System

Abstract

An object of a system according to an embodiment is for an individual to efficiently manage art assets and obtain information in real time.SOLUTION: A system includes an art asset acquisition part, a portfolio management part, a report generation part, and a chat response part. The art asset acquisition part collects data of art assets. The portfolio management unit manages a portfolio on the basis of the data collected by the art asset acquisition unit. The report generation unit generates an asset report based on the portfolio managed by the portfolio management unit. The chat response unit responds to the user's question in real time based on the report generated by the report generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult for individuals to efficiently manage their art assets and obtain information in real time.

[0005] The system according to the embodiment aims to enable individuals to efficiently manage their art assets and obtain information in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes an art asset acquisition unit, a portfolio management unit, a report generation unit, and a chat response unit. The art asset acquisition unit collects art asset data. The portfolio management unit manages a portfolio based on the data collected by the art asset acquisition unit. The report generation unit generates an asset report based on the portfolio managed by the portfolio management unit. The chat response unit responds to user questions in real time based on the report generated by the report generation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows individuals to efficiently manage their art assets and obtain information in real time. [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 art asset management system according to an embodiment of the present invention uses a generative AI to manage a portfolio of expensive art assets that individuals would not normally have access to, and provides asset reports through a chat function, allowing individuals to efficiently and precisely manage their expensive art assets and obtain information in real time.

[0029] An art asset management system according to an embodiment includes an art asset acquisition unit, a portfolio management unit, a report generation unit, and a chat response unit. The art asset acquisition unit collects art asset data. For example, the art asset acquisition unit digitizes and collects data on paintings and sculptures by famous artists, historical artworks, and the like. The art asset acquisition unit can also collect art auction results and museum exhibition information. For example, the art asset acquisition unit collects auction winning bids and exhibition information and stores them in a database. The portfolio management unit manages a portfolio based on the data collected by the art asset acquisition unit. For example, the portfolio management unit analyzes fluctuations in the value of art assets and market trends to build an optimal portfolio. The portfolio management unit can also propose a portfolio based on a user's investment goals and risk tolerance. For example, the portfolio management unit proposes a stable asset allocation with reduced risk based on the user's investment history and market data. The report generation unit generates an asset report based on the portfolio managed by the portfolio management unit. For example, the report generation unit creates a report including the current value of art assets, past value fluctuations, and future value predictions. The report generation unit can also generate customized reports based on the user's investment strategy. For example, the report generation unit provides detailed reports based on the user's investment goals and risk tolerance. The chat response unit responds to the user's questions in real time based on the reports generated by the report generation unit. For example, when a user asks, "What is the value of my current portfolio?", the chat response unit provides an immediate response based on the latest data. Furthermore, when a user asks, "What art asset should I purchase next?", the chat response unit can analyze market trends and suggest optimal art assets. For example, the chat response unit suggests art assets that are likely to increase in value in the future based on the user's investment history and market data. As a result, the art asset management system according to the embodiment allows individuals to efficiently and precisely manage their expensive art assets and obtain information in real time.For example, users can accurately grasp the value of their art assets and make optimal purchase and sale decisions. Real-time information also allows users to stay up to date with the latest market trends.

[0030] The art asset acquisition unit can scan handwritten answers and convert them into digital data. For example, the art asset acquisition unit reads handwritten answers using a scanner and saves them as image data. It then converts the image data into text data using OCR technology. The art asset acquisition unit can also take a photo of a handwritten answer using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The art asset acquisition unit can also write handwritten answers with a dedicated digital pen, which then converts the data into digital data in real time. For example, a sensor can detect the movement of the pen and save it as text data. In this way, converting handwritten answers into digital data makes it easier for the generative AI to analyze.

[0031] The art asset acquisition unit can analyze writing pressure and stroke order to extract the writer's characteristics. The art asset acquisition unit, for example, uses a writing pressure sensor to analyze how an answer is written. For example, it collects data on the strength of writing pressure and extracts the writer's characteristics. In addition, to analyze the stroke order, the art asset acquisition unit tracks the movement of a digital pen and collects data on the writer's stroke order. For example, it analyzes the stroke order pattern and identifies the writer's characteristics. In addition, the art asset acquisition unit combines the data on writing pressure and stroke order to comprehensively analyze the writer's characteristics. For example, it extracts the writer's characteristics based on the degree of agreement between changes in writing pressure and stroke order. In this way, by analyzing writing pressure and stroke order, the writer's characteristics can be grasped in detail.

[0032] The art asset acquisition unit can convert what a student dictates into text using voice input and treat the text as an answer sheet. For example, the art asset acquisition unit records what a student dictates with a microphone and converts it into text data using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. The art asset acquisition unit also creates a system that recognizes what a student dictates in real time and displays it as text data. For example, text is displayed on a screen simultaneously with voice input. The art asset acquisition unit also uses voice input to convert what a student dictates into text and saves the text data as an answer sheet. For example, voice recognition technology is used to perform highly accurate text conversion. As a result, even students who have difficulty writing by hand or typing can submit answer sheets by using voice input.

[0033] The art asset acquisition unit can analyze images and diagrams and include visual information in the evaluation. For example, the art asset acquisition unit analyzes images and diagrams included in an answer sheet using image recognition technology and converts the content into text data. For example, the content of the diagram is automatically analyzed and reflected in the evaluation. The art asset acquisition unit also analyzes images and diagrams included in an answer sheet and builds a system that evaluates based on visual information. For example, the content of the image is analyzed and reflected in the evaluation of the answer. The art asset acquisition unit also analyzes answer sheets that include images and diagrams and integrates the visual information with text data for evaluation. For example, the visual information is analyzed using image recognition technology and reflected in the evaluation of the answer. This makes it possible to perform a more detailed evaluation by analyzing answer sheets that include images and diagrams.

