system

Abstract

The system according to this embodiment aims to enable an AI avatar, based on the user's behavioral history and preferences, to communicate autonomously. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and a communication unit. The collection unit collects the user's behavior history and preferences. The generation unit generates an AI avatar based on the data collected by the collection unit. The communication unit enables the AI ​​avatar generated by the generation unit to communicate with other AI avatars.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, autonomous communication based on a user's behavior history and preferences has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to enable an AI avatar to autonomously communicate based on a user's behavior history and preferences.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, and a communication unit. The collection unit collects the user's behavioral history and preferences. The generation unit generates an AI avatar based on the data collected by the collection unit. The communication unit enables the AI ​​avatar generated by the generation unit to communicate with other AI avatars. [Effects of the Invention]

[0007] The system according to this embodiment allows an AI avatar, based on the user's behavioral history and preferences, to communicate autonomously. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​avatar generation system according to an embodiment of the present invention is a system that learns the user's behavioral history and preferences and creates an AI avatar that reflects the user's personality. This AI avatar generation system learns the user's behavioral history and preferences and autonomously communicates with other AI avatars on behalf of the user, enabling information gathering, negotiation, and networking. For example, the AI ​​avatar generation system automatically collects conversations and behavioral history with the user. For example, it collects data such as which websites the user has visited, what products they have purchased, and what hobbies they have. This allows the system to understand the user's behavioral patterns and preferences. Next, based on the collected data, it generates an AI avatar that reflects the user's personality. The AI ​​avatar learns the user's behavioral history and preferences and autonomously communicates with other AI avatars on behalf of the user. For example, if the user is job hunting, the AI ​​avatar can interview AI avatars of companies and find the most suitable company for the user. Also, if the user is looking for a marriage partner, the AI ​​avatar can communicate with other AI avatars and find the most suitable partner for the user. Furthermore, the AI ​​avatar can autonomously gather information, negotiate, and network. For example, an AI avatar can communicate with other AI avatars to gather information useful to the user. It can also negotiate with other AI avatars to secure favorable terms for the user. Furthermore, it can network with other AI avatars to build valuable connections for the user. This system allows users to efficiently achieve optimal matching. For instance, busy business professionals can network effectively within limited timeframes. Students seeking employment can find the perfect company for them. Individuals seeking marriage can find their ideal partner. In this way, utilizing AI agents and AI avatars saves users time and effort, enabling efficient and optimal matching.This allows the AI ​​avatar generation system to learn the user's behavioral history and preferences, and to autonomously communicate with other AI avatars on behalf of the user, enabling information gathering, negotiation, and networking.

[0029] The AI ​​avatar generation system according to this embodiment comprises a collection unit, a generation unit, and a communication unit. The collection unit collects the user's behavioral history and preferences. For example, the collection unit collects data such as which websites the user has visited, what products they have purchased, and what hobbies they have. The collection unit can collect detailed data in order to understand the user's behavioral patterns and preferences. The generation unit generates an AI avatar based on the data collected by the collection unit. For example, the generation unit learns the user's behavioral history and preferences and generates an AI avatar that reflects the user's personality. In order to generate an AI avatar that reflects the user's personality, the generation unit can generate an AI avatar based on the collected data. The communication unit enables the AI ​​avatar generated by the generation unit to communicate with other AI avatars. For example, the communication unit enables the AI ​​avatar to autonomously collect information, negotiate, and network with other AI avatars. The communication unit enables the AI ​​avatar to communicate with other AI avatars and collect information that is useful to the user. The communication department enables AI avatars to negotiate with other AI avatars to secure favorable terms for the user. The communication department also enables AI avatars to network with other AI avatars to build beneficial connections for the user. As a result, the AI ​​avatar generation system according to this embodiment allows AI avatars that have learned the user's behavioral history and preferences to autonomously communicate, gather information, negotiate, and network.

[0030] The data collection unit collects user behavior history and preferences. For example, it collects data such as which websites users visit, what products they purchase, and what their hobbies are. Specifically, it meticulously records details such as the URLs of websites visited, browsing time, clicked links, categories and prices of purchased products, and review content. It also collects information on events users participate in, social media accounts they follow, and their comment and like history. This data is crucial for understanding user behavior patterns and preferences, and the data collection unit centrally manages this data, integrating with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the generation and communication units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. In addition, the data collection unit ensures security by anonymizing and encrypting data to protect user privacy. This allows users to use the system with peace of mind.

[0031] The generation unit generates AI avatars based on data collected by the collection unit. For example, the generation unit learns the user's behavioral history and preferences to generate AI avatars that reflect the user's personality. Specifically, it uses the collected data to analyze the user's preferences and interests, and customizes the appearance, personality, and speaking style of the AI ​​avatar based on that. For example, if the user likes music, it generates an AI avatar that is knowledgeable about music; if the user likes sports, it generates an AI avatar that is knowledgeable about sports. Furthermore, by learning the user's behavioral patterns, it can predict what kind of information the user will need and when, and generate an AI avatar that can respond accordingly. The generation unit can use AI technology to generate AI avatars that reflect the user's personality based on the collected data. For example, it can use natural language processing technology to learn the characteristics of the user's speech and writing, and set the conversational style of the AI ​​avatar based on that. It can also use image recognition technology to extract physical characteristics from the user's photos and videos, and set the appearance of the AI ​​avatar based on that. This allows the generation unit to generate AI avatars that reflect the user's individuality with high accuracy.

[0032] The Communication Department enables AI avatars generated by the Generation Department to communicate with other AI avatars. For example, the Communication Department allows AI avatars to autonomously gather information, negotiate, and network with other AI avatars. Specifically, AI avatars exchange information with other AI avatars through chat and voice calls, gathering information useful to the user. For example, an AI avatar can gather the latest news and trend information from other AI avatars and provide it to the user. Furthermore, an AI avatar can negotiate with other AI avatars to secure favorable terms for the user. For example, an AI avatar can negotiate prices with other AI avatars to lower the price of products the user purchases. Additionally, an AI avatar can network with other AI avatars to build valuable connections for the user. For example, an AI avatar can collaborate with other AI avatars to connect with experts and influencers in areas of interest to the user. Thus, the Communication Department enables AI avatars to communicate with other AI avatars, gather useful information, negotiate, and network for the user. Furthermore, the communications department can reduce the burden on users and support efficient information gathering, negotiation, and networking by having AI avatars act autonomously on behalf of the users.

