System

Abstract

An object of the system according to the embodiment is to analyze the content and emotion of a conversation and cause a AI character to interrupt at an appropriate timing.SOLUTION: A system according to an embodiment includes an analysis unit, an emotion analysis unit, a learning unit, and a control unit. The analysis unit analyzes the content of the conversation in real time. The emotion analysis unit analyzes an emotion on the basis of the content analyzed by the analysis unit. The learning unit learns the reaction of the user on the basis of the analysis result obtained by the emotion analysis unit. The control unit controls the interruption of the AI character based on the information learned by the learning 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] With conventional technology, it was difficult to analyze the content and emotions of conversations in real time and have an AI character interrupt at the appropriate time.

[0005] The system according to the embodiment aims to analyze the content and emotions of a conversation and have an AI character interrupt at the appropriate time. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an emotion analysis unit, a learning unit, and a control unit. The analysis unit analyzes the content of the conversation in real time. The emotion analysis unit analyzes emotions based on the content analyzed by the analysis unit. The learning unit learns the user's reaction based on the analysis results obtained by the emotion analysis unit. The control unit controls the interruption of the AI ​​character based on the information learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment analyzes the content and emotions of the conversation, and allows an AI character to interrupt at the appropriate time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A messaging app system according to an embodiment of the present invention analyzes conversation content in real time, analyzes emotions, learns user responses, and controls AI character interruptions. The messaging app system plays a role in making communication more enjoyable by livening up conversations and creating new topics. For example, the messaging app system analyzes conversation content in real time. For example, when a specific keyword or phrase appears in a conversation with friends or family, the system analyzes the content and interrupts at the appropriate time. The messaging app system then inserts appropriate comments and questions while being careful not to disrupt the flow of the conversation. Next, the messaging app system provides content to liven up conversations. For example, the system stimulates conversations by providing funny jokes, quizzes, trivia, and the like. Furthermore, the system can broaden the scope of conversations by providing information or news related to specific topics. Furthermore, the messaging app system learns user responses and reflects them in future conversations. For example, the system records how a user responded to a specific comment or question and uses that information to provide more appropriate comments or questions in the next conversation. In this way, the messaging app system plays a role in livening up conversations with friends and family and making communication more enjoyable. For example, when a conversation is about to die down, the messaging app system can insert a funny joke to get the conversation going again. Also, by providing information related to a specific topic, the conversation can be broadened and deeper communication can be achieved. This allows the messaging app system to continuously improve communication with users and provide more enjoyable conversations. For example, when a conversation is about to die down, the messaging app system can insert a funny joke to get the conversation going again. Also, by providing information related to a specific topic, the conversation can be broadened and deeper communication can be achieved.

[0029] A messaging app system according to an embodiment includes an analysis unit, a sentiment analysis unit, a learning unit, and a control unit. The analysis unit analyzes the content of a conversation in real time. The analysis unit analyzes the content of the conversation using, for example, natural language processing technology. The analysis unit can also convert a voice conversation into text using voice recognition technology and analyze the content. For example, the analysis unit detects specific keywords or phrases in the conversation and analyzes the content. The sentiment analysis unit analyzes emotions based on the content analyzed by the analysis unit. The sentiment analysis unit analyzes user emotions using, for example, a sentiment estimation algorithm. The sentiment analysis unit can also capture emotional nuances in the conversation using text analysis technology. For example, the sentiment analysis unit analyzes emotions taking into account the tone and context of the conversation. The learning unit learns user reactions based on the analysis results obtained by the sentiment analysis unit. The learning unit learns user reactions using, for example, a machine learning algorithm. The learning unit can also improve the accuracy of the learning by referring to past user reaction data. For example, the learning unit records how the user responded to a particular comment or question, and based on that information, provides a more appropriate comment or question in the next conversation. The control unit controls the interruption of the AI ​​character based on the information learned by the learning unit. The control unit adjusts the timing of the interruption of the AI ​​character based on, for example, a user setting. The control unit can also determine the priority of the interruption based on the user's emotions. For example, if the user is relaxed, the control unit sets a low priority for the interruption. This allows the messaging app system according to the embodiment to analyze the content of the conversation in real time, analyze emotions, learn the user's reactions, and control the interruption of the AI ​​character.

[0030] The messaging app system includes a content providing unit that provides content based on the results of the analysis unit and the sentiment analysis unit. The content providing unit provides content based on the results of the analysis unit and the sentiment analysis unit. The content providing unit provides content such as funny jokes, quizzes, and trivia. The content providing unit can also provide information and news related to specific topics. For example, the content providing unit selects and provides appropriate content based on the content of the conversation. This makes it possible to provide content such as funny jokes, quizzes, and trivia based on the results of the analysis unit and the sentiment analysis unit. Some or all of the above-described processing in the content providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the content providing unit can provide content using an AI model that receives the results of the analysis unit and the sentiment analysis unit as input and outputs appropriate content.

[0031] The messaging app system includes a recording unit that cooperates with the learning unit to record user responses. The recording unit cooperates with the learning unit to record user responses. The recording unit records, for example, how a user responded to a specific comment or question. The recording unit can also accumulate user response data and reflect it in subsequent conversations. For example, the recording unit analyzes user response data in chronological order and identifies patterns of change in responses. By doing so, by recording user responses in cooperation with the learning unit, it is possible to reflect the patterns of change in responses in subsequent conversations. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input user response data to a generation AI and have the generation AI analyze patterns of change in responses.

[0032] The messaging app system includes a setting management unit for managing user settings in cooperation with the control unit. The setting management unit manages user settings in cooperation with the control unit. For example, the setting management unit manages user setting items and provides them to the control unit. The setting management unit can also adjust the interrupt timing of the AI ​​character based on the user settings. For example, the setting management unit issues instructions to the control unit based on the frequency and timing of interrupts set by the user. In this way, by managing the user settings in cooperation with the control unit, the interruption of the AI ​​character can be controlled based on the user settings. Some or all of the above-described processing in the setting management unit may be performed using AI, for example, or may be performed without using AI. For example, the setting management unit can input user setting data to a generation AI and have the generation AI manage the settings.

[0033] When analyzing the content of a conversation, the analysis unit can improve the accuracy of the analysis by referring to the user's past conversation history. For example, the analysis unit prioritizes analysis of specific phrases or keywords used by the user in the past. The analysis unit can also analyze the user's past conversation patterns and prioritize analysis of similar conversation content. The analysis unit can also prioritize analysis of information related to a specific topic from the user's past conversation history. In this way, by referring to the user's past conversation history, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past conversation history data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0034] When analyzing the content of a conversation, the analysis unit can preferentially detect specific keywords and phrases based on the context of the conversation. For example, the analysis unit preferentially detects important keywords from the context of the conversation. The analysis unit can also analyze the flow of the conversation and preferentially detect related phrases. The analysis unit can also preferentially detect specific keywords according to the topic of the conversation. In this way, important keywords and phrases can be preferentially detected by taking the context of the conversation into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation context data to a generation AI and cause the generation AI to preferentially detect keywords and phrases.

[0035] When analyzing the content of a conversation, the analysis unit can customize the analysis algorithm according to the language or dialect used by the user. The analysis unit can adjust the analysis algorithm according to, for example, the language used by the user. The analysis unit can also use a specific language model to accommodate the user's dialect. The analysis unit can also optimize the analysis algorithm based on the user's language settings. This improves the accuracy of the analysis by customizing the analysis algorithm according to the language or dialect used by the user. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's language data into a generation AI and have the generation AI customize the analysis algorithm.

