System

Abstract

A system is provided.SOLUTION: The system includes a means for receiving the generated business ideas and storing them in a database, a means for identifying and learning the ideas stored in the database, a means for collating the learned ideas with a patent database and selecting the possibility of a patent infringement, and a means for outputting and notifying the ideas having the possibility of the patent infringement as a list.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 field of generative AI, as companies compete to develop new technologies and business ideas, the risk of proposed ideas infringing existing patents is increasing. There is also a need for an effective system to prevent patent infringement and respond quickly when it occurs. However, current systems have difficulty detecting patent infringement risks and formulating and implementing appropriate countermeasures. Therefore, the present invention aims to solve these issues and provide a system that efficiently manages patent infringement risks for generative AI business ideas and maximizes revenue opportunities. [Means for solving the problem]

[0005] The system includes a means for receiving generated business ideas and storing them in a database, a means for identifying and learning from the ideas stored in the database, a means for comparing the learned ideas with a patent database to identify potential patent infringements, and a means for outputting and notifying the potential patent infringing ideas as a list. The system may also include a means for presenting countermeasures to the potential patent infringing ideas and promoting their monetization, and a means for displaying the list of potential patent infringing ideas to an administrator using a user interface for detailed analysis. This enables the business ideas generated by the AI ​​to address patent infringement issues and maximize profits.

[0006] "Generated Business Idea" refers to a new business idea or technology concept submitted by a User through the Contest Platform or other means.

[0007] A "database" is a system or mechanism for storing and managing structured information, and is a place where submitted ideas are stored.

[0008] "Identification" is the process of distinguishing and identifying a particular business idea from other ideas.

[0009] "Learning" is the process by which an AI model extracts features and patterns from the data provided to it and acquires knowledge.

[0010] "Patent Database" means a database for storing and managing existing patent information, including reference data for matching submitted ideas.

[0011] "Matching" is the process of comparing specific data to determine matches or similarities.

[0012] "Potential patent infringement" refers to a situation in which the submitted idea poses a risk of infringing an existing patent right.

[0013] "Sorting" is the process of extracting data that meets specific conditions from a large amount of data.

[0014] A "list" refers to an arrangement and display of specific items in an enumerated format.

[0015] "Notification" is the act of notifying a specific user or system of certain information.

[0016] "Solution" refers to a procedure or solution to a specific problem.

[0017] "Monetization" is the process of leveraging a particular asset or idea in a way that generates revenue.

[0018] A "user interface" is an interface that allows a user to interact with a system, and enables the display and manipulation of information.

[0019] "Administrator" means an individual or group responsible for operating and monitoring a system.

[0020] "Analysis" is the process of examining data and interpreting the information. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] The present invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and ultimately monetizing them. The system includes the following major components:

[0043] 1. Idea submission phase

[0044] Idea Submission

[0045] Users submit new business ideas using generative AI to the system by accessing a web form and providing a summary of the idea, details, and potential use cases. This information is then sent to the server.

[0046] Database storage

[0047] The server receives the submitted ideas and stores them in a database, with each idea being assigned a unique idea ID.

[0048] 2. Idea learning phase

[0049] Data format conversion

[0050] The server converts new ideas stored in the database into the appropriate format to feed to the AI ​​model.

[0051] Input to the AI ​​model

[0052] The server then feeds the converted data into an AI model to train it, which then learns and analyzes specific features of the ideas.

[0053] 3. Patent database matching

[0054] Data Verification

[0055] The AI ​​model compares the ideas it learns with existing patent information stored in a patent database, thereby detecting whether the submitted idea infringes on an existing patent.

[0056] Generating Similarity Scores

[0057] The AI ​​model generates a similarity score between the idea and the patent information and returns the results to the server.

[0058] 4. Patent infringement determination phase

[0059] Evaluating the results

[0060] The server analyzes the similarity scores received from the AI ​​model and marks ideas that exceed a certain score threshold as potentially patent infringing.

[0061] List Generation and Notifications

[0062] The server generates a list of ideas that may be patent infringing and notifies the administrator's terminal.

[0063] 5. Countermeasure decision phase

[0064] Viewing the list

[0065] The terminal displays to the user a list of ideas that may be patent infringing, including details of the ideas and information on similar patents.

[0066] Consideration of countermeasures

[0067] The administrator will evaluate the list and consider countermeasures, which may include proposing joint development, seeking compensation, or collecting usage fees, etc. Countermeasures will be implemented in coordination with the legal department.

[0068] 6. Monetization Phase

[0069] Contract Management

[0070] The server starts the contract management system based on the selected countermeasure. A draft contract is generated and presented to the administrator. Progress management after the contract is concluded is also performed.

[0071] Progress display

[0072] The terminal displays the contract's progress in a user interface and notifies the administrator, along with alerts about the progress of each phase and key milestones.

[0073] Specific examples

[0074] Example 1: A new voice assistant idea

[0075] User A submits an idea for a new voice assistant using generative AI to a contest. The server receives the idea and stores it in a database. The AI ​​model learns from the idea and compares it with existing patent databases. If Patent B is determined to be similar, the server adds it to a patent infringement list and notifies the administrator. The administrator evaluates the infringement risk and enters into a joint development agreement with the holder of Patent B to monetize it.

[0076] As described above, the system of the present invention can efficiently manage the risk of patent infringement for business ideas of generative AI and maximize profits.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] The user enters the generated AI business idea into the system's web form, fills in the necessary details, and then presses the submit button.

[0080] Step 2:

[0081] The server receives the submitted ideas and stores them in a database, where each idea is assigned a unique idea ID.

[0082] Step 3:

[0083] The server identifies newly saved ideas and converts them from the database into the appropriate data format to feed to the AI ​​model.

[0084] Step 4:

[0085] The server inputs the converted data into an AI model to train it, which then learns the characteristics of the ideas.

[0086] Step 5:

[0087] The server compares the ideas learned by the AI ​​model with a patent database and calculates a similarity score to existing patent information.

[0088] Step 6:

[0089] The server analyzes the similarity scores received from the AI ​​model and, if the score exceeds a certain threshold, marks the idea as potentially patent infringing.

[0090] Step 7:

[0091] The server compiles a list of ideas that may be patent infringing and notifies the administrator terminal.

[0092] Step 8:

[0093] The terminal displays a list of ideas that may be infringing patents on the user interface, and the administrator checks the list and considers countermeasures.

[0094] Step 9:

[0095] The administrator works with the legal department to determine the best course of action to address the risk of patent infringement, which may include joint development, seeking compensation, or collecting royalties.

[0096] Step 10:

[0097] The server launches a contract management system based on the selected solution, generates a draft contract, and tracks the progress of the contract.

[0098] Step 11:

[0099] The terminal displays the contract's progress in a user interface and sends notifications to administrators about important milestones and deadlines.

[0100] Through these steps, the system can efficiently manage the patent infringement risk of generated AI business ideas and maximize profits.

[0101] Example 1

[0102] 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."

[0103] Currently, there is a lack of systems that can quickly and efficiently assess the risk of patent infringement for business ideas created using generative AI and take appropriate countermeasures. In particular, there is a need for automation and improved accuracy in the process of detecting the patent infringement risk of ideas. Another issue is the lack of effective management of the process of presenting countermeasures and monetizing ideas after assessing the risk of patent infringement.

[0104] 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.

[0105] In this invention, the server includes means for receiving generated business ideas and storing them in a database, means for identifying the ideas stored in the database and learning them using a generative AI model, means for comparing the learned ideas with a patent information database to determine whether they may infringe a patent, and means for analyzing the similarity scores generated by the AI ​​model and outputting and notifying a list of ideas that may infringe a patent. This makes it possible to accurately and quickly evaluate the patent infringement risk of business ideas generated by generative AI, present appropriate countermeasures, and efficiently manage the monetization process.

[0106] A "generated business idea" is a newly devised commercial concept or plan using a generative AI model.

[0107] The "database" is an information storage system for centrally storing and managing generated business ideas and other related information.

[0108] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to process and learn from data.

[0109] The "Patent Information Database" is a database system that aggregates patent information registered to date and allows for searching and matching.

[0110] A "similarity score" is a numerical representation of the similarity between a business idea and existing patent information, and is an indicator for assessing the possibility of patent infringement based on the level of the similarity.

[0111] A "list" is a list of ideas or information selected based on specific criteria.

[0112] "User interface" refers to the screen or operation panel that administrators and users use to interact with the system.

[0113] "Administrator" means an individual or organization that has the authority to operate the system, evaluate patents, and decide on countermeasures.

[0114] The present invention is a system for efficiently managing business ideas, reducing the risk of patent infringement, and ultimately monetizing them. A specific implementation method for this system is described below.

[0115] System configuration

[0116] The system includes the following main components:

[0117] 1. Client terminal

[0118] 2. Server

[0119] 3. Database

[0120] 4. Generative AI Models

[0121] 5. Patent Information Database

[0122] 6. User Interface

[0123] System Operation

[0124] Idea Submission Phase

[0125] A user accesses a web page using a client device and submits a new business idea using a generative AI model. The submitted idea is sent to the server via an HTTP request. The server stores the received idea information in a database, and each idea is assigned a unique idea ID.

