system

Abstract

The system according to this embodiment aims to provide adaptive responses in real time based on information about the user's hobbies. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a response unit. The collection unit collects information related to the user's hobbies. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a customized response based on the analysis results obtained by the analysis unit. The response unit recognizes emotions in real time and responds adaptively based on the response generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, real-time adaptive responses based on information about the user's hobbies have not been fully performed, and there is room for improvement.

[0005] The system according to the embodiment aims to adaptively respond in real time based on information about the user's hobbies.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a response unit. The collection unit collects information related to the user's hobbies. The analysis unit analyzes the information collected by the collection unit. The generation unit generates a customized response based on the analysis results obtained by the analysis unit. The response unit recognizes emotions in real time and responds adaptively based on the response generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can adaptively respond in real time based on information about the user's hobbies. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that shows deep understanding and empathy for a user's specific niche hobby and engages in real-time conversation. When a user shares information about their hobby, this AI agent system collects and analyzes that information, generates a customized response, recognizes emotions in real time, and responds adaptively. For example, when a user shares information about their hobby, the AI ​​agent system collects that information. Next, it analyzes the collected information and generates a customizable response based on the user's interests. Furthermore, it recognizes emotions in real time and responds adaptively. This allows the user to find someone with whom they can have in-depth conversations about their hobby, thereby reducing feelings of social isolation. For example, the AI ​​agent system receives input from the user about their hobby. For example, when a user shares information about music, the AI ​​agent system collects and analyzes that information and generates a customized response. Next, the AI ​​agent system recognizes emotions in real time and responds adaptively. This allows the user to find someone with whom they can have in-depth conversations about music. Similarly, when a user shares information about movies, the AI ​​agent system collects and analyzes that information and generates a customized response. Next, the AI ​​agent system recognizes emotions in real time and responds adaptively. This allows the user to find someone with whom they can have in-depth conversations about movies. Furthermore, when a user shares information about sports, the AI ​​agent system collects, analyzes, and generates a customized response. The AI ​​agent system then recognizes emotions in real time and responds adaptively. This allows users to find someone with whom they can have in-depth conversations about sports. This enables the AI ​​agent system to collect and analyze information about the user's hobbies, generate customized responses, and recognize and respond to emotions in real time.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a response unit. The collection unit collects information related to the user's hobbies. For example, the collection unit collects information when the user inputs information related to their hobbies. For example, the collection unit collects information when the user inputs information related to music. The collection unit can also collect information when the user inputs information related to movies. Furthermore, the collection unit can also collect information when the user inputs information related to sports. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information and provides data for generating a customizable response based on the user's interests. For example, the analysis unit analyzes the collected information related to music and provides data for generating a customizable response based on the user's interests. The analysis unit can also analyze the collected information related to movies and provide data for generating a customizable response based on the user's interests. Furthermore, the analysis unit can also analyze the collected information related to sports and provide data for generating a customizable response based on the user's interests. The generation unit generates a customized response based on the analysis results obtained by the analysis unit. The generation unit generates customizable responses based on user interests, for example, based on analysis results. For example, the generation unit generates customizable responses based on user interests, based on analysis results related to music. The generation unit can also generate customizable responses based on user interests, based on analysis results related to movies. Furthermore, the generation unit can also generate customizable responses based on user interests, based on analysis results related to sports. The response unit recognizes emotions in real time and responds adaptively based on the responses generated by the generation unit. For example, the response unit recognizes emotions in real time and responds adaptively based on the generated responses. For example, the response unit recognizes emotions in real time and responds adaptively based on the generated responses related to music.Furthermore, the response unit can recognize emotions in real time and respond adaptively based on the generated movie-related responses. Additionally, the response unit can recognize emotions in real time and respond adaptively based on the generated sports-related responses. This enables the AI ​​agent system according to the embodiment to collect and analyze information about the user's hobbies, generate customized responses, and perform real-time emotion recognition and adaptive responses.

[0030] The data collection unit collects information about the user's hobbies. For example, it collects information when the user inputs information about their hobbies. Specifically, when the user inputs information about music, the unit collects that information. For example, the user can input detailed information such as their favorite artists, genres, and recently listened-to songs. The data collection unit can also collect information when the user inputs information about movies. For example, the user can input information such as recently watched movies, their favorite movie genres, directors, and actors. Furthermore, the data collection unit can collect information when the user inputs information about sports. For example, the user can input information such as their favorite team, sport, and recently watched matches. The data collection unit centrally manages this information and builds a database of the user's hobbies. The data collection unit collects the information entered by the user in real time and stores it in the database. This allows the data collection unit to efficiently collect information about the user's hobbies and provide it to the analysis and generation units. Furthermore, the data collection unit can periodically update the user's input information to maintain the latest information. For example, it can adapt if the user develops a new hobby or their interests change. This allows the data collection unit to keep user hobbies information up-to-date at all times, improving the overall accuracy and reliability of the system.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information and provides data to generate customizable responses based on the user's interests. Specifically, the analysis unit analyzes collected information on music and provides data to generate customizable responses based on the user's interests. For example, it generates data to recommend relevant new songs and artists based on the user's favorite artists and genres. The analysis unit can also analyze collected information on movies and provide data to generate customizable responses based on the user's interests. For example, it generates data to recommend relevant new movies and works by directors based on the user's favorite movie genres and directors. Furthermore, the analysis unit can also analyze collected information on sports and provide data to generate customizable responses based on the user's interests. For example, it generates data to provide information on relevant matches and events based on the team the user supports and their favorite sport. The analysis unit uses AI to analyze this information and gain a detailed understanding of the user's interests and preferences. The analysis unit can also analyze past data and trends to predict changes in user interests and new interests. This allows the analysis unit to provide data for generating customizable responses based on user interests, thereby improving the overall accuracy and reliability of the system.