[0034] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0035] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0036] The portfolio management unit can extend the portfolio management function to other asset classes, including real estate and stocks, to provide comprehensive asset management. For example, the portfolio management unit can extend the portfolio management function to real estate assets, allowing users to manage their real estate investments. For example, the unit can analyze real estate value fluctuations and market trends and propose optimal investment strategies. The portfolio management unit can also extend the portfolio management function to stock assets, allowing users to manage their stock investments. For example, the unit can analyze stock price fluctuations and corporate performance and propose optimal investment strategies. The portfolio management unit can also extend the portfolio management function to comprehensive asset management, allowing users to centrally manage multiple asset classes. For example, the unit can manage art assets, real estate, and stocks in an integrated manner and propose optimal portfolios. This makes it possible to provide comprehensive asset management.

[0037] The portfolio management unit can add a function that allows a portfolio proposed by the generation AI to be shared with other users and that allows the user to receive community-based investment advice. The portfolio management unit, for example, adds a function that allows a portfolio proposed by the generation AI to be shared with other users and that allows the user to receive community-based investment advice. For example, it provides a platform where users can exchange opinions and give advice to each other. The portfolio management unit also adds a function that allows the user to share a portfolio proposed by the generation AI and that allows the user to receive feedback from other users. For example, the user can comment on and rate the proposed portfolio. The portfolio management unit also adds a function that allows the user to share a portfolio proposed by the generation AI and that allows the user to receive community-based investment advice. For example, the user can adjust the portfolio by referring to the opinions of other investors. This allows the user to receive community-based investment advice.

[0038] When generating an asset report, the report generation unit can perform a simulation that combines past data and future predictions and present multiple scenarios to the user. In the report generation unit, for example, the generation AI performs a simulation that combines past data and future predictions and presents multiple scenarios to the user. For example, it simulates fluctuations in asset value under different market conditions. In addition, the report generation unit performs a simulation that combines past data and future predictions and presents multiple scenarios to the user. For example, it simulates fluctuations in asset value when different investment strategies are adopted. In addition, the report generation unit performs a simulation that combines past data and future predictions and presents multiple scenarios to the user. For example, it simulates fluctuations in asset value under different economic conditions. This makes it possible to present multiple scenarios to the user.

[0039] The report generation unit can also include physical management information, such as the storage status and insurance information of the art assets, in the asset report. For example, the report generation unit includes the storage status of the art assets in the asset report. For example, it may include details of the storage environment, such as which museum or gallery the work is stored in. The report generation unit also includes insurance information for the art assets in the asset report. For example, it may include the insurance amount for the work, information about the insurance company, and the insurance period. The report generation unit also includes physical management information for the art assets in the asset report. For example, it may include the restoration history, exhibition history, and transportation history of the work. This makes it possible to provide an asset report that also includes physical management information.

[0040] The report generation unit can provide the asset report in other formats, such as a video report or an interactive report, to deepen the user's understanding. For example, the report generation unit can provide the asset report in a video report format to allow the user to understand it visually. For example, value fluctuations and market trends of art assets can be explained through video. The report generation unit can also provide the asset report in an interactive report format to allow the user to manipulate the information themselves. For example, the user can click on a graph or chart to display detailed information. The report generation unit can also provide the asset report in other formats to deepen the user's understanding. For example, the report generation unit can provide an audio report in podcast format with commentary from an art market expert. This allows the asset report to be provided in other formats to deepen the user's understanding.

[0041] The report generation unit can add a function that allows asset reports to be shared with other users and receive feedback. The report generation unit adds, for example, a function that allows asset reports to be shared with other users and receive feedback. For example, a user can share a report and receive comments and ratings from other investors. The report generation unit also adds a function that allows asset reports to be shared with other users and receive feedback. For example, a user can share a report and receive advice from an expert. The report generation unit also adds a function that allows asset reports to be shared with other users and receive feedback. For example, a user can share a report and exchange opinions on a community basis. This allows asset reports to be shared with other users and receive feedback.

[0042] The chat response unit can add a function to analyze a user's past dialogue history and provide individually optimized answers. For example, the chat response unit adds a function in which the generation AI analyzes a user's past dialogue history and provides individually optimized answers. For example, related information is provided based on the content of the user's past questions. The chat response unit also adds a function in which the generation AI analyzes a user's past dialogue history and provides individually optimized answers. For example, information is provided according to the user's preferences and interests. The chat response unit also adds a function in which the generation AI analyzes a user's past dialogue history and provides individually optimized answers. For example, optimal investment advice is provided based on the user's investment history. This makes it possible to provide optimized answers based on the user's past dialogue history.

[0043] The chat response unit allows the generation AI to periodically notify the user of market trends and the status of their portfolio through the chat function. The chat response unit, for example, allows the generation AI to periodically notify the user of market trends through the chat function. For example, weekly market reports and important news are provided via chat. The chat response unit also allows the generation AI to periodically notify the user of the status of their portfolio through the chat function. For example, portfolio value fluctuations and risk assessments are provided via chat. The chat response unit also allows the generation AI to periodically notify the user of market trends and the status of their portfolio through the chat function. For example, advice based on the user's investment strategy is provided via chat. This allows the user to periodically be notified of market trends and the status of their portfolio.

[0044] The chat response unit can make the chat function compatible with other platforms such as smart speakers and mobile apps, allowing users to obtain information anywhere. For example, the chat response unit can make the chat function compatible with smart speakers, allowing users to obtain information by voice. For example, the chat response unit can provide information about art assets through Amazon Echo or Google (registered trademark) Home. The chat response unit can also make the chat function compatible with mobile apps, allowing users to obtain information anywhere. For example, the chat response unit can notify market trends in real time through a smartphone app. The chat response unit can also make the chat function compatible with other platforms, allowing users to obtain information anywhere. For example, the chat response unit can provide information about art assets through a wearable device. This allows users to obtain information anywhere.

[0045] The chat response unit can add a community function that allows users to exchange information and have discussions with other users through the chat function. The chat response unit, for example, adds a community function that allows users to exchange information and have discussions with other users through the chat function. For example, it provides a chat room where users can exchange opinions about art assets. The chat response unit also adds a community function that allows users to exchange information and have discussions with other users through the chat function. For example, it provides a discussion forum where experts and investors participate. The chat response unit also adds a community function that allows users to exchange information and have discussions with other users through the chat function. For example, it provides a platform where users can discuss art market trends and investment strategies. This allows users to exchange information and have discussions with other users.