[0033] The data collection unit can automatically collect conversation and behavioral history with users. For example, the data collection unit can automatically collect conversations with users, such as text messages, voice conversations, and video calls. In order to automatically collect user behavioral history, the data collection unit can collect data such as which websites users have visited, what products they have purchased, and what their hobbies are. The data collection unit can collect detailed data to understand user behavioral patterns and preferences. This allows the data collection unit to collect detailed information about users' behavioral history and preferences. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input conversational data with users into a generating AI and have the generating AI perform analysis of the conversational data.

[0034] The generation unit can generate an AI avatar that reflects the user's personality based on the collected data. For example, the generation unit can learn the user's behavioral history and preferences and generate an AI avatar that reflects the user's personality. The generation unit can generate an AI avatar based on the collected data in order to generate an AI avatar that reflects the user's personality. For example, the generation unit can generate an AI avatar that reflects the user's personality traits, hobbies and preferences, behavioral patterns, etc. The generation unit can generate an AI avatar based on the collected data in order to generate an AI avatar that reflects the user's personality. In this way, the generation unit can generate an AI avatar that reflects the user's personality. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the collected data into a generation AI and have the generation AI perform the generation of an AI avatar that reflects the user's personality.

[0035] The communication unit enables the generated AI avatar to autonomously gather information, negotiate, and network with other AI avatars. For example, the communication unit allows the AI ​​avatar to autonomously gather information with other AI avatars. The communication unit allows the AI ​​avatar to autonomously negotiate with other AI avatars. The communication unit allows the AI ​​avatar to autonomously network with other AI avatars. This enables the communication unit to enable the AI ​​avatar to autonomously gather information, negotiate, and network. Some or all of the above-described processes in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the generated AI avatar into a generating AI and have the generating AI perform communication with other AI avatars.

[0036] The communication unit allows AI avatars to communicate with other AI avatars and collect information useful to the user. For example, the communication unit allows AI avatars to communicate with other AI avatars and collect useful information such as business intelligence, market research, and technical information. The communication unit allows AI avatars to communicate with other AI avatars and collect information beneficial to the user. In this way, the communication unit allows AI avatars to collect information beneficial to the user. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit allows AI avatars to communicate with other AI avatars and has a generating AI perform information collection.

[0037] The communication department allows AI avatars to negotiate with other AI avatars to obtain terms useful to the user. For example, the communication department allows AI avatars to negotiate prices, terms, contracts, etc., with other AI avatars to obtain favorable terms for the user. The communication department allows AI avatars to negotiate with other AI avatars to obtain favorable terms for the user. This enables the communication department to allow AI avatars to obtain favorable terms for the user. Some or all of the above-described processes in the communication department may be performed using AI, for example, or without AI. For example, the communication department allows AI avatars to negotiate with other AI avatars and has the negotiations performed by a generating AI.

[0038] The communication department allows AI avatars to network with other AI avatars and build useful connections for the user. For example, the communication department allows AI avatars to engage in business networking, social networking, professional networking, etc., with other AI avatars, thereby building beneficial connections for the user. The communication department allows AI avatars to network with other AI avatars and build beneficial connections for the user. This enables the communication department to allow AI avatars to build beneficial connections for the user. Some or all of the above-described processes in the communication department may be performed using AI, for example, or without AI. For example, the communication department allows AI avatars to network with other AI avatars and has the networking performed by a generating AI.

[0039] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from websites the user has frequently visited in the past. The data collection unit can collect information on related products based on data on products the user has purchased in the past. The data collection unit can analyze the user's past behavior patterns and select the most efficient data collection method. Thus, the data collection unit can select the optimal data collection method by analyzing the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter the collected behavioral history based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting behavioral history related to topics the user is currently interested in. The data collection unit can filter relevant data based on the user's current lifestyle (work, hobbies, etc.). The data collection unit can efficiently collect data by excluding unnecessary data based on the user's areas of interest. In this way, the data collection unit can efficiently collect data by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant history based on the user's geographical location when collecting behavioral history. For example, if the user is in a specific region, the data collection unit can prioritize the collection of behavioral history related to that region. If the user is traveling, the data collection unit can prioritize the collection of information about their destination. The data collection unit can filter and efficiently collect relevant data based on the user's current location. This allows the data collection unit to efficiently collect data by prioritizing the collection of highly relevant history based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant history.

[0042] The data collection unit can analyze a user's social media activity and collect relevant history when collecting behavioral history. For example, the data collection unit can collect relevant behavioral history based on information shared by the user on social media. The data collection unit can analyze the user's social media activity patterns and select the optimal collection method. The data collection unit can collect relevant data based on information from accounts the user follows. This allows the data collection unit to efficiently collect relevant history by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant history.

[0043] The generation unit can adjust the level of detail of the AI ​​avatar based on the user's personality during generation. For example, if the user prefers detailed information, the generation unit can generate an AI avatar with detailed features. If the user prefers simple information, the generation unit can generate an AI avatar with simple features. The generation unit can generate an AI avatar with an appropriate level of detail based on the user's personality. In this way, the generation unit can generate a more appropriate AI avatar by adjusting the level of detail of the AI ​​avatar based on the user's personality. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user personality data into a generation AI and have the generation AI perform the level of detail adjustment.

[0044] The generation unit can apply different generation algorithms depending on the user's category during generation. For example, if the user is a business person, the generation unit can apply a business-oriented generation algorithm. If the user is a student, the generation unit can apply an academic-oriented generation algorithm. If the user has a hobby, the generation unit can apply a generation algorithm appropriate to that hobby. In this way, the generation unit can generate a more appropriate AI avatar by applying different generation algorithms depending on the user's category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0045] The generation unit can determine the priority of AI avatars based on the timing of the user's behavior history submission during generation. For example, the generation unit can determine the priority of AI avatars based on the user's recent actions. The generation unit can determine the priority of AI avatars based on the user's past actions. The generation unit can generate the optimal AI avatar based on the timing of the user's behavior history submission. As a result, the generation unit can generate a more appropriate AI avatar by determining the priority of AI avatars based on the timing of the user's behavior history submission. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user behavior history data into a generation AI and have the generation AI perform the priority determination.