[0032] The generation unit generates customized responses based on the analysis results obtained by the analysis unit. For example, the generation unit generates customizable responses based on the user's interests based on the analysis results. Specifically, the generation unit generates customizable responses based on the user's interests based on the analysis results regarding music. For example, it generates responses that recommend new songs or artists based on the user's favorite artists or genres. The generation unit can also generate customizable responses based on the user's interests based on the analysis results regarding movies. For example, it generates responses that recommend new movies or works by directors based on the user's favorite movie genres or directors. Furthermore, the generation unit can also generate customizable responses based on the user's interests based on the analysis results regarding sports. For example, it generates responses that provide information on relevant matches or events based on the team the user supports or their favorite sport. The generation unit generates these responses using natural language generation technology and provides them to the user in a natural way. By generating responses based on the user's interests and preferences, the generation unit can improve user satisfaction. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy and quality of the responses. This allows the generation unit to produce customizable responses based on user interests, improving the overall accuracy and reliability of the system.

[0033] The response unit recognizes emotions in real time and responds adaptively based on the responses generated by the generation unit. For example, the response unit recognizes emotions in real time and responds adaptively based on the generated responses. Specifically, the response unit recognizes emotions in real time and responds adaptively based on the generated responses regarding music. For example, if the user shows a positive reaction to a recommended song, the response unit recognizes that emotion and recommends further related songs. The response unit can also recognize emotions in real time and respond adaptively based on the generated responses regarding movies. For example, if the user shows a negative reaction to a recommended movie, the response unit recognizes that emotion and recommends a different movie. Furthermore, the response unit can also recognize emotions in real time and respond adaptively based on the generated responses regarding sports. For example, if the user shows a positive reaction to the results of a game of their favorite team, the response unit recognizes that emotion and provides information about related games and events. The response unit generates these responses using natural language processing technology and provides them to the user in a natural way. By recognizing the user's emotions in real time and responding adaptively, the response unit can improve user satisfaction. Furthermore, the response unit can collect user feedback and continuously improve the accuracy and quality of its responses. This allows the response unit to recognize user emotions in real time and respond adaptively, thereby improving the overall accuracy and reliability of the system.

[0034] The analysis unit can analyze information about the user's hobbies using deep learning. For example, the analysis unit can analyze information about the user's hobbies using deep learning. For example, the analysis unit can analyze information about the user's hobbies using a neural network. The analysis unit can also analyze information about the user's hobbies using training data. For example, the analysis unit can use a large amount of hobby-related data as training data to build a deep learning model. This improves the accuracy of the analysis of information about the user's hobbies by using deep learning. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform the analysis using an AI model that takes information about the user's hobbies as input and outputs the analysis results.

[0035] The response unit can recognize the user's emotions in real time. For example, the response unit can capture the user's facial expressions with a camera and recognize emotions using an emotion estimation algorithm. For example, the response unit can calculate an emotion score based on changes in facial expressions. The response unit can also record the user's voice and recognize emotions using voice analysis technology. For example, the response unit can analyze the tone and speed of the voice and calculate an emotion score. The response unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and recognize emotions using an emotion estimation algorithm. For example, the response unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate responses by recognizing the user's emotions in real time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input image data of the user captured by the camera into the generating AI, allowing the generating AI to perform the recognition of the user's emotions.

[0036] The generation unit can generate customizable responses based on the user's interests. For example, the generation unit can generate customized responses based on the user's past behavioral history. For example, the generation unit can generate responses based on topics the user has shown interest in in the past. The generation unit can also generate customized responses based on the user's current interests. For example, the generation unit can generate responses based on topics the user is currently interested in. The generation unit can also analyze information about the user's hobbies and generate customized responses based on the analysis results. For example, the generation unit analyzes information about the user's hobbies and generates responses based on the analysis results. This deepens interaction with the user by generating customizable responses based on the user's interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate responses using an AI model that takes information about the user's hobbies as input and outputs customized responses.

[0037] The response unit can recognize emotions in real time and respond adaptively. For example, the response unit can capture the user's facial expressions with a camera, recognize emotions using an emotion estimation algorithm, and respond adaptively. For example, the response unit can calculate an emotion score based on changes in facial expressions and respond adaptively based on that score. The response unit can also record the user's voice, recognize emotions using voice analysis technology, and respond adaptively. For example, the response unit can analyze the tone and speed of the voice, calculate an emotion score, and respond adaptively based on that score. Furthermore, the response unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, recognize emotions using an emotion estimation algorithm, and respond adaptively. For example, the response unit can calculate an emotion score based on fluctuations in heart rate and respond adaptively based on that score. This allows for smoother communication with the user by recognizing emotions in real time and responding adaptively. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the response unit may be performed using AI, or not using AI. For example, the response unit can input user image data captured by a camera into the generating AI and have the generating AI perform the recognition of the user's emotions.