[0126] As a specific example, a user can enter a "suggestion for a new voice assistant" and click the submit button, which will save the suggestion to the database.

[0127] Example prompt: "Voice assistants are a new way to manage IoT devices in the home."

[0128] Idea Learning Phase

[0129] The server takes the ideas stored in the database and converts them into a format that the generative AI model can process. The converted data is then fed into the generative AI model to train it, during which it learns specific characteristics of the ideas.

[0130] Patent database matching

[0131] The generative AI model compares the learned ideas with a patent information database to verify the possibility of patent infringement. The AI ​​model generates a similarity score between the idea and the patent information and returns the result to the server.

[0132] Patent infringement determination phase

[0133] The server analyzes the similarity scores and adds ideas that exceed a certain score threshold to a list of potential patent infringements. This list is then sent to an administrator's terminal.

[0134] Countermeasure decision phase

[0135] The administrator's terminal displays a list of ideas that may be infringing patents and performs a detailed analysis. The administrator reviews the list and decides on countermeasures, such as proposing joint development, requesting compensation, or collecting usage fees. These countermeasures are implemented in cooperation with the legal department.

[0136] Monetization Phase

[0137] The server launches the contract management system based on the selected solution, generates a draft contract, and presents it to the administrator. Progress is also monitored after the contract is signed, and alerts are displayed regarding important milestones. The progress of the contract is displayed on the administrator's terminal.

[0138] Specific examples

[0139] As a concrete example, consider the process in which User A submits an idea for a new voice assistant. The server receives the idea and stores it in a database. The generative AI model learns from the idea and compares it with existing patent information databases. If Patent B is determined to be similar, the server adds it to a patent infringement list and notifies the administrator. The administrator evaluates the infringement risk and enters into a joint development agreement with the holder of Patent B to monetize it.

[0140] In this way, the system utilizes generative AI models to efficiently manage patent infringement risks for business ideas and maximize profits.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] Idea Submission

[0144] A user accesses a web page using a client device and submits a new business idea using a generative AI model. When the user fills out the form with a summary of the idea, details, and potential use cases, and clicks the submit button, the information is sent to the server via an HTTP request.

[0145] Input: Idea information from users

[0146] Output: Send to server

[0147] Specific behavior: The user enters a "Suggestion for a new voice assistant" and clicks the submit button.

[0148] Step 2:

[0149] Receiving and storing data

[0150] The server processes the received idea information and stores it in a database, where each idea is assigned a unique idea ID.

[0151] Input: Idea information submitted by the user

[0152] Output: Idea information stored in the database and a unique idea ID

[0153] Specific operation: The server saves the "new voice assistant proposal" in the database and assigns it the ID "AI1234."

[0154] Step 3:

[0155] Data format conversion

[0156] The server takes the idea information stored in the database and converts it into a format that can be processed by the generative AI model. This conversion operation involves preprocessing and shaping the data.

[0157] Input: Idea information stored in the database

[0158] Output: Data in a format that can be processed by the generative AI model (e.g., JSON format)

[0159] Specific operation: The server converts "New voice assistant proposal (ID: AI1234)" into JSON format.

[0160] Step 4:

[0161] Input to generative AI models

[0162] The server loads a pre-trained generative AI model and inputs the transformed data into the model, where the model analyzes the idea information and extracts specific features.

[0163] Input: Data in a format that the generative AI model can process.

[0164] Output: Data with extracted features

[0165] Specific operation: The server inputs the "new voice assistant proposal" in JSON format into the generative AI model and extracts features.

[0166] Step 5:

[0167] Matching with patent information database

[0168] The server uses the features extracted by the generative AI model to access the patent information database, retrieve existing patent information from the patent information database, and compare it with the generative AI model.

[0169] Input: Data from which features have been extracted, information from patent information database

[0170] Output: Similarity score indicating the likelihood of patent infringement

[0171] Specific operation: The server compares the features of the "new voice assistant proposal" with the patent data and generates a similarity score.

[0172] Step 6:

[0173] Analysis of similarity scores

[0174] The server receives and analyzes the similarity scores generated by the generative AI model to determine whether they exceed a certain score threshold.

[0175] Input: Similarity score from the generative AI model

[0176] Output: A list of potentially infringing ideas

[0177] Specific operation: The server analyzes the similarity score "0.85" and adds "New voice assistant proposal" to the patent infringement list.

[0178] Step 7:

[0179] Notifications and List Generation

[0180] The server creates a list of ideas that may be patent infringing and notifies the administrator of this list, which includes a summary of the idea and a similarity score.

[0181] Input: List of potentially infringing ideas

[0182] Output: Notification to administrator

[0183] Specific operation: The server adds the "new voice assistant proposal" to the "patent infringement list" and notifies the administrator.

[0184] Step 8:

[0185] Evaluation by the administrator and consideration of countermeasures

[0186] A list of ideas that may be patent infringing is displayed on the administrator's terminal, and the administrator evaluates the list and considers countermeasures, such as proposing joint development, requesting compensation, or collecting usage fees.

[0187] Input: List of potentially infringing ideas

[0188] Output: Administrator decides what to do

[0189] Specific actions: The administrator evaluates the "New Voice Assistant Proposal" and proposes collaboration with the patent holder.

[0190] Step 9:

[0191] Contract management and progress display

[0192] The server starts the contract management system based on the selected countermeasure, generates a draft contract, manages progress, and displays the progress on the terminal.

[0193] Input: Administrator's decision on how to proceed

[0194] Output: Contract draft, progress display

[0195] Specific operation: The server generates a draft contract "Joint Development Agreement" and presents it to the administrator. The progress is displayed on the terminal and an alert is sent if necessary.

[0196] (Application example 1)

[0197] 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."

[0198] It is extremely important to efficiently manage patent infringement risks for generated business ideas and maximize their potential for monetization. However, conventional systems have the problem of requiring a great deal of time and effort to verify novelty, assess patent infringement risks, and monetize ideas. Furthermore, the process from idea submission to patent infringement checks and monetization is often inconsistent and inefficient. To address these issues, the present invention provides a system for centrally managing these processes and making them run efficiently and quickly.

[0199] 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.

[0200] In this invention, the server includes means for receiving the generated business ideas and storing them in a database, means for identifying and learning the ideas stored in the database, means for comparing the learned ideas with a patent database to screen for possible patent infringement, means for outputting and notifying a list of the ideas that may be infringing, and means for evaluating the possibility of patent infringement regarding the user's idea and presenting countermeasures and monetization means. This makes it possible to quickly and efficiently evaluate the patent infringement risk of the generated business ideas and present appropriate countermeasures and monetization possibilities.

[0201] A "generated business idea" is a new business idea or plan created using a generative AI model.

[0202] A "database" is a collection of data managed on a computer system, and is a place to store generated business ideas and patent information.

[0203] "Identification" refers to properly recognizing the information stored in the database and identifying which idea it is.

[0204] "Learning" is the process by which an AI model analyzes and understands the specific characteristics of the ideas it receives as input.

[0205] A "patent database" is a database system for storing and managing existing patent information.

[0206] "Matching" is the act of comparing learned ideas with information in a patent database to find matches or similarities.

[0207] "Patent infringement" refers to a situation in which a newly generated business idea may infringe an existing patent right.

[0208] A "list" is a list of items organized based on specific criteria.

[0209] "Notification" is the act of informing relevant parties of specific information.

[0210] "Countermeasures" are specific actions or methods taken in response to problems or issues that arise.

[0211] "Monetization" is the process of implementing a business idea and generating revenue.

[0212] A "user interface" is software or hardware that serves as a window through which a user interacts with a system.

[0213] An "administrator" is a person responsible for operating and maintaining a system.

[0214] This invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and ultimately monetizing them. The main components of this system include a server for receiving, storing, learning from, and analyzing generated ideas, a terminal for users to submit ideas, and a function for checking against patent databases and notifying the results.

[0215] 1. Idea submission phase

[0216] Idea Submission

[0217] Users submit their generated business ideas to the system using a client device (smartphone, tablet, etc.). They are provided with a form to enter a summary of their idea, details, and potential use cases. For example, if you have an idea for an "AI-based movie recommendation system," you might use the following prompt:

[0218] Example prompt sentence:

[0219] Title: "AI-based movie recommendation system"

[0220] Summary: "An AI-based movie recommendation system based on a user's viewing history and preferences. It analyzes a user's past viewing data and recommends similar movies."

[0221] 2. Idea learning phase

[0222] The server receives the submitted ideas and stores them in a database. Each saved idea is assigned a unique idea ID. This data is converted into an appropriate format for input into an AI model, which then learns from it. For example, Tensorflow (registered trademark) or PyTorch is used as the AI ​​model.

[0223] 3. Patent database matching

[0224] The learned ideas are then matched against a patent database. The server compares the ideas with existing patent information to assess the likelihood of patent infringement. This process uses an AI model to extract specific features and calculate a similarity score.

[0225] 4. Patent infringement determination phase

[0226] The server analyzes the similarity scores received from the AI ​​model and marks ideas that exceed a certain threshold as potentially infringing. These ideas are then listed and notified to the administrator, who then displays the notification on their device.

[0227] 5. Countermeasures Consideration Phase

[0228] Countermeasures include proposing joint development, requesting compensation, collecting usage fees, etc. The server manages these countermeasures and generates the necessary draft contracts.

