system

Abstract

The system according to this embodiment aims to suggest artworks based on the user's preferences and interests, and to facilitate smooth communication with artists. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a communication unit, and a response unit. The collection unit collects profile information about users. The analysis unit analyzes the information collected by the collection unit and suggests artworks based on the user's preferences and interests. The communication unit communicates with artists based on the artworks suggested by the analysis unit. The response unit immediately responds to unclear techniques or information in the explanations provided by the communication unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to suggest artworks based on a user's preferences and interests and to facilitate smooth communication with artists.

[0005] The system according to this embodiment aims to suggest artworks based on the user's preferences and interests, and to facilitate smooth communication with artists. [Means for solving the problem]

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a communication unit, and a response unit. The collection unit collects user profile information. The analysis unit analyzes the information collected by the collection unit and suggests artworks based on the user's preferences and interests. The communication unit communicates with the artist based on the artworks suggested by the analysis unit. The response unit immediately provides answers regarding unclear techniques or information in the explanations provided by the communication unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest artworks based on the user's preferences and interests, and facilitate communication with artists. [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) The matching-type art-on-demand service according to an embodiment of the present invention is a system that matches users' favorite art with emerging artists. This system involves users registering their own profiles on the platform, and artists registering their works and attaching their creative intent and explanations. The generation AI then suggests artworks based on the user's preferences and interests. Users can also directly input their impressions of the artworks they are interested in. If there is a piece of artwork they would like to view or consider purchasing, they can communicate with the artist, engaging in direct interaction, such as questions and explanations while viewing the artwork. The generation AI instantly answers any unclear techniques or information in the explanation, deepening their understanding. In this way, the generation AI is used for both preference matching and interactive appreciation. For example, a user registers their profile on the platform. At this time, they input detailed information about their preferences and interests, such as their favorite art genre and their impressions of works they have viewed in the past. This allows the generation AI to collect data for analyzing the user's preferences. Next, artists register their works on the platform. By attaching their creative intent and explanations, users can more easily understand the artwork. For example, they can provide detailed descriptions of the artwork's background and the techniques used. This allows users to deepen their understanding of the artwork. The generative AI suggests artworks based on the user's preferences and interests. Users can also directly input their impressions of the artwork they are looking for. For example, they can input specific requests such as "I want to see an abstract painting using bright colors." The generative AI analyzes this information and suggests the artwork that is best suited to the user. If there is a work they would like to see in person or consider purchasing, they can communicate with the artist. Users can directly interact with the artist by asking questions and receiving explanations while viewing the artwork. For example, they can ask about the techniques and background of the work and receive answers directly from the artist. The generative AI will instantly answer any techniques or information they do not understand in the explanation, deepening their understanding. For example, if they want to know more about the techniques used by the artist, the generative AI will provide information about those techniques.This allows users to deepen their understanding of the artwork. By utilizing AI generation in two ways—matching based on preferences and engaging in interactive viewing—users can encounter artwork that aligns with their values ​​and deepen their understanding of the work through direct communication with the artist. In this way, the matching-type art on-demand service matches users' preferred art with emerging artists, allowing users to understand and enjoy artwork more deeply.

[0029] A matching-type art on-demand service according to an embodiment includes a collection unit, an analysis unit, a communication unit, and a response unit. The collection unit collects user profile information. The user profile information includes, but is not limited to, information such as age, gender, hobbies, and past art appreciation history. The collection unit collects information about the user's preferences and interests, for example, through a questionnaire. The collection unit can also analyze the user's behavioral history to understand the user's preferences and interests. For example, the collection unit collects data on artworks the user has viewed in the past and analyzes preference trends. The analysis unit analyzes the information collected by the collection unit and suggests artworks based on the user's preferences and interests. For example, the analysis unit analyzes the user's preferences using a machine learning algorithm and suggests optimal artworks. The analysis unit can also understand the user's interests and suggest artworks using statistical analysis techniques. For example, the analysis unit suggests similar artworks based on the user's past art appreciation history. The communication unit communicates with artists based on the artworks suggested by the analysis unit. For example, the communication unit exchanges messages between the user and the artist using a chat function. The communication unit can also enable direct communication between the user and the artist using a video call function. For example, the communication unit provides a forum where the user can ask questions about artworks and the artist can answer the questions. The answering unit immediately answers questions about unclear techniques or information in the explanations provided by the communication unit. For example, the answering unit answers questions about techniques or information using generative AI. The answering unit can also provide related materials to deepen the user's understanding. For example, the answering unit provides detailed explanations about the techniques used by the artist. As a result, the matching-type art on-demand service according to the embodiment can collect and analyze user profile information, suggest artworks, communicate with artists, and immediately answer questions about unclear techniques or information in the explanations.

[0030] The collection unit can specifically collect information about the user's preferences and interests. The collection unit collects information about the user's preferences and interests, for example, through a questionnaire. For example, the collection unit may ask the user about their favorite art genres and their impressions of works they have viewed in the past. The collection unit can also analyze the user's behavioral history to understand their preferences and interests. For example, the collection unit may collect data on art works that the user has viewed in the past and analyze preference trends. This allows for detailed collection of information about the user's preferences and interests, making it possible to suggest art works with greater accuracy. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit may input questionnaire response data into a generation AI, which may then analyze the user's preferences and interests.

