System

Abstract

An object of the system according to the embodiment is to generate an ideal lover based on the user's preferences and past experience.SOLUTION: A system includes a user information providing unit, a learning unit, and a generation unit. The user information providing unit provides information on the user's preferences and past experiences. The learning unit performs learning based on the information provided by the user information providing unit and data of the real world. The generation part generates an ideal lover on the basis of the information learned by the learning part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have been unable to generate an ideal partner based on a user's preferences and past experiences, and have had the problem of insufficient personalization to meet individual needs.

[0005] The system according to the embodiment aims to generate an ideal partner based on the user's preferences and past experiences. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information providing unit, a learning unit, and a generating unit. The user information providing unit provides information about the user's preferences and past experiences. The learning unit performs learning based on the information provided by the user information providing unit and real-world data. The generating unit generates an ideal partner based on the information learned by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate an ideal partner based on the user's preferences and past experiences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The "My Love Type" system according to an embodiment of the present invention is a system that allows users to create their own ideal partner by utilizing real people in the real world as learning data, and a generation AI generates the ideal partner based on the user's preferences and past experiences. In this way, the "My Love Type" system allows users to obtain their ideal partner based on their preferences and past experiences.

[0029] The "My Love Type" system according to the embodiment includes a user information providing unit, a learning unit, and a generation unit. The user information providing unit provides information about the user's preferences and past experiences. For example, the user may provide information such as "I like guys with black hair and a kind personality." The user information providing unit may also collect information about the user's past romantic experiences and specific events. The learning unit performs learning based on the information provided by the user information providing unit and real-world data. For example, the learning unit may analyze photos, videos, and text data of many people to extract characteristics of appearance and personality. The learning unit may also combine and use the information provided by the user with real-world data. The generation unit generates an ideal lover based on the information learned by the learning unit. For example, the generation unit generates a lover with black hair and a kind personality based on information such as "I like guys with black hair and a kind personality." The generation unit may also generate a customized lover based on the user's preferences and past experiences. This allows the "My Love Type" system according to the embodiment to generate an ideal lover based on the user's preferences and past experiences.

[0030] The user information providing unit can analyze the user's past SNS posts and message history to automatically extract more detailed preferences and experiences. The user information providing unit, for example, analyzes the user's past SNS posts and automatically extracts information about preferences and experiences. For example, it can identify specific hobbies and interests from the content and photos frequently posted by the user. The user information providing unit can also analyze the message history to extract the user's past experiences and emotions. For example, it can analyze the events and emotions the user mentioned in messages with friends. This makes it possible to extract detailed preferences and experiences from the user's past SNS posts and message history.

[0031] The user information providing unit can provide feedback in real time to the information provided by the user, thereby improving the accuracy of the information. For example, when the user provides information, the generation AI provides feedback in real time to improve the accuracy of the information. For example, if the user says, "My favorite color is blue," the generation AI asks, "Which specific shade of blue do you like?" The user information providing unit can also use a chatbot to provide feedback in real time. For example, it can respond immediately to the information provided by the user, improving the accuracy of the information. This makes it possible to improve the accuracy of the information provided by the user in real time.

[0032] The user information providing unit can collect a wider variety of information using voice input and image input. The user information providing unit, for example, collects information using voice input when the user provides information. For example, the unit analyzes the voice of the user talking about a favorite movie and extracts the movie title and genre. The user information providing unit can also collect information using image input. For example, the user provides a photo of a favorite landscape, and the photo is analyzed to extract information. This allows a wider variety of information to be collected using voice input and image input.

[0033] The user information providing unit can anonymously share information provided by a user with other users and match users who share common preferences and experiences. The user information providing unit, for example, builds a system that anonymously shares information provided by a user and matches users who share common preferences and experiences. For example, it automatically matches users who share the same hobbies. The user information providing unit can also protect the privacy of users by using data anonymization technology. For example, it anonymizes the user's personal information and protects the shared information. This makes it possible to match users who share common preferences and experiences.

[0034] The learning unit can take into account the user's geographical location information and reflect regional characteristics when collecting real-world data. For example, the learning unit can take into account the user's geographical location information and reflect regional characteristics when collecting real-world data. For example, the learning unit can include the culture and customs of a specific region in the learning data. The learning unit can also use GPS data or location information services to obtain geographical location information. For example, the learning unit can collect region-specific data based on the user's location information. This allows the learning unit to reflect regional characteristics by taking into account the geographical location information.

