period, the analysis unit extracts the conversation pattern for that time period. The analysis unit also considers the time period and frequency of user comments and identifies conversation patterns for each time period when analyzing a chat log. For example, the analysis unit analyzes the tendency of users to talk about specific topics during specific time periods. This makes it possible to generate more appropriate conversations by considering the time period and frequency of user comments.

[0044] The analysis unit can track changes in a user's writing style and language usage, and generate a conversation that corresponds to the change in writing style. For example, when analyzing a chat log, the analysis unit tracks changes in a user's writing style and language usage, and generates a conversation that corresponds to the change in writing style. For example, if a user changes from a formal to a casual writing style, the analysis unit generates a conversation that corresponds to the change in writing style. The analysis unit also analyzes changes in a user's writing style and language usage, and generates a conversation that corresponds to the change in writing style. For example, if a user uses a different writing style with a specific person, the analysis unit generates a conversation that corresponds to that writing style. The analysis unit also tracks changes in a user's writing style and language usage, and generates a conversation that corresponds to the change in writing style when analyzing a chat log. For example, if a user uses a different writing style for a specific topic, the analysis unit generates a conversation that corresponds to that writing style. This makes it possible to generate a conversation that corresponds to changes in a user's writing style and language usage.

[0045] The chat log upload unit analyzes multimedia data including voice messages and images to generate richer conversations. For example, when uploading a chat log, it analyzes voice messages and extracts text data from the voice data. For example, it uses voice recognition technology to convert voice messages into text and uses the text to generate conversations. The chat log upload unit also analyzes image data and generates conversations based on the content of the images. For example, it uses image recognition technology to analyze the content of images and generates conversations based on the content. The chat log upload unit also analyzes voice messages and image data and generates conversations based on the multimedia data. For example, it analyzes the content of voice messages and images and generates conversations based on the content. In this way, analyzing voice messages and images makes it possible to generate richer conversations.

[0046] The chat log uploading unit can also analyze chat logs from different SNS platforms and compare conversation patterns across multiple platforms. For example, it analyzes chat logs from different SNS platforms and compares conversation patterns across each platform. For example, it analyzes chat logs from Facebook (registered trademark) and Twitter (registered trademark) and compares the content and frequency of user comments. The chat log uploading unit also analyzes chat logs from multiple SNS platforms to identify common conversation patterns. For example, it analyzes common topics and phrases across different platforms. The chat log uploading unit also analyzes chat logs from different SNS platforms and compares conversation patterns across each platform. For example, it analyzes the user's speaking style and writing style on each platform. This makes it possible to compare a wider range of conversation patterns by analyzing chat logs from different SNS platforms.

[0047] The conversation generation unit can understand the context of the user's past statements in detail and generate advanced replies that correspond to the context. For example, when automatically generating a conversation, the unit analyzes the context of the user's past statements and generates advanced replies that correspond to that context. For example, it suggests related topics based on what the user has said in the past. The conversation generation unit also deeply understands the context of the user's past statements and generates replies that correspond to that context. For example, it provides appropriate advice based on what the user has said in the past. The conversation generation unit also analyzes the context of the user's past statements when automatically generating a conversation and generates advanced replies that correspond to that context. For example, it provides related information based on what the user has said in the past. In this way, by deeply understanding the context of the user's past statements, more appropriate replies can be generated and natural communication can be achieved.

[0048] The conversation generation unit can analyze the intention of a user's utterance and generate an appropriate reply according to that intention. For example, when automatically generating a conversation, the conversation generation unit analyzes the intention of a user's utterance and generates an appropriate reply according to that intention. For example, if a user asks a question, a specific answer to the question is generated. The conversation generation unit also analyzes the intention of a user's utterance and generates an appropriate reply according to that intention. For example, if a user makes a suggestion, an opinion for or against the suggestion is generated. The conversation generation unit also analyzes the intention of a user's utterance and generates an appropriate reply according to that intention. For example, if a user expresses gratitude, a reply to that gratitude is generated. In this way, by analyzing the intention of a user's utterance, more appropriate replies can be generated, achieving natural communication.

[0049] The conversation generation unit enables conversation generation in different languages, thereby supporting international communication. For example, conversation generation in different languages ​​is enabled during automatic conversation generation. For example, conversations are generated in multiple languages, such as English, Japanese, and French. The conversation generation unit also enables conversation generation in different languages, thereby supporting international communication. For example, it enables a user to have a natural conversation with a partner who speaks a different language. The conversation generation unit also enables conversation generation in different languages ​​during automatic conversation generation, thereby supporting international communication. For example, it enables a user to communicate smoothly with a partner who speaks a different language. This supports international communication by enabling conversation generation in different languages.