[0031] The analysis unit can suggest appropriate artworks based on the user's preferences and interests. The analysis unit can, for example, use a machine learning algorithm to analyze the user's preferences and suggest optimal artworks. For example, the analysis unit can suggest similar artworks based on the user's past appreciation history. The analysis unit can also use statistical analysis techniques to understand the user's interests and suggest artworks. For example, the analysis unit can analyze the user's behavioral history and suggest artworks that are of high interest to the user. This allows the user to be provided with optimal artworks by suggesting artworks based on the user's preferences and interests. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input data regarding the user's preferences and interests into a generation AI, which can then suggest optimal artworks.

[0032] The communication unit can enable users to communicate directly with artists. For example, the communication unit exchanges messages between users and artists using a chat function. For example, the communication unit provides a forum where users can ask questions about artworks and artists can answer those questions. The communication unit can also enable users to directly interact with artists using a video call function. For example, the communication unit allows users to ask questions about the techniques or background of artworks, and the artists can provide detailed explanations in response to those questions. This allows users to directly interact with artists and deepen their understanding of the artworks. Some or all of the above-described processing in the communication unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the communication unit can input chat data between users and artists into a generation AI, which can then generate appropriate answers.

[0033] The answering unit can provide information about the techniques used by the artist. The answering unit answers questions about techniques and information using, for example, a generation AI. For example, the answering unit provides a detailed explanation of the techniques used by the artist. The answering unit can also provide related materials to deepen the user's understanding. For example, the answering unit can provide literature and videos about the techniques used by the artist. This can deepen the user's understanding by providing information about the techniques used by the artist. Some or all of the above-mentioned processing in the answering unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the answering unit can input the user's question data into a generation AI, which can generate an appropriate answer.

[0034] The analysis unit can enable users to directly input specific impressions of the artwork they desire. For example, the analysis unit provides an interface for users to directly input impressions of the artwork they desire. For example, the analysis unit allows users to input specific requests such as "I want to see abstract paintings with bright colors." The analysis unit can also analyze the user's input data and suggest the most suitable artwork. For example, the analysis unit searches for and suggests relevant artworks based on the user's requests. This allows for the suggestion of artworks that more specifically meet the user's requests by allowing them to directly input impressions of the artwork they desire. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input user request data into a generative AI, which can then suggest the most suitable artwork.

[0035] The collection unit can analyze the user's past art viewing history and select an appropriate collection method. For example, the collection unit can analyze the genres of art works the user has viewed in the past and prioritize collecting information of similar genres. For example, the collection unit can analyze the characteristics of works that the user has given high ratings to in the past and collect information of similar works. The collection unit can also prioritize collecting information of artists that the user has frequently viewed in the past. For example, the collection unit can collect information on new works by a specific artist based on the user's viewing history. In this way, the optimal collection method can be selected by analyzing the user's past art viewing history. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input the user's viewing history data into a generative AI, which can then select the optimal collection method.

[0036] When collecting profile information, the collection unit can filter the information based on the user's current living situation and areas of interest. The collection unit, for example, filters information based on the art genre in which the user is currently interested. For example, the collection unit prioritizes collecting relevant information based on the user's living situation (e.g., work or hobbies). The collection unit can also exclude unnecessary information based on the user's current areas of interest. For example, the collection unit filters information on genres in which the user is not currently interested. This allows more relevant information to be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input data regarding the user's living situation and areas of interest into the generation AI, which can then perform appropriate filtering.

[0037] When collecting profile information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information about nearby art events based on the user's current location. For example, the collection unit prioritizes collecting information about local art works based on the user's geographical location information. The collection unit can also prioritize collecting information about local artists based on the user's location information. For example, the collection unit collects information about new works by local artists based on the user's location information. This allows for the provision of more appropriate information by prioritizing the collection of highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, which then prioritizes the collection of highly relevant information.

[0038] When collecting profile information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the user's social media "like" and share history to collect related artwork information. For example, the collection unit can prioritize collecting information about artists and art-related accounts followed by the user. The collection unit can also analyze the user's social media posts to collect information about art genres of interest. For example, the collection unit can collect related artwork information based on the user's posts. This allows related information to be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activity data into a generation AI, which can then collect related information.

[0039] During analysis, the analysis unit can improve the accuracy of suggestions by taking into account specific changes in the user's preferences. The analysis unit, for example, analyzes the user's past history of evaluations of artworks and makes suggestions by taking into account changes in preferences. For example, the analysis unit considers seasonal changes in the user's preferences and suggests artworks that suit the season. The analysis unit can also make suggestions by taking into account changes in preferences based on the user's life events (e.g., moving or changing jobs). For example, the analysis unit suggests related artworks based on the user's life events. This improves the accuracy of suggestions by taking into account changes in the user's preferences. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's evaluation history data into a generation AI, which then makes suggestions by taking into account changes in preferences.

[0040] During analysis, the analysis unit can suggest appropriate artworks by referring to the user's past purchase history. The analysis unit can suggest similar artworks based on the genre of artworks the user has previously purchased, for example. For example, the analysis unit can suggest new works by a specific artist based on the user's purchase history. The analysis unit can also analyze the user's purchase history and suggest artworks that match the user's purchasing trends. For example, the analysis unit can suggest related artworks based on the user's purchase history. This makes it possible to suggest optimal artworks by referring to the user's past purchase history. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's purchase history data into a generation AI, which can then suggest optimal artworks.

[0041] During analysis, the analysis unit can suggest highly relevant art works by taking into account the user's geographical location information. For example, the analysis unit can prioritize suggesting nearby art event information based on the user's current location. For example, the analysis unit can suggest art works specific to the region based on the user's geographical location information. The analysis unit can also prioritize suggesting works by local artists based on the user's location information. For example, the analysis unit can suggest new works by local artists based on the user's location information. This makes it possible to suggest highly relevant art works by taking the user's geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI, which can then suggest highly relevant art works.

