System

Abstract

An object of a system according to an embodiment is to efficiently combine patent technologies according to market needs and realize matching between companies.SOLUTION: A system includes a core technology database creation part, a patent analyzing part, a generation AI part, and a mediation service part. A core technology database creation part predicts market needs and creates a database of necessary core technologies. The patent analyzing unit analyzes the patent related to the core technology databased by the core technology databasing unit from the database of the patent Office. The generation AI unit generates a plan in which the patent technologies analyzed by the patent analyzing unit are combined. A mediation service part performs Web information provision and matching contract mediation on the basis of the combination plan generated by the generation AI part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently combine patented technologies in accordance with market needs and to match them between companies.

[0005] The system of the embodiment aims to efficiently combine patented technologies in accordance with market needs and realize matching between companies. [Means for solving the problem]

[0006] The system according to the embodiment comprises a core technology database creation unit, a patent analysis unit, a generation AI unit, and an intermediary service unit. The core technology database creation unit predicts market needs and creates a database of necessary core technologies. The patent analysis unit analyzes patents related to the core technologies created in the database by the core technology database creation unit from the Japan Patent Office database. The generation AI unit creates proposals that combine the patent technologies analyzed by the patent analysis unit. The intermediary service unit provides web information and mediates matching contracts based on the combination proposals created by the generation AI unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently combine patented technologies according to market needs and realize matching between companies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The patent matching platform according to an embodiment of the present invention is a system that combines patent technologies from multiple companies and organizations, proposes utilization value to companies that want to create innovative products and services, increases intellectual property revenue through contracts, stabilizes management, and secures new research and development funds. As a result, the patent matching platform promotes the creation of new products and services by combining patent technologies, increases intellectual property revenue for companies, stabilizes management, and secures new research and development funds.

[0029] The patent matching platform according to the embodiment includes a core technology database creation unit, a patent analysis unit, a generation AI unit, and an intermediary service unit. The core technology database creation unit predicts market needs and creates a database of necessary core technologies. For example, AI analyzes market data and lists core technologies such as next-generation energy and medical technologies. The patent analysis unit analyzes patents related to the core technologies created by the core technology database creation unit from the Japan Patent Office database. For example, a patent analysis service is used to extract patents related to specific energy technologies and identify their technical details and scope of rights. The generation AI unit generates proposals that combine the patent technologies analyzed by the patent analysis unit. For example, the generation AI may propose a new medical device that combines next-generation energy technology and medical technology. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate proposals for patent technology combinations. The intermediary service unit provides web information and mediates matching contracts based on the combination proposals created by the generation AI unit. For example, the proposed patent technology combinations are published on a web platform, and if interested companies wish to enter into a contract, the intermediary service unit supports the contract procedures. As a result, the patent matching platform according to the embodiment will promote the creation of new products and services through the combination of patented technologies, increase intellectual property revenue for companies, stabilize management, and secure funds for new research and development.

[0030] The Core Technology Database Division can reflect real-time consumer reactions, including social media posts and comments on review sites. For example, when AI analyzes market data, the Core Technology Database Division collects social media posts and comments on review sites in real time and reflects consumer reactions. For example, if there are many positive comments about a particular technology, that technology will be listed first. This allows for a more accurate understanding of market needs by reflecting real-time consumer reactions.

[0031] The Core Technology Database Creation Department cross-references technological trends in different industries and can discover new market needs. For example, when AI analyzes market data, the Core Technology Database Creation Department cross-references technological trends in different industries and can discover new market needs. For example, it can identify new market needs that combine energy technology and medical technology. This makes it possible to discover new market needs by cross-referencing technological trends in different industries.

[0032] The Patent Analysis Department can analyze not only the technical details of patents but also their commercial success stories. For example, to analyze both the technical details and commercial success stories of patents, the Patent Analysis Department will integrate patent databases and commercial databases and build a system that simultaneously analyzes the technical content of patents and their commercial success stories. This allows for an analysis that includes both the technical details and commercial success stories of patents, making it possible to select patent technologies with higher feasibility.

[0033] The Patent Analysis Department can compare the results of patent analysis with technology trends in different industries to discover new application possibilities. For example, the Patent Analysis Department can integrate patent databases and industry databases to compare the results of patent analysis with technology trends in different industries and build a system to discover new application possibilities. For example, comparing energy technology with medical technology. This makes it possible to discover new application possibilities by comparing technology trends in different industries.