[0036] The emotion analysis unit can improve the accuracy of emotion analysis by referring to the user's past emotion data. For example, the emotion analysis unit improves the accuracy of emotion analysis based on the user's past emotion data. The emotion analysis unit can also improve the accuracy of emotion analysis by analyzing the user's past emotion patterns. The emotion analysis unit can also prioritize analysis of specific emotions from the user's past emotion data. This improves the accuracy of emotion analysis by referring to the user's past emotion data. Some or all of the above-described processing in the emotion analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the emotion analysis unit can input the user's past emotion data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0037] The sentiment analysis unit can analyze sentiment more accurately by taking into account the context and tone of the conversation during sentiment analysis. For example, the sentiment analysis unit accurately analyzes sentiment by taking into account the context of the conversation. The sentiment analysis unit can also accurately analyze sentiment by analyzing the tone of the conversation. The sentiment analysis unit can also accurately analyze sentiment by analyzing the flow of the conversation. This allows for more accurate sentiment analysis by taking into account the context and tone of the conversation. Some or all of the above-described processing in the sentiment analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the sentiment analysis unit can input conversation context data to the generation AI and cause the generation AI to perform an accurate sentiment analysis.

[0038] The sentiment analysis unit can customize the analysis algorithm according to the language or dialect used by the user during sentiment analysis. For example, the sentiment analysis unit adjusts the sentiment analysis algorithm according to the language used by the user. The sentiment analysis unit can also use a specific language model to accommodate the user's dialect. The sentiment analysis unit can also optimize the sentiment analysis algorithm based on the user's language settings. This improves the accuracy of sentiment analysis by customizing the analysis algorithm according to the language or dialect used by the user. Some or all of the above-described processing in the sentiment analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the sentiment analysis unit can input the user's language data into a generation AI and have the generation AI customize the analysis algorithm.

[0039] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, optimizes the learning algorithm based on past learning data. The learning unit can also extract specific patterns from past learning data and optimize the learning algorithm. The learning unit can also analyze past learning data and improve the accuracy of the learning algorithm. In this way, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to optimize the learning algorithm.

[0040] During learning, the learning unit can analyze the user's reaction data and adjust the update frequency of the learning data. The learning unit can, for example, adjust the update frequency of the learning data based on the user's reaction data. The learning unit can also extract specific patterns from the user's reaction data and adjust the update frequency of the learning data. The learning unit can also analyze the user's reaction data and optimize the update frequency of the learning data. In this way, the update frequency of the learning data can be optimized by analyzing the user's reaction data. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input the user's reaction data to the generation AI and cause the generation AI to adjust the update frequency of the learning data.

[0041] During learning, the learning unit can customize the learning algorithm according to the language or dialect used by the user. The learning unit can adjust the learning algorithm according to, for example, the language used by the user. The learning unit can also use a specific language model to accommodate the user's dialect. The learning unit can also optimize the learning algorithm based on the user's language settings. This improves learning accuracy by customizing the learning algorithm according to the language or dialect used by the user. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input the user's language data into a generation AI and cause the generation AI to customize the learning algorithm.

[0042] During control, the control unit can improve the accuracy of interrupts by referring to the user's past reaction data. The control unit improves the accuracy of interrupts, for example, based on the user's past reaction data. The control unit can also extract specific patterns from the user's past reaction data and improve the accuracy of interrupts. The control unit can also analyze the user's past reaction data and optimize the accuracy of interrupts. This improves the accuracy of interrupts by referring to the user's past reaction data. Some or all of the above-described processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input the user's past reaction data into the generation AI and cause the generation AI to improve the accuracy of interrupts.

[0043] During control, the control unit can adjust the timing of interruption based on the context and tone of the conversation. For example, the control unit optimizes the timing of interruption by taking into account the context of the conversation. The control unit can also analyze the tone of the conversation and optimize the timing of interruption. The control unit can also analyze the flow of the conversation and optimize the timing of interruption. In this way, the timing of interruption can be optimized by taking into account the context and tone of the conversation. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input conversation context data to the generation AI and cause the generation AI to optimize the timing of interruption.

[0044] During control, the control unit can customize the interrupt algorithm according to the language or dialect used by the user. The control unit can adjust the interrupt algorithm according to, for example, the language used by the user. The control unit can also use a specific language model to accommodate the user's dialect. The control unit can also optimize the interrupt algorithm based on the user's language settings. This improves the accuracy of interrupts by customizing the interrupt algorithm according to the language or dialect used by the user. Some or all of the above-described processing in the control unit can be performed using, for example, AI, or can be performed without using AI. For example, the control unit can input the user's language data into a generation AI and cause the generation AI to customize the interrupt algorithm.

[0045] During control, the control unit can adjust the timing of interruptions based on the user's geographical location information. The control unit can adjust the timing of interruptions based on, for example, the user's current location. The control unit can also adjust the timing of interruptions by referring to the user's past location information. The control unit can also adjust the timing of interruptions related to a specific topic based on the user's geographical location. This makes it possible to optimize the timing of interruptions by taking the user's geographical location information into consideration. Some or all of the above-described processing in the control unit can be performed using, or without, AI, for example. For example, the control unit can input the user's geographical location data into the generation AI and cause the generation AI to adjust the interruption timing.

[0046] During control, the control unit can analyze the user's social media activities and acquire related information. For example, the control unit can analyze the content of the user's social media posts and acquire related information. The control unit can also acquire related information by referring to the activities of the user's friends on social media. The control unit can also analyze the user's social media check-in information and acquire related information. In this way, by analyzing the user's social media activities, related information can be acquired and the accuracy of interrupts can be improved. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's social media data into the generation AI and cause the generation AI to acquire related information.

[0047] During control, the control unit can customize the interrupt method by reflecting the user's past feedback. The control unit, for example, adjusts the interrupt method based on the user's past feedback. The control unit can also preferentially use a specific interrupt method based on the user's past feedback. The control unit can also improve the accuracy of the interrupt method by reflecting the user's past feedback. In this way, the accuracy of the interrupt method is improved by reflecting the user's past feedback. Some or all of the above-described processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the interrupt method.

[0048] When providing content, the content providing unit can improve the accuracy of the content to be provided based on the user's past reaction data. The content providing unit improves the accuracy of the content to be provided, for example, based on the user's past reaction data. The content providing unit can also extract specific patterns from the user's past reaction data and improve the accuracy of the content to be provided. The content providing unit can also analyze the user's past reaction data and optimize the accuracy of the content to be provided. In this way, the accuracy of the content to be provided is improved by referring to the user's past reaction data. Some or all of the above-described processing in the content providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the content providing unit can input the user's past reaction data into a generation AI and cause the generation AI to improve the accuracy of the content to be provided.

[0049] When providing content, the content providing unit can optimize the content to be provided by taking into account the context and tone of the conversation. For example, the content providing unit optimizes the content to be provided by taking into account the context of the conversation. The content providing unit can also analyze the tone of the conversation and optimize the content to be provided. The content providing unit can also analyze the flow of the conversation and optimize the content to be provided. In this way, the content to be provided can be optimized by taking into account the context and tone of the conversation. Some or all of the above-described processing in the content providing unit may be performed using AI, for example, or may be performed without using AI. For example, the content providing unit can input context data of the conversation to a generation AI and cause the generation AI to optimize the content to be provided.

[0050] When providing content, the content providing unit can customize the content to be provided according to the language or dialect used by the user. The content providing unit can, for example, adjust the content to be provided according to the language used by the user. The content providing unit can also use a specific language model to accommodate the user's dialect. The content providing unit can also optimize the content to be provided based on the user's language settings. This improves the accuracy of the content to be provided by customizing the content to be provided according to the language or dialect used by the user. Some or all of the above-described processing by the content providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the content providing unit can input the user's language data into a generation AI and cause the generation AI to customize the content to be provided.

