System

Abstract

To generate avatars based on user messages and call exchanges by the system in accordance with the embodiment, and to interact with them.SOLUTION: A system according to the embodiment has a collection unit, an analysis unit, a generation unit, and a dialogue unit. The collection unit collects user messages or call exchanges. The analysis unit analyzes the information collected by the collection unit and extracts user characteristics. The generation unit generates avatars based on the features extracted by the analysis unit. The dialog unit is where avatars generated by the generation unit interact with each other.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately generate avatars based on a user's messages and phone calls, and there is room for improvement.

[0005] The system according to the embodiment aims to generate an avatar based on messages and phone calls exchanged between users, and to engage in dialogue with the avatar. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a dialogue unit. The collection unit collects messages or calls of a user. The analysis unit analyzes the information collected by the collection unit and extracts features of the user. The generation unit generates an avatar based on the features extracted by the analysis unit. The dialogue unit allows the avatar generated by the generation unit to engage in dialogue. Effect of the Invention

[0007] The system according to the embodiment can generate an avatar based on messages and phone calls exchanged between users and can engage in conversations. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The interactive avatar system according to the embodiment of the present invention is a system in which messages and telephone conversations of a user are collected, and a generation AI analyzes the characteristics of the user to generate an anthropomorphized avatar. This interactive avatar system generates an interactive avatar that reflects the characteristics of the user, and can converse on behalf of the user in an emergency. For example, messages and telephone conversations of a user are collected, and a generation AI analyzes the characteristics of the user's speaking style and expression. Next, an avatar is generated based on the characteristics analyzed by the generation AI, and this avatar converses on behalf of the user. This allows the user to live a daily life with peace of mind. This allows the interactive avatar system to generate an avatar that reflects the characteristics of the user, and can converse on behalf of the user in an emergency.

[0029] The interactive avatar system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a dialogue unit. The collection unit collects messages or telephone conversations of a user. The collection unit can collect data such as text messages, voice calls, and video calls. The collection unit collects messages and telephone conversations sent by a user in detail and provides these data to the analysis unit. The analysis unit analyzes the collected data and extracts the user's characteristics. The analysis unit analyzes the user's speaking style and expression characteristics using, for example, natural language processing technology. The analysis unit extracts words and phrases frequently used by the user, speech tone, and the like. The generation unit generates an avatar based on the characteristics extracted by the analysis unit. The generation unit generates an avatar that reflects the user's characteristics using a generation AI. The generation AI generates the avatar's dialogue content using, for example, a text generation AI (for example, LLM). The generation unit can generate a 3D model or 2D character that reflects the user's characteristics, an avatar with only voice, and the like. The dialogue unit allows the generated avatar to dialogue on behalf of the user. The dialogue unit conducts dialogue by, for example, a method such as text chat, voice dialogue, or video dialogue. The dialogue unit can conduct dialogue reflecting the characteristics of the user using a generation AI. As a result, the interactive avatar system according to the embodiment can generate an avatar reflecting the characteristics of the user and conduct dialogue on behalf of the user in the event of an emergency. For example, the dialogue unit can cause the generated avatar to conduct dialogue on behalf of the user and take appropriate action. As a result, the user can live his or her daily life with peace of mind.

[0030] The collection unit collects messages or call exchanges of the user. The collection unit can collect data such as text messages, voice calls, and video calls. Specifically, in the case of text messages, character information sent by the user is acquired in real time, and in the case of voice calls, the contents of the calls are recorded and saved as voice data. In the case of video calls, both video and voice are collected to acquire detailed information such as the user's facial expressions and tone of voice. These data are centrally managed by the collection unit and provided to the analysis unit. The collection unit can use encryption technology for privacy protection when collecting data. For example, the collected data is encrypted at the time of collection, and unauthorized access by third parties is prevented until it is transmitted to the analysis unit. In addition, the collection unit has a mechanism for collecting data with the user's consent, and can stop collection if the user refuses data collection. This allows the collection unit to efficiently collect necessary data while protecting the user's privacy. Furthermore, the collection unit has a high-speed data transfer technology for providing the collected data to the analysis unit in real time, thereby improving the response speed of the entire system.

[0031] The analysis unit analyzes the collected data and extracts the user's characteristics. The analysis unit, for example, uses natural language processing technology to analyze the user's speaking style and expression characteristics. Specifically, in the case of text messages, morphological analysis and grammatical analysis are performed to identify words and phrases frequently used by the user. In the case of voice calls, voice data is converted into text using voice recognition technology, and the text data is analyzed. In addition, characteristics such as tone of voice, speaking speed, and emotional expression can also be extracted from the voice data. In the case of video calls, video analysis technology is used to analyze the user's facial expressions and gestures, and the user's emotions and intentions are inferred based on this information. The analysis unit integrates these analysis results to comprehensively grasp the user's characteristics. Furthermore, the analysis unit can learn the user's behavioral patterns and preferences by utilizing past data and user history information. For example, based on past dialogue history, it predicts how the user will react in a specific situation and provides information to realize a more natural dialogue. In addition, the analysis unit can use an anomaly detection algorithm to detect unusual user behavior or remarks and issue a warning as necessary. This enables the analysis unit to accurately grasp the characteristics of the user and improve the dialogue quality of the entire system.

[0032] The generation unit generates an avatar based on the features extracted by the analysis unit. The generation unit uses a generation AI to generate an avatar that reflects the user's features. Specifically, the generation unit uses a text generation AI (e.g., LLM) to generate the dialogue content of the avatar. The generation AI can learn the features of the user's speech and expression, and generate natural dialogue based on the learned features. For example, the generation unit generates dialogue content that incorporates words and phrases frequently used by the user, and creates avatar utterances that resemble the user's speech. The generation unit can also generate 3D models, 2D characters, and voice-only avatars that reflect the user's features. In the case of 3D models, avatars that reproduce the user's appearance and facial expressions can be generated and operated in real time. In the case of 2D characters, illustrations and animations that reflect the user's features are generated and displayed during dialogue. In the case of voice-only avatars, natural voice dialogue is realized using voice synthesis technology that reflects the features of the user's voice. The generation unit provides these generation results to the dialogue unit, and prepares to dialogue on behalf of the user. In addition, the generation unit can evaluate the quality of the generated avatar, and make corrections and improvements as necessary. For example, if the generated dialogue content is unnatural or does not accurately reflect the user's characteristics, the avatar is regenerated to improve quality. This enables the generation unit to generate a high-quality avatar that accurately reflects the user's characteristics, thereby improving the dialogue quality of the entire system.

