System

Abstract

An object of a system according to an embodiment is to efficiently match patent information of a patent authority with need information of a business operator.SOLUTION: A system includes a patent registering unit, a needs registering unit, and a matching unit. The patent registering part registers patent information of a patent right holder. The needs registration unit registers needs information of a business operator. The matching unit performs optimum matching by analyzing the patent information registered by the patent registering unit and the needs information registered by the needs registering unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently match patent information from patent holders with information on the needs of businesses.

[0005] The system according to the embodiment aims to efficiently match patent information of patent holders with needs information of businesses. [Means for solving the problem]

[0006] The system according to the embodiment includes a patent registration unit, a needs registration unit, and a matching unit. The patent registration unit registers patent information of patent holders. The needs registration unit registers needs information of businesses. The matching unit analyzes the patent information registered by the patent registration unit and the needs information registered by the needs registration unit to perform optimal matching. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently match patent information of patent holders with information on the needs of businesses. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The matching system according to an embodiment of the present invention is a system for utilizing patents held by patent holders to commercialize and develop services. This system utilizes generative AI to match patent holders who have registered patents with businesses seeking ideas for new products and services. This allows the matching system to effectively utilize patents held by patent holders and enable businesses to obtain ideas for new products and services.

[0029] The matching system according to the embodiment includes a patent registration unit, a needs registration unit, and a matching unit. The patent registration unit registers patent information of patent holders. For example, the patent holder inputs detailed patent information and related technical documents and stores them in the service's database. Furthermore, when a patent holder registers a patent, the patent registration unit can automatically evaluate the patent's technical strengths and market value using a generation AI and provide feedback to the patent holder. For example, the generation AI can provide the patent's technical advantages and comparison results with competing patents. Furthermore, when a patent holder registers a patent, the generation AI can analyze related literature and existing market data to suggest potential application fields for the patent. For example, the generation AI can suggest the patent's applicability based on relevant academic papers and patent documents. Furthermore, when a patent holder registers a patent, the patent registration unit can use an emotion estimation function to analyze the patent holder's emotions and provide an interface to elicit positive emotions. For example, the patent holder's facial expressions and voice can be analyzed to display positive feedback. The needs registration unit registers the needs information of businesses. For example, the business's desired technical field and specific requirements can be input and stored in the service's database. In addition, when a business registers its needs, the needs registration department can use the generation AI to analyze the business's past projects and market trends and propose optimal patents. For example, it can propose patents based on the success factors of past projects. In addition, when a business registers its needs, the needs registration department can use the generation AI to propose ways to utilize patents based on the business's business model and strategy. For example, it can propose patent application methods that are suitable for the business's business model. Furthermore, when a business registers its needs, the needs registration department can use an emotion estimation function to analyze the business's emotions and provide an interface to elicit positive emotions. For example, it can analyze the business's facial expressions and voice and display positive feedback. The matching department analyzes the patent information registered by the patent registration department and the needs information registered by the needs registration department to make optimal matches. For example, the generation AI conducts technical details of the patents and market analysis to propose optimal matches.The generation AI can also evaluate the applicability and technical strengths of patents and make specific proposals to businesses. Furthermore, when the generation AI analyzes patent information and needs information, it can use an emotion estimation function to provide matching results that take the user's emotions into account. For example, it can prioritize proposals for patents that evoke positive emotions from the user. This allows the matching system according to the embodiment to achieve optimal matching between patent holders and businesses. For example, when a patent holder registers a patent and a business registers its needs, the generation AI can perform optimal matching and support the commercialization and service creation process.

[0030] When a patent holder registers a patent, the Patent Registration Department uses generative AI to automatically evaluate the technical strengths and market value of the patent and provides feedback to the patent holder. For example, when a patent holder registers a patent, the Patent Registration Department uses generative AI to analyze the technical strengths of the patent and feeds back the results to the patent holder. For example, it provides the patent's technical superiority and a comparison with competing patents. The generative AI also automatically evaluates the patent's market value and notifies the patent holder of the evaluation results. For example, it presents the patent's market size and potential profitability in numerical form. Furthermore, when a patent holder registers a patent, the generative AI comprehensively evaluates the patent's technical strengths and market value and feeds back the results to the patent holder. For example, it provides a comprehensive evaluation that combines the patent's technical strengths and market value. This makes it easier for the patent holder to understand the patent's technical strengths and market value.

[0031] When a patent holder registers a patent, the Patent Registration Department uses generative AI to analyze the patent's relevant literature and existing market data to suggest potential application fields for the patent. For example, the Patent Registration Department uses generative AI to analyze the patent's relevant literature and identify the patent's technical background and application fields. For example, it proposes the patent's possible applications based on related academic papers and patent documents. The generative AI also analyzes existing market data to suggest potential application fields for the patent to the patent holder. For example, it identifies market segments and industrial fields to which the patent's technology can be applied. Furthermore, when a patent holder registers a patent, the generative AI comprehensively analyzes the patent's relevant literature and market data to suggest potential application fields for the patent. For example, it proposes application fields that combine the patent's technical strengths with market needs. This makes it easier for the patent holder to understand the patent's potential application fields.