[0051] When providing content, the content providing unit can select content to be provided based on the user's geographical location information. The content providing unit can, for example, provide related content based on the user's current location. The content providing unit can also provide related content by referencing the user's past location information. The content providing unit can also provide content related to a specific topic based on the user's geographical location. This makes it possible to optimize the content to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the content providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the content providing unit can input the user's geographical location data into a generation AI and cause the generation AI to select content to be provided.

[0052] When providing content, the content providing unit can analyze the user's social media activity and acquire related content. For example, the content providing unit can analyze the user's social media posts and provide related content. The content providing unit can also provide related content by referring to the activities of the user's friends on social media. The content providing unit can also analyze the user's social media check-in information and provide related content. In this way, by analyzing the user's social media activity, related content can be acquired and the accuracy of the content to be provided can be improved. Some or all of the above-mentioned processing in the content providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the content providing unit can input the user's social media data into a generation AI and cause the generation AI to acquire related content.

[0053] When providing content, the content providing unit can customize the content to be provided by reflecting the user's past feedback. The content providing unit, for example, adjusts the content to be provided based on the user's past feedback. The content providing unit can also preferentially provide specific content based on the user's past feedback. The content providing unit can also improve the accuracy of the content to be provided by reflecting the user's past feedback. In this way, the accuracy of the content to be provided is improved by reflecting the user's past feedback. Some or all of the above-described processing in the content providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the content providing unit can input the user's past feedback data into a generation AI and have the generation AI customize the content to be provided.

[0054] During recording, the recording unit can improve the accuracy of the recording based on the user's past reaction data. The recording unit improves the accuracy of the recording based on, for example, the user's past reaction data. The recording unit can also extract specific patterns from the user's past reaction data and improve the accuracy of the recording. The recording unit can also analyze the user's past reaction data and optimize the accuracy of the recording. This improves the accuracy of the recording by referring to the user's past reaction data. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's past reaction data into the generation AI and cause the generation AI to improve the accuracy of the recording.

[0055] The recording unit can optimize the data to be recorded by taking into account the context and tone of the conversation during recording. For example, the recording unit optimizes the data to be recorded by taking into account the context of the conversation. The recording unit can also analyze the tone of the conversation and optimize the data to be recorded. The recording unit can also analyze the flow of the conversation and optimize the data to be recorded. In this way, the data to be recorded can be optimized by taking into account the context and tone of the conversation. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input context data of the conversation to a generation AI and cause the generation AI to optimize the data to be recorded.

[0056] The recording unit can customize the recording algorithm according to the language or dialect used by the user during recording. The recording unit can adjust the recording algorithm according to, for example, the language used by the user. The recording unit can also use a specific language model to accommodate the user's dialect. The recording unit can also optimize the recording algorithm based on the user's language settings. This improves the accuracy of recording by customizing the recording algorithm according to the language or dialect used by the user. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input the user's language data into a generation AI and cause the generation AI to customize the recording algorithm.

[0057] When recording, the recording unit can select data to record based on the user's geographical location information. The recording unit, for example, records relevant data based on the user's current location. The recording unit can also record relevant data by referencing the user's past location information. The recording unit can also record data related to a specific topic based on the user's geographical location. This makes it possible to optimize the data to be recorded by taking the user's geographical location information into consideration. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's geographical location data to a generation AI and cause the generation AI to select the data to be recorded.

[0058] The recording unit can analyze the user's social media activities and acquire related data when recording. For example, the recording unit can analyze the content of the user's social media posts and record the related data. The recording unit can also record the related data by referring to the activities of the user's friends on social media. The recording unit can also analyze the user's social media check-in information and record the related data. In this way, by analyzing the user's social media activities, related data can be acquired and the accuracy of recording can be improved. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input the user's social media data into the generation AI and cause the generation AI to acquire the related data.

[0059] The recording unit can customize the recording method by reflecting the user's past feedback during recording. The recording unit can adjust the recording method based on the user's past feedback, for example. The recording unit can also preferentially use a specific recording method based on the user's past feedback. The recording unit can also improve the accuracy of the recording method by reflecting the user's past feedback. In this way, the accuracy of the recording method is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input the user's past feedback data into the generation AI and have the generation AI customize the recording method.

[0060] During setting management, the setting management unit can improve the accuracy of management based on the user's past setting data. The setting management unit improves the accuracy of management based on, for example, the user's past setting data. The setting management unit can also extract specific patterns from the user's past setting data to improve the accuracy of management. The setting management unit can also analyze the user's past setting data and optimize the accuracy of management. This improves the accuracy of management by referring to the user's past setting data. Some or all of the above-described processing in the setting management unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting management unit can input the user's past setting data into a generation AI and have the generation AI improve the accuracy of management.

[0061] The setting management unit can optimize settings by taking into account the context and tone of the conversation during setting management. For example, the setting management unit optimizes settings by taking into account the context of the conversation. The setting management unit can also analyze the tone of the conversation and optimize the settings. The setting management unit can also analyze the flow of the conversation and optimize the settings. In this way, settings can be optimized by taking into account the context and tone of the conversation. Some or all of the above-mentioned processing in the setting management unit may be performed using AI, for example, or may be performed without using AI. For example, the setting management unit can input conversation context data to the generation AI and cause the generation AI to optimize the settings.

[0062] During setting management, the setting management unit can customize the setting management algorithm according to the language or dialect used by the user. The setting management unit adjusts the setting management algorithm according to, for example, the language used by the user. The setting management unit can also use a specific language model to accommodate the user's dialect. The setting management unit can also optimize the setting management algorithm based on the user's language settings. This improves the accuracy of setting management by customizing the setting management algorithm according to the language or dialect used by the user. Some or all of the above-described processing in the setting management unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting management unit can input the user's language data into a generation AI and cause the generation AI to customize the setting management algorithm.

[0063] During setting management, the setting management unit can select settings based on the user's geographic location information. The setting management unit selects relevant settings based on the user's current location, for example. The setting management unit can also select relevant settings by referencing the user's past location information. The setting management unit can also select settings related to a specific topic based on the user's geographic location. This allows settings to be optimized by taking the user's geographic location information into consideration. Some or all of the above-described processing in the setting management unit may be performed using AI, for example, or may be performed without using AI. For example, the setting management unit can input the user's geographic location data into a generation AI and have the generation AI select settings.

[0064] During setting management, the setting management unit can analyze the user's social media activity and acquire related settings. For example, the setting management unit can analyze the user's social media posts and acquire related settings. The setting management unit can also acquire related settings by referring to the activities of the user's friends on social media. The setting management unit can also analyze the user's social media check-in information and acquire related settings. In this way, by analyzing the user's social media activity, related settings can be acquired and the accuracy of setting management can be improved. Some or all of the above-mentioned processing in the setting management unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting management unit can input the user's social media data into the generation AI and cause the generation AI to acquire related settings.

[0065] During setting management, the setting management unit can customize the setting method by reflecting the user's past feedback. The setting management unit, for example, adjusts the setting method based on the user's past feedback. The setting management unit can also preferentially use a specific setting method based on the user's past feedback. The setting management unit can also improve the accuracy of the setting method by reflecting the user's past feedback. In this way, the accuracy of the setting method is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the setting management unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting management unit can input the user's past feedback data into a generation AI and have the generation AI customize the setting method.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The messaging app system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit, for example, measures the user's heart rate and stress level and provides the data to the analysis unit. The analysis unit can provide conversation content that corresponds to the user's health condition based on the data from the health monitoring unit. For example, if the user is in a high-stress state, the analysis unit can provide relaxing topics and advice. Also, if the user's heart rate is high, the analysis unit can provide conversation in a calm tone. This makes it possible to provide appropriate conversation that corresponds to the user's health condition.

