System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving detailed information of a technology or a project under development and storing the detailed information in a database; means for determining whether the received information is patentable using a generative AI; means for storing a result of the determination of patentability and a proposal in the database; and means for providing the stored result to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the patent application process, companies, individuals, and small and medium-sized enterprises are required to quickly determine whether a patent is valid and to file patent applications efficiently without spending a lot of time and money. In particular, those with little knowledge or experience in patents face the risk of missed applications or duplicate applications, which hinders patent applications. The objective of this invention is to solve these problems and improve the efficiency of patent applications while reducing the risks. [Means for solving the problem]

[0005] The present invention is a system that includes a means for receiving detailed information about technologies and projects under development and storing it in a database, a means for using a generation AI to determine whether or not to grant a patent for the received information, a means for saving the results of the patent approval / denial decision and proposals in a database, and a means for providing the saved results to users.Furthermore, by including a means for the generation AI to compare the information with a patent database to check whether it matches an existing patent, and a means for automatically generating documents for patent applications based on the results of the patent approval / denial decision, the system achieves efficiency in patent applications and reduces risk.

[0006] "Detailed information on technologies and projects under development" refers to specific information including the project's progress, technical details, process, materials used, methodology, etc.

[0007] "Means for storing in a database" refers to a system or function for temporarily or permanently storing and managing received information.

[0008] "Means for determining whether or not to grant patent permission for received information using generative AI" refers to a system or function that uses a machine learning model or natural language processing model to evaluate whether input information is suitable for a patent application.

[0009] "Means for storing the results of patent approval / denial and suggestions in a database" refers to a system or function for storing the results of the patent approval / denial evaluation and amendments or suggestions based on the results in a database.

[0010] "Means for providing saved results to users" refers to interfaces or functions that allow users to view and use patent approval / disapproval results and proposals.

[0011] "Means for the generating AI to check against a patent database to see if it matches an existing patent" refers to a system or function that accesses a patent database and checks whether the input information overlaps with an existing patent.

[0012] "Means for automatically generating documents for a patent application based on the result of whether or not a patent application will be granted" refers to a system or function for automatically generating documents and other materials required for a patent application based on the result of the decision on whether or not a patent application will be granted. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The system of the present invention is composed of the following elements: a server, a user, and a generation AI. The specific roles and operations of each element are explained below.

[0035] Server Roles and Operations

[0036] server:

[0037] Establishes a database connection and initializes the necessary tables.

[0038] Provides an interface for receiving detailed information about technologies and projects under development.

[0039] The received information is stored in a database.

[0040] In cooperation with the generation AI, we request a decision on whether or not to grant patent permission.

[0041] The results received from the generation AI are stored in a database and provided to the user.

[0042] User Roles and Actions

[0043] User:

[0044] Enter detailed information about the technology or project under development into the system.

[0045] Check the results and proposals provided by the server.

[0046] Based on the results presented, the development process and patent application procedures will be carried out.

[0047] Role and behavior of generative AI

[0048] Generation AI:

[0049] The detailed information entered by the user is analyzed and compared with the patent database.

[0050] Determine whether a patent is possible and generate the results and proposals.

[0051] Automatically generate documents for patent applications.

[0052] Specific examples

[0053] Data Entry

[0054] Users input "detailed information about new battery materials" into the system. This information includes details necessary for patent applications, such as the chemical composition of the material, manufacturing process, and use cases. The server stores the received information in a database.

[0055] Judgment on whether or not to grant patent permission

[0056] The server passes the saved information to the generation AI and asks it to determine whether or not to grant a patent. The generation AI analyzes the input information and compares it with a patent database to check for overlaps with existing patents. It also automatically generates documents for patent applications.

[0057] Providing results

[0058] The server stores the patent approval / denial results and proposals received from the generation AI in a database and provides them to the user, who then proceeds with the patent application process based on the presented results.

[0059] Specific operation example

[0060] The user inputs the "materials for the new battery."

[0061] The server stores the information in a database and passes it on to the generating AI.

[0062] The generative AI analyzes the input information and compares it with a patent database.

[0063] If there is a high probability of a patent, documents are generated for filing.

[0064] The server stores the results in a database and provides them to the user.

[0065] The user checks the results and proceeds with the patent application process.

[0066] As described above, the system of the present invention appropriately processes detailed information on technologies and projects under development and provides efficient support for patent applications.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The server establishes a database connection and initializes the submissions and results tables, creating them if they do not exist.

[0070] Step 2:

[0071] Users input details of the technology or project they are developing into the system, for example, entering details about a new battery material.

[0072] Step 3:

[0073] The server inserts the information received from the user into the submissions table in the database, giving the inserted row a unique ID.

[0074] Step 4:

[0075] The server prepares the generative AI model, specifically by loading the natural language processing model and preparing for information analysis.

[0076] Step 5:

[0077] The server retrieves the submitted information from the database, fetching details corresponding to the specific ID from the submissions table.

[0078] Step 6:

[0079] The generation AI analyzes the acquired information and compares it with a patent database, specifically checking whether the input information overlaps with existing patents.

[0080] Step 7:

[0081] The AI ​​generates patent approval and rejection results and proposals. If there is a high possibility of patent approval, it also automatically generates documents for patent application.

[0082] Step 8:

[0083] The server stores the generated patent approval results and proposals in a results table in the database.

[0084] Step 9:

[0085] The server retrieves the patent approval / denial results and proposals from the database and provides them to the user.

[0086] Step 10:

[0087] The user checks the provided results and proceeds with the modification of the development process or patent application procedures. Based on the results, the next action is decided.

[0088] Example 1

[0089] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0090] The conventional patent application process lacks an integrated support system that allows users to efficiently manage detailed information about technologies and projects under development and quickly determine whether or not a patent should be granted. This requires users to spend a lot of time and effort manually, which can potentially lower the success rate of patent applications. There is also a need for a system that can consistently determine whether or not a patent should be granted and prepare documents for patent applications.

[0091] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0092] In this invention, the server includes means for establishing a database connection and initializing necessary tables, means for receiving detailed information about technologies and projects under development and storing it in the database, means for determining whether or not to grant a patent for the received information using a generation AI, means for saving the results of the patent approval / denial decision and proposals in the database, and means for providing the saved results to the user. This allows the user to efficiently manage detailed information about technologies and projects under development, and enables quick decisions on whether or not to grant a patent and automatic generation of documents for patent applications.

[0093] The "means for establishing a database connection and initializing the necessary tables" refers to a series of operations in which the server connects to the database system and creates the table structure required in the initial state.

[0094] "Means for receiving detailed information about technologies and projects under development and storing it in a database" refers to the process for receiving detailed technical information entered by users into the system and storing that information in a database.

[0095] "Means for determining whether or not to grant patent permission for received information using generating AI" refers to a series of processes in which the generating AI analyzes the technical information received and compares it with existing patent documents to determine the possibility of granting a patent.

[0096] "Means for storing the results of whether or not to grant a patent and proposals in a database" refers to the operation for recording the results of whether or not to grant a patent determined by the generating AI and proposals based on that result in a database.

[0097] "Means for providing stored results to a user" refers to an interface or method for displaying and providing the patent approval / disapproval results and proposals stored in the database to a user.

[0098] "Means of checking against a patent database to see if it matches existing patents" refers to the process by which the generating AI compares technical information with a patent database to see if the information matches existing patents.

[0099] "Means for automatically generating documents for patent applications" refers to the function that enables the generation AI to automatically create documents necessary for patent applications based on the results of whether or not a patent is granted.

[0100] The system of the present invention is composed of the following elements: a server, a user, and a generation AI. The specific roles and operations of each element are explained below.

[0101] Server Operation

[0102] The server first connects to a database system (e.g., MySQL®) and creates the necessary tables as an initialization process. Here, two tables, projects and patent_results, are created, each with columns for storing technical information and patent approval / denial results. The server uses a backend framework such as Java® or Node.js to provide a REST API for receiving detailed information about technologies and projects entered by users. The received information is stored in the projects table in the database.

[0103] The server then passes the information to the generation AI using a Python API. The generation AI analyzes the received technical information and compares it with existing patent databases (e.g., Google® Patents or the USPTO database) to determine whether or not to grant a patent. The results and related proposals are returned to the server in JSON format. The server stores these results in the patent_results table.

[0104] Finally, the server provides the saved patent approval / denial results to the user, which are displayed via email notification and a web dashboard.

[0105] User Actions

[0106] Users enter detailed information about their technology or project into the system via a web form, for example, by entering "detailed information about new battery materials," and then clicking the submit button. The information entered by the user is then stored in a database via the server.

[0107] The user then checks the results of the patent approval and proposals provided by the server. This allows them to review the development process and proceed with the patent application process. Based on the results, the user can create a specific action plan for filing a patent application.

[0108] Generative AI behavior

[0109] The generation AI analyzes the technical information received from the user and compares it with a patent database to determine whether or not to grant a patent. It uses NLP (natural language processing) technology and machine learning models to check whether the information matches existing patents. Based on the results, it automatically generates a proposal for whether or not to grant a patent and documents for patent applications.

[0110] A concrete example of patent matching performed by a generative AI might be the following prompt:

[0111] "This technical description contains detailed information about new battery materials. Based on this information, search existing patent databases to see if there are any matching patents."

[0112] The generation AI receives this prompt, compares it with the patent database, and returns the results to the server in JSON format.

[0113] Specific examples

[0114] Data Entry

[0115] Users input "detailed information about new battery materials" into the system. This information includes details necessary for patent applications, such as the chemical composition of the material, manufacturing process, and use cases. The server stores the received information in a database.

[0116] Judgment on whether or not to grant patent permission

[0117] The server passes the saved information to the generation AI and asks it to determine whether or not to grant a patent. The generation AI analyzes the input information and compares it with a patent database to check for overlaps with existing patents. It also automatically generates documents for patent applications.

[0118] Providing results

[0119] The server stores the patent approval / denial results and proposals received from the generation AI in a database and provides them to the user, who then proceeds with the patent application process based on the presented results.

[0120] In this way, the system of the present invention efficiently manages detailed information on technologies and projects under development and provides prompt support for patent applications.

[0121] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0122] Step 1: Establishing and Initializing a Database Connection

[0123] The server connects to a database system (e.g., MySQL) and initializes the necessary tables, specifically creating two tables called projects and patent_results, thus establishing a structure for storing technical information and patent results.

[0124] Input: Database connection information, table structure definition

[0125] Output: initialized database

[0126] Step 2: User input

[0127] Users enter details of the technology or project they are developing into a web form, for example, "Details about a new battery material," and click the submit button.

[0128] Input: Technology and project details (e.g., chemical composition, manufacturing process, use cases)

[0129] Output: Information submitted via the web form

[0130] Step 3: Save the input information to the database

[0131] The server stores the information received from the user in the projects table in the database, confirms the save was successful, and checks for errors.

[0132] Input: User-submitted technical information

[0133] Output: Information stored in the projects table in the database

[0134] Step 4: Sending information to the generating AI

[0135] The server retrieves the input information from the database and requests the generation AI to determine whether or not to grant a patent. It sends a request to the generation AI using a Python API.

[0136] Input: Technical information retrieved from the projects table

[0137] Output: The request sent to the generation AI

[0138] Step 5: Patent approval decision and proposal generation

[0139] The generative AI analyzes the information it receives, compares it with a patent database, and uses NLP technology to determine whether a patent is valid and generates proposals.

[0140] Input: Technical information sent from the server

[0141] Output: Patent approval / denial result and proposal (JSON format)

[0142] Step 6: Save the results to a database

[0143] The server analyzes the results received from the generation AI and saves them in the patent_results table. It checks whether the data is saved correctly in the required fields.

[0144] Input: Patent approval / denial results and proposals returned by the generation AI

[0145] Output: Contents stored in the database table patent_results

[0146] Step 7: Providing results to users

[0147] The server provides the saved patent approval results to the user, displaying them using a dashboard and notification system.

[0148] Input: Patent approval / denial results stored in the patent_results table

[0149] Output: Patent approval / denial results provided to the user (dashboard and email notification)

[0150] As described above, each processing step includes specific operations, and the entire system forms a flow that efficiently supports the user's patent application process.

[0151] (Application example 1)

[0152] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0153] Until now, there have only been limited systems that can efficiently process detailed information on technologies and projects under development for patent acquisition, and the processes for determining whether a patent application should be filed and automatically generating application documents were complex and time-consuming. There was also a lack of a way for users to quickly check the results of patent decisions. This resulted in low efficiency in the overall patent acquisition process and placed a heavy burden on engineers.

[0154] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0155] In this invention, the server includes a means for receiving detailed information about technologies and projects under development and storing it in a database, a means for determining whether or not to grant a patent based on the received information using a generation AI, a means for saving the results of the patent application and proposals in a database, a means for inputting technical information and checking the results of patent decisions as an application installed on a smartphone, and a means for automatically generating documents related to patent applications. This makes it possible to improve the efficiency of the patent acquisition process, which has traditionally been a challenge.

[0156] "Detailed information about technologies or projects under development" means specific information about new or ongoing technological ideas or projects, including the purpose, structure, operation, and data related to the technology.

[0157] "Means for storing in a database" refers to a method or device for storing specific information in a database system so that it can be retrieved and used later.

[0158] "Generative AI" refers to artificial intelligence systems that use machine learning and deep learning to analyze data and perform generative tasks for specific purposes.

[0159] "Means for determining whether or not to grant a patent" refers to a method or device that analyzes received technical information and determines whether the content overlaps with existing patents and whether or not a patent can be obtained.

[0160] "Means for storing the results of patent approval denial and proposals in a database" refers to a method or device for recording the results of the patent approval denial and proposals output by the generating AI in a database.

[0161] "Means for providing to the user" refers to a method or device for presenting the information stored in the database and the determination results in a manner that allows the user to easily access them.

[0162] An "application installed on a smartphone" refers to a software program that is installed and runs on a mobile information terminal, and in the case of this invention, has the function of assisting in patent decisions.

[0163] "Means for inputting technical information and checking patent decision results" refers to a method or device in which a user inputs technical details, the generating AI determines whether or not to grant a patent based on that information, and the result is displayed on the user's terminal.

[0164] "Means for automatically generating documents related to patent applications" refers to methods or devices in which generation AI automatically creates documents necessary for patent applications.

[0165] This invention relates to a system that supports the development of new robotics technologies and streamlines patent acquisition. This system consists of a server, a user terminal (such as a smartphone or PC), and a generative AI model. The specific roles and operations of each element are explained below.

[0166] The server provides a means to receive detailed information about technologies and projects under development and store it in a database. Technical information entered by users (for example, detailed information about a new robot arm) is saved in the database via the server. The database also includes a patent database and user information.

[0167] The server then uses the generative AI model to provide a means of determining whether or not to grant a patent for the received technical information. The generative AI model uses machine learning and deep learning to analyze the input technical information and compare it with a patent database to see if it matches any existing patents. The generative AI outputs the patent grant / denial results and recommendations, and the data is then stored back in the database.

[0168] Furthermore, this system includes a means for inputting technical information as an application installed on a smartphone and checking the results of patent decisions. Using this application, users can input technical information under development (for example, the manufacturing process and materials used for a robot arm). The input information is sent to the generation AI via a server, and the result of whether or not a patent is granted is returned to the user.

[0169] The server also provides a means for automatically generating documents for patent applications. The AI ​​automatically creates the documents necessary for patent applications based on the results of the patent approval / denial decision. These automatically generated documents are provided to users in a format that is easily accessible.

[0170] Specifically, when a user enters "technical information for a new robot arm" using an application installed on their smartphone, the information is sent to a server. The server stores this information in a database and sends it to the generation AI. The generation AI compares the information with a patent database and determines whether or not the application is patentable. The results are stored in the database, and the user can check the patent decision results again from the application. Furthermore, the generation AI automatically generates documents for the patent application, which are also provided to the user.

[0171] Examples of prompts to input to a generative AI model include:

[0172] "Details on the new movement control system for the robotic arm.

[0173] Material: Aluminum, Carbon Fiber

[0174] Manufacturing process: Laser cut and 3D printed

[0175] Purpose of use: High-precision transport work within factories

[0176] Instructions to the generative AI model:

[0177] 1. Analyze the input information and match it with the patent database.

[0178] 2. Determine whether the application is patentable and return the results.

[0179] 3. Generate the documents for your patent application.

[0180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0181] Step 1:

[0182] Users enter detailed information about the technology or project they are developing using their smartphone.

[0183] Specific behavior:

[0184] The user launches an application installed on their smartphone and inputs new technology information (e.g., details of a new robot arm), including the purpose of the technology, materials, manufacturing process, and use cases.

[0185] Input: Technical information (e.g., robot arm materials, manufacturing process, etc.)

[0186] Output: User input data

[0187] Step 2:

[0188] The server receives the technical information sent by the user and stores it in a database.

[0189] Specific behavior:

[0190] The smartphone app sends user-entered data to the server, which then establishes a database connection to store the received data in a database and saves the technical information in the appropriate tables.

[0191] Input: User-entered data

[0192] Output: Technical information stored in a database

[0193] Step 3:

[0194] The server passes the stored information to the generation AI and asks it to decide whether or not to grant a patent.

[0195] Specific behavior:

[0196] The server retrieves technical information from the database and generates a prompt to send to the AI. The prompt contains details of the technology and an instruction to the AI ​​to determine whether or not to grant a patent.

[0197] Input: Technical information stored in the database

[0198] Output: prompt statement

[0199] Step 4:

[0200] The generating AI analyzes the input technical information and compares it with a patent database to determine whether or not it is patentable.

[0201] Specific behavior:

[0202] The generation AI receives the prompt, analyzes the technical information, and compares it with a patent database. The patent database stores existing patent information, and compares it with the analysis results to check for overlaps. It determines whether the patent is valid and returns the result to the server.

[0203] Input: prompt statement

[0204] Output: Patent permission / denial result

[0205] Step 5:

[0206] The server stores the results received from the generation AI in a database and provides them to the user.

[0207] Specific behavior:

[0208] The server receives the patent approval / denial results from the generation AI and stores them in a database, which is then returned to the application so that the user can access and check the results.

[0209] Input: Patent approval / denial result

[0210] Output: Patent approval / denial results stored in the database, patent decision results that can be checked by the user

[0211] Step 6:

[0212] The generation AI automatically generates documents for patent applications based on the results of whether or not a patent is granted.