[0032] The patent registration unit provides a function that enables a patent holder to upload a visual demo or prototype video of a patent when registering a patent. The patent registration unit provides a function that enables a patent holder to upload a visual demo or prototype video of a patent when registering a patent. For example, a video that visually explains the patent technology can be uploaded. Furthermore, by uploading a visual demo or prototype video of a patent when registering a patent, a patent holder can visually appeal to the value of the patent. For example, a demo video that actually shows the patent technology in action can be uploaded. Furthermore, a function has been added that enables a patent holder to upload a visual demo or prototype video of a patent when registering a patent, to visually appeal to the value of the patent. For example, an animated video that visually explains the patent technology can be uploaded. This allows a patent holder to visually appeal to the value of the patent.

[0033] The Patent Registration Department provides a generative AI function that suggests collaboration opportunities with other patent holders when a patent holder registers a patent. In the Patent Registration Department, for example, when a patent holder registers a patent, the generative AI suggests collaboration opportunities with other patent holders. For example, it identifies patent holders in related technical fields and proposes collaboration. The generative AI also analyzes the patent information of the patent holder and suggests collaboration opportunities with other patent holders. For example, it identifies patent holders that complement the technical strengths of the patent and proposes collaboration. In addition, a function is added in which the generative AI suggests collaboration opportunities with other patent holders when a patent holder registers a patent. For example, it identifies patent holders that complement the technical strengths of the patent and proposes collaboration. This provides patent holders with opportunities to collaborate with other patent holders.

[0034] In the needs registration department, when a business registers needs, the generation AI proposes ways to utilize patents based on the business's business model and strategy. For example, when a business registers needs, the generation AI analyzes the business's business model and proposes ways to utilize patents. For example, it proposes ways to apply patents that are suitable for the business's business model. The generation AI also analyzes the business's strategy and proposes ways to utilize patents. For example, it proposes how to utilize patented technology based on the business's strategy. In addition, when a business registers needs, the generation AI comprehensively analyzes the business's business model and strategy and proposes ways to utilize patents. For example, it proposes ways to apply patents that combine the business's business model and strategy. In this way, the business is proposed ways to utilize patents based on its business model and strategy.

[0035] When a business registers its needs, the generation AI proposes patents from different industries and fields, promoting crossover innovation. For example, when a business registers its needs, the generation AI proposes patents from different industries, promoting crossover innovation. For example, the generation AI may propose applying a medical patent to consumer products. The generation AI may also propose patents from different fields, promoting crossover innovation tailored to the business's needs. For example, the generation AI may propose applying an energy patent to environmental protection. Furthermore, when a business registers its needs, the generation AI comprehensively analyzes patents from different industries and fields, promoting crossover innovation. For example, it may propose new application methods that combine patents from different technology fields. This allows businesses to be proposed patents from different industries and fields, promoting crossover innovation.

[0036] In the needs registration department, when a business registers its needs, the generation AI proposes joint development projects with patent holders based on the business's needs. For example, when a business registers its needs, the generation AI analyzes the business's needs and proposes joint development projects with patent holders. For example, it identifies patent holders that are suitable for the business's technical requirements and proposes joint development. The generation AI also proposes joint development projects with patent holders based on the business's needs. For example, it identifies patent holders that are suitable for the business's business model and proposes joint development. In addition, when a business registers its needs, the generation AI comprehensively analyzes the business's needs and proposes joint development projects with patent holders. For example, it identifies patent holders that combine the business's technical requirements with its business model and proposes joint development. In this way, the business is proposed joint development projects with patent holders.

[0037] When analyzing patent information and needs information, the matching department conducts technical details of the patent and market analysis to propose the optimal match. In the matching department, for example, the generation AI analyzes patent information and identifies the technical details of the patent. For example, it evaluates the technical strengths and applicability of the patent and proposes the patent that best meets the needs of the business. The generation AI also analyzes needs information and identifies the business's technical requirements and market needs. For example, it proposes patents based on the business's business model and market trends. The generation AI also comprehensively analyzes patent information and needs information, conducts technical details of the patent and market analysis, and proposes the optimal match. For example, it proposes a patent that combines the technical strengths of the patent with market needs. This makes it possible to propose the optimal match based on the technical details of the patent and market analysis.