[0033] In the dialogue unit, the generated avatar converses on behalf of the user. The dialogue unit converses by, for example, text chat, voice dialogue, video dialogue, and the like. Specifically, in the case of text chat, a text message generated by the generation AI is sent on behalf of the user to converse with the other party. In the case of voice dialogue, a voice generated using voice synthesis technology is played back to converse on behalf of the user. In the case of video dialogue, a generated 3D model or 2D character is displayed and a dialogue is conducted while moving in real time. The dialogue unit can use the generation AI to conduct a dialogue that reflects the characteristics of the user. For example, the dialogue unit generates dialogue content that incorporates words and phrases frequently used by the user, and creates avatar remarks that resemble the user's way of speaking. In addition, the dialogue unit can incorporate new dialogue content generated by the generation AI in real time according to the progress of the dialogue. This allows the dialogue unit to realize natural and smooth dialogue and respond appropriately on behalf of the user. Furthermore, the dialogue unit can record the content and progress of the dialogue and later provide it to the analysis unit and generation unit. This allows feedback to be provided to continuously improve the dialogue quality of the entire system. For example, if the content of the dialogue is inappropriate or does not accurately reflect the user's characteristics, the analysis unit or generation unit can make corrections or improvements based on that information. This allows the dialogue unit to conduct high-quality dialogue on behalf of the user, improving the reliability and satisfaction of the entire system.

[0034] The collection unit can collect messages or call contents of the user. The collection unit can collect data such as text messages, voice calls, and video calls. The collection unit collects messages and call contents sent by the user in detail and provides these data to the analysis unit. In this way, by collecting the messages and call contents of the user, the characteristics of the user can be understood. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the messages and call contents of the user to AI, which can analyze the data.

[0035] The analysis unit can analyze the collected data and extract the characteristics of the user's speaking style or expression. The analysis unit can analyze the characteristics of the user's speaking style and expression using, for example, natural language processing technology. The analysis unit extracts words, phrases, and speech tones frequently used by the user. In this way, by extracting the characteristics of the user's speaking style and expression, it is possible to generate an avatar that reflects the user's characteristics. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data to AI, which can analyze the data.

[0036] The generation unit can generate an avatar based on the extracted features. The generation unit uses a generation AI to generate an avatar that reflects the features of the user. The generation AI generates the dialogue content of the avatar using, for example, a text generation AI (for example, LLM). The generation unit can generate a 3D model, a 2D character, an avatar with only voice, and the like that reflects the features of the user. As a result, by generating an avatar based on the extracted features, it is possible to provide an avatar that reflects the features of the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the extracted features to the generation AI, which can generate an avatar.

[0037] The dialogue unit allows the generated avatar to dialogue on behalf of the user. The dialogue unit dialogues by, for example, text chat, voice dialogue, video dialogue, or the like. The dialogue unit can use a generation AI to conduct dialogue that reflects the characteristics of the user. This allows the generated avatar to dialogue on behalf of the user, making it possible to take appropriate action in the event of an emergency. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the dialogue unit can input the generated avatar to an AI, which then conducts the dialogue.

[0038] The dialogue unit can use an algorithm for responding in the event of an emergency. The dialogue unit can take an appropriate response in the event of an emergency, for example, by using an emergency response scenario or an AI technology to be used. As a result, by using an algorithm for taking an appropriate response in the event of an emergency, an appropriate response can be taken on behalf of the user. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the dialogue unit can input an emergency response scenario into the AI, and the AI ​​can take the response.

[0039] The collection unit can analyze the user's past messages and call history and select a collection method. The collection unit can, for example, preferentially collect message apps and call apps that the user has used frequently in the past. In addition, the collection unit can concentrate collection in a specific time period from the user's past messages and call history. Furthermore, the collection unit can analyze the user's past communication patterns and select the most effective collection method. As a result, the optimal collection method can be selected by analyzing the user's past messages and call history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past messages and call history into AI, and the AI ​​can select the collection method.

[0040] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting messages and call contents. For example, when the user is at work, the collection unit can preferentially collect messages and call contents related to work. In addition, when the user is engaged in a hobby-related activity, the collection unit can collect messages and call contents related to the hobby. Furthermore, when the user is traveling, the collection unit can collect messages and call contents related to travel. This makes it possible to collect more relevant data by filtering based on the user's living situation and areas of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's living situation and areas of interest into AI, and the AI ​​can perform filtering.

[0041] The collection unit can collect highly relevant content preferentially based on the geographical location information of the user when collecting messages and call contents. For example, when the user is in a specific area, the collection unit can collect messages and call contents related to the area preferentially. In addition, when the user is traveling, the collection unit can collect messages and call contents related to the travel destination. Furthermore, when the user is at home, the collection unit can collect messages and call contents related to the home preferentially. In this way, by taking into account the geographical location information of the user, highly relevant data can be collected preferentially. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can collect highly relevant content preferentially.

[0042] The collection unit can analyze the user's social media activity and collect related content when collecting messages and call contents. For example, the collection unit can preferentially collect messages and call contents with people with whom the user frequently interacts on social media. The collection unit can also collect messages and call contents related to topics in which the user is interested on social media. Furthermore, the collection unit can collect messages and call contents related to groups and communities in which the user participates on social media. This makes it possible to collect related content by analyzing the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI, and the AI ​​can collect related content.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the message or the call content during the analysis. For example, the analysis unit can perform a detailed analysis of a message or a call content with a high level of importance and extract the features in detail. In addition, the analysis unit can perform a simplified analysis of a message or a call content with a low level of importance and extract only the main features. Furthermore, the analysis unit can quickly analyze a message or a call content with a high level of urgency and immediately extract the features. As a result, the analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance of the message or the call content. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the message or the call content to an AI, and the AI ​​can adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the message or call content during analysis. For example, the analysis unit can apply an algorithm that analyzes formal expressions and technical terms to business-related messages and call content. In addition, the analysis unit can apply an algorithm that analyzes casual expressions and everyday words to private messages and call content. Furthermore, the analysis unit can apply an algorithm for responding quickly to emergency messages and call content. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the message or call content. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the message or call content to AI, and the AI ​​can apply different analysis algorithms.

[0045] The analysis unit can determine the priority of the analysis based on the time of submission of the message or the call content during the analysis. The analysis unit can, for example, give priority to analyzing the message or the call content that was sent recently. The analysis unit can also give priority to analyzing the message or the call content that was sent in a specific time period. Furthermore, the analysis unit can give priority to analyzing the message or the call content that has a high urgency. This allows efficient analysis by determining the priority of the analysis based on the time of submission of the message or the call content. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the time of submission of the message or the call content to an AI, and the AI ​​can determine the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of messages and call contents during analysis. The analysis unit can, for example, prioritize analysis of highly relevant messages and call contents. The analysis unit can also postpone analysis of less relevant messages and call contents. Furthermore, the analysis unit can prioritize analysis of messages and call contents related to a specific topic. This allows analysis to be performed efficiently by adjusting the order of analysis based on the relevance of messages and call contents. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of messages and call contents to AI, and the AI ​​can adjust the order of analysis.