[0042] During the analysis, the analysis unit can analyze the user's social media activity and suggest related artworks. For example, the analysis unit can analyze the user's social media "like" and share history to suggest related artworks. For example, the analysis unit can prioritize suggesting artworks by artists or art-related accounts that the user follows. The analysis unit can also analyze the content of the user's social media posts to suggest artworks in art genres that the user is interested in. For example, the analysis unit can suggest related artworks based on the content of the user's posts. In this way, related artworks can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's social media activity data into a generation AI, which can then suggest related artworks.

[0043] The communication unit can select an appropriate communication method by referring to the user's past interaction history when communicating. For example, the communication unit communicates in a similar style based on the user's past preferred communication style. For example, the communication unit prioritizes providing information on a specific topic from the user's past interaction history. The communication unit can also analyze the user's past interaction history and select an optimal communication method. For example, the communication unit communicates on a related topic based on the user's past interaction history. In this way, the optimal communication method can be selected by referring to the user's past interaction history. Some or all of the above-mentioned processing in the communication unit may be performed using, or without, a generation AI. For example, the communication unit can input the user's interaction history data into the generation AI, which can select the optimal communication method.

[0044] The communication unit can customize the means of communication based on the user's current living situation during communication. For example, if the user is busy, the communication unit communicates with a short message. For example, if the user is relaxed, the communication unit communicates with a message containing detailed information. The communication unit can also select the optimal communication means (e.g., chat, video call) based on the user's living situation. For example, the communication unit selects an appropriate communication means based on the user's living situation. This enables more appropriate communication by customizing the means of communication based on the user's current living situation. Some or all of the above-mentioned processing in the communication unit may be performed using, or without, a generation AI. For example, the communication unit can input the user's living situation data into the generation AI, which then selects the optimal communication means.

[0045] The communication unit can select an appropriate communication method by taking into account the user's geographical location information when communicating. For example, the communication unit provides information about nearby art events based on the user's current location. For example, the communication unit provides information about local art works based on the user's geographical location information. The communication unit can also prioritize communication with local artists based on the user's location information. For example, the communication unit promotes dialogue with local artists based on the user's location information. This allows the optimal communication method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the communication unit may be performed using, or without, a generation AI. For example, the communication unit can input the user's geographical location information into the generation AI, which can then select the optimal communication method.

[0046] The communication unit can analyze the user's social media activity and suggest a means of communication when communicating. For example, the communication unit can analyze the user's social media activity history and suggest the optimal means of communication. For example, the communication unit can prioritize communication with artists the user follows on social media. The communication unit can also analyze the content of the user's social media posts and communicate on topics of interest. For example, the communication unit can promote conversations on related topics based on the content of the user's posts. In this way, the optimal means of communication can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the communication unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the communication unit can input the user's social media activity data into a generation AI, which can then suggest the optimal means of communication.

[0047] When answering a question, the answering unit can select an appropriate answering method by referring to the user's past question history. For example, the answering unit provides an answer in a similar style based on the user's preferred answering style in the past. For example, the answering unit preferentially provides information on a specific topic from the user's past question history. The answering unit can also analyze the user's past question history and select the optimal answering method. For example, the answering unit provides answers on related topics based on the user's past question history. This makes it possible to select the optimal answering method by referring to the user's past question history. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, a generation AI. For example, the answering unit can input the user's question history data into the generation AI, which can select the optimal answering method.

[0048] When answering, the answering unit can customize the content of the answer based on the user's current specific knowledge level. For example, if the user is a beginner, the answering unit provides basic information. For example, if the user is an intermediate user, the answering unit provides detailed information. Furthermore, if the user is an advanced user, the answering unit can also provide specialized information. For example, the answering unit provides appropriate information according to the user's knowledge level. This allows for customizing the content of the answer based on the user's current knowledge level, thereby enabling a more appropriate answer. Some or all of the above-described processing in the answering unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the answering unit can input the user's knowledge level data into the generation AI, which can then generate an appropriate answer.

[0049] When answering, the answering unit can select an appropriate answering method by taking into account the user's geographical location information. The answering unit, for example, provides information about nearby art events based on the user's current location. For example, the answering unit provides information about local art works based on the user's geographical location information. The answering unit can also prioritize providing information about local artists based on the user's location information. For example, the answering unit provides information about new works by local artists based on the user's location information. This allows the optimal answering method to be selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, a generation AI. For example, the answering unit can input the user's geographical location information into the generation AI, which can then select the optimal answering method.

[0050] The response unit can analyze the user's social media activity and suggest appropriate responses when a response is submitted. For example, the response unit can analyze the user's social media activity history and suggest the most suitable response. For example, the response unit can prioritize providing information about artists the user follows on social media. The response unit can also analyze the user's social media posts and provide responses on topics of interest. For example, the response unit can provide responses on relevant topics based on the user's posts. In this way, by analyzing the user's social media activity, the response unit can suggest the most suitable response. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the response unit can input the user's social media activity data into a generative AI, which can then suggest the most suitable response.

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

[0052] The analytics unit can also refer to a user's past purchase and rating history when suggesting artwork based on their preferences and interests. For example, it can suggest similar works based on the genre and artist of artwork the user has previously purchased. It can also analyze the characteristics of artwork that the user has previously given high ratings to and suggest works with similar characteristics. Furthermore, the analytics unit can consider seasonal changes in preferences based on the user's rating history when making suggestions. This makes it possible to suggest artwork with greater accuracy by utilizing the user's past behavioral history.

