system

Abstract

Provide a system. 【Solution means】 Means for obtaining patent information, Means for using a generation model for summarizing and analyzing the patent information, Means for exploring the applicability of technology in different fields based on the analyzed information, Means for generating new business ideas based on the exploration results, Means for visualizing the new business ideas and providing them to the user, Means for modifying ideas based on feedback from the user, Means for proposing a specific business plan to the user, Means for providing new content ideas through an online platform and using them to support content creation in the application field, A system including the above.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] Patent information is vast, and its effective utilization is a difficult task. Despite the invention of many technologies, patents remain dormant, and the creation of new businesses based on them has not progressed, which has become a problem. Also, it is difficult to discover the potential for applying technologies to different fields, which has caused open innovation to stagnate. A method for effectively utilizing patent information to break such a situation is required.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a system that uses a generative model to acquire patent information and summarize and analyze it. This system has the function of automatically exploring the applicability of technology to different fields based on the analyzed information and generating new business ideas based on that. Furthermore, by visualizing the generated ideas and providing them to the user, the system modifies the ideas based on the user's feedback. In this way, it provides a means to propose concrete business plans to the user through a collaboration platform and support the creation of new businesses.

[0006] "Patent information" refers to documentary information about technology and inventions published by the Japan Patent Office, which describes many inventions and their feasibility.

[0007] A "generative model" is a machine learning algorithm used to generate various outputs from specific input data, and is applied to natural language processing and data analysis.

[0008] "Cross-field application potential" refers to the possibility of a single patented technology being put into practical use in different industrial fields, and is a concept for discovering new markets and applications.

[0009] A "new business idea" is a concept for a business or product that does not yet exist in the market, created based on existing technologies and market information.

[0010] "Visualization" is the process of representing analyzed data and information as graphs, charts, and diagrams, presenting them in a way that users can intuitively understand.

[0011] "User" refers to an individual or legal entity that uses this system to create or evaluate business ideas based on patent information.

[0012] A "business plan proposal" is a document created based on a specific business idea, outlining its feasibility and concrete plans for market launch.

[0013] A "collaboration platform" is an online platform that allows multiple users to share ideas and information, and to collaborate on discussions and improvements. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the language used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system that generates new business ideas using patent information and supports users in commercializing them. This system mainly consists of a server, terminals, and users, and proceeds with processing in the following steps.

[0036] First, the server accesses the Japan Patent Office's public patent database to retrieve a vast amount of patent information. This information is stored on the server as raw data, and an AI model summarizes and analyzes it. The server utilizes natural language processing technology to summarize patent documents in a short time and extract key points and features of the technology. This extracted data becomes the foundational data for exploring the possibility of applying the technology to different fields.

[0037] Next, the server uses a generative AI model to construct new business ideas. This AI combines technical information and market data to generate ideas while considering novelty, marketability, and feasibility. Each of these generated business ideas is scored and sent to the terminal.

[0038] The device receives this information and provides it to the user using visualization tools. It displays the characteristics and scoring results of the generated ideas in a dashboard format, making them intuitively understandable to the user. Based on this visualized data, the user evaluates the ideas and selects the most promising ones.

[0039] Users can access the collaboration platform via their devices and discuss ideas with other stakeholders. User feedback is fed to the server, and the AI ​​uses this as a guide to improve the ideas.

[0040] Furthermore, the server activates a strategic consultant AI to assist the user in developing a concrete business plan, providing suggestions and guidance along the way. At this stage, the user develops a business plan that incorporates their own knowledge and requirements while taking the AI's suggestions into account.

[0041] As a concrete example, if patent information for a certain battery technology is retrieved from the server, a summary of that technology is generated, and solutions for drones and mobile devices are proposed as new applications for reusable batteries. The user evaluates this idea and develops a specific product development strategy in cooperation with relevant industry stakeholders.

[0042] This system enables the efficient creation of new businesses utilizing patent information, playing a role in maximizing the value of a company's patent assets.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server accesses the Japan Patent Office's public patent database and retrieves patent information. It filters and selects patents related to specific keywords or technical fields, and converts the retrieved data into an analyzable format.

[0046] Step 2:

[0047] The server uses natural language processing algorithms to automatically summarize the retrieved patent documents. Here, the technical objectives, benefits, and features of the patent are condensed, allowing for the rapid extraction of necessary information.

[0048] Step 3:

[0049] The server uses a generative AI model to analyze summarized patent information and explore the potential for cross-industry applications of the technologies. Based on each technology, it investigates and scores its potential for application in new markets.

[0050] Step 4:

[0051] The server combines patent technology information and market data to generate new business ideas. This is a process in which AI evaluates the novelty, market potential, and feasibility of new businesses and constructs each idea.

[0052] Step 5:

[0053] The terminal receives new business ideas sent from the server and presents them to the user using visualization tools. The score and characteristics of each idea are displayed on a dashboard, allowing the user to understand them intuitively.

[0054] Step 6:

[0055] Users select compelling business ideas based on visual information displayed via their devices and provide feedback. This user feedback is fed back into the system and used for further improvement.

[0056] Step 7:

[0057] Users utilize the collaboration platform on their devices to engage in discussions with other users. This enables the exchange of ideas to improve and concretize selected ideas.

[0058] Step 8:

[0059] The server reuses the generated AI based on user feedback to further refine business ideas. Through this process, ideas that better meet user needs are developed.

[0060] Step 9:

[0061] Users develop concrete business plans with the help of a strategic consultant AI through their device. The AI ​​provides market data and simulations to help users build realistic plans.

[0062] Step 10:

[0063] Users review the business plan developed on their devices and make adjustments as needed. To finalize the plan, users conduct detailed reviews and revisions until they create an actionable business plan.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] Generating new business ideas using patent information presents challenges, as it requires processing a vast amount of information, necessitating significant time and effort for understanding the technology and evaluating its market applicability. Furthermore, effectively sharing and evaluating these ideas among stakeholders is difficult, necessitating the development of efficient and rapid commercialization strategies.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes means for using a generative model to summarize and analyze patent information, means for exploring the potential of cross-disciplinary applications of the technology based on the analyzed information, and means for generating and scoring new business ideas. This makes it possible to quickly generate new business ideas from patent information and to efficiently evaluate and share them.

[0069] "Patent information" refers to technical information officially submitted to protect intellectual property rights, and is derived from publicly available patent documents.

[0070] A "generative model" is a type of artificial intelligence used to infer or generate new information from data, and is particularly applied to natural language processing and data analysis.

[0071] "Cross-disciplinary application potential" refers to the act of exploring the possibility that a particular technology may be useful in areas other than its original field.

[0072] A "business idea" is a concept that plans new directions for corporate activities, products, or services based on market needs and technological potential.

[0073] "Scoring" is a method of numerically evaluating ideas and data based on specific criteria, and it measures their value and potential.

[0074] A "terminal" is a device used by a user to receive or input information, and includes computers and smartphones.

[0075] "Feedback" refers to the opinions and evaluations that users provide after using a system, and is used to improve services and products.

[0076] The "strategy consultant model" is an artificial intelligence model designed to support decision-making when formulating business plans. It analyzes market and technological information to propose the optimal strategy.

[0077] A "collaboration platform" is a platform designed to facilitate information sharing and discussions among participants, and includes online meeting systems and collaboration tools.

[0078] This invention is a system that utilizes patent information to generate new business ideas and efficiently support their commercialization. This system primarily consists of a server, terminals, and users.

[0079] The server accesses the Japan Patent Office's public patent database to retrieve patent information. This information is stored in a database on the server and analyzed using a generative AI model. The server uses natural language processing technology to extract key points from patent documents and explores how the technology can be applied in other fields. The server also generates new business ideas by combining this information with market data and scores these ideas. The generative AI model utilizes an open-source natural language processing library.

[0080] The terminal is a tool for visualizing and providing business ideas received from the server to the user. The terminal uses visualization software to display information in a dashboard format, allowing the user to intuitively understand the details of the ideas and their scoring results.

[0081] Users evaluate business ideas provided via their devices. User feedback is sent to a server and used by the AI ​​to guide the refinement of the ideas. Users can also utilize a collaborative platform to discuss ideas with stakeholders. By activating a strategic consultant model, users can receive advice on developing concrete business plans.

[0082] For example, if patent information for battery technology is obtained, the server compactly summarizes the relevant information and generates ideas for applications in other fields, such as batteries for mobile devices and drones. Users evaluate these ideas and collaborate with industry stakeholders to build a product development strategy.

[0083] Examples of prompt statements include the following:

[0084] "Based on recently acquired patent information, please generate five new business ideas. Also, evaluate the market potential and feasibility of each idea."

[0085] "Based on the patent information for this battery technology, devise new applications for reusable batteries and propose a business plan utilizing them."

[0086] This system will enable the efficient creation of new businesses utilizing patent information and support companies in making the most of their patent assets.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The server accesses the Japan Patent Office's public patent database to retrieve patent information. As input, queries are executed against the patent database. As a result of these queries, a large number of patent documents related to specific technical fields or keywords are output. These documents, along with metadata such as patent number, title, and filing date, are stored in the server's database.

[0090] Step 2:

[0091] The server uses a generating AI model to summarize and analyze the acquired patent data. The input is the text data of the patent documents saved in Step 1. The AI ​​model uses natural language processing techniques to analyze the patent documents and extract key technical points and features. The output is summarized technical information and keywords indicating potential cross-disciplinary applications.

[0092] Step 3:

[0093] The server uses a generative AI model to generate new business ideas based on summarized patent information. The summarized data and market data obtained in step 2 are used as input. The server sends prompts to the generative AI model to instruct it to generate business ideas. The output is a list of scored business ideas. This list evaluates the marketability, novelty, and feasibility of each idea.

[0094] Step 4:

[0095] The terminal visualizes the business ideas received from the server. As input, a list of business ideas generated in step 3 is sent to the terminal. The terminal uses visualization software to graphically display these ideas on a dashboard. As output, the user is provided with a dashboard that visually and clearly presents the characteristics and scoring results of each idea.

[0096] Step 5:

[0097] Users evaluate business ideas provided through their devices. As input, the dashboard information visualized in step 4 is presented to the user. Users submit feedback on the ideas, and this feedback, along with their evaluation, is returned to the server. The output consists of user comments and evaluation results, which serve as a guide for AI-driven idea refinement.

[0098] Step 6:

[0099] The server launches a strategic consultant model to assist the user in developing a concrete business plan. User feedback and scored ideas are used as input. The strategic consultant model analyzes this information to provide optimal business strategies and risk management measures. The output is a concrete business plan proposal for the user to refer to.

[0100] (Application Example 1)

[0101] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0102] The problem lies in the lack of means to generate new business ideas utilizing patent information and to maximize their application potential. In particular, there is a need for systems to generate new content ideas based on patent information and to effectively deliver them to users. Since opportunities for users to utilize patent information to create their own content are limited, providing these new opportunities is a challenge.

[0103] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0104] In this invention, the server includes means for acquiring patent information, means for using a generative model for summarizing and analyzing the patent information, and means for exploring cross-disciplinary application possibilities based on the analyzed information. This enables users to create original content utilizing patent information through the generation and provision of new content ideas.

[0105] "Patent information" refers to data concerning the technical content of an invention that is made public by the Japan Patent Office.

[0106] A "generative model" is a model based on AI technology used to analyze patent information.

[0107] "Cross-disciplinary applicability" refers to the ability to evaluate whether a particular technology can be used in other fields.

[0108] A "new business idea" refers to an original concept for developing a new business, generated based on patent information.

[0109] An "online platform" refers to a system that provides the foundation for digital services accessed via the internet.

[0110] "New content ideas" refer to original ideas for creating new content, generated based on patent information.

[0111] To implement this invention, the server first accesses the Japan Patent Office's public patent database to obtain patent information. This information is then summarized and analyzed by a generative AI model built using Amazon SageMaker. The generative model uses natural language processing technology to analyze the patent documents and explore their potential applications in other fields.

[0112] Based on the analysis results, the server generates new business ideas and provides them to users. Users can access the online platform via devices such as smartphones and smart glasses to view the new content ideas. The user interface, built using React Native, is visualized using D3.js, allowing users to intuitively understand the information.

[0113] Users send feedback from their devices to the server, and this feedback is reflected in the generating AI model. The server uses the AI ​​model to refine the idea and proposes it to the user again. This cycle enables users to create original content based on patent information.

[0114] As a concrete example, if a user wants to create new video content, the server will provide new content ideas based on an analysis of patent information related to that field. The user can then use this and incorporate it as a new element in their project.

[0115] An example of a prompt for a generative AI model could be, "Generate new video content ideas to enhance user engagement." The ideas suggested through this prompt will form the basis for the user's creative activities.

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The server accesses the Japan Patent Office's public patent database and retrieves patent information. The input consists of the URL of the Patent Office database and a search query, while the output is raw patent data. The server then saves this raw data to local storage.

[0119] Step 2:

[0120] The server inputs the raw data of stored patent information into a generating AI model, which is then executed on Amazon SageMaker. The input includes patent documents, and the output consists of summarized text and analysis results. In this process, the server uses natural language processing techniques to quickly summarize the patent information and extract key technical points and features.

[0121] Step 3:

[0122] The server explores potential applications in different fields based on the analysis results. The input consists of key technical points and characteristics, while the output obtained by the generative AI model is a list of ideas applicable to different fields. The server then maps this list to specific application examples.