[0229] 6. Monetization Phase

[0230] The server launches the contract management system based on the selected countermeasure. The progress of the contract is displayed to the administrator through a user interface, allowing the administrator to grasp important milestones and progress in real time.

[0231] This system quickly and efficiently evaluates the patent infringement risk of generated business ideas and proposes appropriate countermeasures and monetization possibilities. The hardware used is AWS (registered trademark) EC2 instances, and the database is AWS RDS (MySQL (registered trademark). In addition, TensorFlow and PyTorch are used as AI models.

[0232] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0233] Step 1:

[0234] A user enters the generated business idea using the idea submission form and clicks the submit button. The user enters a title and a summary, such as "AI-based movie recommendation system." The entered data is sent from the client device to the server as an HTTP POST request. The input data (idea title and summary) is sent, and the server processes the received data based on it.

[0235] Step 2:

[0236] The server stores the received business ideas in a database. When stored, each idea is assigned a unique idea ID. This idea ID is used to identify the idea in subsequent processing. The server stores the input data (idea title and summary) and generates a unique idea ID as its output.

[0237] Step 3:

[0238] The server takes ideas stored in the database and converts them into the appropriate format for input to the AI ​​model. This format conversion prepares the data in a format that the AI ​​model can accurately parse. The server converts the input data (idea details based on idea ID) and generates a data format that is compatible with the AI ​​model as output.

[0239] Step 4:

[0240] The AI ​​model receives the converted idea data and begins the learning process. The AI ​​model analyzes the features of the ideas using TensorFlow or PyTorch and learns specific patterns. The AI ​​model processes the input data (the format-converted idea data) and generates learned feature data as its output.

[0241] Step 5:

[0242] The server compares the trained idea data with a patent database, retrieves related patent information from the patent database, and calculates a similarity score using the AI ​​model. The server performs data matching using input data (trained feature data and patent database information) and generates a similarity score as its output.

[0243] Step 6:

[0244] The server analyzes the similarity scores received from the AI ​​model and evaluates the possibility of patent infringement. It lists ideas that exceed a certain threshold as being at risk of patent infringement and generates information for notification. The server evaluates based on the input data (similarity scores) and generates a list of ideas at risk of patent infringement as its output.

[0245] Step 7:

[0246] The server notifies the administrator's terminal of the generated list of ideas that pose a risk of patent infringement. The terminal receives the information and visually displays it to the administrator. Patent information and idea details are provided to make it easier for the administrator to consider specific countermeasures. The server notifies the administrator of the input data (the list of ideas that pose a risk of patent infringement), and the terminal displays the information to the administrator as output.

[0247] Step 8:

[0248] The administrator uses a terminal to check the displayed list of ideas that pose a risk of patent infringement and consider specific countermeasures, such as proposing joint development, requesting compensation, or collecting usage fees. The administrator decides on specific countermeasures for the ideas and takes concrete action to monetize them.

[0249] Step 9:

[0250] The server starts the contract management system based on the countermeasures decided by the administrator. It generates the necessary draft contracts and displays the progress in real time. The administrator uses a terminal to monitor the contract management system and manage the progress of the contract. The server processes the input data (details of the countermeasures) and generates contract management progress information as its output.

[0251] 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.

[0252] The present invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and ultimately monetizing them, and further combines an emotion engine that recognizes user emotions. The system includes the following main components and processing flow:

[0253] 1. Idea submission phase

[0254] Idea Submission

[0255] Users submit their new business ideas using generative AI by entering details into the system's web form. This information is sent to the server, and the emotion engine simultaneously collects and analyzes the user's emotional data.

[0256] Database storage

[0257] The server receives submitted ideas and emotion data and stores them in a database, where each idea is assigned a unique idea ID.

[0258] 2. Idea learning phase

[0259] Data format conversion

[0260] The server converts new ideas and corresponding emotional data stored in the database into an appropriate data format to provide to the AI ​​model.

[0261] Input to the AI ​​model

[0262] The server then inputs the converted data into an AI model to train it, which then learns the specific characteristics of the idea and the user's emotional state.

[0263] 3. Patent database matching

[0264] Data Verification

[0265] The AI ​​model compares the ideas it learns with existing patent information stored in a patent database, thereby detecting whether the submitted idea infringes on an existing patent.

[0266] Generating Similarity Scores

[0267] The AI ​​model generates a similarity score between the idea and the patent information and returns it to the server along with analysis results that take into account the user's emotional data.

[0268] 4. Patent infringement determination phase

[0269] Evaluating the results

[0270] The server analyzes the similarity scores and sentiment data analysis results received from the AI ​​model, and if they exceed a certain score threshold, marks the idea as potentially patent infringing.

[0271] List Generation and Notifications

[0272] The server compiles a list of ideas that may be patent infringing and notifies the administrator terminal.

[0273] 5. Countermeasure decision phase

[0274] Viewing the list

[0275] The terminal displays a list of ideas that may be infringing patents on the user interface. The administrator checks the list and considers countermeasures.

[0276] Consideration of countermeasures

[0277] The administrator works with the legal department to determine the best course of action to address the risk of patent infringement, which may include joint development, seeking compensation, or collecting royalties.

[0278] 6. Monetization Phase

[0279] Contract Management

[0280] The server starts a contract management system based on the selected solution, generates a draft contract, and tracks the progress of the contract.

[0281] Progress display

[0282] The terminal displays the contract's progress in a user interface and sends notifications to administrators about important milestones and deadlines.

[0283] Specific examples

[0284] Example 1: A new voice assistant idea

[0285] User A submits an idea for a new voice assistant using generative AI to a contest. The server's emotion engine collects and analyzes the idea along with User A's emotion data. The idea and emotion data stored in the database are learned by an AI model and compared with the patent database. If Patent B is determined to be similar, the server adds it to a patent infringement list and notifies the administrator. The administrator considers the risk of infringement and the emotion data, and enters into a joint development agreement with the holder of Patent B to monetize it.

[0286] In this way, the system of the present invention, which combines an emotion engine, can more accurately manage the patent infringement risk of generated AI business ideas and maximize profits.

[0287] The processing flow will be explained below.

[0288] Step 1:

[0289] The user enters the generated AI business idea into the system's web form, provides detailed information (summary, use case, etc.), and presses the submit button. At the same time as submission, the emotion engine collects the user's facial expression and voice data.

[0290] Step 2:

[0291] The server receives the submitted ideas and stores them in a database. A unique idea ID is assigned when the ideas are saved. The server also stores the user's emotional data collected by the emotion engine.

[0292] Step 3:

[0293] The server extracts new ideas and corresponding emotional data stored in the database and converts them into an appropriate data format to provide to the AI ​​model.

[0294] Step 4:

[0295] The server inputs the converted data into the AI ​​model and starts training the model, which learns the content of the idea and the user's emotional state.

[0296] Step 5:

[0297] The AI ​​model uses the training data to match the submitted idea with existing patent information in the database, and also incorporates user sentiment data into the analysis.

[0298] Step 6:

[0299] Based on the matching results, the AI ​​model generates a similarity score that reflects not only the degree of match with the idea content but also the confidence level based on the user's emotional state.

[0300] Step 7:

[0301] The server evaluates the similarity scores received from the AI ​​model and the results of the sentiment data analysis, and marks ideas with scores above a certain threshold as having a high probability of patent infringement.

[0302] Step 8:

[0303] The server creates a list of ideas that are likely to infringe patents and notifies the administrator's terminal, along with user emotional data.

[0304] Step 9:

[0305] The terminal displays a list of ideas that may be patent infringing on the user interface. The administrator checks the list and considers countermeasures based on the idea details and user emotion data.

[0306] Step 10:

[0307] The administrator works with the legal department to determine the best course of action to address patent infringement risk, which may include joint development, seeking compensation, or collecting royalties.

[0308] Step 11:

[0309] The server starts the contract management system based on the selected solution, generates the necessary contract drafts, and tracks the progress of the contract and provides it to the administrator.

[0310] Step 12:

[0311] The terminal displays the contract's progress in a user interface, and administrators can receive notifications based on important milestones and deadlines.

[0312] As a concrete example, when User A submits an idea for a new voice assistant, the server stores the idea and emotional data in a database, and uses an AI model to compare it with a patent database and finds that it is similar to Patent B. The server notifies this information to the administrator, who, taking User A's emotional data into account, decides to enter into a joint development agreement with the holder of Patent B. Through this process, the risk of patent infringement of the idea can be reduced while monetizing it.

[0313] Example 2

[0314] 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."

[0315] In today's business environment, generating and managing new business ideas is an important challenge. However, generated business ideas may infringe existing patents, which can hinder business development. Furthermore, systems for quickly detecting potential patent infringement and taking appropriate countermeasures are often lacking. The present invention aims to efficiently manage generated business ideas, reduce patent infringement risks, and ultimately monetize them. In particular, the present invention aims to provide a system that uses user emotion data to make more accurate judgments and provide appropriate notifications and countermeasures to administrators.