[0035] The learning unit can take into account temporal changes when analyzing real-world data and learn trends and seasonal fluctuations. For example, the learning unit can take into account temporal changes when analyzing real-world data and learn trends and seasonal fluctuations. For example, the learning unit can learn data on seasonal fashion and events. The learning unit can also use an algorithm for analyzing temporal changes. For example, the learning unit can learn based on trend changes and seasonal data. This makes it possible to learn trends and seasonal fluctuations while taking into account temporal changes.

[0036] The learning unit can compare real-world data with data from different cultural spheres or countries to learn from a global perspective. For example, the learning unit can compare real-world data with data from different cultural spheres or countries to learn from a global perspective. For example, the learning data can include the food cultures and lifestyles of different countries. The learning unit can also use algorithms for learning from a global perspective. For example, the learning can be based on international trends and cultural differences. This allows learning from a global perspective.

[0037] The learning unit can integrate real-world data with other datasets to perform more multifaceted learning. For example, the learning unit can integrate real-world data with other datasets to perform more multifaceted learning. For example, movie and music data can be included in the learning data. The learning unit can also use algorithms to integrate other datasets. For example, different data sources can be integrated and analyzed from multiple perspectives. This allows for more multifaceted learning.

[0038] The generation unit can fine-tune the appearance and personality of the generated lover based on real-time feedback from the user. For example, the generation unit fine-tunes the appearance and personality of the generated lover based on real-time feedback from the user. For example, if the user says, "I wish my hair was a little shorter," the generation unit adjusts the appearance in accordance with that request. The generation unit can also use an algorithm for fine-tuning the appearance and personality based on real-time feedback. For example, the generation unit can instantly reflect the user's feedback. This allows the generation unit to fine-tune the appearance and personality of the generated lover based on real-time feedback from the user.

[0039] The generation unit can customize the appearance and personality of the generated lover based on the user's past romantic experiences and emotions. The generation unit, for example, customizes the appearance and personality of the generated lover based on the user's past romantic experiences and emotions. For example, if the user says, "I like the personality of my past lover," the generation unit reflects that personality. The generation unit can also use an algorithm for customizing the appearance and personality based on the user's past romantic experiences and emotions. For example, the generation unit analyzes the user's past romantic experiences and generates an optimal appearance and personality. This makes it possible to customize the appearance and personality of the generated lover based on the user's past romantic experiences and emotions.

[0040] The generation unit can simulate the appearance and personality of the generated lover in different scenarios and contexts to find the optimal combination. The generation unit, for example, simulates the appearance and personality of the generated lover in different scenarios and contexts to find the optimal combination. For example, the generation unit performs simulations in dating scenarios and everyday life scenarios. The generation unit can also use an algorithm to simulate the appearance and personality based on the scenario and context. For example, the generation unit performs simulations using virtual reality or computer simulation. This makes it possible to simulate the appearance and personality of the generated lover in different scenarios and contexts to find the optimal combination.

[0041] The generation unit can share the generated lover with other users and improve it based on feedback. The generation unit, for example, builds a system for sharing the generated lover with other users and improving it based on feedback. For example, the generation unit collects opinions and impressions from other users and improves the generated lover. The generation unit can also use an algorithm for making improvements based on feedback. For example, improvements are made based on user ratings and comments. This allows the generated lover to be shared with other users and improved based on feedback.

[0042] The generation unit can provide a more realistic experience by interacting with the user using voice or video chat. For example, the generation unit can provide a more realistic experience by interacting with the user using voice or video chat. For example, the generation unit can respond in real time using voice recognition or video analysis. The generation unit can also use technology for using voice or video chat. For example, the generation unit can perform interaction using voice recognition technology or video streaming technology. This can provide a more realistic experience using voice or video chat.

[0043] The generation unit can provide enjoyment by interacting with the user through a game or simulation. For example, the generation unit can provide enjoyment by interacting with the user through a game or simulation. For example, the generation unit can interact with the user through a dating simulation game. The generation unit can also use technology for using games or simulations. For example, the generation unit can provide interaction using a role-playing game or a simulation game. This makes it possible to provide enjoyment through games or simulations.