[0123] Step 4:

[0124] The server generates new business ideas using the results of its cross-disciplinary application potential analysis. The input is a list of application ideas, and the output is a concrete new business plan. The generated plan takes into account the novelty and market potential of the business. The server organizes this information and prepares it for transmission to the user's terminal.

[0125] Step 5:

[0126] The terminal visualizes new business ideas received from the server and presents them to the user. The input is a new business plan, and the output is a visually easy-to-understand dashboard display. The terminal displays this using React Native and uses D3.js to enable interactive user interaction.

[0127] Step 6:

[0128] Users review the displayed new business ideas and provide feedback. Input is the user's evaluation and comments, and output is feedback information. Users send this feedback to the server via their device.

[0129] Step 7:

[0130] Based on the received feedback, the server runs the generating AI model again to improve the idea. The input is the feedback information, and the output is the improved business idea. The server uses prompts to instruct the AI ​​on the direction of idea improvement and sends the newly generated business idea back to the terminal.

[0131] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0132] This invention is a system that generates new business ideas using patent information and provides them to users, while also recognizing and reflecting the user's emotions using an emotion engine. This system comprises a server, a terminal, and an emotion engine, and proceeds with processing as follows.

[0133] First, the server accesses the Japan Patent Office's public patent database and retrieves relevant patent information. The retrieved patent information is automatically summarized and analyzed using a natural language processing model. The server extracts the technical features and content and explores their potential applications in other fields. This information is then used to develop new business ideas.

[0134] Next, the server constructs the generated new business ideas and scores each idea based on its novelty, feasibility, and market potential. The terminal receives the business ideas sent from the server and displays them to the user using a visualization tool. During this process, an emotion engine recognizes the user's emotions in real time and adjusts the visuals to optimize the user experience.

[0135] Users evaluate business ideas based on the presented visual information and provide feedback. This feedback is sent to the server via the device, and is analyzed along with user emotion data collected by the emotion engine. This allows user emotions to be used to revise and optimize ideas.

[0136] For example, suppose we have an idea for a "mobile application for sustainable energy" generated through patent analysis. Users consider this idea, and based on the emotional engine's high evaluation of their interests and concerns, the information is tailored to the most suitable presentation format. Based on this information, users interact with other stakeholders through a discussion platform to concretize the business plan.

[0137] The server utilizes strategic consultant AI to provide users with interactive guides and business plan proposals, supporting concrete business development. This allows users to leverage the provided information and emotional feedback to formulate optimal business plans.

[0138] Ultimately, this system provides a mechanism that helps users efficiently create attractive new businesses by effectively utilizing patent information and user sentiment.

[0139] The following describes the processing flow.

[0140] Step 1:

[0141] The server accesses the Japan Patent Office's public patent database, filters and retrieves patent information based on specified keywords and technical fields, and stores this information in a parseable data format.

[0142] Step 2:

[0143] The server uses a natural language processing engine to summarize the retrieved patent documents and extract important technical content and features. In this process, machine learning models are used to analyze the text data and express the patent information concisely.

[0144] Step 3:

[0145] The server uses generated AI models based on the analyzed patent information to explore potential applications in different industries. This allows for the evaluation and scoring of the potential for technology use in other fields.

[0146] Step 4:

[0147] The generative AI model generates new business ideas by combining technical information and market data within the server, and then assigns scores to those ideas based on their marketability and feasibility.

[0148] Step 5:

[0149] The terminal visualizes new business ideas received from the server and presents them to the user in a dashboard format. Here, the emotion engine recognizes the user's reactions and dynamically adjusts the display method.

[0150] Step 6:

[0151] Users select interesting business ideas based on visualized information on their devices and provide feedback. User sentiment data is collected by an emotion engine and incorporated into the evaluation of the ideas.

[0152] Step 7:

[0153] The device sends user feedback and emotion engine data to a server, which then uses this data to execute a process for further optimizing and refining ideas.

[0154] Step 8:

[0155] Users utilize a collaboration platform on their devices to discuss proposed ideas with other users. Through this collaborative work, they advance the concretization of their ideas.

[0156] Step 9:

[0157] The server uses a strategic consultant AI to propose appropriate business plan proposals to users and provides support to improve the specificity and feasibility of those proposals.

[0158] Step 10:

[0159] Users scrutinize the proposed business plan, make revisions via their device as needed, and finalize the business plan. This plan is optimized by taking into account feedback and data from the sentiment engine.

[0160] (Example 2)

[0161] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0162] A system is needed to efficiently generate new business plans using patent information, and to enable flexible feedback and plan revisions that take user sentiment into account. Furthermore, it is necessary to enhance the specificity and market adaptability of the generated business plans.

[0163] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0164] In this invention, the server includes means for acquiring patent information, means for utilizing a generative model for analyzing the patent information, and emotion engine means for recognizing the user's emotions and optimizing the visualization format. This enables the efficient generation of new business plans based on patent information and the modification of plans to suit the user's emotions.

[0165] "Patent information" refers to a collection of documents and data containing technical content that have been made public by the Japan Patent Office.

[0166] A "generative model" is a general term for algorithms and systems that generate new information or patterns based on data.

[0167] "Potential for application in other fields" refers to the possibility that a particular technology or idea can be used in areas outside of one's own area of ​​expertise.

[0168] A "business plan" is a detailed document that describes the strategies, business activities, and marketing tactics for conducting commercial activities.

[0169] "Visualization" is the process of representing data and ideas in visual forms such as shapes, images, and graphs.

[0170] "Users" refer to individuals or organizations that use a system or service.

[0171] An "emotion engine" is a software or hardware system that analyzes a user's emotions and adjusts the system's response based on the results.

[0172] "Feedback" is the process by which users provide opinions and impressions about a system or information.

[0173] A "business plan proposal" is a document that outlines the specific tasks an organization should perform and the means by which they should be performed.

[0174] The embodiment for carrying out this invention is configured as follows.

[0175] The server uses a web scraping tool built in Python to access the Japan Patent Office's public database. This allows the server to obtain patent technical information and then summarize and analyze the data using natural language processing techniques. Specifically, it utilizes generative AI models such as BERT and GPT to perform text analysis on the patent information. The server also performs cluster analysis to evaluate the cross-disciplinary applicability of patent technologies and develops new business plans.

[0176] The terminal receives business ideas and plans sent from the server and presents them visually to the user using visualization tools such as D3.js. In this process, the terminal optimizes the display based on user input under the guidance of an emotion engine. The emotion engine uses OpenCV and TENSORFLOW® to analyze the user's facial expressions and improve the user experience.

[0177] Users evaluate business ideas using the presented visual information. When users provide feedback, data is sent to the server via their device. The server uses this feedback, along with sentiment data, to revise the ideas.

[0178] For example, when the server suggests a "new mobile application for sustainable energy," if the emotion engine detects the user's interest, the information display style will be automatically adjusted. The generative AI model provides the user with a prompt such as, "Please propose a detailed plan to explore new business opportunities in the sustainable energy market by applying this patent."

[0179] This will enable a broader creative process that is more accessible to all stakeholders, and by integrating patent information and user feedback, it will be possible to realize new business plans.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The server accesses the Japan Patent Office's public database to retrieve patent information related to a specific technical field. The input is a patent search query, and the output is a set of retrieved patent documents. Specifically, a web scraping tool using Python is used to collect the necessary patent information data.

[0183] Step 2:

[0184] The server analyzes the acquired patent documents using natural language processing technology. The input is the patent documents acquired in step 1, and the output is a list of summarized patent information and its technical features. Specifically, it uses a generative AI model (such as BERT or GPT) to analyze and summarize the text and extract relevant technologies and keywords.

[0185] Step 3:

[0186] Based on the information analyzed in the previous step, the server explores the potential for cross-disciplinary applications of the patented technology. The input is the list of technical features from step 2, and the output is a list of application ideas in different fields. In this step, cluster analysis is performed to identify other technological areas with similar features and explore new business opportunities.

[0187] Step 4:

[0188] The server generates new business plans based on their potential for cross-disciplinary application. The input is a list of application ideas obtained in step 3, and the output is a concrete business plan proposal. The generation AI model is used to formulate plans that consider business strategy and market adaptability. This process generates specific prompts such as, "How can we devise a new mobile application for sustainable energy?"

[0189] Step 5:

[0190] The terminal receives the business plan sent from the server and presents it to the user in a visualized form. The input is the business plan draft sent from the server, and the output is visual information for the user. The terminal uses visualization tools such as D3.js to graphically display the plan draft and designs the interface to facilitate user understanding.

[0191] Step 6:

[0192] The emotion engine recognizes the user's emotions in real time as they view presented visual information and optimizes the experience. Input is the user's facial expressions and tone of voice, and output is an optimized user interface. This includes specific processes such as performing facial analysis using OpenCV and TensorFlow, and adjusting the colors and layout of the visual display.

[0193] Step 7:

[0194] Users input opinions and suggestions as feedback on the business plan via a terminal. The input consists of user evaluations and feedback, and the output is feedback data sent to the server. Based on this information, the server modifies and optimizes the business plan based on the feedback. A generative AI model is utilized to provide concrete means for improving the proposed content.

[0195] (Application Example 2)

[0196] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0197] Traditionally, when generating new business ideas using patent information, there was a lack of methods to effectively incorporate user emotional responses and improve the customer experience in physical stores. As a result, product displays and promotions in physical stores could not be effectively tailored to customer preferences, leading to challenges in increasing sales and improving customer satisfaction.

[0198] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0199] In this invention, the server includes means for acquiring patent information, means for using a generative model for summarizing and analyzing the patent information, and means for exploring the potential of the technology to be applied to other fields. This makes it possible to recognize user emotions in real time, dynamically adjust in-store displays and promotions to optimize the customer experience and improve sales.

[0200] "Patent information" refers to publicly available documents relating to the technical details of an invention and its intellectual property rights.

[0201] A "generative model" is an algorithm or process for extracting useful information from specific input data and for analyzing and transforming it.

[0202] "Cross-disciplinary applicability" refers to the potential for how a particular technology or idea can be applied in different fields.

[0203] A "new business idea" is an innovative business concept that does not yet exist in the market or that has evolved from an existing business.

[0204] "Visualization" is a technique that makes information and data easier for users to understand by illustrating them.

[0205] "User sentiment" refers to the emotional response that users exhibit when interacting with a product or service.

[0206] A "promotion strategy" is a set of actions and measures planned to promote the sale of a product or service.

[0207] A "physical store" is a physical sales space that customers can visit in person.

[0208] This system acquires and analyzes patent information and generates new business ideas based on the results. The server retrieves patent information from a database and summarizes and analyzes it using a generative model. This analysis explores the potential of the patent information to be applied in different fields and constructs new business ideas. The server visualizes these ideas and provides them to the user. The server is also configured to recognize the user's emotions in real time and dynamically adjust store displays and promotions based on those emotions. This process is carried out via devices such as smart glasses and displays, and user feedback and emotion data are used to optimize the store experience.

[0209] Specifically, if a customer shows no interest in a particular product, the server can automatically adjust the promotional strategy accordingly. For example, in a fashion store, this could involve analyzing the customer's facial expressions through smart glasses and suggesting a try-on event. This allows stores to provide promotions tailored to customer preferences, thereby improving customer satisfaction and increasing sales.

[0210] The entire process described above is designed to be efficient by utilizing a generative AI model. An example of a prompt for analyzing real-time data from a specific channel and formulating optimal promotional ideas is: "What promotional ideas would be best if the customer is not showing interest in the product? Please also consider relevant information from the patent database."

[0211] These processes enable this invention to more personalize and effectively improve the customer experience in physical stores.

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The server retrieves patent information from a database. It uses queries to the patent database as input to collect publicly available patent documents. The output is a set of retrieved patent information. This step involves specific actions to access the patent database via an API to automate the patent information retrieval process.

[0215] Step 2:

[0216] The server uses a generative AI model to summarize and analyze patent information. The patent information obtained in step 1 is used as input. For data processing, natural language processing is used to segment the document and extract key technical features. The output is the summarized patent information and its analysis results. This step includes specific actions in which the generative model automatically identifies the subject matter and applicability of the patent.

[0217] Step 3:

[0218] The server explores the cross-disciplinary applicability of the technology based on the analysis results. The input is the analysis results from step 2. As a data calculation, it retrieves application examples of similar technologies from a database and scores their applicability. The output is a report on the cross-disciplinary applicability. This step includes specific actions that use existing case studies to compare related technologies.

[0219] Step 4:

[0220] The server generates new business ideas based on the search results. The report obtained in step 3 is used as input. For data processing, a generative AI model is utilized to construct ideas with increased applicability. The output is a list of new business ideas. This step includes the specific operation of the generative AI model, which combines different technological elements to generate new ideas.

[0221] Step 5:

[0222] The terminal visualizes and presents new business ideas to the user. The input is the idea list from step 4. As data processing, a visual design for presentation is generated. The output is a visualized business idea that the user can view. This step includes specific actions to generate an intuitive interface using visual tools.

[0223] Step 6:

[0224] Users provide feedback on business ideas, which is sent to the server. The inputs used are user feedback and sentiment data collected from sensors. Data processing involves analyzing user responses and performing analysis using a sentiment engine. The output is the improved business idea and its evaluation. This step includes specific actions to integrate feedback and sentiment data and clarify areas for improvement for the next step.

[0225] Step 7:

[0226] The server generates prompt messages and proposes the optimal promotional strategy obtained by the generative AI model. The input consists of feedback and sentiment data. Data processing involves generating prompt messages and formulating a promotional strategy. The output is the optimized promotional proposal. This step includes specific actions where the generative AI model dynamically adjusts the promotion considering the user's sentiment.