[0213] Specific behavior:

[0214] If the patent application is approved, the AI ​​automatically generates the documents necessary for the patent application (e.g., patent specification). The generated documents are sent to a server and stored in a database.

[0215] Input: Patent approval / denial result

[0216] Output: Auto-generated patent application document

[0217] Step 7:

[0218] The server provides the automatically generated patent application document to the user.

[0219] Specific behavior:

[0220] The server provides the automatically generated document to the user through the application, from which the user can download the document and proceed with the patent application process.

[0221] Input: Auto-generated patent application document

[0222] Output: User downloadable patent application document

[0223] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0224] The system of the present invention is composed of the following elements: a server, a user, a generation AI, and an emotion engine. The specific roles and operations of each element are explained below.

[0225] Server Roles and Operations

[0226] server:

[0227] Establishes a database connection and initializes the necessary tables.

[0228] Provides an interface for receiving detailed information about technologies and projects under development.

[0229] The received information is stored in a database.

[0230] The generative AI and emotion engine work together to perform the necessary data analysis and processing.

[0231] The results received from the generative AI and the analysis results from the emotion engine are stored in a database and provided to the user.

[0232] User Roles and Actions

[0233] User:

[0234] Enter detailed information about the technology or project under development into the system.

[0235] Emotional data is also provided through the system interface.

[0236] Check the provided patent approval / rejection results and proposals, as well as the emotion engine analysis results.

[0237] Based on the results presented, the development process and patent application procedures will be carried out.

[0238] Role and behavior of generative AI

[0239] Generation AI:

[0240] The detailed information entered by the user is analyzed and compared with the patent database.

[0241] Determine whether a patent is possible and generate the results and proposals.

[0242] Automatically generate documents for patent applications.

[0243] Role and operation of the emotion engine

[0244] Emotion Engine:

[0245] Emotional data from users is collected and analyzed when they input information.

[0246] Based on the analysis results, the generative AI provides information for adjusting the patent approval / denial results and proposals.

[0247] Use emotional data to adjust the tone and approach of your interactions with users.

[0248] Specific examples

[0249] Data Entry

[0250] The user inputs emotional data into the system along with "detailed information about the materials of the new battery." The emotional data includes information for analyzing emotions from text and the emotional state selected by the user. The server stores this information in a database.

[0251] Judgment on whether or not to grant patent permission

[0252] The server passes the saved information to the generation AI and asks it to determine whether or not to grant the patent. At the same time, it passes the user's emotional data to the emotion engine and obtains the analysis results. The generation AI analyzes the input information and compares it with a patent database to check for overlaps with existing patents. It also automatically generates documents for patent applications.

[0253] Providing results

[0254] The server stores the patent approval / denial results and proposals received from the generative AI, as well as the analysis results from the emotion engine, in a database and provides them to the user. The user then proceeds with the patent application process based on the presented results. At this time, the way the results are presented is adjusted based on the results of the emotion engine, providing information in the most optimal form for the user.

[0255] Specific operation example

[0256] The user inputs "materials for a new battery" and emotional data at that time.

[0257] The server stores the information in a database and transmits it to the generative AI and emotion engine.

[0258] The generation AI analyzes the input information, compares it with a patent database, and automatically generates documents for patent applications.

[0259] The emotion engine analyzes the emotional data and adjusts the output of the generative AI based on the results.

[0260] The server stores the results in a database and provides them to the user.

[0261] The user checks the results and proceeds with the patent application process.

[0262] As described above, the system of the present invention appropriately processes detailed information about technologies and projects under development and provides efficient and emotional support for patent applications.

[0263] The processing flow will be explained below.

[0264] Step 1:

[0265] The server establishes a database connection and initializes the submissions, results, and emotions tables, creating them if they do not exist.

[0266] Step 2:

[0267] Users enter detailed information about the technology or project they are developing into the system. Specifically, they enter "detailed information about new battery materials." They also select the emotion they felt when entering the information.

[0268] Step 3:

[0269] The server inserts the details and emotion data received from the user into the submissions and emotions tables in the database, and assigns a unique ID to the inserted row.

[0270] Step 4:

[0271] The server prepares the generative AI model, specifically by loading the natural language processing model and preparing for information analysis.

[0272] Step 5:

[0273] The server retrieves the submitted details and emotion data from the database. It fetches the details and emotion data corresponding to the specific ID from the submissions and emotions tables.

[0274] Step 6:

[0275] The generating AI analyzes the acquired information and compares it with a patent database, specifically checking whether the input information overlaps with existing patents.

[0276] Step 7:

[0277] The AI ​​generates patent approval and rejection results and proposals. If there is a high possibility of patent approval, it also automatically generates documents for patent application.

[0278] Step 8:

[0279] The server calls the emotion engine to analyze the user's emotion data. The emotion engine analyzes the emotion information entered by the user and outputs the results.

[0280] Step 9:

[0281] The emotion engine passes the results of the emotion analysis to the generative AI, which then adjusts the tone and approach of the patent approval / rejection results and recommendations. For example, if the user is emotionally unstable, the results will be presented in more friendly language.

[0282] Step 10:

[0283] The server stores the patent approval / denial results and emotion analysis results received from the generation AI and emotion engine in the results table of the database.

[0284] Step 11:

[0285] The server retrieves the results and suggestions for patent approval from the database and provides them to the user. In particular, it presents them in the most suitable form for the user based on the results of the emotion engine.

[0286] Step 12:

[0287] The user checks the provided results and proceeds with the modification of the development process or patent application procedures. Based on the results, the next action is decided.

[0288] Example 2

[0289] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0290] In conventional patent application systems, the process of efficiently analyzing technical information under development and determining whether or not a patent should be granted was cumbersome. Furthermore, because feedback that took into account the user's emotional data was not provided, the process was often mentally burdensome for applicants. This resulted in a decrease in the efficiency of patent applications and a poor user experience.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving information about technologies and projects under development and storing the data, means for evaluating the patentability of the received information using a generative AI model, means for storing the results of the patentability decision and proposals, means for collecting and analyzing emotion data, and means for adjusting the output result of the generative AI model based on the analyzed emotion data. This enables an integrated analysis of technical information and emotion data, enabling efficient and user-friendly support for patent applications.

[0292] "Information about technology and projects" refers to data about technical details and project content currently under development or research.

[0293] "Data storage means" means hardware and software components for securely and efficiently storing received information.

[0294] A "generative AI model" is an artificial intelligence algorithm that uses machine learning to analyze and determine whether or not to grant a patent.

[0295] The "means for assessing whether or not a patent is permitted" is a system that uses a generative AI model to determine the likelihood that the received technical information will be granted a patent.

[0296] The "patent approval / denial result" refers to the judgment result on whether the technical information analyzed by the generative AI model will be granted a patent.

[0297] "Proposal" is information that suggests improvements or additional actions regarding technical information based on the result of the patent approval or denial.

[0298] "Emotion data" refers to the emotional state and psychological information input by the user.

[0299] The "means for collecting and analyzing emotional data" is a system for acquiring emotional information from a user and analyzing it.

[0300] The "means for adjusting the output results of the generative AI model" is a system that adjusts the output and proposal content of the generative AI model based on the results of analyzing emotional data.

[0301] The system of the present invention is composed of the following elements: a server, a user, a generative AI model, and an emotion engine. The specific roles and operations of each element are explained below.

[0302] Server Roles and Operations

[0303] The server receives information about technologies and projects under development and stores it in a database. This database uses a relational database management system such as MySQL or PostgreSQL. The server passes the stored information to a generative AI model, which evaluates whether or not to grant a patent. It also works with an emotion engine to collect and analyze emotion data. The analysis results are stored in the database until they are provided to the user.

[0304] User Roles and Actions

[0305] Users enter detailed information about the technology or project they are developing through the system's interface, which can be provided via a web browser or a dedicated application. Along with the technical information, users also enter emotional data, conveying their emotional state to the system. This data is used for patent evaluation and user feedback.

[0306] Example prompt sentence:

[0307] More information on the new battery material: "This material is a special alloy that extends the life of lithium-ion batteries."

[0308] Emotional data: "Users are currently feeling both excitement and anxiety."

[0309] Roles and behavior of generative AI models

[0310] The generative AI model analyzes technical information entered by the user and compares it with patent databases, such as Google Patents and the USPTO database. The generative AI model determines whether a patent should be granted and automatically generates documents for the patent application based on the results.

[0311] Role and operation of the emotion engine

[0312] The emotion engine collects and analyzes the emotional data entered by the user. The emotion engine evaluates the user's emotional state and adjusts the output of the generative AI model based on the results. This allows information to be provided to the user in the most optimal way.

[0313] Specific examples

[0314] Data Entry

[0315] The user inputs "detailed information about the materials of the new battery" along with emotional data at the time of input. The emotional data includes emotions analyzed from the user's text and the emotional state selected by the user. The server stores this information in a database.

[0316] Judgment on whether or not to grant patent permission

[0317] The server passes the stored information to the generative AI model and requests it to evaluate whether or not to grant a patent. At the same time, it passes the emotional data to the emotion engine and obtains the analysis results. The generative AI model analyzes the technical information and compares it with a patent database to check for any matches with existing patents. It also automatically generates documents for patent applications.

[0318] Providing results

[0319] The server stores the patent approval / denial results and proposals received from the generative AI model, as well as the analysis results from the emotion engine, in a database and provides them to the user. The user then proceeds with the patent application process based on the presented results. At this time, the method of presenting the results is adjusted based on the results of the emotion engine, so that information is provided in the most optimal form for the user.

[0320] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0321] Step 1:

[0322] The user inputs technical information and emotional data. Through a web browser or a dedicated application interface, the user inputs detailed information about the technology or project under development, along with emotional data. The input data includes, for example, technical information such as "a special alloy that extends the life of next-generation lithium-ion batteries," and emotional states such as "expectations and anxiety." The server provides an API endpoint for receiving this data.

[0323] ·input:

[0324] Technical information: "Special alloy extends the life of next-generation lithium-ion batteries"

[0325] Emotional data: "Expectations and Anxieties"

[0326] Specific actions:

[0327] Users enter emotion data into a form along with details of the project under development.

[0328] Step 2:

[0329] The server saves the received information in a database. The server receives the technical information and emotion data from the user and stores it in a database. The database format used is, for example, MySQL or PostgreSQL. Data consistency checks and format conversions are also performed as necessary.

[0330] ·input:

[0331] Technical information and sentiment data from users

[0332] ·output:

[0333] Information stored in the database

[0334] Specific actions:

[0335] The server's API receives the information and stores it in the database using an INSERT statement.

[0336] Step 3:

[0337] The server transfers the stored information to the generative AI model and emotion engine. The server retrieves the stored technical information and emotion data from the database and passes them to the generative AI model and emotion engine, respectively. During this process, information is sent using a RESTful API.

[0338] ·input:

[0339] Information stored in the database

[0340] ·output:

[0341] Data sent to generative AI models and emotion engines

[0342] Specific actions:

[0343] The server generates API requests and sends data to the generative AI model and emotion engine.

[0344] Step 4:

[0345] The generative AI model evaluates whether or not to grant a patent. The generative AI model analyzes the technical information sent from the server and compares it with patent databases. Patent databases include Google Patents and the USPTO database, for example. The generative AI model determines whether or not to grant a patent and returns the result to the server.

[0346] ·input:

[0347] Technical information sent from the server

[0348] ·output:

[0349] Evaluation results for patent approval

[0350] Specific actions:

[0351] The generative AI model analyzes the incoming data using natural language processing (NLP) techniques and compares it with patent databases.

[0352] Step 5:

[0353] The emotion engine analyzes the emotion data. The emotion engine analyzes the emotion data sent from the server and evaluates the user's emotional state. The results of this analysis are used to adjust the output of the generative AI model.

[0354] ·input:

[0355] Emotion data sent from the server

[0356] ·output:

[0357] Emotion analysis results

[0358] Specific actions:

[0359] The emotion engine analyzes the incoming data using natural language processing (NLP) techniques to assess the emotional state.

[0360] Step 6:

[0361] The results of the generative AI model and the emotion engine are integrated. The server integrates the patent approval evaluation results from the generative AI model and the emotion analysis results from the emotion engine, and compiles them into a single data package.

[0362] ·input:

[0363] Evaluation results of patent permission from generative AI model

[0364] Emotion analysis results from the emotion engine

[0365] ·output:

[0366] Integrated Data Package

[0367] Specific actions:

[0368] The server combines the two results in JSON format and stores them in the database.

[0369] Step 7:

[0370] The server provides the results to the user. The server generates data to display the consolidated results in a user interface and provides it to the user, for example, through a web dashboard or a mobile application.

[0371] ·input:

[0372] Integrated Data Package

[0373] ·output:

[0374] Result information provided to the user

[0375] Specific actions:

[0376] The server retrieves the results from the database and displays them on the user's dashboard via an API.

[0377] The above is the specific program processing in the system of the present invention. This system makes it possible to efficiently and comprehensively analyze technical information and emotion data, and to smoothly advance the patent application process.

[0378] (Application example 2)

[0379] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0380] New technologies and projects are frequently developed in modern factories and manufacturing sites. However, determining whether a technology can be patented is a time-consuming process that requires specialized knowledge and is difficult to perform efficiently. Furthermore, providing information without considering the emotional state of the developer can cause stress, which can delay the patent application process. Therefore, there is a need for an efficient and reliable patent approval / denial system that takes the developer's emotions into account.

[0381] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving detailed information about technologies and projects under development and storing it in a database, means for using a generation AI to determine whether or not to grant a patent for the received information, means for saving the results of the patent approval decision and proposals in a database, means for collecting and analyzing emotional data, means for adjusting the output of the generation AI based on the results of the emotion analysis, and means for explaining patent information to developers in an optimal manner. This makes it possible to improve the efficiency and reliability of patent approval decisions while taking into account the emotional state of developers.

[0382] "Developers" are engineers who develop new technologies and projects in the manufacturing field.

[0383] "Detailed information" refers to the specific content, specifications, and examples of new technologies and projects.

[0384] A "database" is a collection of data that organizes and stores information so that it can be searched and used as needed.

[0385] "Generative AI" refers to an artificial intelligence system that analyzes received information and determines whether or not it is patentable.

[0386] "Patent approval or disapproval" refers to the decision on whether a certain technology is eligible for a patent.

[0387] "Suggestions" refer to advice on improvements and next steps provided based on the results of whether or not a patent is granted.

[0388] "Emotion data" is data that represents the emotional state of the developer, and includes, for example, stress and satisfaction.

[0389] An "emotion engine" is a system that analyzes emotional data and outputs the results.

[0390] "Adjustment" refers to optimizing the output content and presentation method of the generative AI based on the results of emotion analysis.

[0391] "Explanation" refers to the act of clearly communicating the results of patent approval or rejection and proposals to developers.

[0392] The system of the present invention is composed of the following elements: a server, a factory robot, a generative AI, and an emotion engine. The specific roles and operations of each element are explained below.

[0393] Server Roles and Operations

[0394] The server establishes a database connection and prepares to initialize project information and emotion data. It stores the detailed information and emotion data of the new technology received from the developer in the database and provides them to the generation AI and emotion engine. It also stores the patent approval decision results from the generation AI and the analysis results from the emotion engine in the database and provides them to the developer.

[0395] Roles and operations of factory robots

[0396] The factory robot receives detailed information about new technologies from developers through voice or manual input, while simultaneously analyzing the developer's emotional data (e.g., stress and satisfaction) in real time using a Kinect sensor and microphone.

[0397] Role and behavior of generative AI

[0398] The generation AI analyzes the received technical information and checks it against a patent database to confirm whether it matches existing patents. It determines whether the technology is patentable and automatically generates documents for patent applications based on that determination. Example prompt: "Based on detailed information about the new battery material, please determine whether this technology is patentable."

[0399] Role and operation of the emotion engine

[0400] The emotion engine analyzes the developer's emotional data and adjusts the output of the generative AI based on the results. The emotion analysis results are used to optimize the way patent information is presented, providing developers with information in a format that is easy to understand and less stressful.

[0401] Specific examples

[0402] A developer inputs information about a "new battery material" into a factory robot. The robot sends the information to a server, simultaneously collecting the developer's emotional data. The server stores the received information in a database and requests analysis from the generation AI and emotion engine. The generation AI analyzes the input information, compares it with a patent database, and automatically generates documents for the patent application. The emotion engine analyzes the emotional data and adjusts the output of the generation AI based on the results. The server provides the developer with the analysis results and the result of the patent approval or denial. This process makes the patent application process more efficient and reduces stress for developers.

[0403] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0404] Step 1:

[0405] The user (the developer who operates the factory robot) inputs detailed information about the new technology into the terminal by voice or manual input. The input information includes the specific content, specifications, and examples of the new technology. At the same time, the user's emotional data (e.g., facial expressions and tone of voice) is collected in real time using the Kinect sensor and microphone.

[0406] Input: Details of new technology, user emotional data

[0407] Output: Data received by factory robots

[0408] Step 2:

[0409] The terminal (factory robot) transmits the received technical information and emotion data to the server, at which point the collected data is stored in the server's database.

[0410] Input: Technical information and emotional data from factory robots

[0411] Output: Sending data to the server

[0412] Step 3:

[0413] The server initializes the received information into a database and provides it to the generative AI and emotion engine, establishing a database connection and properly organizing and storing the received data.

[0414] Input: Received technical and emotional data

[0415] Output: Store in a database

[0416] Step 4:

[0417] The generation AI analyzes the stored technical information, compares it with a patent database, and checks for matches with existing patents. The generation AI determines whether a patent is possible and automatically generates documents for patent applications.

[0418] Input: Technical Information

[0419] Output: Patent approval / denial decision result and patent document

[0420] Step 5:

[0421] The emotion engine analyzes the stored emotional data and adjusts the output of the generative AI based on the results. For example, if the user is feeling stressed, it will adjust the tone and detail of the presented approval / disapproval results.

[0422] Input: Emotion data

[0423] Output: Sentiment analysis results and adjustments to generated AI output

[0424] Step 6:

[0425] The server stores the patent approval / denial decisions and proposals from the generation AI, as well as the analysis results from the emotion engine, in a database and provides them to users. The presentation method is optimized based on the emotion analysis results.