[0038] The data collection unit can analyze the user's past information submission history regarding their hobbies and select the optimal data collection method. For example, the data collection unit may prioritize using information collection methods (text, audio, etc.) that the user has frequently used in the past. For example, the data collection unit may analyze patterns in the information the user has submitted in the past and select the most effective data collection method. The data collection unit can also suggest the optimal data collection method for a specific time period based on the user's past submission history. In this way, the optimal data collection method can be selected by analyzing the user's past information submission history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past information submission history into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter information related to hobbies based on the user's current areas of interest. For example, the data collection unit can collect only information related to a specific topic that the user is currently interested in. For example, the data collection unit can exclude irrelevant information based on the user's current areas of interest. The data collection unit can also prioritize the collection of the most relevant information based on the user's areas of interest. This allows for the collection of highly relevant information by filtering information based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into a generating AI and have the generating AI perform the information filtering.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting information related to hobbies. For example, the data collection unit can prioritize the collection of event and activity information related to the user's current location. For example, the data collection unit can collect information on region-specific hobbies based on the user's geographical location. The data collection unit can also collect information on nearby hobby-related facilities and shops based on the user's location information. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0041] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting information related to hobbies. For example, the data collection unit can collect information based on posts about hobbies that the user has shared on social media. For example, the data collection unit can refer to information shared by the user's social media followers and friends. The data collection unit can also analyze the user's social media activity history and collect relevant information. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the information related to hobbies. For example, the analysis unit performs a detailed analysis on information of high importance, and a concise analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to its importance. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the information related to hobbies. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information related to hobbies into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the hobby category during analysis. For example, the analysis unit applies a music analysis algorithm to information about music. For example, it applies a sports analysis algorithm to information about sports. The analysis unit can also apply an art analysis algorithm to information about art. By applying different analysis algorithms depending on the hobby category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input hobby categories into a generating AI and have the generating AI execute the application of analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on when the hobby-related information was submitted. For example, the analysis unit will prioritize the analysis of recently submitted information. For example, it will lower the priority of older information. The analysis unit can also adjust the level of detail of the analysis according to the submission date. This allows for more effective analysis by determining the priority of analysis based on when the hobby-related information was submitted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the hobby-related information into a generating AI and have the generating AI determine the priority of the analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the information related to hobbies. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, it may postpone the analysis of less relevant information. The analysis unit can also adjust the level of detail of the analysis according to the relevance. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the information related to hobbies. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the information related to hobbies into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The generation unit can adjust the level of detail in responses based on the importance of the information related to hobbies when generating responses. For example, the generation unit provides detailed responses for information of high importance, and concise responses for information of low importance. The generation unit can also determine the priority of responses according to their importance. This allows for more effective responses by adjusting the level of detail in responses based on the importance of the information related to hobbies. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the information related to hobbies into a generation AI and have the generation AI perform the adjustment of the level of detail in the responses.

[0047] The generation unit can apply different response generation algorithms depending on the hobby category when generating responses. For example, the generation unit can apply a music response generation algorithm to information about music. For example, it can apply a sports response generation algorithm to information about sports. Furthermore, the generation unit can also apply an art response generation algorithm to information about art. By applying different response generation algorithms depending on the hobby category, the accuracy of the responses is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input hobby categories into a generation AI and have the generation AI execute the application of response generation algorithms.

[0048] The generation unit can determine the priority of responses based on when the hobby-related information was submitted. For example, the generation unit will prioritize the inclusion of recently submitted information in the response. For example, it will lower the priority of older information. The generation unit can also adjust the level of detail in the response according to the submission date. This allows for more effective responses by determining the priority of responses based on when the hobby-related information was submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission date of the hobby-related information into a generation AI and have the generation AI perform the task of determining the priority of responses.

[0049] The generation unit can adjust the order of responses based on the relevance of the information related to hobbies when generating responses. For example, the generation unit prioritizes reflecting highly relevant information in the response. For example, the generation unit postpones the order of responses for less relevant information. The generation unit can also adjust the level of detail in the responses according to their relevance. This allows for more effective responses by adjusting the order of responses based on the relevance of the information related to hobbies. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the information related to hobbies into a generation AI and have the generation AI perform the adjustment of the order of responses.

[0050] The response unit can adjust the level of detail in its response based on the importance of the information related to the hobby. For example, the response unit provides a detailed response for information of high importance, and a concise response for information of low importance. The response unit can also determine the priority of responses according to their importance. This allows for more effective responses by adjusting the level of detail in the response based on the importance of the information related to the hobby. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the importance of the information related to the hobby into a generating AI and have the generating AI perform the adjustment of the level of detail in the response.

[0051] The response unit can apply different response algorithms depending on the hobby category when responding. For example, the response unit can apply a music response algorithm to information about music. For example, it can apply a sports response algorithm to information about sports. It can also apply an art response algorithm to information about art. By applying different response algorithms depending on the hobby category, the accuracy of the response is improved. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the hobby category into a generating AI and have the generating AI perform the application of the response algorithm.

[0052] The response unit can determine the priority of responses based on when the information about hobbies was submitted. For example, the response unit will prioritize the inclusion of recently submitted information in the response. For example, it will lower the priority of older information. The response unit can also adjust the level of detail in the response according to the submission date. This allows for more effective responses by determining the priority of responses based on when the information about hobbies was submitted. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the submission date of the information about hobbies into a generating AI and have the generating AI perform the determination of response priorities.

[0053] The response unit can adjust the order of responses based on the relevance of the information related to hobbies. For example, the response unit prioritizes reflecting highly relevant information in the response. For example, the response unit postpones the order of responses for less relevant information. The response unit can also adjust the level of detail in the response according to its relevance. This allows for more effective responses by adjusting the order of responses based on the relevance of the information related to hobbies. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the relevance of the information related to hobbies into a generating AI and have the generating AI perform the adjustment of the order of responses.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The analysis unit can consider the user's past behavioral history when analyzing information related to the user's hobbies. For example, the analysis unit can analyze what kind of hobby-related information the user has frequently searched for in the past and reflect the results in the current analysis. It can also analyze what kind of hobby-related information the user has preferred to view in the past and reflect that trend in the current analysis. Furthermore, the analysis unit can analyze what kind of hobby-related information the user has shared in the past and reflect that information in the current analysis. By considering the user's past behavioral history, a more accurate analysis becomes possible.