[0068] The messaging app system may further include an hobby learning unit that learns the user's hobbies and interests. The hobby learning unit learns the user's hobbies and interests based on, for example, keywords searched for by the user in the past and content viewed by the user. The analysis unit can provide conversation content that matches the user's interests based on the data from the hobby learning unit. For example, if the user is interested in sports, the analysis unit can provide the latest sports news and game results. Also, if the user is interested in movies, the analysis unit can provide the latest movie information and reviews. This makes it possible to provide conversation that matches the user's interests.

[0069] The messaging application system may further include a schedule management unit that manages the user's schedule. The schedule management unit, for example, refers to the user's calendar or planner and provides important events and plans to the analysis unit. The analysis unit can provide conversation content according to the user's schedule based on the data from the schedule management unit. For example, if the user is nervous before a meeting, the analysis unit can provide topics that will help the user relax. It can also provide information the user needs before traveling. This makes it possible to provide appropriate conversation according to the user's schedule.

[0070] The messaging app system may further include a music learning unit that learns the user's music preferences. The music learning unit learns the user's music preferences, for example, based on music played by the user in the past and playlists. The analysis unit can provide music-related conversation content that matches the user's preferences based on the data from the music learning unit. For example, if the user likes a particular artist, the analysis unit can provide the latest information and new songs about that artist. Also, if the user likes a particular genre, the analysis unit can provide news and event information related to that genre. This makes it possible to provide conversation that matches the user's music preferences.

[0071] The messaging app system may further include a reading learning unit that learns the user's reading preferences. The reading learning unit learns the user's reading preferences, for example, based on books the user has read in the past and reviews. The analysis unit can provide reading-related conversation content that matches the user's preferences based on the data from the reading learning unit. For example, if the user likes a particular author, the analysis unit can provide the author's latest work and related information. Also, if the user likes a particular genre, the analysis unit can provide recommended books and reviews related to that genre. This makes it possible to provide conversation that matches the user's reading preferences.

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

[0073] Step 1: The analysis unit analyzes the content of the conversation in real time. The analysis unit uses natural language processing and voice recognition technologies to analyze the content of the conversation and detect specific keywords and phrases. Step 2: The sentiment analysis unit analyzes emotions based on the content analyzed by the analysis unit. The sentiment analysis unit uses emotion estimation algorithms and text analysis techniques to analyze emotions, taking into account the tone and context of the conversation. Step 3: The learning unit learns user reactions based on the analysis results obtained by the sentiment analysis unit. The learning unit learns user reactions using a machine learning algorithm and improves the accuracy of the learning by referring to past reaction data. Step 4: The control unit controls the interruption of the AI ​​character based on the information learned by the learning unit. The control unit adjusts the interruption timing and priority based on the user's settings and emotions.

[0074] (Example 2) A messaging app system according to an embodiment of the present invention analyzes conversation content in real time, analyzes emotions, learns user responses, and controls AI character interruptions. The messaging app system plays a role in making communication more enjoyable by livening up conversations and creating new topics. For example, the messaging app system analyzes conversation content in real time. For example, when a specific keyword or phrase appears in a conversation with friends or family, the system analyzes the content and interrupts at the appropriate time. The messaging app system then inserts appropriate comments and questions while being careful not to disrupt the flow of the conversation. Next, the messaging app system provides content to liven up conversations. For example, the system stimulates conversations by providing funny jokes, quizzes, trivia, and the like. Furthermore, the system can broaden the scope of conversations by providing information or news related to specific topics. Furthermore, the messaging app system learns user responses and reflects them in future conversations. For example, the system records how a user responded to a specific comment or question and uses that information to provide more appropriate comments or questions in the next conversation. In this way, the messaging app system plays a role in livening up conversations with friends and family and making communication more enjoyable. For example, when a conversation is about to die down, the messaging app system can insert a funny joke to get the conversation going again. Also, by providing information related to a specific topic, the conversation can be broadened and deeper communication can be achieved. This allows the messaging app system to continuously improve communication with users and provide more enjoyable conversations. For example, when a conversation is about to die down, the messaging app system can insert a funny joke to get the conversation going again. Also, by providing information related to a specific topic, the conversation can be broadened and deeper communication can be achieved.

[0075] A messaging app system according to an embodiment includes an analysis unit, a sentiment analysis unit, a learning unit, and a control unit. The analysis unit analyzes the content of a conversation in real time. The analysis unit analyzes the content of the conversation using, for example, natural language processing technology. The analysis unit can also convert a voice conversation into text using voice recognition technology and analyze the content. For example, the analysis unit detects specific keywords or phrases in the conversation and analyzes the content. The sentiment analysis unit analyzes emotions based on the content analyzed by the analysis unit. The sentiment analysis unit analyzes user emotions using, for example, a sentiment estimation algorithm. The sentiment analysis unit can also capture emotional nuances in the conversation using text analysis technology. For example, the sentiment analysis unit analyzes emotions taking into account the tone and context of the conversation. The learning unit learns user reactions based on the analysis results obtained by the sentiment analysis unit. The learning unit learns user reactions using, for example, a machine learning algorithm. The learning unit can also improve the accuracy of the learning by referring to past user reaction data. For example, the learning unit records how the user responded to a particular comment or question, and based on that information, provides a more appropriate comment or question in the next conversation. The control unit controls the interruption of the AI ​​character based on the information learned by the learning unit. The control unit adjusts the timing of the interruption of the AI ​​character based on, for example, a user setting. The control unit can also determine the priority of the interruption based on the user's emotions. For example, if the user is relaxed, the control unit sets a low priority for the interruption. This allows the messaging app system according to the embodiment to analyze the content of the conversation in real time, analyze emotions, learn the user's reactions, and control the interruption of the AI ​​character.

[0076] The messaging app system includes a content providing unit that provides content based on the results of the analysis unit and the sentiment analysis unit. The content providing unit provides content based on the results of the analysis unit and the sentiment analysis unit. The content providing unit provides content such as funny jokes, quizzes, and trivia. The content providing unit can also provide information and news related to specific topics. For example, the content providing unit selects and provides appropriate content based on the content of the conversation. This makes it possible to provide content such as funny jokes, quizzes, and trivia based on the results of the analysis unit and the sentiment analysis unit. Some or all of the above-described processing in the content providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the content providing unit can provide content using an AI model that receives the results of the analysis unit and the sentiment analysis unit as input and outputs appropriate content.

[0077] The messaging app system includes a recording unit that cooperates with the learning unit to record user responses. The recording unit cooperates with the learning unit to record user responses. The recording unit records, for example, how a user responded to a specific comment or question. The recording unit can also accumulate user response data and reflect it in subsequent conversations. For example, the recording unit analyzes user response data in chronological order and identifies patterns of change in responses. By doing so, by recording user responses in cooperation with the learning unit, it is possible to reflect the patterns of change in responses in subsequent conversations. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input user response data to a generation AI and have the generation AI analyze patterns of change in responses.

[0078] The messaging app system includes a setting management unit for managing user settings in cooperation with the control unit. The setting management unit manages user settings in cooperation with the control unit. For example, the setting management unit manages user setting items and provides them to the control unit. The setting management unit can also adjust the interrupt timing of the AI ​​character based on the user settings. For example, the setting management unit issues instructions to the control unit based on the frequency and timing of interrupts set by the user. In this way, by managing the user settings in cooperation with the control unit, the interruption of the AI ​​character can be controlled based on the user settings. Some or all of the above-described processing in the setting management unit may be performed using AI, for example, or may be performed without using AI. For example, the setting management unit can input user setting data to a generation AI and have the generation AI manage the settings.