[0426] Input: Generative AI results, emotion engine results

[0427] Output: Providing results to the user

[0428] Step 7:

[0429] Users can proceed with the patent application process based on the provided patent approval / denial results, suggestions, and emotional feedback, allowing them to efficiently evaluate the patentability of their technology and take the necessary steps quickly.

[0430] Input: Approval / rejection results, suggestions, emotional feedback

[0431] Output: Patent application process

[0432] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0433] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0434] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0435] [Second embodiment]

[0436] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0437] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0438] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0439] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0440] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0441] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0442] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0443] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0444] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0445] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0446] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0447] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0448] The system of the present invention is composed of the following elements: a server, a user, and a generation AI. The specific roles and operations of each element are explained below.

[0449] Server Roles and Operations

[0450] server:

[0451] Establishes a database connection and initializes the necessary tables.

[0452] Provides an interface for receiving detailed information about technologies and projects under development.

[0453] The received information is stored in a database.

[0454] In cooperation with the generation AI, we request a decision on whether or not to grant patent permission.

[0455] The results received from the generation AI are stored in a database and provided to the user.

[0456] User Roles and Actions

[0457] User:

[0458] Enter detailed information about the technology or project under development into the system.

[0459] Check the results and proposals provided by the server.

[0460] Based on the results presented, the development process and patent application procedures will be carried out.

[0461] Role and behavior of generative AI

[0462] Generation AI:

[0463] The detailed information entered by the user is analyzed and compared with the patent database.

[0464] Determine whether a patent is possible and generate the results and proposals.

[0465] Automatically generate documents for patent applications.

[0466] Specific examples

[0467] Data Entry

[0468] Users input "detailed information about new battery materials" into the system. This information includes details necessary for patent applications, such as the chemical composition of the material, manufacturing process, and use cases. The server stores the received information in a database.

[0469] Judgment on whether or not to grant patent permission

[0470] The server passes the saved information to the generation AI and asks it to determine whether or not to grant a patent. The generation AI analyzes the input information and compares it with a patent database to check for overlaps with existing patents. It also automatically generates documents for patent applications.

[0471] Providing results

[0472] The server stores the patent approval / denial results and proposals received from the generation AI in a database and provides them to the user, who then proceeds with the patent application process based on the presented results.

[0473] Specific operation example

[0474] The user inputs the "materials for the new battery."

[0475] The server stores the information in a database and passes it on to the generating AI.

[0476] The generative AI analyzes the input information and compares it with a patent database.

[0477] If there is a high probability of a patent, documents are generated for filing.

[0478] The server stores the results in a database and provides them to the user.

[0479] The user checks the results and proceeds with the patent application process.

[0480] As described above, the system of the present invention appropriately processes detailed information on technologies and projects under development and provides efficient support for patent applications.

[0481] The processing flow will be explained below.

[0482] Step 1:

[0483] The server establishes a database connection and initializes the submissions and results tables, creating them if they do not exist.

[0484] Step 2:

[0485] Users input details of the technology or project they are developing into the system, for example, entering details about a new battery material.

[0486] Step 3:

[0487] The server inserts the information received from the user into the submissions table in the database, giving the inserted row a unique ID.

[0488] Step 4:

[0489] The server prepares the generative AI model, specifically by loading the natural language processing model and preparing for information analysis.

[0490] Step 5:

[0491] The server retrieves the submitted information from the database, fetching details corresponding to the specific ID from the submissions table.

[0492] Step 6:

[0493] The generation AI analyzes the acquired information and compares it with a patent database, specifically checking whether the input information overlaps with existing patents.

[0494] Step 7:

[0495] The AI ​​generates patent approval and rejection results and proposals. If there is a high possibility of patent approval, it also automatically generates documents for patent application.

[0496] Step 8:

[0497] The server stores the generated patent approval results and proposals in a results table in the database.

[0498] Step 9:

[0499] The server retrieves the patent approval / denial results and proposals from the database and provides them to the user.

[0500] Step 10:

[0501] The user checks the provided results and proceeds with the modification of the development process or patent application procedures. Based on the results, the next action is decided.

[0502] Example 1

[0503] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0504] The conventional patent application process lacks an integrated support system that allows users to efficiently manage detailed information about technologies and projects under development and quickly determine whether or not a patent should be granted. This requires users to spend a lot of time and effort manually, which can potentially lower the success rate of patent applications. There is also a need for a system that can consistently determine whether or not a patent should be granted and prepare documents for patent applications.

[0505] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0506] In this invention, the server includes means for establishing a database connection and initializing necessary tables, means for receiving detailed information about technologies and projects under development and storing it in the database, means for determining whether or not to grant a patent for the received information using a generation AI, means for saving the results of the patent approval / denial decision and proposals in the database, and means for providing the saved results to the user. This allows the user to efficiently manage detailed information about technologies and projects under development, and enables quick decisions on whether or not to grant a patent and automatic generation of documents for patent applications.

[0507] The "means for establishing a database connection and initializing the necessary tables" refers to a series of operations in which the server connects to the database system and creates the table structure required in the initial state.

[0508] "Means for receiving detailed information about technologies and projects under development and storing it in a database" refers to the process for receiving detailed technical information entered by users into the system and storing that information in a database.

[0509] "Means for determining whether or not to grant patent permission for received information using generating AI" refers to a series of processes in which the generating AI analyzes the technical information received and compares it with existing patent documents to determine the possibility of granting a patent.

[0510] "Means for storing the results of whether or not to grant a patent and proposals in a database" refers to the operation for recording the results of whether or not to grant a patent determined by the generating AI and proposals based on that result in a database.

[0511] "Means for providing stored results to a user" refers to an interface or method for displaying and providing the patent approval / disapproval results and proposals stored in the database to a user.

[0512] "Means of checking against a patent database to see if it matches existing patents" refers to the process by which the generating AI compares technical information with a patent database to see if the information matches existing patents.

[0513] "Means for automatically generating documents for patent applications" refers to the function that enables the generation AI to automatically create documents necessary for patent applications based on the results of whether or not a patent is granted.

[0514] The system of the present invention is composed of the following elements: a server, a user, and a generation AI. The specific roles and operations of each element are explained below.

[0515] Server Operation

[0516] The server first connects to a database system (e.g., MySQL) and creates the necessary tables as an initialization process. Here, two tables, projects and patent_results, are created, each with columns for storing technology information and patent approval / disapproval results. The server uses a backend framework such as Java or Node.js to provide a REST API for receiving technology and project details entered by the user. The received information is stored in the projects table in the database.

[0517] The server then passes the information to the Generator AI using a Python API. The Generator AI analyzes the received technical information and compares it with existing patent databases (e.g., Google Patents or the USPTO database) to determine whether or not to grant a patent. The results and related proposals are returned to the server in JSON format. The server stores these results in the patent_results table.

[0518] Finally, the server provides the saved patent approval / denial results to the user, which are displayed via email notification and a web dashboard.

[0519] User Actions

[0520] Users enter detailed information about their technology or project into the system via a web form, for example, by entering "detailed information about new battery materials," and then clicking the submit button. The information entered by the user is then stored in a database via the server.

[0521] The user then checks the results of the patent approval and proposals provided by the server. This allows them to review the development process and proceed with the patent application process. Based on the results, the user can create a specific action plan for filing a patent application.

[0522] Generative AI behavior

[0523] The generation AI analyzes the technical information received from the user and compares it with a patent database to determine whether or not to grant a patent. It uses NLP (natural language processing) technology and machine learning models to check whether the information matches existing patents. Based on the results, it automatically generates a proposal for whether or not to grant a patent and documents for patent applications.

[0524] A concrete example of patent matching performed by a generative AI might be the following prompt:

[0525] "This technical description contains detailed information about new battery materials. Based on this information, search existing patent databases to see if there are any matching patents."

[0526] The generation AI receives this prompt, compares it with the patent database, and returns the results to the server in JSON format.

[0527] Specific examples

[0528] Data Entry

[0529] Users input "detailed information about new battery materials" into the system. This information includes details necessary for patent applications, such as the chemical composition of the material, manufacturing process, and use cases. The server stores the received information in a database.

[0530] Judgment on whether or not to grant patent permission

[0531] The server passes the saved information to the generation AI and asks it to determine whether or not to grant a patent. The generation AI analyzes the input information and compares it with a patent database to check for overlaps with existing patents. It also automatically generates documents for patent applications.

[0532] Providing results

[0533] The server stores the patent approval / denial results and proposals received from the generation AI in a database and provides them to the user, who then proceeds with the patent application process based on the presented results.

[0534] In this way, the system of the present invention efficiently manages detailed information on technologies and projects under development and provides prompt support for patent applications.

[0535] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0536] Step 1: Establishing and Initializing a Database Connection

[0537] The server connects to a database system (e.g., MySQL) and initializes the necessary tables, specifically creating two tables called projects and patent_results, thus establishing a structure for storing technical information and patent results.

[0538] Input: Database connection information, table structure definition

[0539] Output: initialized database

[0540] Step 2: User input

[0541] Users enter details of the technology or project they are developing into a web form, for example, "Details about a new battery material," and click the submit button.

[0542] Input: Technology and project details (e.g., chemical composition, manufacturing process, use cases)

[0543] Output: Information submitted via the web form

[0544] Step 3: Save the input information to the database

[0545] The server stores the information received from the user in the projects table in the database, confirms the save was successful, and checks for errors.

[0546] Input: User-submitted technical information

[0547] Output: Information stored in the projects table in the database

[0548] Step 4: Sending information to the generating AI

[0549] The server retrieves the input information from the database and requests the generation AI to determine whether or not to grant a patent. It sends a request to the generation AI using a Python API.

[0550] Input: Technical information retrieved from the projects table

[0551] Output: The request sent to the generation AI

[0552] Step 5: Patent approval decision and proposal generation

[0553] The generative AI analyzes the information it receives, compares it with a patent database, and uses NLP technology to determine whether a patent is valid and generates proposals.

[0554] Input: Technical information sent from the server

[0555] Output: Patent approval / denial result and proposal (JSON format)

[0556] Step 6: Save the results to a database

[0557] The server analyzes the results received from the generation AI and saves them in the patent_results table. It checks whether the data is saved correctly in the required fields.

[0558] Input: Patent approval / denial results and proposals returned by the generation AI

[0559] Output: Contents stored in the database table patent_results

[0560] Step 7: Providing results to users

[0561] The server provides the saved patent approval results to the user, displaying them using a dashboard and notification system.

[0562] Input: Patent approval / denial results stored in the patent_results table

[0563] Output: Patent approval / denial results provided to the user (dashboard and email notification)

[0564] As described above, each processing step includes specific operations, and the entire system forms a flow that efficiently supports the user's patent application process.

[0565] (Application example 1)

[0566] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0567] Until now, there have only been limited systems that can efficiently process detailed information on technologies and projects under development for patent acquisition, and the processes for determining whether a patent application should be filed and automatically generating application documents were complex and time-consuming. There was also a lack of a way for users to quickly check the results of patent decisions. This resulted in low efficiency in the overall patent acquisition process and placed a heavy burden on engineers.

[0568] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0569] In this invention, the server includes a means for receiving detailed information about technologies and projects under development and storing it in a database, a means for determining whether or not to grant a patent based on the received information using a generation AI, a means for saving the results of the patent application and proposals in a database, a means for inputting technical information and checking the results of patent decisions as an application installed on a smartphone, and a means for automatically generating documents related to patent applications. This makes it possible to improve the efficiency of the patent acquisition process, which has traditionally been a challenge.

[0570] "Detailed information about technologies or projects under development" means specific information about new or ongoing technological ideas or projects, including the purpose, structure, operation, and data related to the technology.

[0571] "Means for storing in a database" refers to a method or device for storing specific information in a database system so that it can be retrieved and used later.

[0572] "Generative AI" refers to artificial intelligence systems that use machine learning and deep learning to analyze data and perform generative tasks for specific purposes.

[0573] "Means for determining whether or not to grant a patent" refers to a method or device that analyzes received technical information and determines whether the content overlaps with existing patents and whether or not a patent can be obtained.

[0574] "Means for storing the results of patent approval denial and proposals in a database" refers to a method or device for recording the results of the patent approval denial and proposals output by the generating AI in a database.

[0575] "Means for providing to the user" refers to a method or device for presenting the information stored in the database and the determination results in a manner that allows the user to easily access them.

[0576] An "application installed on a smartphone" refers to a software program that is installed and runs on a mobile information terminal, and in the case of this invention, has the function of assisting in patent decisions.

[0577] "Means for inputting technical information and checking patent decision results" refers to a method or device in which a user inputs technical details, the generating AI determines whether or not to grant a patent based on that information, and the result is displayed on the user's terminal.

[0578] "Means for automatically generating documents related to patent applications" refers to methods or devices in which generation AI automatically creates documents necessary for patent applications.

[0579] This invention relates to a system that supports the development of new robotics technologies and streamlines patent acquisition. This system consists of a server, a user terminal (such as a smartphone or PC), and a generative AI model. The specific roles and operations of each element are explained below.

[0580] The server provides a means to receive detailed information about technologies and projects under development and store it in a database. Technical information entered by users (for example, detailed information about a new robot arm) is saved in the database via the server. The database also includes a patent database and user information.

[0581] The server then uses the generative AI model to provide a means of determining whether or not to grant a patent for the received technical information. The generative AI model uses machine learning and deep learning to analyze the input technical information and compare it with a patent database to see if it matches any existing patents. The generative AI outputs the patent grant / denial results and recommendations, and the data is then stored back in the database.

[0582] Furthermore, this system includes a means for inputting technical information as an application installed on a smartphone and checking the results of patent decisions. Using this application, users can input technical information under development (for example, the manufacturing process and materials used for a robot arm). The input information is sent to the generation AI via a server, and the result of whether or not a patent is granted is returned to the user.

[0583] The server also provides a means for automatically generating documents for patent applications. The AI ​​automatically creates the documents necessary for patent applications based on the results of the patent approval / denial decision. These automatically generated documents are provided to users in a format that is easily accessible.

[0584] Specifically, when a user enters "technical information for a new robot arm" using an application installed on their smartphone, the information is sent to a server. The server stores this information in a database and sends it to the generation AI. The generation AI compares the information with a patent database and determines whether or not the application is patentable. The results are stored in the database, and the user can check the patent decision results again from the application. Furthermore, the generation AI automatically generates documents for the patent application, which are also provided to the user.

[0585] Examples of prompts to input to a generative AI model include:

[0586] "Details on the new movement control system for the robotic arm.

[0587] Material: Aluminum, Carbon Fiber

[0588] Manufacturing process: Laser cut and 3D printed

[0589] Purpose of use: High-precision transport work within factories

[0590] Instructions to the generative AI model:

[0591] 1. Analyze the input information and match it with the patent database.

[0592] 2. Determine whether the application is patentable and return the results.

[0593] 3. Generate the documents for your patent application.

[0594] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0595] Step 1:

[0596] Users enter detailed information about the technology or project they are developing using their smartphone.

[0597] Specific behavior:

[0598] The user launches an application installed on their smartphone and inputs new technology information (e.g., details of a new robot arm), including the purpose of the technology, materials, manufacturing process, and use cases.

[0599] Input: Technical information (e.g., robot arm materials, manufacturing process, etc.)

[0600] Output: User input data

[0601] Step 2:

[0602] The server receives the technical information sent by the user and stores it in a database.

[0603] Specific behavior:

[0604] The smartphone app sends user-entered data to the server, which then establishes a database connection to store the received data in a database and saves the technical information in the appropriate tables.

[0605] Input: User-entered data

[0606] Output: Technical information stored in a database

[0607] Step 3:

[0608] The server passes the stored information to the generation AI and asks it to decide whether or not to grant a patent.

[0609] Specific behavior:

[0610] The server retrieves technical information from the database and generates a prompt to send to the AI. The prompt contains details of the technology and an instruction to the AI ​​to determine whether or not to grant a patent.

[0611] Input: Technical information stored in the database

[0612] Output: prompt statement

[0613] Step 4:

[0614] The generating AI analyzes the input technical information and compares it with a patent database to determine whether or not it is patentable.

[0615] Specific behavior:

[0616] The generation AI receives the prompt, analyzes the technical information, and compares it with a patent database. The patent database stores existing patent information, and compares it with the analysis results to check for overlaps. It determines whether the patent is valid and returns the result to the server.

[0617] Input: prompt statement

[0618] Output: Patent permission / denial result

[0619] Step 5:

[0620] The server stores the results received from the generation AI in a database and provides them to the user.

[0621] Specific behavior:

[0622] The server receives the patent approval / denial results from the generation AI and stores them in a database, which is then returned to the application so that the user can access and check the results.

[0623] Input: Patent approval / denial result

[0624] Output: Patent approval / denial results stored in the database, patent decision results that can be checked by the user

[0625] Step 6:

[0626] The generation AI automatically generates documents for patent applications based on the results of whether or not a patent is granted.

[0627] Specific behavior:

[0628] If the patent application is approved, the AI ​​automatically generates the documents necessary for the patent application (e.g., patent specification). The generated documents are sent to a server and stored in a database.

[0629] Input: Patent approval / denial result

[0630] Output: Auto-generated patent application document

[0631] Step 7:

[0632] The server provides the automatically generated patent application document to the user.

[0633] Specific behavior:

[0634] The server provides the automatically generated document to the user through the application, from which the user can download the document and proceed with the patent application process.

[0635] Input: Auto-generated patent application document

[0636] Output: User downloadable patent application document

[0637] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0638] The system of the present invention is composed of the following elements: a server, a user, a generation AI, and an emotion engine. The specific roles and operations of each element are explained below.

[0639] Server Roles and Operations

[0640] server:

[0641] Establishes a database connection and initializes the necessary tables.

[0642] Provides an interface for receiving detailed information about technologies and projects under development.

[0643] The received information is stored in a database.

[0644] The generative AI and emotion engine work together to perform the necessary data analysis and processing.

[0645] The results received from the generative AI and the analysis results from the emotion engine are stored in a database and provided to the user.

[0646] User Roles and Actions

[0647] User:

[0648] Enter detailed information about the technology or project under development into the system.

[0649] Emotional data is also provided through the system interface.

[0650] Check the provided patent approval / rejection results and proposals, as well as the emotion engine analysis results.

[0651] Based on the results presented, the development process and patent application procedures will be carried out.

[0652] Role and behavior of generative AI

[0653] Generation AI:

[0654] The detailed information entered by the user is analyzed and compared with the patent database.

[0655] Determine whether a patent is possible and generate the results and proposals.