[0227] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0228] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0229] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0233] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0234] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0235] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0236] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0237] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0238] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0239] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0240] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0241] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0242] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0243] This invention is a system that generates new business ideas using patent information and supports users in commercializing them. This system mainly consists of a server, terminals, and users, and proceeds with processing in the following steps.

[0244] First, the server accesses the Japan Patent Office's public patent database to retrieve a vast amount of patent information. This information is stored on the server as raw data, and an AI model summarizes and analyzes it. The server utilizes natural language processing technology to summarize patent documents in a short time and extract key points and features of the technology. This extracted data becomes the foundational data for exploring the possibility of applying the technology to different fields.

[0245] Next, the server uses a generative AI model to construct new business ideas. This AI combines technical information and market data to generate ideas while considering novelty, marketability, and feasibility. Each of these generated business ideas is scored and sent to the terminal.

[0246] The device receives this information and provides it to the user using visualization tools. It displays the characteristics and scoring results of the generated ideas in a dashboard format, making them intuitively understandable to the user. Based on this visualized data, the user evaluates the ideas and selects the most promising ones.

[0247] Users can access the collaboration platform via their devices and discuss ideas with other stakeholders. User feedback is fed to the server, and the AI ​​uses this as a guide to improve the ideas.

[0248] Furthermore, the server activates a strategic consultant AI to assist the user in developing a concrete business plan, providing suggestions and guidance along the way. At this stage, the user develops a business plan that incorporates their own knowledge and requirements while taking the AI's suggestions into account.

[0249] As a concrete example, if patent information for a certain battery technology is retrieved from the server, a summary of that technology is generated, and solutions for drones and mobile devices are proposed as new applications for reusable batteries. The user evaluates this idea and develops a specific product development strategy in cooperation with relevant industry stakeholders.

[0250] This system enables the efficient creation of new businesses utilizing patent information, playing a role in maximizing the value of a company's patent assets.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The server accesses the Japan Patent Office's public patent database and retrieves patent information. It filters and selects patents related to specific keywords or technical fields, and converts the retrieved data into an analyzable format.

[0254] Step 2:

[0255] The server uses natural language processing algorithms to automatically summarize the retrieved patent documents. Here, the technical objectives, benefits, and features of the patent are condensed, allowing for the rapid extraction of necessary information.

[0256] Step 3:

[0257] The server uses a generative AI model to analyze summarized patent information and explore the potential for cross-industry applications of the technologies. Based on each technology, it investigates and scores its potential for application in new markets.

[0258] Step 4:

[0259] The server combines patent technology information and market data to generate new business ideas. This is a process in which AI evaluates the novelty, market potential, and feasibility of new businesses and constructs each idea.

[0260] Step 5:

[0261] The terminal receives new business ideas sent from the server and presents them to the user using visualization tools. The score and characteristics of each idea are displayed on a dashboard, allowing the user to understand them intuitively.

[0262] Step 6:

[0263] Users select compelling business ideas based on visual information displayed via their devices and provide feedback. This user feedback is fed back into the system and used for further improvement.

[0264] Step 7:

[0265] Users utilize the collaboration platform on their devices to engage in discussions with other users. This enables the exchange of ideas to improve and concretize selected ideas.

[0266] Step 8:

[0267] The server reuses the generated AI based on user feedback to further refine business ideas. Through this process, ideas that better meet user needs are developed.

[0268] Step 9:

[0269] Users develop concrete business plans with the help of a strategic consultant AI through their device. The AI ​​provides market data and simulations to help users build realistic plans.

[0270] Step 10:

[0271] Users review the business plan developed on their devices and make adjustments as needed. To finalize the plan, users conduct detailed reviews and revisions until they create an actionable business plan.

[0272] (Example 1)

[0273] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0274] Generating new business ideas using patent information presents challenges, as it requires processing a vast amount of information, necessitating significant time and effort for understanding the technology and evaluating its market applicability. Furthermore, effectively sharing and evaluating these ideas among stakeholders is difficult, necessitating the development of efficient and rapid commercialization strategies.

[0275] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0276] In this invention, the server includes means for using a generative model to summarize and analyze patent information, means for exploring the potential of cross-disciplinary applications of the technology based on the analyzed information, and means for generating and scoring new business ideas. This makes it possible to quickly generate new business ideas from patent information and to efficiently evaluate and share them.

[0277] "Patent information" refers to technical information officially submitted to protect intellectual property rights, and is derived from publicly available patent documents.

[0278] A "generative model" is a type of artificial intelligence used to infer or generate new information from data, and is particularly applied to natural language processing and data analysis.

[0279] "Cross-disciplinary application potential" refers to the act of exploring the possibility that a particular technology may be useful in areas other than its original field.

[0280] A "business idea" is a concept that plans new directions for corporate activities, products, or services based on market needs and technological potential.

[0281] "Scoring" is a method of numerically evaluating ideas and data based on specific criteria, and it measures their value and potential.

[0282] A "terminal" is a device used by a user to receive and input information, such as a computer or a smartphone.

[0283] "Feedback" refers to opinions and evaluations provided by users after using a system, which are used to improve services and products.

[0284] A "strategic consultant model" is an artificial intelligence model used to support decision-making when formulating business plans, and it plays a role in analyzing market information and technical information to propose optimal strategies.

[0285] A "cooperation platform" is a platform for promoting information sharing and discussion among participants, including online meeting systems and collaboration tools.

[0286] This invention is a system that utilizes patent information to generate new business ideas and efficiently supports commercialization. This system is mainly composed of a server, a terminal, and a user.

[0287] The server accesses the public patent database of the Patent Office to obtain patent information. This information is stored in the server's database and analyzed using a generation AI model. The server extracts the key points of patent documents using natural language processing technology and explores how the technology can be applied in other fields. In addition, the server generates new business ideas by combining with market data and scores these ideas. As the generation AI model, an open-source natural language processing library is utilized.

[0288] The terminal is a tool for visualizing the business ideas received from the server and providing them to the user. The terminal uses visualization software to display information in a dashboard format, enabling the user to intuitively understand the details of the ideas and the scoring results.

[0289] Users evaluate business ideas provided via their devices. User feedback is sent to a server and used by the AI ​​to guide the refinement of the ideas. Users can also utilize a collaborative platform to discuss ideas with stakeholders. By activating a strategic consultant model, users can receive advice on developing concrete business plans.

[0290] For example, if patent information for battery technology is obtained, the server compactly summarizes the relevant information and generates ideas for applications in other fields, such as batteries for mobile devices and drones. Users evaluate these ideas and collaborate with industry stakeholders to build a product development strategy.

[0291] Examples of prompt statements include the following:

[0292] "Based on recently acquired patent information, please generate five new business ideas. Also, evaluate the market potential and feasibility of each idea."

[0293] "Based on the patent information for this battery technology, devise new applications for reusable batteries and propose a business plan utilizing them."

[0294] This system will enable the efficient creation of new businesses utilizing patent information and support companies in making the most of their patent assets.

[0295] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0296] Step 1:

[0297] The server accesses the Japan Patent Office's public patent database to retrieve patent information. As input, queries are executed against the patent database. As a result of these queries, a large number of patent documents related to specific technical fields or keywords are output. These documents, along with metadata such as patent number, title, and filing date, are stored in the server's database.

[0298] Step 2:

[0299] The server uses a generating AI model to summarize and analyze the acquired patent data. The input is the text data of the patent documents saved in Step 1. The AI ​​model uses natural language processing techniques to analyze the patent documents and extract key technical points and features. The output is summarized technical information and keywords indicating potential cross-disciplinary applications.

[0300] Step 3:

[0301] The server uses a generative AI model to generate new business ideas based on summarized patent information. The summarized data and market data obtained in step 2 are used as input. The server sends prompts to the generative AI model to instruct it to generate business ideas. The output is a list of scored business ideas. This list evaluates the marketability, novelty, and feasibility of each idea.

[0302] Step 4:

[0303] The terminal visualizes the business ideas received from the server. As input, a list of business ideas generated in step 3 is sent to the terminal. The terminal uses visualization software to graphically display these ideas on a dashboard. As output, the user is provided with a dashboard that visually and clearly presents the characteristics and scoring results of each idea.

[0304] Step 5:

[0305] The user evaluates the business ideas provided through the terminal. As input, the dashboard information visualized in Step 4 is presented to the user. The user sends feedback on the ideas, which is then returned to the server along with the evaluation. The output is the user's comments and evaluation results, which serve as guidelines for idea improvement by AI.

[0306] Step 6:

[0307] The server activates the strategic consultant model to assist the user in formulating a specific business plan. As input, the user's feedback and scored ideas are used. The strategic consultant model analyzes this information and provides an optimal business strategy and risk management plan. The output is a specific business plan proposal for the user to refer to.

[0308] (Application Example 1)

[0309] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0310] There is a lack of means to maximize the creation of new business ideas using patent information and its applicability. In particular, there is a demand for a system for generating new content ideas based on patent information and effectively providing this to users. Since the opportunities for users to utilize patent information for their own content creation are limited, it is an issue to provide such new opportunities.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0312] In this invention, the server includes means for acquiring patent information, means for using a generative model for summarizing and analyzing the patent information, and means for exploring cross-disciplinary application possibilities based on the analyzed information. This enables users to create original content utilizing patent information through the generation and provision of new content ideas.

[0313] "Patent information" refers to data concerning the technical content of an invention that is made public by the Japan Patent Office.

[0314] A "generative model" is a model based on AI technology used to analyze patent information.

[0315] "Cross-disciplinary applicability" refers to the ability to evaluate whether a particular technology can be used in other fields.

[0316] A "new business idea" refers to an original concept for developing a new business, generated based on patent information.

[0317] An "online platform" refers to a system that provides the foundation for digital services accessed via the internet.

[0318] "New content ideas" refer to original ideas for creating new content, generated based on patent information.

[0319] To implement this invention, the server first accesses the Japan Patent Office's public patent database to obtain patent information. This information is then summarized and analyzed by a generative AI model built using Amazon SageMaker. The generative model uses natural language processing technology to analyze the patent documents and explore their potential applications in other fields.

[0320] Based on the analysis results, the server generates new business ideas and provides them to users. Users can access the online platform via devices such as smartphones and smart glasses to view the new content ideas. The user interface, built using React Native, is visualized using D3.js, allowing users to intuitively understand the information.

[0321] Users send feedback from their devices to the server, and this feedback is reflected in the generating AI model. The server uses the AI ​​model to refine the idea and proposes it to the user again. This cycle enables users to create original content based on patent information.

[0322] As a concrete example, if a user wants to create new video content, the server will provide new content ideas based on an analysis of patent information related to that field. The user can then use this and incorporate it as a new element in their project.

[0323] An example of a prompt for a generative AI model could be, "Generate new video content ideas to enhance user engagement." The ideas suggested through this prompt will form the basis for the user's creative activities.

[0324] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0325] Step 1:

[0326] The server accesses the Japan Patent Office's public patent database and retrieves patent information. The input consists of the URL of the Patent Office database and a search query, while the output is raw patent data. The server then saves this raw data to local storage.

[0327] Step 2:

[0328] The server inputs the raw data of stored patent information into a generating AI model, which is then executed on Amazon SageMaker. The input includes patent documents, and the output consists of summarized text and analysis results. In this process, the server uses natural language processing techniques to quickly summarize the patent information and extract key technical points and features.

[0329] Step 3:

[0330] The server explores potential applications in different fields based on the analysis results. The input consists of key technical points and characteristics, while the output obtained by the generative AI model is a list of ideas applicable to different fields. The server then maps this list to specific application examples.

[0331] Step 4:

[0332] The server generates new business ideas using the results of its cross-disciplinary application potential analysis. The input is a list of application ideas, and the output is a concrete new business plan. The generated plan takes into account the novelty and market potential of the business. The server organizes this information and prepares it for transmission to the user's terminal.

[0333] Step 5:

[0334] The terminal visualizes new business ideas received from the server and presents them to the user. The input is a new business plan, and the output is a visually easy-to-understand dashboard display. The terminal displays this using React Native and uses D3.js to enable interactive user interaction.

[0335] Step 6:

[0336] Users review the displayed new business ideas and provide feedback. Input is the user's evaluation and comments, and output is feedback information. Users send this feedback to the server via their device.

[0337] Step 7:

[0338] Based on the received feedback, the server runs the generating AI model again to improve the idea. The input is the feedback information, and the output is the improved business idea. The server uses prompts to instruct the AI ​​on the direction of idea improvement and sends the newly generated business idea back to the terminal.

[0339] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0340] This invention is a system that generates new business ideas using patent information and provides them to users, while also recognizing and reflecting the user's emotions using an emotion engine. This system comprises a server, a terminal, and an emotion engine, and proceeds with processing as follows.

[0341] First, the server accesses the Japan Patent Office's public patent database and retrieves relevant patent information. The retrieved patent information is automatically summarized and analyzed using a natural language processing model. The server extracts the technical features and content and explores their potential applications in other fields. This information is then used to develop new business ideas.

[0342] Next, the server constructs the generated new business ideas and scores each idea based on its novelty, feasibility, and market potential. The terminal receives the business ideas sent from the server and displays them to the user using a visualization tool. During this process, an emotion engine recognizes the user's emotions in real time and adjusts the visuals to optimize the user experience.

[0343] Users evaluate business ideas based on the presented visual information and provide feedback. This feedback is sent to the server via the device, and is analyzed along with user emotion data collected by the emotion engine. This allows user emotions to be used to revise and optimize ideas.