[0079] The analysis unit can estimate the user's emotions and adjust the conversation analysis method based on the estimated user emotions. For example, if the user is having fun, the analysis unit can analyze the conversation slowly to maintain a natural flow. Furthermore, if the user is angry, the analysis unit can quickly analyze the conversation and provide an appropriate response. Furthermore, if the user is sad, the analysis unit can carefully analyze the conversation and provide comments that take the user's emotions into consideration. This enables more appropriate analysis by adjusting the conversation analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the analysis method based on the emotion.

[0080] When analyzing the content of a conversation, the analysis unit can improve the accuracy of the analysis by referring to the user's past conversation history. For example, the analysis unit prioritizes analysis of specific phrases or keywords used by the user in the past. The analysis unit can also analyze the user's past conversation patterns and prioritize analysis of similar conversation content. The analysis unit can also prioritize analysis of information related to a specific topic from the user's past conversation history. In this way, by referring to the user's past conversation history, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past conversation history data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0081] When analyzing the content of a conversation, the analysis unit can preferentially detect specific keywords and phrases based on the context of the conversation. For example, the analysis unit preferentially detects important keywords from the context of the conversation. The analysis unit can also analyze the flow of the conversation and preferentially detect related phrases. The analysis unit can also preferentially detect specific keywords according to the topic of the conversation. In this way, important keywords and phrases can be preferentially detected by taking the context of the conversation into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input conversation context data to a generation AI and cause the generation AI to preferentially detect keywords and phrases.

[0082] When analyzing the content of a conversation, the analysis unit can customize the analysis algorithm according to the language or dialect used by the user. The analysis unit can adjust the analysis algorithm according to, for example, the language used by the user. The analysis unit can also use a specific language model to accommodate the user's dialect. The analysis unit can also optimize the analysis algorithm based on the user's language settings. This improves the accuracy of the analysis by customizing the analysis algorithm according to the language or dialect used by the user. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's language data into a generation AI and have the generation AI customize the analysis algorithm.

[0083] The emotion analysis unit can estimate the user's emotion and adjust the emotion analysis algorithm based on the estimated user emotion. For example, if the user is relaxed, the emotion analysis unit can gradually adjust the emotion analysis algorithm. Furthermore, if the user is nervous, the emotion analysis unit can quickly adjust the emotion analysis algorithm. Furthermore, if the user is excited, the emotion analysis unit can finely adjust the emotion analysis algorithm. This enables more accurate emotion analysis by adjusting the emotion analysis algorithm based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the emotion analysis unit can be performed using, for example, an AI, or without an AI. For example, the emotion analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the emotion analysis algorithm.

[0084] The emotion analysis unit can improve the accuracy of emotion analysis by referring to the user's past emotion data. For example, the emotion analysis unit improves the accuracy of emotion analysis based on the user's past emotion data. The emotion analysis unit can also improve the accuracy of emotion analysis by analyzing the user's past emotion patterns. The emotion analysis unit can also prioritize analysis of specific emotions from the user's past emotion data. This improves the accuracy of emotion analysis by referring to the user's past emotion data. Some or all of the above-described processing in the emotion analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the emotion analysis unit can input the user's past emotion data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0085] The sentiment analysis unit can analyze sentiment more accurately by taking into account the context and tone of the conversation during sentiment analysis. For example, the sentiment analysis unit accurately analyzes sentiment by taking into account the context of the conversation. The sentiment analysis unit can also accurately analyze sentiment by analyzing the tone of the conversation. The sentiment analysis unit can also accurately analyze sentiment by analyzing the flow of the conversation. This allows for more accurate sentiment analysis by taking into account the context and tone of the conversation. Some or all of the above-described processing in the sentiment analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the sentiment analysis unit can input conversation context data to the generation AI and cause the generation AI to perform an accurate sentiment analysis.

[0086] The sentiment analysis unit can customize the analysis algorithm according to the language or dialect used by the user during sentiment analysis. For example, the sentiment analysis unit adjusts the sentiment analysis algorithm according to the language used by the user. The sentiment analysis unit can also use a specific language model to accommodate the user's dialect. The sentiment analysis unit can also optimize the sentiment analysis algorithm based on the user's language settings. This improves the accuracy of sentiment analysis by customizing the analysis algorithm according to the language or dialect used by the user. Some or all of the above-described processing in the sentiment analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the sentiment analysis unit can input the user's language data into a generation AI and have the generation AI customize the analysis algorithm.

[0087] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit selects training data related to the relaxed state. Furthermore, if the user is tense, the learning unit can select training data related to the tense state. Furthermore, if the user is excited, the learning unit can select training data related to the excited state. This enables more appropriate learning by selecting training data based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the learning unit can input the user's emotion data into the generation AI and have the generation AI select the training data.

[0088] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit, for example, optimizes the learning algorithm based on past learning data. The learning unit can also extract specific patterns from past learning data and optimize the learning algorithm. The learning unit can also analyze past learning data and improve the accuracy of the learning algorithm. In this way, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data into a generation AI and cause the generation AI to optimize the learning algorithm.

[0089] During learning, the learning unit can analyze the user's reaction data and adjust the update frequency of the learning data. The learning unit can, for example, adjust the update frequency of the learning data based on the user's reaction data. The learning unit can also extract specific patterns from the user's reaction data and adjust the update frequency of the learning data. The learning unit can also analyze the user's reaction data and optimize the update frequency of the learning data. In this way, the update frequency of the learning data can be optimized by analyzing the user's reaction data. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input the user's reaction data to the generation AI and cause the generation AI to adjust the update frequency of the learning data.

[0090] During learning, the learning unit can customize the learning algorithm according to the language or dialect used by the user. The learning unit can adjust the learning algorithm according to, for example, the language used by the user. The learning unit can also use a specific language model to accommodate the user's dialect. The learning unit can also optimize the learning algorithm based on the user's language settings. This improves learning accuracy by customizing the learning algorithm according to the language or dialect used by the user. Some or all of the above-described processing in the learning unit can be performed using, for example, AI, or can be performed without using AI. For example, the learning unit can input the user's language data into a generation AI and cause the generation AI to customize the learning algorithm.

[0091] The control unit can estimate the user's emotions and adjust the interrupt timing of the AI ​​character based on the estimated user emotions. For example, if the user is relaxed, the control unit can set the interrupt timing to be gentle. If the user is nervous, the control unit can also set the interrupt timing to be rapid. If the user is excited, the control unit can also set the interrupt timing to be moderate. This allows for more appropriate interrupts by adjusting the interrupt timing of the AI ​​character based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the control unit can be performed using an AI, for example, or without an AI. For example, the control unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the interrupt timing.

[0092] During control, the control unit can improve the accuracy of interrupts by referring to the user's past reaction data. The control unit improves the accuracy of interrupts, for example, based on the user's past reaction data. The control unit can also extract specific patterns from the user's past reaction data and improve the accuracy of interrupts. The control unit can also analyze the user's past reaction data and optimize the accuracy of interrupts. This improves the accuracy of interrupts by referring to the user's past reaction data. Some or all of the above-described processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input the user's past reaction data into the generation AI and cause the generation AI to improve the accuracy of interrupts.

[0093] During control, the control unit can adjust the timing of interruption based on the context and tone of the conversation. For example, the control unit optimizes the timing of interruption by taking into account the context of the conversation. The control unit can also analyze the tone of the conversation and optimize the timing of interruption. The control unit can also analyze the flow of the conversation and optimize the timing of interruption. In this way, the timing of interruption can be optimized by taking into account the context and tone of the conversation. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input conversation context data to the generation AI and cause the generation AI to optimize the timing of interruption.

[0094] During control, the control unit can customize the interrupt algorithm according to the language or dialect used by the user. The control unit can adjust the interrupt algorithm according to, for example, the language used by the user. The control unit can also use a specific language model to accommodate the user's dialect. The control unit can also optimize the interrupt algorithm based on the user's language settings. This improves the accuracy of interrupts by customizing the interrupt algorithm according to the language or dialect used by the user. Some or all of the above-described processing in the control unit can be performed using, for example, AI, or can be performed without using AI. For example, the control unit can input the user's language data into a generation AI and cause the generation AI to customize the interrupt algorithm.