[0050] The conversation generation unit can predict a future conversation scenario based on the user's past conversation history and generate a reply based on the prediction. For example, during automatic conversation generation, the user's past conversation history is analyzed to predict a future conversation scenario. For example, a topic that the user is likely to talk about next is predicted based on content that the user has spoken in the past. The conversation generation unit also predicts a future conversation scenario based on the user's past conversation history and generates a reply based on the prediction. For example, a reply is generated to a topic that the user is likely to talk about next. The conversation generation unit also analyzes the user's past conversation history to predict a future conversation scenario during automatic conversation generation. For example, a topic that the user is likely to talk about next is predicted and a reply is generated to that topic. In this way, a future conversation scenario is predicted based on the user's past conversation history and a more appropriate reply is generated.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The chat log upload unit uploads the user's chat log. For example, the user can upload a chat log from an SNS (LINE (registered trademark)) to the service. Step 2: The analysis unit analyzes the chat logs uploaded by the chat log upload unit. For example, the generation AI analyzes the user's chat log and extracts conversation patterns and characteristics. The generation AI uses text generation AI (e.g., LLM) to analyze the user's frequently used phrases, topics, and other people's reactions. Step 3: The conversation generation unit generates a conversation based on the results of the analysis by the analysis unit. For example, the generation AI may refer to the other person's reactions from past conversations to generate natural responses. The generation AI may also use multimodal generation AI to generate the content of the conversation. Step 4: The matching unit suggests compatible partners based on the results of the analysis by the analysis unit. For example, the generation AI finds people who share common hobbies and interests, or who have a similar conversational rhythm, and makes suggestions to the user.

[0053] (Example 2) The Talk Matchmaker according to an embodiment of the present invention is a system that automatically generates conversations using chat logs from a social networking service (LINE (registered trademark)). This system allows users to upload their own chat logs, and a generation AI analyzes the data to recreate conversations with other users. This allows the Talk Matchmaker to automatically generate conversations based on past chat history, saving time and effort while enabling natural communication. The system also includes an automatic matching function that can automatically suggest conversations with compatible users.

[0054] A talk matchmaker according to an embodiment includes a chat log uploading unit, an analysis unit, a conversation generation unit, and a matching unit. The chat log uploading unit uploads a user's chat log. For example, a user can upload a chat log from a social networking service (LINE (registered trademark)) to the service. The analysis unit analyzes the chat log uploaded by the chat log uploading unit. For example, a generation AI analyzes the user's chat log and extracts conversation patterns and characteristics. The generation AI uses a text generation AI (e.g., LLM) to analyze the user's frequently used phrases, topics, and the other person's reactions. The conversation generation unit generates a conversation based on the results of the analysis by the analysis unit. For example, the generation AI generates natural responses by referring to the other person's reactions from past conversations. The generation AI can also generate conversation content using a multimodal generation AI. The matching unit suggests compatible partners based on the results of the analysis by the analysis unit. For example, the generation AI finds partners with common hobbies and interests, or partners with a similar conversational tempo, and suggests them to the user. As a result, the talk matchmaker according to the embodiment can analyze the user's chat log, generate natural conversations, and suggest compatible partners, thereby realizing efficient and natural communication.

[0055] The analysis unit can estimate a user's emotions, track changes in emotions, and extract emotional patterns. For example, when analyzing a chat log, the analysis unit estimates emotions for each user's statement and tracks changes in emotions over time. For example, the analysis unit calculates an emotion score such as joy, anger, or sadness for each statement and extracts emotional patterns. The analysis unit also uses the emotion estimation function to analyze changes in emotions in the user's statements and detect specific emotional patterns. For example, it analyzes the user's emotions regarding a specific topic. The analysis unit also tracks changes in the user's emotions and extracts emotional patterns when analyzing the chat log. For example, it classifies statements made by the user into those with positive emotions and those with negative emotions, and identifies emotional patterns. In this way, by tracking changes in the user's emotions and extracting emotional patterns, more natural conversations can be generated.