[0038] When analyzing patent information and needs information, the matching department evaluates the patent's applicability and technical strengths and makes specific proposals to businesses. In the matching department, for example, the generation AI analyzes patent information and evaluates the patent's applicability. For example, it identifies the fields in which the patent's technology can be applied and makes proposals to businesses. The generation AI also analyzes needs information and evaluates the patent's technical strengths based on the business's technical requirements and market needs. For example, it evaluates the extent to which the patent's technology meets the business's needs and makes proposals. The generation AI also comprehensively analyzes patent information and needs information, evaluates the patent's applicability and technical strengths, and makes specific proposals to businesses. For example, it proposes specific application methods that combine the patent's technical strengths with market needs. This enables specific proposals to be made based on the patent's applicability and technical strengths.

[0039] When analyzing patent information and needs information, the matching unit makes new proposals that combine patents from different industries and fields. For example, the matching unit uses a generation AI to analyze patent information and make new proposals that combine patents from different industries. For example, it may propose a new application method that combines a patent in the medical field with a patent in a consumer product. The generation AI also analyzes needs information and makes new proposals that combine patents from different fields. For example, it may propose a new application method that combines a patent in the energy field with a patent in environmental protection. The generation AI also comprehensively analyzes patent information and needs information and makes new proposals that combine patents from different industries and fields. For example, it may propose a new application method that combines patents in different technology fields. This makes it possible to make new proposals that combine patents from different industries and fields.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] When a patent holder registers a patent, the Patent Registration Department can not only evaluate the patent's technical strength and market value, but also provide a function to evaluate the patent's environmental impact. For example, the Generative AI can analyze the environmental impact of the patent's technology and provide feedback on the results to the patent holder. It can also evaluate the extent to which the patent's technology contributes to the Sustainable Development Goals (SDGs) and notify the patent holder of the evaluation results. Furthermore, when a patent holder registers a patent, the Generative AI can comprehensively evaluate the patent's environmental impact and provide feedback on the results to the patent holder. This makes it easier for patent holders to understand the patent's environmental impact.

[0042] When a patent holder registers a patent, the Patent Registration Department not only evaluates the patent's technical strengths and market value, but also provides a function to evaluate the patent's social impact. For example, the generative AI can analyze the impact of the patent's technology on society and provide feedback on the results to the patent holder. It can also evaluate the extent to which the patent's technology contributes to solving social issues and notify the patent holder of the evaluation results. Furthermore, when a patent holder registers a patent, the generative AI can comprehensively evaluate the patent's social impact and provide feedback on the results to the patent holder. This makes it easier for patent holders to understand the patent's social impact.

[0043] When a patent holder registers a patent, the Patent Registration Department can not only evaluate the patent's technical strengths and market value, but also provide a function to evaluate the patent's ethical impact. For example, the generative AI can analyze whether the patented technology is ethically sound and provide feedback on the results to the patent holder. It can also evaluate the extent to which the patented technology complies with ethical standards and notify the patent holder of the evaluation results. Furthermore, when a patent holder registers a patent, the generative AI can comprehensively evaluate the patent's ethical impact and provide feedback on the results to the patent holder. This makes it easier for patent holders to understand the ethical impact of their patents.

[0044] When a patent holder registers a patent, the Patent Registration Department not only evaluates the patent's technical strengths and market value, but also provides a function to evaluate the patent's legal risks. For example, the Generative AI can analyze whether the patent's technology is legally sound and provide feedback on the results to the patent holder. It can also evaluate the extent to which the patent's technology complies with legal standards and notify the patent holder of the evaluation results. Furthermore, when a patent holder registers a patent, the Generative AI can comprehensively evaluate the patent's legal risks and provide feedback on the results to the patent holder. This makes it easier for patent holders to understand the legal risks of their patents.

[0045] When a patent holder registers a patent, the Patent Registration Department can not only evaluate the patent's technical strength and market value, but also provide a function to evaluate the patent's economic impact. For example, the Generative AI can analyze the economic impact of the patent's technology and provide feedback on the results to the patent holder. It can also evaluate the extent to which the patent's technology contributes to economic growth and notify the patent holder of the evaluation results. Furthermore, when a patent holder registers a patent, the Generative AI can comprehensively evaluate the patent's economic impact and provide feedback on the results to the patent holder. This makes it easier for patent holders to understand the patent's economic impact.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The patent registration department registers the patent holder's patent information. For example, the patent holder enters detailed patent information and related technical documents and stores them in the service's database. When the patent holder registers a patent, the patent registration department can also use generative AI to automatically evaluate the patent's technical strengths and market value and provide feedback to the patent holder. Furthermore, the generative AI can analyze the patent's related literature and existing market data to suggest potential application areas for the patent. It can also use emotion estimation to analyze the patent holder's emotions and provide an interface to elicit positive emotions. Step 2: The needs registration unit registers the business's needs information. For example, the business enters the technology field and specific requirements it is seeking, and stores them in the service database. When a business registers its needs, the needs registration unit also allows the generation AI to analyze the business's past projects and market trends and propose optimal patents. Furthermore, the generation AI can suggest ways to utilize patents based on the business's business model and strategy. The emotion estimation function can also be used to analyze the business's emotions and provide an interface to elicit positive emotions. Step 3: The matching unit analyzes the patent information registered by the patent registration unit and the needs information registered by the needs registration unit to perform optimal matching. For example, the generation AI performs technical details of the patent and market analysis to propose optimal matching. The generation AI can also evaluate the patent's applicability and technical strengths and make specific proposals to businesses. Furthermore, when the generation AI analyzes the patent information and needs information, it can use an emotion estimation function to provide matching results that take the user's emotions into account.