[0046] The generation unit can adjust the order of AI avatars based on user relevance during generation. For example, if a user performs a highly relevant action, the generation unit can adjust the order of AI avatars based on that action. If a user performs a less relevant action, the generation unit can adjust the order of AI avatars based on that action. The generation unit can generate AI avatars in the optimal order based on user relevance. In this way, the generation unit can generate more appropriate AI avatars by adjusting the order of AI avatars based on user relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user relevance data into a generation AI and have the generation AI perform the order adjustment.

[0047] The communication unit can improve the accuracy of communication based on the relationships between AI avatars during communication. For example, the communication unit can perform optimal communication based on past interactions between AI avatars. The communication unit can perform appropriate communication by considering the relationships between AI avatars. The communication unit can analyze the relationships between AI avatars and select the optimal communication method. In this way, the communication unit can improve the accuracy of communication by considering the relationships between AI avatars. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input data on the relationships between AI avatars into a generating AI and have the generating AI perform the improvement of communication accuracy.

[0048] The communication unit can communicate based on the attribute information of the AI ​​avatar during communication. For example, the communication unit can communicate appropriately based on the attribute information of the AI ​​avatar (age, gender, etc.). The communication unit can select the optimal communication method by considering the attribute information of the AI ​​avatar. The communication unit can communicate appropriately based on the attribute information of the AI ​​avatar. As a result, the communication unit can communicate more appropriately by considering the attribute information of the AI ​​avatar. Some or all of the above processing in the communication unit may be performed using AI, for example, or without using AI. For example, the communication unit can input the attribute information data of the AI ​​avatar into a generating AI and have the generating AI execute the communication.

[0049] The communication unit can communicate based on the geographical distribution of the AI ​​avatars. For example, the communication unit can perform optimal communication based on the geographical distribution of the AI ​​avatars. The communication unit can select an appropriate communication method considering the geographical distribution of the AI ​​avatars. The communication unit can perform appropriate communication based on the geographical distribution of the AI ​​avatars. As a result, the communication unit can perform more appropriate communication by considering the geographical distribution of the AI ​​avatars. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the geographical distribution data of the AI ​​avatars into a generating AI and have the generating AI perform the communication.

[0050] The communication unit can improve the accuracy of communication based on the relevant literature for the AI ​​avatar during communication. For example, the communication unit can perform optimal communication based on the relevant literature for the AI ​​avatar. The communication unit can select an appropriate communication method by referring to the relevant literature for the AI ​​avatar. The communication unit can perform appropriate communication based on the relevant literature for the AI ​​avatar. In this way, the communication unit can improve the accuracy of communication by referring to the relevant literature for the AI ​​avatar. Some or all of the above processing in the communication unit may be performed using AI, for example, or without using AI. For example, the communication unit can input the relevant literature data for the AI ​​avatar into a generating AI and have the generating AI perform the improvement of communication accuracy.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The data collection unit can collect user health data in addition to user behavior history and preferences. For example, it can collect data such as heart rate, steps, and sleep patterns from the user's fitness tracker or smartwatch. This allows the data collection unit to understand the user's health status and provide health advice. It can also collect the user's food diary and calorie intake to support healthy eating habits. Furthermore, it can monitor the user's stress level and provide suggestions for stress management. In this way, by collecting user health data, the data collection unit can provide more comprehensive support.

[0053] The communication department can refer to a user's past communication history when an AI avatar communicates with other AI avatars. For example, the communication department analyzes what topics a user has communicated with and with whom in the past, and selects the optimal communication strategy based on that data. This allows the communication department to leverage the user's past communication history to achieve more effective communication. Furthermore, the communication department can learn patterns of successful negotiations and networking from the user's past and reflect those patterns in the AI ​​avatar. In addition, the communication department can adjust the AI ​​avatar's communication style based on the user's past communication history. This allows the communication department to achieve more effective communication by referring to the user's past communication history.

[0054] The data collection unit can collect not only user behavior history and preferences, but also user social media activity. For example, the data collection unit collects data such as what users post on social media and what content they "like" or comment on. This allows the data collection unit to understand users' social media activity and gain a more detailed understanding of their preferences and interests. The data collection unit can also collect information on accounts that users follow and groups they participate in, allowing them to understand users' networks. Furthermore, the data collection unit can analyze users' social media activity patterns and collect data at the optimal time. This allows the data collection unit to gain a more detailed understanding of users' preferences and interests by collecting their social media activity.

[0055] The generation unit can consider the user's cultural background when generating AI avatars based on the user's behavioral history and preferences. For example, the generation unit can adjust the AI ​​avatar's language and accent based on the user's place of origin and upbringing. This allows the generation unit to generate AI avatars that reflect the user's cultural background. The generation unit can also adjust the AI ​​avatar's clothing and gestures based on the user's cultural background. Furthermore, the generation unit can adjust the AI ​​avatar's communication style, taking the user's cultural background into consideration. As a result, by considering the user's cultural background, the generation unit can generate more natural and approachable AI avatars.

[0056] The data collection unit can collect not only user behavior history and preferences, but also user purchase history. For example, the data collection unit collects data on products that users have purchased in the past and analyzes user purchase patterns based on that data. This allows the data collection unit to understand user purchase preferences and suggest the most suitable products to users. The data collection unit can also collect reviews and ratings of products that users have purchased in the past and analyze user satisfaction based on that data. Furthermore, the data collection unit can collect information on related products based on the user's purchase history and provide it to the user. In this way, by collecting user purchase history, the data collection unit can understand more detailed purchase preferences and suggest the most suitable products to users.

[0057] The generation unit can predict the user's future behavior when generating AI avatars based on the user's behavioral history and preferences. For example, the generation unit predicts what actions the user is likely to take in the future based on the user's past behavioral data, and incorporates this prediction data into the generation of the AI ​​avatar. This allows the generation unit to generate AI avatars that predict the user's future behavior. Furthermore, by predicting the user's future behavior, the generation unit can enable the AI ​​avatar to offer suggestions and advice to the user at a more appropriate time. In addition, by predicting the user's future behavior, the generation unit can enable the AI ​​avatar to proactively respond to the user's needs. This allows the generation unit to generate more appropriate AI avatars by predicting the user's future behavior.