[0047] The generation unit can adjust the level of detail of the avatar based on the importance of the user's features when generating the avatar. For example, when the user's features are clear, the generation unit can generate a detailed avatar. Furthermore, when the user's features are vague, the generation unit can generate a simplified avatar. Furthermore, when the user's features are diverse, the generation unit can generate an avatar that emphasizes the main features. As a result, a more appropriate avatar can be generated by adjusting the level of detail of the avatar based on the importance of the user's features. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the user's features to AI, and the AI ​​can adjust the level of detail of the avatar.

[0048] The generation unit can apply different generation algorithms depending on the user category when generating an avatar. For example, the generation unit can apply a generation algorithm having a formal expression or action to an avatar for business use. In addition, the generation unit can apply a generation algorithm having a casual expression or action to an avatar for private use. Furthermore, the generation unit can apply a generation algorithm for quick response to an avatar in an emergency. In this way, by applying different generation algorithms depending on the user category, a more appropriate avatar can be generated. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user category to AI and apply a generation algorithm with a different AI.

[0049] The generation unit can determine the priority of generation based on the user's past avatar usage history when generating an avatar. For example, the generation unit can preferentially reflect the features of avatars that the user has frequently used in the past. In addition, the generation unit can predict and generate an avatar to be used in a specific time period from the user's past avatar usage history. Furthermore, the generation unit can analyze the user's past avatar usage pattern and generate the most effective avatar. As a result, a more appropriate avatar can be generated by determining the priority of generation based on the user's past avatar usage history. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past avatar usage history into AI, and the AI ​​can determine the priority of generation.

[0050] The generation unit can improve the accuracy of the generation when generating an avatar by referring to the user's related data. The generation unit can adjust the appearance and personality of the avatar, for example, by referring to the user's social media profile and posted content. The generation unit can also adjust the avatar's speaking style and expression by referring to the user's past messages and call content. Furthermore, the generation unit can customize the characteristics of the avatar based on the user's hobbies and interests. This can improve the accuracy of the generation by referring to the user's related data. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the user's related data into AI, which can improve the accuracy of the generation.

[0051] The dialogue unit can select a dialogue method by referring to the user's past dialogue history during dialogue. The dialogue unit can, for example, incorporate expressions and phrases that the user has used in the past into the dialogue. The dialogue unit can also select a dialogue method related to a specific topic from the user's past dialogue history. Furthermore, the dialogue unit can analyze the user's past dialogue patterns and select the most effective dialogue method. This makes it possible to select an optimal dialogue method by referring to the user's past dialogue history. A part or all of the above-mentioned processing in the dialogue unit can be performed, for example, using AI or can be performed without using AI. For example, the dialogue unit can input the user's past dialogue history into AI, which can then select a dialogue method.

[0052] The dialogue unit can customize the dialogue means based on the user's current situation during dialogue. For example, when the user is at work, the dialogue unit can conduct a businesslike dialogue. Also, when the user is relaxed, the dialogue unit can conduct a casual dialogue. Furthermore, when the user is in an emergency, the dialogue unit can conduct a quick and concise dialogue. This allows for a more appropriate dialogue by customizing the dialogue means based on the user's current situation. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the user's current situation into the AI, which can then customize the dialogue means.

[0053] The dialogue unit can select the optimal dialogue method in consideration of the geographical location information of the user during dialogue. For example, when the user is in a specific area, the dialogue unit can conduct a dialogue to provide information related to the area. Furthermore, when the user is traveling, the dialogue unit can conduct a dialogue to provide information related to the travel destination. Furthermore, when the user is at home, the dialogue unit can conduct a dialogue to provide information related to the home. In this way, the optimal dialogue method can be selected by considering the geographical location information of the user. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the geographical location information of the user to the AI, which can select the optimal dialogue method.

[0054] The dialogue unit can analyze the user's social media activity and suggest a means of dialogue during dialogue. For example, the dialogue unit can prioritize dialogue with people with whom the user frequently interacts on social media. The dialogue unit can also conduct dialogue related to topics in which the user is interested on social media. Furthermore, the dialogue unit can conduct dialogue related to groups and communities in which the user participates on social media. This makes it possible to suggest optimal dialogue means by analyzing the user's social media activity. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's social media activity into AI, which can then suggest a means of dialogue.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The interactive avatar system can further collect health data of the user and reflect it in the dialogue content of the avatar. For example, the collection unit can collect heart rate and sleep data from the user's wearable device. The analysis unit can analyze these data and grasp the user's health condition. The generation unit can provide the avatar with health advice based on the user's health condition. The dialogue unit can conduct dialogue to encourage relaxation or exercise according to the user's health condition. In this way, more personalized support can be provided by conducting dialogue that takes the user's health condition into consideration.

[0057] The collection unit can collect environmental data of the user and reflect it in the dialogue content of the avatar. For example, the collection unit can collect environmental data such as the temperature, humidity, and noise level around the user. The analysis unit can analyze these data and grasp the environmental state of the user. The generation unit can provide advice for the avatar to maintain a comfortable environment based on the environmental state of the user. The dialogue unit can carry out an appropriate dialogue according to the environmental state of the user. This can support a more comfortable life by carrying out a dialogue that takes into account the environmental state of the user.

[0058] The analysis unit can analyze the user's hobbies and interests and reflect them in the content of the avatar's dialogue. For example, the analysis unit can analyze data on keywords searched for by the user in the past and websites visited by the user. The generation unit can provide the avatar with topics related to the user's hobbies and interests based on this data. The dialogue unit can suggest related information and activities according to the user's hobbies and interests. This makes it possible to provide a more interesting dialogue by conducting a dialogue taking into account the user's hobbies and interests.

[0059] The generation unit can refer to the user's past dialogue history and customize the dialogue content of the avatar. For example, the generation unit can incorporate expressions and phrases that the user has used in the past into the dialogue of the avatar. The dialogue unit can select a dialogue method for a specific topic from the user's past dialogue history. Furthermore, the generation unit can analyze the user's past dialogue patterns and select the most effective dialogue method. In this way, a more personalized dialogue can be provided by referring to the user's past dialogue history.