[0048] (Example 2) The matching system according to an embodiment of the present invention is a system for utilizing patents held by patent holders to commercialize and develop services. This system utilizes generative AI to match patent holders who have registered patents with businesses seeking ideas for new products and services. This allows the matching system to effectively utilize patents held by patent holders and enable businesses to obtain ideas for new products and services.

[0049] The matching system according to the embodiment includes a patent registration unit, a needs registration unit, and a matching unit. The patent registration unit registers patent information of patent holders. For example, the patent holder inputs detailed patent information and related technical documents and stores them in the service's database. Furthermore, when a patent holder registers a patent, the patent registration unit can automatically evaluate the patent's technical strengths and market value using a generation AI and provide feedback to the patent holder. For example, the generation AI can provide the patent's technical advantages and comparison results with competing patents. Furthermore, when a patent holder registers a patent, the generation AI can analyze related literature and existing market data to suggest potential application fields for the patent. For example, the generation AI can suggest the patent's applicability based on relevant academic papers and patent documents. Furthermore, when a patent holder registers a patent, the patent registration unit can use an emotion estimation function to analyze the patent holder's emotions and provide an interface to elicit positive emotions. For example, the patent holder's facial expressions and voice can be analyzed to display positive feedback. The needs registration unit registers the needs information of businesses. For example, the business's desired technical field and specific requirements can be input and stored in the service's database. In addition, when a business registers its needs, the needs registration department can use the generation AI to analyze the business's past projects and market trends and propose optimal patents. For example, it can propose patents based on the success factors of past projects. In addition, when a business registers its needs, the needs registration department can use the generation AI to propose ways to utilize patents based on the business's business model and strategy. For example, it can propose patent application methods that are suitable for the business's business model. Furthermore, when a business registers its needs, the needs registration department can use an emotion estimation function to analyze the business's emotions and provide an interface to elicit positive emotions. For example, it can analyze the business's facial expressions and voice and display positive feedback. The matching department analyzes the patent information registered by the patent registration department and the needs information registered by the needs registration department to make optimal matches. For example, the generation AI conducts technical details of the patents and market analysis to propose optimal matches.The generation AI can also evaluate the applicability and technical strengths of patents and make specific proposals to businesses. Furthermore, when the generation AI analyzes patent information and needs information, it can use an emotion estimation function to provide matching results that take the user's emotions into account. For example, it can prioritize proposals for patents that evoke positive emotions from the user. This allows the matching system according to the embodiment to achieve optimal matching between patent holders and businesses. For example, when a patent holder registers a patent and a business registers its needs, the generation AI can perform optimal matching and support the commercialization and service creation process.

[0050] When a patent holder registers a patent, the Patent Registration Department uses generative AI to automatically evaluate the technical strengths and market value of the patent and provides feedback to the patent holder. For example, when a patent holder registers a patent, the Patent Registration Department uses generative AI to analyze the technical strengths of the patent and feeds back the results to the patent holder. For example, it provides the patent's technical superiority and a comparison with competing patents. The generative AI also automatically evaluates the patent's market value and notifies the patent holder of the evaluation results. For example, it presents the patent's market size and potential profitability in numerical form. Furthermore, when a patent holder registers a patent, the generative AI comprehensively evaluates the patent's technical strengths and market value and feeds back the results to the patent holder. For example, it provides a comprehensive evaluation that combines the patent's technical strengths and market value. This makes it easier for the patent holder to understand the patent's technical strengths and market value.

[0051] When a patent holder registers a patent, the Patent Registration Department uses generative AI to analyze the patent's relevant literature and existing market data to suggest potential application fields for the patent. For example, the Patent Registration Department uses generative AI to analyze the patent's relevant literature and identify the patent's technical background and application fields. For example, it proposes the patent's possible applications based on related academic papers and patent documents. The generative AI also analyzes existing market data to suggest potential application fields for the patent to the patent holder. For example, it identifies market segments and industrial fields to which the patent's technology can be applied. Furthermore, when a patent holder registers a patent, the generative AI comprehensively analyzes the patent's relevant literature and market data to suggest potential application fields for the patent. For example, it proposes application fields that combine the patent's technical strengths with market needs. This makes it easier for the patent holder to understand the patent's potential application fields.