[0056] The generation unit can make suggestions that will interest the user based on information about their hobbies. For example, if the user inputs information about music, the generation unit can suggest new artists and songs based on that information. Similarly, if the user inputs information about movies, the generation unit can suggest new movies and directors based on that information. Furthermore, if the user inputs information about sports, the generation unit can suggest new sporting events and athletes based on that information. By making suggestions that will interest the user, it is possible to further increase the user's interest in their hobbies.

[0057] The data collection unit can consider the user's current activity status when collecting information about the user's hobbies. For example, if the user is exercising, the data collection unit can prioritize collecting information related to exercise. Similarly, if the user is resting, the data collection unit can prioritize collecting information related to relaxation. Furthermore, if the user is working, the data collection unit can prioritize collecting information related to work. This allows for the collection of more relevant information by considering the user's current activity status.

[0058] The generation unit can suggest events that will interest the user based on information about the user's hobbies. For example, if the user inputs information about music, the generation unit can suggest music events based on that information. Similarly, if the user inputs information about movies, the generation unit can suggest movie screenings based on that information. Furthermore, if the user inputs information about sports, the generation unit can suggest sports events based on that information. This allows the system to suggest events that will interest the user, thereby further increasing their interest in their hobbies.

[0059] The data collection unit can consider a user's past information gathering history when collecting information about their hobbies. For example, the unit can analyze what kind of information a user has frequently collected in the past and reflect that result in current information gathering. It can also analyze what kind of information a user has preferred to collect in the past and reflect that trend in current information gathering. Furthermore, the unit can analyze what kind of information a user has shared in the past and reflect that information in current information gathering. By considering a user's past information gathering history, more accurate information gathering becomes possible.

[0060] The generation unit can suggest communities that might interest the user based on information about their hobbies. For example, if the user inputs information about music, the generation unit can suggest music communities based on that information. Similarly, if the user inputs information about movies, the generation unit can suggest movie communities based on that information. Furthermore, if the user inputs information about sports, the generation unit can suggest sports communities based on that information. By suggesting communities that might interest the user, it is possible to further increase the user's interest in their hobbies.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects information about the user's interests. For example, if the user enters information about music, movies, or sports, the unit collects that information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the collected information on music, movies, and sports to provide data for generating customizable responses based on the user's interests. Step 3: The generation unit generates a customized response based on the analysis results obtained by the analysis unit. For example, it generates a customizable response based on the user's interests based on analysis results related to music, movies, and sports. Step 4: The response unit recognizes emotions in real time and responds adaptively based on the responses generated by the generation unit. For example, it recognizes emotions in real time and responds adaptively based on the generated responses related to music, movies, and sports.

[0063] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that shows deep understanding and empathy for a user's specific niche hobby and engages in real-time conversation. When a user shares information about their hobby, this AI agent system collects and analyzes that information, generates a customized response, recognizes emotions in real time, and responds adaptively. For example, when a user shares information about their hobby, the AI ​​agent system collects that information. Next, it analyzes the collected information and generates a customizable response based on the user's interests. Furthermore, it recognizes emotions in real time and responds adaptively. This allows the user to find someone with whom they can have in-depth conversations about their hobby, thereby reducing feelings of social isolation. For example, the AI ​​agent system receives input from the user about their hobby. For example, when a user shares information about music, the AI ​​agent system collects and analyzes that information and generates a customized response. Next, the AI ​​agent system recognizes emotions in real time and responds adaptively. This allows the user to find someone with whom they can have in-depth conversations about music. Similarly, when a user shares information about movies, the AI ​​agent system collects and analyzes that information and generates a customized response. Next, the AI ​​agent system recognizes emotions in real time and responds adaptively. This allows the user to find someone with whom they can have in-depth conversations about movies. Furthermore, when a user shares information about sports, the AI ​​agent system collects, analyzes, and generates a customized response. The AI ​​agent system then recognizes emotions in real time and responds adaptively. This allows users to find someone with whom they can have in-depth conversations about sports. This enables the AI ​​agent system to collect and analyze information about the user's hobbies, generate customized responses, and recognize and respond to emotions in real time.

[0064] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a response unit. The collection unit collects information related to the user's hobbies. For example, the collection unit collects information when the user inputs information related to their hobbies. For example, the collection unit collects information when the user inputs information related to music. The collection unit can also collect information when the user inputs information related to movies. Furthermore, the collection unit can also collect information when the user inputs information related to sports. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information and provides data for generating a customizable response based on the user's interests. For example, the analysis unit analyzes the collected information related to music and provides data for generating a customizable response based on the user's interests. The analysis unit can also analyze the collected information related to movies and provide data for generating a customizable response based on the user's interests. Furthermore, the analysis unit can also analyze the collected information related to sports and provide data for generating a customizable response based on the user's interests. The generation unit generates a customized response based on the analysis results obtained by the analysis unit. The generation unit generates customizable responses based on user interests, for example, based on analysis results. For example, the generation unit generates customizable responses based on user interests, based on analysis results related to music. The generation unit can also generate customizable responses based on user interests, based on analysis results related to movies. Furthermore, the generation unit can also generate customizable responses based on user interests, based on analysis results related to sports. The response unit recognizes emotions in real time and responds adaptively based on the responses generated by the generation unit. For example, the response unit recognizes emotions in real time and responds adaptively based on the generated responses. For example, the response unit recognizes emotions in real time and responds adaptively based on the generated responses related to music.Furthermore, the response unit can recognize emotions in real time and respond adaptively based on the generated movie-related responses. Additionally, the response unit can recognize emotions in real time and respond adaptively based on the generated sports-related responses. This enables the AI ​​agent system according to the embodiment to collect and analyze information about the user's hobbies, generate customized responses, and perform real-time emotion recognition and adaptive responses.