[0344] For example, suppose we have an idea for a "mobile application for sustainable energy" generated through patent analysis. Users consider this idea, and based on the emotional engine's high evaluation of their interests and concerns, the information is tailored to the most suitable presentation format. Based on this information, users interact with other stakeholders through a discussion platform to concretize the business plan.

[0345] The server utilizes strategic consultant AI to provide users with interactive guides and business plan proposals, supporting concrete business development. This allows users to leverage the provided information and emotional feedback to formulate optimal business plans.

[0346] Ultimately, this system provides a mechanism that helps users efficiently create attractive new businesses by effectively utilizing patent information and user sentiment.

[0347] The following describes the processing flow.

[0348] Step 1:

[0349] The server accesses the Japan Patent Office's public patent database, filters and retrieves patent information based on specified keywords and technical fields, and stores this information in a parseable data format.

[0350] Step 2:

[0351] The server uses a natural language processing engine to summarize the retrieved patent documents and extract important technical content and features. In this process, machine learning models are used to analyze the text data and express the patent information concisely.

[0352] Step 3:

[0353] The server uses generated AI models based on the analyzed patent information to explore potential applications in different industries. This allows for the evaluation and scoring of the potential for technology use in other fields.

[0354] Step 4:

[0355] The generative AI model generates new business ideas by combining technical information and market data within the server, and then assigns scores to those ideas based on their marketability and feasibility.

[0356] Step 5:

[0357] The terminal visualizes new business ideas received from the server and presents them to the user in a dashboard format. Here, the emotion engine recognizes the user's reactions and dynamically adjusts the display method.

[0358] Step 6:

[0359] Users select interesting business ideas based on visualized information on their devices and provide feedback. User sentiment data is collected by an emotion engine and incorporated into the evaluation of the ideas.

[0360] Step 7:

[0361] The device sends user feedback and emotion engine data to a server, which then uses this data to execute a process for further optimizing and refining ideas.

[0362] Step 8:

[0363] Users utilize a collaboration platform on their devices to discuss proposed ideas with other users. Through this collaborative work, they advance the concretization of their ideas.

[0364] Step 9:

[0365] The server uses a strategic consultant AI to propose appropriate business plan proposals to users and provides support to improve the specificity and feasibility of those proposals.

[0366] Step 10:

[0367] Users scrutinize the proposed business plan, make revisions via their device as needed, and finalize the business plan. This plan is optimized by taking into account feedback and data from the sentiment engine.

[0368] (Example 2)

[0369] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0370] A system is needed to efficiently generate new business plans using patent information, and to enable flexible feedback and plan revisions that take user sentiment into account. Furthermore, it is necessary to enhance the specificity and market adaptability of the generated business plans.

[0371] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0372] In this invention, the server includes means for acquiring patent information, means for utilizing a generative model for analyzing the patent information, and emotion engine means for recognizing the user's emotions and optimizing the visualization format. This enables the efficient generation of new business plans based on patent information and the modification of plans to suit the user's emotions.

[0373] "Patent information" refers to a collection of documents and data containing technical content that have been made public by the Japan Patent Office.

[0374] A "generative model" is a general term for algorithms and systems that generate new information or patterns based on data.

[0375] "Potential for application in other fields" refers to the possibility that a particular technology or idea can be used in areas outside of one's own area of ​​expertise.

[0376] A "business plan" is a detailed document that describes the strategies, business activities, and marketing tactics for conducting commercial activities.

[0377] "Visualization" is the process of representing data and ideas in visual forms such as shapes, images, and graphs.

[0378] "Users" refer to individuals or organizations that use a system or service.

[0379] An "emotion engine" is a software or hardware system that analyzes a user's emotions and adjusts the system's response based on the results.

[0380] "Feedback" is the process by which users provide opinions and impressions about a system or information.

[0381] A "business plan proposal" is a document that outlines the specific tasks an organization should perform and the means by which they should be performed.

[0382] The embodiment for carrying out this invention is configured as follows.

[0383] The server uses a web scraping tool built in Python to access the Japan Patent Office's public database. This allows the server to obtain patent technical information and then summarize and analyze the data using natural language processing techniques. Specifically, it utilizes generative AI models such as BERT and GPT to perform text analysis on the patent information. The server also performs cluster analysis to evaluate the cross-disciplinary applicability of patent technologies and develops new business plans.

[0384] The terminal receives business ideas and plans sent from the server and presents them visually to the user using visualization tools such as D3.js. In this process, the terminal optimizes the display based on user input under the guidance of an emotion engine. The emotion engine uses OpenCV and TensorFlow to analyze the user's facial expressions and improve the user experience.

[0385] Users evaluate business ideas using the presented visual information. When users provide feedback, data is sent to the server via their device. The server uses this feedback, along with sentiment data, to revise the ideas.

[0386] For example, when the server suggests a "new mobile application for sustainable energy," if the emotion engine detects the user's interest, the information display style will be automatically adjusted. The generative AI model provides the user with a prompt such as, "Please propose a detailed plan to explore new business opportunities in the sustainable energy market by applying this patent."

[0387] This will enable a broader creative process that is more accessible to all stakeholders, and by integrating patent information and user feedback, it will be possible to realize new business plans.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] The server accesses the Japan Patent Office's public database to retrieve patent information related to a specific technical field. The input is a patent search query, and the output is a set of retrieved patent documents. Specifically, a web scraping tool using Python is used to collect the necessary patent information data.

[0391] Step 2:

[0392] The server analyzes the acquired patent documents using natural language processing technology. The input is the patent documents acquired in step 1, and the output is a list of summarized patent information and its technical features. Specifically, it uses a generative AI model (such as BERT or GPT) to analyze and summarize the text and extract relevant technologies and keywords.

[0393] Step 3:

[0394] Based on the information analyzed in the previous step, the server explores the potential for cross-disciplinary applications of the patented technology. The input is the list of technical features from step 2, and the output is a list of application ideas in different fields. In this step, cluster analysis is performed to identify other technological areas with similar features and explore new business opportunities.

[0395] Step 4:

[0396] The server generates new business plans based on their potential for cross-disciplinary application. The input is a list of application ideas obtained in step 3, and the output is a concrete business plan proposal. The generation AI model is used to formulate plans that consider business strategy and market adaptability. This process generates specific prompts such as, "How can we devise a new mobile application for sustainable energy?"

[0397] Step 5:

[0398] The terminal receives the business plan sent from the server and presents it to the user in a visualized form. The input is the business plan draft sent from the server, and the output is visual information for the user. The terminal uses visualization tools such as D3.js to graphically display the plan draft and designs the interface to facilitate user understanding.

[0399] Step 6:

[0400] The emotion engine recognizes the user's emotions in real time as they view presented visual information and optimizes the experience. Input is the user's facial expressions and tone of voice, and output is an optimized user interface. This includes specific processes such as performing facial analysis using OpenCV and TensorFlow, and adjusting the colors and layout of the visual display.

[0401] Step 7:

[0402] Users input opinions and suggestions as feedback on the business plan via a terminal. The input consists of user evaluations and feedback, and the output is feedback data sent to the server. Based on this information, the server modifies and optimizes the business plan based on the feedback. A generative AI model is utilized to provide concrete means for improving the proposed content.

[0403] (Application Example 2)

[0404] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0405] Traditionally, when generating new business ideas using patent information, there was a lack of methods to effectively incorporate user emotional responses and improve the customer experience in physical stores. As a result, product displays and promotions in physical stores could not be effectively tailored to customer preferences, leading to challenges in increasing sales and improving customer satisfaction.

[0406] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0407] In this invention, the server includes means for acquiring patent information, means for using a generative model for summarizing and analyzing the patent information, and means for exploring the potential of the technology to be applied to other fields. This makes it possible to recognize user emotions in real time, dynamically adjust in-store displays and promotions to optimize the customer experience and improve sales.

[0408] "Patent information" refers to publicly available documents relating to the technical details of an invention and its intellectual property rights.

[0409] A "generative model" is an algorithm or process for extracting useful information from specific input data and for analyzing and transforming it.

[0410] "Cross-disciplinary applicability" refers to the potential for how a particular technology or idea can be applied in different fields.

[0411] A "new business idea" is an innovative business concept that does not yet exist in the market or that has evolved from an existing business.

[0412] "Visualization" is a technique that makes information and data easier for users to understand by illustrating them.

[0413] "User sentiment" refers to the emotional response that users exhibit when interacting with a product or service.

[0414] A "promotion strategy" is a set of actions and measures planned to promote the sale of a product or service.

[0415] A "physical store" is a physical sales space that customers can visit in person.

[0416] This system acquires and analyzes patent information and generates new business ideas based on the results. The server retrieves patent information from a database and summarizes and analyzes it using a generative model. This analysis explores the potential of the patent information to be applied in different fields and constructs new business ideas. The server visualizes these ideas and provides them to the user. The server is also configured to recognize the user's emotions in real time and dynamically adjust store displays and promotions based on those emotions. This process is carried out via devices such as smart glasses and displays, and user feedback and emotion data are used to optimize the store experience.

[0417] Specifically, if a customer shows no interest in a particular product, the server can automatically adjust the promotional strategy accordingly. For example, in a fashion store, this could involve analyzing the customer's facial expressions through smart glasses and suggesting a try-on event. This allows stores to provide promotions tailored to customer preferences, thereby improving customer satisfaction and increasing sales.

[0418] The entire process described above is designed to be efficient by utilizing a generative AI model. An example of a prompt for analyzing real-time data from a specific channel and formulating optimal promotional ideas is: "What promotional ideas would be best if the customer is not showing interest in the product? Please also consider relevant information from the patent database."

[0419] These processes enable this invention to more personalize and effectively improve the customer experience in physical stores.

[0420] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0421] Step 1:

[0422] The server retrieves patent information from a database. It uses queries to the patent database as input to collect publicly available patent documents. The output is a set of retrieved patent information. This step involves specific actions to access the patent database via an API to automate the patent information retrieval process.

[0423] Step 2:

[0424] The server uses a generative AI model to summarize and analyze patent information. The patent information obtained in step 1 is used as input. For data processing, natural language processing is used to segment the document and extract key technical features. The output is the summarized patent information and its analysis results. This step includes specific actions in which the generative model automatically identifies the subject matter and applicability of the patent.

[0425] Step 3:

[0426] The server explores the cross-disciplinary applicability of the technology based on the analysis results. The input is the analysis results from step 2. As a data calculation, it retrieves application examples of similar technologies from a database and scores their applicability. The output is a report on the cross-disciplinary applicability. This step includes specific actions that use existing case studies to compare related technologies.

[0427] Step 4:

[0428] The server generates new business ideas based on the search results. The report obtained in step 3 is used as input. For data processing, a generative AI model is utilized to construct ideas with increased applicability. The output is a list of new business ideas. This step includes the specific operation of the generative AI model, which combines different technological elements to generate new ideas.

[0429] Step 5:

[0430] The terminal visualizes and presents new business ideas to the user. The input is the idea list from step 4. As data processing, a visual design for presentation is generated. The output is a visualized business idea that the user can view. This step includes specific actions to generate an intuitive interface using visual tools.

[0431] Step 6:

[0432] Users provide feedback on business ideas, which is sent to the server. The inputs used are user feedback and sentiment data collected from sensors. Data processing involves analyzing user responses and performing analysis using a sentiment engine. The output is the improved business idea and its evaluation. This step includes specific actions to integrate feedback and sentiment data and clarify areas for improvement for the next step.

[0433] Step 7:

[0434] The server generates prompt messages and proposes the optimal promotional strategy obtained by the generative AI model. The input consists of feedback and sentiment data. Data processing involves generating prompt messages and formulating a promotional strategy. The output is the optimized promotional proposal. This step includes specific actions where the generative AI model dynamically adjusts the promotion considering the user's sentiment.

[0435] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0436] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0437] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0438] [Third Embodiment]

[0439] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0440] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0441] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0442] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0443] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0444] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0445] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0446] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0447] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0448] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0449] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0450] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0451] This invention is a system that generates new business ideas using patent information and supports users in commercializing them. This system mainly consists of a server, terminals, and users, and proceeds with processing in the following steps.

[0452] First, the server accesses the Japan Patent Office's public patent database to retrieve a vast amount of patent information. This information is stored on the server as raw data, and an AI model summarizes and analyzes it. The server utilizes natural language processing technology to summarize patent documents in a short time and extract key points and features of the technology. This extracted data becomes the foundational data for exploring the possibility of applying the technology to different fields.

[0453] Next, the server uses a generative AI model to construct new business ideas. This AI combines technical information and market data to generate ideas while considering novelty, marketability, and feasibility. Each of these generated business ideas is scored and sent to the terminal.

[0454] The device receives this information and provides it to the user using visualization tools. It displays the characteristics and scoring results of the generated ideas in a dashboard format, making them intuitively understandable to the user. Based on this visualized data, the user evaluates the ideas and selects the most promising ones.

[0455] Users can access the collaboration platform via their devices and discuss ideas with other stakeholders. User feedback is fed to the server, and the AI ​​uses this as a guide to improve the ideas.

[0456] Furthermore, the server activates a strategic consultant AI to assist the user in developing a concrete business plan, providing suggestions and guidance along the way. At this stage, the user develops a business plan that incorporates their own knowledge and requirements while taking the AI's suggestions into account.

[0457] As a concrete example, if patent information for a certain battery technology is retrieved from the server, a summary of that technology is generated, and solutions for drones and mobile devices are proposed as new applications for reusable batteries. The user evaluates this idea and develops a specific product development strategy in cooperation with relevant industry stakeholders.