[0656] Automatically generate documents for patent applications.

[0657] Role and operation of the emotion engine

[0658] Emotion Engine:

[0659] Emotional data from users is collected and analyzed when they input information.

[0660] Based on the analysis results, the generative AI provides information for adjusting the patent approval / denial results and proposals.

[0661] Use emotional data to adjust the tone and approach of your interactions with users.

[0662] Specific examples

[0663] Data Entry

[0664] The user inputs emotional data into the system along with "detailed information about the materials of the new battery." The emotional data includes information for analyzing emotions from text and the emotional state selected by the user. The server stores this information in a database.

[0665] Judgment on whether or not to grant patent permission

[0666] The server passes the saved information to the generation AI and asks it to determine whether or not to grant the patent. At the same time, it passes the user's emotional data to the emotion engine and obtains the analysis results. The generation AI analyzes the input information and compares it with a patent database to check for overlaps with existing patents. It also automatically generates documents for patent applications.

[0667] Providing results

[0668] The server stores the patent approval / denial results and proposals received from the generative AI, as well as the analysis results from the emotion engine, in a database and provides them to the user. The user then proceeds with the patent application process based on the presented results. At this time, the way the results are presented is adjusted based on the results of the emotion engine, providing information in the most optimal form for the user.

[0669] Specific operation example

[0670] The user inputs "materials for a new battery" and emotional data at that time.

[0671] The server stores the information in a database and transmits it to the generative AI and emotion engine.

[0672] The generation AI analyzes the input information, compares it with a patent database, and automatically generates documents for patent applications.

[0673] The emotion engine analyzes the emotional data and adjusts the output of the generative AI based on the results.

[0674] The server stores the results in a database and provides them to the user.

[0675] The user checks the results and proceeds with the patent application process.

[0676] As described above, the system of the present invention appropriately processes detailed information about technologies and projects under development and provides efficient and emotional support for patent applications.

[0677] The processing flow will be explained below.

[0678] Step 1:

[0679] The server establishes a database connection and initializes the submissions, results, and emotions tables, creating them if they do not exist.

[0680] Step 2:

[0681] Users enter detailed information about the technology or project they are developing into the system. Specifically, they enter "detailed information about new battery materials." They also select the emotion they felt when entering the information.

[0682] Step 3:

[0683] The server inserts the details and emotion data received from the user into the submissions and emotions tables in the database, and assigns a unique ID to the inserted row.

[0684] Step 4:

[0685] The server prepares the generative AI model, specifically by loading the natural language processing model and preparing for information analysis.

[0686] Step 5:

[0687] The server retrieves the submitted details and emotion data from the database. It fetches the details and emotion data corresponding to the specific ID from the submissions and emotions tables.

[0688] Step 6:

[0689] The generating AI analyzes the acquired information and compares it with a patent database, specifically checking whether the input information overlaps with existing patents.

[0690] Step 7:

[0691] The AI ​​generates patent approval and rejection results and proposals. If there is a high possibility of patent approval, it also automatically generates documents for patent application.

[0692] Step 8:

[0693] The server calls the emotion engine to analyze the user's emotion data. The emotion engine analyzes the emotion information entered by the user and outputs the results.

[0694] Step 9:

[0695] The emotion engine passes the results of the emotion analysis to the generative AI, which then adjusts the tone and approach of the patent approval / rejection results and recommendations. For example, if the user is emotionally unstable, the results will be presented in more friendly language.

[0696] Step 10:

[0697] The server stores the patent approval / denial results and emotion analysis results received from the generation AI and emotion engine in the results table of the database.

[0698] Step 11:

[0699] The server retrieves the results and suggestions for patent approval from the database and provides them to the user. In particular, it presents them in the most suitable form for the user based on the results of the emotion engine.

[0700] Step 12:

[0701] The user checks the provided results and proceeds with the modification of the development process or patent application procedures. Based on the results, the next action is decided.

[0702] Example 2

[0703] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0704] In conventional patent application systems, the process of efficiently analyzing technical information under development and determining whether or not a patent should be granted was cumbersome. Furthermore, because feedback that took into account the user's emotional data was not provided, the process was often mentally burdensome for applicants. This resulted in a decrease in the efficiency of patent applications and a poor user experience.

[0705] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving information about technologies and projects under development and storing the data, means for evaluating the patentability of the received information using a generative AI model, means for storing the results of the patentability decision and proposals, means for collecting and analyzing emotion data, and means for adjusting the output result of the generative AI model based on the analyzed emotion data. This enables an integrated analysis of technical information and emotion data, enabling efficient and user-friendly support for patent applications.

[0706] "Information about technology and projects" refers to data about technical details and project content currently under development or research.

[0707] "Data storage means" means hardware and software components for securely and efficiently storing received information.

[0708] A "generative AI model" is an artificial intelligence algorithm that uses machine learning to analyze and determine whether or not to grant a patent.

[0709] The "means for assessing whether or not a patent is permitted" is a system that uses a generative AI model to determine the likelihood that the received technical information will be granted a patent.

[0710] The "patent approval / denial result" refers to the judgment result on whether the technical information analyzed by the generative AI model will be granted a patent.

[0711] "Proposal" is information that suggests improvements or additional actions regarding technical information based on the result of the patent approval or denial.

[0712] "Emotion data" refers to the emotional state and psychological information input by the user.

[0713] The "means for collecting and analyzing emotional data" is a system for acquiring emotional information from a user and analyzing it.

[0714] The "means for adjusting the output results of the generative AI model" is a system that adjusts the output and proposal content of the generative AI model based on the results of analyzing emotional data.

[0715] The system of the present invention is composed of the following elements: a server, a user, a generative AI model, and an emotion engine. The specific roles and operations of each element are explained below.

[0716] Server Roles and Operations

[0717] The server receives information about technologies and projects under development and stores it in a database. This database uses a relational database management system such as MySQL or PostgreSQL. The server passes the stored information to a generative AI model, which evaluates whether or not to grant a patent. It also works with an emotion engine to collect and analyze emotion data. The analysis results are stored in the database until they are provided to the user.

[0718] User Roles and Actions

[0719] Users enter detailed information about the technology or project they are developing through the system's interface, which can be provided via a web browser or a dedicated application. Along with the technical information, users also enter emotional data, conveying their emotional state to the system. This data is used for patent evaluation and user feedback.

[0720] Example prompt sentence:

[0721] More information on the new battery material: "This material is a special alloy that extends the life of lithium-ion batteries."

[0722] Emotional data: "Users are currently feeling both excitement and anxiety."

[0723] Roles and behavior of generative AI models

[0724] The generative AI model analyzes technical information entered by the user and compares it with patent databases, such as Google Patents and the USPTO database. The generative AI model determines whether a patent should be granted and automatically generates documents for the patent application based on the results.

[0725] Role and operation of the emotion engine

[0726] The emotion engine collects and analyzes the emotional data entered by the user. The emotion engine evaluates the user's emotional state and adjusts the output of the generative AI model based on the results. This allows information to be provided to the user in the most optimal way.

[0727] Specific examples

[0728] Data Entry

[0729] The user inputs "detailed information about the materials of the new battery" along with emotional data at the time of input. The emotional data includes emotions analyzed from the user's text and the emotional state selected by the user. The server stores this information in a database.

[0730] Judgment on whether or not to grant patent permission

[0731] The server passes the stored information to the generative AI model and requests it to evaluate whether or not to grant a patent. At the same time, it passes the emotional data to the emotion engine and obtains the analysis results. The generative AI model analyzes the technical information and compares it with a patent database to check for any matches with existing patents. It also automatically generates documents for patent applications.

[0732] Providing results

[0733] The server stores the patent approval / denial results and proposals received from the generative AI model, as well as the analysis results from the emotion engine, in a database and provides them to the user. The user then proceeds with the patent application process based on the presented results. At this time, the method of presenting the results is adjusted based on the results of the emotion engine, so that information is provided in the most optimal form for the user.

[0734] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0735] Step 1:

[0736] The user inputs technical information and emotional data. Through a web browser or a dedicated application interface, the user inputs detailed information about the technology or project under development, along with emotional data. The input data includes, for example, technical information such as "a special alloy that extends the life of next-generation lithium-ion batteries," and emotional states such as "expectations and anxiety." The server provides an API endpoint for receiving this data.

[0737] ·input:

[0738] Technical information: "Special alloy extends the life of next-generation lithium-ion batteries"

[0739] Emotional data: "Expectations and Anxieties"

[0740] Specific actions:

[0741] Users enter emotion data into a form along with details of the project under development.

[0742] Step 2:

[0743] The server saves the received information in a database. The server receives the technical information and emotion data from the user and stores it in a database. The database format used is, for example, MySQL or PostgreSQL. Data consistency checks and format conversions are also performed as necessary.

[0744] ·input:

[0745] Technical information and sentiment data from users

[0746] ·output:

[0747] Information stored in the database

[0748] Specific actions:

[0749] The server's API receives the information and stores it in the database using an INSERT statement.

[0750] Step 3:

[0751] The server transfers the stored information to the generative AI model and emotion engine. The server retrieves the stored technical information and emotion data from the database and passes them to the generative AI model and emotion engine, respectively. During this process, information is sent using a RESTful API.

[0752] ·input:

[0753] Information stored in the database

[0754] ·output:

[0755] Data sent to generative AI models and emotion engines

[0756] Specific actions:

[0757] The server generates API requests and sends data to the generative AI model and emotion engine.

[0758] Step 4:

[0759] The generative AI model evaluates whether or not to grant a patent. The generative AI model analyzes the technical information sent from the server and compares it with patent databases. Patent databases include Google Patents and the USPTO database, for example. The generative AI model determines whether or not to grant a patent and returns the result to the server.

[0760] ·input:

[0761] Technical information sent from the server

[0762] ·output:

[0763] Evaluation results for patent approval

[0764] Specific actions:

[0765] The generative AI model analyzes the incoming data using natural language processing (NLP) techniques and compares it with patent databases.

[0766] Step 5:

[0767] The emotion engine analyzes the emotion data. The emotion engine analyzes the emotion data sent from the server and evaluates the user's emotional state. The results of this analysis are used to adjust the output of the generative AI model.

[0768] ·input:

[0769] Emotion data sent from the server

[0770] ·output:

[0771] Emotion analysis results

[0772] Specific actions:

[0773] The emotion engine analyzes the incoming data using natural language processing (NLP) techniques to assess the emotional state.

[0774] Step 6:

[0775] The results of the generative AI model and the emotion engine are integrated. The server integrates the patent approval evaluation results from the generative AI model and the emotion analysis results from the emotion engine, and compiles them into a single data package.

[0776] ·input:

[0777] Evaluation results of patent permission from generative AI model

[0778] Emotion analysis results from the emotion engine

[0779] ·output:

[0780] Integrated Data Package

[0781] Specific actions:

[0782] The server combines the two results in JSON format and stores them in the database.

[0783] Step 7:

[0784] The server provides the results to the user. The server generates data to display the consolidated results in a user interface and provides it to the user, for example, through a web dashboard or a mobile application.

[0785] ·input:

[0786] Integrated Data Package

[0787] ·output:

[0788] Result information provided to the user

[0789] Specific actions:

[0790] The server retrieves the results from the database and displays them on the user's dashboard via an API.

[0791] The above is the specific program processing in the system of the present invention. This system makes it possible to efficiently and comprehensively analyze technical information and emotion data, and to smoothly advance the patent application process.

[0792] (Application example 2)

[0793] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0794] New technologies and projects are frequently developed in modern factories and manufacturing sites. However, determining whether a technology can be patented is a time-consuming process that requires specialized knowledge and is difficult to perform efficiently. Furthermore, providing information without considering the emotional state of the developer can cause stress, which can delay the patent application process. Therefore, there is a need for an efficient and reliable patent approval / denial system that takes the developer's emotions into account.

[0795] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving detailed information about technologies and projects under development and storing it in a database, means for using a generation AI to determine whether or not to grant a patent for the received information, means for saving the results of the patent approval decision and proposals in a database, means for collecting and analyzing emotional data, means for adjusting the output of the generation AI based on the results of the emotion analysis, and means for explaining patent information to developers in an optimal manner. This makes it possible to improve the efficiency and reliability of patent approval decisions while taking into account the emotional state of developers.

[0796] "Developers" are engineers who develop new technologies and projects in the manufacturing field.

[0797] "Detailed information" refers to the specific content, specifications, and examples of new technologies and projects.

[0798] A "database" is a collection of data that organizes and stores information so that it can be searched and used as needed.

[0799] "Generative AI" refers to an artificial intelligence system that analyzes received information and determines whether or not it is patentable.

[0800] "Patent approval or disapproval" refers to the decision on whether a certain technology is eligible for a patent.

[0801] "Suggestions" refer to advice on improvements and next steps provided based on the results of whether or not a patent is granted.

[0802] "Emotion data" is data that represents the emotional state of the developer, and includes, for example, stress and satisfaction.

[0803] An "emotion engine" is a system that analyzes emotional data and outputs the results.

[0804] "Adjustment" refers to optimizing the output content and presentation method of the generative AI based on the results of emotion analysis.

[0805] "Explanation" refers to the act of clearly communicating the results of patent approval or rejection and proposals to developers.

[0806] The system of the present invention is composed of the following elements: a server, a factory robot, a generative AI, and an emotion engine. The specific roles and operations of each element are explained below.

[0807] Server Roles and Operations

[0808] The server establishes a database connection and prepares to initialize project information and emotion data. It stores the detailed information and emotion data of the new technology received from the developer in the database and provides them to the generation AI and emotion engine. It also stores the patent approval decision results from the generation AI and the analysis results from the emotion engine in the database and provides them to the developer.

[0809] Roles and operations of factory robots

[0810] The factory robot receives detailed information about new technologies from developers through voice or manual input, while simultaneously analyzing the developer's emotional data (e.g., stress and satisfaction) in real time using a Kinect sensor and microphone.

[0811] Role and behavior of generative AI

[0812] The generation AI analyzes the received technical information and checks it against a patent database to confirm whether it matches existing patents. It determines whether the technology is patentable and automatically generates documents for patent applications based on that determination. Example prompt: "Based on detailed information about the new battery material, please determine whether this technology is patentable."

[0813] Role and operation of the emotion engine

[0814] The emotion engine analyzes the developer's emotional data and adjusts the output of the generative AI based on the results. The emotion analysis results are used to optimize the way patent information is presented, providing developers with information in a format that is easy to understand and less stressful.

[0815] Specific examples

[0816] A developer inputs information about a "new battery material" into a factory robot. The robot sends the information to a server, simultaneously collecting the developer's emotional data. The server stores the received information in a database and requests analysis from the generation AI and emotion engine. The generation AI analyzes the input information, compares it with a patent database, and automatically generates documents for the patent application. The emotion engine analyzes the emotional data and adjusts the output of the generation AI based on the results. The server provides the developer with the analysis results and the result of the patent approval or denial. This process makes the patent application process more efficient and reduces stress for developers.

[0817] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0818] Step 1:

[0819] The user (the developer who operates the factory robot) inputs detailed information about the new technology into the terminal by voice or manual input. The input information includes the specific content, specifications, and examples of the new technology. At the same time, the user's emotional data (e.g., facial expressions and tone of voice) is collected in real time using the Kinect sensor and microphone.

[0820] Input: Details of new technology, user emotional data

[0821] Output: Data received by factory robots

[0822] Step 2:

[0823] The terminal (factory robot) transmits the received technical information and emotion data to the server, at which point the collected data is stored in the server's database.

[0824] Input: Technical information and emotional data from factory robots

[0825] Output: Sending data to the server

[0826] Step 3:

[0827] The server initializes the received information into a database and provides it to the generative AI and emotion engine, establishing a database connection and properly organizing and storing the received data.

[0828] Input: Received technical and emotional data

[0829] Output: Store in a database

[0830] Step 4:

[0831] The generation AI analyzes the stored technical information, compares it with a patent database, and checks for matches with existing patents. The generation AI determines whether a patent is possible and automatically generates documents for patent applications.

[0832] Input: Technical Information

[0833] Output: Patent approval / denial decision result and patent document

[0834] Step 5:

[0835] The emotion engine analyzes the stored emotional data and adjusts the output of the generative AI based on the results. For example, if the user is feeling stressed, it will adjust the tone and detail of the presented approval / disapproval results.

[0836] Input: Emotion data

[0837] Output: Sentiment analysis results and adjustments to generated AI output

[0838] Step 6:

[0839] The server stores the patent approval / denial decisions and proposals from the generation AI, as well as the analysis results from the emotion engine, in a database and provides them to users. The presentation method is optimized based on the emotion analysis results.

[0840] Input: Generative AI results, emotion engine results

[0841] Output: Providing results to the user

[0842] Step 7:

[0843] Users can proceed with the patent application process based on the provided patent approval / denial results, suggestions, and emotional feedback, allowing them to efficiently evaluate the patentability of their technology and take the necessary steps quickly.

[0844] Input: Approval / rejection results, suggestions, emotional feedback

[0845] Output: Patent application process

[0846] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0847] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0848] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0849] [Third embodiment]

[0850] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0851] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0852] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0853] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0854] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0855] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0856] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0857] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0858] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0859] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0860] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0861] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0862] The system of the present invention is composed of the following elements: a server, a user, and a generation AI. The specific roles and operations of each element are explained below.

[0863] Server Roles and Operations

[0864] server:

[0865] Establishes a database connection and initializes the necessary tables.

[0866] Provides an interface for receiving detailed information about technologies and projects under development.

[0867] The received information is stored in a database.

[0868] In cooperation with the generation AI, we request a decision on whether or not to grant patent permission.

[0869] The results received from the generation AI are stored in a database and provided to the user.

[0870] User Roles and Actions

[0871] User:

[0872] Enter detailed information about the technology or project under development into the system.

[0873] Check the results and proposals provided by the server.

[0874] Based on the results presented, the development process and patent application procedures will be carried out.

[0875] Role and behavior of generative AI

[0876] Generation AI:

[0877] The detailed information entered by the user is analyzed and compared with the patent database.

[0878] Determine whether a patent is possible and generate the results and proposals.

[0879] Automatically generate documents for patent applications.

[0880] Specific examples

[0881] Data Entry

[0882] Users input "detailed information about new battery materials" into the system. This information includes details necessary for patent applications, such as the chemical composition of the material, manufacturing process, and use cases. The server stores the received information in a database.

