system

Abstract

The system according to the embodiment aims to provide an appropriate personality AI based on the type of person the user wants to talk to. [Solution] A system according to an embodiment includes a reception unit, a generation unit, and a conversation unit. The reception unit receives a request from a user for a character they would like to talk to. The generation unit creates an appropriate personality AI based on the request received by the reception unit. The conversation unit initiates a conversation between the personality AI created by the generation unit and the user.

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 techniques have had the problem of making it difficult to provide an appropriate conversation partner based on the image of the person the user wants to talk to.

[0005] The system according to the embodiment aims to provide an appropriate personality AI based on the type of person the user wants to talk to. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a conversation unit. The reception unit receives a request from a user for a character they would like to talk to. The generation unit creates an appropriate personality AI based on the request received by the reception unit. The conversation unit starts a conversation between the personality AI created by the generation unit and the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide an appropriate personality AI based on the type of person the user wants to talk to. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A conversation system according to an embodiment of the present invention accepts a user's request for a character they would like to talk to, and a generation AI creates an appropriate personality AI based on the request and begins a conversation with the user. The conversation system responds to the need to talk to someone when lonely or bored. For example, the conversation system allows a user to request a specific character they would like to talk to. The user can input a specific character description and characteristics. For example, a request could be "a kind old man" or "a lively friend." This information is then input into the generation AI. The generation AI then analyzes the input information and creates an appropriate personality AI. The generation AI learns from the information provider's chat data, call data, photos, videos, etc. to create a personality AI of a real person. For example, by learning the information provider's message data, it is possible to reproduce that person's language, voice, and facial expressions. The generated personality AI then begins a conversation with the user. For example, when a user asks, "How was your day?", the AI ​​personality responds by reproducing the information provider's language and facial expressions. This allows users to experience conversations as if they were having a conversation with a real person. This allows the conversation system to talk to someone when users feel lonely or bored. For example, by using this service when users want to talk to someone, such as when they are alone in the middle of the night or during a long wait, they can easily enjoy a conversation. Furthermore, by creating an AI personality based on a real person, users can enjoy a more realistic conversational experience. Furthermore, because this service provides conversations based on a specific persona, it can provide a personalized experience tailored to the user's needs. For example, if a user wants to talk to a "cheerful friend," an AI personality based on the user's request is created and a conversation begins with the user. In this way, users can enjoy conversations tailored to their mood and situation.

[0029] A conversation system according to an embodiment includes a reception unit, a generation unit, and a conversation unit. The reception unit receives a request from a user for a character they would like to talk to. The user can input, for example, a specific character description and characteristics. For example, a request such as "a kind old man" or "a lively friend" is possible. The generation unit uses a generation AI to create an appropriate personality AI based on the request received by the reception unit. The generation AI learns, for example, chat data, call data, photos, videos, etc., of an information provider to create a personality AI of a real person. For example, the generation AI can reproduce the information provider's language, voice, and facial expressions by learning the information provider's message data. The conversation unit initiates a conversation between the personality AI created by the generation unit and the user. For example, the conversation unit responds to questions input by the user while reproducing the information provider's language and facial expressions. As a result, the conversation system according to an embodiment can create an appropriate personality AI based on the user's request and initiate a conversation.

[0030] The generation unit can learn the informant's chat data, call data, photos, and videos to create a personality AI of a real person. The generation unit can, for example, learn the informant's chat data and reproduce that person's language and expressions. For example, the generation unit analyzes text messages and voice messages to learn the informant's language. The generation unit can also learn the informant's call data and reproduce that person's voice and speaking style. For example, the generation unit analyzes voice call and video call data to learn the informant's tone of voice and intonation. The generation unit can also learn the informant's photos and videos to reproduce that person's facial expressions and movements. For example, the generation unit analyzes still images, sequential photos, short videos, and long videos to learn the informant's facial expressions and movements. In this way, the generation unit can create a personality AI of a real person by learning the informant's data.

[0031] The conversation unit can respond to questions input by the user while reproducing the information provider's language or facial expression. The conversation unit, for example, responds to questions input by the user while reproducing the information provider's language. For example, the conversation unit reproduces the information provider's tone of voice, vocabulary, and grammar. The conversation unit can also respond to questions input by the user while reproducing the information provider's facial expression. For example, the conversation unit reproduces the information provider's smile, anger, surprise, and other facial expressions. In this way, the conversation unit can provide a more realistic conversation experience by reproducing the information provider's language and facial expression.

[0032] The reception unit can accept the user's input of a specific person image and characteristics. The reception unit, for example, accepts the user's input of a specific person image and characteristics. For example, the user can input information such as age, gender, occupation, and hobbies. This allows the reception unit to provide a more personalized conversation experience by allowing the user to input a specific person image and characteristics.

[0033] The generation unit may include an algorithm that creates an appropriate personality AI based on a user request. The generation unit may include, for example, an algorithm that creates an appropriate personality AI based on a user request. For example, the generation unit may use a machine learning algorithm or a rule-based algorithm to create an appropriate personality AI based on a user request. This allows the generation unit to create an appropriate personality AI based on a user request.