[0046] Furthermore, the art asset management system is equipped with an art market trend analysis unit. When analyzing trends in the art market, this art market trend analysis unit analyzes past auction results and exhibition information in chronological order to identify long-term trends. In the art market trend analysis unit, for example, the generation AI analyzes past auction results in chronological order to identify long-term trends in the art market. For example, it analyzes price fluctuations for specific artists or genres. In addition, the generation AI analyzes exhibition information in chronological order to identify long-term trends in the art market. For example, it predicts market trends based on the exhibition history of art museums and galleries. In addition, the generation AI integrates past auction results and exhibition information to identify long-term trends in the art market. For example, it analyzes the relationship between winning bid prices at auctions and the number of exhibitions. This makes it possible to identify long-term trends in the art market.

[0047] The Art Market Trend Analysis Department incorporates information from social media and online forums into its art market trend analysis, enabling more comprehensive market analysis. In the Art Market Trend Analysis Department, for example, the Generative AI analyzes social media posts to understand art market trends and popular works. For example, market trends are predicted based on posts on Twitter (registered trademark) and Instagram (registered trademark). In addition, the Generative AI analyzes posts on online forums to understand art market trends and investor opinions. For example, market analysis is performed based on posts on Reddit and specialized forums. In addition, the Generative AI integrates data from social media and online forums to perform comprehensive market analysis. For example, trends are predicted by combining online topics with actual market data. This enables comprehensive market analysis.

[0048] The art market trend analysis unit can apply art market trend analysis to other markets (for example, the fashion market or the music market) to clarify the relationships between different markets. For example, the generative AI in the art market trend analysis unit applies art market trend analysis to the fashion market to clarify the relationships between different markets. For example, it can analyze the commonalities between art and fashion trends. The art market trend analysis unit can also apply art market trend analysis to the music market to clarify the relationships between different markets. For example, it can analyze the commonalities between popular art and music works and artists. The art market trend analysis unit can also apply art market trend analysis to other markets to clarify the relationships between different markets. For example, it can analyze the commonalities between art and technology trends. This can clarify the relationships between different markets.

[0049] The art market trend analysis unit can display the market trends analyzed by the generating AI on a customizable dashboard, allowing the user to select information that interests them. For example, the art market trend analysis unit can display the market trends analyzed by the generating AI on a customizable dashboard, allowing the user to select information that interests them. For example, the user can prioritize displaying information about a specific artist or genre. The art market trend analysis unit can also display the market trends analyzed by the generating AI on a customizable dashboard, allowing the user to select information that interests them. For example, the user can filter information for a specific period or region. The art market trend analysis unit can also display the market trends analyzed by the generating AI on a customizable dashboard, allowing the user to select information that interests them. For example, the user can display information related to a specific investment strategy. This allows the user to select information that interests them.

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

[0051] The art asset management system may further include a recommendation unit that recommends art assets based on the user's preferences. For example, the recommendation unit may analyze the user's past purchase history and browsing history to recommend art assets that the user may be interested in. The recommendation unit may also provide information about new exhibitions and auctions of art assets based on the user's preferences and interests. For example, if the user is interested in a particular artist or genre, related information may be provided preferentially. The recommendation unit may also analyze the user's social media activity to identify and recommend art assets in which the user is interested. This makes it possible to recommend art assets based on the user's preferences.

[0052] The art asset management system may further include an environmental monitoring unit that monitors the storage environment of the art assets. For example, the environmental monitoring unit may use sensors to measure the temperature and humidity of the location where the art assets are stored, and verify that an appropriate environment is being maintained. The environmental monitoring unit may also monitor the security status of the art asset storage location to reduce the risk of unauthorized access or theft. For example, security of the storage location may be enhanced using surveillance cameras or an intrusion detection system. The environmental monitoring unit may also include a function to notify the user if an abnormality occurs in the art asset storage environment. This allows for appropriate management of the art asset storage environment.

[0053] The art asset management system may further include a restoration management unit that manages the restoration history of art assets. For example, the restoration management unit may store the restoration history of art assets as digital data and manage detailed restoration information. The restoration management unit may also recommend an appropriate restoration expert when an art asset requires restoration. For example, the restoration management unit may select the most appropriate restoration expert depending on the type of restoration and the characteristics of the art asset. The restoration management unit may also include a function for monitoring the progress of restoration work in real time and reporting it to the user. This allows for detailed management of the restoration history of art assets and allows for appropriate restoration.

[0054] The art asset management system can further include an insurance management unit that manages insurance information for art assets. For example, the insurance management unit stores insurance contract information for art assets as digital data and manages detailed insurance information. The insurance management unit can also have a function that notifies users when it is time to renew their art asset insurance. For example, it can send a reminder to users when the insurance expiration date is approaching. The insurance management unit can also manage the insurance amount for art assets and information about insurance companies, and support insurance review and renewal. This allows for appropriate management of art asset insurance information and smooth insurance renewal and review.

[0055] The art asset management system can further include an exhibition management unit that manages the exhibition history of art assets. For example, the exhibition management unit stores the past exhibition history of art assets as digital data and manages detailed exhibition information. The exhibition management unit can also have a function to notify users of upcoming exhibitions of art assets. For example, it can inform users of the date, time, and location of exhibitions. The exhibition management unit can also analyze the effectiveness and influence of exhibitions based on the exhibition history of art assets and report this to users. This allows for detailed management of the exhibition history of art assets and evaluation of the effectiveness of exhibitions.

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

[0057] Step 1: The Art Asset Acquisition Department collects art asset data. For example, it digitizes and collects data on paintings and sculptures by famous artists and historical artworks. It also collects art auction results and museum exhibition information and stores them in a database. Step 2: The Portfolio Management Department manages the portfolio based on the data collected by the Art Asset Acquisition Department. For example, it analyzes fluctuations in the value of art assets and market trends to build an optimal portfolio. It also proposes a portfolio that matches the user's investment goals and risk tolerance. Step 3: The report generation unit generates asset reports based on the portfolio managed by the portfolio management unit. For example, it creates reports including the current value of art assets, past value fluctuations, and future value forecasts. It also generates customized reports according to the user's investment strategy. Step 4: The chat response unit responds to the user's questions in real time based on the reports generated by the report generation unit. For example, if a user asks, "What is the value of my current portfolio?", the chat response unit will provide an immediate answer based on the latest data. Similarly, if a user asks, "What art asset should I purchase next?", the chat response unit will analyze market trends and suggest the most suitable art asset.