[0034] The Patent Analysis Department can automatically translate the results of patent analysis into different languages ​​and obtain feedback from an international perspective. The Patent Analysis Department, for example, builds a system that automatically translates the results of patent analysis into different languages ​​and collects feedback from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. This allows the results of patent analysis to be automatically translated into different languages ​​and feedback to be obtained from an international perspective, making it possible to evaluate patent technologies from a more multifaceted perspective.

[0035] The Patent Analysis Department can convert the results of patent analysis into visual notes or mind maps to make them easier to understand visually. For example, the Patent Analysis Department can convert the results of patent analysis into visual notes and build a system that visually displays the key points and relevance of patents. For example, the technical content of patents can be shown using diagrams and icons. In this way, converting the results of patent analysis into visual notes or mind maps makes them easier to understand visually.

[0036] The Generative AI Department cross-references technologies from different industries and can propose combinations that meet new market needs. For example, when the Generative AI creates combination proposals, it cross-references technologies from different industries and builds a system that proposes combinations that meet new market needs. For example, it identifies a new market need that combines energy technology and medical technology. This makes it possible to propose combinations that meet new market needs by cross-referencing technologies from different industries.

[0037] The Generative AI Department can refer to commercially successful cases of patented technologies and propose combinations that are highly feasible. For example, when the Generative AI creates combination proposals, it references commercially successful cases of patented technologies and builds a system that proposes highly feasible combinations. For example, it automatically generates optimal combinations based on past successful cases. This makes it possible to propose highly feasible combinations by referring to commercially successful cases of patented technologies.

[0038] The Generative AI Department customizes combination proposals to meet the market needs of different regions and cultural spheres, making it possible to make technical proposals from a global perspective. For example, in order to customize the combination proposals created by the Generative AI to meet the market needs of different regions and cultural spheres, the Generative AI Department uses AI to analyze market data for each region and make technical proposals. For example, it makes technical proposals for the Asian market. This makes it possible to make technical proposals from a global perspective by customizing them to meet the market needs of different regions and cultural spheres.

[0039] The generation AI unit can visualize and provide combination proposals, allowing users to intuitively understand the relevance of technologies. For example, to visualize the combination proposals created by the generation AI, the generation AI unit builds a system in which the AI ​​displays the relevance of technologies in graphs and charts. For example, it provides an interface that visually shows the relevance of technologies. In this way, by visualizing the combination proposals, users can intuitively understand the relevance of technologies.

[0040] The intermediary service department cross-references technology trends in different industries on a web platform and can provide information that meets new market needs. The intermediary service department, for example, builds a system that cross-references technology trends in different industries on a web platform and provides information that meets new market needs. For example, a new market need that combines energy technology and medical technology is identified. This makes it possible to provide information that meets new market needs by cross-referencing technology trends in different industries.

[0041] The intermediary service department can refer to commercially successful cases of patented technologies on a web platform and make highly feasible technical proposals. The intermediary service department will, for example, build a system that refers to commercially successful cases of patented technologies on a web platform and makes highly feasible technical proposals. For example, it will automatically generate optimal technical proposals based on past success cases. This will enable highly feasible technical proposals to be made by referring to commercially successful cases of patented technologies.

[0042] The intermediary service department will automatically translate the information provided on the web platform into different languages ​​and obtain feedback from an international perspective. The intermediary service department will, for example, build a system that automatically translates the information provided on the web platform into different languages ​​and collects feedback from an international perspective. For example, it will translate into multiple languages, such as English, French, and Chinese. This will enable the information provided on the web platform to be automatically translated into different languages ​​and receive feedback from an international perspective, making it possible to provide more multifaceted information.

[0043] The intermediary service unit can convert information provided on the web platform into visual notes or mind maps to make it easier to understand visually. For example, the intermediary service unit converts information provided on the web platform into visual notes and builds a system that visually displays the main points and relationships of the information. For example, the technical content of the information is shown using diagrams and icons. In this way, converting information provided on the web platform into visual notes or mind maps makes it easier to understand visually.

[0044] The intermediary service department customizes the information provided on the web platform to meet the market needs of different regions and cultural spheres, and can make technical proposals from a global perspective. For example, in order to customize the information provided on the web platform to meet the market needs of different regions and cultural spheres, the intermediary service department uses AI to analyze market data for each region and make technical proposals. For example, it makes technical proposals for the Asian market. This makes it possible to make technical proposals from a global perspective by customizing the information provided on the web platform to meet the market needs of different regions and cultural spheres.