[0065] The data collection unit collects information about the user's hobbies. For example, it collects information when the user inputs information about their hobbies. Specifically, when the user inputs information about music, the unit collects that information. For example, the user can input detailed information such as their favorite artists, genres, and recently listened-to songs. The data collection unit can also collect information when the user inputs information about movies. For example, the user can input information such as recently watched movies, their favorite movie genres, directors, and actors. Furthermore, the data collection unit can collect information when the user inputs information about sports. For example, the user can input information such as their favorite team, sport, and recently watched matches. The data collection unit centrally manages this information and builds a database of the user's hobbies. The data collection unit collects the information entered by the user in real time and stores it in the database. This allows the data collection unit to efficiently collect information about the user's hobbies and provide it to the analysis and generation units. Furthermore, the data collection unit can periodically update the user's input information to maintain the latest information. For example, it can adapt if the user develops a new hobby or their interests change. This allows the data collection unit to keep user hobbies information up-to-date at all times, improving the overall accuracy and reliability of the system.

[0066] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the collected information and provides data to generate customizable responses based on the user's interests. Specifically, the analysis unit analyzes collected information on music and provides data to generate customizable responses based on the user's interests. For example, it generates data to recommend relevant new songs and artists based on the user's favorite artists and genres. The analysis unit can also analyze collected information on movies and provide data to generate customizable responses based on the user's interests. For example, it generates data to recommend relevant new movies and works by directors based on the user's favorite movie genres and directors. Furthermore, the analysis unit can also analyze collected information on sports and provide data to generate customizable responses based on the user's interests. For example, it generates data to provide information on relevant matches and events based on the team the user supports and their favorite sport. The analysis unit uses AI to analyze this information and gain a detailed understanding of the user's interests and preferences. The analysis unit can also analyze past data and trends to predict changes in user interests and new interests. This allows the analysis unit to provide data for generating customizable responses based on user interests, thereby improving the overall accuracy and reliability of the system.

[0067] The generation unit generates customized responses based on the analysis results obtained by the analysis unit. For example, the generation unit generates customizable responses based on the user's interests based on the analysis results. Specifically, the generation unit generates customizable responses based on the user's interests based on the analysis results regarding music. For example, it generates responses that recommend new songs or artists based on the user's favorite artists or genres. The generation unit can also generate customizable responses based on the user's interests based on the analysis results regarding movies. For example, it generates responses that recommend new movies or works by directors based on the user's favorite movie genres or directors. Furthermore, the generation unit can also generate customizable responses based on the user's interests based on the analysis results regarding sports. For example, it generates responses that provide information on relevant matches or events based on the team the user supports or their favorite sport. The generation unit generates these responses using natural language generation technology and provides them to the user in a natural way. By generating responses based on the user's interests and preferences, the generation unit can improve user satisfaction. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy and quality of the responses. This allows the generation unit to produce customizable responses based on user interests, improving the overall accuracy and reliability of the system.

[0068] The response unit recognizes emotions in real time and responds adaptively based on the responses generated by the generation unit. For example, the response unit recognizes emotions in real time and responds adaptively based on the generated responses. Specifically, the response unit recognizes emotions in real time and responds adaptively based on the generated responses regarding music. For example, if the user shows a positive reaction to a recommended song, the response unit recognizes that emotion and recommends further related songs. The response unit can also recognize emotions in real time and respond adaptively based on the generated responses regarding movies. For example, if the user shows a negative reaction to a recommended movie, the response unit recognizes that emotion and recommends a different movie. Furthermore, the response unit can also recognize emotions in real time and respond adaptively based on the generated responses regarding sports. For example, if the user shows a positive reaction to the results of a game of their favorite team, the response unit recognizes that emotion and provides information about related games and events. The response unit generates these responses using natural language processing technology and provides them to the user in a natural way. By recognizing the user's emotions in real time and responding adaptively, the response unit can improve user satisfaction. Furthermore, the response unit can collect user feedback and continuously improve the accuracy and quality of its responses. This allows the response unit to recognize user emotions in real time and respond adaptively, thereby improving the overall accuracy and reliability of the system.

[0069] The analysis unit can analyze information about the user's hobbies using deep learning. For example, the analysis unit can analyze information about the user's hobbies using deep learning. For example, the analysis unit can analyze information about the user's hobbies using a neural network. The analysis unit can also analyze information about the user's hobbies using training data. For example, the analysis unit can use a large amount of hobby-related data as training data to build a deep learning model. This improves the accuracy of the analysis of information about the user's hobbies by using deep learning. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform the analysis using an AI model that takes information about the user's hobbies as input and outputs the analysis results.

[0070] The response unit can recognize the user's emotions in real time. For example, the response unit can capture the user's facial expressions with a camera and recognize emotions using an emotion estimation algorithm. For example, the response unit can calculate an emotion score based on changes in facial expressions. The response unit can also record the user's voice and recognize emotions using voice analysis technology. For example, the response unit can analyze the tone and speed of the voice and calculate an emotion score. The response unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and recognize emotions using an emotion estimation algorithm. For example, the response unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate responses by recognizing the user's emotions in real time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input image data of the user captured by the camera into the generating AI, allowing the generating AI to perform the recognition of the user's emotions.

[0071] The generation unit can generate customizable responses based on the user's interests. For example, the generation unit can generate customized responses based on the user's past behavioral history. For example, the generation unit can generate responses based on topics the user has shown interest in in the past. The generation unit can also generate customized responses based on the user's current interests. For example, the generation unit can generate responses based on topics the user is currently interested in. The generation unit can also analyze information about the user's hobbies and generate customized responses based on the analysis results. For example, the generation unit analyzes information about the user's hobbies and generates responses based on the analysis results. This deepens interaction with the user by generating customizable responses based on the user's interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate responses using an AI model that takes information about the user's hobbies as input and outputs customized responses.