[0316] The identification process by the identification 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 generated business ideas and storing them in a database; means for converting the ideas and emotion data stored in the database into an appropriate data format; means for inputting the converted idea data and emotion data into an AI model and training the model; means for comparing the learned ideas with a patent database to screen for possible patent infringement; and means for outputting and notifying the ideas with the possibility of patent infringement as a list. This enables the patent infringement risk of generated business ideas to be quickly and accurately determined and notified to an administrator in a timely manner. Furthermore, by including means for displaying the list on a user interface and allowing an administrator to identify ideas with the possibility of patent infringement, the administrator can easily perform detailed analysis and consider countermeasures. Furthermore, by including means for presenting optimal countermeasures for the ideas with the possibility of patent infringement and promoting monetization, the system can realize appropriate utilization of business ideas and maximize profits.

[0317] A "generated business idea" is a new business concept or plan devised by a user using a generative AI model.

[0318] A "database" is a digital recording device that structures and stores information such as business ideas and emotional data, allowing it to be searched and retrieved efficiently later.

[0319] "Emotional Data" refers to data that represents the user's emotional state and is collected and analyzed by the Emotion Engine.

[0320] A "data format" is a set of standards or rules for arranging digital data into a particular structure or form.

[0321] An "AI model" is an artificial intelligence system built using machine learning algorithms that learns specific data and makes predictions and judgments.

[0322] A "patent database" is a digital recording device that aggregates existing patent information and allows for searching and matching.

[0323] The "similarity score" is a numerical indicator that shows the degree of similarity when the AI ​​model compares the generated idea with existing patent information.

[0324] "User interface" refers to the screen and input device that a user uses to operate and exchange information with a system.

[0325] The "administrator" is a person whose role is to supervise and operate the entire system, assess the risk of patent infringement, and decide on countermeasures.

[0326] "Countermeasures" refer to specific actions and methods to be taken when a risk of patent infringement is discovered, and include joint development, claims for compensation, and collection of usage fees.

[0327] "Monetization" is the process of obtaining economic benefits from generated business ideas and associated patents.

[0328] The present invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and monetizing them. This system includes multiple components that are operated mainly by a server, a terminal, and a user. Details for specifically implementing the present invention will now be described.

[0329] System Overview

[0330] In this system, users input new business ideas using a generative AI model, which are then processed by a server. Specifically, the server receives the ideas, stores them in a database, and then converts them into the appropriate format and inputs them into the AI ​​model. The AI ​​model compares the learned ideas with an existing patent database to evaluate the possibility of patent infringement. This information is sent to an administrator's terminal, which determines appropriate countermeasures and monetizes the ideas, forming a series of steps.

[0331] Hardware and software used

[0332] Server: The server is the central component that stores data, transforms it, inputs it into the AI ​​model, and checks it against the patent database. The server runs a MySQL database, TensorFlow, and other applications.

[0333] Terminal: The administrator terminal has a user interface and uses front-end technologies such as React to support list display, notifications, and detailed analysis.

[0334] Generative AI models: Generative AI models such as GPT-4® are used to support the generation of new business ideas.

[0335] Emotion engine: Emotion recognition uses Azure® Cognitive Services to analyze the user's emotional state in real time.

[0336] Processing flow

[0337] 1. Idea Submission:

[0338] The user uses the generative AI to generate new business ideas and enter them into the system's web form. For example, the user enters the following prompt into the generative AI:

[0339] "We are thinking of an idea for a new voice assistant. Please propose an assistant that can not only control home appliances with voice commands, but also detect the user's emotional state and change its response accordingly."

[0340] This idea and emotion data is sent to the server when the user submits it in a web form.

[0341] 2. Database storage:

[0342] The server stores the received ideas and emotion data in a MySQL database and assigns each idea a unique ID.

[0343] 3. Data format conversion:

[0344] The server converts the stored ideas and emotional data into a format such as JSON, making it recognizable to the AI ​​model.

[0345] 4. Input to the AI ​​model:

[0346] The server inputs the converted data into an AI model trained using TensorFlow for learning.

[0347] 5. Patent database matching:

[0348] The AI ​​model compares the learned ideas with a patent database to assess the likelihood of patent infringement, and the matching results are returned to the server as a similarity score.

[0349] 6. Evaluation and Notification of Results:

[0350] Based on the similarity score and the results of the emotion data analysis, the server generates a list of ideas that may be patent infringing and notifies the administrator terminal.

[0351] Specific examples

[0352] User A inputs an idea for a new voice assistant, and the server collects and analyzes the idea along with emotional data. The idea and emotional data stored in the database are converted into an appropriate format by the server and input into the AI ​​model. The AI ​​model compares the idea with a patent database, and if it is determined to be similar to patent B, the server adds it to a patent infringement list and notifies the administrator. The administrator checks this list on the user interface on their device, and, taking into consideration the risk of patent infringement and the emotional data, seeks to monetize the idea by entering into a joint development agreement with the holder of patent B.

[0353] In this way, the system of the present invention can accurately manage the patent infringement risk of generated business ideas and maximize profitability.

[0354] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0355] System processing flow

[0356] Step 1: Submit your idea

[0357] Specific behavior:

[0358] Using a generative AI model (such as GPT-4), users input their new business idea into a web form, and the emotion engine collects and analyzes the user's emotional data. For example, the following prompt sentence can be input into the generative AI model:

[0359] "We are thinking of an idea for a new voice assistant. Please propose an assistant that can not only control home appliances with voice commands, but also detect the user's emotional state and change its response accordingly."

[0360] input:

[0361] Ideas from users and idea details and sentiment data generated by the generative AI model.

[0362] output:

[0363] Idea details and sentiment data are submitted to the server through a web form.

[0364] Step 2: Save to database

[0365] Specific behavior:

[0366] The server stores idea details and sentiment data received through the web form in a MySQL database, and assigns each idea a unique idea ID.

[0367] input:

[0368] Idea details and sentiment data submitted via web forms.

[0369] output:

[0370] Idea details and sentiment data, idea ID stored in the database.

[0371] Step 3: Convert data formats

[0372] Specific behavior:

[0373] The server converts the idea details and emotion data stored in the database into a data format (e.g., JSON) that the AI ​​model can recognize.

[0374] input:

[0375] Idea details and sentiment data stored in a database.

[0376] output:

[0377] The converted idea details and sentiment data in JSON format.

[0378] Step 4: Input to the AI ​​model

[0379] Specific behavior:

[0380] The server inputs the converted data into an AI model trained using TensorFlow to learn the features and sentiment data of the ideas.

[0381] input:

[0382] Idea details and sentiment data converted into JSON format.

[0383] output:

[0384] AI model parameters generated as a result of learning.

[0385] Step 5: Matching with patent databases

[0386] Specific behavior:

[0387] The AI ​​model matches the learned ideas with existing patent information stored in a patent database by sending queries to the patent database to retrieve relevant patent information and generate a similarity score.

[0388] input:

[0389] Parameters of the trained AI model, patent information from the patent database.

[0390] output:

[0391] Similarity score.

[0392] Step 6: Evaluate and communicate results

[0393] Specific behavior:

[0394] The server integrates and analyzes the similarity scores and emotion data analysis results received from the AI ​​model to create a list of ideas that may be infringing patents, and notifies the administrator terminal of the list.

[0395] input:

[0396] Similarity scores, emotional data analysis results.

[0397] output:

[0398] A list of ideas that may be infringing patents is sent to the administrator's terminal.

[0399] Step 7: View the list

[0400] Specific behavior:

[0401] The terminal (for administrator) displays a list of ideas that may be infringing patents on a user interface. The administrator can check the list and individually review the details of each idea.

[0402] input:

[0403] A list of ideas that may be subject to patent infringement.

[0404] output:

[0405] The idea list and detailed information displayed in the user interface.

[0406] Step 8: Consider and decide on countermeasures

[0407] Specific behavior:

[0408] The administrator will work with the legal department to determine the best course of action to address the risk of patent infringement, including joint development, seeking compensation, and collecting usage fees.

[0409] input:

[0410] A list of potentially infringing ideas and detailed information about them.

[0411] output:

[0412] Determining countermeasures to patent infringement risks.

[0413] Step 9: Manage and monetize your contracts

[0414] Specific behavior:

[0415] Based on the selected action, the server will launch the contract management system to generate a draft contract, track the contract's progress, and notify the administrator of important milestones and deadlines.

[0416] input:

[0417] The decided response measures.

[0418] output:

[0419] Draft contract, progress notification.

[0420] (Application example 2)

[0421] 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."

[0422] Conventional business idea management systems lack a means to efficiently assess the patent infringement risk of generated ideas, often hindering the monetization of inventions. There is also a need for an idea evaluation system that takes user emotional data into account and for improved accuracy in assessing patent infringement risk. Furthermore, idea management in the advertising industry poses unique challenges, necessitating a means to evaluate advertising effectiveness in real time using emotional data.

[0423] The identification process by the identification 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 generated business ideas and storing them in a database; means for identifying and learning the ideas stored in the database; means for comparing the learned ideas with a patent database to screen for possible patent infringement; means for outputting and notifying the ideas that may be patent infringing as a list; means for collecting and analyzing emotional data; and means for inputting the emotional data into a generative AI model and generating prompt sentences. This enables efficient management of patent infringement risk and highly accurate idea evaluation by taking user emotional data into account. Particularly in the advertising industry, emotional data can be used to evaluate advertising effectiveness in real time, thereby maximizing profits.