[0045] The intermediary service unit can visualize and provide information provided on the web platform, allowing users to intuitively understand the relevance of technologies. For example, in order to visualize the information provided on the web platform, the intermediary service unit builds a system in which AI displays the relevance of technologies in graphs and charts. For example, it provides an interface that visually shows the relevance of technologies. In this way, by visualizing the information, users can intuitively understand the relevance of technologies.

[0046] The intermediary service department cross-references technology trends in different industries on a web platform and can provide information that meets new market needs. The intermediary service department, for example, builds a system that cross-references technology trends in different industries on a web platform and provides information that meets new market needs. For example, a new market need that combines energy technology and medical technology is identified. This makes it possible to provide information that meets new market needs by cross-referencing technology trends in different industries.

[0047] The intermediary service department can refer to commercially successful cases of patented technologies on a web platform and make highly feasible technical proposals. The intermediary service department will, for example, build a system that refers to commercially successful cases of patented technologies on a web platform and makes highly feasible technical proposals. For example, it will automatically generate optimal technical proposals based on past success cases. This will enable highly feasible technical proposals to be made by referring to commercially successful cases of patented technologies.

[0048] When making a new plan proposal, the generation AI unit can learn from past successes and failures and generate a proposal with the highest probability of success. For example, the generation AI unit can have the generation AI learn from past successes and failures and generate a proposal with the highest probability of success when making a new plan proposal. For example, it can automatically generate optimal plans based on past data. In this way, by learning from past successes and failures, it can generate a proposal with the highest probability of success.

[0049] The generation AI unit can collect user feedback on proposed plans in real time and make improvements immediately. For example, the generation AI unit can build a system that collects user feedback on proposed plans in real time and makes improvements immediately based on the results. For example, it uses online surveys and comment functions. This allows user feedback to be collected in real time and improvements to be made immediately, making it possible to propose more effective plans.

[0050] The Generation AI Department can automatically generate planning proposals specialized for different markets and regions. For example, the Generation AI Department uses Generation AI to build a system that automatically generates planning proposals specialized for different markets and regions. For example, it makes proposals according to market needs and regional characteristics. This enables more effective market response by automatically generating planning proposals specialized for different markets and regions.

[0051] The Generative AI Department can automatically generate visual prototypes of proposed plans, making them easier to understand visually. For example, the Generative AI Department will use generative AI to develop a system that automatically generates visual prototypes of proposed plans. For example, it will visually display product designs and service flows. By automatically generating visual prototypes, the content of the plans will be easier to understand visually.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The patent matching platform may further include a generation AI unit that cross-references technology trends in different industries and generates proposals for patent technology combinations that meet new market needs. For example, the generation AI unit can identify a new market need that combines energy technology and medical technology and propose patent technology combinations that meet that need. This makes it possible to provide proposals for patent technology combinations that meet new market needs by cross-referencing technology trends in different industries.

[0054] The patent matching platform may further include a generation AI unit that refers to commercially successful cases of patent technologies and generates highly feasible patent technology combination proposals. For example, the generation AI unit can automatically generate optimal patent technology combinations based on past successful cases. This makes it possible to provide highly feasible patent technology combination proposals by referring to commercially successful cases of patent technologies.

[0055] The patent matching platform may further include a generation AI unit that customizes proposed patent technology combinations to meet the market needs of different regions and cultural spheres and makes technology proposals from a global perspective. For example, the generation AI unit can analyze market data for each region and propose patent technology combinations for the Asian market. This enables technology proposals to be made from a global perspective by customizing them to meet the market needs of different regions and cultural spheres.

[0056] The patent matching platform may further include a generation AI unit that visualizes and provides proposed combinations of patent technologies, allowing users to intuitively understand the relevance of the technologies. For example, the generation AI unit may build a system that displays the relevance of patent technologies in graphs and charts. This allows users to intuitively understand the relevance of the technologies by visualizing proposed combinations of patent technologies.