[0058] The generation unit can consider the user's life stage when generating AI avatars based on the user's behavioral history and preferences. For example, if the user is a student, the generation unit can generate an AI avatar that provides information related to academics. If the user is a new graduate entering the workforce, it can generate an AI avatar that provides information related to career development. If the user is raising children, it can generate an AI avatar that provides information related to childcare. In this way, the generation unit can generate AI avatars that provide more appropriate information by considering the user's life stage. Furthermore, the generation unit can adjust the appearance and communication style of the AI ​​avatar according to the user's life stage. In this way, the generation unit can generate more approachable AI avatars by considering the user's life stage.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The data collection unit collects user behavior history and preferences. For example, it collects data such as which websites users have visited, what products they have purchased, and what their hobbies are. The data collection unit can collect detailed data to understand user behavior patterns and preferences. Step 2: The generation unit generates an AI avatar based on the data collected by the collection unit. For example, it learns the user's behavioral history and preferences to generate an AI avatar that reflects the user's personality. The generation unit can generate an AI avatar based on the collected data. Step 3: The communication unit enables the AI ​​avatars generated by the generation unit to communicate with other AI avatars. For example, an AI avatar can autonomously gather information, negotiate, and network with other AI avatars. The communication unit enables the AI ​​avatars to communicate with other AI avatars and gather information useful to the user. The AI ​​avatars can negotiate with other AI avatars to secure favorable terms for the user. The AI ​​avatars can network with other AI avatars to build useful connections for the user.

[0061] (Example of form 2) The AI ​​avatar generation system according to an embodiment of the present invention is a system that learns the user's behavioral history and preferences and creates an AI avatar that reflects the user's personality. This AI avatar generation system learns the user's behavioral history and preferences and autonomously communicates with other AI avatars on behalf of the user, enabling information gathering, negotiation, and networking. For example, the AI ​​avatar generation system automatically collects conversations and behavioral history with the user. For example, it collects data such as which websites the user has visited, what products they have purchased, and what hobbies they have. This allows the system to understand the user's behavioral patterns and preferences. Next, based on the collected data, it generates an AI avatar that reflects the user's personality. The AI ​​avatar learns the user's behavioral history and preferences and autonomously communicates with other AI avatars on behalf of the user. For example, if the user is job hunting, the AI ​​avatar can interview AI avatars of companies and find the most suitable company for the user. Also, if the user is looking for a marriage partner, the AI ​​avatar can communicate with other AI avatars and find the most suitable partner for the user. Furthermore, the AI ​​avatar can autonomously gather information, negotiate, and network. For example, an AI avatar can communicate with other AI avatars to gather information useful to the user. It can also negotiate with other AI avatars to secure favorable terms for the user. Furthermore, it can network with other AI avatars to build valuable connections for the user. This system allows users to efficiently achieve optimal matching. For instance, busy business professionals can network effectively within limited timeframes. Students seeking employment can find the perfect company for them. Individuals seeking marriage can find their ideal partner. In this way, utilizing AI agents and AI avatars saves users time and effort, enabling efficient and optimal matching.This allows the AI ​​avatar generation system to learn the user's behavioral history and preferences, and to autonomously communicate with other AI avatars on behalf of the user, enabling information gathering, negotiation, and networking.

[0062] The AI ​​avatar generation system according to this embodiment comprises a collection unit, a generation unit, and a communication unit. The collection unit collects the user's behavioral history and preferences. For example, the collection unit collects data such as which websites the user has visited, what products they have purchased, and what hobbies they have. The collection unit can collect detailed data in order to understand the user's behavioral patterns and preferences. The generation unit generates an AI avatar based on the data collected by the collection unit. For example, the generation unit learns the user's behavioral history and preferences and generates an AI avatar that reflects the user's personality. In order to generate an AI avatar that reflects the user's personality, the generation unit can generate an AI avatar based on the collected data. The communication unit enables the AI ​​avatar generated by the generation unit to communicate with other AI avatars. For example, the communication unit enables the AI ​​avatar to autonomously collect information, negotiate, and network with other AI avatars. The communication unit enables the AI ​​avatar to communicate with other AI avatars and collect information that is useful to the user. The communication department enables AI avatars to negotiate with other AI avatars to secure favorable terms for the user. The communication department also enables AI avatars to network with other AI avatars to build beneficial connections for the user. As a result, the AI ​​avatar generation system according to this embodiment allows AI avatars that have learned the user's behavioral history and preferences to autonomously communicate, gather information, negotiate, and network.

[0063] The data collection unit collects user behavior history and preferences. For example, it collects data such as which websites users visit, what products they purchase, and what their hobbies are. Specifically, it meticulously records details such as the URLs of websites visited, browsing time, clicked links, categories and prices of purchased products, and review content. It also collects information on events users participate in, social media accounts they follow, and their comment and like history. This data is crucial for understanding user behavior patterns and preferences, and the data collection unit centrally manages this data, integrating with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the generation and communication units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance. In addition, the data collection unit ensures security by anonymizing and encrypting data to protect user privacy. This allows users to use the system with peace of mind.

[0064] The generation unit generates AI avatars based on data collected by the collection unit. For example, the generation unit learns the user's behavioral history and preferences to generate AI avatars that reflect the user's personality. Specifically, it uses the collected data to analyze the user's preferences and interests, and customizes the appearance, personality, and speaking style of the AI ​​avatar based on that. For example, if the user likes music, it generates an AI avatar that is knowledgeable about music; if the user likes sports, it generates an AI avatar that is knowledgeable about sports. Furthermore, by learning the user's behavioral patterns, it can predict what kind of information the user will need and when, and generate an AI avatar that can respond accordingly. The generation unit can use AI technology to generate AI avatars that reflect the user's personality based on the collected data. For example, it can use natural language processing technology to learn the characteristics of the user's speech and writing, and set the conversational style of the AI ​​avatar based on that. It can also use image recognition technology to extract physical characteristics from the user's photos and videos, and set the appearance of the AI ​​avatar based on that. This allows the generation unit to generate AI avatars that reflect the user's individuality with high accuracy.