[0034] The conversation unit can take specific security measures to protect the user's privacy. The conversation unit, for example, takes specific security measures to protect the user's privacy. For example, the conversation unit takes security measures such as data encryption, access control, and the formulation of a privacy policy. This allows the conversation unit to take security measures to protect the user's privacy.

[0035] The reception unit can analyze the user's past request history and suggest an appropriate request method. The reception unit, for example, analyzes the user's past request history and suggests an appropriate request method. For example, it automatically displays as candidates images of people that the user has requested in the past. It also analyzes the characteristics of images of people that the user has preferred in the past and suggests similar images of people. It also predicts and suggests images of people that the user will prefer at specific time periods from the user's past request history. In this way, the reception unit can suggest the optimal request method by analyzing the user's past request history.

[0036] The reception unit can filter the request content based on the user's current psychological state and areas of interest when receiving a request. For example, the reception unit filters the request content based on the user's current psychological state and areas of interest when receiving a request. For example, the reception unit proposes a character image related to a topic in which the user is currently interested. Furthermore, the reception unit filters and proposes a character image that is relaxing or stimulating according to the user's psychological state. Furthermore, the reception unit proposes a character image with related expertise based on the user's current areas of interest. In this way, the reception unit can propose more appropriate character images by filtering the request content based on the user's psychological state and areas of interest.

[0037] The reception unit can select an appropriate reception means depending on the user's input method when receiving a request. For example, the reception unit selects an appropriate reception means depending on the user's input method when receiving a request. For example, if the user selects voice input, the request is received using voice recognition technology. If the user selects text input, the request is received using text analysis technology. If the user uploads an image, the request is received using image analysis technology. In this way, the reception unit can select the optimal reception means depending on the user's input method, thereby improving the efficiency of request reception.

[0038] The reception unit can prioritize receiving highly relevant requests in consideration of the user's geographical location information when receiving a request. For example, the reception unit prioritizes receiving highly relevant requests in consideration of the user's geographical location information when receiving a request. For example, if the user is in a specific area, it prioritizes suggesting a person image related to that area. Also, if the user is traveling, it prioritizes suggesting a person image related to the travel destination. Also, if the user is at home, it prioritizes suggesting a person image that makes the user feel relaxed. In this way, the reception unit can prioritize receiving highly relevant requests by considering the user's geographical location information.

[0039] The reception unit can analyze the user's social media activity when receiving a request and suggest related requests. For example, the reception unit can analyze the user's social media activity when receiving a request and suggest related requests. For example, the reception unit can suggest a person profile related to a topic in which the user has shown interest on social media. The reception unit can also analyze the content of the user's social media posts and suggest related person profiles. The reception unit can also suggest related person profiles based on the activity of the user's friends on social media. This allows the reception unit to suggest related requests by analyzing the user's social media activity.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a request. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving a request. For example, the reception unit preferentially suggests request methods that the user has preferred in the past. The reception unit also customizes the request reception interface based on the user's past feedback. The reception unit also analyzes the user's past feedback and suggests the optimal request method. In this way, the reception unit can customize the reception method by reflecting the user's past feedback.

[0041] The generation unit can adjust the level of detail of the generation based on the importance of the information provider's data at the time of generation. For example, the generation unit adjusts the level of detail of the generation based on the importance of the information provider's data at the time of generation. For example, if the information provider has a wealth of chat data, a detailed personality AI is generated. Also, if the information provider has a wealth of call data, the reproduction of the human voice is increased. Also, if the information provider has a wealth of photos and videos, the reproduction of facial expressions is increased. In this way, the generation unit can generate a more detailed personality AI by adjusting the level of detail of the generation based on the importance of the information provider's data.

[0042] The generation unit can apply different generation algorithms depending on the category of the information provider during generation. For example, the generation unit applies different generation algorithms depending on the category of the information provider during generation. For example, if the information provider is a friend, an approachable algorithm is applied. Also, if the information provider is a family member, an intimate algorithm is applied. Also, if the information provider is a coworker, a businesslike algorithm is applied. In this way, the generation unit can generate a more appropriate personality AI by applying different generation algorithms depending on the category of the information provider.

[0043] The generation unit can improve the accuracy of generation by referring to the user's past generation results at the time of generation. The generation unit, for example, improves the accuracy of generation by referring to the user's past generation results at the time of generation. For example, the generation unit improves the accuracy of generation by reflecting the characteristics of personality AI that the user has preferred in the past. In addition, the generation unit extracts areas for improvement from the user's past generation results and improves the accuracy of generation. In addition, the generation unit improves accuracy by adjusting the generation algorithm based on the user's past feedback. In this way, the generation unit can improve the accuracy of generation by referring to the user's past generation results.

[0044] The generation unit can determine the generation priority based on the time of submission of the information provider's data at the time of generation. The generation unit, for example, determines the generation priority based on the time of submission of the information provider's data at the time of generation. For example, the generation unit generates a personality AI by using the latest data preferentially. In addition, older data is also taken into consideration and generation is performed to achieve an overall balance. In addition, data importance is adjusted according to the time of submission. In this way, the generation unit can prioritize the use of the latest data by determining the generation priority based on the time of submission of the information provider's data.