[0883] Judgment on whether or not to grant patent permission

[0884] The server passes the saved information to the generation AI and asks it to determine whether or not to grant a patent. The generation AI analyzes the input information and compares it with a patent database to check for overlaps with existing patents. It also automatically generates documents for patent applications.

[0885] Providing results

[0886] The server stores the patent approval / denial results and proposals received from the generation AI in a database and provides them to the user, who then proceeds with the patent application process based on the presented results.

[0887] Specific operation example

[0888] The user inputs the "materials for the new battery."

[0889] The server stores the information in a database and passes it on to the generating AI.

[0890] The generative AI analyzes the input information and compares it with a patent database.

[0891] If there is a high probability of a patent, documents are generated for filing.

[0892] The server stores the results in a database and provides them to the user.

[0893] The user checks the results and proceeds with the patent application process.

[0894] As described above, the system of the present invention appropriately processes detailed information on technologies and projects under development and provides efficient support for patent applications.

[0895] The processing flow will be explained below.

[0896] Step 1:

[0897] The server establishes a database connection and initializes the submissions and results tables, creating them if they do not exist.

[0898] Step 2:

[0899] Users input details of the technology or project they are developing into the system, for example, entering details about a new battery material.

[0900] Step 3:

[0901] The server inserts the information received from the user into the submissions table in the database, giving the inserted row a unique ID.

[0902] Step 4:

[0903] The server prepares the generative AI model, specifically by loading the natural language processing model and preparing for information analysis.

[0904] Step 5:

[0905] The server retrieves the submitted information from the database, fetching details corresponding to the specific ID from the submissions table.

[0906] Step 6:

[0907] The generation AI analyzes the acquired information and compares it with a patent database, specifically checking whether the input information overlaps with existing patents.

[0908] Step 7:

[0909] The AI ​​generates patent approval and rejection results and proposals. If there is a high possibility of patent approval, it also automatically generates documents for patent application.

[0910] Step 8:

[0911] The server stores the generated patent approval results and proposals in a results table in the database.

[0912] Step 9:

[0913] The server retrieves the patent approval / denial results and proposals from the database and provides them to the user.

[0914] Step 10:

[0915] The user checks the provided results and proceeds with the modification of the development process or patent application procedures. Based on the results, the next action is decided.

[0916] Example 1

[0917] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0918] The conventional patent application process lacks an integrated support system that allows users to efficiently manage detailed information about technologies and projects under development and quickly determine whether or not a patent should be granted. This requires users to spend a lot of time and effort manually, which can potentially lower the success rate of patent applications. There is also a need for a system that can consistently determine whether or not a patent should be granted and prepare documents for patent applications.

[0919] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0920] In this invention, the server includes means for establishing a database connection and initializing necessary tables, means for receiving detailed information about technologies and projects under development and storing it in the database, means for determining whether or not to grant a patent for the received information using a generation AI, means for saving the results of the patent approval / denial decision and proposals in the database, and means for providing the saved results to the user. This allows the user to efficiently manage detailed information about technologies and projects under development, and enables quick decisions on whether or not to grant a patent and automatic generation of documents for patent applications.

[0921] The "means for establishing a database connection and initializing the necessary tables" refers to a series of operations in which the server connects to the database system and creates the table structure required in the initial state.

[0922] "Means for receiving detailed information about technologies and projects under development and storing it in a database" refers to the process for receiving detailed technical information entered by users into the system and storing that information in a database.

[0923] "Means for determining whether or not to grant patent permission for received information using generating AI" refers to a series of processes in which the generating AI analyzes the technical information received and compares it with existing patent documents to determine the possibility of granting a patent.

[0924] "Means for storing the results of whether or not to grant a patent and proposals in a database" refers to the operation for recording the results of whether or not to grant a patent determined by the generating AI and proposals based on that result in a database.

[0925] "Means for providing stored results to a user" refers to an interface or method for displaying and providing the patent approval / disapproval results and proposals stored in the database to a user.

[0926] "Means of checking against a patent database to see if it matches existing patents" refers to the process by which the generating AI compares technical information with a patent database to see if the information matches existing patents.

[0927] "Means for automatically generating documents for patent applications" refers to the function that enables the generation AI to automatically create documents necessary for patent applications based on the results of whether or not a patent is granted.

[0928] The system of the present invention is composed of the following elements: a server, a user, and a generation AI. The specific roles and operations of each element are explained below.

[0929] Server Operation

[0930] The server first connects to a database system (e.g., MySQL) and creates the necessary tables as an initialization process. Here, two tables, projects and patent_results, are created, each with columns for storing technology information and patent approval / disapproval results. The server uses a backend framework such as Java or Node.js to provide a REST API for receiving technology and project details entered by the user. The received information is stored in the projects table in the database.

[0931] The server then passes the information to the Generator AI using a Python API. The Generator AI analyzes the received technical information and compares it with existing patent databases (e.g., Google Patents or the USPTO database) to determine whether or not to grant a patent. The results and related proposals are returned to the server in JSON format. The server stores these results in the patent_results table.

[0932] Finally, the server provides the saved patent approval / denial results to the user, which are displayed via email notification and a web dashboard.

[0933] User Actions

[0934] Users enter detailed information about their technology or project into the system via a web form, for example, by entering "detailed information about new battery materials," and then clicking the submit button. The information entered by the user is then stored in a database via the server.

[0935] The user then checks the results of the patent approval and proposals provided by the server. This allows them to review the development process and proceed with the patent application process. Based on the results, the user can create a specific action plan for filing a patent application.

[0936] Generative AI behavior

[0937] The generation AI analyzes the technical information received from the user and compares it with a patent database to determine whether or not to grant a patent. It uses NLP (natural language processing) technology and machine learning models to check whether the information matches existing patents. Based on the results, it automatically generates a proposal for whether or not to grant a patent and documents for patent applications.

[0938] A concrete example of patent matching performed by a generative AI might be the following prompt:

[0939] "This technical description contains detailed information about new battery materials. Based on this information, search existing patent databases to see if there are any matching patents."

[0940] The generation AI receives this prompt, compares it with the patent database, and returns the results to the server in JSON format.

[0941] Specific examples

[0942] Data Entry

[0943] Users input "detailed information about new battery materials" into the system. This information includes details necessary for patent applications, such as the chemical composition of the material, manufacturing process, and use cases. The server stores the received information in a database.

[0944] Judgment on whether or not to grant patent permission

[0945] The server passes the saved information to the generation AI and asks it to determine whether or not to grant a patent. The generation AI analyzes the input information and compares it with a patent database to check for overlaps with existing patents. It also automatically generates documents for patent applications.

[0946] Providing results

[0947] The server stores the patent approval / denial results and proposals received from the generation AI in a database and provides them to the user, who then proceeds with the patent application process based on the presented results.

[0948] In this way, the system of the present invention efficiently manages detailed information on technologies and projects under development and provides prompt support for patent applications.

[0949] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0950] Step 1: Establishing and Initializing a Database Connection

[0951] The server connects to a database system (e.g., MySQL) and initializes the necessary tables, specifically creating two tables called projects and patent_results, thus establishing a structure for storing technical information and patent results.

[0952] Input: Database connection information, table structure definition

[0953] Output: initialized database

[0954] Step 2: User input

[0955] Users enter details of the technology or project they are developing into a web form, for example, "Details about a new battery material," and click the submit button.

[0956] Input: Technology and project details (e.g., chemical composition, manufacturing process, use cases)

[0957] Output: Information submitted via the web form

[0958] Step 3: Save the input information to the database

[0959] The server stores the information received from the user in the projects table in the database, confirms the save was successful, and checks for errors.

[0960] Input: User-submitted technical information

[0961] Output: Information stored in the projects table in the database

[0962] Step 4: Sending information to the generating AI

[0963] The server retrieves the input information from the database and requests the generation AI to determine whether or not to grant a patent. It sends a request to the generation AI using a Python API.

[0964] Input: Technical information retrieved from the projects table

[0965] Output: The request sent to the generation AI

[0966] Step 5: Patent approval decision and proposal generation

[0967] The generative AI analyzes the information it receives, compares it with a patent database, and uses NLP technology to determine whether a patent is valid and generates proposals.

[0968] Input: Technical information sent from the server

[0969] Output: Patent approval / denial result and proposal (JSON format)

[0970] Step 6: Save the results to a database

[0971] The server analyzes the results received from the generation AI and saves them in the patent_results table. It checks whether the data is saved correctly in the required fields.

[0972] Input: Patent approval / denial results and proposals returned by the generation AI

[0973] Output: Contents stored in the database table patent_results

[0974] Step 7: Providing results to users

[0975] The server provides the saved patent approval results to the user, displaying them using a dashboard and notification system.

[0976] Input: Patent approval / denial results stored in the patent_results table

[0977] Output: Patent approval / denial results provided to the user (dashboard and email notification)

[0978] As described above, each processing step includes specific operations, and the entire system forms a flow that efficiently supports the user's patent application process.

[0979] (Application example 1)

[0980] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0981] Until now, there have only been limited systems that can efficiently process detailed information on technologies and projects under development for patent acquisition, and the processes for determining whether a patent application should be filed and automatically generating application documents were complex and time-consuming. There was also a lack of a way for users to quickly check the results of patent decisions. This resulted in low efficiency in the overall patent acquisition process and placed a heavy burden on engineers.

[0982] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0983] In this invention, the server includes a means for receiving detailed information about technologies and projects under development and storing it in a database, a means for determining whether or not to grant a patent based on the received information using a generation AI, a means for saving the results of the patent application and proposals in a database, a means for inputting technical information and checking the results of patent decisions as an application installed on a smartphone, and a means for automatically generating documents related to patent applications. This makes it possible to improve the efficiency of the patent acquisition process, which has traditionally been a challenge.

[0984] "Detailed information about technologies or projects under development" means specific information about new or ongoing technological ideas or projects, including the purpose, structure, operation, and data related to the technology.

[0985] "Means for storing in a database" refers to a method or device for storing specific information in a database system so that it can be retrieved and used later.

[0986] "Generative AI" refers to artificial intelligence systems that use machine learning and deep learning to analyze data and perform generative tasks for specific purposes.

[0987] "Means for determining whether or not to grant a patent" refers to a method or device that analyzes received technical information and determines whether the content overlaps with existing patents and whether or not a patent can be obtained.

[0988] "Means for storing the results of patent approval denial and proposals in a database" refers to a method or device for recording the results of the patent approval denial and proposals output by the generating AI in a database.

[0989] "Means for providing to the user" refers to a method or device for presenting the information stored in the database and the determination results in a manner that allows the user to easily access them.

[0990] An "application installed on a smartphone" refers to a software program that is installed and runs on a mobile information terminal, and in the case of this invention, has the function of assisting in patent decisions.

[0991] "Means for inputting technical information and checking patent decision results" refers to a method or device in which a user inputs technical details, the generating AI determines whether or not to grant a patent based on that information, and the result is displayed on the user's terminal.

[0992] "Means for automatically generating documents related to patent applications" refers to methods or devices in which generation AI automatically creates documents necessary for patent applications.

[0993] This invention relates to a system that supports the development of new robotics technologies and streamlines patent acquisition. This system consists of a server, a user terminal (such as a smartphone or PC), and a generative AI model. The specific roles and operations of each element are explained below.

[0994] The server provides a means to receive detailed information about technologies and projects under development and store it in a database. Technical information entered by users (for example, detailed information about a new robot arm) is saved in the database via the server. The database also includes a patent database and user information.

[0995] The server then uses the generative AI model to provide a means of determining whether or not to grant a patent for the received technical information. The generative AI model uses machine learning and deep learning to analyze the input technical information and compare it with a patent database to see if it matches any existing patents. The generative AI outputs the patent grant / denial results and recommendations, and the data is then stored back in the database.

[0996] Furthermore, this system includes a means for inputting technical information as an application installed on a smartphone and checking the results of patent decisions. Using this application, users can input technical information under development (for example, the manufacturing process and materials used for a robot arm). The input information is sent to the generation AI via a server, and the result of whether or not a patent is granted is returned to the user.

[0997] The server also provides a means for automatically generating documents for patent applications. The AI ​​automatically creates the documents necessary for patent applications based on the results of the patent approval / denial decision. These automatically generated documents are provided to users in a format that is easily accessible.

[0998] Specifically, when a user enters "technical information for a new robot arm" using an application installed on their smartphone, the information is sent to a server. The server stores this information in a database and sends it to the generation AI. The generation AI compares the information with a patent database and determines whether or not the application is patentable. The results are stored in the database, and the user can check the patent decision results again from the application. Furthermore, the generation AI automatically generates documents for the patent application, which are also provided to the user.

[0999] Examples of prompts to input to a generative AI model include:

[1000] "Details on the new movement control system for the robotic arm.

[1001] Material: Aluminum, Carbon Fiber

[1002] Manufacturing process: Laser cut and 3D printed

[1003] Purpose of use: High-precision transport work within factories

[1004] Instructions to the generative AI model:

[1005] 1. Analyze the input information and match it with the patent database.

[1006] 2. Determine whether the application is patentable and return the results.

[1007] 3. Generate the documents for your patent application.

[1008] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1009] Step 1:

[1010] Users enter detailed information about the technology or project they are developing using their smartphone.

[1011] Specific behavior:

[1012] The user launches an application installed on their smartphone and inputs new technology information (e.g., details of a new robot arm), including the purpose of the technology, materials, manufacturing process, and use cases.

[1013] Input: Technical information (e.g., robot arm materials, manufacturing process, etc.)

[1014] Output: User input data

[1015] Step 2:

[1016] The server receives the technical information sent by the user and stores it in a database.

[1017] Specific behavior:

[1018] The smartphone app sends user-entered data to the server, which then establishes a database connection to store the received data in a database and saves the technical information in the appropriate tables.

[1019] Input: User-entered data

[1020] Output: Technical information stored in a database

[1021] Step 3:

[1022] The server passes the stored information to the generation AI and asks it to decide whether or not to grant a patent.

[1023] Specific behavior:

[1024] The server retrieves technical information from the database and generates a prompt to send to the AI. The prompt contains details of the technology and an instruction to the AI ​​to determine whether or not to grant a patent.

[1025] Input: Technical information stored in the database

[1026] Output: prompt statement

[1027] Step 4:

[1028] The generating AI analyzes the input technical information and compares it with a patent database to determine whether or not it is patentable.

[1029] Specific behavior:

[1030] The generation AI receives the prompt, analyzes the technical information, and compares it with a patent database. The patent database stores existing patent information, and compares it with the analysis results to check for overlaps. It determines whether the patent is valid and returns the result to the server.

[1031] Input: prompt statement

[1032] Output: Patent permission / denial result

[1033] Step 5:

[1034] The server stores the results received from the generation AI in a database and provides them to the user.

[1035] Specific behavior:

[1036] The server receives the patent approval / denial results from the generation AI and stores them in a database, which is then returned to the application so that the user can access and check the results.

[1037] Input: Patent approval / denial result

[1038] Output: Patent approval / denial results stored in the database, patent decision results that can be checked by the user

[1039] Step 6:

[1040] The generation AI automatically generates documents for patent applications based on the results of whether or not a patent is granted.

[1041] Specific behavior:

[1042] If the patent application is approved, the AI ​​automatically generates the documents necessary for the patent application (e.g., patent specification). The generated documents are sent to a server and stored in a database.

[1043] Input: Patent approval / denial result

[1044] Output: Auto-generated patent application document

[1045] Step 7:

[1046] The server provides the automatically generated patent application document to the user.

[1047] Specific behavior:

[1048] The server provides the automatically generated document to the user through the application, from which the user can download the document and proceed with the patent application process.

[1049] Input: Auto-generated patent application document

[1050] Output: User downloadable patent application document

[1051] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1052] The system of the present invention is composed of the following elements: a server, a user, a generation AI, and an emotion engine. The specific roles and operations of each element are explained below.

[1053] Server Roles and Operations

[1054] server:

[1055] Establishes a database connection and initializes the necessary tables.

[1056] Provides an interface for receiving detailed information about technologies and projects under development.

[1057] The received information is stored in a database.

[1058] The generative AI and emotion engine work together to perform the necessary data analysis and processing.

[1059] The results received from the generative AI and the analysis results from the emotion engine are stored in a database and provided to the user.

[1060] User Roles and Actions

[1061] User:

[1062] Enter detailed information about the technology or project under development into the system.

[1063] Emotional data is also provided through the system interface.

[1064] Check the provided patent approval / rejection results and proposals, as well as the emotion engine analysis results.

[1065] Based on the results presented, the development process and patent application procedures will be carried out.

[1066] Role and behavior of generative AI

[1067] Generation AI:

[1068] The detailed information entered by the user is analyzed and compared with the patent database.

[1069] Determine whether a patent is possible and generate the results and proposals.

[1070] Automatically generate documents for patent applications.

[1071] Role and operation of the emotion engine

[1072] Emotion Engine:

[1073] Emotional data from users is collected and analyzed when they input information.

[1074] Based on the analysis results, the generative AI provides information for adjusting the patent approval / denial results and proposals.

[1075] Use emotional data to adjust the tone and approach of your interactions with users.

[1076] Specific examples

[1077] Data Entry

[1078] The user inputs emotional data into the system along with "detailed information about the materials of the new battery." The emotional data includes information for analyzing emotions from text and the emotional state selected by the user. The server stores this information in a database.

[1079] Judgment on whether or not to grant patent permission

[1080] The server passes the saved information to the generation AI and asks it to determine whether or not to grant the patent. At the same time, it passes the user's emotional data to the emotion engine and obtains the analysis results. The generation AI analyzes the input information and compares it with a patent database to check for overlaps with existing patents. It also automatically generates documents for patent applications.

[1081] Providing results

[1082] The server stores the patent approval / denial results and proposals received from the generative AI, as well as the analysis results from the emotion engine, in a database and provides them to the user. The user then proceeds with the patent application process based on the presented results. At this time, the way the results are presented is adjusted based on the results of the emotion engine, providing information in the most optimal form for the user.

[1083] Specific operation example

[1084] The user inputs "materials for a new battery" and emotional data at that time.

[1085] The server stores the information in a database and transmits it to the generative AI and emotion engine.

[1086] The generation AI analyzes the input information, compares it with a patent database, and automatically generates documents for patent applications.

[1087] The emotion engine analyzes the emotional data and adjusts the output of the generative AI based on the results.