[0458] This system enables the efficient creation of new businesses utilizing patent information, playing a role in maximizing the value of a company's patent assets.

[0459] The following describes the processing flow.

[0460] Step 1:

[0461] The server accesses the Japan Patent Office's public patent database and retrieves patent information. It filters and selects patents related to specific keywords or technical fields, and converts the retrieved data into an analyzable format.

[0462] Step 2:

[0463] The server uses natural language processing algorithms to automatically summarize the retrieved patent documents. Here, the technical objectives, benefits, and features of the patent are condensed, allowing for the rapid extraction of necessary information.

[0464] Step 3:

[0465] The server uses a generative AI model to analyze summarized patent information and explore the potential for cross-industry applications of the technologies. Based on each technology, it investigates and scores its potential for application in new markets.

[0466] Step 4:

[0467] The server combines patent technology information and market data to generate new business ideas. This is a process in which AI evaluates the novelty, market potential, and feasibility of new businesses and constructs each idea.

[0468] Step 5:

[0469] The terminal receives new business ideas sent from the server and presents them to the user using visualization tools. The score and characteristics of each idea are displayed on a dashboard, allowing the user to understand them intuitively.

[0470] Step 6:

[0471] Users select compelling business ideas based on visual information displayed via their devices and provide feedback. This user feedback is fed back into the system and used for further improvement.

[0472] Step 7:

[0473] Users utilize the collaboration platform on their devices to engage in discussions with other users. This enables the exchange of ideas to improve and concretize selected ideas.

[0474] Step 8:

[0475] The server reuses the generated AI based on user feedback to further refine business ideas. Through this process, ideas that better meet user needs are developed.

[0476] Step 9:

[0477] Users develop concrete business plans with the help of a strategic consultant AI through their device. The AI ​​provides market data and simulations to help users build realistic plans.

[0478] Step 10:

[0479] Users review the business plan developed on their devices and make adjustments as needed. To finalize the plan, users conduct detailed reviews and revisions until they create an actionable business plan.

[0480] (Example 1)

[0481] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0482] Generating new business ideas using patent information presents challenges, as it requires processing a vast amount of information, necessitating significant time and effort for understanding the technology and evaluating its market applicability. Furthermore, effectively sharing and evaluating these ideas among stakeholders is difficult, necessitating the development of efficient and rapid commercialization strategies.

[0483] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0484] In this invention, the server includes means for using a generative model to summarize and analyze patent information, means for exploring the potential of cross-disciplinary applications of the technology based on the analyzed information, and means for generating and scoring new business ideas. This makes it possible to quickly generate new business ideas from patent information and to efficiently evaluate and share them.

[0485] "Patent information" refers to technical information officially submitted to protect intellectual property rights, and is derived from publicly available patent documents.

[0486] A "generative model" is a type of artificial intelligence used to infer or generate new information from data, and is particularly applied to natural language processing and data analysis.

[0487] "Cross-disciplinary application potential" refers to the act of exploring the possibility that a particular technology may be useful in areas other than its original field.

[0488] A "business idea" is a concept that plans new directions for corporate activities, products, or services based on market needs and technological potential.

[0489] "Scoring" is a method of numerically evaluating ideas and data based on specific criteria, and it measures their value and potential.

[0490] A "terminal" is a device used by a user to receive or input information, and includes computers and smartphones.

[0491] "Feedback" refers to the opinions and evaluations that users provide after using a system, and is used to improve services and products.

[0492] The "strategy consultant model" is an artificial intelligence model designed to support decision-making when formulating business plans. It analyzes market and technological information to propose the optimal strategy.

[0493] A "collaboration platform" is a platform designed to facilitate information sharing and discussions among participants, and includes online meeting systems and collaboration tools.

[0494] This invention is a system that generates new business ideas using patent information and efficiently supports their commercialization. This system mainly consists of a server, terminals, and users.

[0495] The server accesses the Japan Patent Office's public patent database to retrieve patent information. This information is stored in a database on the server and analyzed using a generative AI model. The server uses natural language processing technology to extract key points from patent documents and explores how the technology can be applied in other fields. The server also generates new business ideas by combining this information with market data and scores these ideas. The generative AI model utilizes an open-source natural language processing library.

[0496] The terminal is a tool for visualizing and providing business ideas received from the server to the user. The terminal uses visualization software to display information in a dashboard format, allowing the user to intuitively understand the details of the ideas and their scoring results.

[0497] Users evaluate business ideas provided via their devices. User feedback is sent to a server and used by the AI ​​to guide the refinement of the ideas. Users can also utilize a collaborative platform to discuss ideas with stakeholders. By activating a strategic consultant model, users can receive advice on developing concrete business plans.

[0498] For example, if patent information for battery technology is obtained, the server compactly summarizes the relevant information and generates ideas for applications in other fields, such as batteries for mobile devices and drones. Users evaluate these ideas and collaborate with industry stakeholders to build a product development strategy.

[0499] Examples of prompt statements include the following:

[0500] "Based on recently acquired patent information, please generate five new business ideas. Also, evaluate the market potential and feasibility of each idea."

[0501] "Based on the patent information for this battery technology, devise new applications for reusable batteries and propose a business plan utilizing them."

[0502] This system will enable the efficient creation of new businesses utilizing patent information and support companies in making the most of their patent assets.

[0503] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0504] Step 1:

[0505] The server accesses the Japan Patent Office's public patent database to retrieve patent information. As input, queries are executed against the patent database. As a result of these queries, a large number of patent documents related to specific technical fields or keywords are output. These documents, along with metadata such as patent number, title, and filing date, are stored in the server's database.

[0506] Step 2:

[0507] The server uses a generating AI model to summarize and analyze the acquired patent data. The input is the text data of the patent documents saved in Step 1. The AI ​​model uses natural language processing techniques to analyze the patent documents and extract key technical points and features. The output is summarized technical information and keywords indicating potential cross-disciplinary applications.

[0508] Step 3:

[0509] The server uses a generative AI model to generate new business ideas based on summarized patent information. The summarized data and market data obtained in step 2 are used as input. The server sends prompts to the generative AI model to instruct it to generate business ideas. The output is a list of scored business ideas. This list evaluates the marketability, novelty, and feasibility of each idea.

[0510] Step 4:

[0511] The terminal visualizes the business ideas received from the server. As input, a list of business ideas generated in step 3 is sent to the terminal. The terminal uses visualization software to graphically display these ideas on a dashboard. As output, the user is provided with a dashboard that visually and clearly presents the characteristics and scoring results of each idea.

[0512] Step 5:

[0513] Users evaluate business ideas provided through their devices. As input, the dashboard information visualized in step 4 is presented to the user. Users submit feedback on the ideas, and this feedback, along with their evaluation, is returned to the server. The output consists of user comments and evaluation results, which serve as a guide for AI-driven idea refinement.

[0514] Step 6:

[0515] The server launches a strategic consultant model to assist the user in developing a concrete business plan. User feedback and scored ideas are used as input. The strategic consultant model analyzes this information to provide optimal business strategies and risk management measures. The output is a concrete business plan proposal for the user to refer to.

[0516] (Application Example 1)

[0517] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0518] The problem lies in the lack of means to generate new business ideas utilizing patent information and to maximize their application potential. In particular, there is a need for systems to generate new content ideas based on patent information and to effectively deliver them to users. Since opportunities for users to utilize patent information to create their own content are limited, providing these new opportunities is a challenge.

[0519] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0520] In this invention, the server includes means for acquiring patent information, means for using a generative model for summarizing and analyzing the patent information, and means for exploring cross-disciplinary application possibilities based on the analyzed information. This enables users to create original content utilizing patent information through the generation and provision of new content ideas.

[0521] "Patent information" refers to data concerning the technical content of an invention that is made public by the Japan Patent Office.

[0522] A "generative model" is a model based on AI technology used to analyze patent information.

[0523] "Cross-disciplinary applicability" refers to the ability to evaluate whether a particular technology can be used in other fields.

[0524] A "new business idea" refers to an original concept for developing a new business, generated based on patent information.

[0525] An "online platform" refers to a system that provides the foundation for digital services accessed via the internet.

[0526] "New content ideas" refer to original ideas for creating new content, generated based on patent information.

[0527] To implement this invention, the server first accesses the Japan Patent Office's public patent database to obtain patent information. This information is then summarized and analyzed by a generative AI model built using Amazon SageMaker. The generative model uses natural language processing technology to analyze the patent documents and explore their potential applications in other fields.

[0528] Based on the analysis results, the server generates new business ideas and provides them to users. Users can access the online platform via devices such as smartphones and smart glasses to view the new content ideas. The user interface, built using React Native, is visualized using D3.js, allowing users to intuitively understand the information.

[0529] Users send feedback from their devices to the server, and this feedback is reflected in the generating AI model. The server uses the AI ​​model to refine the idea and proposes it to the user again. This cycle enables users to create original content based on patent information.

[0530] As a concrete example, if a user wants to create new video content, the server will provide new content ideas based on an analysis of patent information related to that field. The user can then use this and incorporate it as a new element in their project.

[0531] An example of a prompt for a generative AI model could be, "Generate new video content ideas to enhance user engagement." The ideas suggested through this prompt will form the basis for the user's creative activities.

[0532] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0533] Step 1:

[0534] The server accesses the Japan Patent Office's public patent database and retrieves patent information. The input consists of the URL of the Patent Office database and a search query, while the output is raw patent data. The server then saves this raw data to local storage.

[0535] Step 2:

[0536] The server inputs the raw data of stored patent information into a generating AI model, which is then executed on Amazon SageMaker. The input includes patent documents, and the output consists of summarized text and analysis results. In this process, the server uses natural language processing techniques to quickly summarize the patent information and extract key technical points and features.

[0537] Step 3:

[0538] The server explores potential applications in different fields based on the analysis results. The input consists of key technical points and characteristics, while the output obtained by the generative AI model is a list of ideas applicable to different fields. The server then maps this list to specific application examples.

[0539] Step 4:

[0540] The server generates new business ideas using the results of its cross-disciplinary application potential analysis. The input is a list of application ideas, and the output is a concrete new business plan. The generated plan takes into account the novelty and market potential of the business. The server organizes this information and prepares it for transmission to the user's terminal.

[0541] Step 5:

[0542] The terminal visualizes new business ideas received from the server and presents them to the user. The input is a new business plan, and the output is a visually easy-to-understand dashboard display. The terminal displays this using React Native and uses D3.js to enable interactive user interaction.

[0543] Step 6:

[0544] Users review the displayed new business ideas and provide feedback. Input is the user's evaluation and comments, and output is feedback information. Users send this feedback to the server via their device.

[0545] Step 7:

[0546] Based on the received feedback, the server runs the generating AI model again to improve the idea. The input is the feedback information, and the output is the improved business idea. The server uses prompts to instruct the AI ​​on the direction of idea improvement and sends the newly generated business idea back to the terminal.

[0547] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0548] This invention is a system that generates new business ideas using patent information and provides them to users, while also recognizing and reflecting the user's emotions using an emotion engine. This system comprises a server, a terminal, and an emotion engine, and proceeds with processing as follows.

[0549] First, the server accesses the Japan Patent Office's public patent database and retrieves relevant patent information. The retrieved patent information is automatically summarized and analyzed using a natural language processing model. The server extracts the technical features and content and explores their potential applications in other fields. This information is then used to develop new business ideas.

[0550] Next, the server constructs the generated new business ideas and scores each idea based on its novelty, feasibility, and market potential. The terminal receives the business ideas sent from the server and displays them to the user using a visualization tool. During this process, an emotion engine recognizes the user's emotions in real time and adjusts the visuals to optimize the user experience.

[0551] Users evaluate business ideas based on the presented visual information and provide feedback. This feedback is sent to the server via the device, and is analyzed along with user emotion data collected by the emotion engine. This allows user emotions to be used to revise and optimize ideas.

[0552] For example, suppose we have an idea for a "mobile application for sustainable energy" generated through patent analysis. Users consider this idea, and based on the emotional engine's high evaluation of their interests and concerns, the information is tailored to the most suitable presentation format. Based on this information, users interact with other stakeholders through a discussion platform to concretize the business plan.

[0553] The server utilizes strategic consultant AI to provide users with interactive guides and business plan proposals, supporting concrete business development. This allows users to leverage the provided information and emotional feedback to formulate optimal business plans.

[0554] Ultimately, this system provides a mechanism that helps users efficiently create attractive new businesses by effectively utilizing patent information and user sentiment.

[0555] The following describes the processing flow.

[0556] Step 1:

[0557] The server accesses the Japan Patent Office's public patent database, filters and retrieves patent information based on specified keywords and technical fields, and stores this information in a parseable data format.

[0558] Step 2:

[0559] The server uses a natural language processing engine to summarize the retrieved patent documents and extract important technical content and features. In this process, machine learning models are used to analyze the text data and express the patent information concisely.

[0560] Step 3:

[0561] The server uses generated AI models based on the analyzed patent information to explore potential applications in different industries. This allows for the evaluation and scoring of the potential for technology use in other fields.

[0562] Step 4:

[0563] The generative AI model generates new business ideas by combining technical information and market data within the server, and then assigns scores to those ideas based on their marketability and feasibility.

[0564] Step 5:

[0565] The terminal visualizes new business ideas received from the server and presents them to the user in a dashboard format. Here, the emotion engine recognizes the user's reactions and dynamically adjusts the display method.

[0566] Step 6:

[0567] Users select interesting business ideas based on visualized information on their devices and provide feedback. User sentiment data is collected by an emotion engine and incorporated into the evaluation of the ideas.