[0072] The response unit can recognize emotions in real time and respond adaptively. For example, the response unit can capture the user's facial expressions with a camera, recognize emotions using an emotion estimation algorithm, and respond adaptively. For example, the response unit can calculate an emotion score based on changes in facial expressions and respond adaptively based on that score. The response unit can also record the user's voice, recognize emotions using voice analysis technology, and respond adaptively. For example, the response unit can analyze the tone and speed of the voice, calculate an emotion score, and respond adaptively based on that score. Furthermore, the response unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors, recognize emotions using an emotion estimation algorithm, and respond adaptively. For example, the response unit can calculate an emotion score based on fluctuations in heart rate and respond adaptively based on that score. This allows for smoother communication with the user by recognizing emotions in real time and responding adaptively. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the response unit may be performed using AI, or not using AI. For example, the response unit can input user image data captured by a camera into the generating AI and have the generating AI perform the recognition of the user's emotions.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of collecting information about their hobbies based on those emotions. For example, if the user is excited, the data collection unit can collect information immediately and respond in real time. If the user is relaxed, the data collection unit can collect information at a slower pace and respond at appropriate intervals. The data collection unit can also temporarily refrain from collecting information if the user is stressed and wait until the user calms down. By adjusting the timing of information collection based on the user's emotions, information can be collected at a more appropriate time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into a generating AI and have the generating AI adjust the timing of the collection.

[0074] The data collection unit can analyze the user's past information submission history regarding their hobbies and select the optimal data collection method. For example, the data collection unit may prioritize using information collection methods (text, audio, etc.) that the user has frequently used in the past. For example, the data collection unit may analyze patterns in the information the user has submitted in the past and select the most effective data collection method. The data collection unit can also suggest the optimal data collection method for a specific time period based on the user's past submission history. In this way, the optimal data collection method can be selected by analyzing the user's past information submission history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past information submission history into a generating AI and have the generating AI select the optimal data collection method.

[0075] The data collection unit can filter information related to hobbies based on the user's current areas of interest. For example, the data collection unit can collect only information related to a specific topic that the user is currently interested in. For example, the data collection unit can exclude irrelevant information based on the user's current areas of interest. The data collection unit can also prioritize the collection of the most relevant information based on the user's areas of interest. This allows for the collection of highly relevant information by filtering information based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into a generating AI and have the generating AI perform the information filtering.

[0076] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting the information that is most interesting to them. If the user is relaxed, the data collection unit will collect a wide range of information in a balanced manner. Furthermore, if the user is stressed, the data collection unit can also prioritize collecting information that promotes relaxation. In this way, by prioritizing information based on the user's emotions, more appropriate information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0077] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting information related to hobbies. For example, the data collection unit can prioritize the collection of event and activity information related to the user's current location. For example, the data collection unit can collect information on region-specific hobbies based on the user's geographical location. The data collection unit can also collect information on nearby hobby-related facilities and shops based on the user's location information. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant information.

[0078] The data collection unit can collect relevant information by analyzing the user's social media activity when collecting information related to hobbies. For example, the data collection unit can collect information based on posts about hobbies that the user has shared on social media. For example, the data collection unit can refer to information shared by the user's social media followers and friends. The data collection unit can also analyze the user's social media activity history and collect relevant information. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit provides concise analysis results. The analysis unit can also provide visually appealing analysis results if the user is excited. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the information related to hobbies. For example, the analysis unit performs a detailed analysis on information of high importance, and a concise analysis on information of low importance. The analysis unit can also determine the priority of the analysis according to its importance. This allows for more effective analysis by adjusting the level of detail of the analysis based on the importance of the information related to hobbies. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information related to hobbies into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the hobby category during analysis. For example, the analysis unit applies a music analysis algorithm to information about music. For example, it applies a sports analysis algorithm to information about sports. The analysis unit can also apply an art analysis algorithm to information about art. By applying different analysis algorithms depending on the hobby category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input hobby categories into a generating AI and have the generating AI execute the application of analysis algorithms.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. If the user is relaxed, the analysis unit provides a detailed analysis. The analysis unit can also provide a visually appealing analysis if the user is excited. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0083] The analysis unit can determine the priority of analysis based on when the hobby-related information was submitted. For example, the analysis unit will prioritize the analysis of recently submitted information. For example, it will lower the priority of older information. The analysis unit can also adjust the level of detail of the analysis according to the submission date. This allows for more effective analysis by determining the priority of analysis based on when the hobby-related information was submitted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the hobby-related information into a generating AI and have the generating AI determine the priority of the analysis.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the information related to hobbies. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, it may postpone the analysis of less relevant information. The analysis unit can also adjust the level of detail of the analysis according to the relevance. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the information related to hobbies. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the information related to hobbies into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0085] The generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit will provide a detailed response. If the user is in a hurry, the generation unit will provide a concise response. The generation unit can also provide a visually appealing response if the user is excited. This allows for the provision of more appropriate responses by adjusting the way the response is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the way the response is expressed.

[0086] The generation unit can adjust the level of detail in responses based on the importance of the information related to hobbies when generating responses. For example, the generation unit provides detailed responses for information of high importance, and concise responses for information of low importance. The generation unit can also determine the priority of responses according to their importance. This allows for more effective responses by adjusting the level of detail in responses based on the importance of the information related to hobbies. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the information related to hobbies into a generation AI and have the generation AI perform the adjustment of the level of detail in the responses.