[0058] (Example 2) The art asset management system according to an embodiment of the present invention uses a generative AI to manage a portfolio of expensive art assets that individuals would not normally have access to, and provides asset reports through a chat function, allowing individuals to efficiently and precisely manage their expensive art assets and obtain information in real time.

[0059] An art asset management system according to an embodiment includes an art asset acquisition unit, a portfolio management unit, a report generation unit, and a chat response unit. The art asset acquisition unit collects art asset data. For example, the art asset acquisition unit digitizes and collects data on paintings and sculptures by famous artists, historical artworks, and the like. The art asset acquisition unit can also collect art auction results and museum exhibition information. For example, the art asset acquisition unit collects auction winning bids and exhibition information and stores them in a database. The portfolio management unit manages a portfolio based on the data collected by the art asset acquisition unit. For example, the portfolio management unit analyzes fluctuations in the value of art assets and market trends to build an optimal portfolio. The portfolio management unit can also propose a portfolio based on a user's investment goals and risk tolerance. For example, the portfolio management unit proposes a stable asset allocation with reduced risk based on the user's investment history and market data. The report generation unit generates an asset report based on the portfolio managed by the portfolio management unit. For example, the report generation unit creates a report including the current value of art assets, past value fluctuations, and future value predictions. The report generation unit can also generate customized reports based on the user's investment strategy. For example, the report generation unit provides detailed reports based on the user's investment goals and risk tolerance. The chat response unit responds to the user's questions in real time based on the reports generated by the report generation unit. For example, when a user asks, "What is the value of my current portfolio?", the chat response unit provides an immediate response based on the latest data. Furthermore, when a user asks, "What art asset should I purchase next?", the chat response unit can analyze market trends and suggest optimal art assets. For example, the chat response unit suggests art assets that are likely to increase in value in the future based on the user's investment history and market data. As a result, the art asset management system according to the embodiment allows individuals to efficiently and precisely manage their expensive art assets and obtain information in real time.For example, users can accurately grasp the value of their art assets and make optimal purchase and sale decisions. Real-time information also allows users to stay up to date with the latest market trends.

[0060] The art asset acquisition unit can scan handwritten answers and convert them into digital data. For example, the art asset acquisition unit reads handwritten answers using a scanner and saves them as image data. It then converts the image data into text data using OCR technology. The art asset acquisition unit can also take a photo of a handwritten answer using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The art asset acquisition unit can also write handwritten answers with a dedicated digital pen, which then converts the data into digital data in real time. For example, a sensor can detect the movement of the pen and save it as text data. In this way, converting handwritten answers into digital data makes it easier for the generative AI to analyze.

[0061] The art asset acquisition unit can analyze writing pressure and stroke order to extract the writer's characteristics. The art asset acquisition unit, for example, uses a writing pressure sensor to analyze how an answer is written. For example, it collects data on the strength of writing pressure and extracts the writer's characteristics. In addition, to analyze the stroke order, the art asset acquisition unit tracks the movement of a digital pen and collects data on the writer's stroke order. For example, it analyzes the stroke order pattern and identifies the writer's characteristics. In addition, the art asset acquisition unit combines the data on writing pressure and stroke order to comprehensively analyze the writer's characteristics. For example, it extracts the writer's characteristics based on the degree of agreement between changes in writing pressure and stroke order. In this way, by analyzing writing pressure and stroke order, the writer's characteristics can be grasped in detail.

[0062] The art asset acquisition unit can estimate the student's emotions using an emotion estimation function and reflect the emotion data in the evaluation of the answer sheet. For example, the art asset acquisition unit uses a camera to capture the student's facial expression while writing the answer sheet and analyzes the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expression. The art asset acquisition unit also records the student's voice while writing the answer sheet and estimates the emotion using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates the emotion score. The art asset acquisition unit also uses a sensor to collect the student's biometric data (heart rate and electrodermal activity) while writing the answer sheet and analyzes the emotion using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate. This allows the student's emotions to be reflected in the evaluation, making it possible to perform a more comprehensive evaluation.

[0063] The art asset acquisition unit can convert what a student dictates into text using voice input and treat the text as an answer sheet. For example, the art asset acquisition unit records what a student dictates with a microphone and converts it into text data using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. The art asset acquisition unit also creates a system that recognizes what a student dictates in real time and displays it as text data. For example, text is displayed on a screen simultaneously with voice input. The art asset acquisition unit also uses voice input to convert what a student dictates into text and saves the text data as an answer sheet. For example, voice recognition technology is used to perform highly accurate text conversion. As a result, even students who have difficulty writing by hand or typing can submit answer sheets by using voice input.

[0064] The art asset acquisition unit can analyze images and diagrams and include visual information in the evaluation. For example, the art asset acquisition unit analyzes images and diagrams included in an answer sheet using image recognition technology and converts the content into text data. For example, the content of the diagram is automatically analyzed and reflected in the evaluation. The art asset acquisition unit also analyzes images and diagrams included in an answer sheet and builds a system that evaluates based on visual information. For example, the content of the image is analyzed and reflected in the evaluation of the answer. The art asset acquisition unit also analyzes answer sheets that include images and diagrams and integrates the visual information with text data for evaluation. For example, the visual information is analyzed using image recognition technology and reflected in the evaluation of the answer. This makes it possible to perform a more detailed evaluation by analyzing answer sheets that include images and diagrams.