[0060] The dialogue unit can customize the dialogue content of the avatar by taking into account the geographical location information of the user. For example, when the user is in a particular area, the dialogue unit can perform a dialogue that provides information related to the area. When the user is traveling, the dialogue unit can perform a dialogue that provides information related to the travel destination. When the user is at home, the dialogue unit can perform a dialogue that provides information related to the home. In this way, by taking into account the geographical location information of the user, it is possible to provide a more relevant dialogue.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects messages or call exchanges of the user. The collection unit can collect data such as text messages, voice calls, and video calls. The collection unit collects details of messages and call contents sent by the user and provides these data to the analysis unit. Step 2: The analysis unit analyzes the collected data and extracts the user's characteristics. For example, the analysis unit uses natural language processing technology to analyze the user's speaking style and expression characteristics. The analysis unit extracts words and phrases that the user frequently uses, the tone of speech, etc. Step 3: The generation unit generates an avatar based on the features extracted by the analysis unit. The generation unit uses a generation AI to generate an avatar that reflects the user's features. The generation AI generates the avatar's dialogue content using, for example, a text generation AI (e.g., LLM). The generation unit can generate a 3D model or 2D character that reflects the user's features, or an avatar with only voice. Step 4: The dialogue unit uses the generated avatar to dialogue on behalf of the user. The dialogue unit dialogues by methods such as text chat, voice dialogue, video dialogue, etc. The dialogue unit can use a generation AI to conduct dialogue that reflects the characteristics of the user. As a result, the interactive avatar system according to the embodiment can generate an avatar that reflects the characteristics of the user and can dialogue on behalf of the user in the event of an emergency.

[0063] (Example 2) The interactive avatar system according to the embodiment of the present invention is a system in which messages and telephone conversations of a user are collected, and a generation AI analyzes the characteristics of the user to generate an anthropomorphized avatar. This interactive avatar system generates an interactive avatar that reflects the characteristics of the user, and can converse on behalf of the user in an emergency. For example, messages and telephone conversations of a user are collected, and a generation AI analyzes the characteristics of the user's speaking style and expression. Next, an avatar is generated based on the characteristics analyzed by the generation AI, and this avatar converses on behalf of the user. This allows the user to live a daily life with peace of mind. This allows the interactive avatar system to generate an avatar that reflects the characteristics of the user, and can converse on behalf of the user in an emergency.

[0064] The interactive avatar system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a dialogue unit. The collection unit collects messages or telephone conversations of a user. The collection unit can collect data such as text messages, voice calls, and video calls. The collection unit collects messages and telephone conversations sent by a user in detail and provides these data to the analysis unit. The analysis unit analyzes the collected data and extracts the user's characteristics. The analysis unit analyzes the user's speaking style and expression characteristics using, for example, natural language processing technology. The analysis unit extracts words and phrases frequently used by the user, speech tone, and the like. The generation unit generates an avatar based on the characteristics extracted by the analysis unit. The generation unit generates an avatar that reflects the user's characteristics using a generation AI. The generation AI generates the avatar's dialogue content using, for example, a text generation AI (for example, LLM). The generation unit can generate a 3D model or 2D character that reflects the user's characteristics, an avatar with only voice, and the like. The dialogue unit allows the generated avatar to dialogue on behalf of the user. The dialogue unit conducts dialogue by, for example, a method such as text chat, voice dialogue, or video dialogue. The dialogue unit can conduct dialogue reflecting the characteristics of the user using a generation AI. As a result, the interactive avatar system according to the embodiment can generate an avatar reflecting the characteristics of the user and conduct dialogue on behalf of the user in the event of an emergency. For example, the dialogue unit can cause the generated avatar to conduct dialogue on behalf of the user and take appropriate action. As a result, the user can live his or her daily life with peace of mind.

[0065] The collection unit collects messages or call exchanges of the user. The collection unit can collect data such as text messages, voice calls, and video calls. Specifically, in the case of text messages, character information sent by the user is acquired in real time, and in the case of voice calls, the contents of the calls are recorded and saved as voice data. In the case of video calls, both video and voice are collected to acquire detailed information such as the user's facial expressions and tone of voice. These data are centrally managed by the collection unit and provided to the analysis unit. The collection unit can use encryption technology for privacy protection when collecting data. For example, the collected data is encrypted at the time of collection, and unauthorized access by third parties is prevented until it is transmitted to the analysis unit. In addition, the collection unit has a mechanism for collecting data with the user's consent, and can stop collection if the user refuses data collection. This allows the collection unit to efficiently collect necessary data while protecting the user's privacy. Furthermore, the collection unit has a high-speed data transfer technology for providing the collected data to the analysis unit in real time, thereby improving the response speed of the entire system.

[0066] The analysis unit analyzes the collected data and extracts the user's characteristics. The analysis unit, for example, uses natural language processing technology to analyze the user's speaking style and expression characteristics. Specifically, in the case of text messages, morphological analysis and grammatical analysis are performed to identify words and phrases frequently used by the user. In the case of voice calls, voice data is converted into text using voice recognition technology, and the text data is analyzed. In addition, characteristics such as tone of voice, speaking speed, and emotional expression can also be extracted from the voice data. In the case of video calls, video analysis technology is used to analyze the user's facial expressions and gestures, and the user's emotions and intentions are inferred based on this information. The analysis unit integrates these analysis results to comprehensively grasp the user's characteristics. Furthermore, the analysis unit can learn the user's behavioral patterns and preferences by utilizing past data and user history information. For example, based on past dialogue history, it predicts how the user will react in a specific situation and provides information to realize a more natural dialogue. In addition, the analysis unit can use an anomaly detection algorithm to detect unusual user behavior or remarks and issue a warning as necessary. This enables the analysis unit to accurately grasp the characteristics of the user and improve the dialogue quality of the entire system.

[0067] The generation unit generates an avatar based on the features extracted by the analysis unit. The generation unit uses a generation AI to generate an avatar that reflects the user's features. Specifically, the generation unit uses a text generation AI (e.g., LLM) to generate the dialogue content of the avatar. The generation AI can learn the features of the user's speech and expression, and generate natural dialogue based on the learned features. For example, the generation unit generates dialogue content that incorporates words and phrases frequently used by the user, and creates avatar utterances that resemble the user's speech. The generation unit can also generate 3D models, 2D characters, and voice-only avatars that reflect the user's features. In the case of 3D models, avatars that reproduce the user's appearance and facial expressions can be generated and operated in real time. In the case of 2D characters, illustrations and animations that reflect the user's features are generated and displayed during dialogue. In the case of voice-only avatars, natural voice dialogue is realized using voice synthesis technology that reflects the features of the user's voice. The generation unit provides these generation results to the dialogue unit, and prepares to dialogue on behalf of the user. In addition, the generation unit can evaluate the quality of the generated avatar, and make corrections and improvements as necessary. For example, if the generated dialogue content is unnatural or does not accurately reflect the user's characteristics, the avatar is regenerated to improve quality. This enables the generation unit to generate a high-quality avatar that accurately reflects the user's characteristics, thereby improving the dialogue quality of the entire system.