[0056] The analysis unit can identify conversation patterns for each time period based on the time period and frequency of user comments. For example, when analyzing a chat log, the analysis unit considers the time period of user comments and identifies conversation patterns for each time period. For example, the analysis unit analyzes the content and frequency of user comments for each time period: morning, afternoon, and evening. The analysis unit also analyzes the frequency of user comments and identifies conversation patterns for specific time periods. For example, if a user frequently comments during a specific time period, the analysis unit extracts the conversation pattern for that time period. The analysis unit also considers the time period and frequency of user comments and identifies conversation patterns for each time period when analyzing a chat log. For example, the analysis unit analyzes the tendency of users to talk about specific topics during specific time periods. This makes it possible to generate more appropriate conversations by considering the time period and frequency of user comments.

[0057] The analysis unit can track changes in a user's writing style and language usage and generate a conversation that corresponds to the change in writing style. For example, when analyzing a chat log, the analysis unit tracks changes in a user's writing style and language usage and generates a conversation that corresponds to the change in writing style. For example, if a user changes from a formal to a casual writing style, the analysis unit generates a conversation that corresponds to the change in writing style. The analysis unit also analyzes changes in a user's writing style and language usage and generates a conversation that corresponds to the change in writing style. For example, if a user uses a different writing style with a specific person, the analysis unit generates a conversation that corresponds to that writing style. The analysis unit also tracks changes in a user's writing style and language usage and generates a conversation that corresponds to the change in writing style when analyzing a chat log. For example, if a user uses a different writing style for a specific topic, the analysis unit generates a conversation that corresponds to that writing style. This makes it possible to generate a conversation that corresponds to changes in a user's writing style and language usage.

[0058] The chat log upload unit analyzes multimedia data including voice messages and images, and can generate richer conversations. For example, when uploading a chat log, the chat log upload unit analyzes voice messages and extracts text data from the voice data. For example, it converts voice messages into text using voice recognition technology and uses the text to generate conversations. The chat log upload unit also analyzes image data and generates conversations based on the content of the images. For example, it analyzes the content of images using image recognition technology and generates conversations based on the content. The chat log upload unit also analyzes voice messages and image data and generates conversations based on the multimedia data. For example, it analyzes the content of voice messages and the content of images and generates conversations based on the content. In this way, analyzing voice messages and images makes it possible to generate richer conversations.

[0059] The chat log uploading unit can also analyze chat logs from different SNS platforms and compare conversation patterns across multiple platforms. For example, the chat log uploading unit analyzes chat logs from different SNS platforms and compares conversation patterns across each platform. For example, it analyzes chat logs from Facebook (registered trademark) and Twitter (registered trademark) and compares the content and frequency of user comments. The chat log uploading unit also analyzes chat logs from multiple SNS platforms and identifies common conversation patterns. For example, it analyzes common topics and phrases across different platforms. The chat log uploading unit also analyzes chat logs from different SNS platforms and compares conversation patterns across each platform. For example, it analyzes the user's speaking style and writing style on each platform. This makes it possible to compare a wider range of conversation patterns by analyzing chat logs from different SNS platforms.

[0060] The chat log uploading unit can use the emotion estimation function to estimate the user's emotion in real time when uploading a chat log and provide feedback to elicit positive emotions. For example, the chat log uploading unit estimates the user's emotion in real time when uploading a chat log and provides feedback to elicit positive emotions. For example, if the user has negative emotions, it displays an encouraging message. The chat log uploading unit also uses the emotion estimation function to analyze the user's emotion in real time when uploading a chat log and makes suggestions to elicit positive emotions. For example, it suggests topics that the user feels positive about. The chat log uploading unit also estimates the user's emotion in real time when uploading a chat log and provides feedback to elicit positive emotions. For example, it highlights comments that the user feels positive about. In this way, the user's satisfaction is improved by estimating the user's emotion in real time and providing feedback to elicit positive emotions.

[0061] The conversation generation unit uses the emotion estimation function to generate a response that corresponds to the user's emotion, thereby promoting emotional empathy. The conversation generation unit, for example, estimates the user's emotion in real time during automatic conversation generation and generates a response that corresponds to that emotion. For example, if the user is sad, it generates words of comfort. The conversation generation unit also uses the emotion estimation function to generate a response that corresponds to the user's emotion and promotes emotional empathy. For example, if the user is happy, it generates words of empathy. The conversation generation unit also analyzes the user's emotion during automatic conversation generation and generates a response that corresponds to that emotion. For example, if the user is angry, it generates words that encourage the user to respond calmly. In this way, generating a response that corresponds to the user's emotion promotes emotional empathy and achieves more natural communication.