[0095] The control unit can estimate the user's emotions and determine the priority of interrupts based on the estimated user emotions. For example, if the user is relaxed, the control unit can set the priority of interrupts to low. If the user is nervous, the control unit can also set the priority of interrupts to high. If the user is excited, the control unit can also set the priority of interrupts to medium. This enables more appropriate interrupts by determining the priority of interrupts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the control unit can be performed using an AI, for example, or without an AI. For example, the control unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of interrupts.

[0096] During control, the control unit can adjust the timing of interruptions based on the user's geographical location information. The control unit can adjust the timing of interruptions based on, for example, the user's current location. The control unit can also adjust the timing of interruptions by referring to the user's past location information. The control unit can also adjust the timing of interruptions related to a specific topic based on the user's geographical location. This makes it possible to optimize the timing of interruptions by taking the user's geographical location information into consideration. Some or all of the above-described processing in the control unit can be performed using, or without, AI, for example. For example, the control unit can input the user's geographical location data into the generation AI and cause the generation AI to adjust the interruption timing.

[0097] During control, the control unit can analyze the user's social media activities and acquire related information. For example, the control unit can analyze the content of the user's social media posts and acquire related information. The control unit can also acquire related information by referring to the activities of the user's friends on social media. The control unit can also analyze the user's social media check-in information and acquire related information. In this way, by analyzing the user's social media activities, related information can be acquired and the accuracy of interrupts can be improved. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's social media data into the generation AI and cause the generation AI to acquire related information.

[0098] During control, the control unit can customize the interrupt method by reflecting the user's past feedback. The control unit, for example, adjusts the interrupt method based on the user's past feedback. The control unit can also preferentially use a specific interrupt method based on the user's past feedback. The control unit can also improve the accuracy of the interrupt method by reflecting the user's past feedback. In this way, the accuracy of the interrupt method is improved by reflecting the user's past feedback. Some or all of the above-described processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the interrupt method.

[0099] The content providing unit can estimate the user's emotions and adjust the type of content to be provided based on the estimated user's emotions. For example, if the user is relaxed, the content providing unit can provide content related to a relaxed state. Furthermore, if the user is tense, the content providing unit can provide content related to a tense state. Furthermore, if the user is excited, the content providing unit can provide content related to an excited state. By adjusting the type of content to be provided based on the user's emotions, more appropriate content can be provided. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the content providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the content providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the type of content to be provided.

[0100] When providing content, the content providing unit can improve the accuracy of the content to be provided based on the user's past reaction data. The content providing unit improves the accuracy of the content to be provided, for example, based on the user's past reaction data. The content providing unit can also extract specific patterns from the user's past reaction data and improve the accuracy of the content to be provided. The content providing unit can also analyze the user's past reaction data and optimize the accuracy of the content to be provided. In this way, the accuracy of the content to be provided is improved by referring to the user's past reaction data. Some or all of the above-described processing in the content providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the content providing unit can input the user's past reaction data into a generation AI and cause the generation AI to improve the accuracy of the content to be provided.

[0101] When providing content, the content providing unit can optimize the content to be provided by taking into account the context and tone of the conversation. For example, the content providing unit optimizes the content to be provided by taking into account the context of the conversation. The content providing unit can also analyze the tone of the conversation and optimize the content to be provided. The content providing unit can also analyze the flow of the conversation and optimize the content to be provided. In this way, the content to be provided can be optimized by taking into account the context and tone of the conversation. Some or all of the above-described processing in the content providing unit may be performed using AI, for example, or may be performed without using AI. For example, the content providing unit can input context data of the conversation to a generation AI and cause the generation AI to optimize the content to be provided.

[0102] When providing content, the content providing unit can customize the content to be provided according to the language or dialect used by the user. The content providing unit can, for example, adjust the content to be provided according to the language used by the user. The content providing unit can also use a specific language model to accommodate the user's dialect. The content providing unit can also optimize the content to be provided based on the user's language settings. This improves the accuracy of the content to be provided by customizing the content to be provided according to the language or dialect used by the user. Some or all of the above-described processing by the content providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the content providing unit can input the user's language data into a generation AI and cause the generation AI to customize the content to be provided.

[0103] The content providing unit can estimate the user's emotions and determine the priority of content to be provided based on the estimated user's emotions. For example, if the user is relaxed, the content providing unit can set a low priority for content to be provided. Furthermore, if the user is nervous, the content providing unit can set a high priority for content to be provided. Furthermore, if the user is excited, the content providing unit can set a medium priority for content to be provided. This allows for more appropriate content to be provided by determining the priority of content to be provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the content providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the content providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of content to be provided.

[0104] When providing content, the content providing unit can select content to be provided based on the user's geographical location information. The content providing unit can, for example, provide related content based on the user's current location. The content providing unit can also provide related content by referencing the user's past location information. The content providing unit can also provide content related to a specific topic based on the user's geographical location. This makes it possible to optimize the content to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the content providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the content providing unit can input the user's geographical location data into a generation AI and cause the generation AI to select content to be provided.

[0105] When providing content, the content providing unit can analyze the user's social media activity and acquire related content. For example, the content providing unit can analyze the user's social media posts and provide related content. The content providing unit can also provide related content by referring to the activities of the user's friends on social media. The content providing unit can also analyze the user's social media check-in information and provide related content. In this way, by analyzing the user's social media activity, related content can be acquired and the accuracy of the content to be provided can be improved. Some or all of the above-mentioned processing in the content providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the content providing unit can input the user's social media data into a generation AI and cause the generation AI to acquire related content.

[0106] When providing content, the content providing unit can customize the content to be provided by reflecting the user's past feedback. The content providing unit, for example, adjusts the content to be provided based on the user's past feedback. The content providing unit can also preferentially provide specific content based on the user's past feedback. The content providing unit can also improve the accuracy of the content to be provided by reflecting the user's past feedback. In this way, the accuracy of the content to be provided is improved by reflecting the user's past feedback. Some or all of the above-described processing in the content providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the content providing unit can input the user's past feedback data into a generation AI and have the generation AI customize the content to be provided.

[0107] The recording unit can estimate the user's emotion and adjust the type of data to be recorded based on the estimated user's emotion. For example, if the user is relaxed, the recording unit records data related to the relaxed state. Furthermore, if the user is tense, the recording unit can also record data related to the tense state. Furthermore, if the user is excited, the recording unit can also record data related to the excited state. By adjusting the type of data to be recorded based on the user's emotion, more appropriate data can be recorded. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recording unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the type of data to be recorded.

[0108] During recording, the recording unit can improve the accuracy of the recording based on the user's past reaction data. The recording unit improves the accuracy of the recording based on, for example, the user's past reaction data. The recording unit can also extract specific patterns from the user's past reaction data and improve the accuracy of the recording. The recording unit can also analyze the user's past reaction data and optimize the accuracy of the recording. This improves the accuracy of the recording by referring to the user's past reaction data. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's past reaction data into the generation AI and cause the generation AI to improve the accuracy of the recording.

[0109] The recording unit can optimize the data to be recorded by taking into account the context and tone of the conversation during recording. For example, the recording unit optimizes the data to be recorded by taking into account the context of the conversation. The recording unit can also analyze the tone of the conversation and optimize the data to be recorded. The recording unit can also analyze the flow of the conversation and optimize the data to be recorded. In this way, the data to be recorded can be optimized by taking into account the context and tone of the conversation. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input context data of the conversation to a generation AI and cause the generation AI to optimize the data to be recorded.