[0053] The collection unit may also analyze the user's social media activity when collecting information about the user's preferences and interests. For example, the collection unit may analyze the user's social media "like" and share history to collect related artwork information. The collection unit may also prioritize collection of information about artists and art-related accounts that the user follows. Furthermore, the collection unit may analyze the content of the user's social media posts to collect information about art genres that interest the user. This allows the collection of more relevant information by utilizing the user's social media activity.

[0054] The analysis unit may also take into account the user's geographic location information when suggesting artworks based on the user's preferences and interests. For example, the analysis unit may prioritize suggesting nearby art event information based on the user's current location. The analysis unit may also suggest local artworks based on the user's geographic location information. Furthermore, the analysis unit may prioritize suggesting artworks by local artists based on the user's location information. This allows the analysis unit to suggest more relevant artworks by taking the user's geographic location information into account.

[0055] The communication unit can also refer to the user's past interaction history when the user directly communicates with the artist. For example, the communication unit can communicate in a similar style based on the user's past preferred communication style. The communication unit can also prioritize information on specific topics based on the user's past interaction history. Furthermore, the communication unit can analyze the user's past interaction history and select the optimal communication method. This allows for more appropriate communication by utilizing the user's past interaction history.

[0056] When providing information about the techniques used by the artist, the answering section may customize the content of the answer based on the user's current specific knowledge level. For example, the answering section may provide basic information if the user is a beginner. The answering section may provide detailed information if the user is an intermediate artist. Furthermore, the answering section may provide specialized information if the user is an advanced artist. This allows the answering section to provide more appropriate answers by customizing the content of the answer based on the user's current knowledge level.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects user profile information. User profile information includes age, gender, hobbies, and past art appreciation history. The collection unit collects information about the user's preferences and interests through questionnaires. The collection unit can also analyze the user's behavioral history to understand preferences and interests. For example, the collection unit collects data on art works that the user has viewed in the past and analyzes preference trends. Step 2: The analysis unit analyzes the information collected by the collection unit and suggests artworks based on the user's preferences and interests. The analysis unit uses machine learning algorithms and statistical analysis methods to analyze the user's preferences and suggest the most suitable artworks. For example, it may suggest similar artworks based on the user's past viewing history. Step 3: The communication unit communicates with the artist based on the artwork proposed by the analysis unit. The communication unit uses chat and video call functions to exchange messages and engage in direct dialogue between the user and the artist. For example, the communication unit provides a forum where users can ask questions about the artwork and the artist can respond to those questions. Step 4: The Answering Department immediately answers questions about techniques and information unclear in the explanations provided by the Communication Department. The Answering Department uses generative AI to answer questions about techniques and information and provide relevant materials, such as detailed explanations of the techniques used by the artist.

[0059] (Example 2) The matching-type art-on-demand service according to an embodiment of the present invention is a system that matches users' favorite art with emerging artists. This system involves users registering their own profiles on the platform, and artists registering their works and attaching their creative intent and explanations. The generation AI then suggests artworks based on the user's preferences and interests. Users can also directly input their impressions of the artworks they are interested in. If there is a piece of artwork they would like to view or consider purchasing, they can communicate with the artist, engaging in direct interaction, such as questions and explanations while viewing the artwork. The generation AI instantly answers any unclear techniques or information in the explanation, deepening their understanding. In this way, the generation AI is used for both preference matching and interactive appreciation. For example, a user registers their profile on the platform. At this time, they input detailed information about their preferences and interests, such as their favorite art genre and their impressions of works they have viewed in the past. This allows the generation AI to collect data for analyzing the user's preferences. Next, artists register their works on the platform. By attaching their creative intent and explanations, users can more easily understand the artwork. For example, they can provide detailed descriptions of the artwork's background and the techniques used. This allows users to deepen their understanding of the artwork. The generative AI suggests artworks based on the user's preferences and interests. Users can also directly input their impressions of the artwork they are looking for. For example, they can input specific requests such as "I want to see an abstract painting using bright colors." The generative AI analyzes this information and suggests the artwork that is best suited to the user. If there is a work they would like to see in person or consider purchasing, they can communicate with the artist. Users can directly interact with the artist by asking questions and receiving explanations while viewing the artwork. For example, they can ask about the techniques and background of the work and receive answers directly from the artist. The generative AI will instantly answer any techniques or information they do not understand in the explanation, deepening their understanding. For example, if they want to know more about the techniques used by the artist, the generative AI will provide information about those techniques.This allows users to deepen their understanding of the artwork. By utilizing AI generation in two ways—matching based on preferences and engaging in interactive viewing—users can encounter artwork that aligns with their values ​​and deepen their understanding of the work through direct communication with the artist. In this way, the matching-type art on-demand service matches users' preferred art with emerging artists, allowing users to understand and enjoy artwork more deeply.