[0062] The conversation generation unit can understand the context of the user's past statements in detail and generate advanced replies that correspond to the context. For example, when automatically generating a conversation, the conversation generation unit analyzes the context of the user's past statements and generates advanced replies that correspond to the context. For example, it suggests related topics based on what the user has said in the past. The conversation generation unit also deeply understands the context of the user's past statements and generates replies that correspond to the context. For example, it provides appropriate advice based on what the user has said in the past. The conversation generation unit also analyzes the context of the user's past statements and generates advanced replies that correspond to the context. For example, it provides related information based on what the user has said in the past. In this way, by deeply understanding the context of the user's past statements, more appropriate replies can be generated and natural communication can be achieved.

[0063] The conversation generation unit can analyze the intention of a user's utterance and generate an appropriate reply according to the intention. The conversation generation unit, for example, during automatic conversation generation, analyzes the intention of a user's utterance and generates an appropriate reply according to the intention. For example, if a user asks a question, it generates a specific answer to the question. The conversation generation unit also analyzes the intention of a user's utterance and generates an appropriate reply according to the intention. For example, if a user makes a suggestion, it generates opinions for or against the suggestion. The conversation generation unit also analyzes the intention of a user's utterance during automatic conversation generation and generates an appropriate reply according to the intention. For example, if a user expresses gratitude, it generates a reply to that gratitude. In this way, by analyzing the intention of a user's utterance, more appropriate replies can be generated, achieving natural communication.

[0064] The conversation generation unit enables conversation generation in different languages, thereby supporting international communication. The conversation generation unit, for example, enables conversation generation in different languages ​​during automatic conversation generation. For example, conversations are generated in multiple languages, such as English, Japanese, and French. The conversation generation unit also enables conversation generation in different languages, thereby supporting international communication. For example, it enables a user to have a natural conversation with a partner who speaks a different language. The conversation generation unit also enables conversation generation in different languages ​​during automatic conversation generation, thereby supporting international communication. For example, it enables a user to communicate smoothly with a partner who speaks a different language. This supports international communication by enabling conversation generation in different languages.

[0065] The conversation generation unit can predict a future conversation scenario based on the user's past conversation history and generate a reply based on the prediction. The conversation generation unit, for example, analyzes the user's past conversation history and predicts a future conversation scenario when automatically generating a conversation. For example, it predicts a topic that the user is likely to talk about next based on content that the user has spoken in the past. The conversation generation unit also predicts a future conversation scenario based on the user's past conversation history and generates a reply based on the prediction. For example, it generates a reply to a topic that the user is likely to talk about next. The conversation generation unit also analyzes the user's past conversation history and predicts a future conversation scenario when automatically generating a conversation. For example, it predicts a topic that the user is likely to talk about next and generates a reply to that topic. In this way, a future conversation scenario is predicted based on the user's past conversation history and a more appropriate reply is generated.

[0066] The conversation generation unit uses the emotion estimation function to monitor the user's emotions in real time when using the automatic conversation generation, and can generate a conversation according to the emotions. The conversation generation unit, for example, monitors the user's emotions in real time when generating the automatic conversation, and generates a conversation according to the emotions. For example, if the user has positive emotions, it generates a response according to the emotions. The conversation generation unit also uses the emotion estimation function to analyze the user's emotions in real time when using the automatic conversation generation, and generates a conversation according to the emotions. For example, if the user has negative emotions, it generates a response according to the emotions. The conversation generation unit also monitors the user's emotions in real time when generating the automatic conversation, and generates a conversation according to the emotions. For example, if the user has neutral emotions, it generates a response according to the emotions. In this way, by monitoring the user's emotions in real time and generating a conversation according to the emotions, more natural communication is achieved.

[0067] The matching unit can use the emotion estimation function to suggest compatible partners based on the user's emotions. For example, during automatic matching, the matching unit estimates the user's emotions in real time and suggests compatible partners based on those emotions. For example, it preferentially suggests partners about whom the user has positive emotions. The matching unit also uses the emotion estimation function to suggest compatible partners based on the user's emotions. For example, it suggests that the user avoid partners about whom the user has negative emotions. The matching unit also analyzes the user's emotions during automatic matching and suggests compatible partners based on those emotions. For example, it suggests partners about whom the user has neutral emotions. This allows for more appropriate matching by suggesting compatible partners based on the user's emotions.