[0110] The recording unit can customize the recording algorithm according to the language or dialect used by the user during recording. The recording unit can adjust the recording algorithm according to, for example, the language used by the user. The recording unit can also use a specific language model to accommodate the user's dialect. The recording unit can also optimize the recording algorithm based on the user's language settings. This improves the accuracy of recording by customizing the recording algorithm according to the language or dialect used by the user. Some or all of the above-described processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input the user's language data into a generation AI and cause the generation AI to customize the recording algorithm.

[0111] The recording unit can estimate the user's emotions and determine the priority of data to be recorded based on the estimated user's emotions. For example, if the user is relaxed, the recording unit can set a low priority for the data to be recorded. Furthermore, if the user is nervous, the recording unit can also set a high priority for the data to be recorded. Furthermore, if the user is excited, the recording unit can also set a medium priority for the data to be recorded. This allows more appropriate data to be recorded by determining the priority of data to be recorded based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the recording unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recording unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of data to be recorded.

[0112] When recording, the recording unit can select data to record based on the user's geographical location information. The recording unit, for example, records relevant data based on the user's current location. The recording unit can also record relevant data by referencing the user's past location information. The recording unit can also record data related to a specific topic based on the user's geographical location. This makes it possible to optimize the data to be recorded by taking the user's geographical location information into consideration. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user's geographical location data to a generation AI and cause the generation AI to select the data to be recorded.

[0113] The recording unit can analyze the user's social media activities and acquire related data when recording. For example, the recording unit can analyze the content of the user's social media posts and record the related data. The recording unit can also record the related data by referring to the activities of the user's friends on social media. The recording unit can also analyze the user's social media check-in information and record the related data. In this way, by analyzing the user's social media activities, related data can be acquired and the accuracy of recording can be improved. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input the user's social media data into the generation AI and cause the generation AI to acquire the related data.

[0114] The recording unit can customize the recording method by reflecting the user's past feedback during recording. The recording unit can adjust the recording method based on the user's past feedback, for example. The recording unit can also preferentially use a specific recording method based on the user's past feedback. The recording unit can also improve the accuracy of the recording method by reflecting the user's past feedback. In this way, the accuracy of the recording method is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the recording unit can be performed using, for example, AI, or can be performed without using AI. For example, the recording unit can input the user's past feedback data into the generation AI and have the generation AI customize the recording method.

[0115] The setting management unit can estimate the user's emotions and adjust the setting management method based on the estimated user's emotions. For example, the setting management unit can gently adjust the setting management method when the user is relaxed. The setting management unit can also quickly adjust the setting management method when the user is nervous. The setting management unit can also finely adjust the setting management method when the user is excited. This enables more appropriate setting management by adjusting the setting management method based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the setting management unit can be performed using, for example, an AI, or without an AI. For example, the setting management unit can input the user's emotion data into the generation AI and have the generation AI adjust the setting management method.

[0116] During setting management, the setting management unit can improve the accuracy of management based on the user's past setting data. The setting management unit improves the accuracy of management based on, for example, the user's past setting data. The setting management unit can also extract specific patterns from the user's past setting data to improve the accuracy of management. The setting management unit can also analyze the user's past setting data and optimize the accuracy of management. This improves the accuracy of management by referring to the user's past setting data. Some or all of the above-described processing in the setting management unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting management unit can input the user's past setting data into a generation AI and have the generation AI improve the accuracy of management.

[0117] The setting management unit can optimize settings by taking into account the context and tone of the conversation during setting management. For example, the setting management unit optimizes settings by taking into account the context of the conversation. The setting management unit can also analyze the tone of the conversation and optimize the settings. The setting management unit can also analyze the flow of the conversation and optimize the settings. In this way, settings can be optimized by taking into account the context and tone of the conversation. Some or all of the above-mentioned processing in the setting management unit may be performed using AI, for example, or may be performed without using AI. For example, the setting management unit can input conversation context data to the generation AI and cause the generation AI to optimize the settings.

[0118] During setting management, the setting management unit can customize the setting management algorithm according to the language or dialect used by the user. The setting management unit adjusts the setting management algorithm according to, for example, the language used by the user. The setting management unit can also use a specific language model to accommodate the user's dialect. The setting management unit can also optimize the setting management algorithm based on the user's language settings. This improves the accuracy of setting management by customizing the setting management algorithm according to the language or dialect used by the user. Some or all of the above-described processing in the setting management unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting management unit can input the user's language data into a generation AI and cause the generation AI to customize the setting management algorithm.

[0119] The setting management unit can estimate the user's emotions and determine the priority of settings based on the estimated user emotions. For example, if the user is relaxed, the setting management unit can set the priority of settings to low. If the user is nervous, the setting management unit can also set the priority of settings to high. If the user is excited, the setting management unit can also set the priority of settings to medium. This enables more appropriate setting management by determining the priority of settings based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the setting management unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the setting management unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of settings.

[0120] During setting management, the setting management unit can select settings based on the user's geographic location information. The setting management unit selects relevant settings based on the user's current location, for example. The setting management unit can also select relevant settings by referencing the user's past location information. The setting management unit can also select settings related to a specific topic based on the user's geographic location. This allows settings to be optimized by taking the user's geographic location information into consideration. Some or all of the above-described processing in the setting management unit may be performed using AI, for example, or may be performed without using AI. For example, the setting management unit can input the user's geographic location data into a generation AI and have the generation AI select settings.

[0121] During setting management, the setting management unit can analyze the user's social media activity and acquire related settings. For example, the setting management unit can analyze the user's social media posts and acquire related settings. The setting management unit can also acquire related settings by referring to the activities of the user's friends on social media. The setting management unit can also analyze the user's social media check-in information and acquire related settings. In this way, by analyzing the user's social media activity, related settings can be acquired and the accuracy of setting management can be improved. Some or all of the above-mentioned processing in the setting management unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting management unit can input the user's social media data into the generation AI and cause the generation AI to acquire related settings.

[0122] During setting management, the setting management unit can customize the setting method by reflecting the user's past feedback. The setting management unit, for example, adjusts the setting method based on the user's past feedback. The setting management unit can also preferentially use a specific setting method based on the user's past feedback. The setting management unit can also improve the accuracy of the setting method by reflecting the user's past feedback. In this way, the accuracy of the setting method is improved by reflecting the user's past feedback. Some or all of the above-mentioned processing in the setting management unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting management unit can input the user's past feedback data into a generation AI and have the generation AI customize the setting method. === Hard Collateral 1-1 === Each of the multiple elements, including the analysis unit, emotion analysis unit, learning unit, control unit, content providing unit, recording unit, and setting management unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes the content of the conversation in real time. The emotion analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes emotions based on the analyzed content. The learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns the user's reactions. The control unit is implemented by the control unit 46A of the smart device 14 and controls the interruption of the AI ​​character. The content providing unit is implemented by the control unit 46A of the smart device 14 and provides content based on the results of the analysis unit and emotion analysis unit. The recording unit is implemented by the specific processing unit 290 of the data processing device 12 and records the user's reactions. The setting management unit is implemented by the control unit 46A of the smart device 14 and manages the user's settings. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned analysis unit, emotion analysis unit, learning unit, control unit, content providing unit, recording unit, and setting management unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 and analyzes the content of the conversation in real time. The emotion analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes emotions based on the analyzed content. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's reactions. The control unit is realized by the control unit 46A of the smart glasses 214 and controls the interruption of the AI ​​character. The content providing unit is realized by the control unit 46A of the smart glasses 214 and provides content based on the results of the analysis unit and the emotion analysis unit. The recording unit is realized by the specific processing unit 290 of the data processing device 12 and records the user's reactions. The setting management unit is realized by the control unit 46A of the smart glasses 214 and manages the user's settings. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned analysis unit, emotion analysis unit, learning unit, control unit, content providing unit, recording unit, and setting management unit, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 and analyzes the content of the conversation in real time. The emotion analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes emotions based on the analyzed content. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's reactions. The control unit is realized by the control unit 46A of the headset type terminal 314 and controls the interruption of the AI ​​character. The content providing unit is realized by the control unit 46A of the headset type terminal 314 and provides content based on the results of the analysis unit and the emotion analysis unit. The recording unit is realized by the specific processing unit 290 of the data processing device 12 and records the user's reactions. The setting management unit is realized by the control unit 46A of the headset type terminal 314 and manages the user's settings. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned analysis unit, emotion analysis unit, learning unit, control unit, content providing unit, recording unit, and setting management unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 and analyzes the content of the conversation in real time. The emotion analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes emotions based on the analyzed content. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns the user's reactions. The control unit is realized by the control unit 46A of the robot 414 and controls the interruption of the AI ​​character. The content providing unit is realized by the control unit 46A of the robot 414 and provides content based on the results of the analysis unit and emotion analysis unit. The recording unit is realized by the specific processing unit 290 of the data processing device 12 and records the user's reactions. The setting management unit is realized by the control unit 46A of the robot 414 and manages the user's settings.