[0065] The Communication Department enables AI avatars generated by the Generation Department to communicate with other AI avatars. For example, the Communication Department allows AI avatars to autonomously gather information, negotiate, and network with other AI avatars. Specifically, AI avatars exchange information with other AI avatars through chat and voice calls, gathering information useful to the user. For example, an AI avatar can gather the latest news and trend information from other AI avatars and provide it to the user. Furthermore, an AI avatar can negotiate with other AI avatars to secure favorable terms for the user. For example, an AI avatar can negotiate prices with other AI avatars to lower the price of products the user purchases. Additionally, an AI avatar can network with other AI avatars to build valuable connections for the user. For example, an AI avatar can collaborate with other AI avatars to connect with experts and influencers in areas of interest to the user. Thus, the Communication Department enables AI avatars to communicate with other AI avatars, gather useful information, negotiate, and network for the user. Furthermore, the communications department can reduce the burden on users and support efficient information gathering, negotiation, and networking by having AI avatars act autonomously on behalf of the users.

[0066] The data collection unit can automatically collect conversation and behavioral history with users. For example, the data collection unit can automatically collect conversations with users, such as text messages, voice conversations, and video calls. In order to automatically collect user behavioral history, the data collection unit can collect data such as which websites users have visited, what products they have purchased, and what their hobbies are. The data collection unit can collect detailed data to understand user behavioral patterns and preferences. This allows the data collection unit to collect detailed information about users' behavioral history and preferences. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input conversational data with users into a generating AI and have the generating AI perform analysis of the conversational data.

[0067] The generation unit can generate an AI avatar that reflects the user's personality based on the collected data. For example, the generation unit can learn the user's behavioral history and preferences and generate an AI avatar that reflects the user's personality. The generation unit can generate an AI avatar based on the collected data in order to generate an AI avatar that reflects the user's personality. For example, the generation unit can generate an AI avatar that reflects the user's personality traits, hobbies and preferences, behavioral patterns, etc. The generation unit can generate an AI avatar based on the collected data in order to generate an AI avatar that reflects the user's personality. In this way, the generation unit can generate an AI avatar that reflects the user's personality. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the collected data into a generation AI and have the generation AI perform the generation of an AI avatar that reflects the user's personality.

[0068] The communication unit enables the generated AI avatar to autonomously gather information, negotiate, and network with other AI avatars. For example, the communication unit allows the AI ​​avatar to autonomously gather information with other AI avatars. The communication unit allows the AI ​​avatar to autonomously negotiate with other AI avatars. The communication unit allows the AI ​​avatar to autonomously network with other AI avatars. This enables the communication unit to enable the AI ​​avatar to autonomously gather information, negotiate, and network. Some or all of the above-described processes in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the generated AI avatar into a generating AI and have the generating AI perform communication with other AI avatars.

[0069] The communication unit allows AI avatars to communicate with other AI avatars and collect information useful to the user. For example, the communication unit allows AI avatars to communicate with other AI avatars and collect useful information such as business intelligence, market research, and technical information. The communication unit allows AI avatars to communicate with other AI avatars and collect information beneficial to the user. In this way, the communication unit allows AI avatars to collect information beneficial to the user. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit allows AI avatars to communicate with other AI avatars and has a generating AI perform information collection.

[0070] The communication department allows AI avatars to negotiate with other AI avatars to obtain terms useful to the user. For example, the communication department allows AI avatars to negotiate prices, terms, contracts, etc., with other AI avatars to obtain favorable terms for the user. The communication department allows AI avatars to negotiate with other AI avatars to obtain favorable terms for the user. This enables the communication department to allow AI avatars to obtain favorable terms for the user. Some or all of the above-described processes in the communication department may be performed using AI, for example, or without AI. For example, the communication department allows AI avatars to negotiate with other AI avatars and has the negotiations performed by a generating AI.

[0071] The communication department allows AI avatars to network with other AI avatars and build useful connections for the user. For example, the communication department allows AI avatars to engage in business networking, social networking, professional networking, etc., with other AI avatars, thereby building beneficial connections for the user. The communication department allows AI avatars to network with other AI avatars and build beneficial connections for the user. This enables the communication department to allow AI avatars to build beneficial connections for the user. Some or all of the above-described processes in the communication department may be performed using AI, for example, or without AI. For example, the communication department allows AI avatars to network with other AI avatars and has the networking performed by a generating AI.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of collecting behavioral history based on the estimated emotions. For example, the data collection unit can obtain more natural data by collecting behavioral history when the user is relaxed. The data collection unit can refrain from collecting data when the user is stressed and attempt to collect it again later. The data collection unit can obtain more detailed data by collecting behavioral history when the user is focused. In this way, the data collection unit can obtain more natural data by adjusting the timing of collecting behavioral history according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0073] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from websites the user has frequently visited in the past. The data collection unit can collect information on related products based on data on products the user has purchased in the past. The data collection unit can analyze the user's past behavior patterns and select the most efficient data collection method. Thus, the data collection unit can select the optimal data collection method by analyzing the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history data into a generating AI and have the generating AI select the optimal data collection method.

[0074] The data collection unit can filter the collected behavioral history based on the user's current lifestyle and areas of interest. For example, the data collection unit can prioritize collecting behavioral history related to topics the user is currently interested in. The data collection unit can filter relevant data based on the user's current lifestyle (work, hobbies, etc.). The data collection unit can efficiently collect data by excluding unnecessary data based on the user's areas of interest. In this way, the data collection unit can efficiently collect data by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of behavioral history to collect based on the estimated user emotions. For example, when the user is excited, the data collection unit can prioritize collecting relevant behavioral history. When the user is relaxed, the data collection unit can collect detailed behavioral history. When the user is stressed, the data collection unit can refrain from collecting data and try again later. In this way, the data collection unit can prioritize the collection of more important data by determining the priority of behavioral history according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The data collection unit can prioritize the collection of highly relevant history based on the user's geographical location when collecting behavioral history. For example, if the user is in a specific region, the data collection unit can prioritize the collection of behavioral history related to that region. If the user is traveling, the data collection unit can prioritize the collection of information about their destination. The data collection unit can filter and efficiently collect relevant data based on the user's current location. This allows the data collection unit to efficiently collect data by prioritizing the collection of highly relevant history based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI and have the generating AI perform the collection of highly relevant history.