[0068] The matching unit can analyze the user's past conversation history in detail and identify compatible partners based on the conversation tempo and style. For example, during automatic matching, the matching unit analyzes the user's past conversation history in detail and identifies compatible partners based on the conversation tempo and style. For example, if the user speaks at a fast tempo, the matching unit suggests partners who speak at the same tempo. The matching unit also analyzes the user's past conversation history and identifies compatible partners based on the conversation style. For example, if the user uses a formal writing style, the matching unit suggests partners who use the same writing style. The matching unit also analyzes the user's past conversation history in detail during automatic matching and identifies compatible partners based on the conversation tempo and style. For example, if the user uses a casual writing style, the matching unit suggests partners who use the same writing style. In this way, by analyzing the user's past conversation history in detail, compatible partners based on the conversation tempo and style are identified.

[0069] The matching unit can analyze the user's hobbies and interests in detail and suggest partners who share the same hobbies and interests. For example, during automatic matching, the matching unit analyzes the user's hobbies and interests in detail and suggests partners who share the same hobbies and interests. For example, if the user is interested in music, it suggests partners who share the same hobbies. The matching unit also analyzes the user's hobbies and interests in detail and suggests partners who share the same hobbies and interests. For example, if the user is interested in sports, it suggests partners who share the same hobbies. The matching unit also analyzes the user's hobbies and interests in detail during automatic matching and suggests partners who share the same hobbies and interests. For example, if the user is interested in movies, it suggests partners who share the same hobbies. In this way, by analyzing the user's hobbies and interests in detail, it suggests partners who share the same hobbies and interests.

[0070] The matching unit enables matching between users of different SNS platforms, thereby providing opportunities to meet a wider range of potential partners. The matching unit enables matching between users of different SNS platforms, for example, during automatic matching. For example, it matches users of Facebook (registered trademark) and Twitter (registered trademark). The matching unit also enables matching between users of different SNS platforms, thereby providing opportunities to meet a wider range of potential partners. For example, it matches users of Instagram (registered trademark) and LinkedIn. The matching unit also enables matching between users of different SNS platforms, during automatic matching, thereby providing opportunities to meet a wider range of potential partners. For example, it matches users of LINE (registered trademark) and WhatsApp. This enables matching between users of different SNS platforms, thereby providing opportunities to meet a wider range of potential partners.

[0071] The matching unit can preferentially suggest nearby partners based on the user's geographical location information. For example, during automatic matching, the matching unit takes into account the user's geographical location information and preferentially suggests nearby partners. For example, partners who live in the same city or area are suggested. The matching unit also analyzes the user's geographical location information and preferentially suggests nearby partners. For example, partners are suggested based on the area where the user lives. The matching unit also takes into account the user's geographical location information and preferentially suggests nearby partners during automatic matching. For example, partners are suggested based on the area where the user commutes to. In this way, nearby partners are preferentially suggested by taking into account the user's geographical location information.

[0072] The matching unit can use the emotion estimation function to monitor the emotions of the user when using automatic matching in real time and suggest partners according to the emotions. For example, the matching unit monitors the user's emotions in real time during automatic matching and suggests partners according to those emotions. For example, it preferentially suggests partners for whom the user has positive emotions. The matching unit also uses the emotion estimation function to analyze the emotions of the user when using automatic matching in real time and suggests partners according to those emotions. For example, it suggests that the user avoid partners for whom the user has negative emotions. The matching unit also monitors the user's emotions in real time during automatic matching and suggests partners according to those emotions. For example, it suggests partners for whom the user has neutral emotions. In this way, by monitoring the user's emotions in real time and suggesting partners according to their emotions, more appropriate matching is possible.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The analysis unit can identify conversation patterns for each time period based on the time period and frequency of user comments. For example, when analyzing a chat log, the analysis unit considers the time period of user comments and identifies conversation patterns for each time period. For example, the analysis unit analyzes the content and frequency of user comments for each time period: morning, afternoon, and evening. The analysis unit also analyzes the frequency of user comments and identifies conversation patterns for specific time periods. For example, if a user frequently comments during a specific time period, the analysis unit extracts the conversation pattern for that time period. The analysis unit also considers the time period and frequency of user comments and identifies conversation patterns for each time period when analyzing a chat log. For example, the analysis unit analyzes the tendency of users to talk about specific topics during specific time periods. This makes it possible to generate more appropriate conversations by considering the time period and frequency of user comments.