[1088] The server stores the results in a database and provides them to the user.

[1089] The user checks the results and proceeds with the patent application process.

[1090] As described above, the system of the present invention appropriately processes detailed information about technologies and projects under development and provides efficient and emotional support for patent applications.

[1091] The processing flow will be explained below.

[1092] Step 1:

[1093] The server establishes a database connection and initializes the submissions, results, and emotions tables, creating them if they do not exist.

[1094] Step 2:

[1095] Users enter detailed information about the technology or project they are developing into the system. Specifically, they enter "detailed information about new battery materials." They also select the emotion they felt when entering the information.

[1096] Step 3:

[1097] The server inserts the details and emotion data received from the user into the submissions and emotions tables in the database, and assigns a unique ID to the inserted row.

[1098] Step 4:

[1099] The server prepares the generative AI model, specifically by loading the natural language processing model and preparing for information analysis.

[1100] Step 5:

[1101] The server retrieves the submitted details and emotion data from the database. It fetches the details and emotion data corresponding to the specific ID from the submissions and emotions tables.

[1102] Step 6:

[1103] The generating AI analyzes the acquired information and compares it with a patent database, specifically checking whether the input information overlaps with existing patents.

[1104] Step 7:

[1105] The AI ​​generates patent approval and rejection results and proposals. If there is a high possibility of patent approval, it also automatically generates documents for patent application.

[1106] Step 8:

[1107] The server calls the emotion engine to analyze the user's emotion data. The emotion engine analyzes the emotion information entered by the user and outputs the results.

[1108] Step 9:

[1109] The emotion engine passes the results of the emotion analysis to the generative AI, which then adjusts the tone and approach of the patent approval / rejection results and recommendations. For example, if the user is emotionally unstable, the results will be presented in more friendly language.

[1110] Step 10:

[1111] The server stores the patent approval / denial results and emotion analysis results received from the generation AI and emotion engine in the results table of the database.

[1112] Step 11:

[1113] The server retrieves the results and suggestions for patent approval from the database and provides them to the user. In particular, it presents them in the most suitable form for the user based on the results of the emotion engine.

[1114] Step 12:

[1115] The user checks the provided results and proceeds with the modification of the development process or patent application procedures. Based on the results, the next action is decided.

[1116] Example 2

[1117] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1118] In conventional patent application systems, the process of efficiently analyzing technical information under development and determining whether or not a patent should be granted was cumbersome. Furthermore, because feedback that took into account the user's emotional data was not provided, the process was often mentally burdensome for applicants. This resulted in a decrease in the efficiency of patent applications and a poor user experience.

[1119] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving information about technologies and projects under development and storing the data, means for evaluating the patentability of the received information using a generative AI model, means for storing the results of the patentability decision and proposals, means for collecting and analyzing emotion data, and means for adjusting the output result of the generative AI model based on the analyzed emotion data. This enables an integrated analysis of technical information and emotion data, enabling efficient and user-friendly support for patent applications.

[1120] "Information about technology and projects" refers to data about technical details and project content currently under development or research.

[1121] "Data storage means" means hardware and software components for securely and efficiently storing received information.

[1122] A "generative AI model" is an artificial intelligence algorithm that uses machine learning to analyze and determine whether or not to grant a patent.

[1123] The "means for assessing whether or not a patent is permitted" is a system that uses a generative AI model to determine the likelihood that the received technical information will be granted a patent.

[1124] The "patent approval / denial result" refers to the judgment result on whether the technical information analyzed by the generative AI model will be granted a patent.

[1125] "Proposal" is information that suggests improvements or additional actions regarding technical information based on the result of the patent approval or denial.

[1126] "Emotion data" refers to the emotional state and psychological information input by the user.

[1127] The "means for collecting and analyzing emotional data" is a system for acquiring emotional information from a user and analyzing it.

[1128] The "means for adjusting the output results of the generative AI model" is a system that adjusts the output and proposal content of the generative AI model based on the results of analyzing emotional data.

[1129] The system of the present invention is composed of the following elements: a server, a user, a generative AI model, and an emotion engine. The specific roles and operations of each element are explained below.

[1130] Server Roles and Operations

[1131] The server receives information about technologies and projects under development and stores it in a database. This database uses a relational database management system such as MySQL or PostgreSQL. The server passes the stored information to a generative AI model, which evaluates whether or not to grant a patent. It also works with an emotion engine to collect and analyze emotion data. The analysis results are stored in the database until they are provided to the user.

[1132] User Roles and Actions

[1133] Users enter detailed information about the technology or project they are developing through the system's interface, which can be provided via a web browser or a dedicated application. Along with the technical information, users also enter emotional data, conveying their emotional state to the system. This data is used for patent evaluation and user feedback.

[1134] Example prompt sentence:

[1135] More information on the new battery material: "This material is a special alloy that extends the life of lithium-ion batteries."

[1136] Emotional data: "Users are currently feeling both excitement and anxiety."

[1137] Roles and behavior of generative AI models

[1138] The generative AI model analyzes technical information entered by the user and compares it with patent databases, such as Google Patents and the USPTO database. The generative AI model determines whether a patent should be granted and automatically generates documents for the patent application based on the results.

[1139] Role and operation of the emotion engine

[1140] The emotion engine collects and analyzes the emotional data entered by the user. The emotion engine evaluates the user's emotional state and adjusts the output of the generative AI model based on the results. This allows information to be provided to the user in the most optimal way.

[1141] Specific examples

[1142] Data Entry

[1143] The user inputs "detailed information about the materials of the new battery" along with emotional data at the time of input. The emotional data includes emotions analyzed from the user's text and the emotional state selected by the user. The server stores this information in a database.

[1144] Judgment on whether or not to grant patent permission

[1145] The server passes the stored information to the generative AI model and requests it to evaluate whether or not to grant a patent. At the same time, it passes the emotional data to the emotion engine and obtains the analysis results. The generative AI model analyzes the technical information and compares it with a patent database to check for any matches with existing patents. It also automatically generates documents for patent applications.

[1146] Providing results

[1147] The server stores the patent approval / denial results and proposals received from the generative AI model, as well as the analysis results from the emotion engine, in a database and provides them to the user. The user then proceeds with the patent application process based on the presented results. At this time, the method of presenting the results is adjusted based on the results of the emotion engine, so that information is provided in the most optimal form for the user.

[1148] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1149] Step 1:

[1150] The user inputs technical information and emotional data. Through a web browser or a dedicated application interface, the user inputs detailed information about the technology or project under development, along with emotional data. The input data includes, for example, technical information such as "a special alloy that extends the life of next-generation lithium-ion batteries," and emotional states such as "expectations and anxiety." The server provides an API endpoint for receiving this data.

[1151] ·input:

[1152] Technical information: "Special alloy extends the life of next-generation lithium-ion batteries"

[1153] Emotional data: "Expectations and Anxieties"

[1154] Specific actions:

[1155] Users enter emotion data into a form along with details of the project under development.

[1156] Step 2:

[1157] The server saves the received information in a database. The server receives the technical information and emotion data from the user and stores it in a database. The database format used is, for example, MySQL or PostgreSQL. Data consistency checks and format conversions are also performed as necessary.

[1158] ·input:

[1159] Technical information and sentiment data from users

[1160] ·output:

[1161] Information stored in the database

[1162] Specific actions:

[1163] The server's API receives the information and stores it in the database using an INSERT statement.

[1164] Step 3:

[1165] The server transfers the stored information to the generative AI model and emotion engine. The server retrieves the stored technical information and emotion data from the database and passes them to the generative AI model and emotion engine, respectively. During this process, information is sent using a RESTful API.

[1166] ·input:

[1167] Information stored in the database

[1168] ·output:

[1169] Data sent to generative AI models and emotion engines

[1170] Specific actions:

[1171] The server generates API requests and sends data to the generative AI model and emotion engine.

[1172] Step 4:

[1173] The generative AI model evaluates whether or not to grant a patent. The generative AI model analyzes the technical information sent from the server and compares it with patent databases. Patent databases include Google Patents and the USPTO database, for example. The generative AI model determines whether or not to grant a patent and returns the result to the server.

[1174] ·input:

[1175] Technical information sent from the server

[1176] ·output:

[1177] Evaluation results for patent approval

[1178] Specific actions:

[1179] The generative AI model analyzes the incoming data using natural language processing (NLP) techniques and compares it with patent databases.

[1180] Step 5:

[1181] The emotion engine analyzes the emotion data. The emotion engine analyzes the emotion data sent from the server and evaluates the user's emotional state. The results of this analysis are used to adjust the output of the generative AI model.

[1182] ·input:

[1183] Emotion data sent from the server

[1184] ·output:

[1185] Emotion analysis results

[1186] Specific actions:

[1187] The emotion engine analyzes the incoming data using natural language processing (NLP) techniques to assess the emotional state.

[1188] Step 6:

[1189] The results of the generative AI model and the emotion engine are integrated. The server integrates the patent approval evaluation results from the generative AI model and the emotion analysis results from the emotion engine, and compiles them into a single data package.

[1190] ·input:

[1191] Evaluation results of patent permission from generative AI model

[1192] Emotion analysis results from the emotion engine

[1193] ·output:

[1194] Integrated Data Package

[1195] Specific actions:

[1196] The server combines the two results in JSON format and stores them in the database.

[1197] Step 7:

[1198] The server provides the results to the user. The server generates data to display the consolidated results in a user interface and provides it to the user, for example, through a web dashboard or a mobile application.

[1199] ·input:

[1200] Integrated Data Package

[1201] ·output:

[1202] Result information provided to the user

[1203] Specific actions:

[1204] The server retrieves the results from the database and displays them on the user's dashboard via an API.

[1205] The above is the specific program processing in the system of the present invention. This system makes it possible to efficiently and comprehensively analyze technical information and emotion data, and to smoothly advance the patent application process.

[1206] (Application example 2)

[1207] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1208] New technologies and projects are frequently developed in modern factories and manufacturing sites. However, determining whether a technology can be patented is a time-consuming process that requires specialized knowledge and is difficult to perform efficiently. Furthermore, providing information without considering the emotional state of the developer can cause stress, which can delay the patent application process. Therefore, there is a need for an efficient and reliable patent approval / denial system that takes the developer's emotions into account.

[1209] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving detailed information about technologies and projects under development and storing it in a database, means for using a generation AI to determine whether or not to grant a patent for the received information, means for saving the results of the patent approval decision and proposals in a database, means for collecting and analyzing emotional data, means for adjusting the output of the generation AI based on the results of the emotion analysis, and means for explaining patent information to developers in an optimal manner. This makes it possible to improve the efficiency and reliability of patent approval decisions while taking into account the emotional state of developers.

[1210] "Developers" are engineers who develop new technologies and projects in the manufacturing field.

[1211] "Detailed information" refers to the specific content, specifications, and examples of new technologies and projects.

[1212] A "database" is a collection of data that organizes and stores information so that it can be searched and used as needed.

[1213] "Generative AI" refers to an artificial intelligence system that analyzes received information and determines whether or not it is patentable.

[1214] "Patent approval or disapproval" refers to the decision on whether a certain technology is eligible for a patent.

[1215] "Suggestions" refer to advice on improvements and next steps provided based on the results of whether or not a patent is granted.

[1216] "Emotion data" is data that represents the emotional state of the developer, and includes, for example, stress and satisfaction.

[1217] An "emotion engine" is a system that analyzes emotional data and outputs the results.

[1218] "Adjustment" refers to optimizing the output content and presentation method of the generative AI based on the results of emotion analysis.

[1219] "Explanation" refers to the act of clearly communicating the results of patent approval or rejection and proposals to developers.

[1220] The system of the present invention is composed of the following elements: a server, a factory robot, a generative AI, and an emotion engine. The specific roles and operations of each element are explained below.

[1221] Server Roles and Operations

[1222] The server establishes a database connection and prepares to initialize project information and emotion data. It stores the detailed information and emotion data of the new technology received from the developer in the database and provides them to the generation AI and emotion engine. It also stores the patent approval decision results from the generation AI and the analysis results from the emotion engine in the database and provides them to the developer.

[1223] Roles and operations of factory robots

[1224] The factory robot receives detailed information about new technologies from developers through voice or manual input, while simultaneously analyzing the developer's emotional data (e.g., stress and satisfaction) in real time using a Kinect sensor and microphone.

[1225] Role and behavior of generative AI

[1226] The generation AI analyzes the received technical information and checks it against a patent database to confirm whether it matches existing patents. It determines whether the technology is patentable and automatically generates documents for patent applications based on that determination. Example prompt: "Based on detailed information about the new battery material, please determine whether this technology is patentable."

[1227] Role and operation of the emotion engine

[1228] The emotion engine analyzes the developer's emotional data and adjusts the output of the generative AI based on the results. The emotion analysis results are used to optimize the way patent information is presented, providing developers with information in a format that is easy to understand and less stressful.

[1229] Specific examples

[1230] A developer inputs information about a "new battery material" into a factory robot. The robot sends the information to a server, simultaneously collecting the developer's emotional data. The server stores the received information in a database and requests analysis from the generation AI and emotion engine. The generation AI analyzes the input information, compares it with a patent database, and automatically generates documents for the patent application. The emotion engine analyzes the emotional data and adjusts the output of the generation AI based on the results. The server provides the developer with the analysis results and the result of the patent approval or denial. This process makes the patent application process more efficient and reduces stress for developers.

[1231] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1232] Step 1:

[1233] The user (the developer who operates the factory robot) inputs detailed information about the new technology into the terminal by voice or manual input. The input information includes the specific content, specifications, and examples of the new technology. At the same time, the user's emotional data (e.g., facial expressions and tone of voice) is collected in real time using the Kinect sensor and microphone.

[1234] Input: Details of new technology, user emotional data

[1235] Output: Data received by factory robots

[1236] Step 2:

[1237] The terminal (factory robot) transmits the received technical information and emotion data to the server, at which point the collected data is stored in the server's database.

[1238] Input: Technical information and emotional data from factory robots

[1239] Output: Sending data to the server

[1240] Step 3:

[1241] The server initializes the received information into a database and provides it to the generative AI and emotion engine, establishing a database connection and properly organizing and storing the received data.

[1242] Input: Received technical and emotional data

[1243] Output: Store in a database

[1244] Step 4:

[1245] The generation AI analyzes the stored technical information, compares it with a patent database, and checks for matches with existing patents. The generation AI determines whether a patent is possible and automatically generates documents for patent applications.

[1246] Input: Technical Information

[1247] Output: Patent approval / denial decision result and patent document

[1248] Step 5:

[1249] The emotion engine analyzes the stored emotional data and adjusts the output of the generative AI based on the results. For example, if the user is feeling stressed, it will adjust the tone and detail of the presented approval / disapproval results.

[1250] Input: Emotion data

[1251] Output: Sentiment analysis results and adjustments to generated AI output

[1252] Step 6:

[1253] The server stores the patent approval / denial decisions and proposals from the generation AI, as well as the analysis results from the emotion engine, in a database and provides them to users. The presentation method is optimized based on the emotion analysis results.

[1254] Input: Generative AI results, emotion engine results

[1255] Output: Providing results to the user

[1256] Step 7:

[1257] Users can proceed with the patent application process based on the provided patent approval / denial results, suggestions, and emotional feedback, allowing them to efficiently evaluate the patentability of their technology and take the necessary steps quickly.

[1258] Input: Approval / rejection results, suggestions, emotional feedback

[1259] Output: Patent application process

[1260] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1261] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1262] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1263] [Fourth embodiment]

[1264] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1265] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1266] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1267] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1268] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1269] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1270] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1271] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1272] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1273] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1274] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1275] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1276] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1277] The system of the present invention is composed of the following elements: a server, a user, and a generation AI. The specific roles and operations of each element are explained below.

[1278] Server Roles and Operations

[1279] server:

[1280] Establishes a database connection and initializes the necessary tables.

[1281] Provides an interface for receiving detailed information about technologies and projects under development.

[1282] The received information is stored in a database.

[1283] In cooperation with the generation AI, we request a decision on whether or not to grant patent permission.

[1284] The results received from the generation AI are stored in a database and provided to the user.

[1285] User Roles and Actions

[1286] User:

[1287] Enter detailed information about the technology or project under development into the system.

[1288] Check the results and proposals provided by the server.

[1289] Based on the results presented, the development process and patent application procedures will be carried out.

[1290] Role and behavior of generative AI

[1291] Generation AI:

[1292] The detailed information entered by the user is analyzed and compared with the patent database.

[1293] Determine whether a patent is possible and generate the results and proposals.

[1294] Automatically generate documents for patent applications.

[1295] Specific examples

[1296] Data Entry

[1297] Users input "detailed information about new battery materials" into the system. This information includes details necessary for patent applications, such as the chemical composition of the material, manufacturing process, and use cases. The server stores the received information in a database.

[1298] Judgment on whether or not to grant patent permission

[1299] The server passes the saved information to the generation AI and asks it to determine whether or not to grant a patent. The generation AI analyzes the input information and compares it with a patent database to check for overlaps with existing patents. It also automatically generates documents for patent applications.

[1300] Providing results

[1301] The server stores the patent approval / denial results and proposals received from the generation AI in a database and provides them to the user, who then proceeds with the patent application process based on the presented results.

[1302] Specific operation example

[1303] The user inputs the "materials for the new battery."

[1304] The server stores the information in a database and passes it on to the generating AI.

[1305] The generative AI analyzes the input information and compares it with a patent database.

[1306] If there is a high probability of a patent, documents are generated for filing.

[1307] The server stores the results in a database and provides them to the user.

[1308] The user checks the results and proceeds with the patent application process.

[1309] As described above, the system of the present invention appropriately processes detailed information on technologies and projects under development and provides efficient support for patent applications.

[1310] The processing flow will be explained below.

[1311] Step 1:

[1312] The server establishes a database connection and initializes the submissions and results tables, creating them if they do not exist.

[1313] Step 2:

[1314] Users input details of the technology or project they are developing into the system, for example, entering details about a new battery material.

[1315] Step 3:

[1316] The server inserts the information received from the user into the submissions table in the database, giving the inserted row a unique ID.

[1317] Step 4:

[1318] The server prepares the generative AI model, specifically by loading the natural language processing model and preparing for information analysis.

[1319] Step 5:

[1320] The server retrieves the submitted information from the database, fetching details corresponding to the specific ID from the submissions table.