[0045] The generation unit can adjust the order of generation based on the relevance of the information provider at the time of generation. The generation unit adjusts the order of generation based on the relevance of the information provider at the time of generation, for example. For example, if the information provider is an important person to the user, the data is used preferentially. Also, if the information provider is a friend of the user, the data is used preferentially. Also, if the information provider is a family member of the user, the data is used preferentially. In this way, the generation unit can use more important data preferentially by adjusting the order of generation based on the relevance of the information provider.

[0046] The generation unit may adjust the use of technical terminology in the generated speech depending on the user's level of expertise during generation. For example, the generation unit adjusts the use of technical terminology in the generated speech depending on the user's level of expertise during generation. For example, if the user has specialized knowledge, the generation unit uses a lot of technical terminology. On the other hand, if the user is a beginner, the generation unit avoids technical terminology and uses simple words. The generation unit also adjusts the use of appropriate technical terminology depending on the user's level of expertise. In this way, the generation unit can provide a more appropriate conversation experience by adjusting the use of technical terminology depending on the user's level of expertise.

[0047] The conversation unit can adjust the degree of reproduction of the information provider's language and facial expressions during a conversation. The conversation unit, for example, adjusts the degree of reproduction of the information provider's language and facial expressions during a conversation. For example, the conversation unit faithfully reproduces the information provider's language. Furthermore, the conversation unit reproduces the information provider's facial expressions in real time. Furthermore, the conversation unit reproduces the tone of voice and intonation of the information provider. In this way, the conversation unit can provide a more realistic conversation experience by adjusting the degree of reproduction of the information provider's language and facial expressions.

[0048] The conversation unit can improve the accuracy of the conversation by referring to the user's past conversation history during the conversation. For example, the conversation unit improves the accuracy of the conversation by referring to the user's past conversation history during the conversation. For example, the conversation unit provides related topics based on what the user has said in the past. The conversation unit also extracts and provides preferred topics from the user's past conversation history. The conversation unit also analyzes the user's past conversation history to smooth the flow of the conversation. In this way, the conversation unit can improve the accuracy of the conversation by referring to the user's past conversation history.

[0049] The conversation unit can customize the conversation content based on the user's current psychological state during a conversation. For example, the conversation unit customizes the conversation content based on the user's current psychological state during a conversation. For example, if the user is relaxed, a calm topic is provided. If the user is excited, a lively topic is provided. If the user is feeling sad, a comforting topic is provided. In this way, the conversation unit can provide a more appropriate conversation experience by customizing the conversation content based on the user's current psychological state.

[0050] The conversation unit can provide optimal conversation content by taking into account the user's geographical location information during a conversation. The conversation unit, for example, provides optimal conversation content by taking into account the user's geographical location information during a conversation. For example, if the user is in a specific area, topics related to that area are provided. Also, if the user is traveling, topics related to the travel destination are provided. Also, if the user is at home, topics that will help the user relax are provided. In this way, the conversation unit can provide optimal conversation content by taking into account the user's geographical location information.

[0051] The conversation unit can analyze the user's social media activity during a conversation and suggest related conversation content. The conversation unit, for example, analyzes the user's social media activity during a conversation and suggests related conversation content. For example, the conversation unit provides topics related to topics in which the user has shown interest on social media. The conversation unit can also provide related topics by analyzing the content posted by the user on social media. The conversation unit can also provide related topics by taking into account the activities of the user's friends on social media. In this way, the conversation unit can suggest related conversation content by analyzing the user's social media activity.

[0052] The conversation unit can customize the content of the conversation by reflecting the user's past feedback during the conversation. For example, the conversation unit customizes the content of the conversation by reflecting the user's past feedback during the conversation. For example, it provides topics that the user has previously preferred preferentially. It also customizes the tone and style of the conversation based on the user's past feedback. It also analyzes the user's past feedback and suggests optimal content of the conversation. In this way, the conversation unit can customize the content of the conversation by reflecting the user's past feedback.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When accepting a user's request, the accepting unit can refer to the user's past conversation history and automatically complete the request content. For example, if the user previously requested to talk to a "cheerful friend," a similar person profile will be automatically suggested the next time the user makes a request. Also, if the user has previously liked a particular topic, it is possible to suggest a person profile related to that topic. Furthermore, it is possible to analyze the characteristics of the person profile requested by the user in the past and suggest more specific request content. In this way, the accepting unit can utilize the user's past conversation history to realize more personalized request acceptance.

[0055] The generation unit can reflect the user's past feedback in the personality AI that is generated based on the user's request. For example, a new personality AI can be generated by reflecting the characteristics of personality AI that the user previously preferred. It can also generate a new personality AI by improving points that the user previously dissatisfied with. Furthermore, it is possible to analyze the user's past feedback and apply the optimal generation algorithm. In this way, the generation unit can utilize the user's past feedback to generate a personality AI that is more satisfying.