[0057] The patent matching platform may further include a generation AI unit that customizes proposed patent technology combinations to meet the market needs of different regions and cultural spheres and makes technology proposals from a global perspective. For example, the generation AI unit can analyze market data for each region and propose patent technology combinations for the Asian market. This enables technology proposals to be made from a global perspective by customizing them to meet the market needs of different regions and cultural spheres.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The Core Technology Database Department predicts market needs and creates a database of necessary core technologies. For example, AI analyzes market data and lists core technologies such as next-generation energy technologies and medical technologies. Step 2: The Patent Analysis Department analyzes patents related to the core technologies compiled by the Core Technology Database Department from the Patent Office database. For example, using a patent analysis service, they extract patents related to specific energy technologies and understand their technical details and scope of rights. Step 3: The Generative AI department generates proposals that combine the patent technologies analyzed by the Patent Analysis department. For example, the Generative AI may "propose a new medical device that combines next-generation energy technology and medical technology." The Generative AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate proposals for combining patent technologies. Step 4: The Intermediary Service Department provides online information and mediates contract matching based on the combination proposals generated by the AI ​​Generation Department. For example, the proposed combinations of patented technologies are published on a web platform, and if an interested company wishes to enter into a contract, the department will support the contract procedures.

[0060] (Example 2) The patent matching platform according to an embodiment of the present invention is a system that combines patent technologies from multiple companies and organizations, proposes utilization value to companies that want to create innovative products and services, increases intellectual property revenue through contracts, stabilizes management, and secures new research and development funds. As a result, the patent matching platform promotes the creation of new products and services by combining patent technologies, increases intellectual property revenue for companies, stabilizes management, and secures new research and development funds.

[0061] The patent matching platform according to the embodiment includes a core technology database creation unit, a patent analysis unit, a generation AI unit, and an intermediary service unit. The core technology database creation unit predicts market needs and creates a database of necessary core technologies. For example, AI analyzes market data and lists core technologies such as next-generation energy and medical technologies. The patent analysis unit analyzes patents related to the core technologies created by the core technology database creation unit from the Japan Patent Office database. For example, a patent analysis service is used to extract patents related to specific energy technologies and identify their technical details and scope of rights. The generation AI unit generates proposals that combine the patent technologies analyzed by the patent analysis unit. For example, the generation AI may propose a new medical device that combines next-generation energy technology and medical technology. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate proposals for patent technology combinations. The intermediary service unit provides web information and mediates matching contracts based on the combination proposals created by the generation AI unit. For example, the proposed patent technology combinations are published on a web platform, and if interested companies wish to enter into a contract, the intermediary service unit supports the contract procedures. As a result, the patent matching platform according to the embodiment will promote the creation of new products and services through the combination of patented technologies, increase intellectual property revenue for companies, stabilize management, and secure funds for new research and development.

[0062] The core technology database creation unit can use the emotion estimation function to analyze consumer emotional trends and prioritize a list of technologies that elicit positive emotions. For example, when the AI ​​analyzes market data, the core technology database creation unit collects social media posts and comments on review sites to analyze consumer emotional trends, and uses the emotion estimation function to identify technologies that elicit positive emotions. This enables more effective technology proposals by analyzing consumer emotional trends and prioritizing a list of technologies that elicit positive emotions.

[0063] The Core Technology Database Division can reflect real-time consumer reactions, including social media posts and comments on review sites. For example, when AI analyzes market data, the Core Technology Database Division collects social media posts and comments on review sites in real time and reflects consumer reactions. For example, if there are many positive comments about a particular technology, that technology will be listed first. This allows for a more accurate understanding of market needs by reflecting real-time consumer reactions.

[0064] The Core Technology Database Creation Department cross-references technological trends in different industries and can discover new market needs. For example, when AI analyzes market data, the Core Technology Database Creation Department cross-references technological trends in different industries and can discover new market needs. For example, it can identify new market needs that combine energy technology and medical technology. This makes it possible to discover new market needs by cross-referencing technological trends in different industries.

[0065] The patent analysis unit uses the emotion estimation function to analyze the market's emotional reaction to patent technology and can prioritize extract patent technologies with a high number of positive reactions. For example, to analyze the market's emotional reaction to patent technology, the patent analysis unit uses the emotion estimation function to analyze comments on social media and review sites and prioritize extract patent technologies with a high number of positive reactions. This enables more effective patent technology selection by analyzing the market's emotional reaction and prioritize extracting patent technologies with a high number of positive reactions.

[0066] The Patent Analysis Department can analyze not only the technical details of patents but also their commercial success stories. For example, to analyze both the technical details and commercial success stories of patents, the Patent Analysis Department will integrate patent databases and commercial databases and build a system that simultaneously analyzes the technical content of patents and their commercial success stories. This allows for an analysis that includes both the technical details and commercial success stories of patents, making it possible to select patent technologies with higher feasibility.