[0065] The art asset acquisition unit can use an emotion estimation function to monitor students' emotions in real time and provide feedback according to their emotions. For example, the art asset acquisition unit uses a camera to capture the student's facial expression when reading an answer sheet and uses an emotion estimation algorithm to analyze the student's emotions in real time. For example, it calculates an emotion score based on changes in facial expression and provides feedback. The art asset acquisition unit also records the student's voice when reading an answer sheet and uses voice analysis technology to estimate the student's emotion in real time. For example, it analyzes the tone and speed of the voice, calculates an emotion score, and provides feedback. The art asset acquisition unit also uses a sensor to collect the student's biometric data (heart rate and electrodermal activity) when reading an answer sheet and uses an emotion estimation algorithm to analyze the student's emotion in real time. For example, it calculates an emotion score based on fluctuations in heart rate and provides feedback. In this way, the student's emotions can be monitored in real time and appropriate feedback can be provided, thereby improving learning effectiveness.

[0066] The summary generation unit can refer to background information and topic models to understand the context. For example, when the generation AI creates a summary, the summary generation unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The summary generation unit also uses topic models to understand the context when the generation AI creates a summary. For example, it extracts related keywords and phrases based on the topic model. The summary generation unit also references related background information and topic models when the generation AI creates a summary, building a system for understanding the context. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0067] The summary generation unit can analyze the logical structure of an answer and the development of the arguments to generate a logical summary. For example, the summary generation unit uses a generation AI to analyze the logical structure of an answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The summary generation unit also analyzes the development of arguments in an answer and builds a system in which the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The summary generation unit also develops an algorithm for the generation AI to analyze the logical structure of an answer and the development of arguments to generate a logical summary. For example, it evaluates the logical consistency and the importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of an answer and the development of arguments in a question.

[0068] The summary generation unit uses the emotion estimation function to generate a summary that captures the emotional nuances of the answer, allowing the emotional elements to be reflected in the evaluation. For example, when the generation AI summarizes, the summary generation unit uses the emotion estimation function to capture the emotional nuances of the answer. For example, it generates a summary based on an emotion score. The summary generation unit also uses the emotion estimation function to build a system in which the generation AI reflects the emotional elements of the answer in the evaluation. For example, it performs the evaluation based on the emotion score. The summary generation unit also develops an algorithm for the generation AI to use the emotion estimation function to generate a summary that captures the emotional nuances of the answer. For example, it generates a summary based on the emotion score and reflects that in the evaluation. In this way, by generating a summary that captures the emotional nuances, the emotional elements can also be reflected in the evaluation.

[0069] The portfolio management unit can extend the portfolio management function to other asset classes, including real estate and stocks, to provide comprehensive asset management. For example, the portfolio management unit can extend the portfolio management function to real estate assets, allowing users to manage their real estate investments. For example, the unit can analyze real estate value fluctuations and market trends and propose optimal investment strategies. The portfolio management unit can also extend the portfolio management function to stock assets, allowing users to manage their stock investments. For example, the unit can analyze stock price fluctuations and corporate performance and propose optimal investment strategies. The portfolio management unit can also extend the portfolio management function to comprehensive asset management, allowing users to centrally manage multiple asset classes. For example, the unit can manage art assets, real estate, and stocks in an integrated manner and propose optimal portfolios. This makes it possible to provide comprehensive asset management.

[0070] The portfolio management unit can add a function that allows a portfolio proposed by the generation AI to be shared with other users and that allows the user to receive community-based investment advice. The portfolio management unit, for example, adds a function that allows a portfolio proposed by the generation AI to be shared with other users and that allows the user to receive community-based investment advice. For example, it provides a platform where users can exchange opinions and give advice to each other. The portfolio management unit also adds a function that allows the user to share a portfolio proposed by the generation AI and that allows the user to receive feedback from other users. For example, the user can comment on and rate the proposed portfolio. The portfolio management unit also adds a function that allows the user to share a portfolio proposed by the generation AI and that allows the user to receive community-based investment advice. For example, the user can adjust the portfolio by referring to the opinions of other investors. This allows the user to receive community-based investment advice.

[0071] The portfolio management unit can use the emotion estimation function to monitor the user's emotions regarding investments in real time and propose risk management measures according to the emotions. The portfolio management unit, for example, uses the emotion estimation function to monitor the user's emotions regarding investments in real time and proposes risk management measures according to the emotions. For example, if the user is feeling anxious, it proposes an investment strategy that reduces risk. The portfolio management unit also uses the emotion estimation function to monitor the user's emotions regarding investments in real time and proposes risk management measures according to the emotions. For example, if the user is excited, it provides advice to encourage calm decision-making. The portfolio management unit also uses the emotion estimation function to monitor the user's emotions regarding investments in real time and proposes risk management measures according to the emotions. For example, it proposes an asset allocation that gives the user a sense of security. In this way, it is possible to propose risk management measures according to the user's emotions.

[0072] When generating an asset report, the report generation unit can perform a simulation that combines past data and future predictions and present multiple scenarios to the user. In the report generation unit, for example, the generation AI performs a simulation that combines past data and future predictions and presents multiple scenarios to the user. For example, it simulates fluctuations in asset value under different market conditions. In addition, the report generation unit performs a simulation that combines past data and future predictions and presents multiple scenarios to the user. For example, it simulates fluctuations in asset value when different investment strategies are adopted. In addition, the report generation unit performs a simulation that combines past data and future predictions and presents multiple scenarios to the user. For example, it simulates fluctuations in asset value under different economic conditions. This makes it possible to present multiple scenarios to the user.

[0073] The report generation unit can also include physical management information, such as the storage status and insurance information of the art assets, in the asset report. For example, the report generation unit includes the storage status of the art assets in the asset report. For example, it may include details of the storage environment, such as which museum or gallery the work is stored in. The report generation unit also includes insurance information for the art assets in the asset report. For example, it may include the insurance amount for the work, information about the insurance company, and the insurance period. The report generation unit also includes physical management information for the art assets in the asset report. For example, it may include the restoration history, exhibition history, and transportation history of the work. This makes it possible to provide an asset report that also includes physical management information.

[0074] The report generation unit uses the emotion estimation function to analyze the emotion of the user when viewing a report and customize the report according to the emotion. The report generation unit, for example, uses the emotion estimation function to analyze the emotion of the user when viewing a report and customize the report according to the emotion. For example, if the user is feeling anxious, information that gives a sense of security is emphasized. The report generation unit also uses the emotion estimation function to analyze the emotion of the user when viewing a report and customize the report according to the emotion. For example, if the user is excited, information that encourages calm decision-making is provided. The report generation unit also uses the emotion estimation function to analyze the emotion of the user when viewing a report and customize the report according to the emotion. For example, information that the user is interested in is preferentially displayed. This makes it possible to customize the report according to the user's emotion.