[0056] The conversation unit not only reproduces the language and facial expressions of the information provider in response to questions entered by the user, but also customizes the content of the response based on the user's current areas of interest. For example, it can provide information related to topics that the user is currently interested in. Also, if the user has specific hobbies or interests, it can provide topics related to those hobbies or interests. Furthermore, it can suggest people with relevant expertise based on the user's current areas of interest. This allows the conversation unit to provide answers that are tailored to the user's areas of interest, creating a more interesting conversation experience.

[0057] The generation unit can reflect the user's geographical location information in the personality AI generated based on the user's request. For example, if the user is in a specific area, a character image related to that area can be generated. Also, if the user is traveling, a character image related to the travel destination can be generated. Furthermore, if the user is at home, a character image that makes them feel relaxed can be generated. In this way, the generation unit can provide a more appropriate conversation experience by generating a personality AI according to the user's geographical location information.

[0058] The conversation unit not only reproduces the language and facial expressions of the information provider in response to questions entered by the user, but also customizes the content of the response by referring to the user's past conversation history. For example, it can provide related topics based on what the user has previously talked about. It can also provide topics that the user has previously preferred preferentially. It can also analyze the user's past conversation history to smooth the flow of the conversation. In this way, the conversation unit can utilize the user's past conversation history to provide a more personalized conversation experience.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives a request from the user for the type of person they would like to talk to. The user can input a specific person's image and characteristics. For example, a request could be "a kind old man" or "a lively friend." Step 2: The generation unit uses the generation AI to create an appropriate personality AI based on the request received by the reception unit. The generation AI studies the information provider's chat data, call data, photos, videos, etc., to create a personality AI of a real person. For example, by studying the information provider's message data, the generation AI can reproduce that person's language, voice, and facial expressions. Step 3: The conversation unit initiates a conversation between the user and the personality AI created by the generation unit. The conversation unit responds to questions entered by the user by reproducing the wording and facial expressions of the informant.

[0061] (Example 2) A conversation system according to an embodiment of the present invention accepts a user's request for a character they would like to talk to, and a generation AI creates an appropriate personality AI based on the request and begins a conversation with the user. The conversation system responds to the need to talk to someone when lonely or bored. For example, the conversation system allows a user to request a specific character they would like to talk to. The user can input a specific character description and characteristics. For example, a request could be "a kind old man" or "a lively friend." This information is then input into the generation AI. The generation AI then analyzes the input information and creates an appropriate personality AI. The generation AI learns from the information provider's chat data, call data, photos, videos, etc. to create a personality AI of a real person. For example, by learning the information provider's message data, it is possible to reproduce that person's language, voice, and facial expressions. The generated personality AI then begins a conversation with the user. For example, when a user asks, "How was your day?", the AI ​​personality responds by reproducing the information provider's language and facial expressions. This allows users to experience conversations as if they were having a conversation with a real person. This allows the conversation system to talk to someone when users feel lonely or bored. For example, by using this service when users want to talk to someone, such as when they are alone in the middle of the night or during a long wait, they can easily enjoy a conversation. Furthermore, by creating an AI personality based on a real person, users can enjoy a more realistic conversational experience. Furthermore, because this service provides conversations based on a specific persona, it can provide a personalized experience tailored to the user's needs. For example, if a user wants to talk to a "cheerful friend," an AI personality based on the user's request is created and a conversation begins with the user. In this way, users can enjoy conversations tailored to their mood and situation.

[0062] A conversation system according to an embodiment includes a reception unit, a generation unit, and a conversation unit. The reception unit receives a request from a user for a character they would like to talk to. The user can input, for example, a specific character description and characteristics. For example, a request such as "a kind old man" or "a lively friend" is possible. The generation unit uses a generation AI to create an appropriate personality AI based on the request received by the reception unit. The generation AI learns, for example, chat data, call data, photos, videos, etc., of an information provider to create a personality AI of a real person. For example, the generation AI can reproduce the information provider's language, voice, and facial expressions by learning the information provider's message data. The conversation unit initiates a conversation between the personality AI created by the generation unit and the user. For example, the conversation unit responds to questions input by the user while reproducing the information provider's language and facial expressions. As a result, the conversation system according to an embodiment can create an appropriate personality AI based on the user's request and initiate a conversation.

[0063] The generation unit can learn the informant's chat data, call data, photos, and videos to create a personality AI of a real person. The generation unit can, for example, learn the informant's chat data and reproduce that person's language and expressions. For example, the generation unit analyzes text messages and voice messages to learn the informant's language. The generation unit can also learn the informant's call data and reproduce that person's voice and speaking style. For example, the generation unit analyzes voice call and video call data to learn the informant's tone of voice and intonation. The generation unit can also learn the informant's photos and videos to reproduce that person's facial expressions and movements. For example, the generation unit analyzes still images, sequential photos, short videos, and long videos to learn the informant's facial expressions and movements. In this way, the generation unit can create a personality AI of a real person by learning the informant's data.