[0568] Step 7:

[0569] The device sends user feedback and emotion engine data to a server, which then uses this data to execute a process for further optimizing and refining ideas.

[0570] Step 8:

[0571] Users utilize a collaboration platform on their devices to discuss proposed ideas with other users. Through this collaborative work, they advance the concretization of their ideas.

[0572] Step 9:

[0573] The server uses a strategic consultant AI to propose appropriate business plan proposals to users and provides support to improve the specificity and feasibility of those proposals.

[0574] Step 10:

[0575] Users scrutinize the proposed business plan, make revisions via their device as needed, and finalize the business plan. This plan is optimized by taking into account feedback and data from the sentiment engine.

[0576] (Example 2)

[0577] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0578] A system is needed to efficiently generate new business plans using patent information, and to enable flexible feedback and plan revisions that take user sentiment into account. Furthermore, it is necessary to enhance the specificity and market adaptability of the generated business plans.

[0579] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0580] In this invention, the server includes means for acquiring patent information, means for utilizing a generative model for analyzing the patent information, and emotion engine means for recognizing the user's emotions and optimizing the visualization format. This enables the efficient generation of new business plans based on patent information and the modification of plans to suit the user's emotions.

[0581] "Patent information" refers to a collection of documents and data containing technical content that have been made public by the Japan Patent Office.

[0582] A "generative model" is a general term for algorithms and systems that generate new information or patterns based on data.

[0583] "Potential for application in other fields" refers to the possibility that a particular technology or idea can be used in areas outside of one's own area of ​​expertise.

[0584] A "business plan" is a detailed document that describes the strategies, business activities, and marketing tactics for conducting commercial activities.

[0585] "Visualization" is the process of representing data and ideas in visual forms such as shapes, images, and graphs.

[0586] "Users" refer to individuals or organizations that use a system or service.

[0587] An "emotion engine" is a software or hardware system that analyzes a user's emotions and adjusts the system's response based on the results.

[0588] "Feedback" is the process by which users provide opinions and impressions about a system or information.

[0589] A "business plan proposal" is a document that outlines the specific tasks an organization should perform and the means by which they should be performed.

[0590] The embodiment for carrying out this invention is configured as follows.

[0591] The server uses a web scraping tool built in Python to access the Japan Patent Office's public database. This allows the server to obtain patent technical information and then summarize and analyze the data using natural language processing techniques. Specifically, it utilizes generative AI models such as BERT and GPT to perform text analysis on the patent information. The server also performs cluster analysis to evaluate the cross-disciplinary applicability of patent technologies and develops new business plans.

[0592] The terminal receives business ideas and plans sent from the server and presents them visually to the user using visualization tools such as D3.js. In this process, the terminal optimizes the display based on user input under the guidance of an emotion engine. The emotion engine uses OpenCV and TensorFlow to analyze the user's facial expressions and improve the user experience.

[0593] Users evaluate business ideas using the presented visual information. When users provide feedback, data is sent to the server via their device. The server uses this feedback, along with sentiment data, to revise the ideas.

[0594] For example, when the server suggests a "new mobile application for sustainable energy," if the emotion engine detects the user's interest, the information display style will be automatically adjusted. The generative AI model provides the user with a prompt such as, "Please propose a detailed plan to explore new business opportunities in the sustainable energy market by applying this patent."

[0595] This will enable a broader creative process that is more accessible to all stakeholders, and by integrating patent information and user feedback, it will be possible to realize new business plans.

[0596] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0597] Step 1:

[0598] The server accesses the Japan Patent Office's public database to retrieve patent information related to a specific technical field. The input is a patent search query, and the output is a set of retrieved patent documents. Specifically, a web scraping tool using Python is used to collect the necessary patent information data.

[0599] Step 2:

[0600] The server analyzes the acquired patent documents using natural language processing technology. The input is the patent documents acquired in step 1, and the output is a list of summarized patent information and its technical features. Specifically, it uses a generative AI model (such as BERT or GPT) to analyze and summarize the text and extract relevant technologies and keywords.

[0601] Step 3:

[0602] Based on the information analyzed in the previous step, the server explores the potential for cross-disciplinary applications of the patented technology. The input is the list of technical features from step 2, and the output is a list of application ideas in different fields. In this step, cluster analysis is performed to identify other technological areas with similar features and explore new business opportunities.

[0603] Step 4:

[0604] The server generates new business plans based on their potential for cross-disciplinary application. The input is a list of application ideas obtained in step 3, and the output is a concrete business plan proposal. The generation AI model is used to formulate plans that consider business strategy and market adaptability. This process generates specific prompts such as, "How can we devise a new mobile application for sustainable energy?"

[0605] Step 5:

[0606] The terminal receives the business plan sent from the server and presents it to the user in a visualized form. The input is the business plan draft sent from the server, and the output is visual information for the user. The terminal uses visualization tools such as D3.js to graphically display the plan draft and designs the interface to facilitate user understanding.

[0607] Step 6:

[0608] The emotion engine recognizes the user's emotions in real time as they view presented visual information and optimizes the experience. Input is the user's facial expressions and tone of voice, and output is an optimized user interface. This includes specific processes such as performing facial analysis using OpenCV and TensorFlow, and adjusting the colors and layout of the visual display.

[0609] Step 7:

[0610] Users input opinions and suggestions as feedback on the business plan via a terminal. The input consists of user evaluations and feedback, and the output is feedback data sent to the server. Based on this information, the server modifies and optimizes the business plan based on the feedback. A generative AI model is utilized to provide concrete means for improving the proposed content.

[0611] (Application Example 2)

[0612] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0613] Traditionally, when generating new business ideas using patent information, there was a lack of methods to effectively incorporate user emotional responses and improve the customer experience in physical stores. As a result, product displays and promotions in physical stores could not be effectively tailored to customer preferences, leading to challenges in increasing sales and improving customer satisfaction.

[0614] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0615] In this invention, the server includes means for acquiring patent information, means for using a generative model for summarizing and analyzing the patent information, and means for exploring the potential of the technology to be applied to other fields. This makes it possible to recognize user emotions in real time, dynamically adjust in-store displays and promotions to optimize the customer experience and improve sales.

[0616] "Patent information" refers to publicly available documents relating to the technical details of an invention and its intellectual property rights.

[0617] A "generative model" is an algorithm or process for extracting useful information from specific input data and for analyzing and transforming it.

[0618] "Cross-disciplinary applicability" refers to the potential for how a particular technology or idea can be applied in different fields.

[0619] A "new business idea" is an innovative business concept that does not yet exist in the market or that has evolved from an existing business.

[0620] "Visualization" is a technique that makes information and data easier for users to understand by illustrating them.

[0621] "User sentiment" refers to the emotional response that users exhibit when interacting with a product or service.

[0622] A "promotion strategy" is a set of actions and measures planned to promote the sale of a product or service.

[0623] A "physical store" is a physical sales space that customers can visit in person.

[0624] This system acquires and analyzes patent information and generates new business ideas based on the results. The server retrieves patent information from a database and summarizes and analyzes it using a generative model. This analysis explores the potential of the patent information to be applied in different fields and constructs new business ideas. The server visualizes these ideas and provides them to the user. The server is also configured to recognize the user's emotions in real time and dynamically adjust store displays and promotions based on those emotions. This process is carried out via devices such as smart glasses and displays, and user feedback and emotion data are used to optimize the store experience.

[0625] Specifically, if a customer shows no interest in a particular product, the server can automatically adjust the promotional strategy accordingly. For example, in a fashion store, this could involve analyzing the customer's facial expressions through smart glasses and suggesting a try-on event. This allows stores to provide promotions tailored to customer preferences, thereby improving customer satisfaction and increasing sales.

[0626] The entire process described above is designed to be efficient by utilizing a generative AI model. An example of a prompt for analyzing real-time data from a specific channel and formulating optimal promotional ideas is: "What promotional ideas would be best if the customer is not showing interest in the product? Please also consider relevant information from the patent database."

[0627] These processes enable this invention to more personalize and effectively improve the customer experience in physical stores.

[0628] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0629] Step 1:

[0630] The server retrieves patent information from a database. It uses queries to the patent database as input to collect publicly available patent documents. The output is a set of retrieved patent information. This step involves specific actions to access the patent database via an API to automate the patent information retrieval process.

[0631] Step 2:

[0632] The server uses a generative AI model to summarize and analyze patent information. The patent information obtained in step 1 is used as input. For data processing, natural language processing is used to segment the document and extract key technical features. The output is the summarized patent information and its analysis results. This step includes specific actions in which the generative model automatically identifies the subject matter and applicability of the patent.

[0633] Step 3:

[0634] The server explores the cross-disciplinary applicability of the technology based on the analysis results. The input is the analysis results from step 2. As a data calculation, it retrieves application examples of similar technologies from a database and scores their applicability. The output is a report on the cross-disciplinary applicability. This step includes specific actions that use existing case studies to compare related technologies.

[0635] Step 4:

[0636] The server generates new business ideas based on the search results. The report obtained in step 3 is used as input. For data processing, a generative AI model is utilized to construct ideas with increased applicability. The output is a list of new business ideas. This step includes the specific operation of the generative AI model, which combines different technological elements to generate new ideas.

[0637] Step 5:

[0638] The terminal visualizes and presents new business ideas to the user. The input is the idea list from step 4. As data processing, a visual design for presentation is generated. The output is a visualized business idea that the user can view. This step includes specific actions to generate an intuitive interface using visual tools.

[0639] Step 6:

[0640] Users provide feedback on business ideas, which is sent to the server. The inputs used are user feedback and sentiment data collected from sensors. Data processing involves analyzing user responses and performing analysis using a sentiment engine. The output is the improved business idea and its evaluation. This step includes specific actions to integrate feedback and sentiment data and clarify areas for improvement for the next step.

[0641] Step 7:

[0642] The server generates prompt messages and proposes the optimal promotional strategy obtained by the generative AI model. The input consists of feedback and sentiment data. Data processing involves generating prompt messages and formulating a promotional strategy. The output is the optimized promotional proposal. This step includes specific actions where the generative AI model dynamically adjusts the promotion considering the user's sentiment.

[0643] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0644] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0645] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0646] [Fourth Embodiment]

[0647] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0648] As shown in Figure 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.

[0649] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0650] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0651] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0652] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0653] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0654] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0655] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0656] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0657] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0658] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0659] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] This invention is a system that generates new business ideas using patent information and supports users in commercializing them. This system mainly consists of a server, terminals, and users, and proceeds with processing in the following steps.

[0661] First, the server accesses the Japan Patent Office's public patent database to retrieve a vast amount of patent information. This information is stored on the server as raw data, and an AI model summarizes and analyzes it. The server utilizes natural language processing technology to summarize patent documents in a short time and extract key points and features of the technology. This extracted data becomes the foundational data for exploring the possibility of applying the technology to different fields.

[0662] Next, the server uses a generative AI model to construct new business ideas. This AI combines technical information and market data to generate ideas while considering novelty, marketability, and feasibility. Each of these generated business ideas is scored and sent to the terminal.

[0663] The device receives this information and provides it to the user using visualization tools. It displays the characteristics and scoring results of the generated ideas in a dashboard format, making them intuitively understandable to the user. Based on this visualized data, the user evaluates the ideas and selects the most promising ones.

[0664] Users can access the collaboration platform via their devices and discuss ideas with other stakeholders. User feedback is fed to the server, and the AI ​​uses this as a guide to improve the ideas.

[0665] Furthermore, the server activates a strategic consultant AI to assist the user in developing a concrete business plan, providing suggestions and guidance along the way. At this stage, the user develops a business plan that incorporates their own knowledge and requirements while taking the AI's suggestions into account.

[0666] As a concrete example, if patent information for a certain battery technology is retrieved from the server, a summary of that technology is generated, and solutions for drones and mobile devices are proposed as new applications for reusable batteries. The user evaluates this idea and develops a specific product development strategy in cooperation with relevant industry stakeholders.

[0667] This system enables the efficient creation of new businesses utilizing patent information, playing a role in maximizing the value of a company's patent assets.

[0668] The following describes the processing flow.

[0669] Step 1:

[0670] The server accesses the Japan Patent Office's public patent database and retrieves patent information. It filters and selects patents related to specific keywords and technical fields, and converts the retrieved data into an analyzable format.

[0671] Step 2:

[0672] The server uses natural language processing algorithms to automatically summarize the retrieved patent documents. Here, the technical objectives, benefits, and features of the patent are condensed, allowing for the rapid extraction of necessary information.

[0673] Step 3:

[0674] The server uses a generative AI model to analyze summarized patent information and explore the potential for cross-industry applications of the technologies. Based on each technology, it investigates and scores its potential for application in new markets.

[0675] Step 4:

[0676] The server combines patent technology information and market data to generate new business ideas. This is a process in which AI evaluates the novelty, market potential, and feasibility of new businesses and constructs each idea.

[0677] Step 5:

[0678] The terminal receives new business ideas sent from the server and presents them to the user using visualization tools. The score and characteristics of each idea are displayed on a dashboard, allowing the user to understand them intuitively.

[0679] Step 6:

[0680] Users select compelling business ideas based on visual information displayed via their devices and provide feedback. This user feedback is fed back into the system and used for further improvement.

[0681] Step 7:

[0682] Users utilize the collaboration platform on their devices to engage in discussions with other users. This enables the exchange of ideas to improve and concretize selected ideas.

[0683] Step 8:

[0684] The server reuses the generated AI based on user feedback to further refine business ideas. Through this process, ideas that better meet user needs are developed.