[0052] The patent registration unit uses an emotion estimation function to analyze the emotions of a patent holder when registering a patent, and provides an interface for eliciting positive emotions. For example, when a patent holder registers a patent, the patent registration unit uses the emotion estimation function to analyze the emotions of the patent holder in real time, and provides an interface for eliciting positive emotions. For example, the patent registration unit analyzes the patent holder's facial expressions and voice and displays positive feedback. The emotion estimation function is also used to analyze the emotions of the patent holder when registering a patent, and displays messages and animations to elicit positive emotions. For example, the emotion estimation function is used to make the patent holder feel positive emotions during the registration process. The patent registration unit also uses the emotion estimation function to monitor the emotions of the patent holder when registering a patent, and provides an interface for eliciting positive emotions. For example, interactive elements are added to make the patent holder feel positive emotions during the registration process. This allows the patent holder to register a patent with positive emotions.

[0053] The patent registration unit provides a function that enables a patent holder to upload a visual demo or prototype video of a patent when registering a patent. The patent registration unit provides a function that enables a patent holder to upload a visual demo or prototype video of a patent when registering a patent. For example, a video that visually explains the patent technology can be uploaded. Furthermore, by uploading a visual demo or prototype video of a patent when registering a patent, a patent holder can visually appeal to the value of the patent. For example, a demo video that actually shows the patent technology in action can be uploaded. Furthermore, a function has been added that enables a patent holder to upload a visual demo or prototype video of a patent when registering a patent, to visually appeal to the value of the patent. For example, an animated video that visually explains the patent technology can be uploaded. This allows a patent holder to visually appeal to the value of the patent.

[0054] The Patent Registration Department provides a generative AI function that suggests collaboration opportunities with other patent holders when a patent holder registers a patent. In the Patent Registration Department, for example, when a patent holder registers a patent, the generative AI suggests collaboration opportunities with other patent holders. For example, it identifies patent holders in related technical fields and proposes collaboration. The generative AI also analyzes the patent information of the patent holder and suggests collaboration opportunities with other patent holders. For example, it identifies patent holders that complement the technical strengths of the patent and proposes collaboration. In addition, a function is added in which the generative AI suggests collaboration opportunities with other patent holders when a patent holder registers a patent. For example, it identifies patent holders that complement the technical strengths of the patent and proposes collaboration. This provides patent holders with opportunities to collaborate with other patent holders.

[0055] The patent registration unit uses an emotion estimation function to monitor the emotions of a patent holder in real time when the patent holder is registering a patent, and provides feedback to improve the registration process. For example, the patent registration unit uses the emotion estimation function to monitor the emotions of a patent holder in real time when the patent holder is registering a patent, and provides feedback to improve the registration process. For example, the emotion estimation function makes suggestions for relaxing if the patent holder is feeling stressed. The patent registration unit also uses the emotion estimation function to monitor the emotions of a patent holder in real time when the patent holder is registering a patent, and provides feedback to improve the registration process. For example, the emotion estimation function provides an interface to make the patent holder feel positive emotions. The patent registration unit also uses the emotion estimation function to monitor the emotions of a patent holder in real time when the patent holder is registering a patent, and provides feedback to improve the registration process. For example, the emotion estimation function adds interactive elements to make the patent holder feel positive emotions. This allows the patent holder to register a patent with positive emotions.

[0056] In the needs registration department, when a business registers needs, the generation AI proposes ways to utilize patents based on the business's business model and strategy. For example, when a business registers needs, the generation AI analyzes the business's business model and proposes ways to utilize patents. For example, it proposes ways to apply patents that are suitable for the business's business model. The generation AI also analyzes the business's strategy and proposes ways to utilize patents. For example, it proposes how to utilize patented technology based on the business's strategy. In addition, when a business registers needs, the generation AI comprehensively analyzes the business's business model and strategy and proposes ways to utilize patents. For example, it proposes ways to apply patents that combine the business's business model and strategy. In this way, the business is proposed ways to utilize patents based on its business model and strategy.

[0057] The needs registration unit uses an emotion estimation function to analyze the emotions of a business operator when the business operator registers their needs, and provides an interface for eliciting positive emotions. The needs registration unit, for example, uses the emotion estimation function to analyze the emotions of a business operator in real time when the business operator registers their needs, and provides an interface for eliciting positive emotions. For example, the needs registration unit analyzes the business operator's facial expressions and voice and displays positive feedback. The emotion estimation function is also used to analyze the emotions of the business operator when registering their needs, and displays messages and animations to elicit positive emotions. For example, the emotion estimation function is used to make the business operator feel positive emotions during the registration process. The emotion estimation function is also used to monitor the business operator's emotions when the business operator registers their needs, and provides an interface for eliciting positive emotions. For example, interactive elements are added to make the business operator feel positive emotions during the registration process. This allows the business operator to register their needs while feeling positive emotions.