[0067] The Patent Analysis Department can compare the results of patent analysis with technology trends in different industries to discover new application possibilities. For example, the Patent Analysis Department can integrate patent databases and industry databases to compare the results of patent analysis with technology trends in different industries and build a system to discover new application possibilities. For example, comparing energy technology with medical technology. This makes it possible to discover new application possibilities by comparing technology trends in different industries.

[0068] The Patent Analysis Department can automatically translate the results of patent analysis into different languages ​​and obtain feedback from an international perspective. The Patent Analysis Department, for example, builds a system that automatically translates the results of patent analysis into different languages ​​and collects feedback from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. This allows the results of patent analysis to be automatically translated into different languages ​​and feedback to be obtained from an international perspective, making it possible to evaluate patent technologies from a more multifaceted perspective.

[0069] The Patent Analysis Department can convert the results of patent analysis into visual notes or mind maps to make them easier to understand visually. For example, the Patent Analysis Department can convert the results of patent analysis into visual notes and build a system that visually displays the key points and relevance of patents. For example, the technical content of patents can be shown using diagrams and icons. In this way, converting the results of patent analysis into visual notes or mind maps makes them easier to understand visually.

[0070] The patent analysis department can use the emotion estimation function to collect users' emotional reactions to the results of patent analysis and improve the accuracy of the analysis based on that. For example, the patent analysis department can collect users' emotional reactions to the results of patent analysis in real time and build a system that improves the accuracy of patent analysis based on that data. For example, patents with a high number of positive reactions can be listed preferentially. In this way, by collecting users' emotional reactions and improving the accuracy of patent analysis based on that, more effective patent technology selection becomes possible.

[0071] When combining patented technologies, the Generative AI Unit can use the emotion estimation function to predict the user's emotional response and prioritize combinations that will elicit a positive response. For example, when the Generative AI combines patented technologies, the Generative AI Unit builds a system that uses the emotion estimation function to predict the user's emotional response and identify combinations that will elicit a positive response. For example, it will prioritize suggesting combinations with high emotion scores. This allows for more effective combinations of patented technologies by predicting the user's emotional response and prioritizing combinations that will elicit a positive response.

[0072] The Generative AI Department cross-references technologies from different industries and can propose combinations that meet new market needs. For example, when the Generative AI creates combination proposals, it cross-references technologies from different industries and builds a system that proposes combinations that meet new market needs. For example, it identifies a new market need that combines energy technology and medical technology. This makes it possible to propose combinations that meet new market needs by cross-referencing technologies from different industries.

[0073] The Generative AI Department can refer to commercially successful cases of patented technologies and propose combinations that are highly feasible. For example, when the Generative AI creates combination proposals, it references commercially successful cases of patented technologies and builds a system that proposes highly feasible combinations. For example, it automatically generates optimal combinations based on past successful cases. This makes it possible to propose highly feasible combinations by referring to commercially successful cases of patented technologies.

[0074] The Generative AI Department customizes combination proposals to meet the market needs of different regions and cultural spheres, making it possible to make technical proposals from a global perspective. For example, in order to customize the combination proposals created by the Generative AI to meet the market needs of different regions and cultural spheres, the Generative AI Department uses AI to analyze market data for each region and make technical proposals. For example, it makes technical proposals for the Asian market. This makes it possible to make technical proposals from a global perspective by customizing them to meet the market needs of different regions and cultural spheres.

[0075] The generation AI unit can visualize and provide combination proposals, allowing users to intuitively understand the relevance of technologies. For example, to visualize the combination proposals created by the generation AI, the generation AI unit builds a system in which the AI ​​displays the relevance of technologies in graphs and charts. For example, it provides an interface that visually shows the relevance of technologies. In this way, by visualizing the combination proposals, users can intuitively understand the relevance of technologies.

[0076] The generation AI unit can collect users' emotional responses to the proposed combinations and prioritize proposals that receive a lot of positive responses. The generation AI unit, for example, uses an emotion estimation function to build a system that collects users' emotional responses to the proposed combinations created by the generation AI. For example, it can analyze the user's facial expressions and voice and calculate an emotional score. This allows for more effective technology proposals by collecting users' emotional responses and prioritizing proposals that receive a lot of positive responses.