[0044] The generation unit can share interactions with a user with other users and match users who share common interests. The generation unit, for example, builds a system that shares interactions with a user with other users and matches users who share common interests. For example, users who share the same hobbies are automatically matched. The generation unit can also use an algorithm to match users who share common interests. For example, matching is performed based on common hobbies or event participation history. This makes it possible to match users who share common interests.

[0045] The generation unit can provide various experiences by performing interactions with the user on different devices. For example, the generation unit can perform interactions with the user on different devices to provide various experiences. For example, the generation unit can perform interactions using a smartphone or a VR device. The generation unit can also use technology for using different devices. For example, the generation unit can perform interactions using a smartphone app or a VR app. This makes it possible to provide various experiences on different devices.

[0046] The generation unit can reflect user feedback in real time during continuous learning and make improvements immediately. The generation unit can reflect user feedback in real time during continuous learning and make improvements immediately. For example, if a user says, "I want this part changed," the request is reflected immediately. The generation unit can also use technology to reflect feedback in real time. For example, real-time data analysis and real-time updates are used to make improvements. This allows user feedback to be reflected in real time and improvements to be made immediately.

[0047] The generation unit can adaptively perform learning by taking into account long-term changes and trends of the user during continuous learning. For example, the generation unit adaptively performs learning by taking into account long-term changes and trends of the user during continuous learning. For example, if the user's preferences change over time, the change is reflected in the learning data. The generation unit can also use an algorithm for analyzing long-term changes and trends. For example, learning is performed based on changes in the user's behavioral patterns and long-term fluctuations in interests. This allows adaptive learning by taking into account long-term changes and trends of the user.

[0048] The generation unit can integrate data from different users during continuous learning and learn common patterns and trends. For example, the generation unit can integrate data from different users during continuous learning and learn common patterns and trends. For example, the generation unit can analyze the preferences and behavioral patterns of multiple users and extract common features. The generation unit can also use an algorithm for learning common patterns and trends. For example, the generation unit can learn based on frequently used words and common behavioral patterns. This allows the generation unit to integrate data from different users and learn common patterns and trends.

[0049] The generation unit can refer to other datasets during continuous learning to perform more multifaceted learning. For example, the generation unit can refer to other datasets (e.g., psychology or sociology data) during continuous learning to perform more multifaceted learning. For example, the generation unit can analyze user behavior based on psychology research data. The generation unit can also use an algorithm for referencing other datasets. For example, different data sources can be integrated and analyzed from multiple perspectives. This allows the generation unit to refer to other datasets to perform more multifaceted learning.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The user information providing unit can collect the user's health data and reflect it in generating an ideal partner. For example, data on the user's exercise habits and dietary habits can be collected to generate a partner with a healthy lifestyle. The user information providing unit can also generate a partner with common health goals based on the user's health data. This makes it possible to generate an ideal partner that reflects the user's health data.

[0052] The user information providing unit can generate lovers who share common hobbies based on the user's hobbies and interests. For example, if the user is interested in music or sports, the unit can generate lovers who share those hobbies. The user information providing unit can also collect data on the user's hobbies and interests and use an algorithm to generate lovers who share common hobbies. This makes it possible to generate lovers who share common hobbies.

[0053] The user information providing unit can generate lovers who share a common occupation or career based on the user's occupation or career. For example, if the user works in the medical field, the unit generates lovers who share that occupation. The user information providing unit can also collect data on the user's occupation or career and use an algorithm to generate lovers who share a common occupation or career. This makes it possible to generate lovers who share a common occupation or career.

[0054] The user information providing unit can analyze the user's travel history and generate lovers who share common travel destinations and interests. For example, based on travel destinations and places of interest that the user has visited in the past, lovers who share those places are generated. The user information providing unit can also collect data on the user's travel history and use an algorithm to generate lovers who share common travel destinations and interests. This makes it possible to generate lovers who share common travel destinations and interests.