[0077] The data collection unit can analyze a user's social media activity and collect relevant history when collecting behavioral history. For example, the data collection unit can collect relevant behavioral history based on information shared by the user on social media. The data collection unit can analyze the user's social media activity patterns and select the optimal collection method. The data collection unit can collect relevant data based on information from accounts the user follows. This allows the data collection unit to efficiently collect relevant history by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant history.

[0078] The generation unit can estimate the user's emotions and adjust the expression of the AI ​​avatar based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate an AI avatar with a calm expression. If the user is excited, the generation unit can generate an AI avatar with an energetic expression. If the user is stressed, the generation unit can generate an AI avatar with a calm expression. In this way, the generation unit can generate a more natural AI avatar by adjusting the expression of the AI ​​avatar according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0079] The generation unit can adjust the level of detail of the AI ​​avatar based on the user's personality during generation. For example, if the user prefers detailed information, the generation unit can generate an AI avatar with detailed features. If the user prefers simple information, the generation unit can generate an AI avatar with simple features. The generation unit can generate an AI avatar with an appropriate level of detail based on the user's personality. In this way, the generation unit can generate a more appropriate AI avatar by adjusting the level of detail of the AI ​​avatar based on the user's personality. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user personality data into a generation AI and have the generation AI perform the level of detail adjustment.

[0080] The generation unit can apply different generation algorithms depending on the user's category during generation. For example, if the user is a business person, the generation unit can apply a business-oriented generation algorithm. If the user is a student, the generation unit can apply an academic-oriented generation algorithm. If the user has a hobby, the generation unit can apply a generation algorithm appropriate to that hobby. In this way, the generation unit can generate a more appropriate AI avatar by applying different generation algorithms depending on the user's category. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0081] The generation unit can estimate the user's emotions and adjust the appearance of the AI ​​avatar based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate an AI avatar with a calm appearance. If the user is excited, the generation unit can generate an AI avatar with an energetic appearance. If the user is stressed, the generation unit can generate an AI avatar with a calm appearance. In this way, the generation unit can generate a more natural AI avatar by adjusting its appearance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0082] The generation unit can determine the priority of AI avatars based on the timing of the user's behavior history submission during generation. For example, the generation unit can determine the priority of AI avatars based on the user's recent actions. The generation unit can determine the priority of AI avatars based on the user's past actions. The generation unit can generate the optimal AI avatar based on the timing of the user's behavior history submission. As a result, the generation unit can generate a more appropriate AI avatar by determining the priority of AI avatars based on the timing of the user's behavior history submission. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user behavior history data into a generation AI and have the generation AI perform the priority determination.

[0083] The generation unit can adjust the order of AI avatars based on user relevance during generation. For example, if a user performs a highly relevant action, the generation unit can adjust the order of AI avatars based on that action. If a user performs a less relevant action, the generation unit can adjust the order of AI avatars based on that action. The generation unit can generate AI avatars in the optimal order based on user relevance. In this way, the generation unit can generate more appropriate AI avatars by adjusting the order of AI avatars based on user relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user relevance data into a generation AI and have the generation AI perform the order adjustment.

[0084] The communication unit can estimate the user's emotions and adjust the communication criteria based on the estimated emotions. For example, if the user is relaxed, the communication unit will communicate calmly. If the user is excited, the communication unit will communicate actively. If the user is stressed, the communication unit will communicate calmly. In this way, the communication unit can communicate more appropriately by adjusting the communication criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The communication unit can improve the accuracy of communication based on the relationships between AI avatars during communication. For example, the communication unit can perform optimal communication based on past interactions between AI avatars. The communication unit can perform appropriate communication by considering the relationships between AI avatars. The communication unit can analyze the relationships between AI avatars and select the optimal communication method. In this way, the communication unit can improve the accuracy of communication by considering the relationships between AI avatars. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input data on the relationships between AI avatars into a generating AI and have the generating AI perform the improvement of communication accuracy.

[0086] The communication unit can communicate based on the attribute information of the AI ​​avatar during communication. For example, the communication unit can communicate appropriately based on the attribute information of the AI ​​avatar (age, gender, etc.). The communication unit can select the optimal communication method by considering the attribute information of the AI ​​avatar. The communication unit can communicate appropriately based on the attribute information of the AI ​​avatar. As a result, the communication unit can communicate more appropriately by considering the attribute information of the AI ​​avatar. Some or all of the above processing in the communication unit may be performed using AI, for example, or without using AI. For example, the communication unit can input the attribute information data of the AI ​​avatar into a generating AI and have the generating AI execute the communication.

[0087] The communication unit can estimate the user's emotions and adjust the order in which it displays communication results based on the estimated emotions. For example, if the user is relaxed, the communication unit can prioritize displaying calm results. If the user is excited, the communication unit can prioritize displaying lively results. If the user is stressed, the communication unit can prioritize displaying calm results. In this way, the communication unit can display more appropriate results by adjusting the order in which it displays communication results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The communication unit can communicate based on the geographical distribution of the AI ​​avatars. For example, the communication unit can perform optimal communication based on the geographical distribution of the AI ​​avatars. The communication unit can select an appropriate communication method considering the geographical distribution of the AI ​​avatars. The communication unit can perform appropriate communication based on the geographical distribution of the AI ​​avatars. As a result, the communication unit can perform more appropriate communication by considering the geographical distribution of the AI ​​avatars. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the geographical distribution data of the AI ​​avatars into a generating AI and have the generating AI perform the communication.

[0089] The communication unit can improve the accuracy of communication based on the relevant literature for the AI ​​avatar during communication. For example, the communication unit can perform optimal communication based on the relevant literature for the AI ​​avatar. The communication unit can select an appropriate communication method by referring to the relevant literature for the AI ​​avatar. The communication unit can perform appropriate communication based on the relevant literature for the AI ​​avatar. In this way, the communication unit can improve the accuracy of communication by referring to the relevant literature for the AI ​​avatar. Some or all of the above processing in the communication unit may be performed using AI, for example, or without using AI. For example, the communication unit can input the relevant literature data for the AI ​​avatar into a generating AI and have the generating AI perform the improvement of communication accuracy.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The data collection unit can collect user health data in addition to user behavior history and preferences. For example, it can collect data such as heart rate, steps, and sleep patterns from the user's fitness tracker or smartwatch. This allows the data collection unit to understand the user's health status and provide health advice. It can also collect the user's food diary and calorie intake to support healthy eating habits. Furthermore, it can monitor the user's stress level and provide suggestions for stress management. In this way, by collecting user health data, the data collection unit can provide more comprehensive support.