[0087] The generation unit can apply different response generation algorithms depending on the hobby category when generating responses. For example, the generation unit can apply a music response generation algorithm to information about music. For example, it can apply a sports response generation algorithm to information about sports. Furthermore, the generation unit can also apply an art response generation algorithm to information about art. By applying different response generation algorithms depending on the hobby category, the accuracy of the responses is improved. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input hobby categories into a generation AI and have the generation AI execute the application of response generation algorithms.

[0088] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, the generation unit will provide a short, concise response. If the user is relaxed, the generation unit will provide a detailed response. The generation unit can also provide a visually appealing response if the user is excited. By adjusting the length of the response based on the user's emotions, a more appropriate response can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the response.

[0089] The generation unit can determine the priority of responses based on when the hobby-related information was submitted. For example, the generation unit will prioritize the inclusion of recently submitted information in the response. For example, it will lower the priority of older information. The generation unit can also adjust the level of detail in the response according to the submission date. This allows for more effective responses by determining the priority of responses based on when the hobby-related information was submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the submission date of the hobby-related information into a generation AI and have the generation AI perform the task of determining the priority of responses.

[0090] The generation unit can adjust the order of responses based on the relevance of the information related to hobbies when generating responses. For example, the generation unit prioritizes reflecting highly relevant information in the response. For example, the generation unit postpones the order of responses for less relevant information. The generation unit can also adjust the level of detail in the responses according to their relevance. This allows for more effective responses by adjusting the order of responses based on the relevance of the information related to hobbies. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the information related to hobbies into a generation AI and have the generation AI perform the adjustment of the order of responses.

[0091] The response unit can estimate the user's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the user is relaxed, the response unit will provide a detailed response. If the user is in a hurry, the response unit will provide a concise response. The response unit can also provide a visually appealing response if the user is excited. By adjusting the way it expresses its response based on the user's emotions, it can provide a more appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input user emotion data into a generative AI and have the generative AI adjust the way it expresses its response.

[0092] The response unit can adjust the level of detail in its response based on the importance of the information related to the hobby. For example, the response unit provides a detailed response for information of high importance, and a concise response for information of low importance. The response unit can also determine the priority of responses according to their importance. This allows for more effective responses by adjusting the level of detail in the response based on the importance of the information related to the hobby. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the importance of the information related to the hobby into a generating AI and have the generating AI perform the adjustment of the level of detail in the response.

[0093] The response unit can apply different response algorithms depending on the hobby category when responding. For example, the response unit can apply a music response algorithm to information about music. For example, it can apply a sports response algorithm to information about sports. It can also apply an art response algorithm to information about art. By applying different response algorithms depending on the hobby category, the accuracy of the response is improved. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the hobby category into a generating AI and have the generating AI perform the application of the response algorithm.

[0094] The response unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, the response unit will provide a short, to-the-point response. If the user is relaxed, the response unit will provide a detailed response. The response unit can also provide a visually appealing response if the user is excited. By adjusting the length of the response based on the user's emotions, a more appropriate response can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not using AI. For example, the response unit can input user emotion data into a generative AI and have the generative AI adjust the length of the response.

[0095] The response unit can determine the priority of responses based on when the information about hobbies was submitted. For example, the response unit will prioritize the inclusion of recently submitted information in the response. For example, it will lower the priority of older information. The response unit can also adjust the level of detail in the response according to the submission date. This allows for more effective responses by determining the priority of responses based on when the information about hobbies was submitted. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the submission date of the information about hobbies into a generating AI and have the generating AI perform the determination of response priorities.

[0096] The response unit can adjust the order of responses based on the relevance of the information related to hobbies. For example, the response unit prioritizes reflecting highly relevant information in the response. For example, the response unit postpones the order of responses for less relevant information. The response unit can also adjust the level of detail in the response according to its relevance. This allows for more effective responses by adjusting the order of responses based on the relevance of the information related to hobbies. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input the relevance of the information related to hobbies into a generating AI and have the generating AI perform the adjustment of the order of responses.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The analysis unit can consider the user's past behavioral history when analyzing information related to the user's hobbies. For example, the analysis unit can analyze what kind of hobby-related information the user has frequently searched for in the past and reflect the results in the current analysis. It can also analyze what kind of hobby-related information the user has preferred to view in the past and reflect that trend in the current analysis. Furthermore, the analysis unit can analyze what kind of hobby-related information the user has shared in the past and reflect that information in the current analysis. By considering the user's past behavioral history, a more accurate analysis becomes possible.

[0099] The generation unit can make suggestions that will interest the user based on information about their hobbies. For example, if the user inputs information about music, the generation unit can suggest new artists and songs based on that information. Similarly, if the user inputs information about movies, the generation unit can suggest new movies and directors based on that information. Furthermore, if the user inputs information about sports, the generation unit can suggest new sporting events and athletes based on that information. By making suggestions that will interest the user, it is possible to further increase the user's interest in their hobbies.

[0100] The response unit can estimate the user's emotions and adjust the tone of its response based on those emotions. For example, if the user is excited, the response unit can respond in a more energetic tone. If the user is relaxed, the response unit can respond in a calmer tone. Furthermore, if the user is stressed, the response unit can respond in a more comforting tone. By adjusting the tone of response based on the user's emotions, a more appropriate response becomes possible.

[0101] The data collection unit can consider the user's current activity status when collecting information about the user's hobbies. For example, if the user is exercising, the data collection unit can prioritize collecting information related to exercise. Similarly, if the user is resting, the data collection unit can prioritize collecting information related to relaxation. Furthermore, if the user is working, the data collection unit can prioritize collecting information related to work. This allows for the collection of more relevant information by considering the user's current activity status.