[0055] The user information providing unit can generate lovers who share a common lifestyle based on the user's lifestyle. For example, if the user goes to bed early and gets up early, the unit generates lovers who share that lifestyle. The user information providing unit can also collect data about the user's lifestyle and use an algorithm to generate lovers who share a common lifestyle. This makes it possible to generate lovers who share a common lifestyle.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user information providing unit provides information about the user's preferences and past experiences. For example, the user provides information such as "I like guys with black hair and kind personalities." The user information providing unit can also collect information about the user's past romantic experiences and specific events. Step 2: The learning unit performs learning based on the information provided by the user information providing unit and real-world data. For example, the learning unit analyzes photos, videos, and text data of many people to extract characteristics of appearance and personality. The learning unit can also combine information provided by the user with real-world data. Step 3: The generation unit generates an ideal lover based on the information learned by the learning unit. For example, based on information such as "I like people with black hair and a kind personality," the generation unit generates a lover with black hair and a kind personality. The generation unit can also generate a customized lover based on the user's preferences and past experiences.

[0058] (Example 2) The "My Love Type" system according to an embodiment of the present invention is a system that allows users to create their own ideal partner by utilizing real people in the real world as learning data, and a generation AI generates the ideal partner based on the user's preferences and past experiences. In this way, the "My Love Type" system allows users to obtain their ideal partner based on their preferences and past experiences.

[0059] The "My Love Type" system according to the embodiment includes a user information providing unit, a learning unit, and a generation unit. The user information providing unit provides information about the user's preferences and past experiences. For example, the user may provide information such as "I like guys with black hair and a kind personality." The user information providing unit may also collect information about the user's past romantic experiences and specific events. The learning unit performs learning based on the information provided by the user information providing unit and real-world data. For example, the learning unit may analyze photos, videos, and text data of many people to extract characteristics of appearance and personality. The learning unit may also combine and use the information provided by the user with real-world data. The generation unit generates an ideal lover based on the information learned by the learning unit. For example, the generation unit generates a lover with black hair and a kind personality based on information such as "I like guys with black hair and a kind personality." The generation unit may also generate a customized lover based on the user's preferences and past experiences. This allows the "My Love Type" system according to the embodiment to generate an ideal lover based on the user's preferences and past experiences.

[0060] The user information providing unit can perform sentiment analysis on information provided by the user and evaluate the importance of the information based on the intensity and type of emotion. For example, the user information providing unit uses a generation AI to perform sentiment analysis on information provided by the user and quantify the intensity and type of emotion. For example, if the user says, "This event was very enjoyable," the emotion score is set high and the importance is evaluated. The user information providing unit can also use an algorithm to evaluate the importance of information based on the intensity and type of emotion. For example, it prioritizes evaluation of information with a high emotional intensity. This makes it possible to evaluate the importance of information based on the user's emotions.

[0061] The user information providing unit can analyze the user's past SNS posts and message history to automatically extract more detailed preferences and experiences. The user information providing unit, for example, analyzes the user's past SNS posts and automatically extracts information about preferences and experiences. For example, it can identify specific hobbies and interests from the content and photos frequently posted by the user. The user information providing unit can also analyze the message history to extract the user's past experiences and emotions. For example, it can analyze the events and emotions the user mentioned in messages with friends. This makes it possible to extract detailed preferences and experiences from the user's past SNS posts and message history.

[0062] The user information providing unit can provide feedback in real time to the information provided by the user, thereby improving the accuracy of the information. For example, when the user provides information, the generation AI provides feedback in real time to improve the accuracy of the information. For example, if the user says, "My favorite color is blue," the generation AI asks, "Which specific shade of blue do you like?" The user information providing unit can also use a chatbot to provide feedback in real time. For example, it can respond immediately to the information provided by the user, improving the accuracy of the information. This makes it possible to improve the accuracy of the information provided by the user in real time.

[0063] The user information providing unit can collect a wider variety of information using voice input and image input. The user information providing unit, for example, collects information using voice input when the user provides information. For example, the unit analyzes the voice of the user talking about a favorite movie and extracts the movie title and genre. The user information providing unit can also collect information using image input. For example, the user provides a photo of a favorite landscape, and the photo is analyzed to extract information. This allows a wider variety of information to be collected using voice input and image input.

[0064] The user information providing unit can anonymously share information provided by a user with other users and match users who share common preferences and experiences. The user information providing unit, for example, builds a system that anonymously shares information provided by a user and matches users who share common preferences and experiences. For example, it automatically matches users who share the same hobbies. The user information providing unit can also protect the privacy of users by using data anonymization technology. For example, it anonymizes the user's personal information and protects the shared information. This makes it possible to match users who share common preferences and experiences.