[0075] The report generation unit can provide the asset report in other formats, such as a video report or an interactive report, to deepen the user's understanding. For example, the report generation unit can provide the asset report in a video report format to allow the user to understand it visually. For example, value fluctuations and market trends of art assets can be explained through video. The report generation unit can also provide the asset report in an interactive report format to allow the user to manipulate the information themselves. For example, the user can click on a graph or chart to display detailed information. The report generation unit can also provide the asset report in other formats to deepen the user's understanding. For example, the report generation unit can provide an audio report in podcast format with commentary from an art market expert. This allows the asset report to be provided in other formats to deepen the user's understanding.

[0076] The report generation unit can add a function that allows asset reports to be shared with other users and receive feedback. The report generation unit adds, for example, a function that allows asset reports to be shared with other users and receive feedback. For example, a user can share a report and receive comments and ratings from other investors. The report generation unit also adds a function that allows asset reports to be shared with other users and receive feedback. For example, a user can share a report and receive advice from an expert. The report generation unit also adds a function that allows asset reports to be shared with other users and receive feedback. For example, a user can share a report and exchange opinions on a community basis. This allows asset reports to be shared with other users and receive feedback.

[0077] The report generation unit uses the emotion estimation function to analyze the emotion of the user when viewing a report in real time, and can provide additional information according to the emotion. The report generation unit, for example, uses the emotion estimation function to analyze the emotion of the user when viewing a report in real time, and can provide additional information according to the emotion. For example, if the user is feeling anxious, information that gives a sense of security is added. The report generation unit also uses the emotion estimation function to analyze the emotion of the user when viewing a report in real time, and can provide additional information according to the emotion. For example, if the user is excited, information that encourages the user to make a calm decision is added. The report generation unit also uses the emotion estimation function to analyze the emotion of the user when viewing a report in real time, and can provide additional information according to the emotion. For example, information that the user is interested in is preferentially displayed. This makes it possible to provide additional information according to the user's emotion.

[0078] The chat response unit can add a function to analyze a user's past dialogue history and provide individually optimized answers. For example, the chat response unit adds a function in which the generation AI analyzes a user's past dialogue history and provides individually optimized answers. For example, related information is provided based on the content of the user's past questions. The chat response unit also adds a function in which the generation AI analyzes a user's past dialogue history and provides individually optimized answers. For example, information is provided according to the user's preferences and interests. The chat response unit also adds a function in which the generation AI analyzes a user's past dialogue history and provides individually optimized answers. For example, optimal investment advice is provided based on the user's investment history. This makes it possible to provide optimized answers based on the user's past dialogue history.

[0079] The chat response unit allows the generation AI to periodically notify the user of market trends and the status of their portfolio through the chat function. The chat response unit, for example, allows the generation AI to periodically notify the user of market trends through the chat function. For example, weekly market reports and important news are provided via chat. The chat response unit also allows the generation AI to periodically notify the user of the status of their portfolio through the chat function. For example, portfolio value fluctuations and risk assessments are provided via chat. The chat response unit also allows the generation AI to periodically notify the user of market trends and the status of their portfolio through the chat function. For example, advice based on the user's investment strategy is provided via chat. This allows the user to periodically be notified of market trends and the status of their portfolio.

[0080] The chat response unit can use the emotion estimation function to analyze the emotion of the user in response to the question and provide an answer that corresponds to the emotion. The chat response unit, for example, uses the emotion estimation function to analyze the emotion of the user in response to the question and provide an answer that corresponds to the emotion. For example, if the user is feeling anxious, an answer that gives a sense of security is provided. The chat response unit also uses the emotion estimation function to analyze the emotion of the user in response to the question and provide an answer that corresponds to the emotion. For example, if the user is excited, an answer that encourages the user to make a calm decision is provided. The chat response unit also uses the emotion estimation function to analyze the emotion of the user in response to the question and provide an answer that corresponds to the emotion. For example, if the user is interested, detailed information is provided. In this way, an answer that corresponds to the user's emotion can be provided.

[0081] The chat response unit can make the chat function compatible with other platforms such as smart speakers and mobile apps, allowing users to obtain information anywhere. For example, the chat response unit can make the chat function compatible with smart speakers, allowing users to obtain information by voice. For example, the chat response unit can provide information about art assets through Amazon Echo or Google (registered trademark) Home. The chat response unit can also make the chat function compatible with mobile apps, allowing users to obtain information anywhere. For example, the chat response unit can notify market trends in real time through a smartphone app. The chat response unit can also make the chat function compatible with other platforms, allowing users to obtain information anywhere. For example, the chat response unit can provide information about art assets through a wearable device. This allows users to obtain information anywhere.

[0082] The chat response unit can add a community function that allows users to exchange information and have discussions with other users through the chat function. The chat response unit, for example, adds a community function that allows users to exchange information and have discussions with other users through the chat function. For example, it provides a chat room where users can exchange opinions about art assets. The chat response unit also adds a community function that allows users to exchange information and have discussions with other users through the chat function. For example, it provides a discussion forum where experts and investors participate. The chat response unit also adds a community function that allows users to exchange information and have discussions with other users through the chat function. For example, it provides a platform where users can discuss art market trends and investment strategies. This allows users to exchange information and have discussions with other users.

[0083] The chat response unit can use the emotion estimation function to analyze the user's emotion in response to the question in real time and provide additional information or advice according to the emotion. The chat response unit, for example, uses the emotion estimation function to analyze the user's emotion in response to the question in real time and provide additional information according to the emotion. For example, if the user is feeling anxious, information that gives a sense of security is added. The chat response unit can also use the emotion estimation function to analyze the user's emotion in response to the question in real time and provide advice according to the emotion. For example, if the user is excited, advice is given to encourage the user to make a calm decision. The chat response unit can also use the emotion estimation function to analyze the user's emotion in response to the question in real time and provide additional information or advice according to the emotion. For example, if the user is interested, detailed information is provided. This makes it possible to provide additional information or advice according to the user's emotion.