[0123] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0124] The messaging app system may further include a health monitoring unit that monitors the user's health condition. The health monitoring unit, for example, measures the user's heart rate and stress level and provides the data to the analysis unit. The analysis unit can provide conversation content that corresponds to the user's health condition based on the data from the health monitoring unit. For example, if the user is in a high-stress state, the analysis unit can provide relaxing topics and advice. Also, if the user's heart rate is high, the analysis unit can provide conversation in a calm tone. This makes it possible to provide appropriate conversation that corresponds to the user's health condition.

[0125] The messaging app system may further include an hobby learning unit that learns the user's hobbies and interests. The hobby learning unit learns the user's hobbies and interests based on, for example, keywords searched for by the user in the past and content viewed by the user. The analysis unit can provide conversation content that matches the user's interests based on the data from the hobby learning unit. For example, if the user is interested in sports, the analysis unit can provide the latest sports news and game results. Also, if the user is interested in movies, the analysis unit can provide the latest movie information and reviews. This makes it possible to provide conversation that matches the user's interests.

[0126] The messaging application system may further include a schedule management unit that manages the user's schedule. The schedule management unit, for example, refers to the user's calendar or planner and provides important events and plans to the analysis unit. The analysis unit can provide conversation content according to the user's schedule based on the data from the schedule management unit. For example, if the user is nervous before a meeting, the analysis unit can provide topics that will help the user relax. It can also provide information the user needs before traveling. This makes it possible to provide appropriate conversation according to the user's schedule.

[0127] The messaging app system may further include a music learning unit that learns the user's music preferences. The music learning unit learns the user's music preferences, for example, based on music played by the user in the past and playlists. The analysis unit can provide music-related conversation content that matches the user's preferences based on the data from the music learning unit. For example, if the user likes a particular artist, the analysis unit can provide the latest information and new songs about that artist. Also, if the user likes a particular genre, the analysis unit can provide news and event information related to that genre. This makes it possible to provide conversation that matches the user's music preferences.

[0128] The messaging app system may further include a reading learning unit that learns the user's reading preferences. The reading learning unit learns the user's reading preferences, for example, based on books the user has read in the past and reviews. The analysis unit can provide reading-related conversation content that matches the user's preferences based on the data from the reading learning unit. For example, if the user likes a particular author, the analysis unit can provide the author's latest work and related information. Also, if the user likes a particular genre, the analysis unit can provide recommended books and reviews related to that genre. This makes it possible to provide conversation that matches the user's reading preferences.

[0129] The messaging app system can further estimate the user's emotions and adjust the tone of the conversation based on the estimated user's emotions. For example, if the user is sad, the conversation can be conducted in a comforting tone. If the user is excited, the conversation can be conducted in a tone that shares the user's excitement. Furthermore, if the user is relaxed, the conversation can be conducted in a relaxed tone. This allows the conversation to be conducted in an appropriate tone according to the user's emotions.

[0130] The messaging app system can further estimate the user's emotions and adjust the type of content provided based on the estimated user emotions. For example, if the user is relaxed, it can provide relaxing music or videos. If the user is tense, it can provide relaxation advice or meditation guides. If the user is excited, it can provide news or event information that allows the user to share their excitement. This makes it possible to provide appropriate content according to the user's emotions.

[0131] The messaging app system can further estimate the user's emotions and adjust the content of the conversation based on the estimated user's emotions. For example, if the user is sad, it can provide a topic that comforts the user. If the user is excited, it can provide a topic that allows the user to share their excitement. If the user is relaxed, it can provide a topic that helps the user to relax. This makes it possible to provide appropriate conversation content according to the user's emotions.

[0132] The messaging app system can further estimate the user's emotions and adjust the conversation speed based on the estimated user's emotions. For example, if the user is relaxed, the conversation can proceed at a slow pace. If the user is nervous, the conversation can proceed quickly. Furthermore, if the user is excited, the conversation can proceed at a moderate pace. This makes it possible to provide an appropriate conversation speed according to the user's emotions.

[0133] The messaging app system can also estimate the user's emotions and filter the content of the conversation based on the estimated user's emotions. For example, if the user is sad, negative topics can be avoided. If the user is excited, positive topics can be prioritized. Furthermore, if the user is relaxed, relaxing topics can be provided. This allows the system to provide appropriate conversation content according to the user's emotions.

[0134] The processing flow of the second embodiment will be briefly explained below.

[0135] Step 1: The analysis unit analyzes the content of the conversation in real time. The analysis unit uses natural language processing and voice recognition technologies to analyze the content of the conversation and detect specific keywords and phrases. Step 2: The sentiment analysis unit analyzes emotions based on the content analyzed by the analysis unit. The sentiment analysis unit uses emotion estimation algorithms and text analysis techniques to analyze emotions, taking into account the tone and context of the conversation. Step 3: The learning unit learns user reactions based on the analysis results obtained by the sentiment analysis unit. The learning unit learns user reactions using a machine learning algorithm and improves the accuracy of the learning by referring to past reaction data. Step 4: The control unit controls the interruption of the AI ​​character based on the information learned by the learning unit. The control unit adjusts the interruption timing and priority based on the user's settings and emotions.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Explanation of symbols]

[0208] 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. An analysis unit that analyzes the content of the conversation in real time, an emotion analysis unit that analyzes emotions based on the content analyzed by the analysis unit; a learning unit that learns user reactions based on the analysis results obtained by the emotion analysis unit; a control unit that controls the interruption of an AI character based on the information learned by the learning unit; A system characterized by:

2. a content providing unit that provides content based on the results of the analysis unit and the emotion analysis unit; 2. The system of claim 1.

3. A recording unit that cooperates with the learning unit to record the user's response 2. The system of claim 1.

4. a setting management unit for managing user settings in cooperation with the control unit; 2. The system of claim 1.

5. The analysis unit Estimate user emotions and adjust conversation analysis methods based on the estimated user emotions 2. The system of claim 1.

6. The analysis unit When analyzing the content of a conversation, the accuracy of the analysis is improved by referring to the user's past conversation history.

2. The system of claim 1.

7. The analysis unit When analyzing conversation content, prioritize the detection of specific keywords and phrases based on the context of the conversation.

2. The system of claim 1.

8. The analysis unit When analyzing the content of conversations, the analysis algorithm is customized depending on the language and dialect used by the user.

2. The system of claim 1.

9. The emotion analysis unit Inferring user sentiment and adjusting the sentiment analysis algorithm based on the estimated user sentiment 2. The system of claim 1.

Images