[0685] Step 9:

[0686] Users develop concrete business plans with the help of a strategic consultant AI through their device. The AI ​​provides market data and simulations to help users build realistic plans.

[0687] Step 10:

[0688] Users review the business plan developed on their devices and make adjustments as needed. To finalize the plan, users conduct detailed reviews and revisions until they create an actionable business plan.

[0689] (Example 1)

[0690] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] Generating new business ideas using patent information presents challenges, as it requires processing a vast amount of information, necessitating significant time and effort for understanding the technology and evaluating its market applicability. Furthermore, effectively sharing and evaluating these ideas among stakeholders is difficult, necessitating the development of efficient and rapid commercialization strategies.

[0692] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0693] In this invention, the server includes means for using a generative model to summarize and analyze patent information, means for exploring the potential of cross-disciplinary applications of the technology based on the analyzed information, and means for generating and scoring new business ideas. This makes it possible to quickly generate new business ideas from patent information and to efficiently evaluate and share them.

[0694] "Patent information" refers to technical information officially submitted to protect intellectual property rights, and is derived from publicly available patent documents.

[0695] A "generative model" is a type of artificial intelligence used to infer or generate new information from data, and is particularly applied to natural language processing and data analysis.

[0696] "Cross-disciplinary application potential" refers to the act of exploring the possibility that a particular technology may be useful in areas other than its original field.

[0697] A "business idea" is a concept that plans new directions for corporate activities, products, or services based on market needs and technological potential.

[0698] "Scoring" is a method of numerically evaluating ideas and data based on specific criteria, and it measures their value and potential.

[0699] A "terminal" is a device used by a user to receive or input information, and includes computers and smartphones.

[0700] "Feedback" refers to the opinions and evaluations that users provide after using a system, and is used to improve services and products.

[0701] The "strategy consultant model" is an artificial intelligence model designed to support decision-making when formulating business plans. It analyzes market and technological information to propose the optimal strategy.

[0702] A "collaboration platform" is a platform designed to facilitate information sharing and discussions among participants, and includes online meeting systems and collaboration tools.

[0703] This invention is a system that utilizes patent information to generate new business ideas and efficiently support their commercialization. This system primarily consists of a server, terminals, and users.

[0704] The server accesses the Japan Patent Office's public patent database to retrieve patent information. This information is stored in a database on the server and analyzed using a generative AI model. The server uses natural language processing technology to extract key points from patent documents and explores how the technology can be applied in other fields. The server also generates new business ideas by combining this information with market data and scores these ideas. The generative AI model utilizes an open-source natural language processing library.

[0705] The terminal is a tool for visualizing and providing business ideas received from the server to the user. The terminal uses visualization software to display information in a dashboard format, allowing the user to intuitively understand the details of the ideas and their scoring results.

[0706] Users evaluate business ideas provided via their devices. User feedback is sent to a server and used by the AI ​​to guide the refinement of the ideas. Users can also utilize a collaborative platform to discuss ideas with stakeholders. By activating a strategic consultant model, users can receive advice on developing concrete business plans.

[0707] For example, if patent information for battery technology is obtained, the server compactly summarizes the relevant information and generates ideas for applications in other fields, such as batteries for mobile devices and drones. Users evaluate these ideas and collaborate with industry stakeholders to build a product development strategy.

[0708] Examples of prompt statements include the following:

[0709] "Based on recently acquired patent information, please generate five new business ideas. Also, evaluate the market potential and feasibility of each idea."

[0710] "Based on the patent information for this battery technology, devise new applications for reusable batteries and propose a business plan utilizing them."

[0711] This system will enable the efficient creation of new businesses utilizing patent information and support companies in making the most of their patent assets.

[0712] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0713] Step 1:

[0714] The server accesses the Japan Patent Office's public patent database to retrieve patent information. As input, queries are executed against the patent database. As a result of these queries, a large number of patent documents related to specific technical fields or keywords are output. These documents, along with metadata such as patent number, title, and filing date, are stored in the server's database.

[0715] Step 2:

[0716] The server uses a generating AI model to summarize and analyze the acquired patent data. The input is the text data of the patent documents saved in Step 1. The AI ​​model uses natural language processing techniques to analyze the patent documents and extract key technical points and features. The output is summarized technical information and keywords indicating potential cross-disciplinary applications.

[0717] Step 3:

[0718] The server uses a generative AI model to generate new business ideas based on summarized patent information. The summarized data and market data obtained in step 2 are used as input. The server sends prompts to the generative AI model to instruct it to generate business ideas. The output is a list of scored business ideas. This list evaluates the marketability, novelty, and feasibility of each idea.

[0719] Step 4:

[0720] The terminal visualizes the business ideas received from the server. As input, a list of business ideas generated in step 3 is sent to the terminal. The terminal uses visualization software to graphically display these ideas on a dashboard. As output, the user is provided with a dashboard that visually and clearly presents the characteristics and scoring results of each idea.

[0721] Step 5:

[0722] Users evaluate business ideas provided through their devices. As input, the dashboard information visualized in step 4 is presented to the user. Users submit feedback on the ideas, and this feedback, along with their evaluation, is returned to the server. The output consists of user comments and evaluation results, which serve as a guide for AI-driven idea refinement.

[0723] Step 6:

[0724] The server launches a strategic consultant model to assist the user in developing a concrete business plan. User feedback and scored ideas are used as input. The strategic consultant model analyzes this information to provide optimal business strategies and risk management measures. The output is a concrete business plan proposal for the user to refer to.

[0725] (Application Example 1)

[0726] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0727] The problem lies in the lack of means to generate new business ideas utilizing patent information and to maximize their application potential. In particular, there is a need for systems to generate new content ideas based on patent information and to effectively deliver them to users. Since opportunities for users to utilize patent information to create their own content are limited, providing these new opportunities is a challenge.

[0728] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0729] In this invention, the server includes means for acquiring patent information, means for using a generative model for summarizing and analyzing the patent information, and means for exploring cross-disciplinary application possibilities based on the analyzed information. This enables users to create original content utilizing patent information through the generation and provision of new content ideas.

[0730] "Patent information" refers to data concerning the technical content of an invention that is made public by the Japan Patent Office.

[0731] A "generative model" is a model based on AI technology used to analyze patent information.

[0732] "Cross-disciplinary applicability" refers to the ability to evaluate whether a particular technology can be used in other fields.

[0733] A "new business idea" refers to an original concept for developing a new business, generated based on patent information.

[0734] An "online platform" refers to a system that provides the foundation for digital services accessed via the internet.

[0735] "New content ideas" refer to original ideas for creating new content, generated based on patent information.

[0736] To implement this invention, the server first accesses the Japan Patent Office's public patent database to obtain patent information. This information is then summarized and analyzed by a generative AI model built using Amazon SageMaker. The generative model uses natural language processing technology to analyze the patent documents and explore their potential applications in other fields.

[0737] Based on the analysis results, the server generates new business ideas and provides them to users. Users can access the online platform via devices such as smartphones and smart glasses to view the new content ideas. The user interface, built using React Native, is visualized using D3.js, allowing users to intuitively understand the information.

[0738] Users send feedback from their devices to the server, and this feedback is reflected in the generating AI model. The server uses the AI ​​model to refine the idea and proposes it to the user again. This cycle enables users to create original content based on patent information.

[0739] As a concrete example, if a user wants to create new video content, the server will provide new content ideas based on an analysis of patent information related to that field. The user can then use this and incorporate it as a new element in their project.

[0740] An example of a prompt for a generative AI model could be, "Generate new video content ideas to enhance user engagement." The ideas suggested through this prompt will form the basis for the user's creative activities.

[0741] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0742] Step 1:

[0743] The server accesses the Japan Patent Office's public patent database and retrieves patent information. The input consists of the URL of the Patent Office database and a search query, while the output is raw patent data. The server then saves this raw data to local storage.

[0744] Step 2:

[0745] The server inputs the raw data of stored patent information into a generating AI model, which is then executed on Amazon SageMaker. The input includes patent documents, and the output consists of summarized text and analysis results. In this process, the server uses natural language processing techniques to quickly summarize the patent information and extract key technical points and features.

[0746] Step 3:

[0747] The server explores potential applications in different fields based on the analysis results. The input consists of key technical points and characteristics, while the output obtained by the generative AI model is a list of ideas applicable to different fields. The server then maps this list to specific application examples.

[0748] Step 4:

[0749] The server generates new business ideas using the results of its cross-disciplinary application potential analysis. The input is a list of application ideas, and the output is a concrete new business plan. The generated plan takes into account the novelty and market potential of the business. The server organizes this information and prepares it for transmission to the user's terminal.

[0750] Step 5:

[0751] The terminal visualizes new business ideas received from the server and presents them to the user. The input is a new business plan, and the output is a visually easy-to-understand dashboard display. The terminal displays this using React Native and uses D3.js to enable interactive user interaction.

[0752] Step 6:

[0753] Users review the displayed new business ideas and provide feedback. Input is the user's evaluation and comments, and output is feedback information. Users send this feedback to the server via their device.

[0754] Step 7:

[0755] Based on the received feedback, the server runs the generating AI model again to improve the idea. The input is the feedback information, and the output is the improved business idea. The server uses prompts to instruct the AI ​​on the direction of idea improvement and sends the newly generated business idea back to the terminal.

[0756] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0757] This invention is a system that generates new business ideas using patent information and provides them to users, while also recognizing and reflecting the user's emotions using an emotion engine. This system comprises a server, a terminal, and an emotion engine, and proceeds with processing as follows.

[0758] First, the server accesses the Japan Patent Office's public patent database and retrieves relevant patent information. The retrieved patent information is automatically summarized and analyzed using a natural language processing model. The server extracts the technical features and content and explores their potential applications in other fields. This information is then used to develop new business ideas.

[0759] Next, the server constructs the generated new business ideas and scores each idea based on its novelty, feasibility, and market potential. The terminal receives the business ideas sent from the server and displays them to the user using a visualization tool. During this process, an emotion engine recognizes the user's emotions in real time and adjusts the visuals to optimize the user experience.

[0760] Users evaluate business ideas based on the presented visual information and provide feedback. This feedback is sent to the server via the device, and is analyzed along with user emotion data collected by the emotion engine. This allows user emotions to be used to revise and optimize ideas.

[0761] For example, suppose we have an idea for a "mobile application for sustainable energy" generated through patent analysis. Users consider this idea, and based on the emotional engine's high evaluation of their interests and concerns, the information is tailored to the most suitable presentation format. Based on this information, users interact with other stakeholders through a discussion platform to concretize the business plan.

[0762] The server utilizes strategic consultant AI to provide users with interactive guides and business plan proposals, supporting concrete business development. This allows users to leverage the provided information and emotional feedback to formulate optimal business plans.

[0763] Ultimately, this system provides a mechanism that helps users efficiently create attractive new businesses by effectively utilizing patent information and user sentiment.

[0764] The following describes the processing flow.

[0765] Step 1:

[0766] The server accesses the Japan Patent Office's public patent database, filters and retrieves patent information based on specified keywords and technical fields, and stores this information in a parseable data format.

[0767] Step 2:

[0768] The server uses a natural language processing engine to summarize the retrieved patent documents and extract important technical content and features. In this process, machine learning models are used to analyze the text data and express the patent information concisely.

[0769] Step 3:

[0770] The server uses generated AI models based on the analyzed patent information to explore potential applications in different industries. This allows for the evaluation and scoring of the potential for technology use in other fields.

[0771] Step 4:

[0772] The generative AI model generates new business ideas by combining technical information and market data within the server, and then assigns scores to those ideas based on their marketability and feasibility.

[0773] Step 5:

[0774] The terminal visualizes new business ideas received from the server and presents them to the user in a dashboard format. Here, the emotion engine recognizes the user's reactions and dynamically adjusts the display method.

[0775] Step 6:

[0776] Users select interesting business ideas based on visualized information on their devices and provide feedback. User sentiment data is collected by an emotion engine and incorporated into the evaluation of the ideas.

[0777] Step 7:

[0778] The device sends user feedback and emotion engine data to a server, which then uses this data to execute a process for further optimizing and refining ideas.

[0779] Step 8:

[0780] Users utilize a collaboration platform on their devices to discuss proposed ideas with other users. Through this collaborative work, they advance the concretization of their ideas.

[0781] Step 9:

[0782] The server uses a strategic consultant AI to propose appropriate business plan proposals to users and provides support to improve the specificity and feasibility of those proposals.

[0783] Step 10:

[0784] Users scrutinize the proposed business plan, make revisions via their device as needed, and finalize the business plan. This plan is optimized by taking into account feedback and data from the sentiment engine.

[0785] (Example 2)

[0786] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0787] A system is needed to efficiently generate new business plans using patent information, and to enable flexible feedback and plan revisions that take user sentiment into account. Furthermore, it is necessary to enhance the specificity and market adaptability of the generated business plans.

[0788] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0789] In this invention, the server includes means for acquiring patent information, means for utilizing a generative model for analyzing the patent information, and emotion engine means for recognizing the user's emotions and optimizing the visualization format. This enables the efficient generation of new business plans based on patent information and the modification of plans to suit the user's emotions.

[0790] "Patent information" refers to a collection of documents and data containing technical content that have been made public by the Japan Patent Office.

[0791] A "generative model" is a general term for algorithms and systems that generate new information or patterns based on data.

[0792] "Potential for application in other fields" refers to the possibility that a particular technology or idea can be used in areas outside of one's own area of ​​expertise.