[0065] The user information providing unit can use the emotion estimation function to estimate the emotion of the user when providing information in real time and make suggestions that elicit positive emotions. For example, the user information providing unit can use the emotion estimation function to estimate the emotion of the user when providing information in real time and make suggestions that elicit positive emotions. For example, if the user says, "I'm a little anxious," the generation AI can suggest, "Why not try listening to some relaxing music?" The user information providing unit can also use the emotion estimation function to analyze the user's emotions and use an algorithm to elicit positive emotions. For example, it can make suggestions based on the user's emotion score. This makes it possible to estimate the user's emotions in real time and make suggestions that elicit positive emotions.

[0066] The learning unit can perform sentiment analysis on real-world data and preferentially learn emotionally positive data. The learning unit, for example, performs sentiment analysis on real-world data and preferentially learns data with positive emotions. For example, it preferentially analyzes data that includes smiling photos and positive comments. The learning unit can also use an algorithm for selecting emotionally positive data. For example, it selects data based on positive reviews and favorable comments. This makes it possible to preferentially learn emotionally positive data.

[0067] The learning unit can take into account the user's geographical location information and reflect regional characteristics when collecting real-world data. For example, the learning unit can take into account the user's geographical location information and reflect regional characteristics when collecting real-world data. For example, the learning unit can include the culture and customs of a specific region in the learning data. The learning unit can also use GPS data or location information services to obtain geographical location information. For example, the learning unit can collect region-specific data based on the user's location information. This allows the learning unit to reflect regional characteristics by taking into account the geographical location information.

[0068] The learning unit can take into account temporal changes when analyzing real-world data and learn trends and seasonal fluctuations. For example, the learning unit can take into account temporal changes when analyzing real-world data and learn trends and seasonal fluctuations. For example, the learning unit can learn data on seasonal fashion and events. The learning unit can also use an algorithm for analyzing temporal changes. For example, the learning unit can learn based on trend changes and seasonal data. This makes it possible to learn trends and seasonal fluctuations while taking into account temporal changes.

[0069] The learning unit can compare real-world data with data from different cultural spheres or countries to learn from a global perspective. For example, the learning unit can compare real-world data with data from different cultural spheres or countries to learn from a global perspective. For example, the learning data can include the food cultures and lifestyles of different countries. The learning unit can also use algorithms for learning from a global perspective. For example, the learning can be based on international trends and cultural differences. This allows learning from a global perspective.

[0070] The learning unit can integrate real-world data with other datasets to perform more multifaceted learning. For example, the learning unit can integrate real-world data with other datasets to perform more multifaceted learning. For example, movie and music data can be included in the learning data. The learning unit can also use algorithms to integrate other datasets. For example, different data sources can be integrated and analyzed from multiple perspectives. This allows for more multifaceted learning.

[0071] The generation unit can perform sentiment analysis on the appearance and personality of the generated lover and identify the combination that will evoke the most positive emotions from the user. For example, the generation unit can perform sentiment analysis on the appearance and personality of the generated lover and identify the combination that will evoke the most positive emotions from the user. For example, if the user states, "I like this appearance," that appearance is preferentially generated. The generation unit can also use an algorithm for sentiment analysis. For example, the generation unit can identify the optimal combination based on the user's sentiment score. This can identify the combination that will evoke the most positive emotions from the user.

[0072] The generation unit can fine-tune the appearance and personality of the generated lover based on real-time feedback from the user. For example, the generation unit fine-tunes the appearance and personality of the generated lover based on real-time feedback from the user. For example, if the user says, "I wish my hair was a little shorter," the generation unit adjusts the appearance in accordance with that request. The generation unit can also use an algorithm for fine-tuning the appearance and personality based on real-time feedback. For example, the generation unit can instantly reflect the user's feedback. This allows the generation unit to fine-tune the appearance and personality of the generated lover based on real-time feedback from the user.