[0064] The conversation unit can respond to questions input by the user while reproducing the information provider's language or facial expression. The conversation unit, for example, responds to questions input by the user while reproducing the information provider's language. For example, the conversation unit reproduces the information provider's tone of voice, vocabulary, and grammar. The conversation unit can also respond to questions input by the user while reproducing the information provider's facial expression. For example, the conversation unit reproduces the information provider's smile, anger, surprise, and other facial expressions. In this way, the conversation unit can provide a more realistic conversation experience by reproducing the information provider's language and facial expression.

[0065] The reception unit can accept the user's input of a specific person image and characteristics. The reception unit, for example, accepts the user's input of a specific person image and characteristics. For example, the user can input information such as age, gender, occupation, and hobbies. This allows the reception unit to provide a more personalized conversation experience by allowing the user to input a specific person image and characteristics.

[0066] The generation unit may include an algorithm that creates an appropriate personality AI based on a user request. The generation unit may include, for example, an algorithm that creates an appropriate personality AI based on a user request. For example, the generation unit may use a machine learning algorithm or a rule-based algorithm to create an appropriate personality AI based on a user request. This allows the generation unit to create an appropriate personality AI based on a user request.

[0067] The conversation unit can take specific security measures to protect the user's privacy. The conversation unit, for example, takes specific security measures to protect the user's privacy. For example, the conversation unit takes security measures such as data encryption, access control, and the formulation of a privacy policy. This allows the conversation unit to take security measures to protect the user's privacy.

[0068] The reception unit can estimate the user's emotions and adjust the requested content of the character they want to talk to based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the requested content of the character they want to talk to based on the estimated user emotions. For example, if the user feels lonely, the reception unit adjusts the requested content to a character with a kind personality. If the user feels stressed, the reception unit adjusts the requested content to a character that makes them feel relaxed. If the user wants to have fun, the reception unit adjusts the requested content to a character with a humorous personality. In this way, the reception unit can propose a more appropriate character by adjusting the requested content based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The reception unit can analyze the user's past request history and suggest an appropriate request method. The reception unit, for example, analyzes the user's past request history and suggests an appropriate request method. For example, it automatically displays as candidates images of people that the user has requested in the past. It also analyzes the characteristics of images of people that the user has preferred in the past and suggests similar images of people. It also predicts and suggests images of people that the user will prefer at specific time periods from the user's past request history. In this way, the reception unit can suggest the optimal request method by analyzing the user's past request history.

[0070] The reception unit can filter the request content based on the user's current psychological state and areas of interest when receiving a request. For example, the reception unit filters the request content based on the user's current psychological state and areas of interest when receiving a request. For example, the reception unit proposes a character image related to a topic in which the user is currently interested. Furthermore, the reception unit filters and proposes a character image that is relaxing or stimulating according to the user's psychological state. Furthermore, the reception unit proposes a character image with related expertise based on the user's current areas of interest. In this way, the reception unit can propose more appropriate character images by filtering the request content based on the user's psychological state and areas of interest.

[0071] The reception unit can select an appropriate reception means depending on the user's input method when receiving a request. For example, the reception unit selects an appropriate reception means depending on the user's input method when receiving a request. For example, if the user selects voice input, the request is received using voice recognition technology. If the user selects text input, the request is received using text analysis technology. If the user uploads an image, the request is received using image analysis technology. In this way, the reception unit can select the optimal reception means depending on the user's input method, thereby improving the efficiency of request reception.

[0072] The reception unit can estimate the user's emotion and determine the priority of requests based on the estimated user emotion. The reception unit, for example, estimates the user's emotion and determines the priority of requests based on the estimated user emotion. For example, if the user feels very lonely, the reception unit processes the request with the highest priority. Also, if the user feels stressed, the reception unit processes the request with the highest priority. Also, if the user feels like having fun, the reception unit processes the request with the highest priority. In this way, the reception unit can determine the priority of requests based on the user's emotion, thereby processing more appropriate requests with the highest priority. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The reception unit can prioritize receiving highly relevant requests in consideration of the user's geographical location information when receiving a request. For example, the reception unit prioritizes receiving highly relevant requests in consideration of the user's geographical location information when receiving a request. For example, if the user is in a specific area, it prioritizes suggesting a person image related to that area. Also, if the user is traveling, it prioritizes suggesting a person image related to the travel destination. Also, if the user is at home, it prioritizes suggesting a person image that makes the user feel relaxed. In this way, the reception unit can prioritize receiving highly relevant requests by considering the user's geographical location information.

[0074] The reception unit can analyze the user's social media activity when receiving a request and suggest related requests. For example, the reception unit can analyze the user's social media activity when receiving a request and suggest related requests. For example, the reception unit can suggest a person profile related to a topic in which the user has shown interest on social media. The reception unit can also analyze the content of the user's social media posts and suggest related person profiles. The reception unit can also suggest related person profiles based on the activity of the user's friends on social media. This allows the reception unit to suggest related requests by analyzing the user's social media activity.