[0060] A matching-type art on-demand service according to an embodiment includes a collection unit, an analysis unit, a communication unit, and a response unit. The collection unit collects user profile information. The user profile information includes, but is not limited to, information such as age, gender, hobbies, and past art appreciation history. The collection unit collects information about the user's preferences and interests, for example, through a questionnaire. The collection unit can also analyze the user's behavioral history to understand the user's preferences and interests. For example, the collection unit collects data on artworks the user has viewed in the past and analyzes preference trends. The analysis unit analyzes the information collected by the collection unit and suggests artworks based on the user's preferences and interests. For example, the analysis unit analyzes the user's preferences using a machine learning algorithm and suggests optimal artworks. The analysis unit can also understand the user's interests and suggest artworks using statistical analysis techniques. For example, the analysis unit suggests similar artworks based on the user's past art appreciation history. The communication unit communicates with artists based on the artworks suggested by the analysis unit. For example, the communication unit exchanges messages between the user and the artist using a chat function. The communication unit can also enable direct communication between the user and the artist using a video call function. For example, the communication unit provides a forum where the user can ask questions about artworks and the artist can answer the questions. The answering unit immediately answers questions about unclear techniques or information in the explanations provided by the communication unit. For example, the answering unit answers questions about techniques or information using generative AI. The answering unit can also provide related materials to deepen the user's understanding. For example, the answering unit provides detailed explanations about the techniques used by the artist. As a result, the matching-type art on-demand service according to the embodiment can collect and analyze user profile information, suggest artworks, communicate with artists, and immediately answer questions about unclear techniques or information in the explanations.

[0061] The collection unit can specifically collect information about the user's preferences and interests. The collection unit collects information about the user's preferences and interests, for example, through a questionnaire. For example, the collection unit may ask the user about their favorite art genres and their impressions of works they have viewed in the past. The collection unit can also analyze the user's behavioral history to understand their preferences and interests. For example, the collection unit may collect data on art works that the user has viewed in the past and analyze preference trends. This allows for detailed collection of information about the user's preferences and interests, making it possible to suggest art works with greater accuracy. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit may input questionnaire response data into a generation AI, which may then analyze the user's preferences and interests.

[0062] The analysis unit can suggest appropriate artworks based on the user's preferences and interests. The analysis unit can, for example, use a machine learning algorithm to analyze the user's preferences and suggest optimal artworks. For example, the analysis unit can suggest similar artworks based on the user's past appreciation history. The analysis unit can also use statistical analysis techniques to understand the user's interests and suggest artworks. For example, the analysis unit can analyze the user's behavioral history and suggest artworks that are of high interest to the user. This allows the user to be provided with optimal artworks by suggesting artworks based on the user's preferences and interests. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input data regarding the user's preferences and interests into a generation AI, which can then suggest optimal artworks.

[0063] The communication unit can enable users to communicate directly with artists. For example, the communication unit exchanges messages between users and artists using a chat function. For example, the communication unit provides a forum where users can ask questions about artworks and artists can answer those questions. The communication unit can also enable users to directly interact with artists using a video call function. For example, the communication unit allows users to ask questions about the techniques or background of artworks, and the artists can provide detailed explanations in response to those questions. This allows users to directly interact with artists and deepen their understanding of the artworks. Some or all of the above-described processing in the communication unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the communication unit can input chat data between users and artists into a generation AI, which can then generate appropriate answers.

[0064] The answering unit can provide information about the techniques used by the artist. The answering unit answers questions about techniques and information using, for example, a generation AI. For example, the answering unit provides a detailed explanation of the techniques used by the artist. The answering unit can also provide related materials to deepen the user's understanding. For example, the answering unit can provide literature and videos about the techniques used by the artist. This can deepen the user's understanding by providing information about the techniques used by the artist. Some or all of the above-mentioned processing in the answering unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the answering unit can input the user's question data into a generation AI, which can generate an appropriate answer.

[0065] The analysis unit can enable users to directly input specific impressions of the artwork they desire. For example, the analysis unit provides an interface for users to directly input impressions of the artwork they desire. For example, the analysis unit allows users to input specific requests such as "I want to see abstract paintings with bright colors." The analysis unit can also analyze the user's input data and suggest the most suitable artwork. For example, the analysis unit searches for and suggests relevant artworks based on the user's requests. This allows for the suggestion of artworks that more specifically meet the user's requests by allowing them to directly input impressions of the artwork they desire. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input user request data into a generative AI, which can then suggest the most suitable artwork.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of profile information collection based on the estimated emotions. For example, the data collection unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in facial expressions. The data collection unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice and calculate an emotion score. The data collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate. This allows for the collection of profile information at a more appropriate time by adjusting the timing of collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0067] The collection unit can analyze the user's past art viewing history and select an appropriate collection method. For example, the collection unit can analyze the genres of art works the user has viewed in the past and prioritize collecting information of similar genres. For example, the collection unit can analyze the characteristics of works that the user has given high ratings to in the past and collect information of similar works. The collection unit can also prioritize collecting information of artists that the user has frequently viewed in the past. For example, the collection unit can collect information on new works by a specific artist based on the user's viewing history. In this way, the optimal collection method can be selected by analyzing the user's past art viewing history. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can input the user's viewing history data into a generative AI, which can then select the optimal collection method.

[0068] When collecting profile information, the collection unit can filter the information based on the user's current living situation and areas of interest. The collection unit, for example, filters information based on the art genre in which the user is currently interested. For example, the collection unit prioritizes collecting relevant information based on the user's living situation (e.g., work or hobbies). The collection unit can also exclude unnecessary information based on the user's current areas of interest. For example, the collection unit filters information on genres in which the user is not currently interested. This allows more relevant information to be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input data regarding the user's living situation and areas of interest into the generation AI, which can then perform appropriate filtering.