[0075] The analysis unit can track changes in a user's writing style and language usage, and generate a conversation that corresponds to the change in writing style. For example, when analyzing a chat log, the analysis unit tracks changes in a user's writing style and language usage, and generates a conversation that corresponds to the change in writing style. For example, if a user changes from a formal to a casual writing style, the analysis unit generates a conversation that corresponds to the change in writing style. The analysis unit also analyzes changes in a user's writing style and language usage, and generates a conversation that corresponds to the change in writing style. For example, if a user uses a different writing style with a specific person, the analysis unit generates a conversation that corresponds to that writing style. The analysis unit also tracks changes in a user's writing style and language usage, and generates a conversation that corresponds to the change in writing style when analyzing a chat log. For example, if a user uses a different writing style for a specific topic, the analysis unit generates a conversation that corresponds to that writing style. This makes it possible to generate a conversation that corresponds to changes in a user's writing style and language usage.

[0076] The chat log upload unit analyzes multimedia data including voice messages and images to generate richer conversations. For example, when uploading a chat log, it analyzes voice messages and extracts text data from the voice data. For example, it uses voice recognition technology to convert voice messages into text and uses the text to generate conversations. The chat log upload unit also analyzes image data and generates conversations based on the content of the images. For example, it uses image recognition technology to analyze the content of images and generates conversations based on the content. The chat log upload unit also analyzes voice messages and image data and generates conversations based on the multimedia data. For example, it analyzes the content of voice messages and images and generates conversations based on the content. In this way, analyzing voice messages and images makes it possible to generate richer conversations.

[0077] The chat log uploading unit can also analyze chat logs from different SNS platforms and compare conversation patterns across multiple platforms. For example, it analyzes chat logs from different SNS platforms and compares conversation patterns across each platform. For example, it analyzes chat logs from Facebook (registered trademark) and Twitter (registered trademark) and compares the content and frequency of user comments. The chat log uploading unit also analyzes chat logs from multiple SNS platforms to identify common conversation patterns. For example, it analyzes common topics and phrases across different platforms. The chat log uploading unit also analyzes chat logs from different SNS platforms and compares conversation patterns across each platform. For example, it analyzes the user's speaking style and writing style on each platform. This makes it possible to compare a wider range of conversation patterns by analyzing chat logs from different SNS platforms.

[0078] The chat log uploading unit can use the emotion estimation function to estimate the user's emotion in real time when uploading a chat log and provide feedback to elicit positive emotions. For example, when uploading a chat log, the unit estimates the user's emotion in real time and provides feedback to elicit positive emotions. For example, if the user has negative emotions, the unit displays an encouraging message. The chat log uploading unit also uses the emotion estimation function to analyze the user's emotion in real time when uploading a chat log and makes suggestions to elicit positive emotions. For example, the unit suggests topics that the user feels positive about. The chat log uploading unit also estimates the user's emotion in real time when uploading a chat log and provides feedback to elicit positive emotions. For example, the unit highlights comments that the user feels positive about. In this way, the unit estimates the user's emotion in real time and provides feedback to elicit positive emotions, thereby improving user satisfaction.

[0079] The conversation generation unit can use the emotion estimation function to generate a response that corresponds to the user's emotion and promote emotional empathy. For example, during automatic conversation generation, the user's emotion is estimated in real time and a response that corresponds to that emotion is generated. For example, if the user is sad, words of comfort are generated. The conversation generation unit also uses the emotion estimation function to generate a response that corresponds to the user's emotion and promote emotional empathy. For example, if the user is happy, words of empathy are generated. The conversation generation unit also analyzes the user's emotion during automatic conversation generation and generates a response that corresponds to that emotion. For example, if the user is angry, words that encourage a calm response are generated. In this way, generating a response that corresponds to the user's emotion promotes emotional empathy and achieves more natural communication.

[0080] The conversation generation unit can understand the context of the user's past statements in detail and generate advanced replies that correspond to the context. For example, when automatically generating a conversation, the unit analyzes the context of the user's past statements and generates advanced replies that correspond to that context. For example, it suggests related topics based on what the user has said in the past. The conversation generation unit also deeply understands the context of the user's past statements and generates replies that correspond to that context. For example, it provides appropriate advice based on what the user has said in the past. The conversation generation unit also analyzes the context of the user's past statements when automatically generating a conversation and generates advanced replies that correspond to that context. For example, it provides related information based on what the user has said in the past. In this way, by deeply understanding the context of the user's past statements, more appropriate replies can be generated and natural communication can be achieved.