[0073] The generation unit can customize the appearance and personality of the generated lover based on the user's past romantic experiences and emotions. The generation unit, for example, customizes the appearance and personality of the generated lover based on the user's past romantic experiences and emotions. For example, if the user says, "I like the personality of my past lover," the generation unit reflects that personality. The generation unit can also use an algorithm for customizing the appearance and personality based on the user's past romantic experiences and emotions. For example, the generation unit analyzes the user's past romantic experiences and generates an optimal appearance and personality. This makes it possible to customize the appearance and personality of the generated lover based on the user's past romantic experiences and emotions.

[0074] The generation unit can simulate the appearance and personality of the generated lover in different scenarios and contexts to find the optimal combination. The generation unit, for example, simulates the appearance and personality of the generated lover in different scenarios and contexts to find the optimal combination. For example, the generation unit performs simulations in dating scenarios and everyday life scenarios. The generation unit can also use an algorithm to simulate the appearance and personality based on the scenario and context. For example, the generation unit performs simulations using virtual reality or computer simulation. This makes it possible to simulate the appearance and personality of the generated lover in different scenarios and contexts to find the optimal combination.

[0075] The generation unit can share the generated lover with other users and improve it based on feedback. The generation unit, for example, builds a system for sharing the generated lover with other users and improving it based on feedback. For example, the generation unit collects opinions and impressions from other users and improves the generated lover. The generation unit can also use an algorithm for making improvements based on feedback. For example, improvements are made based on user ratings and comments. This allows the generated lover to be shared with other users and improved based on feedback.

[0076] The generation unit can use the emotion estimation function to monitor the user's emotional reactions to the generated lover in real time and continuously generate the optimal lover. The generation unit can, for example, use the emotion estimation function to monitor the user's emotional reactions to the generated lover in real time and continuously generate the optimal lover. For example, the generation unit can analyze the user's facial expressions and voice and calculate an emotion score. The generation unit can also use sensor technology and real-time data analysis to monitor the emotional reactions in real time. For example, the generation unit can instantly analyze the user's emotional reactions and generate the optimal lover. This allows the generation unit to monitor the user's emotional reactions in real time and continuously generate the optimal lover.

[0077] The generation unit can perform sentiment analysis on interactions with a user and generate a response that corresponds to the user's emotions. For example, if a user says, "I'm tired today," the generation AI responds, "I'll suggest ways to relax." The generation unit can also use an algorithm for sentiment analysis. For example, it can generate an optimal response based on the user's sentiment score. This makes it possible to generate a response that corresponds to the user's emotions.

[0078] The generation unit can provide a more realistic experience by interacting with the user using voice or video chat. For example, the generation unit can provide a more realistic experience by interacting with the user using voice or video chat. For example, the generation unit can respond in real time using voice recognition or video analysis. The generation unit can also use technology for using voice or video chat. For example, the generation unit can perform interaction using voice recognition technology or video streaming technology. This can provide a more realistic experience using voice or video chat.

[0079] The generation unit can provide enjoyment by interacting with the user through a game or simulation. For example, the generation unit can provide enjoyment by interacting with the user through a game or simulation. For example, the generation unit can interact with the user through a dating simulation game. The generation unit can also use technology for using games or simulations. For example, the generation unit can provide interaction using a role-playing game or a simulation game. This makes it possible to provide enjoyment through games or simulations.

[0080] The generation unit can share interactions with a user with other users and match users who share common interests. The generation unit, for example, builds a system that shares interactions with a user with other users and matches users who share common interests. For example, users who share the same hobbies are automatically matched. The generation unit can also use an algorithm to match users who share common interests. For example, matching is performed based on common hobbies or event participation history. This makes it possible to match users who share common interests.

[0081] The generation unit can provide various experiences by performing interactions with the user on different devices. For example, the generation unit can perform interactions with the user on different devices to provide various experiences. For example, the generation unit can perform interactions using a smartphone or a VR device. The generation unit can also use technology for using different devices. For example, the generation unit can perform interactions using a smartphone app or a VR app. This makes it possible to provide various experiences on different devices.

[0082] The generation unit can use the emotion estimation function to analyze emotions during interactions with the user in real time and generate the optimal response. For example, the generation unit can use the emotion estimation function to analyze emotions during interactions with the user in real time and generate the optimal response. For example, if the user says "I'm sad," the generation AI can respond with "What's wrong?" The generation unit can also use sensor technology and real-time data analysis to analyze emotions in real time. For example, it can analyze the user's facial expressions and voice and generate a response based on the emotion score. This makes it possible to analyze emotions during interactions with the user in real time and generate the optimal response.