[0068] In the dialogue unit, the generated avatar converses on behalf of the user. The dialogue unit converses by, for example, text chat, voice dialogue, video dialogue, and the like. Specifically, in the case of text chat, a text message generated by the generation AI is sent on behalf of the user to converse with the other party. In the case of voice dialogue, a voice generated using voice synthesis technology is played back to converse on behalf of the user. In the case of video dialogue, a generated 3D model or 2D character is displayed and a dialogue is conducted while moving in real time. The dialogue unit can use the generation AI to conduct a dialogue that reflects the characteristics of the user. For example, the dialogue unit generates dialogue content that incorporates words and phrases frequently used by the user, and creates avatar remarks that resemble the user's way of speaking. In addition, the dialogue unit can incorporate new dialogue content generated by the generation AI in real time according to the progress of the dialogue. This allows the dialogue unit to realize natural and smooth dialogue and respond appropriately on behalf of the user. Furthermore, the dialogue unit can record the content and progress of the dialogue and later provide it to the analysis unit and generation unit. This allows feedback to be provided to continuously improve the dialogue quality of the entire system. For example, if the content of the dialogue is inappropriate or does not accurately reflect the user's characteristics, the analysis unit or generation unit can make corrections or improvements based on that information. This allows the dialogue unit to conduct high-quality dialogue on behalf of the user, improving the reliability and satisfaction of the entire system.

[0069] The collection unit can collect messages or call contents of the user. The collection unit can collect data such as text messages, voice calls, and video calls. The collection unit collects messages and call contents sent by the user in detail and provides these data to the analysis unit. In this way, by collecting the messages and call contents of the user, the characteristics of the user can be understood. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the messages and call contents of the user to AI, which can analyze the data.

[0070] The analysis unit can analyze the collected data and extract the characteristics of the user's speaking style or expression. The analysis unit can analyze the characteristics of the user's speaking style and expression using, for example, natural language processing technology. The analysis unit extracts words, phrases, and speech tones frequently used by the user. In this way, by extracting the characteristics of the user's speaking style and expression, it is possible to generate an avatar that reflects the user's characteristics. A part or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data to AI, which can analyze the data.

[0071] The generation unit can generate an avatar based on the extracted features. The generation unit uses a generation AI to generate an avatar that reflects the features of the user. The generation AI generates the dialogue content of the avatar using, for example, a text generation AI (for example, LLM). The generation unit can generate a 3D model, a 2D character, an avatar with only voice, and the like that reflects the features of the user. As a result, by generating an avatar based on the extracted features, it is possible to provide an avatar that reflects the features of the user. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the extracted features to the generation AI, which can generate an avatar.

[0072] The dialogue unit allows the generated avatar to dialogue on behalf of the user. The dialogue unit dialogues by, for example, text chat, voice dialogue, video dialogue, or the like. The dialogue unit can use a generation AI to conduct dialogue that reflects the characteristics of the user. This allows the generated avatar to dialogue on behalf of the user, making it possible to take appropriate action in the event of an emergency. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the dialogue unit can input the generated avatar to an AI, which then conducts the dialogue.

[0073] The dialogue unit can use an algorithm for responding in the event of an emergency. The dialogue unit can take an appropriate response in the event of an emergency, for example, by using an emergency response scenario or an AI technology to be used. As a result, by using an algorithm for taking an appropriate response in the event of an emergency, an appropriate response can be taken on behalf of the user. Some or all of the above-mentioned processing in the dialogue unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the dialogue unit can input an emergency response scenario into the AI, and the AI ​​can take the response.

[0074] The collection unit can estimate the user's emotion and adjust the collection timing of messages and call contents based on the estimated user's emotion. The collection unit estimates the user's emotion using technologies such as voice analysis, facial expression recognition, and text analysis. When the user is feeling stressed, the collection unit can temporarily stop collection of messages and call contents and resume collection when the user is relaxed. In addition, when the user is relaxed, the collection unit can proactively collect messages and call contents and obtain detailed data. Furthermore, when the user is in a hurry, the collection unit can preferentially collect important messages and call contents in a short time. This allows data to be collected at a more appropriate timing by adjusting the collection timing based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit inputs the user's emotion data into the generation AI, and the generation AI can adjust the collection timing.

[0075] The collection unit can analyze the user's past messages and call history and select a collection method. The collection unit can, for example, preferentially collect message apps and call apps that the user has used frequently in the past. In addition, the collection unit can concentrate collection in a specific time period from the user's past messages and call history. Furthermore, the collection unit can analyze the user's past communication patterns and select the most effective collection method. As a result, the optimal collection method can be selected by analyzing the user's past messages and call history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past messages and call history into AI, and the AI ​​can select the collection method.

[0076] The collection unit can perform filtering based on the user's current living situation and areas of interest when collecting messages and call contents. For example, when the user is at work, the collection unit can preferentially collect messages and call contents related to work. In addition, when the user is engaged in a hobby-related activity, the collection unit can collect messages and call contents related to the hobby. Furthermore, when the user is traveling, the collection unit can collect messages and call contents related to travel. This makes it possible to collect more relevant data by filtering based on the user's living situation and areas of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's living situation and areas of interest into AI, and the AI ​​can perform filtering.

[0077] The collection unit can estimate the user's emotion and determine the priority of messages and call contents to be collected based on the estimated user's emotion. The collection unit estimates the user's emotion using, for example, technologies such as voice analysis, facial expression recognition, and text analysis. When the user is stressed, the collection unit can prioritize collecting messages and call contents of high importance. Furthermore, when the user is relaxed, the collection unit can collect all messages and call contents evenly. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting messages and call contents of high urgency. As a result, important data can be collected preferentially by determining the priority of messages and call contents to be collected based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit inputs the user's emotion data into the generation AI, and the generation AI can determine the priority of collection.

[0078] The collection unit can collect highly relevant content preferentially based on the geographical location information of the user when collecting messages and call contents. For example, when the user is in a specific area, the collection unit can collect messages and call contents related to the area preferentially. In addition, when the user is traveling, the collection unit can collect messages and call contents related to the travel destination. Furthermore, when the user is at home, the collection unit can collect messages and call contents related to the home preferentially. In this way, by taking into account the geographical location information of the user, highly relevant data can be collected preferentially. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can collect highly relevant content preferentially.

[0079] The collection unit can analyze the user's social media activity and collect related content when collecting messages and call contents. For example, the collection unit can preferentially collect messages and call contents with people with whom the user frequently interacts on social media. The collection unit can also collect messages and call contents related to topics in which the user is interested on social media. Furthermore, the collection unit can collect messages and call contents related to groups and communities in which the user participates on social media. This makes it possible to collect related content by analyzing the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI, and the AI ​​can collect related content.