[0058] When a business registers its needs, the generation AI proposes patents from different industries and fields, promoting crossover innovation. For example, when a business registers its needs, the generation AI proposes patents from different industries, promoting crossover innovation. For example, the generation AI may propose applying a medical patent to consumer products. The generation AI may also propose patents from different fields, promoting crossover innovation tailored to the business's needs. For example, the generation AI may propose applying an energy patent to environmental protection. Furthermore, when a business registers its needs, the generation AI comprehensively analyzes patents from different industries and fields, promoting crossover innovation. For example, it may propose new application methods that combine patents from different technology fields. This allows businesses to be proposed patents from different industries and fields, promoting crossover innovation.

[0059] In the needs registration department, when a business registers its needs, the generation AI proposes joint development projects with patent holders based on the business's needs. For example, when a business registers its needs, the generation AI analyzes the business's needs and proposes joint development projects with patent holders. For example, it identifies patent holders that are suitable for the business's technical requirements and proposes joint development. The generation AI also proposes joint development projects with patent holders based on the business's needs. For example, it identifies patent holders that are suitable for the business's business model and proposes joint development. In addition, when a business registers its needs, the generation AI comprehensively analyzes the business's needs and proposes joint development projects with patent holders. For example, it identifies patent holders that combine the business's technical requirements with its business model and proposes joint development. In this way, the business is proposed joint development projects with patent holders.

[0060] The needs registration unit uses an emotion estimation function to monitor the emotions of the business operator in real time when the business operator registers their needs, and provides feedback to improve the registration process. The needs registration unit, for example, uses the emotion estimation function to monitor the emotions of the business operator in real time when the business operator registers their needs, and provides feedback to improve the registration process. For example, the emotion estimation function makes suggestions for relaxing if the business operator is feeling stressed. The needs registration unit also uses the emotion estimation function to monitor the emotions of the business operator in real time when the business operator registers their needs, and provides feedback to improve the registration process. For example, the needs registration unit provides an interface to make the business operator feel positive emotions. The needs registration unit also uses the emotion estimation function to monitor the emotions of the business operator in real time when the business operator registers their needs, and provides feedback to improve the registration process. For example, an interactive element to make the business operator feel positive emotions is added. This allows the business operator to register their needs while feeling positive emotions.

[0061] When analyzing patent information and needs information, the matching department conducts technical details of the patent and market analysis to propose the optimal match. In the matching department, for example, the generation AI analyzes patent information and identifies the technical details of the patent. For example, it evaluates the technical strengths and applicability of the patent and proposes the patent that best meets the needs of the business. The generation AI also analyzes needs information and identifies the business's technical requirements and market needs. For example, it proposes patents based on the business's business model and market trends. The generation AI also comprehensively analyzes patent information and needs information, conducts technical details of the patent and market analysis, and proposes the optimal match. For example, it proposes a patent that combines the technical strengths of the patent with market needs. This makes it possible to propose the optimal match based on the technical details of the patent and market analysis.

[0062] When analyzing patent information and needs information, the matching department evaluates the patent's applicability and technical strengths and makes specific proposals to businesses. In the matching department, for example, the generation AI analyzes patent information and evaluates the patent's applicability. For example, it identifies the fields in which the patent's technology can be applied and makes proposals to businesses. The generation AI also analyzes needs information and evaluates the patent's technical strengths based on the business's technical requirements and market needs. For example, it evaluates the extent to which the patent's technology meets the business's needs and makes proposals. The generation AI also comprehensively analyzes patent information and needs information, evaluates the patent's applicability and technical strengths, and makes specific proposals to businesses. For example, it proposes specific application methods that combine the patent's technical strengths with market needs. This enables specific proposals to be made based on the patent's applicability and technical strengths.

[0063] The matching unit uses an emotion estimation function to provide matching results that take user emotions into consideration when analyzing patent information and needs information. For example, when the generation AI analyzes patent information and needs information, the matching unit uses the emotion estimation function to provide matching results that take user emotions into consideration. For example, patents that evoke positive emotions from the user are preferentially suggested. Furthermore, the emotion estimation function is used to monitor user emotions in real time when the generation AI analyzes patent information and needs information, and provide matching results that take emotions into consideration. For example, patents that evoke positive emotions from the user are preferentially suggested. Furthermore, when the generation AI analyzes patent information and needs information, the emotion estimation function is used to provide matching results that take user emotions into consideration. For example, an interface is provided for preferentially suggesting patents that evoke positive emotions from the user. This makes it possible to provide matching results that take user emotions into consideration.