[0083] The generation unit can perform sentiment analysis on interaction data with a user and preferentially learn emotionally positive data. For example, the generation unit can perform sentiment analysis on interaction data with a user and preferentially learn data with positive emotions. For example, the generation unit can preferentially learn interaction data in which the user stated that the interaction was "fun." The generation unit can also use an algorithm for selecting emotionally positive data. For example, the generation unit can select data based on positive reviews and favorable comments. This allows the generation unit to preferentially learn emotionally positive interaction data.

[0084] The generation unit can reflect user feedback in real time during continuous learning and make improvements immediately. The generation unit can reflect user feedback in real time during continuous learning and make improvements immediately. For example, if a user says, "I want this part changed," the request is reflected immediately. The generation unit can also use technology to reflect feedback in real time. For example, real-time data analysis and real-time updates are used to make improvements. This allows user feedback to be reflected in real time and improvements to be made immediately.

[0085] The generation unit can adaptively perform learning by taking into account long-term changes and trends of the user during continuous learning. For example, the generation unit adaptively performs learning by taking into account long-term changes and trends of the user during continuous learning. For example, if the user's preferences change over time, the change is reflected in the learning data. The generation unit can also use an algorithm for analyzing long-term changes and trends. For example, learning is performed based on changes in the user's behavioral patterns and long-term fluctuations in interests. This allows adaptive learning by taking into account long-term changes and trends of the user.

[0086] The generation unit can integrate data from different users during continuous learning and learn common patterns and trends. For example, the generation unit can integrate data from different users during continuous learning and learn common patterns and trends. For example, the generation unit can analyze the preferences and behavioral patterns of multiple users and extract common features. The generation unit can also use an algorithm for learning common patterns and trends. For example, the generation unit can learn based on frequently used words and common behavioral patterns. This allows the generation unit to integrate data from different users and learn common patterns and trends.

[0087] The generation unit can refer to other datasets during continuous learning to perform more multifaceted learning. For example, the generation unit can refer to other datasets (e.g., psychology or sociology data) during continuous learning to perform more multifaceted learning. For example, the generation unit can analyze user behavior based on psychology research data. The generation unit can also use an algorithm for referencing other datasets. For example, different data sources can be integrated and analyzed from multiple perspectives. This allows the generation unit to refer to other datasets to perform more multifaceted learning.

[0088] The generation unit uses the emotion estimation function to monitor the user's emotional reactions during continuous learning in real time, thereby enabling continuous optimal learning. The generation unit, for example, uses the emotion estimation function to monitor the user's emotional reactions during continuous learning in real time, thereby enabling continuous optimal learning. For example, the generation unit may analyze the user's facial expressions and voice and calculate an emotion score. The generation unit may also use sensor technology or real-time data analysis to monitor emotional reactions in real time. For example, the generation unit may instantly analyze the user's emotional reactions and adjust the learning content. This allows the user's emotional reactions to be monitored in real time, thereby enabling continuous optimal learning.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The user information providing unit can collect the user's health data and reflect it in generating an ideal partner. For example, data on the user's exercise habits and dietary habits can be collected to generate a partner with a healthy lifestyle. The user information providing unit can also generate a partner with common health goals based on the user's health data. This makes it possible to generate an ideal partner that reflects the user's health data.

[0091] The user information providing unit can generate lovers who share common hobbies based on the user's hobbies and interests. For example, if the user is interested in music or sports, the unit can generate lovers who share those hobbies. The user information providing unit can also collect data on the user's hobbies and interests and use an algorithm to generate lovers who share common hobbies. This makes it possible to generate lovers who share common hobbies.

[0092] The user information providing unit can generate lovers who share a common occupation or career based on the user's occupation or career. For example, if the user works in the medical field, the unit generates lovers who share that occupation. The user information providing unit can also collect data on the user's occupation or career and use an algorithm to generate lovers who share a common occupation or career. This makes it possible to generate lovers who share a common occupation or career.

[0093] The user information providing unit can analyze the user's travel history and generate lovers who share common travel destinations and interests. For example, based on travel destinations and places of interest that the user has visited in the past, lovers who share those places are generated. The user information providing unit can also collect data on the user's travel history and use an algorithm to generate lovers who share common travel destinations and interests. This makes it possible to generate lovers who share common travel destinations and interests.