[0084] Furthermore, the art asset management system is equipped with an art market trend analysis unit. When analyzing trends in the art market, this art market trend analysis unit analyzes past auction results and exhibition information in chronological order to identify long-term trends. In the art market trend analysis unit, for example, the generation AI analyzes past auction results in chronological order to identify long-term trends in the art market. For example, it analyzes price fluctuations for specific artists or genres. In addition, the generation AI analyzes exhibition information in chronological order to identify long-term trends in the art market. For example, it predicts market trends based on the exhibition history of art museums and galleries. In addition, the generation AI integrates past auction results and exhibition information to identify long-term trends in the art market. For example, it analyzes the relationship between winning bid prices at auctions and the number of exhibitions. This makes it possible to identify long-term trends in the art market.

[0085] The Art Market Trend Analysis Department incorporates information from social media and online forums into its art market trend analysis, enabling more comprehensive market analysis. In the Art Market Trend Analysis Department, for example, the Generative AI analyzes social media posts to understand art market trends and popular works. For example, market trends are predicted based on posts on Twitter (registered trademark) and Instagram (registered trademark). In addition, the Generative AI analyzes posts on online forums to understand art market trends and investor opinions. For example, market analysis is performed based on posts on Reddit and specialized forums. In addition, the Generative AI integrates data from social media and online forums to perform comprehensive market analysis. For example, trends are predicted by combining online topics with actual market data. This enables comprehensive market analysis.

[0086] The art market trend analysis unit uses the emotion estimation function to analyze public sentiment toward the art market and make emotion-based market predictions. For example, the art market trend analysis unit uses the emotion estimation function to analyze public sentiment toward the art market from social media posts and news articles. For example, it identifies artworks and artists with a high level of positive sentiment. The art market trend analysis unit also uses the emotion estimation function to analyze public sentiment toward the art market and make emotion-based market predictions. For example, it predicts price increases for artworks with high emotion scores. The art market trend analysis unit also uses the emotion estimation function to analyze public sentiment toward the art market in real time and make emotion-based market predictions. For example, it predicts market trends according to emotional fluctuations. This makes it possible to make market predictions based on public sentiment.

[0087] The art market trend analysis unit can apply art market trend analysis to other markets (for example, the fashion market or the music market) to clarify the relationships between different markets. For example, the generative AI in the art market trend analysis unit applies art market trend analysis to the fashion market to clarify the relationships between different markets. For example, it can analyze the commonalities between art and fashion trends. The art market trend analysis unit can also apply art market trend analysis to the music market to clarify the relationships between different markets. For example, it can analyze the commonalities between popular art and music works and artists. The art market trend analysis unit can also apply art market trend analysis to other markets to clarify the relationships between different markets. For example, it can analyze the commonalities between art and technology trends. This can clarify the relationships between different markets.

[0088] The art market trend analysis unit can display the market trends analyzed by the generating AI on a customizable dashboard, allowing the user to select information that interests them. For example, the art market trend analysis unit can display the market trends analyzed by the generating AI on a customizable dashboard, allowing the user to select information that interests them. For example, the user can prioritize displaying information about a specific artist or genre. The art market trend analysis unit can also display the market trends analyzed by the generating AI on a customizable dashboard, allowing the user to select information that interests them. For example, the user can filter information for a specific period or region. The art market trend analysis unit can also display the market trends analyzed by the generating AI on a customizable dashboard, allowing the user to select information that interests them. For example, the user can display information related to a specific investment strategy. This allows the user to select information that interests them.

[0089] The art market trend analysis unit can use the emotion estimation function to analyze public sentiment toward the art market in real time and provide market forecasts and advice based on those sentiments. The art market trend analysis unit, for example, uses the emotion estimation function to analyze public sentiment toward the art market in real time and provide market forecasts based on those sentiments. For example, it predicts price increases for artworks with a high level of positive sentiment. The art market trend analysis unit also uses the emotion estimation function to analyze public sentiment toward the art market in real time and provide advice based on those sentiments. For example, it recommends purchasing artworks with a high emotion score. The art market trend analysis unit also uses the emotion estimation function to analyze public sentiment toward the art market in real time and provide market forecasts and advice based on those sentiments. For example, it adjusts investment strategies based on emotional fluctuations. This makes it possible to provide market forecasts and advice based on public sentiment.

[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 art asset management system may further include a recommendation unit that recommends art assets based on the user's preferences. For example, the recommendation unit may analyze the user's past purchase history and browsing history to recommend art assets that the user may be interested in. The recommendation unit may also provide information about new exhibitions and auctions of art assets based on the user's preferences and interests. For example, if the user is interested in a particular artist or genre, related information may be provided preferentially. The recommendation unit may also analyze the user's social media activity to identify and recommend art assets in which the user is interested. This makes it possible to recommend art assets based on the user's preferences.

[0092] The art asset management system may further include an environmental monitoring unit that monitors the storage environment of the art assets. For example, the environmental monitoring unit may use sensors to measure the temperature and humidity of the location where the art assets are stored, and verify that an appropriate environment is being maintained. The environmental monitoring unit may also monitor the security status of the art asset storage location to reduce the risk of unauthorized access or theft. For example, security of the storage location may be enhanced using surveillance cameras or an intrusion detection system. The environmental monitoring unit may also include a function to notify the user if an abnormality occurs in the art asset storage environment. This allows for appropriate management of the art asset storage environment.

[0093] The art asset management system may further include a restoration management unit that manages the restoration history of art assets. For example, the restoration management unit may store the restoration history of art assets as digital data and manage detailed restoration information. The restoration management unit may also recommend an appropriate restoration expert when an art asset requires restoration. For example, the restoration management unit may select the most appropriate restoration expert depending on the type of restoration and the characteristics of the art asset. The restoration management unit may also include a function for monitoring the progress of restoration work in real time and reporting it to the user. This allows for detailed management of the restoration history of art assets and allows for appropriate restoration.