[1321] Step 6:

[1322] The generation AI analyzes the acquired information and compares it with a patent database, specifically checking whether the input information overlaps with existing patents.

[1323] Step 7:

[1324] The AI ​​generates patent approval and rejection results and proposals. If there is a high possibility of patent approval, it also automatically generates documents for patent application.

[1325] Step 8:

[1326] The server stores the generated patent approval results and proposals in a results table in the database.

[1327] Step 9:

[1328] The server retrieves the patent approval / denial results and proposals from the database and provides them to the user.

[1329] Step 10:

[1330] The user checks the provided results and proceeds with the modification of the development process or patent application procedures. Based on the results, the next action is decided.

[1331] Example 1

[1332] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1333] The conventional patent application process lacks an integrated support system that allows users to efficiently manage detailed information about technologies and projects under development and quickly determine whether or not a patent should be granted. This requires users to spend a lot of time and effort manually, which can potentially lower the success rate of patent applications. There is also a need for a system that can consistently determine whether or not a patent should be granted and prepare documents for patent applications.

[1334] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1335] In this invention, the server includes means for establishing a database connection and initializing necessary tables, means for receiving detailed information about technologies and projects under development and storing it in the database, means for determining whether or not to grant a patent for the received information using a generation AI, means for saving the results of the patent approval / denial decision and proposals in the database, and means for providing the saved results to the user. This allows the user to efficiently manage detailed information about technologies and projects under development, and enables quick decisions on whether or not to grant a patent and automatic generation of documents for patent applications.

[1336] The "means for establishing a database connection and initializing the necessary tables" refers to a series of operations in which the server connects to the database system and creates the table structure required in the initial state.

[1337] "Means for receiving detailed information about technologies and projects under development and storing it in a database" refers to the process for receiving detailed technical information entered by users into the system and storing that information in a database.

[1338] "Means for determining whether or not to grant patent permission for received information using generating AI" refers to a series of processes in which the generating AI analyzes the technical information received and compares it with existing patent documents to determine the possibility of granting a patent.

[1339] "Means for storing the results of whether or not to grant a patent and proposals in a database" refers to the operation for recording the results of whether or not to grant a patent determined by the generating AI and proposals based on that result in a database.

[1340] "Means for providing stored results to a user" refers to an interface or method for displaying and providing the patent approval / disapproval results and proposals stored in the database to a user.

[1341] "Means of checking against a patent database to see if it matches existing patents" refers to the process by which the generating AI compares technical information with a patent database to see if the information matches existing patents.

[1342] "Means for automatically generating documents for patent applications" refers to the function that enables the generation AI to automatically create documents necessary for patent applications based on the results of whether or not a patent is granted.

[1343] The system of the present invention is composed of the following elements: a server, a user, and a generation AI. The specific roles and operations of each element are explained below.

[1344] Server Operation

[1345] The server first connects to a database system (e.g., MySQL) and creates the necessary tables as an initialization process. Here, two tables, projects and patent_results, are created, each with columns for storing technology information and patent approval / disapproval results. The server uses a backend framework such as Java or Node.js to provide a REST API for receiving technology and project details entered by the user. The received information is stored in the projects table in the database.

[1346] The server then passes the information to the Generator AI using a Python API. The Generator AI analyzes the received technical information and compares it with existing patent databases (e.g., Google Patents or the USPTO database) to determine whether or not to grant a patent. The results and related proposals are returned to the server in JSON format. The server stores these results in the patent_results table.

[1347] Finally, the server provides the saved patent approval / denial results to the user, which are displayed via email notification and a web dashboard.

[1348] User Actions

[1349] Users enter detailed information about their technology or project into the system via a web form, for example, by entering "detailed information about new battery materials," and then clicking the submit button. The information entered by the user is then stored in a database via the server.

[1350] The user then checks the results of the patent approval and proposals provided by the server. This allows them to review the development process and proceed with the patent application process. Based on the results, the user can create a specific action plan for filing a patent application.

[1351] Generative AI behavior

[1352] The generation AI analyzes the technical information received from the user and compares it with a patent database to determine whether or not to grant a patent. It uses NLP (natural language processing) technology and machine learning models to check whether the information matches existing patents. Based on the results, it automatically generates a proposal for whether or not to grant a patent and documents for patent applications.

[1353] A concrete example of patent matching performed by a generative AI might be the following prompt:

[1354] "This technical description contains detailed information about new battery materials. Based on this information, search existing patent databases to see if there are any matching patents."

[1355] The generation AI receives this prompt, compares it with the patent database, and returns the results to the server in JSON format.

[1356] Specific examples

[1357] Data Entry

[1358] Users input "detailed information about new battery materials" into the system. This information includes details necessary for patent applications, such as the chemical composition of the material, manufacturing process, and use cases. The server stores the received information in a database.

[1359] Judgment on whether or not to grant patent permission

[1360] The server passes the saved information to the generation AI and asks it to determine whether or not to grant a patent. The generation AI analyzes the input information and compares it with a patent database to check for overlaps with existing patents. It also automatically generates documents for patent applications.

[1361] Providing results

[1362] The server stores the patent approval / denial results and proposals received from the generation AI in a database and provides them to the user, who then proceeds with the patent application process based on the presented results.

[1363] In this way, the system of the present invention efficiently manages detailed information on technologies and projects under development and provides prompt support for patent applications.

[1364] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1365] Step 1: Establishing and Initializing a Database Connection

[1366] The server connects to a database system (e.g., MySQL) and initializes the necessary tables, specifically creating two tables called projects and patent_results, thus establishing a structure for storing technical information and patent results.

[1367] Input: Database connection information, table structure definition

[1368] Output: initialized database

[1369] Step 2: User input

[1370] Users enter details of the technology or project they are developing into a web form, for example, "Details about a new battery material," and click the submit button.

[1371] Input: Technology and project details (e.g., chemical composition, manufacturing process, use cases)

[1372] Output: Information submitted via the web form

[1373] Step 3: Save the input information to the database

[1374] The server stores the information received from the user in the projects table in the database, confirms the save was successful, and checks for errors.

[1375] Input: User-submitted technical information

[1376] Output: Information stored in the projects table in the database

[1377] Step 4: Sending information to the generating AI

[1378] The server retrieves the input information from the database and requests the generation AI to determine whether or not to grant a patent. It sends a request to the generation AI using a Python API.

[1379] Input: Technical information retrieved from the projects table

[1380] Output: The request sent to the generation AI

[1381] Step 5: Patent approval decision and proposal generation

[1382] The generative AI analyzes the information it receives, compares it with a patent database, and uses NLP technology to determine whether a patent is valid and generates proposals.

[1383] Input: Technical information sent from the server

[1384] Output: Patent approval / denial result and proposal (JSON format)

[1385] Step 6: Save the results to a database

[1386] The server analyzes the results received from the generation AI and saves them in the patent_results table. It checks whether the data is saved correctly in the required fields.

[1387] Input: Patent approval / denial results and proposals returned by the generation AI

[1388] Output: Contents stored in the database table patent_results

[1389] Step 7: Providing results to users

[1390] The server provides the saved patent approval results to the user, displaying them using a dashboard and notification system.

[1391] Input: Patent approval / denial results stored in the patent_results table

[1392] Output: Patent approval / denial results provided to the user (dashboard and email notification)

[1393] As described above, each processing step includes specific operations, and the entire system forms a flow that efficiently supports the user's patent application process.

[1394] (Application example 1)

[1395] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1396] Until now, there have only been limited systems that can efficiently process detailed information on technologies and projects under development for patent acquisition, and the processes for determining whether a patent application should be filed and automatically generating application documents were complex and time-consuming. There was also a lack of a way for users to quickly check the results of patent decisions. This resulted in low efficiency in the overall patent acquisition process and placed a heavy burden on engineers.

[1397] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1398] In this invention, the server includes a means for receiving detailed information about technologies and projects under development and storing it in a database, a means for determining whether or not to grant a patent based on the received information using a generation AI, a means for saving the results of the patent application and proposals in a database, a means for inputting technical information and checking the results of patent decisions as an application installed on a smartphone, and a means for automatically generating documents related to patent applications. This makes it possible to improve the efficiency of the patent acquisition process, which has traditionally been a challenge.

[1399] "Detailed information about technologies or projects under development" means specific information about new or ongoing technological ideas or projects, including the purpose, structure, operation, and data related to the technology.

[1400] "Means for storing in a database" refers to a method or device for storing specific information in a database system so that it can be retrieved and used later.

[1401] "Generative AI" refers to artificial intelligence systems that use machine learning and deep learning to analyze data and perform generative tasks for specific purposes.

[1402] "Means for determining whether or not to grant a patent" refers to a method or device that analyzes received technical information and determines whether the content overlaps with existing patents and whether or not a patent can be obtained.

[1403] "Means for storing the results of patent approval denial and proposals in a database" refers to a method or device for recording the results of the patent approval denial and proposals output by the generating AI in a database.

[1404] "Means for providing to the user" refers to a method or device for presenting the information stored in the database and the determination results in a manner that allows the user to easily access them.

[1405] An "application installed on a smartphone" refers to a software program that is installed and runs on a mobile information terminal, and in the case of this invention, has the function of assisting in patent decisions.

[1406] "Means for inputting technical information and checking patent decision results" refers to a method or device in which a user inputs technical details, the generating AI determines whether or not to grant a patent based on that information, and the result is displayed on the user's terminal.

[1407] "Means for automatically generating documents related to patent applications" refers to methods or devices in which generation AI automatically creates documents necessary for patent applications.

[1408] This invention relates to a system that supports the development of new robotics technologies and streamlines patent acquisition. This system consists of a server, a user terminal (such as a smartphone or PC), and a generative AI model. The specific roles and operations of each element are explained below.

[1409] The server provides a means to receive detailed information about technologies and projects under development and store it in a database. Technical information entered by users (for example, detailed information about a new robot arm) is saved in the database via the server. The database also includes a patent database and user information.

[1410] The server then uses the generative AI model to provide a means of determining whether or not to grant a patent for the received technical information. The generative AI model uses machine learning and deep learning to analyze the input technical information and compare it with a patent database to see if it matches any existing patents. The generative AI outputs the patent grant / denial results and recommendations, and the data is then stored back in the database.

[1411] Furthermore, this system includes a means for inputting technical information as an application installed on a smartphone and checking the results of patent decisions. Using this application, users can input technical information under development (for example, the manufacturing process and materials used for a robot arm). The input information is sent to the generation AI via a server, and the result of whether or not a patent is granted is returned to the user.

[1412] The server also provides a means for automatically generating documents for patent applications. The AI ​​automatically creates the documents necessary for patent applications based on the results of the patent approval / denial decision. These automatically generated documents are provided to users in a format that is easily accessible.

[1413] Specifically, when a user enters "technical information for a new robot arm" using an application installed on their smartphone, the information is sent to a server. The server stores this information in a database and sends it to the generation AI. The generation AI compares the information with a patent database and determines whether or not the application is patentable. The results are stored in the database, and the user can check the patent decision results again from the application. Furthermore, the generation AI automatically generates documents for the patent application, which are also provided to the user.

[1414] Examples of prompts to input to a generative AI model include:

[1415] "Details on the new movement control system for the robotic arm.

[1416] Material: Aluminum, Carbon Fiber

[1417] Manufacturing process: Laser cut and 3D printed

[1418] Purpose of use: High-precision transport work within factories

[1419] Instructions to the generative AI model:

[1420] 1. Analyze the input information and match it with the patent database.

[1421] 2. Determine whether the application is patentable and return the results.

[1422] 3. Generate the documents for your patent application.

[1423] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1424] Step 1:

[1425] Users enter detailed information about the technology or project they are developing using their smartphone.

[1426] Specific behavior:

[1427] The user launches an application installed on their smartphone and inputs new technology information (e.g., details of a new robot arm), including the purpose of the technology, materials, manufacturing process, and use cases.

[1428] Input: Technical information (e.g., robot arm materials, manufacturing process, etc.)

[1429] Output: User input data

[1430] Step 2:

[1431] The server receives the technical information sent by the user and stores it in a database.

[1432] Specific behavior:

[1433] The smartphone app sends user-entered data to the server, which then establishes a database connection to store the received data in a database and saves the technical information in the appropriate tables.

[1434] Input: User-entered data

[1435] Output: Technical information stored in a database

[1436] Step 3:

[1437] The server passes the stored information to the generation AI and asks it to decide whether or not to grant a patent.

[1438] Specific behavior:

[1439] The server retrieves technical information from the database and generates a prompt to send to the AI. The prompt contains details of the technology and an instruction to the AI ​​to determine whether or not to grant a patent.

[1440] Input: Technical information stored in the database

[1441] Output: prompt statement

[1442] Step 4:

[1443] The generating AI analyzes the input technical information and compares it with a patent database to determine whether or not it is patentable.

[1444] Specific behavior:

[1445] The generation AI receives the prompt, analyzes the technical information, and compares it with a patent database. The patent database stores existing patent information, and compares it with the analysis results to check for overlaps. It determines whether the patent is valid and returns the result to the server.

[1446] Input: prompt statement

[1447] Output: Patent permission / denial result

[1448] Step 5:

[1449] The server stores the results received from the generation AI in a database and provides them to the user.

[1450] Specific behavior:

[1451] The server receives the patent approval / denial results from the generation AI and stores them in a database, which is then returned to the application so that the user can access and check the results.

[1452] Input: Patent approval / denial result

[1453] Output: Patent approval / denial results stored in the database, patent decision results that can be checked by the user

[1454] Step 6:

[1455] The generation AI automatically generates documents for patent applications based on the results of whether or not a patent is granted.

[1456] Specific behavior:

[1457] If the patent application is approved, the AI ​​automatically generates the documents necessary for the patent application (e.g., patent specification). The generated documents are sent to a server and stored in a database.

[1458] Input: Patent approval / denial result

[1459] Output: Auto-generated patent application document

[1460] Step 7:

[1461] The server provides the automatically generated patent application document to the user.

[1462] Specific behavior:

[1463] The server provides the automatically generated document to the user through the application, from which the user can download the document and proceed with the patent application process.

[1464] Input: Auto-generated patent application document

[1465] Output: User downloadable patent application document

[1466] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1467] The system of the present invention is composed of the following elements: a server, a user, a generation AI, and an emotion engine. The specific roles and operations of each element are explained below.

[1468] Server Roles and Operations

[1469] server:

[1470] Establishes a database connection and initializes the necessary tables.

[1471] Provides an interface for receiving detailed information about technologies and projects under development.

[1472] The received information is stored in a database.

[1473] The generative AI and emotion engine work together to perform the necessary data analysis and processing.

[1474] The results received from the generative AI and the analysis results from the emotion engine are stored in a database and provided to the user.

[1475] User Roles and Actions

[1476] User:

[1477] Enter detailed information about the technology or project under development into the system.

[1478] Emotional data is also provided through the system interface.

[1479] Check the provided patent approval / rejection results and proposals, as well as the emotion engine analysis results.

[1480] Based on the results presented, the development process and patent application procedures will be carried out.

[1481] Role and behavior of generative AI

[1482] Generation AI:

[1483] The detailed information entered by the user is analyzed and compared with the patent database.

[1484] Determine whether a patent is possible and generate the results and proposals.

[1485] Automatically generate documents for patent applications.

[1486] Role and operation of the emotion engine

[1487] Emotion Engine:

[1488] Emotional data from users is collected and analyzed when they input information.

[1489] Based on the analysis results, the generative AI provides information for adjusting the patent approval / denial results and proposals.

[1490] Use emotional data to adjust the tone and approach of your interactions with users.

[1491] Specific examples

[1492] Data Entry

[1493] The user inputs emotional data into the system along with "detailed information about the materials of the new battery." The emotional data includes information for analyzing emotions from text and the emotional state selected by the user. The server stores this information in a database.

[1494] Judgment on whether or not to grant patent permission

[1495] The server passes the saved information to the generation AI and asks it to determine whether or not to grant the patent. At the same time, it passes the user's emotional data to the emotion engine and obtains the analysis results. The generation AI analyzes the input information and compares it with a patent database to check for overlaps with existing patents. It also automatically generates documents for patent applications.

[1496] Providing results

[1497] The server stores the patent approval / denial results and proposals received from the generative AI, as well as the analysis results from the emotion engine, in a database and provides them to the user. The user then proceeds with the patent application process based on the presented results. At this time, the way the results are presented is adjusted based on the results of the emotion engine, providing information in the most optimal form for the user.

[1498] Specific operation example

[1499] The user inputs "materials for a new battery" and emotional data at that time.

[1500] The server stores the information in a database and transmits it to the generative AI and emotion engine.

[1501] The generation AI analyzes the input information, compares it with a patent database, and automatically generates documents for patent applications.

[1502] The emotion engine analyzes the emotional data and adjusts the output of the generative AI based on the results.

[1503] The server stores the results in a database and provides them to the user.

[1504] The user checks the results and proceeds with the patent application process.

[1505] As described above, the system of the present invention appropriately processes detailed information about technologies and projects under development and provides efficient and emotional support for patent applications.

[1506] The processing flow will be explained below.

[1507] Step 1:

[1508] The server establishes a database connection and initializes the submissions, results, and emotions tables, creating them if they do not exist.

[1509] Step 2:

[1510] Users enter detailed information about the technology or project they are developing into the system. Specifically, they enter "detailed information about new battery materials." They also select the emotion they felt when entering the information.

[1511] Step 3:

[1512] The server inserts the details and emotion data received from the user into the submissions and emotions tables in the database, and assigns a unique ID to the inserted row.

[1513] Step 4:

[1514] The server prepares the generative AI model, specifically by loading the natural language processing model and preparing for information analysis.

[1515] Step 5:

[1516] The server retrieves the submitted details and emotion data from the database. It fetches the details and emotion data corresponding to the specific ID from the submissions and emotions tables.

[1517] Step 6:

[1518] The generating AI analyzes the acquired information and compares it with a patent database, specifically checking whether the input information overlaps with existing patents.

[1519] Step 7:

[1520] The AI ​​generates patent approval and rejection results and proposals. If there is a high possibility of patent approval, it also automatically generates documents for patent application.

[1521] Step 8:

[1522] The server calls the emotion engine to analyze the user's emotion data. The emotion engine analyzes the emotion information entered by the user and outputs the results.