[0424] "Generated business ideas" refer to new business concepts and plans generated using AI models.

[0425] A "database" is a system for systematically storing and managing digital information.

[0426] "Means of learning" refers to the process of using an AI model to analyze data, understand its patterns and characteristics, and learn from them.

[0427] A "patent database" is a digital system that stores and manages existing patent information.

[0428] A "potential patent infringement screening process" is a process for determining whether a newly generated business idea infringes an existing patent.

[0429] The "means of outputting and notifying as a list" refers to a system for listing ideas that may constitute patent infringement and notifying relevant parties.

[0430] "Emotional data" is data that quantifies and analyzes the user's emotional state.

[0431] A "generative AI model" is an artificial intelligence model that analyzes generated business ideas and emotional data to generate new business ideas.

[0432] A "prompt" is text that contains instructions or questions that are input to a generative AI model.

[0433] This invention is a system for efficiently managing business ideas created using generative AI, reducing the risk of patent infringement, and ultimately monetizing them. This system also collects and analyzes user emotional data to more accurately assess the risk of patent infringement. It is particularly intended for use in the advertising industry, with the system intended for use on smartphones, smart glasses, and head-mounted displays.

[0434] Hardware and software used

[0435] 1. Hardware:

[0436] Smartphone

[0437] Smart Glasses

[0438] head-mounted display

[0439] 2. Software:

[0440] Emotion Engine Module (EmotionEngine)

[0441] Generative AI Models

[0442] Database System

[0443] Server API

[0444] System program description

[0445] The server operates the system through the following steps:

[0446] 1. Idea Submission Phase:

[0447] Users use their smartphones, smart glasses, or head-mounted displays to input new advertising ideas that are then used by generative AI models, and the submitted data is sent to a server.

[0448] At the same time, the emotion engine module is used to collect the user's emotion data in real time, which is also sent to the server.

[0449] 2. Database storage:

[0450] The server receives the submitted idea and emotion data and stores them in a database along with a unique idea ID.

[0451] 3. Idea learning phase:

[0452] The server converts the ideas and emotion data stored in the database into an appropriate data format and inputs it into a generative AI model for learning.

[0453] 4. Patent database matching:

[0454] The learned ideas are compared with a patent database to evaluate the possibility of patent infringement. Here, a similarity score is generated between the idea and existing information in the patent database, and analyzed together with sentiment data.

[0455] 5. Patent Infringement Determination Phase:

[0456] Based on the analysis results of the similarity score and emotion data, ideas that pose a risk of patent infringement are selected and listed.

[0457] The listed ideas are notified to the administrator and displayed through the user interface.

[0458] 6. Countermeasure decision phase:

[0459] The administrator reviews the list and works with the legal department to determine the best course of action, such as joint development or a claim for compensation.

[0460] 7. Monetization Phase:

[0461] Contract management is performed and the server tracks progress, providing notifications about important milestones and deadlines.

[0462] Specific examples

[0463] When a user submits an idea for a new advertising campaign, they enter a prompt like this:

[0464] "Submit your new ad campaign idea. Use generative AI to fill in your idea and allow the sentiment engine to collect data."

[0465] Based on this input, the server collects and analyzes ideas and emotional data. The analysis results are compared with a patent database to assess the risk of patent infringement. If there is a risk, an administrator is notified and appropriate countermeasures are taken. Emotional data can also be used to evaluate the effectiveness of advertising in real time, allowing for the development of optimal monetization strategies.

[0466] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0467] Step 1:

[0468] A user uses a smartphone, smart glasses, or head-mounted display to input ideas for a new advertising campaign using a generative AI model. The input prompt is, "Submit a new advertising campaign idea. Please enter your idea using generative AI and allow the emotion engine to collect data." The emotion engine analyzes the user's facial expressions and voice while they are inputting their idea and generates emotion data. The generated idea and emotion data are sent to the server. The input is the idea and emotion data entered by the user, and the output is the idea and emotion data sent to the server.

[0469] Step 2:

[0470] The server receives the submitted idea and emotion data and stores them in a database with a unique idea ID, ensuring the data is available for reference in later processing steps. The input is the idea and emotion data submitted in step 1, and the output is the idea and emotion data stored in the database.

[0471] Step 3:

[0472] The server converts the idea and emotion data stored in the database into an appropriate format and inputs it into the generative AI model. The generative AI model then learns new ideas based on the received data. The input here is the idea and emotion data stored in the database, and the output is the data converted into the format input into the generative AI model.

[0473] Step 4:

[0474] The server compares the learned ideas with a patent database to assess their potential for patent infringement. This process involves comparing the ideas with existing patent information stored in the patent database and calculating a similarity score. Sentiment data is also taken into account. The input is the idea learned by the generative AI model, and the output is a similarity score indicating the potential for patent infringement.

[0475] Step 5:

[0476] The server lists ideas that may be at risk of patent infringement and notifies the administrator. The list is displayed on the administrator's terminal, and detailed information about ideas that are at risk of patent infringement is provided. The input is a similarity score indicating the possibility of patent infringement, and the output is a risk list displayed on the administrator's terminal.

[0477] Step 6:

[0478] The administrator checks the risk list through a terminal and works with the legal department to consider the best course of action. Specific countermeasures are considered, such as joint development, claims for compensation, and the collection of usage fees. The input is the risk list and detailed information displayed on the terminal, and the output is the decision on the countermeasure.

[0479] Step 7:

[0480] Based on the determined action, the server manages the contract and tracks the progress. It notifies the administrator about important milestones and deadlines of the contract. The input is the determined action, and the output is progress tracking and notification.

[0481] The above are the specific processing steps in the embodiment of the invention.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] [Second embodiment]

[0486] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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).

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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.

[0496] 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.

[0497] 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."

[0498] The present invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and ultimately monetizing them. The system includes the following major components:

[0499] 1. Idea submission phase

[0500] Idea Submission

[0501] Users submit new business ideas using generative AI to the system by accessing a web form and providing a summary of the idea, details, and potential use cases. This information is then sent to the server.

[0502] Database storage

[0503] The server receives the submitted ideas and stores them in a database, with each idea being assigned a unique idea ID.

[0504] 2. Idea learning phase

[0505] Data format conversion

[0506] The server converts new ideas stored in the database into the appropriate format to feed to the AI ​​model.

[0507] Input to the AI ​​model

[0508] The server then feeds the converted data into an AI model to train it, which then learns and analyzes specific features of the ideas.

[0509] 3. Patent database matching

[0510] Data Verification

[0511] The AI ​​model compares the ideas it learns with existing patent information stored in a patent database, thereby detecting whether the submitted idea infringes on an existing patent.

[0512] Generating Similarity Scores

[0513] The AI ​​model generates a similarity score between the idea and the patent information and returns the results to the server.

[0514] 4. Patent infringement determination phase

[0515] Evaluating the results

[0516] The server analyzes the similarity scores received from the AI ​​model and marks ideas that exceed a certain score threshold as potentially patent infringing.

[0517] List Generation and Notifications

[0518] The server generates a list of ideas that may be patent infringing and notifies the administrator's terminal.

[0519] 5. Countermeasure decision phase

[0520] Viewing the list

[0521] The terminal displays to the user a list of ideas that may be patent infringing, including details of the ideas and information on similar patents.

[0522] Consideration of countermeasures

[0523] The administrator will evaluate the list and consider countermeasures, which may include proposing joint development, seeking compensation, or collecting usage fees, etc. Countermeasures will be implemented in coordination with the legal department.

[0524] 6. Monetization Phase

[0525] Contract Management

[0526] The server starts the contract management system based on the selected countermeasure. A draft contract is generated and presented to the administrator. Progress management after the contract is concluded is also performed.

[0527] Progress display

[0528] The terminal displays the contract's progress in a user interface and notifies the administrator, along with alerts about the progress of each phase and key milestones.

[0529] Specific examples

[0530] Example 1: A new voice assistant idea

[0531] User A submits an idea for a new voice assistant using generative AI to a contest. The server receives the idea and stores it in a database. The AI ​​model learns from the idea and compares it with existing patent databases. If Patent B is determined to be similar, the server adds it to a patent infringement list and notifies the administrator. The administrator evaluates the infringement risk and enters into a joint development agreement with the holder of Patent B to monetize it.

[0532] As described above, the system of the present invention can efficiently manage the risk of patent infringement for business ideas of generative AI and maximize profits.

[0533] The processing flow will be explained below.

[0534] Step 1:

[0535] The user enters the generated AI business idea into the system's web form, fills in the necessary details, and then presses the submit button.

[0536] Step 2:

[0537] The server receives the submitted ideas and stores them in a database, where each idea is assigned a unique idea ID.

[0538] Step 3:

[0539] The server identifies newly saved ideas and converts them from the database into the appropriate data format to feed to the AI ​​model.

[0540] Step 4:

[0541] The server inputs the converted data into an AI model to train it, which then learns the characteristics of the ideas.

[0542] Step 5:

[0543] The server compares the ideas learned by the AI ​​model with a patent database and calculates a similarity score to existing patent information.

[0544] Step 6:

[0545] The server analyzes the similarity scores received from the AI ​​model and, if the score exceeds a certain threshold, marks the idea as potentially patent infringing.