[0081] The conversation generation unit can analyze the intention of a user's utterance and generate an appropriate reply according to that intention. For example, when automatically generating a conversation, the conversation generation unit analyzes the intention of a user's utterance and generates an appropriate reply according to that intention. For example, if a user asks a question, a specific answer to the question is generated. The conversation generation unit also analyzes the intention of a user's utterance and generates an appropriate reply according to that intention. For example, if a user makes a suggestion, an opinion for or against the suggestion is generated. The conversation generation unit also analyzes the intention of a user's utterance and generates an appropriate reply according to that intention. For example, if a user expresses gratitude, a reply to that gratitude is generated. In this way, by analyzing the intention of a user's utterance, more appropriate replies can be generated, achieving natural communication.

[0082] The conversation generation unit enables conversation generation in different languages, thereby supporting international communication. For example, conversation generation in different languages ​​is enabled during automatic conversation generation. For example, conversations are generated in multiple languages, such as English, Japanese, and French. The conversation generation unit also enables conversation generation in different languages, thereby supporting international communication. For example, it enables a user to have a natural conversation with a partner who speaks a different language. The conversation generation unit also enables conversation generation in different languages ​​during automatic conversation generation, thereby supporting international communication. For example, it enables a user to communicate smoothly with a partner who speaks a different language. This supports international communication by enabling conversation generation in different languages.

[0083] The conversation generation unit can predict a future conversation scenario based on the user's past conversation history and generate a reply based on the prediction. For example, during automatic conversation generation, the user's past conversation history is analyzed to predict a future conversation scenario. For example, a topic that the user is likely to talk about next is predicted based on content that the user has spoken in the past. The conversation generation unit also predicts a future conversation scenario based on the user's past conversation history and generates a reply based on the prediction. For example, a reply is generated to a topic that the user is likely to talk about next. The conversation generation unit also analyzes the user's past conversation history to predict a future conversation scenario during automatic conversation generation. For example, a topic that the user is likely to talk about next is predicted and a reply is generated to that topic. In this way, a future conversation scenario is predicted based on the user's past conversation history and a more appropriate reply is generated.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The chat log upload unit uploads the user's chat log. For example, the user can upload a chat log from an SNS (LINE (registered trademark)) to the service. Step 2: The analysis unit analyzes the chat logs uploaded by the chat log upload unit. For example, the generation AI analyzes the user's chat log and extracts conversation patterns and characteristics. The generation AI uses text generation AI (e.g., LLM) to analyze the user's frequently used phrases, topics, and other people's reactions. Step 3: The conversation generation unit generates a conversation based on the results of the analysis by the analysis unit. For example, the generation AI may refer to the other person's reactions from past conversations to generate natural responses. The generation AI may also use multimodal generation AI to generate the content of the conversation. Step 4: The matching unit suggests compatible partners based on the results of the analysis by the analysis unit. For example, the generation AI finds people who share common hobbies and interests, or who have a similar conversational rhythm, and makes suggestions to the user.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[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 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.

[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. 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.

[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 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.

[0099] 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.

[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 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.

[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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[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 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.

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

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0111] Fig. 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.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0114] In the headset type terminal 314, 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 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 specific processing unit 290 using these models.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The 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.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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, in order to avoid confusion and to 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.

[0152] 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]

[0153] 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 chat log upload unit for uploading a user's chat log; an analysis unit that analyzes the chat log uploaded by the chat log upload unit; a conversation generation unit that generates a conversation based on the analysis result by the analysis unit; a matching unit that proposes a compatible partner based on the analysis result by the analysis unit; A system characterized by:

2. The analysis unit Estimating the user's emotions, tracking changes in emotions, and extracting patterns of emotions.

2. The system of claim 1.

3. The analysis unit Identifying conversation patterns for each time period based on the time period and frequency of the user's speech 2. The system of claim 1.

4. The analysis unit Tracking changes in the user's writing style and vocabulary, and generating conversations that reflect those changes 2. The system of claim 1.

5. The chat log upload unit Analyzes multimedia data, including voice messages and images, to generate richer conversations 2. The system of claim 1.

Images