[0077] The intermediary service unit can use an emotion estimation function to display users' emotional reactions in real time to the information provided on the web platform, and prioritize displaying information with a high number of positive reactions. The intermediary service unit, for example, builds a system that incorporates an emotion estimation function into information provided on the web platform and displays users' emotional reactions in real time. For example, it prioritizes displaying information with a high positive emotion score. This enables more effective information provision by displaying users' emotional reactions to information provided on the web platform in real time and prioritize displaying information with a high number of positive reactions.

[0078] The intermediary service department cross-references technology trends in different industries on a web platform and can provide information that meets new market needs. The intermediary service department, for example, builds a system that cross-references technology trends in different industries on a web platform and provides information that meets new market needs. For example, a new market need that combines energy technology and medical technology is identified. This makes it possible to provide information that meets new market needs by cross-referencing technology trends in different industries.

[0079] The intermediary service department can refer to commercially successful cases of patented technologies on a web platform and make highly feasible technical proposals. The intermediary service department will, for example, build a system that refers to commercially successful cases of patented technologies on a web platform and makes highly feasible technical proposals. For example, it will automatically generate optimal technical proposals based on past success cases. This will enable highly feasible technical proposals to be made by referring to commercially successful cases of patented technologies.

[0080] The intermediary service department will automatically translate the information provided on the web platform into different languages ​​and obtain feedback from an international perspective. The intermediary service department will, for example, build a system that automatically translates the information provided on the web platform into different languages ​​and collects feedback from an international perspective. For example, it will translate into multiple languages, such as English, French, and Chinese. This will enable the information provided on the web platform to be automatically translated into different languages ​​and receive feedback from an international perspective, making it possible to provide more multifaceted information.

[0081] The intermediary service unit can convert information provided on the web platform into visual notes or mind maps to make it easier to understand visually. For example, the intermediary service unit converts information provided on the web platform into visual notes and builds a system that visually displays the main points and relationships of the information. For example, the technical content of the information is shown using diagrams and icons. In this way, converting information provided on the web platform into visual notes or mind maps makes it easier to understand visually.

[0082] The intermediary service unit can use the emotion estimation function to collect users' emotional reactions to information provided on the web platform and improve the accuracy of the information based on that. For example, the intermediary service unit can collect users' emotional reactions to information provided on the web platform in real time and build a system that improves the accuracy of the information based on that data. For example, it can prioritize displaying information with a high number of positive reactions. This makes it possible to provide more effective information by collecting users' emotional reactions and improving the accuracy of the information based on that.

[0083] The intermediary service department customizes the information provided on the web platform to meet the market needs of different regions and cultural spheres, and can make technical proposals from a global perspective. For example, in order to customize the information provided on the web platform to meet the market needs of different regions and cultural spheres, the intermediary service department uses AI to analyze market data for each region and make technical proposals. For example, it makes technical proposals for the Asian market. This makes it possible to make technical proposals from a global perspective by customizing the information provided on the web platform to meet the market needs of different regions and cultural spheres.

[0084] The intermediary service unit can visualize and provide information provided on the web platform, allowing users to intuitively understand the relevance of technologies. For example, in order to visualize the information provided on the web platform, the intermediary service unit builds a system in which AI displays the relevance of technologies in graphs and charts. For example, it provides an interface that visually shows the relevance of technologies. In this way, by visualizing the information, users can intuitively understand the relevance of technologies.

[0085] The intermediary service unit can use the emotion estimation function to collect users' emotional reactions to information provided on the web platform and prioritize displaying information with a high number of positive reactions. The intermediary service unit, for example, builds a system that incorporates the emotion estimation function into information provided on the web platform and displays users' emotional reactions in real time. For example, it prioritizes displaying information with a high positive emotion score. This enables more effective information provision by collecting users' emotional reactions and prioritize displaying information with a high number of positive reactions.

[0086] The intermediary service department cross-references technology trends in different industries on a web platform and can provide information that meets new market needs. The intermediary service department, for example, builds a system that cross-references technology trends in different industries on a web platform and provides information that meets new market needs. For example, a new market need that combines energy technology and medical technology is identified. This makes it possible to provide information that meets new market needs by cross-referencing technology trends in different industries.