[0546] Step 7:

[0547] The server compiles a list of ideas that may be patent infringing and notifies the administrator terminal.

[0548] Step 8:

[0549] The terminal displays a list of ideas that may be infringing patents on the user interface, and the administrator checks the list and considers countermeasures.

[0550] Step 9:

[0551] The administrator works with the legal department to determine the best course of action to address the risk of patent infringement, which may include joint development, seeking compensation, or collecting royalties.

[0552] Step 10:

[0553] The server launches a contract management system based on the selected solution, generates a draft contract, and tracks the progress of the contract.

[0554] Step 11:

[0555] The terminal displays the contract's progress in a user interface and sends notifications to administrators about important milestones and deadlines.

[0556] Through these steps, the system can efficiently manage the patent infringement risk of generated AI business ideas and maximize profits.

[0557] Example 1

[0558] 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."

[0559] Currently, there is a lack of systems that can quickly and efficiently assess the risk of patent infringement for business ideas created using generative AI and take appropriate countermeasures. In particular, there is a need for automation and improved accuracy in the process of detecting the patent infringement risk of ideas. Another issue is the lack of effective management of the process of presenting countermeasures and monetizing ideas after assessing the risk of patent infringement.

[0560] 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.

[0561] In this invention, the server includes means for receiving generated business ideas and storing them in a database, means for identifying the ideas stored in the database and learning them using a generative AI model, means for comparing the learned ideas with a patent information database to determine whether they may infringe a patent, and means for analyzing the similarity scores generated by the AI ​​model and outputting and notifying a list of ideas that may infringe a patent. This makes it possible to accurately and quickly evaluate the patent infringement risk of business ideas generated by generative AI, present appropriate countermeasures, and efficiently manage the monetization process.

[0562] A "generated business idea" is a newly devised commercial concept or plan using a generative AI model.

[0563] The "database" is an information storage system for centrally storing and managing generated business ideas and other related information.

[0564] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to process and learn from data.

[0565] The "Patent Information Database" is a database system that aggregates patent information registered to date and allows for searching and matching.

[0566] A "similarity score" is a numerical representation of the similarity between a business idea and existing patent information, and is an indicator for assessing the possibility of patent infringement based on the level of the similarity.

[0567] A "list" is a list of ideas or information selected based on specific criteria.

[0568] "User interface" refers to the screen or operation panel that administrators and users use to interact with the system.

[0569] "Administrator" means an individual or organization that has the authority to operate the system, evaluate patents, and decide on countermeasures.

[0570] The present invention is a system for efficiently managing business ideas, reducing the risk of patent infringement, and ultimately monetizing them. A specific implementation method for this system is described below.

[0571] System configuration

[0572] The system includes the following main components:

[0573] 1. Client terminal

[0574] 2. Server

[0575] 3. Database

[0576] 4. Generative AI Models

[0577] 5. Patent Information Database

[0578] 6. User Interface

[0579] System Operation

[0580] Idea Submission Phase

[0581] A user accesses a web page using a client device and submits a new business idea using a generative AI model. The submitted idea is sent to the server via an HTTP request. The server stores the received idea information in a database, and each idea is assigned a unique idea ID.

[0582] As a specific example, a user can enter a "suggestion for a new voice assistant" and click the submit button, which will save the suggestion to the database.

[0583] Example prompt: "Voice assistants are a new way to manage IoT devices in the home."

[0584] Idea Learning Phase

[0585] The server takes the ideas stored in the database and converts them into a format that the generative AI model can process. The converted data is then fed into the generative AI model to train it, during which it learns specific characteristics of the ideas.

[0586] Patent database matching

[0587] The generative AI model compares the learned ideas with a patent information database to verify the possibility of patent infringement. The AI ​​model generates a similarity score between the idea and the patent information and returns the result to the server.

[0588] Patent infringement determination phase

[0589] The server analyzes the similarity scores and adds ideas that exceed a certain score threshold to a list of potential patent infringements. This list is then sent to an administrator's terminal.

[0590] Countermeasure decision phase

[0591] The administrator's terminal displays a list of ideas that may be infringing patents and performs a detailed analysis. The administrator reviews the list and decides on countermeasures, such as proposing joint development, requesting compensation, or collecting usage fees. These countermeasures are implemented in cooperation with the legal department.

[0592] Monetization Phase

[0593] The server launches the contract management system based on the selected solution, generates a draft contract, and presents it to the administrator. Progress is also monitored after the contract is signed, and alerts are displayed regarding important milestones. The progress of the contract is displayed on the administrator's terminal.

[0594] Specific examples

[0595] As a concrete example, consider the process in which User A submits an idea for a new voice assistant. The server receives the idea and stores it in a database. The generative AI model learns from the idea and compares it with existing patent information databases. If Patent B is determined to be similar, the server adds it to a patent infringement list and notifies the administrator. The administrator evaluates the infringement risk and enters into a joint development agreement with the holder of Patent B to monetize it.

[0596] In this way, the system utilizes generative AI models to efficiently manage patent infringement risks for business ideas and maximize profits.

[0597] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0598] Step 1:

[0599] Idea Submission

[0600] A user accesses a web page using a client device and submits a new business idea using a generative AI model. When the user fills out the form with a summary of the idea, details, and potential use cases, and clicks the submit button, the information is sent to the server via an HTTP request.

[0601] Input: Idea information from users

[0602] Output: Send to server

[0603] Specific behavior: The user enters a "Suggestion for a new voice assistant" and clicks the submit button.

[0604] Step 2:

[0605] Receiving and storing data

[0606] The server processes the received idea information and stores it in a database, where each idea is assigned a unique idea ID.

[0607] Input: Idea information submitted by the user

[0608] Output: Idea information stored in the database and a unique idea ID

[0609] Specific operation: The server saves the "new voice assistant proposal" in the database and assigns it the ID "AI1234."

[0610] Step 3:

[0611] Data format conversion

[0612] The server takes the idea information stored in the database and converts it into a format that can be processed by the generative AI model. This conversion operation involves preprocessing and shaping the data.

[0613] Input: Idea information stored in the database

[0614] Output: Data in a format that can be processed by the generative AI model (e.g., JSON format)

[0615] Specific operation: The server converts "New voice assistant proposal (ID: AI1234)" into JSON format.

[0616] Step 4:

[0617] Input to generative AI models

[0618] The server loads a pre-trained generative AI model and inputs the transformed data into the model, where the model analyzes the idea information and extracts specific features.

[0619] Input: Data in a format that the generative AI model can process.

[0620] Output: Data with extracted features

[0621] Specific operation: The server inputs the "new voice assistant proposal" in JSON format into the generative AI model and extracts features.

[0622] Step 5:

[0623] Matching with patent information database

[0624] The server uses the features extracted by the generative AI model to access the patent information database, retrieve existing patent information from the patent information database, and compare it with the generative AI model.

[0625] Input: Data from which features have been extracted, information from patent information database

[0626] Output: Similarity score indicating the likelihood of patent infringement

[0627] Specific operation: The server compares the features of the "new voice assistant proposal" with the patent data and generates a similarity score.

[0628] Step 6:

[0629] Analysis of similarity scores

[0630] The server receives and analyzes the similarity scores generated by the generative AI model to determine whether they exceed a certain score threshold.

[0631] Input: Similarity score from the generative AI model

[0632] Output: A list of potentially infringing ideas

[0633] Specific operation: The server analyzes the similarity score "0.85" and adds "New voice assistant proposal" to the patent infringement list.

[0634] Step 7:

[0635] Notifications and List Generation

[0636] The server creates a list of ideas that may be patent infringing and notifies the administrator of this list, which includes a summary of the idea and a similarity score.

[0637] Input: List of potentially infringing ideas

[0638] Output: Notification to administrator

[0639] Specific operation: The server adds the "new voice assistant proposal" to the "patent infringement list" and notifies the administrator.

[0640] Step 8:

[0641] Evaluation by the administrator and consideration of countermeasures

[0642] A list of ideas that may be patent infringing is displayed on the administrator's terminal, and the administrator evaluates the list and considers countermeasures, such as proposing joint development, requesting compensation, or collecting usage fees.

[0643] Input: List of potentially infringing ideas

[0644] Output: Administrator decides what to do

[0645] Specific actions: The administrator evaluates the "New Voice Assistant Proposal" and proposes collaboration with the patent holder.

[0646] Step 9:

[0647] Contract management and progress display

[0648] The server starts the contract management system based on the selected countermeasure, generates a draft contract, manages progress, and displays the progress on the terminal.

[0649] Input: Administrator's decision on how to proceed

[0650] Output: Contract draft, progress display

[0651] Specific operation: The server generates a draft contract "Joint Development Agreement" and presents it to the administrator. The progress is displayed on the terminal and an alert is sent if necessary.

[0652] (Application example 1)

[0653] 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."

[0654] It is extremely important to efficiently manage patent infringement risks for generated business ideas and maximize their potential for monetization. However, conventional systems have the problem of requiring a great deal of time and effort to verify novelty, assess patent infringement risks, and monetize ideas. Furthermore, the process from idea submission to patent infringement checks and monetization is often inconsistent and inefficient. To address these issues, the present invention provides a system for centrally managing these processes and making them run efficiently and quickly.

[0655] 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.