[1523] Step 9:

[1524] The emotion engine passes the results of the emotion analysis to the generative AI, which then adjusts the tone and approach of the patent approval / rejection results and recommendations. For example, if the user is emotionally unstable, the results will be presented in more friendly language.

[1525] Step 10:

[1526] The server stores the patent approval / denial results and emotion analysis results received from the generation AI and emotion engine in the results table of the database.

[1527] Step 11:

[1528] The server retrieves the results and suggestions for patent approval from the database and provides them to the user. In particular, it presents them in the most suitable form for the user based on the results of the emotion engine.

[1529] Step 12:

[1530] The user checks the provided results and proceeds with the modification of the development process or patent application procedures. Based on the results, the next action is decided.

[1531] Example 2

[1532] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1533] In conventional patent application systems, the process of efficiently analyzing technical information under development and determining whether or not a patent should be granted was cumbersome. Furthermore, because feedback that took into account the user's emotional data was not provided, the process was often mentally burdensome for applicants. This resulted in a decrease in the efficiency of patent applications and a poor user experience.

[1534] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving information about technologies and projects under development and storing the data, means for evaluating the patentability of the received information using a generative AI model, means for storing the results of the patentability decision and proposals, means for collecting and analyzing emotion data, and means for adjusting the output result of the generative AI model based on the analyzed emotion data. This enables an integrated analysis of technical information and emotion data, enabling efficient and user-friendly support for patent applications.

[1535] "Information about technology and projects" refers to data about technical details and project content currently under development or research.

[1536] "Data storage means" means hardware and software components for securely and efficiently storing received information.

[1537] A "generative AI model" is an artificial intelligence algorithm that uses machine learning to analyze and determine whether or not to grant a patent.

[1538] The "means for assessing whether or not a patent is permitted" is a system that uses a generative AI model to determine the likelihood that the received technical information will be granted a patent.

[1539] The "patent approval / denial result" refers to the judgment result on whether the technical information analyzed by the generative AI model will be granted a patent.

[1540] "Proposal" is information that suggests improvements or additional actions regarding technical information based on the result of the patent approval or denial.

[1541] "Emotion data" refers to the emotional state and psychological information input by the user.

[1542] The "means for collecting and analyzing emotional data" is a system for acquiring emotional information from a user and analyzing it.

[1543] The "means for adjusting the output results of the generative AI model" is a system that adjusts the output and proposal content of the generative AI model based on the results of analyzing emotional data.

[1544] The system of the present invention is composed of the following elements: a server, a user, a generative AI model, and an emotion engine. The specific roles and operations of each element are explained below.

[1545] Server Roles and Operations

[1546] The server receives information about technologies and projects under development and stores it in a database. This database uses a relational database management system such as MySQL or PostgreSQL. The server passes the stored information to a generative AI model, which evaluates whether or not to grant a patent. It also works with an emotion engine to collect and analyze emotion data. The analysis results are stored in the database until they are provided to the user.

[1547] User Roles and Actions

[1548] Users enter detailed information about the technology or project they are developing through the system's interface, which can be provided via a web browser or a dedicated application. Along with the technical information, users also enter emotional data, conveying their emotional state to the system. This data is used for patent evaluation and user feedback.

[1549] Example prompt sentence:

[1550] More information on the new battery material: "This material is a special alloy that extends the life of lithium-ion batteries."

[1551] Emotional data: "Users are currently feeling both excitement and anxiety."

[1552] Roles and behavior of generative AI models

[1553] The generative AI model analyzes technical information entered by the user and compares it with patent databases, such as Google Patents and the USPTO database. The generative AI model determines whether a patent should be granted and automatically generates documents for the patent application based on the results.

[1554] Role and operation of the emotion engine

[1555] The emotion engine collects and analyzes the emotional data entered by the user. The emotion engine evaluates the user's emotional state and adjusts the output of the generative AI model based on the results. This allows information to be provided to the user in the most optimal way.

[1556] Specific examples

[1557] Data Entry

[1558] The user inputs "detailed information about the materials of the new battery" along with emotional data at the time of input. The emotional data includes emotions analyzed from the user's text and the emotional state selected by the user. The server stores this information in a database.

[1559] Judgment on whether or not to grant patent permission

[1560] The server passes the stored information to the generative AI model and requests it to evaluate whether or not to grant a patent. At the same time, it passes the emotional data to the emotion engine and obtains the analysis results. The generative AI model analyzes the technical information and compares it with a patent database to check for any matches with existing patents. It also automatically generates documents for patent applications.

[1561] Providing results

[1562] The server stores the patent approval / denial results and proposals received from the generative AI model, as well as the analysis results from the emotion engine, in a database and provides them to the user. The user then proceeds with the patent application process based on the presented results. At this time, the method of presenting the results is adjusted based on the results of the emotion engine, so that information is provided in the most optimal form for the user.

[1563] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1564] Step 1:

[1565] The user inputs technical information and emotional data. Through a web browser or a dedicated application interface, the user inputs detailed information about the technology or project under development, along with emotional data. The input data includes, for example, technical information such as "a special alloy that extends the life of next-generation lithium-ion batteries," and emotional states such as "expectations and anxiety." The server provides an API endpoint for receiving this data.

[1566] ·input:

[1567] Technical information: "Special alloy extends the life of next-generation lithium-ion batteries"

[1568] Emotional data: "Expectations and Anxieties"

[1569] Specific actions:

[1570] Users enter emotion data into a form along with details of the project under development.

[1571] Step 2:

[1572] The server saves the received information in a database. The server receives the technical information and emotion data from the user and stores it in a database. The database format used is, for example, MySQL or PostgreSQL. Data consistency checks and format conversions are also performed as necessary.

[1573] ·input:

[1574] Technical information and sentiment data from users

[1575] ·output:

[1576] Information stored in the database

[1577] Specific actions:

[1578] The server's API receives the information and stores it in the database using an INSERT statement.

[1579] Step 3:

[1580] The server transfers the stored information to the generative AI model and emotion engine. The server retrieves the stored technical information and emotion data from the database and passes them to the generative AI model and emotion engine, respectively. During this process, information is sent using a RESTful API.

[1581] ·input:

[1582] Information stored in the database

[1583] ·output:

[1584] Data sent to generative AI models and emotion engines

[1585] Specific actions:

[1586] The server generates API requests and sends data to the generative AI model and emotion engine.

[1587] Step 4:

[1588] The generative AI model evaluates whether or not to grant a patent. The generative AI model analyzes the technical information sent from the server and compares it with patent databases. Patent databases include Google Patents and the USPTO database, for example. The generative AI model determines whether or not to grant a patent and returns the result to the server.

[1589] ·input:

[1590] Technical information sent from the server

[1591] ·output:

[1592] Evaluation results for patent approval

[1593] Specific actions:

[1594] The generative AI model analyzes the incoming data using natural language processing (NLP) techniques and compares it with patent databases.

[1595] Step 5:

[1596] The emotion engine analyzes the emotion data. The emotion engine analyzes the emotion data sent from the server and evaluates the user's emotional state. The results of this analysis are used to adjust the output of the generative AI model.

[1597] ·input:

[1598] Emotion data sent from the server

[1599] ·output:

[1600] Emotion analysis results

[1601] Specific actions:

[1602] The emotion engine analyzes the incoming data using natural language processing (NLP) techniques to assess the emotional state.

[1603] Step 6:

[1604] The results of the generative AI model and the emotion engine are integrated. The server integrates the patent approval evaluation results from the generative AI model and the emotion analysis results from the emotion engine, and compiles them into a single data package.

[1605] ·input:

[1606] Evaluation results of patent permission from generative AI model

[1607] Emotion analysis results from the emotion engine

[1608] ·output:

[1609] Integrated Data Package

[1610] Specific actions:

[1611] The server combines the two results in JSON format and stores them in the database.

[1612] Step 7:

[1613] The server provides the results to the user. The server generates data to display the consolidated results in a user interface and provides it to the user, for example, through a web dashboard or a mobile application.

[1614] ·input:

[1615] Integrated Data Package

[1616] ·output:

[1617] Result information provided to the user

[1618] Specific actions:

[1619] The server retrieves the results from the database and displays them on the user's dashboard via an API.

[1620] The above is the specific program processing in the system of the present invention. This system makes it possible to efficiently and comprehensively analyze technical information and emotion data, and to smoothly advance the patent application process.

[1621] (Application example 2)

[1622] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1623] New technologies and projects are frequently developed in modern factories and manufacturing sites. However, determining whether a technology can be patented is a time-consuming process that requires specialized knowledge and is difficult to perform efficiently. Furthermore, providing information without considering the emotional state of the developer can cause stress, which can delay the patent application process. Therefore, there is a need for an efficient and reliable patent approval / denial system that takes the developer's emotions into account.

[1624] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving detailed information about technologies and projects under development and storing it in a database, means for using a generation AI to determine whether or not to grant a patent for the received information, means for saving the results of the patent approval decision and proposals in a database, means for collecting and analyzing emotional data, means for adjusting the output of the generation AI based on the results of the emotion analysis, and means for explaining patent information to developers in an optimal manner. This makes it possible to improve the efficiency and reliability of patent approval decisions while taking into account the emotional state of developers.

[1625] "Developers" are engineers who develop new technologies and projects in the manufacturing field.

[1626] "Detailed information" refers to the specific content, specifications, and examples of new technologies and projects.

[1627] A "database" is a collection of data that organizes and stores information so that it can be searched and used as needed.

[1628] "Generative AI" refers to an artificial intelligence system that analyzes received information and determines whether or not it is patentable.

[1629] "Patent approval or disapproval" refers to the decision on whether a certain technology is eligible for a patent.

[1630] "Suggestions" refer to advice on improvements and next steps provided based on the results of whether or not a patent is granted.

[1631] "Emotion data" is data that represents the emotional state of the developer, and includes, for example, stress and satisfaction.

[1632] An "emotion engine" is a system that analyzes emotional data and outputs the results.

[1633] "Adjustment" refers to optimizing the output content and presentation method of the generative AI based on the results of emotion analysis.

[1634] "Explanation" refers to the act of clearly communicating the results of patent approval or rejection and proposals to developers.

[1635] The system of the present invention is composed of the following elements: a server, a factory robot, a generative AI, and an emotion engine. The specific roles and operations of each element are explained below.

[1636] Server Roles and Operations

[1637] The server establishes a database connection and prepares to initialize project information and emotion data. It stores the detailed information and emotion data of the new technology received from the developer in the database and provides them to the generation AI and emotion engine. It also stores the patent approval decision results from the generation AI and the analysis results from the emotion engine in the database and provides them to the developer.

[1638] Roles and operations of factory robots

[1639] The factory robot receives detailed information about new technologies from developers through voice or manual input, while simultaneously analyzing the developer's emotional data (e.g., stress and satisfaction) in real time using a Kinect sensor and microphone.

[1640] Role and behavior of generative AI

[1641] The generation AI analyzes the received technical information and checks it against a patent database to confirm whether it matches existing patents. It determines whether the technology is patentable and automatically generates documents for patent applications based on that determination. Example prompt: "Based on detailed information about the new battery material, please determine whether this technology is patentable."

[1642] Role and operation of the emotion engine

[1643] The emotion engine analyzes the developer's emotional data and adjusts the output of the generative AI based on the results. The emotion analysis results are used to optimize the way patent information is presented, providing developers with information in a format that is easy to understand and less stressful.

[1644] Specific examples

[1645] A developer inputs information about a "new battery material" into a factory robot. The robot sends the information to a server, simultaneously collecting the developer's emotional data. The server stores the received information in a database and requests analysis from the generation AI and emotion engine. The generation AI analyzes the input information, compares it with a patent database, and automatically generates documents for the patent application. The emotion engine analyzes the emotional data and adjusts the output of the generation AI based on the results. The server provides the developer with the analysis results and the result of the patent approval or denial. This process makes the patent application process more efficient and reduces stress for developers.

[1646] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1647] Step 1:

[1648] The user (the developer who operates the factory robot) inputs detailed information about the new technology into the terminal by voice or manual input. The input information includes the specific content, specifications, and examples of the new technology. At the same time, the user's emotional data (e.g., facial expressions and tone of voice) is collected in real time using the Kinect sensor and microphone.

[1649] Input: Details of new technology, user emotional data

[1650] Output: Data received by factory robots

[1651] Step 2:

[1652] The terminal (factory robot) transmits the received technical information and emotion data to the server, at which point the collected data is stored in the server's database.

[1653] Input: Technical information and emotional data from factory robots

[1654] Output: Sending data to the server

[1655] Step 3:

[1656] The server initializes the received information into a database and provides it to the generative AI and emotion engine, establishing a database connection and properly organizing and storing the received data.

[1657] Input: Received technical and emotional data

[1658] Output: Store in a database

[1659] Step 4:

[1660] The generation AI analyzes the stored technical information, compares it with a patent database, and checks for matches with existing patents. The generation AI determines whether a patent is possible and automatically generates documents for patent applications.

[1661] Input: Technical Information

[1662] Output: Patent approval / denial decision result and patent document

[1663] Step 5:

[1664] The emotion engine analyzes the stored emotional data and adjusts the output of the generative AI based on the results. For example, if the user is feeling stressed, it will adjust the tone and detail of the presented approval / disapproval results.

[1665] Input: Emotion data

[1666] Output: Sentiment analysis results and adjustments to generated AI output

[1667] Step 6:

[1668] The server stores the patent approval / denial decisions and proposals from the generation AI, as well as the analysis results from the emotion engine, in a database and provides them to users. The presentation method is optimized based on the emotion analysis results.

[1669] Input: Generative AI results, emotion engine results

[1670] Output: Providing results to the user

[1671] Step 7:

[1672] Users can proceed with the patent application process based on the provided patent approval / denial results, suggestions, and emotional feedback, allowing them to efficiently evaluate the patentability of their technology and take the necessary steps quickly.

[1673] Input: Approval / rejection results, suggestions, emotional feedback

[1674] Output: Patent application process

[1675] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1676] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1677] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1678] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1679] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1680] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1681] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1682] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1683] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1684] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1685] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1686] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1687] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1688] 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.

[1689] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1690] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1691] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1692] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1693] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1694] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1695] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1696] The following is further disclosed regarding the above embodiment.

[1697] (Claim 1)

[1698] A means for receiving and storing detailed information about technologies and projects under development in a database;

[1699] A means for determining whether or not to grant patent permission for the received information using the generation AI;

[1700] a means for storing the results and proposals of patent approval or denial in a database;

[1701] means for providing the stored results to a user;

[1702] A system including:

[1703] (Claim 2)

[1704] The system of claim 1, further comprising means for the generating AI to check against a patent database to determine whether the generated AI matches an existing patent.

[1705] (Claim 3)

[1706] The system according to claim 1, further comprising means for the generation AI to automatically generate documents for a patent application based on the result of whether or not the patent is granted.

[1707] "Example 1"

[1708] (Claim 1)

[1709] A means of establishing a database connection and initializing the necessary tables;

[1710] A means for receiving and storing detailed information about technologies and projects under development in a database;

[1711] A means for determining whether or not to grant patent permission for the received information using the generation AI;

[1712] a means for storing the results and proposals of patent approval or denial in a database;

[1713] means for providing the stored results to a user;

[1714] A system including:

[1715] (Claim 2)

[1716] The system of claim 1, further comprising means for the generating AI to check against a patent database to determine whether the generated AI matches an existing patent.

[1717] (Claim 3)

[1718] The system according to claim 1, further comprising means for the generation AI to automatically generate documents for a patent application based on the result of whether or not the patent is granted.

[1719] "Application Example 1"

[1720] (Claim 1)

[1721] A means for receiving and storing detailed information about technologies and projects under development in a database;

[1722] A means for determining whether or not to grant patent permission for the received information using the generation AI;

[1723] a means for storing the results and proposals of patent approval or denial in a database;

[1724] means for providing the stored results to a user;

[1725] As an application installed on a smartphone, it is a means to input technical information and check the results of patent decisions,

[1726] means for automatically generating documents relating to a patent application;

[1727] A system including:

[1728] (Claim 2)

[1729] The system of claim 1, further comprising means for the generating AI to check against a patent database to determine whether the generated AI matches an existing patent.

[1730] (Claim 3)

[1731] The system according to claim 1, further comprising means for the generation AI to automatically generate documents for a patent application based on the result of whether or not the patent is granted.

[1732] "Example 2: Combining Emotion Engines"

[1733] (Claim 1)

[1734] A means of receiving and storing information about technologies and projects under development;

[1735] A means for evaluating the patentability of the received information using a generative AI model;

[1736] A means for storing the results and proposals of patent approval and rejection;

[1737] means for providing the stored results to a user;

[1738] a means for collecting and analyzing emotion data;

[1739] A means to adjust the output results of the generative AI model based on the analyzed emotion data; and

[1740] A system including:

[1741] (Claim 2)

[1742] 10. The system of claim 1, further comprising means for checking the generative AI model against a patent database to determine whether it matches an existing patent.

[1743] (Claim 3)

[1744] The system of claim 1, further comprising means for automatically generating documents for a patent application based on the results of the generative AI model's patent application approval or denial.

[1745] "Application example 2 when combining emotion engines"

[1746] (Claim 1)

[1747] A means for receiving and storing detailed information about technologies and projects under development in a database;

[1748] A means for determining whether or not to grant patent permission for the received information using the generation AI;

[1749] a means for storing the results and proposals of patent approval or denial in a database;

[1750] means for providing the stored results to a user;

[1751] a means for collecting and analyzing emotion data;

[1752] A means for adjusting the output of the generative AI based on the results of emotion analysis;

[1753] A means to explain patent information in an optimal way to developers, and

[1754] A system including:

[1755] (Claim 2)

[1756] The system of claim 1, further comprising means for the generating AI to check against a patent database to determine whether the generated AI matches an existing patent.

[1757] (Claim 3)

[1758] The system according to claim 1, further comprising means for the generation AI to automatically generate documents for a patent application based on the result of whether or not the patent is granted. [Explanation of symbols]

[1759] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving and storing detailed information about technologies and projects under development in a database; A means for determining whether or not to grant patent permission for the received information using the generation AI; a means for storing the results and proposals of patent approval or denial in a database; means for providing the stored results to a user; A system including:

2. The system of claim 1 further comprising means for the generating AI to check against a patent database to determine whether the generated AI matches an existing patent.

3. The system according to claim 1, further comprising means for the generation AI to automatically generate documents for a patent application based on the result of whether or not the patent is granted.

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