[0092] The generation unit can consider the user's past emotional data when generating AI avatars based on the user's behavioral history and preferences. For example, the generation unit analyzes what emotions the user has felt in the past in various situations and reflects that emotional data in the generation of the AI ​​avatar. This allows the generation unit to generate AI avatars that reflect the user's emotional patterns. Furthermore, the generation unit can learn the user's behavioral patterns when they experience specific emotions and reflect those patterns in the AI ​​avatar. In addition, the generation unit can adjust the AI ​​avatar's facial expressions and voice tone based on the user's emotional data. As a result, by considering the user's emotional data, the generation unit can generate more natural and realistic AI avatars.

[0093] The communication department can refer to a user's past communication history when an AI avatar communicates with other AI avatars. For example, the communication department analyzes what topics a user has communicated with and with whom in the past, and selects the optimal communication strategy based on that data. This allows the communication department to leverage the user's past communication history to achieve more effective communication. Furthermore, the communication department can learn patterns of successful negotiations and networking from the user's past and reflect those patterns in the AI ​​avatar. In addition, the communication department can adjust the AI ​​avatar's communication style based on the user's past communication history. This allows the communication department to achieve more effective communication by referring to the user's past communication history.

[0094] The data collection unit can collect not only user behavior history and preferences, but also user social media activity. For example, the data collection unit collects data such as what users post on social media and what content they "like" or comment on. This allows the data collection unit to understand users' social media activity and gain a more detailed understanding of their preferences and interests. The data collection unit can also collect information on accounts that users follow and groups they participate in, allowing them to understand users' networks. Furthermore, the data collection unit can analyze users' social media activity patterns and collect data at the optimal time. This allows the data collection unit to gain a more detailed understanding of users' preferences and interests by collecting their social media activity.

[0095] The generation unit can consider the user's cultural background when generating AI avatars based on the user's behavioral history and preferences. For example, the generation unit can adjust the AI ​​avatar's language and accent based on the user's place of origin and upbringing. This allows the generation unit to generate AI avatars that reflect the user's cultural background. The generation unit can also adjust the AI ​​avatar's clothing and gestures based on the user's cultural background. Furthermore, the generation unit can adjust the AI ​​avatar's communication style, taking the user's cultural background into consideration. As a result, by considering the user's cultural background, the generation unit can generate more natural and approachable AI avatars.

[0096] The communication unit can monitor the user's emotions in real time when an AI avatar communicates with other AI avatars, and adjust the content of the communication based on those emotions. For example, if the user is relaxed, the communication unit can communicate in a calm tone. If the user is excited, it can communicate in an energetic tone. Also, if the user is stressed, it can communicate in a calm tone. In this way, the communication unit can achieve more appropriate communication by adjusting the content of the communication according to the user's emotions. Furthermore, by monitoring the user's emotions, the communication unit can avoid topics that the user may find offensive. In this way, the communication unit can achieve more appropriate communication by monitoring the user's emotions in real time.

[0097] The data collection unit can collect not only user behavior history and preferences, but also user purchase history. For example, the data collection unit collects data on products that users have purchased in the past and analyzes user purchase patterns based on that data. This allows the data collection unit to understand user purchase preferences and suggest the most suitable products to users. The data collection unit can also collect reviews and ratings of products that users have purchased in the past and analyze user satisfaction based on that data. Furthermore, the data collection unit can collect information on related products based on the user's purchase history and provide it to the user. In this way, by collecting user purchase history, the data collection unit can understand more detailed purchase preferences and suggest the most suitable products to users.

[0098] The generation unit can predict the user's future behavior when generating AI avatars based on the user's behavioral history and preferences. For example, the generation unit predicts what actions the user is likely to take in the future based on the user's past behavioral data, and incorporates this prediction data into the generation of the AI ​​avatar. This allows the generation unit to generate AI avatars that predict the user's future behavior. Furthermore, by predicting the user's future behavior, the generation unit can enable the AI ​​avatar to offer suggestions and advice to the user at a more appropriate time. In addition, by predicting the user's future behavior, the generation unit can enable the AI ​​avatar to proactively respond to the user's needs. This allows the generation unit to generate more appropriate AI avatars by predicting the user's future behavior.

[0099] The communication unit can estimate the user's emotions when an AI avatar communicates with another AI avatar, and adjust the timing of communication based on those emotions. For example, the communication unit can achieve more effective communication by initiating communication when the user is relaxed. When the user is stressed, it can refrain from communication and try again later. Also, by communicating when the user is focused, it can gather detailed information. In this way, the communication unit can achieve more effective communication by adjusting the timing of communication according to the user's emotions. Furthermore, by estimating the user's emotions, the communication unit can communicate at the time when the user is most receptive. In this way, the communication unit can achieve more effective communication by estimating the user's emotions.