[0102] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on those emotions. For example, if the user is excited, the analysis unit can perform a more detailed analysis. If the user is relaxed, the analysis unit can perform a simpler analysis. Furthermore, if the user is stressed, the analysis unit can perform a more comforting analysis. By adjusting the depth of the analysis based on the user's emotions, a more appropriate analysis becomes possible.

[0103] The generation unit can suggest events that will interest the user based on information about the user's hobbies. For example, if the user inputs information about music, the generation unit can suggest music events based on that information. Similarly, if the user inputs information about movies, the generation unit can suggest movie screenings based on that information. Furthermore, if the user inputs information about sports, the generation unit can suggest sports events based on that information. This allows the system to suggest events that will interest the user, thereby further increasing their interest in their hobbies.

[0104] The response unit can estimate the user's emotions and adjust the content of its response based on those emotions. For example, if the user is excited, the response unit can respond with more energetic content. If the user is relaxed, the response unit can respond with more calming content. Furthermore, if the user is stressed, the response unit can respond with more comforting content. By adjusting the content of the response based on the user's emotions, a more appropriate response becomes possible.

[0105] The data collection unit can consider a user's past information gathering history when collecting information about their hobbies. For example, the unit can analyze what kind of information a user has frequently collected in the past and reflect that result in current information gathering. It can also analyze what kind of information a user has preferred to collect in the past and reflect that trend in current information gathering. Furthermore, the unit can analyze what kind of information a user has shared in the past and reflect that information in current information gathering. By considering a user's past information gathering history, more accurate information gathering becomes possible.

[0106] The analysis unit can estimate the user's emotions and adjust the visual representation of the analysis based on those emotions. For example, if the user is excited, the analysis unit can provide a more colorful and visually appealing analysis result. If the user is relaxed, the analysis unit can provide a simpler and calmer visual representation. Furthermore, if the user is stressed, the analysis unit can provide a more comforting visual representation. By adjusting the visual representation of the analysis based on the user's emotions, more appropriate analysis results can be provided.

[0107] The generation unit can suggest communities that might interest the user based on information about their hobbies. For example, if the user inputs information about music, the generation unit can suggest music communities based on that information. Similarly, if the user inputs information about movies, the generation unit can suggest movie communities based on that information. Furthermore, if the user inputs information about sports, the generation unit can suggest sports communities based on that information. By suggesting communities that might interest the user, it is possible to further increase the user's interest in their hobbies.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit collects information about the user's interests. For example, if the user enters information about music, movies, or sports, the unit collects that information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the collected information on music, movies, and sports to provide data for generating customizable responses based on the user's interests. Step 3: The generation unit generates a customized response based on the analysis results obtained by the analysis unit. For example, it generates a customizable response based on the user's interests based on analysis results related to music, movies, and sports. Step 4: The response unit recognizes emotions in real time and responds adaptively based on the responses generated by the generation unit. For example, it recognizes emotions in real time and responds adaptively based on the generated responses related to music, movies, and sports.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and response unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information about the user's hobbies. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a customized response based on the analysis results. The response unit is implemented by the control unit 46A of the smart device 14 and recognizes emotions in real time and responds adaptively based on the generated response. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 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.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and response unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information about the user's hobbies. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates a customized response based on the analysis results. The response unit is implemented by the control unit 46A of the smart glasses 214 and recognizes emotions in real time based on the generated response and responds adaptively. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and response unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information about the user's hobbies. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a customized response based on the analysis results. The response unit is implemented by the control unit 46A of the headset terminal 314 and recognizes emotions in real time and responds adaptively based on the generated response. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and response unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information about the user's hobbies. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a customized response based on the analysis results. The response unit is implemented by the control unit 46A of the robot 414 and recognizes emotions in real time and responds adaptively based on the generated response. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A collection unit that collects information about users' hobbies, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates a customized response based on the analysis results obtained by the analysis unit, The system includes a response unit that recognizes emotions in real time and responds adaptively based on the response generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Deep learning is used to analyze information about users' hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The response unit is Recognize user emotions in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate customizable responses based on user interests. The system described in Appendix 1, characterized by the features described herein. (Note 5) The response unit is Recognizes emotions in real time and responds adaptively. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting information about their hobbies based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past information submission history regarding their hobbies and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting information related to hobbies, filter the results based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting information related to hobbies, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information about hobbies, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information related to hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the hobby category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the information regarding hobbies was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of information related to hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a response, adjust the level of detail in the response based on the importance of the information related to the hobby. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating responses, different response generation algorithms are applied depending on the hobby category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the response length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating responses, prioritize responses based on when the information about hobbies was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating responses, the order of responses is adjusted based on the relevance of the information related to hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 24) The response unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The response unit is When responding, adjust the level of detail in your response based on the importance of the information related to your hobbies. The system described in Appendix 1, characterized by the features described herein. (Note 26) The response unit is When responding, different response algorithms are applied depending on the hobby category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The response unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The response unit is When responding, prioritize responses based on when information about hobbies was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The response unit is When responding, the order of responses will be adjusted based on the relevance of the information regarding hobbies. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information about users' hobbies, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates a customized response based on the analysis results obtained by the analysis unit, The system includes a response unit that recognizes emotions in real time and responds adaptively based on the response generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Deep learning is used to analyze information about users' hobbies. The system according to feature 1.

3. The response unit is Recognize user emotions in real time. The system according to feature 1.

4. The generating unit is Generate customizable responses based on user interests. The system according to feature 1.

5. The response unit is Recognizes emotions in real time and responds adaptively. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting information about their hobbies based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past information submission history regarding their hobbies and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting information related to hobbies, filter the results based on the user's current areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting information related to hobbies, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

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