[0087] The intermediary service department can refer to commercially successful cases of patented technologies on a web platform and make highly feasible technical proposals. The intermediary service department will, for example, build a system that refers to commercially successful cases of patented technologies on a web platform and makes highly feasible technical proposals. For example, it will automatically generate optimal technical proposals based on past success cases. This will enable highly feasible technical proposals to be made by referring to commercially successful cases of patented technologies.

[0088] The intermediary service unit can use the emotion estimation function to collect users' emotional reactions to information provided on the web platform and improve the accuracy of the information based on that. For example, the intermediary service unit can collect users' emotional reactions to information provided on the web platform in real time and build a system that improves the accuracy of the information based on that data. For example, it can prioritize displaying information with a high number of positive reactions. This makes it possible to provide more effective information by collecting users' emotional reactions and improving the accuracy of the information based on that.

[0089] When making a new plan proposal, the generation AI unit can learn from past successes and failures and generate a proposal with the highest probability of success. For example, the generation AI unit can have the generation AI learn from past successes and failures and generate a proposal with the highest probability of success when making a new plan proposal. For example, it can automatically generate optimal plans based on past data. In this way, by learning from past successes and failures, it can generate a proposal with the highest probability of success.

[0090] The generation AI unit can collect user feedback on proposed plans in real time and make improvements immediately. For example, the generation AI unit can build a system that collects user feedback on proposed plans in real time and makes improvements immediately based on the results. For example, it uses online surveys and comment functions. This allows user feedback to be collected in real time and improvements to be made immediately, making it possible to propose more effective plans.

[0091] The generation AI unit can use the emotion estimation function to make personalized proposals based on the user's emotions when proposing new plans. For example, the generation AI unit uses the emotion estimation function to build a system that makes personalized proposals based on the user's emotions. For example, it proposes optimal plans based on the user's emotion score. This makes it possible to make more effective proposals by making personalized proposals based on the user's emotions.

[0092] The Generation AI Department can automatically generate planning proposals specialized for different markets and regions. For example, the Generation AI Department uses Generation AI to build a system that automatically generates planning proposals specialized for different markets and regions. For example, it makes proposals according to market needs and regional characteristics. This enables more effective market response by automatically generating planning proposals specialized for different markets and regions.

[0093] The Generative AI Department can automatically generate visual prototypes of proposed plans, making them easier to understand visually. For example, the Generative AI Department will use generative AI to develop a system that automatically generates visual prototypes of proposed plans. For example, it will visually display product designs and service flows. By automatically generating visual prototypes, the content of the plans will be easier to understand visually.

[0094] The Generative AI Department will use the emotion estimation function to monitor users' emotional reactions to new project proposals, enabling it to continuously improve the quality of proposals. For example, the Generative AI Department will develop a system that uses the emotion estimation function to monitor users' emotional reactions to new project proposals in real time. For example, it will analyze the user's facial expressions and voice and calculate an emotional score. This will enable it to monitor users' emotional reactions and continuously improve the quality of proposals, enabling it to make more effective project proposals.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The patent matching platform may further include a generation AI unit that estimates user emotions and generates patent technology combination proposals based on the estimated user emotions. For example, the generation AI unit can collect users' emotional reactions in real time and prioritize proposals for patent technology combinations that generate positive reactions. This allows for more effective technology proposals by providing patent technology combination proposals based on user emotions.

[0097] The patent matching platform may further include a generation AI unit that cross-references technology trends in different industries and generates proposals for patent technology combinations that meet new market needs. For example, the generation AI unit can identify a new market need that combines energy technology and medical technology and propose patent technology combinations that meet that need. This makes it possible to provide proposals for patent technology combinations that meet new market needs by cross-referencing technology trends in different industries.

[0098] The patent matching platform may further include a generation AI unit that refers to commercially successful cases of patent technologies and generates highly feasible patent technology combination proposals. For example, the generation AI unit can automatically generate optimal patent technology combinations based on past successful cases. This makes it possible to provide highly feasible patent technology combination proposals by referring to commercially successful cases of patent technologies.

[0099] The patent matching platform may further include a generation AI unit that customizes proposed patent technology combinations to meet the market needs of different regions and cultural spheres and makes technology proposals from a global perspective. For example, the generation AI unit can analyze market data for each region and propose patent technology combinations for the Asian market. This enables technology proposals to be made from a global perspective by customizing them to meet the market needs of different regions and cultural spheres.