[0793] A "business plan" is a detailed document that describes the strategies, business activities, and marketing tactics for conducting commercial activities.

[0794] "Visualization" is the process of representing data and ideas in visual forms such as shapes, images, and graphs.

[0795] "Users" refer to individuals or organizations that use a system or service.

[0796] An "emotion engine" is a software or hardware system that analyzes a user's emotions and adjusts the system's response based on the results.

[0797] "Feedback" is the process by which users provide opinions and impressions about a system or information.

[0798] A "business plan proposal" is a document that outlines the specific tasks an organization should perform and the means by which they should be performed.

[0799] The embodiment for carrying out this invention is configured as follows.

[0800] The server uses a web scraping tool built in Python to access the Japan Patent Office's public database. This allows the server to obtain patent technical information and then summarize and analyze the data using natural language processing techniques. Specifically, it utilizes generative AI models such as BERT and GPT to perform text analysis on the patent information. The server also performs cluster analysis to evaluate the cross-disciplinary applicability of patent technologies and develops new business plans.

[0801] The terminal receives business ideas and plans sent from the server and presents them visually to the user using visualization tools such as D3.js. In this process, the terminal optimizes the display based on user input under the guidance of an emotion engine. The emotion engine uses OpenCV and TensorFlow to analyze the user's facial expressions and improve the user experience.

[0802] Users evaluate business ideas using the presented visual information. When users provide feedback, data is sent to the server via their device. The server uses this feedback, along with sentiment data, to revise the ideas.

[0803] For example, when the server suggests a "new mobile application for sustainable energy," if the emotion engine detects the user's interest, the information display style will be automatically adjusted. The generative AI model provides the user with a prompt such as, "Please propose a detailed plan to explore new business opportunities in the sustainable energy market by applying this patent."

[0804] This will enable a broader creative process that is more accessible to all stakeholders, and by integrating patent information and user feedback, it will be possible to realize new business plans.

[0805] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0806] Step 1:

[0807] The server accesses the Japan Patent Office's public database to retrieve patent information related to a specific technical field. The input is a patent search query, and the output is a set of retrieved patent documents. Specifically, a web scraping tool using Python is used to collect the necessary patent information data.

[0808] Step 2:

[0809] The server analyzes the acquired patent documents using natural language processing technology. The input is the patent documents acquired in step 1, and the output is a list of summarized patent information and its technical features. Specifically, it uses a generative AI model (such as BERT or GPT) to analyze and summarize the text and extract relevant technologies and keywords.

[0810] Step 3:

[0811] Based on the information analyzed in the previous step, the server explores the potential for cross-disciplinary applications of the patented technology. The input is the list of technical features from step 2, and the output is a list of application ideas in different fields. In this step, cluster analysis is performed to identify other technological areas with similar features and explore new business opportunities.

[0812] Step 4:

[0813] The server generates new business plans based on their potential for cross-disciplinary application. The input is a list of application ideas obtained in step 3, and the output is a concrete business plan proposal. The generation AI model is used to formulate plans that consider business strategy and market adaptability. This process generates specific prompts such as, "How can we devise a new mobile application for sustainable energy?"

[0814] Step 5:

[0815] The terminal receives the business plan sent from the server and presents it to the user in a visualized form. The input is the business plan draft sent from the server, and the output is visual information for the user. The terminal uses visualization tools such as D3.js to graphically display the plan draft and designs the interface to facilitate user understanding.

[0816] Step 6:

[0817] The emotion engine recognizes the user's emotions in real time as they view presented visual information and optimizes the experience. Input is the user's facial expressions and tone of voice, and output is an optimized user interface. This includes specific processes such as performing facial analysis using OpenCV and TensorFlow, and adjusting the colors and layout of the visual display.

[0818] Step 7:

[0819] Users input opinions and suggestions as feedback on the business plan via a terminal. The input consists of user evaluations and feedback, and the output is feedback data sent to the server. Based on this information, the server modifies and optimizes the business plan based on the feedback. A generative AI model is utilized to provide concrete means for improving the proposed content.

[0820] (Application Example 2)

[0821] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0822] Traditionally, when generating new business ideas using patent information, there was a lack of methods to effectively incorporate user emotional responses and improve the customer experience in physical stores. As a result, product displays and promotions in physical stores could not be effectively tailored to customer preferences, leading to challenges in increasing sales and improving customer satisfaction.

[0823] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0824] In this invention, the server includes means for acquiring patent information, means for using a generative model for summarizing and analyzing the patent information, and means for exploring the potential of the technology to be applied to other fields. This makes it possible to recognize user emotions in real time, dynamically adjust in-store displays and promotions to optimize the customer experience and improve sales.

[0825] "Patent information" refers to publicly available documents relating to the technical details of an invention and its intellectual property rights.

[0826] A "generative model" is an algorithm or process for extracting useful information from specific input data and for analyzing and transforming it.

[0827] "Cross-disciplinary applicability" refers to the potential for how a particular technology or idea can be applied in different fields.

[0828] A "new business idea" is an innovative business concept that does not yet exist in the market or that has evolved from an existing business.

[0829] "Visualization" is a technique that makes information and data easier for users to understand by illustrating them.

[0830] "User sentiment" refers to the emotional response that users exhibit when interacting with a product or service.

[0831] A "promotion strategy" is a set of actions and measures planned to promote the sale of a product or service.

[0832] A "physical store" is a physical sales space that customers can visit in person.

[0833] This system acquires and analyzes patent information and generates new business ideas based on the results. The server retrieves patent information from a database and summarizes and analyzes it using a generative model. This analysis explores the potential of the patent information to be applied in different fields and constructs new business ideas. The server visualizes these ideas and provides them to the user. The server is also configured to recognize the user's emotions in real time and dynamically adjust store displays and promotions based on those emotions. This process is carried out via devices such as smart glasses and displays, and user feedback and emotion data are used to optimize the store experience.

[0834] Specifically, if a customer shows no interest in a particular product, the server can automatically adjust the promotional strategy accordingly. For example, in a fashion store, this could involve analyzing the customer's facial expressions through smart glasses and suggesting a try-on event. This allows stores to provide promotions tailored to customer preferences, thereby improving customer satisfaction and increasing sales.

[0835] The entire process described above is designed to be efficient by utilizing a generative AI model. An example of a prompt for analyzing real-time data from a specific channel and formulating optimal promotional ideas is: "What promotional ideas would be best if the customer is not showing interest in the product? Please also consider relevant information from the patent database."

[0836] These processes enable this invention to more personalize and effectively improve the customer experience in physical stores.

[0837] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0838] Step 1:

[0839] The server retrieves patent information from a database. It uses queries to the patent database as input to collect publicly available patent documents. The output is a set of retrieved patent information. This step involves specific actions to access the patent database via an API to automate the patent information retrieval process.

[0840] Step 2:

[0841] The server uses a generative AI model to summarize and analyze patent information. The patent information obtained in step 1 is used as input. For data processing, natural language processing is used to segment the document and extract key technical features. The output is the summarized patent information and its analysis results. This step includes specific actions in which the generative model automatically identifies the subject matter and applicability of the patent.

[0842] Step 3:

[0843] The server explores the cross-disciplinary applicability of the technology based on the analysis results. The input is the analysis results from step 2. As a data calculation, it retrieves application examples of similar technologies from a database and scores their applicability. The output is a report on the cross-disciplinary applicability. This step includes specific actions that use existing case studies to compare related technologies.

[0844] Step 4:

[0845] The server generates new business ideas based on the search results. The report obtained in step 3 is used as input. For data processing, a generative AI model is utilized to construct ideas with increased applicability. The output is a list of new business ideas. This step includes the specific operation of the generative AI model, which combines different technological elements to generate new ideas.

[0846] Step 5:

[0847] The terminal visualizes and presents new business ideas to the user. The input is the idea list from step 4. As data processing, a visual design for presentation is generated. The output is a visualized business idea that the user can view. This step includes specific actions to generate an intuitive interface using visual tools.

[0848] Step 6:

[0849] Users provide feedback on business ideas, which is sent to the server. The inputs used are user feedback and sentiment data collected from sensors. Data processing involves analyzing user responses and performing analysis using a sentiment engine. The output is the improved business idea and its evaluation. This step includes specific actions to integrate feedback and sentiment data and clarify areas for improvement for the next step.

[0850] Step 7:

[0851] The server generates prompt messages and proposes the optimal promotional strategy obtained by the generative AI model. The input consists of feedback and sentiment data. Data processing involves generating prompt messages and formulating a promotional strategy. The output is the optimized promotional proposal. This step includes specific actions where the generative AI model dynamically adjusts the promotion considering the user's sentiment.

[0852] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0853] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0854] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0855] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0856] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0857] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0858] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0859] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0860] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0861] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0862] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0863] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0864] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0865] 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.

[0866] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0867] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0868] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0869] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0870] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0871] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0872] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0873] The following is further disclosed regarding the embodiments described above.

[0874] (Claim 1)

[0875] Means for obtaining patent information,

[0876] Means for using a generative model to summarize and analyze the aforementioned patent information,

[0877] A means for exploring the potential for applying the technology to other fields based on the analyzed information,

[0878] A means for generating new business ideas based on the aforementioned search results,

[0879] A means of visualizing the aforementioned new business idea and providing it to users,

[0880] A means of revising ideas based on user feedback,

[0881] A means of proposing a concrete business plan to the user,

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, wherein the generative model analyzes patent documents using natural language processing technology.

[0885] (Claim 3)

[0886] The system according to claim 1, having a function to support discussion of new business ideas through a collaboration platform.

[0887] "Example 1"

[0888] (Claim 1)

[0889] Means for obtaining patent information,

[0890] Means for using a generative model to summarize and analyze the aforementioned patent information,

[0891] A means for exploring the potential for applying the technology to other fields based on the analyzed information,

[0892] A means for generating and scoring new business ideas based on the aforementioned search results,

[0893] A means of visualizing the aforementioned new business idea and providing it to users via a terminal,

[0894] A means of improving ideas based on user feedback,

[0895] A means of helping users formulate concrete business plans based on proposals using a strategic consultant model,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, wherein the generative model analyzes a patent document using natural language processing technology and extracts key technical points.

[0899] (Claim 3)

[0900] The system according to claim 1, which has the function of supporting discussion of new business ideas through a collaborative platform and promoting communication among stakeholders.

[0901] "Application Example 1"

[0902] (Claim 1)

[0903] Means for obtaining patent information,

[0904] Means for using a generative model to summarize and analyze the aforementioned patent information,

[0905] A means for exploring the potential for applying the technology to other fields based on the analyzed information,

[0906] A means for generating new business ideas based on the aforementioned search results,

[0907] A means of visualizing the aforementioned new business idea and providing it to users,

[0908] A means of revising ideas based on user feedback,

[0909] A means of proposing a concrete business plan to the user,

[0910] A means of providing new content ideas through an online platform and using them to support content creation in applied fields,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, wherein the generative model analyzes patent documents using natural language processing technology and generates new content ideas.

[0914] (Claim 3)

[0915] The system according to claim 1, having a function to support discussion of new content ideas through a collaboration platform.

[0916] "Example 2 of combining an emotion engine"

[0917] (Claim 1)

[0918] Means for obtaining patent information,

[0919] Means for using a generative model to summarize and analyze the aforementioned patent information,

[0920] A means for exploring the potential for applying the technology to other fields based on the analyzed information,

[0921] A means for generating a new business plan based on the aforementioned search results,

[0922] A means of visualizing the aforementioned new business plan and providing it to users,

[0923] An emotion engine means that recognizes the user's emotions and optimizes the form of visualization,

[0924] A means of modifying the plan based on user feedback and sentiment data,

[0925] A means of proposing a concrete business plan to the user,

[0926] A system that includes this.

[0927] (Claim 2)

[0928] The system according to claim 1, wherein the generative model analyzes a patent document using natural language processing technology and uses prompt sentences generated by the generative AI model.

[0929] (Claim 3)

[0930] The system according to claim 1, which supports discussion of new business plans through a collaboration platform and has a feedback analysis function that takes into account the emotions of users.

[0931] "Application example 2 when combining with an emotional engine"

[0932] (Claim 1)

[0933] Means for obtaining patent information,

[0934] Means for using a generative model to summarize and analyze the aforementioned patent information,

[0935] A means for exploring the potential for applying the technology to other fields based on the analyzed information,

[0936] A means for generating new business ideas based on the aforementioned search results,

[0937] A means of visualizing the aforementioned new business idea and providing it to users,

[0938] A means of revising ideas based on user feedback,

[0939] A means of recognizing user emotions and dynamically adjusting in-store displays and promotions,

[0940] A means of proposing promotional strategies to optimize the customer experience in physical stores,

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, wherein the generative model analyzes patent documents using natural language processing technology.

[0944] (Claim 3)

[0945] The system according to claim 1, having a function to support discussion of new business ideas through a collaboration platform. [Explanation of symbols]

[0946] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means for obtaining patent information, Means for using a generative model to summarize and analyze the aforementioned patent information, A means for exploring the potential for applying the technology to other fields based on the analyzed information, A means for generating new business ideas based on the aforementioned search results, A means of visualizing the aforementioned new business idea and providing it to users, A means of revising ideas based on user feedback, A means of proposing a concrete business plan to the user, A means of providing new content ideas through an online platform and using them to support content creation in applied fields, A system that includes this.

2. The system according to claim 1, wherein the generative model analyzes patent documents using natural language processing technology and generates new content ideas.

3. The system according to claim 1, which has a function to support discussion of new content ideas through a collaboration platform.

Images