[0075] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a request. For example, the reception unit customizes the reception method by reflecting the user's past feedback when receiving a request. For example, the reception unit preferentially suggests request methods that the user has preferred in the past. The reception unit also customizes the request reception interface based on the user's past feedback. The reception unit also analyzes the user's past feedback and suggests the optimal request method. In this way, the reception unit can customize the reception method by reflecting the user's past feedback.

[0076] The generation unit can estimate the user's emotions and adjust the expression method of the personality AI to be generated based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the expression method of the personality AI to be generated based on the estimated user emotions. For example, if the user is relaxed, a calm expression method is used. If the user is excited, a lively expression method is used. If the user is sad, a comforting expression method is used. In this way, the generation unit can provide a more appropriate conversation experience by adjusting the expression method of the personality AI based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0077] The generation unit can adjust the level of detail of the generation based on the importance of the information provider's data at the time of generation. For example, the generation unit adjusts the level of detail of the generation based on the importance of the information provider's data at the time of generation. For example, if the information provider has a wealth of chat data, a detailed personality AI is generated. Also, if the information provider has a wealth of call data, the reproduction of the human voice is increased. Also, if the information provider has a wealth of photos and videos, the reproduction of facial expressions is increased. In this way, the generation unit can generate a more detailed personality AI by adjusting the level of detail of the generation based on the importance of the information provider's data.

[0078] The generation unit can apply different generation algorithms depending on the category of the information provider during generation. For example, the generation unit applies different generation algorithms depending on the category of the information provider during generation. For example, if the information provider is a friend, an approachable algorithm is applied. Also, if the information provider is a family member, an intimate algorithm is applied. Also, if the information provider is a coworker, a businesslike algorithm is applied. In this way, the generation unit can generate a more appropriate personality AI by applying different generation algorithms depending on the category of the information provider.

[0079] The generation unit can improve the accuracy of generation by referring to the user's past generation results at the time of generation. The generation unit, for example, improves the accuracy of generation by referring to the user's past generation results at the time of generation. For example, the generation unit improves the accuracy of generation by reflecting the characteristics of personality AI that the user has preferred in the past. In addition, the generation unit extracts areas for improvement from the user's past generation results and improves the accuracy of generation. In addition, the generation unit improves accuracy by adjusting the generation algorithm based on the user's past feedback. In this way, the generation unit can improve the accuracy of generation by referring to the user's past generation results.

[0080] The generation unit can estimate the user's emotions and adjust the length of the personality AI to be generated based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the personality AI to be generated based on the estimated user emotions. For example, if the user is in a hurry, the generation unit adjusts the length so that the conversation is completed in a short time. Alternatively, if the user is relaxed, the generation unit adjusts the length so that the conversation can be enjoyed for a long time. Alternatively, if the user is excited, the generation unit adjusts the length so that the conversation has a fast tempo. In this way, the generation unit can provide a more appropriate conversation experience by adjusting the length of the personality AI based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The generation unit can determine the generation priority based on the time of submission of the information provider's data at the time of generation. The generation unit, for example, determines the generation priority based on the time of submission of the information provider's data at the time of generation. For example, the generation unit generates a personality AI by using the latest data preferentially. In addition, older data is also taken into consideration and generation is performed to achieve an overall balance. In addition, data importance is adjusted according to the time of submission. In this way, the generation unit can prioritize the use of the latest data by determining the generation priority based on the time of submission of the information provider's data.

[0082] The generation unit can adjust the order of generation based on the relevance of the information provider at the time of generation. The generation unit adjusts the order of generation based on the relevance of the information provider at the time of generation, for example. For example, if the information provider is an important person to the user, the data is used preferentially. Also, if the information provider is a friend of the user, the data is used preferentially. Also, if the information provider is a family member of the user, the data is used preferentially. In this way, the generation unit can use more important data preferentially by adjusting the order of generation based on the relevance of the information provider.

[0083] The generation unit may adjust the use of technical terminology in the generated speech depending on the user's level of expertise during generation. For example, the generation unit adjusts the use of technical terminology in the generated speech depending on the user's level of expertise during generation. For example, if the user has specialized knowledge, the generation unit uses a lot of technical terminology. On the other hand, if the user is a beginner, the generation unit avoids technical terminology and uses simple words. The generation unit also adjusts the use of appropriate technical terminology depending on the user's level of expertise. In this way, the generation unit can provide a more appropriate conversation experience by adjusting the use of technical terminology depending on the user's level of expertise.

[0084] The conversation unit can estimate the user's emotions and adjust the way the conversation is expressed based on the estimated user emotions. For example, the conversation unit estimates the user's emotions and adjusts the way the conversation is expressed based on the estimated user emotions. For example, if the user is relaxed, the conversation may proceed in a calm tone. If the user is excited, the conversation may proceed in a lively tone. If the user is sad, the conversation may proceed in a comforting tone. This allows the conversation unit to provide a more appropriate conversation experience by adjusting the way the conversation is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0085] The conversation unit can adjust the degree of reproduction of the information provider's language and facial expressions during a conversation. The conversation unit, for example, adjusts the degree of reproduction of the information provider's language and facial expressions during a conversation. For example, the conversation unit faithfully reproduces the information provider's language. Furthermore, the conversation unit reproduces the information provider's facial expressions in real time. Furthermore, the conversation unit reproduces the tone of voice and intonation of the information provider. In this way, the conversation unit can provide a more realistic conversation experience by adjusting the degree of reproduction of the information provider's language and facial expressions.