[0656] In this invention, the server includes means for receiving the generated business ideas and storing them in a database, means for identifying and learning the ideas stored in the database, means for comparing the learned ideas with a patent database to screen for possible patent infringement, means for outputting and notifying a list of the ideas that may be infringing, and means for evaluating the possibility of patent infringement regarding the user's idea and presenting countermeasures and monetization means. This makes it possible to quickly and efficiently evaluate the patent infringement risk of the generated business ideas and present appropriate countermeasures and monetization possibilities.

[0657] A "generated business idea" is a new business idea or plan created using a generative AI model.

[0658] A "database" is a collection of data managed on a computer system, and is a place to store generated business ideas and patent information.

[0659] "Identification" refers to properly recognizing the information stored in the database and identifying which idea it is.

[0660] "Learning" is the process by which an AI model analyzes and understands the specific characteristics of the ideas it receives as input.

[0661] A "patent database" is a database system for storing and managing existing patent information.

[0662] "Matching" is the act of comparing learned ideas with information in a patent database to find matches or similarities.

[0663] "Patent infringement" refers to a situation in which a newly generated business idea may infringe an existing patent right.

[0664] A "list" is a list of items organized based on specific criteria.

[0665] "Notification" is the act of informing relevant parties of specific information.

[0666] "Countermeasures" are specific actions or methods taken in response to problems or issues that arise.

[0667] "Monetization" is the process of implementing a business idea and generating revenue.

[0668] A "user interface" is software or hardware that serves as a window through which a user interacts with a system.

[0669] An "administrator" is a person responsible for operating and maintaining a system.

[0670] This invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and ultimately monetizing them. The main components of this system include a server for receiving, storing, learning from, and analyzing generated ideas, a terminal for users to submit ideas, and a function for checking against patent databases and notifying the results.

[0671] 1. Idea submission phase

[0672] Idea Submission

[0673] Users submit their generated business ideas to the system using a client device (smartphone, tablet, etc.). They are provided with a form to enter a summary of their idea, details, and potential use cases. For example, if you have an idea for an "AI-based movie recommendation system," you might use the following prompt:

[0674] Example prompt sentence:

[0675] Title: "AI-based movie recommendation system"

[0676] Summary: "An AI-based movie recommendation system based on a user's viewing history and preferences. It analyzes a user's past viewing data and recommends similar movies."

[0677] 2. Idea learning phase

[0678] The server receives the submitted ideas and stores them in a database. Each saved idea is assigned a unique idea ID. This data is converted into an appropriate format for input into an AI model, which then learns from it. For example, TensorFlow or PyTorch are used as AI models.

[0679] 3. Patent database matching

[0680] The learned ideas are then matched against a patent database. The server compares the ideas with existing patent information to assess the likelihood of patent infringement. This process uses an AI model to extract specific features and calculate a similarity score.

[0681] 4. Patent infringement determination phase

[0682] The server analyzes the similarity scores received from the AI ​​model and marks ideas that exceed a certain threshold as potentially infringing. These ideas are then listed and notified to the administrator, who then displays the notification on their device.

[0683] 5. Countermeasures Consideration Phase

[0684] Countermeasures include proposing joint development, requesting compensation, collecting usage fees, etc. The server manages these countermeasures and generates the necessary draft contracts.

[0685] 6. Monetization Phase

[0686] The server launches the contract management system based on the selected countermeasure. The progress of the contract is displayed to the administrator through a user interface, allowing the administrator to grasp important milestones and progress in real time.

[0687] This system quickly and efficiently evaluates the patent infringement risk of generated business ideas and proposes appropriate countermeasures and monetization possibilities. The hardware used is AWS EC2 instances, and the database is AWS RDS (MySQL). The AI ​​models used are TensorFlow and PyTorch.

[0688] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0689] Step 1:

[0690] A user enters the generated business idea using the idea submission form and clicks the submit button. The user enters a title and a summary, such as "AI-based movie recommendation system." The entered data is sent from the client device to the server as an HTTP POST request. The input data (idea title and summary) is sent, and the server processes the received data based on it.

[0691] Step 2:

[0692] The server stores the received business ideas in a database. When stored, each idea is assigned a unique idea ID. This idea ID is used to identify the idea in subsequent processing. The server stores the input data (idea title and summary) and generates a unique idea ID as its output.

[0693] Step 3:

[0694] The server takes ideas stored in the database and converts them into the appropriate format for input to the AI ​​model. This format conversion prepares the data in a format that the AI ​​model can accurately parse. The server converts the input data (idea details based on idea ID) and generates a data format that is compatible with the AI ​​model as output.

[0695] Step 4:

[0696] The AI ​​model receives the converted idea data and begins the learning process. The AI ​​model analyzes the features of the ideas using TensorFlow or PyTorch and learns specific patterns. The AI ​​model processes the input data (the format-converted idea data) and generates learned feature data as its output.

[0697] Step 5:

[0698] The server compares the trained idea data with a patent database, retrieves related patent information from the patent database, and calculates a similarity score using the AI ​​model. The server performs data matching using input data (trained feature data and patent database information) and generates a similarity score as its output.

[0699] Step 6:

[0700] The server analyzes the similarity scores received from the AI ​​model and evaluates the possibility of patent infringement. It lists ideas that exceed a certain threshold as being at risk of patent infringement and generates information for notification. The server evaluates based on the input data (similarity scores) and generates a list of ideas at risk of patent infringement as its output.

[0701] Step 7:

[0702] The server notifies the administrator's terminal of the generated list of ideas that pose a risk of patent infringement. The terminal receives the information and visually displays it to the administrator. Patent information and idea details are provided to make it easier for the administrator to consider specific countermeasures. The server notifies the administrator of the input data (the list of ideas that pose a risk of patent infringement), and the terminal displays the information to the administrator as output.

[0703] Step 8:

[0704] The administrator uses a terminal to check the displayed list of ideas that pose a risk of patent infringement and consider specific countermeasures, such as proposing joint development, requesting compensation, or collecting usage fees. The administrator decides on specific countermeasures for the ideas and takes concrete action to monetize them.

[0705] Step 9:

[0706] The server starts the contract management system based on the countermeasures decided by the administrator. It generates the necessary draft contracts and displays the progress in real time. The administrator uses a terminal to monitor the contract management system and manage the progress of the contract. The server processes the input data (details of the countermeasures) and generates contract management progress information as its output.

[0707] 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.

[0708] The present invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and ultimately monetizing them, and further combines an emotion engine that recognizes user emotions. The system includes the following main components and processing flow:

[0709] 1. Idea submission phase

[0710] Idea Submission

[0711] Users submit their new business ideas using generative AI by entering details into the system's web form. This information is sent to the server, and the emotion engine simultaneously collects and analyzes the user's emotional data.

[0712] Database storage

[0713] The server receives submitted ideas and emotion data and stores them in a database, where each idea is assigned a unique idea ID.

[0714] 2. Idea learning phase

[0715] Data format conversion

[0716] The server converts new ideas and corresponding emotional data stored in the database into an appropriate data format to provide to the AI ​​model.

[0717] Input to the AI ​​model

[0718] The server then inputs the converted data into an AI model to train it, which then learns the specific characteristics of the idea and the user's emotional state.

[0719] 3. Patent database matching

[0720] Data Verification

[0721] The AI ​​model compares the ideas it learns with existing patent information stored in a patent database, thereby detecting whether the submitted idea infringes on an existing patent.

[0722] Generating Similarity Scores

[0723] The AI ​​model generates a similarity score between the idea and the patent information and returns it to the server along with analysis results that take into account the user's emotional data.

[0724] 4. Patent infringement determination phase

[0725] Evaluating the results

[0726] The server analyzes the similarity scores and sentiment data analysis results received from the AI ​​model, and if they exceed a certain score threshold, marks the idea as potentially patent infringing.

[0727] List Generation and Notifications

[0728] The server compiles a list of ideas that may be patent infringing and notifies the administrator terminal.

[0729] 5. Countermeasure decision phase

[0730] Viewing the list

[0731] The terminal displays a list of ideas that may be infringing patents on the user interface. The administrator checks the list and considers countermeasures.

[0732] Consideration of countermeasures

[0733] The administrator works with the legal department to determine the best course of action to address the risk of patent infringement, which may include joint development, seeking compensation, or collecting royalties.

[0734] 6. Monetization Phase

[0735] Contract Management

[0736] The server starts a contract management system based on the selected solution, generates a draft contract, and tracks the progress of the contract.

[0737] Progress display

[0738] The terminal displays the contract's progress in a user interface and sends notifications to administrators about important milestones and deadlines.

[0739] Specific examples

[0740] Example 1: A new voice assistant idea

[0741] User A submits an idea for a new voice assistant using generative AI to a contest. The server's emotion engine collects and analyzes the idea along with User A's emotion data. The idea and emotion data stored in the database are learned by an AI model and compared with the patent database. If Patent B is determined to be similar, the server adds it to a patent infringement list and notifies the administrator. The administrator considers the risk of infringement and the emotion data, and enters into a joint development agreement with the holder of Patent B to monetize it.

[0742] In this way, the system of the present invention, which combines an emotion engine, can more accurately manage the patent infringement risk of generated AI business ideas and maximize profits.

[0743] The processing flow will be explained below.