[0080] The analysis unit can estimate the user's emotion and analyze the characteristics of the speech and expression based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, technologies such as voice analysis, facial expression recognition, and text analysis. The analysis unit can analyze a gentle tone and a slow speech when the user is relaxed. In addition, the analysis unit can analyze a fast-paced and strong tone of speech when the user is stressed. In addition, the analysis unit can analyze an emotional expression and emphasized words when the user is excited. This allows more accurate features to be extracted by analyzing the characteristics of the speech and expression based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI, and the generation AI can analyze the characteristics of the speech and expression.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the message or the call content during the analysis. For example, the analysis unit can perform a detailed analysis of a message or a call content with a high level of importance and extract the features in detail. In addition, the analysis unit can perform a simplified analysis of a message or a call content with a low level of importance and extract only the main features. Furthermore, the analysis unit can quickly analyze a message or a call content with a high level of urgency and immediately extract the features. As a result, the analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance of the message or the call content. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the message or the call content to an AI, and the AI ​​can adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the message or call content during analysis. For example, the analysis unit can apply an algorithm that analyzes formal expressions and technical terms to business-related messages and call content. In addition, the analysis unit can apply an algorithm that analyzes casual expressions and everyday words to private messages and call content. Furthermore, the analysis unit can apply an algorithm for responding quickly to emergency messages and call content. This allows for more accurate analysis by applying different analysis algorithms depending on the category of the message or call content. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of the message or call content to AI, and the AI ​​can apply different analysis algorithms.

[0083] The analysis unit can estimate the user's emotion and determine the analysis priority based on the estimated user's emotion. The analysis unit estimates the user's emotion using, for example, technologies such as voice analysis, facial expression recognition, and text analysis. When the user is stressed, the analysis unit can prioritize analyzing messages and call contents with high importance. Moreover, when the user is relaxed, the analysis unit can analyze all messages and call contents evenly. Furthermore, when the user is in a hurry, the analysis unit can prioritize analyzing messages and call contents with high urgency. As a result, important data can be analyzed preferentially by determining the analysis priority based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit inputs the user's emotion data into the generation AI, and the generation AI can determine the analysis priority.

[0084] The analysis unit can determine the priority of the analysis based on the time of submission of the message or the call content during the analysis. The analysis unit can, for example, give priority to analyzing the message or the call content that was sent recently. The analysis unit can also give priority to analyzing the message or the call content that was sent in a specific time period. Furthermore, the analysis unit can give priority to analyzing the message or the call content that has a high urgency. This allows efficient analysis by determining the priority of the analysis based on the time of submission of the message or the call content. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the time of submission of the message or the call content to an AI, and the AI ​​can determine the priority of the analysis.

[0085] The analysis unit can adjust the order of analysis based on the relevance of messages and call contents during analysis. The analysis unit can, for example, prioritize analysis of highly relevant messages and call contents. The analysis unit can also postpone analysis of less relevant messages and call contents. Furthermore, the analysis unit can prioritize analysis of messages and call contents related to a specific topic. This allows analysis to be performed efficiently by adjusting the order of analysis based on the relevance of messages and call contents. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of messages and call contents to AI, and the AI ​​can adjust the order of analysis.

[0086] The generation unit can estimate the user's emotion and adjust the expression method of the avatar based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, technologies such as voice analysis, facial expression recognition, and text analysis. The generation unit can generate an avatar with a calm expression and movement when the user is relaxed. Furthermore, the generation unit can generate an avatar with a calm expression and movement when the user is stressed. Furthermore, the generation unit can generate an avatar with an active expression and movement when the user is excited. This allows a more appropriate avatar to be generated by adjusting the expression method of the avatar based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI, and the generation AI can adjust the expression method of the avatar.

[0087] The generation unit can adjust the level of detail of the avatar based on the importance of the user's features when generating the avatar. For example, when the user's features are clear, the generation unit can generate a detailed avatar. Furthermore, when the user's features are vague, the generation unit can generate a simplified avatar. Furthermore, when the user's features are diverse, the generation unit can generate an avatar that emphasizes the main features. As a result, a more appropriate avatar can be generated by adjusting the level of detail of the avatar based on the importance of the user's features. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the importance of the user's features to AI, and the AI ​​can adjust the level of detail of the avatar.

[0088] The generation unit can apply different generation algorithms depending on the user category when generating an avatar. For example, the generation unit can apply a generation algorithm having a formal expression or action to an avatar for business use. In addition, the generation unit can apply a generation algorithm having a casual expression or action to an avatar for private use. Furthermore, the generation unit can apply a generation algorithm for quick response to an avatar in an emergency. In this way, by applying different generation algorithms depending on the user category, a more appropriate avatar can be generated. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user category to AI and apply a generation algorithm with a different AI.

[0089] The generation unit can estimate the user's emotion and adjust the appearance and behavior of the avatar based on the estimated user's emotion. The generation unit estimates the user's emotion using, for example, techniques such as voice analysis, facial expression recognition, and text analysis. The generation unit can generate an avatar with a calm expression and behavior when the user is relaxed. Furthermore, the generation unit can generate an avatar with a calm expression and behavior when the user is stressed. Furthermore, the generation unit can generate an avatar with an active expression and behavior when the user is excited. This allows a more appropriate avatar to be generated by adjusting the appearance and behavior of the avatar based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI, and the generation AI can adjust the appearance and behavior of the avatar.

[0090] The generation unit can determine the priority of generation based on the user's past avatar usage history when generating an avatar. For example, the generation unit can preferentially reflect the features of avatars that the user has frequently used in the past. In addition, the generation unit can predict and generate an avatar to be used in a specific time period from the user's past avatar usage history. Furthermore, the generation unit can analyze the user's past avatar usage pattern and generate the most effective avatar. As a result, a more appropriate avatar can be generated by determining the priority of generation based on the user's past avatar usage history. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past avatar usage history into AI, and the AI ​​can determine the priority of generation.

[0091] The generation unit can improve the accuracy of the generation when generating an avatar by referring to the user's related data. The generation unit can adjust the appearance and personality of the avatar, for example, by referring to the user's social media profile and posted content. The generation unit can also adjust the avatar's speaking style and expression by referring to the user's past messages and call content. Furthermore, the generation unit can customize the characteristics of the avatar based on the user's hobbies and interests. This can improve the accuracy of the generation by referring to the user's related data. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input the user's related data into AI, which can improve the accuracy of the generation.

[0092] The dialogue unit can estimate the user's emotion and adjust the expression method of the dialogue based on the estimated user's emotion. The dialogue unit estimates the user's emotion using, for example, technologies such as voice analysis, facial expression recognition, and text analysis. The dialogue unit can conduct the dialogue in a calm tone when the user is relaxed. Furthermore, the dialogue unit can conduct the dialogue in a calm tone when the user is stressed. Furthermore, the dialogue unit can conduct the dialogue in an active tone when the user is excited. This allows a more appropriate dialogue to be conducted by adjusting the expression method of the dialogue based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit inputs the user's emotion data into the generation AI, and the generation AI can adjust the expression method of the dialogue.