[0100] The generation unit can consider the user's life stage when generating AI avatars based on the user's behavioral history and preferences. For example, if the user is a student, the generation unit can generate an AI avatar that provides information related to academics. If the user is a new graduate entering the workforce, it can generate an AI avatar that provides information related to career development. If the user is raising children, it can generate an AI avatar that provides information related to childcare. In this way, the generation unit can generate AI avatars that provide more appropriate information by considering the user's life stage. Furthermore, the generation unit can adjust the appearance and communication style of the AI ​​avatar according to the user's life stage. In this way, the generation unit can generate more approachable AI avatars by considering the user's life stage.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The data collection unit collects user behavior history and preferences. For example, it collects data such as which websites users have visited, what products they have purchased, and what their hobbies are. The data collection unit can collect detailed data to understand user behavior patterns and preferences. Step 2: The generation unit generates an AI avatar based on the data collected by the collection unit. For example, it learns the user's behavioral history and preferences to generate an AI avatar that reflects the user's personality. The generation unit can generate an AI avatar based on the collected data. Step 3: The communication unit enables the AI ​​avatars generated by the generation unit to communicate with other AI avatars. For example, an AI avatar can autonomously gather information, negotiate, and network with other AI avatars. The communication unit enables the AI ​​avatars to communicate with other AI avatars and gather information useful to the user. The AI ​​avatars can negotiate with other AI avatars to secure favorable terms for the user. The AI ​​avatars can network with other AI avatars to build useful connections for the user.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] Each of the multiple elements described above, including the collection unit, generation unit, and communication unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's behavior history and preferences using the camera 42 and microphone 38B of the smart device 14, and processes the data with the control unit 46A. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, and generates an AI avatar that reflects the user's personality based on the collected data. The communication unit is implemented in the control unit 46A of the smart device 14, and the generated AI avatar autonomously communicates with other AI avatars. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the collection unit, generation unit, and communication unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's behavior history and preferences using the camera 42 and microphone 238 of the smart glasses 214, and processes the data with the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates an AI avatar that reflects the user's personality based on the collected data. The communication unit is implemented, for example, by the control unit 46A of the smart glasses 214, and the generated AI avatar autonomously communicates with other AI avatars. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the collection unit, generation unit, and communication unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's behavioral history and preferences using the camera 42 and microphone 238 of the headset terminal 314, and processes the data with the control unit 46A. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12, and generates an AI avatar that reflects the user's personality based on the collected data. The communication unit is implemented in the control unit 46A of the headset terminal 314, and the generated AI avatar autonomously communicates with other AI avatars. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the collection unit, generation unit, and communication unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's behavior history and preferences using the camera 42 and microphone 238 of the robot 414, and processes the data with the control unit 46A. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates an AI avatar that reflects the user's personality based on the collected data. The communication unit is implemented, for example, by the control unit 46A of the robot 414, and the generated AI avatar autonomously communicates with other AI avatars. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) A system characterized by comprising: a collection unit that collects the user's behavioral history and preferences; a generation unit that generates an AI avatar based on the data collected by the collection unit; and a communication unit that enables the AI ​​avatar generated by the generation unit to communicate with other AI avatars. (Note 2) The system described in Appendix 1 is characterized in that the collection unit automatically collects conversation and behavioral history with the user. (Note 3) The generating unit is Based on collected data, an AI avatar is generated that reflects the user's personality. The system described in Appendix 1, characterized by the features described herein. (Note 4) The communication unit is the system described in Appendix 1, characterized in that the generated AI avatar autonomously collects information, negotiates, and networks with other AI avatars. (Note 5) The system described in Appendix 1 is characterized in that the communication unit allows AI avatars to communicate with other AI avatars and collects information useful to the user. (Note 6) The aforementioned communication unit is the system described in Appendix 1, characterized in that an AI avatar negotiates with other AI avatars to extract conditions useful to the user. (Note 7) The aforementioned communication unit is the system described in Appendix 1, characterized in that the AI ​​avatar networked with other AI avatars and built a useful network of contacts for the user. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting behavioral history based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The system described in Appendix 1 is characterized in that the collection unit analyzes the user's past behavior history and selects an appropriate collection method. (Note 10) The system according to Appendix 1, characterized in that the collection unit filters the user's current living situation and areas of interest when collecting behavioral history. (Note 11) The system according to Appendix 1, characterized in that the collection unit estimates the user's emotions and determines the priority of behavioral history to be collected based on the estimated user's emotions. (Note 12) The system described in Appendix 1, characterized in that the collection unit prioritizes collecting highly relevant history based on the user's geographical location information when collecting behavioral history. (Note 13) The system described in Appendix 1 is characterized in that the collection unit analyzes the user's social media activity and collects relevant history when collecting behavioral history. (Note 14) The generating unit is It estimates the user's emotions and adjusts the AI ​​avatar's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The system according to Appendix 1, characterized in that the generation unit adjusts the level of detail of the AI ​​avatar based on the user's individuality during generation. (Note 16) The system according to Appendix 1, characterized in that the generation unit applies different generation algorithms depending on the user category during generation. (Note 17) The generating unit is It estimates the user's emotions and adjusts the appearance of the AI ​​avatar based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The system according to Appendix 1, characterized in that the generation unit determines the priority of AI avatars based on the timing of the user's behavior history submission during generation. (Note 19) The system according to Appendix 1, characterized in that the generation unit adjusts the order of AI avatars based on user relevance during generation. (Note 20) The aforementioned communications department, It estimates the user's emotions and adjusts communication standards based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The system described in Appendix 1, wherein the communication unit improves the accuracy of communication based on the interrelationships of AI avatars during communication. (Note 22) The system described in Appendix 1, wherein the communication unit performs communication based on the attribute information of the AI ​​avatar during communication. (Note 23) The aforementioned communications department, It estimates the user's emotions and adjusts the order in which communication results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The system according to Appendix 1, characterized in that the communication unit performs communication based on the geographical distribution of the AI ​​avatar during communication. (Note 25) The system described in Appendix 1, wherein the communication unit improves the accuracy of communication based on relevant literature for the AI ​​avatar during communication. [Explanation of Symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user behavior history and preferences, A generation unit that generates an AI avatar based on the data collected by the aforementioned collection unit, The system includes a communication unit that enables the AI ​​avatar generated by the generation unit to communicate with other AI avatars. A system characterized by the following features.

2. The aforementioned collection unit is Automatically collects user conversation and behavioral history. The system according to feature 1.

3. The generating unit is Based on the collected data, an AI avatar is generated that reflects the user's personality. The system according to feature 1.

4. The aforementioned communications department, The generated AI avatars autonomously gather information, negotiate, and network with other AI avatars. The system according to feature 1.

5. The aforementioned communications department, AI avatars communicate with other AI avatars and collect information useful to the user. The system according to feature 1.

6. The aforementioned communications department, AI avatars negotiate with other AI avatars to extract terms that are useful to the user. The system according to feature 1.

7. The aforementioned communications department, AI avatars network with other AI avatars, building useful connections for the user. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting behavioral history based on those estimated emotions. The system according to feature 1.

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