[0064] When analyzing patent information and needs information, the matching unit makes new proposals that combine patents from different industries and fields. For example, the matching unit uses a generation AI to analyze patent information and make new proposals that combine patents from different industries. For example, it may propose a new application method that combines a patent in the medical field with a patent in a consumer product. The generation AI also analyzes needs information and makes new proposals that combine patents from different fields. For example, it may propose a new application method that combines a patent in the energy field with a patent in environmental protection. The generation AI also comprehensively analyzes patent information and needs information and makes new proposals that combine patents from different industries and fields. For example, it may propose a new application method that combines patents in different technology fields. This makes it possible to make new proposals that combine patents from different industries and fields.

[0065] The matching unit uses the emotion estimation function to monitor the user's emotions in real time when analyzing patent information and needs information, and provides the optimal matching result. For example, when the generation AI analyzes patent information and needs information, the matching unit uses the emotion estimation function to monitor the user's emotions in real time, and provides the optimal matching result. For example, it prioritizes suggesting patents that evoke positive emotions. In addition, the generation AI uses the emotion estimation function to monitor the user's emotions in real time when analyzing patent information and needs information, and provides the optimal matching result. For example, it prioritizes suggesting patents that evoke positive emotions. In addition, the generation AI uses the emotion estimation function to monitor the user's emotions in real time when analyzing patent information and needs information, and provides the optimal matching result. For example, it provides an interface for preferentially suggesting patents that evoke positive emotions. This makes it possible to monitor the user's emotions in real time and provide the optimal matching result.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] When a patent holder registers a patent, the Patent Registration Department can not only evaluate the patent's technical strength and market value, but also provide a function to evaluate the patent's environmental impact. For example, the Generative AI can analyze the environmental impact of the patent's technology and provide feedback on the results to the patent holder. It can also evaluate the extent to which the patent's technology contributes to the Sustainable Development Goals (SDGs) and notify the patent holder of the evaluation results. Furthermore, when a patent holder registers a patent, the Generative AI can comprehensively evaluate the patent's environmental impact and provide feedback on the results to the patent holder. This makes it easier for patent holders to understand the patent's environmental impact.

[0068] When a patent holder registers a patent, the Patent Registration Department not only evaluates the patent's technical strengths and market value, but also provides a function to evaluate the patent's social impact. For example, the generative AI can analyze the impact of the patent's technology on society and provide feedback on the results to the patent holder. It can also evaluate the extent to which the patent's technology contributes to solving social issues and notify the patent holder of the evaluation results. Furthermore, when a patent holder registers a patent, the generative AI can comprehensively evaluate the patent's social impact and provide feedback on the results to the patent holder. This makes it easier for patent holders to understand the patent's social impact.

[0069] When a patent holder registers a patent, the Patent Registration Department can not only evaluate the patent's technical strengths and market value, but also provide a function to evaluate the patent's ethical impact. For example, the generative AI can analyze whether the patented technology is ethically sound and provide feedback on the results to the patent holder. It can also evaluate the extent to which the patented technology complies with ethical standards and notify the patent holder of the evaluation results. Furthermore, when a patent holder registers a patent, the generative AI can comprehensively evaluate the patent's ethical impact and provide feedback on the results to the patent holder. This makes it easier for patent holders to understand the ethical impact of their patents.

[0070] When a patent holder registers a patent, the Patent Registration Department not only evaluates the patent's technical strengths and market value, but also provides a function to evaluate the patent's legal risks. For example, the Generative AI can analyze whether the patent's technology is legally sound and provide feedback on the results to the patent holder. It can also evaluate the extent to which the patent's technology complies with legal standards and notify the patent holder of the evaluation results. Furthermore, when a patent holder registers a patent, the Generative AI can comprehensively evaluate the patent's legal risks and provide feedback on the results to the patent holder. This makes it easier for patent holders to understand the legal risks of their patents.

[0071] When a patent holder registers a patent, the Patent Registration Department can not only evaluate the patent's technical strength and market value, but also provide a function to evaluate the patent's economic impact. For example, the Generative AI can analyze the economic impact of the patent's technology and provide feedback on the results to the patent holder. It can also evaluate the extent to which the patent's technology contributes to economic growth and notify the patent holder of the evaluation results. Furthermore, when a patent holder registers a patent, the Generative AI can comprehensively evaluate the patent's economic impact and provide feedback on the results to the patent holder. This makes it easier for patent holders to understand the patent's economic impact.

[0072] When a patent holder registers a patent, the patent registration department uses an emotion estimation function to analyze the patent holder's emotions and not only provides an interface to elicit positive emotions, but also customizes the patent registration process based on the patent holder's emotions. For example, if a patent holder is feeling stressed, the department makes suggestions to simplify the registration process. In addition to providing an interface to make the patent holder feel positive emotions, the department can also adjust the registration process based on the patent holder's emotions. For example, it provides an interface that helps the patent holder relax. This allows the patent holder to register a patent with positive emotions.