[0069] The collection unit can estimate the user's emotions and prioritize the profile information to be collected based on the estimated user emotions. The collection unit, for example, captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This allows more appropriate information to be collected by prioritizing the profile information to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0070] When collecting profile information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting information about nearby art events based on the user's current location. For example, the collection unit prioritizes collecting information about local art works based on the user's geographical location information. The collection unit can also prioritize collecting information about local artists based on the user's location information. For example, the collection unit collects information about new works by local artists based on the user's location information. This allows for the provision of more appropriate information by prioritizing the collection of highly relevant information by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information into the generation AI, which then prioritizes the collection of highly relevant information.

[0071] When collecting profile information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit can analyze the user's social media "like" and share history to collect related artwork information. For example, the collection unit can prioritize collecting information about artists and art-related accounts followed by the user. The collection unit can also analyze the user's social media posts to collect information about art genres of interest. For example, the collection unit can collect related artwork information based on the user's posts. This allows related information to be collected by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, or without, a generation AI. For example, the collection unit can input the user's social media activity data into a generation AI, which can then collect related information.

[0072] The analysis unit can estimate the user's emotions and adjust the method for suggesting artworks based on the estimated user emotions. The analysis unit, for example, captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This enables more appropriate suggestions by adjusting the method for suggesting artworks based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0073] During analysis, the analysis unit can improve the accuracy of suggestions by taking into account specific changes in the user's preferences. The analysis unit, for example, analyzes the user's past history of evaluations of artworks and makes suggestions by taking into account changes in preferences. For example, the analysis unit considers seasonal changes in the user's preferences and suggests artworks that suit the season. The analysis unit can also make suggestions by taking into account changes in preferences based on the user's life events (e.g., moving or changing jobs). For example, the analysis unit suggests related artworks based on the user's life events. This improves the accuracy of suggestions by taking into account changes in the user's preferences. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's evaluation history data into a generation AI, which then makes suggestions by taking into account changes in preferences.

[0074] During analysis, the analysis unit can suggest appropriate artworks by referring to the user's past purchase history. The analysis unit can suggest similar artworks based on the genre of artworks the user has previously purchased, for example. For example, the analysis unit can suggest new works by a specific artist based on the user's purchase history. The analysis unit can also analyze the user's purchase history and suggest artworks that match the user's purchasing trends. For example, the analysis unit can suggest related artworks based on the user's purchase history. This makes it possible to suggest optimal artworks by referring to the user's past purchase history. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's purchase history data into a generation AI, which can then suggest optimal artworks.

[0075] The analysis unit can estimate the user's emotions and prioritize the artworks to be suggested based on the estimated user emotions. The analysis unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This enables more appropriate suggestions by prioritizing the artworks to be suggested based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0076] During analysis, the analysis unit can suggest highly relevant art works by taking into account the user's geographical location information. For example, the analysis unit can prioritize suggesting nearby art event information based on the user's current location. For example, the analysis unit can suggest art works specific to the region based on the user's geographical location information. The analysis unit can also prioritize suggesting works by local artists based on the user's location information. For example, the analysis unit can suggest new works by local artists based on the user's location information. This makes it possible to suggest highly relevant art works by taking the user's geographical location information into consideration. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's geographical location information into the generation AI, which can then suggest highly relevant art works.

[0077] During the analysis, the analysis unit can analyze the user's social media activity and suggest related artworks. For example, the analysis unit can analyze the user's social media "like" and share history to suggest related artworks. For example, the analysis unit can prioritize suggesting artworks by artists or art-related accounts that the user follows. The analysis unit can also analyze the content of the user's social media posts to suggest artworks in art genres that the user is interested in. For example, the analysis unit can suggest related artworks based on the content of the user's posts. In this way, related artworks can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's social media activity data into a generation AI, which can then suggest related artworks.

[0078] The communication unit can estimate the user's emotions and adjust the communication method based on the estimated emotions. For example, the communication unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the communication unit can calculate an emotion score based on changes in facial expressions. The communication unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the communication unit can analyze the tone and speed of the voice and calculate an emotion score. The communication unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the communication unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate communication by adjusting the communication method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the communication unit may be performed using, for example, a generative AI, or without a generative AI. For example, the communication unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0079] The communication unit can select an appropriate communication method by referring to the user's past interaction history when communicating. For example, the communication unit communicates in a similar style based on the user's past preferred communication style. For example, the communication unit prioritizes providing information on a specific topic from the user's past interaction history. The communication unit can also analyze the user's past interaction history and select an optimal communication method. For example, the communication unit communicates on a related topic based on the user's past interaction history. In this way, the optimal communication method can be selected by referring to the user's past interaction history. Some or all of the above-mentioned processing in the communication unit may be performed using, or without, a generation AI. For example, the communication unit can input the user's interaction history data into the generation AI, which can select the optimal communication method.

[0080] The communication unit can customize the means of communication based on the user's current living situation during communication. For example, if the user is busy, the communication unit communicates with a short message. For example, if the user is relaxed, the communication unit communicates with a message containing detailed information. The communication unit can also select the optimal communication means (e.g., chat, video call) based on the user's living situation. For example, the communication unit selects an appropriate communication means based on the user's living situation. This enables more appropriate communication by customizing the means of communication based on the user's current living situation. Some or all of the above-mentioned processing in the communication unit may be performed using, or without, a generation AI. For example, the communication unit can input the user's living situation data into the generation AI, which then selects the optimal communication means.