[0093] The dialogue unit can select a dialogue method by referring to the user's past dialogue history during dialogue. The dialogue unit can, for example, incorporate expressions and phrases that the user has used in the past into the dialogue. The dialogue unit can also select a dialogue method related to a specific topic from the user's past dialogue history. Furthermore, the dialogue unit can analyze the user's past dialogue patterns and select the most effective dialogue method. This makes it possible to select an optimal dialogue method by referring to the user's past dialogue history. A part or all of the above-mentioned processing in the dialogue unit can be performed, for example, using AI or can be performed without using AI. For example, the dialogue unit can input the user's past dialogue history into AI, which can then select a dialogue method.

[0094] The dialogue unit can customize the dialogue means based on the user's current situation during dialogue. For example, when the user is at work, the dialogue unit can conduct a businesslike dialogue. Also, when the user is relaxed, the dialogue unit can conduct a casual dialogue. Furthermore, when the user is in an emergency, the dialogue unit can conduct a quick and concise dialogue. This allows for a more appropriate dialogue by customizing the dialogue means based on the user's current situation. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the user's current situation into the AI, which can then customize the dialogue means.

[0095] The dialogue unit can estimate the user's emotion and determine the priority of dialogues based on the estimated user's emotion. The dialogue unit estimates the user's emotion using, for example, technologies such as voice analysis, facial expression recognition, and text analysis. When the user is stressed, the dialogue unit can prioritize dialogues with high importance. Moreover, when the user is relaxed, the dialogue unit can equally perform all dialogues. Furthermore, when the user is in a hurry, the dialogue unit can prioritize dialogues with high urgency. As a result, important dialogues can be prioritized by determining the priority of dialogues based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit inputs the user's emotion data into the generation AI, and the generation AI can determine the priority of dialogues.

[0096] The dialogue unit can select the optimal dialogue method in consideration of the geographical location information of the user during dialogue. For example, when the user is in a specific area, the dialogue unit can conduct a dialogue to provide information related to the area. Furthermore, when the user is traveling, the dialogue unit can conduct a dialogue to provide information related to the travel destination. Furthermore, when the user is at home, the dialogue unit can conduct a dialogue to provide information related to the home. In this way, the optimal dialogue method can be selected by considering the geographical location information of the user. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the geographical location information of the user to the AI, which can select the optimal dialogue method.

[0097] The dialogue unit can analyze the user's social media activity and suggest a means of dialogue during dialogue. For example, the dialogue unit can prioritize dialogue with people with whom the user frequently interacts on social media. The dialogue unit can also conduct dialogue related to topics in which the user is interested on social media. Furthermore, the dialogue unit can conduct dialogue related to groups and communities in which the user participates on social media. This makes it possible to suggest optimal dialogue means by analyzing the user's social media activity. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input the user's social media activity into AI, which can then suggest a means of dialogue.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The interactive avatar system can further collect health data of the user and reflect it in the dialogue content of the avatar. For example, the collection unit can collect heart rate and sleep data from the user's wearable device. The analysis unit can analyze these data and grasp the user's health condition. The generation unit can provide the avatar with health advice based on the user's health condition. The dialogue unit can conduct dialogue to encourage relaxation or exercise according to the user's health condition. In this way, more personalized support can be provided by conducting dialogue that takes the user's health condition into consideration.

[0100] The collection unit can collect environmental data of the user and reflect it in the dialogue content of the avatar. For example, the collection unit can collect environmental data such as the temperature, humidity, and noise level around the user. The analysis unit can analyze these data and grasp the environmental state of the user. The generation unit can provide advice for the avatar to maintain a comfortable environment based on the environmental state of the user. The dialogue unit can carry out an appropriate dialogue according to the environmental state of the user. This can support a more comfortable life by carrying out a dialogue that takes into account the environmental state of the user.

[0101] The analysis unit can analyze the user's hobbies and interests and reflect them in the content of the avatar's dialogue. For example, the analysis unit can analyze data on keywords searched for by the user in the past and websites visited by the user. The generation unit can provide the avatar with topics related to the user's hobbies and interests based on this data. The dialogue unit can suggest related information and activities according to the user's hobbies and interests. This makes it possible to provide a more interesting dialogue by conducting a dialogue taking into account the user's hobbies and interests.

[0102] The generation unit can refer to the user's past dialogue history and customize the dialogue content of the avatar. For example, the generation unit can incorporate expressions and phrases that the user has used in the past into the dialogue of the avatar. The dialogue unit can select a dialogue method for a specific topic from the user's past dialogue history. Furthermore, the generation unit can analyze the user's past dialogue patterns and select the most effective dialogue method. In this way, a more personalized dialogue can be provided by referring to the user's past dialogue history.

[0103] The dialogue unit can customize the dialogue content of the avatar by taking into account the geographical location information of the user. For example, when the user is in a particular area, the dialogue unit can perform a dialogue that provides information related to the area. When the user is traveling, the dialogue unit can perform a dialogue that provides information related to the travel destination. When the user is at home, the dialogue unit can perform a dialogue that provides information related to the home. In this way, by taking into account the geographical location information of the user, it is possible to provide a more relevant dialogue.

[0104] The collection unit can estimate the user's emotion and adjust the type of data to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit can collect music and video data for relaxation. When the user is relaxed, the collection unit can collect data useful for learning or work. When the user is excited, the collection unit can collect data related to entertainment. In this way, by adjusting the type of data to be collected based on the user's emotion, more appropriate data can be provided.

[0105] The analysis unit can estimate the user's emotion and adjust the level of detail of the analysis based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit can perform a detailed analysis and identify the cause of the stress. When the user is relaxed, the analysis unit can perform a simplified analysis and extract only the main features. Furthermore, when the user is excited, the analysis unit can perform a quick analysis and instantly extract the features. This allows a more appropriate analysis to be performed by adjusting the level of detail of the analysis based on the user's emotion.

[0106] The generation unit can estimate the user's emotion and adjust the appearance and behavior of the avatar based on the estimated user's emotion. For example, when the user is relaxed, the generation unit can generate an avatar with a calm expression and behavior. When the user is stressed, the generation unit can generate an avatar with a calm expression and behavior. When the user is excited, the generation unit can generate an avatar with an active expression and behavior. This makes it possible to generate a more appropriate avatar by adjusting the appearance and behavior of the avatar based on the user's emotion.

[0107] The dialogue unit can estimate the user's emotion and adjust the manner of expression of the dialogue based on the estimated user's emotion. For example, the dialogue unit can conduct the dialogue in a gentle tone when the user is relaxed. Furthermore, the dialogue unit can conduct the dialogue in a calm tone when the user is stressed. Furthermore, the dialogue unit can conduct the dialogue in a lively tone when the user is excited. In this way, a more appropriate dialogue can be conducted by adjusting the manner of expression of the dialogue based on the user's emotion.