[0100] The patent matching platform may further include a generation AI unit that visualizes and provides proposed combinations of patent technologies, allowing users to intuitively understand the relevance of the technologies. For example, the generation AI unit may build a system that displays the relevance of patent technologies in graphs and charts. This allows users to intuitively understand the relevance of the technologies by visualizing proposed combinations of patent technologies.

[0101] The patent matching platform may further include a generation AI unit that estimates user emotions, collects users' emotional responses to proposed patent technology combinations based on the estimated user emotions, and prioritizes proposing proposals that receive a large number of positive responses. For example, the generation AI unit can analyze the user's facial expressions and voice and calculate an emotional score. This allows for more effective technology proposals by collecting users' emotional responses and prioritizing proposals that receive a large number of positive responses.

[0102] The patent matching platform may further include a generation AI unit that estimates user emotions and generates patent technology combination proposals based on the estimated user emotions. For example, the generation AI unit can collect users' emotional reactions in real time and prioritize proposals for patent technology combinations that generate positive reactions. This allows for more effective technology proposals by providing patent technology combination proposals based on user emotions.

[0103] The patent matching platform may further include a generation AI unit that estimates user emotions, collects users' emotional responses to proposed patent technology combinations based on the estimated user emotions, and prioritizes proposing proposals that receive a large number of positive responses. For example, the generation AI unit can analyze the user's facial expressions and voice and calculate an emotional score. This allows for more effective technology proposals by collecting users' emotional responses and prioritizing proposals that receive a large number of positive responses.

[0104] The patent matching platform may further include a generation AI unit that estimates user emotions and generates patent technology combination proposals based on the estimated user emotions. For example, the generation AI unit can collect users' emotional reactions in real time and prioritize proposals for patent technology combinations that generate positive reactions. This allows for more effective technology proposals by providing patent technology combination proposals based on user emotions.

[0105] The patent matching platform may further include a generation AI unit that customizes proposed patent technology combinations to meet the market needs of different regions and cultural spheres and makes technology proposals from a global perspective. For example, the generation AI unit can analyze market data for each region and propose patent technology combinations for the Asian market. This enables technology proposals to be made from a global perspective by customizing them to meet the market needs of different regions and cultural spheres.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The Core Technology Database Department predicts market needs and creates a database of necessary core technologies. For example, AI analyzes market data and lists core technologies such as next-generation energy technologies and medical technologies. Step 2: The Patent Analysis Department analyzes patents related to the core technologies compiled by the Core Technology Database Department from the Patent Office database. For example, using a patent analysis service, they extract patents related to specific energy technologies and understand their technical details and scope of rights. Step 3: The Generative AI department generates proposals that combine the patent technologies analyzed by the Patent Analysis department. For example, the Generative AI may "propose a new medical device that combines next-generation energy technology and medical technology." The Generative AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate proposals for combining patent technologies. Step 4: The Intermediary Service Department provides online information and mediates contract matching based on the combination proposals generated by the AI ​​Generation Department. For example, the proposed combinations of patented technologies are published on a web platform, and if an interested company wishes to enter into a contract, the department will support the contract procedures.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

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

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

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0152] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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]

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

Claims

1. The Core Technology Database Department, which predicts market needs and creates a database of necessary core technologies, a patent analysis unit that analyzes patents related to the core technology that have been databased by the core technology database creation unit from the patent office database; a generation AI unit that generates a proposal combining the patent technologies analyzed by the patent analysis unit; and an intermediation service unit that provides web information and mediates matching contracts based on the combination proposals generated by the generation AI unit. A system characterized by:

2. The core technology database creation unit Analyze consumer sentiment trends and prioritize technologies that elicit positive emotions 2. The system of claim 1.

3. The patent analysis unit Analyze market sentiment towards the patented technology and prioritize the patented technology with the most positive reactions.

2. The system of claim 1.

4. The generation AI unit When combining the patented technologies, the system predicts the user's emotional response and prioritizes combinations that will generate positive reactions.

2. The system of claim 1.

5. The intermediation service unit The web platform displays users' emotional reactions to information provided on the web platform in real time, and preferentially displays the information with the most positive reactions.

2. The system of claim 1.

6. The core technology database creation unit Cross-reference technology trends in different industries to discover new market needs 2. The system of claim 1.

7. The patent analysis unit Compare the results of the patent analysis with technology trends in different industries to discover new application possibilities.

2. The system of claim 1.

8. The generation AI unit Cross-reference technologies from different industries and propose combinations that meet new market needs 2. The system of claim 1.

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