[0081] The communication unit can estimate the user's emotions and determine communication priorities based on those estimated emotions. For example, the communication unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the communication unit can calculate an emotion score based on changes in facial expressions. The communication unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the communication unit can analyze the tone and speed of the voice and calculate an emotion score. The communication unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the communication unit can calculate an emotion score based on fluctuations in heart rate. This allows for more appropriate communication by determining communication priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the communication unit may be performed using, for example, a generative AI, or without a generative AI. For example, the communication unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0082] The communication unit can select an appropriate communication method by taking into account the user's geographical location information when communicating. For example, the communication unit provides information about nearby art events based on the user's current location. For example, the communication unit provides information about local art works based on the user's geographical location information. The communication unit can also prioritize communication with local artists based on the user's location information. For example, the communication unit promotes dialogue with local artists based on the user's location information. This allows the optimal communication method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the communication unit may be performed using, or without, a generation AI. For example, the communication unit can input the user's geographical location information into the generation AI, which can then select the optimal communication method.

[0083] The communication unit can analyze the user's social media activity and suggest a means of communication when communicating. For example, the communication unit can analyze the user's social media activity history and suggest the optimal means of communication. For example, the communication unit can prioritize communication with artists the user follows on social media. The communication unit can also analyze the content of the user's social media posts and communicate on topics of interest. For example, the communication unit can promote conversations on related topics based on the content of the user's posts. In this way, the optimal means of communication can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the communication unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the communication unit can input the user's social media activity data into a generation AI, which can then suggest the optimal means of communication.

[0084] The answering unit can estimate the user's emotions and adjust the answering method based on the estimated user's emotions. For example, the answering unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the answering unit calculates an emotion score based on changes in facial expression. The answering unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the answering unit analyzes the tone and speed of the voice and calculates an emotion score. The answering unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the answering unit calculates an emotion score based on heart rate fluctuations. This enables a more appropriate answer by adjusting the answering method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the answering unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the answering unit may input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0085] When answering a question, the answering unit can select an appropriate answering method by referring to the user's past question history. For example, the answering unit provides an answer in a similar style based on the user's preferred answering style in the past. For example, the answering unit preferentially provides information on a specific topic from the user's past question history. The answering unit can also analyze the user's past question history and select the optimal answering method. For example, the answering unit provides answers on related topics based on the user's past question history. This makes it possible to select the optimal answering method by referring to the user's past question history. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, a generation AI. For example, the answering unit can input the user's question history data into the generation AI, which can select the optimal answering method.

[0086] When answering, the answering unit can customize the content of the answer based on the user's current specific knowledge level. For example, if the user is a beginner, the answering unit provides basic information. For example, if the user is an intermediate user, the answering unit provides detailed information. Furthermore, if the user is an advanced user, the answering unit can also provide specialized information. For example, the answering unit provides appropriate information according to the user's knowledge level. This allows for customizing the content of the answer based on the user's current knowledge level, thereby enabling a more appropriate answer. Some or all of the above-described processing in the answering unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the answering unit can input the user's knowledge level data into the generation AI, which can then generate an appropriate answer.

[0087] The answering unit can estimate the user's emotions and prioritize answers based on the estimated user emotions. For example, the answering unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the answering unit can calculate an emotion score based on changes in facial expression. The answering unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the answering unit can analyze the tone and speed of the voice and calculate an emotion score. The answering unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the answering unit can calculate an emotion score based on heart rate fluctuations. This enables more appropriate answers to be provided by prioritizing answers according to 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the answering unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the answering unit may input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0088] When answering, the answering unit can select an appropriate answering method by taking into account the user's geographical location information. The answering unit, for example, provides information about nearby art events based on the user's current location. For example, the answering unit provides information about local art works based on the user's geographical location information. The answering unit can also prioritize providing information about local artists based on the user's location information. For example, the answering unit provides information about new works by local artists based on the user's location information. This allows the optimal answering method to be selected by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the answering unit may be performed using, or without, a generation AI. For example, the answering unit can input the user's geographical location information into the generation AI, which can then select the optimal answering method.

[0089] The response unit can analyze the user's social media activity and suggest appropriate responses when a response is submitted. For example, the response unit can analyze the user's social media activity history and suggest the most suitable response. For example, the response unit can prioritize providing information about artists the user follows on social media. The response unit can also analyze the user's social media posts and provide responses on topics of interest. For example, the response unit can provide responses on relevant topics based on the user's posts. In this way, by analyzing the user's social media activity, the response unit can suggest the most suitable response. Some or all of the above processing in the response unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the response unit can input the user's social media activity data into a generative AI, which can then suggest the most suitable response. === Hard Collateral 1-1 === Each of the multiple elements described above, including the collection unit, analysis unit, communication unit, and response unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user profile information using the control unit 46A of the smart device 14 and analyzes it using the identification processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the user's preferences and interests based on the collected information. The communication unit is implemented, for example, by the control unit 46A of the smart device 14 and facilitates messaging and video calls between the user and the artist. The response unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and uses generative AI to answer questions about techniques and information. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, communication unit, and response unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user profile information via the control unit 46A of the smart glasses 214, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's preferences and interests based on the collected information. The communication unit is realized, for example, by the control unit 46A of the smart glasses 214, and exchanges messages and video calls between the user and the artist. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and answers questions about techniques and information using a generation AI. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, communication unit, and response unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user profile information via the control unit 46A of the headset type terminal 314, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's preferences and interests based on the collected information. The communication unit is realized, for example, by the control unit 46A of the headset type terminal 314, and exchanges messages and video calls between the user and the artist. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and answers questions about techniques and information using a generation AI. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, communication unit, and response unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user profile information via the control unit 46A of the robot 414, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's preferences and interests based on the collected information. The communication unit is realized, for example, by the control unit 46A of the robot 414, and exchanges messages and video calls between the user and the artist. The response unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and answers questions about techniques and information using a generative AI.