[0108] The dialogue unit can estimate the user's emotions and determine the priority of dialogues based on the estimated user's emotions. For example, when the user is feeling stressed, the dialogue unit can prioritize dialogues of high importance. Also, when the user is relaxed, the dialogue unit can equally carry out all dialogues. Furthermore, when the user is in a hurry, the dialogue unit can prioritize dialogues of high urgency. In this way, by determining the priority of dialogues based on the user's emotions, important dialogues can be carried out with priority.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The collection unit collects messages or call exchanges of the user. The collection unit can collect data such as text messages, voice calls, and video calls. The collection unit collects details of messages and call contents sent by the user and provides these data to the analysis unit. Step 2: The analysis unit analyzes the collected data and extracts the user's characteristics. For example, the analysis unit uses natural language processing technology to analyze the user's speaking style and expression characteristics. The analysis unit extracts words and phrases that the user frequently uses, the tone of speech, etc. Step 3: The generation unit generates an avatar based on the features extracted by the analysis unit. The generation unit uses a generation AI to generate an avatar that reflects the user's features. The generation AI generates the avatar's dialogue content using, for example, a text generation AI (e.g., LLM). The generation unit can generate a 3D model or 2D character that reflects the user's features, or an avatar with only voice. Step 4: The dialogue unit uses the generated avatar to dialogue on behalf of the user. The dialogue unit dialogues by methods such as text chat, voice dialogue, video dialogue, etc. The dialogue unit can use a generation AI to conduct dialogue that reflects the characteristics of the user. As a result, the interactive avatar system according to the embodiment can generate an avatar that reflects the characteristics of the user and can dialogue on behalf of the user in the event of an emergency.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and dialogue unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects messages and telephone conversations of the user using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to extract the user's characteristics. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an avatar that reflects the user's characteristics using a generation AI. The dialogue unit is realized, for example, by the control unit 46A of the smart device 14, and the generated avatar dialogues on behalf of the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and dialogue unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects messages and calls of the user using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to extract the user's characteristics. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an avatar that reflects the user's characteristics using a generation AI. The dialogue unit is realized, for example, by the control unit 46A of the smart glasses 214, and the generated avatar dialogues on behalf of the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and dialogue unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects messages and telephone conversations of the user using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to extract the user's characteristics. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an avatar that reflects the user's characteristics using a generation AI. The dialogue unit is realized, for example, by the control unit 46A of the headset type terminal 314, and the generated avatar dialogues on behalf of the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and dialogue unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects messages and telephone conversations of the user using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to extract the user's characteristics. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an avatar that reflects the user's characteristics using a generation AI. The dialogue unit is realized, for example, by the control unit 46A of the robot 414, and the generated avatar dialogues on behalf of the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A collection unit that collects messages or call exchanges of users; an analysis unit that analyzes the information collected by the collection unit and extracts user characteristics; a generation unit that generates an avatar based on the features extracted by the analysis unit; A dialogue unit with which the avatar generated by the generation unit can have a dialogue. system. (Appendix 2) The collecting unit includes: Collect user messages or call transcripts 2. The system of claim 1, (Appendix 3) The analysis unit is Analyzing the collected data and extracting characteristics of the user's speech or expression 2. The system of claim 1, (Appendix 4) The generation unit is Generate an avatar based on the extracted features 2. The system of claim 1, (Appendix 5) The dialogue unit includes: The generated avatar interacts on behalf of the user. 2. The system of claim 1, (Appendix 6) The dialogue unit includes: Use algorithms to respond in emergencies 2. The system of claim 1, (Appendix 7) The collecting unit includes: Estimate user emotions and adjust the timing of collection of messages and call contents based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze the user's past messages and call history to determine the collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: Filter messages and calls as they are collected based on the user's current life situation and interests 2. The system of claim 1, (Appendix 10) The collecting unit includes: Estimate user emotions and prioritize messages and call content to be collected based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting messages and call content, prioritize collection of content that is more relevant based on the user's geographic location 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting messages and call content, analyze the user's social media activity and collect related content. 2. The system of claim 1, (Appendix 13) The analysis unit is Estimate the user's emotions and analyze the characteristics of speech and expressions based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the message or call content 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of message or call content. 2. The system of claim 1, (Appendix 16) The analysis unit is Estimate user emotions and determine analysis priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when messages and calls were submitted 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on the relevance of messages and calls 2. The system of claim 1, (Appendix 19) The generation unit is Estimate the user's emotions and adjust the avatar's expression based on the estimated user's emotions. 2. The system of claim 1, (Appendix 20) The generation unit is When generating an avatar, adjust the level of detail of the avatar based on the importance of the user's features. 2. The system of claim 1, (Appendix 21) The generation unit is When generating avatars, different generation algorithms are applied depending on the user category. 2. The system of claim 1, (Appendix 22) The generation unit is Estimate the user's emotions and adjust the avatar's appearance and behavior based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The generation unit is When generating avatars, the priority of generation is determined based on the user's past avatar usage history. 2. The system of claim 1, (Appendix 24) The generation unit is When generating an avatar, the accuracy of the generation is improved by referring to the user's related data. 2. The system of claim 1, (Appendix 25) The dialogue unit includes: Estimate the user's emotions and adjust the way the dialogue is expressed based on the estimated user's emotions. 2. The system of claim 1, (Appendix 26) The dialogue unit includes: When interacting with a user, the interaction method is selected by referring to the user's past interaction history. 2. The system of claim 1, (Appendix 27) The dialogue unit includes: During an interaction, customize the interaction vehicle based on the user's current situation 2. The system of claim 1, (Appendix 28) The dialogue unit includes: Estimate user emotions and prioritize interactions based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The dialogue unit includes: During dialogue, the optimal dialogue method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The dialogue unit includes: During interaction, the system analyzes the user's social media activity to suggest ways to interact 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects messages or call exchanges of users; an analysis unit that analyzes the information collected by the collection unit and extracts user characteristics; a generation unit that generates an avatar based on the features extracted by the analysis unit; A dialogue unit with which the avatar generated by the generation unit can have a dialogue. system.

2. The collecting unit includes: Collect user messages or call transcripts 2. The system of claim 1 .

3. The analysis unit is Analyzing the collected data and extracting characteristics of the user's speech or expression 2. The system of claim 1 .

4. The dialogue unit includes: Use algorithms to respond in emergencies 2. The system of claim 1 .

5. The collecting unit includes: Estimate user emotions and adjust the timing of collection of messages and call contents based on the estimated user emotions 2. The system of claim 1 .

6. The collecting unit includes: Analyze the user's past messages and call history to determine the collection method 2. The system of claim 1 .

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