[0086] The conversation unit can improve the accuracy of the conversation by referring to the user's past conversation history during the conversation. For example, the conversation unit improves the accuracy of the conversation by referring to the user's past conversation history during the conversation. For example, the conversation unit provides related topics based on what the user has said in the past. The conversation unit also extracts and provides preferred topics from the user's past conversation history. The conversation unit also analyzes the user's past conversation history to smooth the flow of the conversation. In this way, the conversation unit can improve the accuracy of the conversation by referring to the user's past conversation history.

[0087] The conversation unit can customize the conversation content based on the user's current psychological state during a conversation. For example, the conversation unit customizes the conversation content based on the user's current psychological state during a conversation. For example, if the user is relaxed, a calm topic is provided. If the user is excited, a lively topic is provided. If the user is feeling sad, a comforting topic is provided. In this way, the conversation unit can provide a more appropriate conversation experience by customizing the conversation content based on the user's current psychological state.

[0088] The conversation unit can estimate the user's emotions and adjust the length of the conversation based on the estimated user emotions. The conversation unit, for example, estimates the user's emotions and adjusts the length of the conversation based on the estimated user emotions. For example, if the user is in a hurry, the conversation is adjusted to be completed in a short time. Alternatively, if the user is relaxed, the conversation is adjusted to be enjoyable for a long time. Alternatively, if the user is excited, the conversation is adjusted to provide a fast-paced conversation. In this way, the conversation unit can provide a more appropriate conversation experience by adjusting the length of the conversation based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The conversation unit can provide optimal conversation content by taking into account the user's geographical location information during a conversation. The conversation unit, for example, provides optimal conversation content by taking into account the user's geographical location information during a conversation. For example, if the user is in a specific area, topics related to that area are provided. Also, if the user is traveling, topics related to the travel destination are provided. Also, if the user is at home, topics that will help the user relax are provided. In this way, the conversation unit can provide optimal conversation content by taking into account the user's geographical location information.

[0090] The conversation unit can analyze the user's social media activity during a conversation and suggest related conversation content. The conversation unit, for example, analyzes the user's social media activity during a conversation and suggests related conversation content. For example, the conversation unit provides topics related to topics in which the user has shown interest on social media. The conversation unit can also provide related topics by analyzing the content posted by the user on social media. The conversation unit can also provide related topics by taking into account the activities of the user's friends on social media. In this way, the conversation unit can suggest related conversation content by analyzing the user's social media activity.

[0091] The conversation unit can customize the content of the conversation by reflecting the user's past feedback during the conversation. For example, the conversation unit customizes the content of the conversation by reflecting the user's past feedback during the conversation. For example, it provides topics that the user has previously preferred preferentially. It also customizes the tone and style of the conversation based on the user's past feedback. It also analyzes the user's past feedback and suggests optimal content of the conversation. In this way, the conversation unit can customize the content of the conversation by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and conversation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives a request from the user for a character image to talk to. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an appropriate personality AI using the generation AI. The conversation unit is realized by the control unit 46A of the smart device 14 and starts a conversation between the generated personality AI and the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and conversation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a request from the user for a character image to talk to. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an appropriate personality AI using the generation AI. The conversation unit is realized by the control unit 46A of the smart glasses 214 and starts a conversation between the generated personality AI and the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and conversation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives a request from the user for a character image to talk to. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an appropriate personality AI using the generation AI. The conversation unit is realized by the control unit 46A of the headset type terminal 314 and starts a conversation between the generated personality AI and the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and conversation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives a request from the user for a character image to talk to. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates an appropriate personality AI using the generation AI. The conversation unit is realized by the control unit 46A of the robot 414 and starts a conversation between the generated personality AI and the user.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] When accepting a user's request, the accepting unit can refer to the user's past conversation history and automatically complete the request content. For example, if the user previously requested to talk to a "cheerful friend," a similar person profile will be automatically suggested the next time the user makes a request. Also, if the user has previously liked a particular topic, it is possible to suggest a person profile related to that topic. Furthermore, it is possible to analyze the characteristics of the person profile requested by the user in the past and suggest more specific request content. In this way, the accepting unit can utilize the user's past conversation history to realize more personalized request acceptance.

[0094] The generation unit can reflect the user's current psychological state in the personality AI generated based on the user's request. For example, if the user is relaxed, a personality AI with a calm personality can be generated. Also, if the user is excited, a personality AI with a lively personality can be generated. Furthermore, if the user is feeling sad, a personality AI with a comforting personality can be generated. In this way, the generation unit can provide a more appropriate conversation experience by generating a personality AI according to the user's psychological state.