[0073] When a patent holder registers a patent, the Patent Registration Department uses emotion estimation functionality to monitor the patent holder's emotions in real time, providing feedback to improve the registration process and personalizing the registration process based on the patent holder's emotions. For example, if a patent holder is feeling stressed, the Department can not only provide suggestions for relaxation, but also adjust the registration process based on the patent holder's emotions. For example, the Department can provide an interface that helps the patent holder relax. This allows the patent holder to register a patent with positive emotions.

[0074] When a patent holder registers a patent, the patent registration department uses an emotion estimation function to monitor the patent holder's emotions in real time, not only providing feedback to improve the registration process but also guiding the registration process based on the patent holder's emotions. For example, if a patent holder is feeling stressed, the department can not only make suggestions to help them relax, but also navigate the registration process based on the patent holder's emotions. For example, it can provide an interface that helps the patent holder relax. This allows the patent holder to register a patent with positive emotions.

[0075] When a patent holder registers a patent, the patent registration department uses an emotion estimation function to monitor the patent holder's emotions in real time, not only providing feedback to improve the registration process but also optimizing the registration process based on the patent holder's emotions. For example, if a patent holder is feeling stressed, the department can not only make suggestions to help them relax, but also optimize the registration process based on the patent holder's emotions. For example, it can provide an interface that helps the patent holder relax. This allows the patent holder to register a patent with positive emotions.

[0076] When a patent holder registers a patent, the Patent Registration Department uses an emotion estimation function to monitor the patent holder's emotions in real time, providing feedback to improve the registration process and customizing the registration process based on the patent holder's emotions. For example, if a patent holder is feeling stressed, the Department can not only provide suggestions for relaxation, but also adjust the registration process based on the patent holder's emotions. For example, the Department can provide an interface that helps the patent holder relax. This allows the patent holder to register a patent with positive emotions.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The patent registration department registers the patent holder's patent information. For example, the patent holder enters detailed patent information and related technical documents and stores them in the service's database. When the patent holder registers a patent, the patent registration department can also use generative AI to automatically evaluate the patent's technical strengths and market value and provide feedback to the patent holder. Furthermore, the generative AI can analyze the patent's related literature and existing market data to suggest potential application areas for the patent. It can also use emotion estimation to analyze the patent holder's emotions and provide an interface to elicit positive emotions. Step 2: The needs registration unit registers the business's needs information. For example, the business enters the technology field and specific requirements it is seeking, and stores them in the service database. When a business registers its needs, the needs registration unit also allows the generation AI to analyze the business's past projects and market trends and propose optimal patents. Furthermore, the generation AI can suggest ways to utilize patents based on the business's business model and strategy. The emotion estimation function can also be used to analyze the business's emotions and provide an interface to elicit positive emotions. Step 3: The matching unit analyzes the patent information registered by the patent registration unit and the needs information registered by the needs registration unit to perform optimal matching. For example, the generation AI performs technical details of the patent and market analysis to propose optimal matching. The generation AI can also evaluate the patent's applicability and technical strengths and make specific proposals to businesses. Furthermore, when the generation AI analyzes the patent information and needs information, it can use an emotion estimation function to provide matching results that take the user's emotions into account.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0092] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0093] 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.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a patent registration department that registers patent information of patent holders; a needs registration unit that registers needs information of businesses; a matching unit that analyzes the patent information registered by the patent registration unit and the needs information registered by the needs registration unit and performs optimal matching. A system characterized by:

2. The Patent Registration Department: When the patent holder registers a patent, generative AI is used to automatically evaluate the technical strength and market value of the patent and provide feedback to the patent holder.

2. The system of claim 1.

3. The Patent Registration Department: When the patent owner registers a patent, the generative AI analyzes the patent's related literature and existing market data to suggest potential application areas for the patent.

2. The system of claim 1.

4. The Patent Registration Department: When the patentee registers a patent, an interface is provided to analyze the patentee's emotions and elicit positive emotions.

2. The system of claim 1.

5. The Patent Registration Department: When a patent owner registers a patent, the patent owner is provided with the ability to upload a visual demo or prototype video of the patent.

2. The system of claim 1.

6. The Patent Registration Department: Providing a generative AI function that suggests collaboration opportunities with other patent owners when the patent owner files a patent 2. The system of claim 1.

7. The Patent Registration Department: Monitor patent owner sentiment in real time as they file their patents and provide feedback to improve the registration process 2. The system of claim 1.

8. The needs registration unit When the business registers its needs, the AI ​​analyzes the business's past projects and market trends to propose optimal patents.

2. The system of claim 1.

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