[0094] The art asset management system can further include an insurance management unit that manages insurance information for art assets. For example, the insurance management unit stores insurance contract information for art assets as digital data and manages detailed insurance information. The insurance management unit can also have a function that notifies users when it is time to renew their art asset insurance. For example, it can send a reminder to users when the insurance expiration date is approaching. The insurance management unit can also manage the insurance amount for art assets and information about insurance companies, and support insurance review and renewal. This allows for appropriate management of art asset insurance information and smooth insurance renewal and review.

[0095] The art asset management system can further include an exhibition management unit that manages the exhibition history of art assets. For example, the exhibition management unit stores the past exhibition history of art assets as digital data and manages detailed exhibition information. The exhibition management unit can also have a function to notify users of upcoming exhibitions of art assets. For example, it can inform users of the date, time, and location of exhibitions. The exhibition management unit can also analyze the effectiveness and influence of exhibitions based on the exhibition history of art assets and report this to users. This allows for detailed management of the exhibition history of art assets and evaluation of the effectiveness of exhibitions.

[0096] The art asset management system can further use an emotion estimation function to recommend art assets based on the user's emotions. For example, a camera can capture the user's facial expression while viewing art assets, and an emotion estimation algorithm can be used to analyze the emotion. For example, if the user is excited, art assets that are likely to evoke a similar emotion can be recommended. The system can also record the user's voice and estimate their emotions using voice analysis technology. For example, if the user is relaxed, art assets that have a relaxing effect can be recommended. The system can also collect the user's biometric data (heart rate and electrodermal activity) using sensors, and analyze their emotions using an emotion estimation algorithm. This makes it possible to recommend art assets based on the user's emotions.

[0097] The art asset management system can also use an emotion estimation function to provide investment advice tailored to the user's emotions. For example, a camera can capture a user's facial expression while they are considering purchasing an art asset, and an emotion estimation algorithm can be used to analyze their emotions. For example, if the user is feeling anxious, investment advice to reduce risk can be provided. The system can also record the user's voice and use voice analysis technology to estimate their emotions. For example, if the user is excited, advice is provided to encourage them to make calm decisions. The system can also collect the user's biometric data (heart rate and electrodermal activity) using sensors and analyze their emotions using an emotion estimation algorithm. This makes it possible to provide investment advice tailored to the user's emotions.

[0098] The art asset management system can also use an emotion estimation function to customize reports based on the user's emotions. For example, a camera can capture the user's facial expression while viewing a report, and an emotion estimation algorithm can be used to analyze the user's emotions. For example, if the user is feeling anxious, information that provides a sense of security can be emphasized. The system can also record the user's voice and use voice analysis technology to estimate emotions. For example, if the user is excited, information that encourages calm decision-making can be provided. The system can also collect the user's biometric data (heart rate and electrodermal activity) using sensors, and analyze the user's emotions using an emotion estimation algorithm. This makes it possible to customize reports based on the user's emotions.

[0099] The art asset management system can further use an emotion estimation function to provide feedback according to the user's emotions. For example, a camera can capture the user's facial expression while they are viewing art asset information, and an emotion estimation algorithm can be used to analyze their emotions. For example, if the user is excited, detailed information can be provided. The system can also record the user's voice and use voice analysis technology to estimate their emotions. For example, if the user is feeling anxious, it can provide feedback that gives a sense of security. The system can also collect the user's biometric data (heart rate and electrodermal activity) using sensors, and analyze their emotions using an emotion estimation algorithm. This makes it possible to provide feedback according to the user's emotions.

[0100] The art asset management system can further use an emotion estimation function to evaluate art assets based on the user's emotions. For example, a camera can capture the user's facial expression while viewing an art asset, and an emotion estimation algorithm can be used to analyze the emotion. For example, if the user shows positive emotions, the evaluation of the art asset can be increased. The system can also record the user's voice and use voice analysis technology to estimate emotions. For example, if the user shows negative emotions, the evaluation of the art asset can be decreased. The system can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor, and use an emotion estimation algorithm to analyze emotions. This makes it possible to evaluate art assets based on the user's emotions.

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

[0102] Step 1: The Art Asset Acquisition Department collects art asset data. For example, it digitizes and collects data on paintings and sculptures by famous artists and historical artworks. It also collects art auction results and museum exhibition information and stores them in a database. Step 2: The Portfolio Management Department manages the portfolio based on the data collected by the Art Asset Acquisition Department. For example, it analyzes fluctuations in the value of art assets and market trends to build an optimal portfolio. It also proposes a portfolio that matches the user's investment goals and risk tolerance. Step 3: The report generation unit generates asset reports based on the portfolio managed by the portfolio management unit. For example, it creates reports including the current value of art assets, past value fluctuations, and future value forecasts. It also generates customized reports according to the user's investment strategy. Step 4: The chat response unit responds to the user's questions in real time based on the reports generated by the report generation unit. For example, if a user asks, "What is the value of my current portfolio?", the chat response unit will provide an immediate answer based on the latest data. Similarly, if a user asks, "What art asset should I purchase next?", the chat response unit will analyze market trends and suggest the most suitable art asset.

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

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

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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. an art asset acquisition unit that collects data on art assets; a portfolio management unit that manages a portfolio based on the data collected by the art asset acquisition unit; a report generator for generating asset reports based on the portfolio managed by the portfolio manager; a chat response unit that responds in real time to questions from users based on the report generated by the report generation unit. A system characterized by:

2. The art asset acquisition unit When digitizing artworks, the 3D scanning technology is used to generate three-dimensional data of the artworks, allowing the user to view them in a 360-degree view.

2. The system of claim 1.

3. The art asset acquisition unit The historical background and the artist's intentions are analyzed for the digital data of the art assets, and the results are provided to the user.

2. The system of claim 1.

4. The art asset acquisition unit Analyzing the emotions of the user when viewing the art assets and recommending artworks that match the preferences of the user.

2. The system of claim 1.

5. The art asset acquisition unit Using the digital data of the art assets, we provide a viewing experience in a virtual reality (VR) environment.

2. The system of claim 1.

6. The art asset acquisition unit Share digital data of the art assets with educational institutions and museums to promote their use for educational purposes.

2. The system of claim 1.

Images