[0095] The conversation unit not only reproduces the language and facial expressions of the information provider in response to questions entered by the user, but also adjusts the tone and content of the response according to the user's emotions. For example, if the user is relaxed, the conversation unit can respond in a calm tone. If the user is excited, the conversation unit can respond in a lively tone. Furthermore, if the user is feeling sad, the conversation unit can respond in a comforting tone. This allows the conversation unit to provide answers that correspond to the user's emotions, thereby realizing a more realistic conversation experience.

[0096] When a user inputs a request, the reception unit can accept not only voice input and text input, but also gesture input and input methods using facial expression recognition. For example, if the user smiles, it can be determined that the user wishes to have a pleasant conversation. Also, if the user frowns, it can be determined that the user is feeling stressed, and a relaxed image can be suggested. Furthermore, a specific request can be input by the user making a gesture such as waving their hand. This allows the reception unit to accommodate a variety of user input methods, thereby realizing more intuitive request reception.

[0097] The generation unit can reflect the user's past feedback in the personality AI that is generated based on the user's request. For example, a new personality AI can be generated by reflecting the characteristics of personality AI that the user previously preferred. It can also generate a new personality AI by improving points that the user previously dissatisfied with. Furthermore, it is possible to analyze the user's past feedback and apply the optimal generation algorithm. In this way, the generation unit can utilize the user's past feedback to generate a personality AI that is more satisfying.

[0098] The conversation unit not only reproduces the language and facial expressions of the information provider in response to questions entered by the user, but also customizes the content of the response based on the user's current areas of interest. For example, it can provide information related to topics that the user is currently interested in. Also, if the user has specific hobbies or interests, it can provide topics related to those hobbies or interests. Furthermore, it can suggest people with relevant expertise based on the user's current areas of interest. This allows the conversation unit to provide answers that are tailored to the user's areas of interest, creating a more interesting conversation experience.

[0099] The reception unit can estimate the user's emotions and filter the request content based on the estimated user emotions. For example, if the user feels lonely, a person with a kind personality can be suggested. Also, if the user feels stressed, a person who can relax can be suggested. Furthermore, if the user wants to have fun, a person with a humorous personality can be suggested. In this way, the reception unit can suggest more appropriate person images by filtering the request content based on the user's emotions.

[0100] The generation unit can reflect the user's geographical location information in the personality AI generated based on the user's request. For example, if the user is in a specific area, a character image related to that area can be generated. Also, if the user is traveling, a character image related to the travel destination can be generated. Furthermore, if the user is at home, a character image that makes them feel relaxed can be generated. In this way, the generation unit can provide a more appropriate conversation experience by generating a personality AI according to the user's geographical location information.

[0101] The conversation unit not only reproduces the language and facial expressions of the information provider in response to questions entered by the user, but also customizes the content of the response by referring to the user's past conversation history. For example, it can provide related topics based on what the user has previously talked about. It can also provide topics that the user has previously preferred preferentially. It can also analyze the user's past conversation history to smooth the flow of the conversation. In this way, the conversation unit can utilize the user's past conversation history to provide a more personalized conversation experience.

[0102] The reception unit can estimate the user's emotions and determine the priority of requests based on the estimated user's emotions. For example, if the user feels very lonely, the request can be processed with the highest priority. Also, if the user feels stressed, the request can be processed with the highest priority. Furthermore, if the user feels like having fun, the request can be processed with the highest priority. In this way, the reception unit can prioritize requests based on the user's emotions, thereby allowing more appropriate requests to be processed with the highest priority.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit receives a request from the user for the type of person they would like to talk to. The user can input a specific person's image and characteristics. For example, a request could be "a kind old man" or "a lively friend." Step 2: The generation unit uses the generation AI to create an appropriate personality AI based on the request received by the reception unit. The generation AI studies the information provider's chat data, call data, photos, videos, etc., to create a personality AI of a real person. For example, by studying the information provider's message data, the generation AI can reproduce that person's language, voice, and facial expressions. Step 3: The conversation unit initiates a conversation between the user and the personality AI created by the generation unit. The conversation unit responds to questions entered by the user by reproducing the wording and facial expressions of the informant.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 reception unit that receives a request from a user for a person profile that the user wants to talk to; a generation unit that generates an appropriate personality AI based on the request received by the reception unit; a conversation unit that starts a conversation between the personality AI created by the generation unit and the user. A system characterized by:

2. The generation unit By studying the chat data, call data, photos, and videos of informants, an AI with the personality of a real person is created.

2. The system of claim 1.

3. The conversation unit is Answers questions entered by the user while reproducing the words or facial expressions of the informant 2. The system of claim 1.

4. The reception unit Accepts user input of specific persona and characteristics 2. The system of claim 1.

5. The generation unit Includes an algorithm that creates an appropriate personality AI based on user requests.

2. The system of claim 1.

6. The conversation unit is Take specific security measures to protect user privacy 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the request content of the person you want to talk to based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past request history and suggest the appropriate request method 2. The system of claim 1.

9. The reception unit When a request is received, it filters it based on the user's current state of mind and interests.

2. The system of claim 1.

10. The reception unit When accepting a request, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

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