[0094] The user information providing unit can generate lovers who share a common lifestyle based on the user's lifestyle. For example, if the user goes to bed early and gets up early, the unit generates lovers who share that lifestyle. The user information providing unit can also collect data about the user's lifestyle and use an algorithm to generate lovers who share a common lifestyle. This makes it possible to generate lovers who share a common lifestyle.

[0095] The user information providing unit can estimate the user's emotions and provide a relaxing environment for the user based on the estimated emotions. For example, if the user is feeling stressed, it can provide relaxing music or videos. The user information providing unit can also use an emotion estimation function to analyze the user's emotions and use an algorithm to provide a relaxing environment. This makes it possible to provide a relaxing environment based on the user's emotions.

[0096] The user information providing unit can estimate the user's emotions and suggest activities that the user can enjoy based on the estimated emotions. For example, if the user is bored, the unit can suggest activities that will interest the user. The user information providing unit can also use an emotion estimation function to analyze the user's emotions and use an algorithm to suggest activities that the user can enjoy. This makes it possible to suggest activities that the user can enjoy based on their emotions.

[0097] The user information providing unit can estimate the user's emotions and provide a reassuring message to the user based on the estimated emotions. For example, if the user is feeling anxious, the unit can send a reassuring message. The user information providing unit can also use an emotion estimation function to analyze the user's emotions and use an algorithm to provide a reassuring message. This makes it possible to provide a reassuring message based on the user's emotions.

[0098] The user information providing unit can estimate the user's emotions and make suggestions to cheer up the user based on the estimated emotions. For example, if the user is feeling down, the unit can suggest activities or messages to cheer up the user. The user information providing unit can also use an emotion estimation function to analyze the user's emotions and use an algorithm to make suggestions to cheer up the user. This makes it possible to make suggestions to cheer up the user based on the user's emotions.

[0099] The user information providing unit can estimate the user's emotion and suggest ways for the user to refresh themselves based on the estimated emotion. For example, if the user is tired, the unit can suggest ways for the user to refresh themselves. The user information providing unit can also use an emotion estimation function to analyze the user's emotion and use an algorithm to suggest ways for the user to refresh themselves. This makes it possible to suggest ways for the user to refresh themselves based on the user's emotion.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The user information providing unit provides information about the user's preferences and past experiences. For example, the user provides information such as "I like guys with black hair and kind personalities." The user information providing unit can also collect information about the user's past romantic experiences and specific events. Step 2: The learning unit performs learning based on the information provided by the user information providing unit and real-world data. For example, the learning unit analyzes photos, videos, and text data of many people to extract characteristics of appearance and personality. The learning unit can also combine information provided by the user with real-world data. Step 3: The generation unit generates an ideal lover based on the information learned by the learning unit. For example, based on information such as "I like people with black hair and a kind personality," the generation unit generates a lover with black hair and a kind personality. The generation unit can also generate a customized lover based on the user's preferences and past experiences.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0146] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] 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.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user information providing unit that provides information about a user's preferences and past experiences; a learning unit that performs learning based on the information provided by the user information providing unit and real-world data; A generation unit that generates an ideal lover based on the information learned by the learning unit. A system characterized by:

2. The user information providing unit Analyze users' past SNS posts and message history to automatically extract more detailed preferences and experiences.

2. The system of claim 1.

3. The learning unit Sentiment analysis is performed on real-world data, and emotionally positive data is given priority for learning.

2. The system of claim 1.

4. The generation unit A sentiment analysis is performed on the appearance and personality of the created lover, and the combination that gives the user the most positive sentiment is identified.

2. The system of claim 1.

5. The generation unit Performing a sentiment analysis on the interaction with the user and generating a response according to the sentiment of the user.

2. The system of claim 1.

6. The user information providing unit Use voice and image input to collect more diverse information.

2. The system of claim 1.

7. The learning unit Integrate real-world data with other datasets to conduct more diversified learning 2. The system of claim 1.

8. The generation unit Using an emotion estimation function, the user's emotional reactions during continuous learning are monitored in real time, and optimal learning is performed continuously.

2. The system of claim 1.

Images