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

[0091] The analytics unit can also refer to a user's past purchase and rating history when suggesting artwork based on their preferences and interests. For example, it can suggest similar works based on the genre and artist of artwork the user has previously purchased. It can also analyze the characteristics of artwork that the user has previously given high ratings to and suggest works with similar characteristics. Furthermore, the analytics unit can consider seasonal changes in preferences based on the user's rating history when making suggestions. This makes it possible to suggest artwork with greater accuracy by utilizing the user's past behavioral history.

[0092] The collection unit may also analyze the user's social media activity when collecting information about the user's preferences and interests. For example, the collection unit may analyze the user's social media "like" and share history to collect related artwork information. The collection unit may also prioritize collection of information about artists and art-related accounts that the user follows. Furthermore, the collection unit may analyze the content of the user's social media posts to collect information about art genres that interest the user. This allows the collection of more relevant information by utilizing the user's social media activity.

[0093] The analysis unit may also take into account the user's geographic location information when suggesting artworks based on the user's preferences and interests. For example, the analysis unit may prioritize suggesting nearby art event information based on the user's current location. The analysis unit may also suggest local artworks based on the user's geographic location information. Furthermore, the analysis unit may prioritize suggesting artworks by local artists based on the user's location information. This allows the analysis unit to suggest more relevant artworks by taking the user's geographic location information into account.

[0094] The communication unit can also refer to the user's past interaction history when the user directly communicates with the artist. For example, the communication unit can communicate in a similar style based on the user's past preferred communication style. The communication unit can also prioritize information on specific topics based on the user's past interaction history. Furthermore, the communication unit can analyze the user's past interaction history and select the optimal communication method. This allows for more appropriate communication by utilizing the user's past interaction history.

[0095] When providing information about the techniques used by the artist, the answering section may customize the content of the answer based on the user's current specific knowledge level. For example, the answering section may provide basic information if the user is a beginner. The answering section may provide detailed information if the user is an intermediate artist. Furthermore, the answering section may provide specialized information if the user is an advanced artist. This allows the answering section to provide more appropriate answers by customizing the content of the answer based on the user's current knowledge level.

[0096] The analysis unit can also estimate the user's emotions when the user directly inputs their specific impressions of the artwork they are looking for, and prioritize the artworks to be suggested based on the estimated user emotions. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more appropriate suggestions by prioritizing the artworks to be suggested based on the user's emotions.

[0097] The collection unit can estimate the user's emotions and adjust the timing of collecting profile information based on the estimated user emotions. For example, the collection unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the timing of collecting profile information to be adjusted according to the user's emotions, making it possible to collect information at a more appropriate time.

[0098] The analysis unit can estimate the user's emotions and adjust the method of suggesting artworks based on the estimated user emotions. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. This allows for more appropriate suggestions by adjusting the method of suggesting artworks according to the user's emotions.

[0099] The communication unit can estimate the user's emotions and adjust the communication method based on the estimated user emotions. For example, the communication unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The communication unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the communication unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. This allows for more appropriate communication by adjusting the communication method according to the user's emotions.

[0100] The answering unit can estimate the user's emotions and adjust the answering method based on the estimated user emotions. For example, the answering unit can capture the user's facial expression with a camera and estimate the emotions using an emotion estimation algorithm. The answering unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the answering unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. This allows the answering method to be adjusted according to the user's emotions, making it possible to provide more appropriate answers.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects user profile information. User profile information includes age, gender, hobbies, and past art appreciation history. The collection unit collects information about the user's preferences and interests through questionnaires. The collection unit can also analyze the user's behavioral history to understand preferences and interests. For example, the collection unit collects data on art works that the user has viewed in the past and analyzes preference trends. Step 2: The analysis unit analyzes the information collected by the collection unit and suggests artworks based on the user's preferences and interests. The analysis unit uses machine learning algorithms and statistical analysis methods to analyze the user's preferences and suggest the most suitable artworks. For example, it may suggest similar artworks based on the user's past viewing history. Step 3: The communication unit communicates with the artist based on the artwork proposed by the analysis unit. The communication unit uses chat and video call functions to exchange messages and engage in direct dialogue between the user and the artist. For example, the communication unit provides a forum where users can ask questions about the artwork and the artist can respond to those questions. Step 4: The Answering Department immediately answers questions about techniques and information unclear in the explanations provided by the Communication Department. The Answering Department uses generative AI to answer questions about techniques and information and provide relevant materials, such as detailed explanations of the techniques used by the artist.

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

[0104] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

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

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

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

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

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

Claims

1. a collection unit that collects user profile information; an analysis unit that analyzes the information collected by the collection unit and suggests artworks based on the user's preferences and interests; a communication unit that communicates with artists based on the artworks proposed by the analysis unit; a replying section that immediately replies to unclear techniques or information in the explanations given by the communication section; Equipped with A system characterized by:

2. The collecting unit Specifically collect information about your preferences and interests The system of claim 1 .

3. The analysis unit Suggesting appropriate artworks based on user preferences and interests The system of claim 1 .

4. The communication unit Allows users to communicate directly with artists The system of claim 1 .

5. The answering section Provides information about the techniques used by the artist The system of claim 1 .

6. The analysis unit Allows users to directly input their specific impressions of the artwork they are looking for The system of claim 1 .

7. The collecting unit The user's emotions are estimated, and the timing of collecting profile information is adjusted based on the estimated user's emotions. The system of claim 1 .

8. The collecting unit Analyze users' past art appreciation history and select the appropriate collection method The system of claim 1 .

Images