[0744] Step 1:

[0745] The user enters the generated AI business idea into the system's web form, provides detailed information (summary, use case, etc.), and presses the submit button. At the same time as submission, the emotion engine collects the user's facial expression and voice data.

[0746] Step 2:

[0747] The server receives the submitted ideas and stores them in a database. A unique idea ID is assigned when the ideas are saved. The server also stores the user's emotional data collected by the emotion engine.

[0748] Step 3:

[0749] The server extracts new ideas and corresponding emotional data stored in the database and converts them into an appropriate data format to provide to the AI ​​model.

[0750] Step 4:

[0751] The server inputs the converted data into the AI ​​model and starts training the model, which learns the content of the idea and the user's emotional state.

[0752] Step 5:

[0753] The AI ​​model uses the training data to match the submitted idea with existing patent information in the database, and also incorporates user sentiment data into the analysis.

[0754] Step 6:

[0755] Based on the matching results, the AI ​​model generates a similarity score that reflects not only the degree of match with the idea content but also the confidence level based on the user's emotional state.

[0756] Step 7:

[0757] The server evaluates the similarity scores received from the AI ​​model and the results of the sentiment data analysis, and marks ideas with scores above a certain threshold as having a high probability of patent infringement.

[0758] Step 8:

[0759] The server creates a list of ideas that are likely to infringe patents and notifies the administrator's terminal, along with user emotional data.

[0760] Step 9:

[0761] The terminal displays a list of ideas that may be patent infringing on the user interface. The administrator checks the list and considers countermeasures based on the idea details and user emotion data.

[0762] Step 10:

[0763] The administrator works with the legal department to determine the best course of action to address patent infringement risk, which may include joint development, seeking compensation, or collecting royalties.

[0764] Step 11:

[0765] The server starts the contract management system based on the selected solution, generates the necessary contract drafts, and tracks the progress of the contract and provides it to the administrator.

[0766] Step 12:

[0767] The terminal displays the contract's progress in a user interface, and administrators can receive notifications based on important milestones and deadlines.

[0768] As a concrete example, when User A submits an idea for a new voice assistant, the server stores the idea and emotional data in a database, and uses an AI model to compare it with a patent database and finds that it is similar to Patent B. The server notifies this information to the administrator, who, taking User A's emotional data into account, decides to enter into a joint development agreement with the holder of Patent B. Through this process, the risk of patent infringement of the idea can be reduced while monetizing it.

[0769] Example 2

[0770] 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."

[0771] In today's business environment, generating and managing new business ideas is an important challenge. However, generated business ideas may infringe existing patents, which can hinder business development. Furthermore, systems for quickly detecting potential patent infringement and taking appropriate countermeasures are often lacking. The present invention aims to efficiently manage generated business ideas, reduce patent infringement risks, and ultimately monetize them. In particular, the present invention aims to provide a system that uses user emotion data to make more accurate judgments and provide appropriate notifications and countermeasures to administrators.

[0772] The identification process by the identification 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 generated business ideas and storing them in a database; means for converting the ideas and emotion data stored in the database into an appropriate data format; means for inputting the converted idea data and emotion data into an AI model and training the model; means for comparing the learned ideas with a patent database to screen for possible patent infringement; and means for outputting and notifying the ideas with the possibility of patent infringement as a list. This enables the patent infringement risk of generated business ideas to be quickly and accurately determined and notified to an administrator in a timely manner. Furthermore, by including means for displaying the list on a user interface and allowing an administrator to identify ideas with the possibility of patent infringement, the administrator can easily perform detailed analysis and consider countermeasures. Furthermore, by including means for presenting optimal countermeasures for the ideas with the possibility of patent infringement and promoting monetization, the system can realize appropriate utilization of business ideas and maximize profits.

[0773] A "generated business idea" is a new business concept or plan devised by a user using a generative AI model.

[0774] A "database" is a digital recording device that structures and stores information such as business ideas and emotional data, allowing it to be searched and retrieved efficiently later.

[0775] "Emotional Data" refers to data that represents the user's emotional state and is collected and analyzed by the Emotion Engine.

[0776] A "data format" is a set of standards or rules for arranging digital data into a particular structure or form.

[0777] An "AI model" is an artificial intelligence system built using machine learning algorithms that learns specific data and makes predictions and judgments.

[0778] A "patent database" is a digital recording device that aggregates existing patent information and allows for searching and matching.

[0779] The "similarity score" is a numerical indicator that shows the degree of similarity when the AI ​​model compares the generated idea with existing patent information.

[0780] "User interface" refers to the screen and input device that a user uses to operate and exchange information with a system.

[0781] The "administrator" is a person whose role is to supervise and operate the entire system, assess the risk of patent infringement, and decide on countermeasures.

[0782] "Countermeasures" refer to specific actions and methods to be taken when a risk of patent infringement is discovered, and include joint development, claims for compensation, and collection of usage fees.

[0783] "Monetization" is the process of obtaining economic benefits from generated business ideas and associated patents.

[0784] The present invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and monetizing them. This system includes multiple components that are operated mainly by a server, a terminal, and a user. Details for specifically implementing the present invention will now be described.

[0785] System Overview

[0786] In this system, users input new business ideas using a generative AI model, which are then processed by a server. Specifically, the server receives the ideas, stores them in a database, and then converts them into the appropriate format and inputs them into the AI ​​model. The AI ​​model compares the learned ideas with an existing patent database to evaluate the possibility of patent infringement. This information is sent to an administrator's terminal, which determines appropriate countermeasures and monetizes the ideas, forming a series of steps.

[0787] Hardware and software used

[0788] Server: The server is the central component that stores data, transforms it, inputs it into the AI ​​model, and checks it against the patent database. The server runs a MySQL database, TensorFlow, and other applications.

[0789] Terminal: The administrator terminal has a user interface and uses front-end technologies such as React to support list display, notifications, and detailed analysis.

[0790] Generative AI models: Generative AI models such as GPT-4 are used to support the generation of new business ideas.

[0791] Emotion engine: Emotion recognition uses Azure Cognitive Services to analyze the user's emotional state in real time.

[0792] Processing flow

[0793] 1. Idea Submission:

[0794] The user uses the generative AI to generate new business ideas and enter them into the system's web form. For example, the user enters the following prompt into the generative AI:

[0795] "We are thinking of an idea for a new voice assistant. Please propose an assistant that can not only control home appliances with voice commands, but also detect the user's emotional state and change its response accordingly."

[0796] This idea and emotion data is sent to the server when the user submits it in a web form.

[0797] 2. Database storage:

[0798] The server stores the received ideas and emotion data in a MySQL database and assigns each idea a unique ID.

[0799] 3. Data format conversion:

[0800] The server converts the stored ideas and emotional data into a format such as JSON, making it recognizable to the AI ​​model.

[0801] 4. Input to the AI ​​model:

[0802] The server inputs the converted data into an AI model trained using TensorFlow for learning.

[0803] 5. Patent database matching:

[0804] The AI ​​model compares the learned ideas with a patent database to assess the likelihood of patent infringement, and the matching results are returned to the server as a similarity score.

[0805] 6. Evaluation and Notification of Results:

[0806] Based on the similarity score and the results of the emotion data analysis, the server generates a list of ideas that may be patent infringing and notifies the administrator terminal.

[0807] Specific examples

[0808] User A inputs an idea for a new voice assistant, and the server collects and analyzes the idea along with emotional data. The idea and emotional data stored in the database are converted into an appropriate format by the server and input into the AI ​​model. The AI ​​model compares the idea with a patent database, and if it is determined to be similar to patent B, the server adds it to a patent infringement list and notifies the administrator. The administrator checks this list on the user interface on their device, and, taking into consideration the risk of patent infringement and the emotional data, seeks to monetize the idea by entering into a joint development agreement with the holder of patent B.

[0809] In this way, the system of the present invention can accurately manage the patent infringement risk of generated business ideas and maximize profitability.

[0810] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0811] System processing flow

[0812] Step 1: Submit your idea

[0813] Specific behavior:

[0814] Using a generative AI model (such as GPT-4), users input their new business idea into a web form, and the emotion engine collects and analyzes the user's emotional data. For example, the following prompt sentence can be input into the generative AI model:

[0815] "We are thinking of an idea for a new voice assistant. Please propose an assistant that can not only control home appliances with voice commands, but also detect the user's emotional state and change its response accordingly."

[0816] input:

[0817] Ideas from users and idea details and sentiment data generated by the generative AI model.

[0818] output:

[0819] Idea details and sentiment data are submitted to the server through a web form.

[0820] Step 2: Save to database

[0821] Specific behavior:

[0822] The server stores idea details and sentiment data received through the web form in a MySQL database, and assigns each idea a unique idea ID.

[0823] input:

[0824] Idea details and sentiment data submitted via web forms.

[0825] output:

[0826] Idea details and sentiment data, idea ID stored in the database.

[0827] Step 3: Convert data formats

[0828] Specific behavior:

[0829] The server converts the idea details and emotion data stored in the database into a data format (e.g., JSON) that the AI ​​model can recognize.

[0830] input:

[0831] Idea details and sentiment data stored in a database.

[0832] output:

[0833] The converted idea details and sentiment data in JSON format.

[0834] Step 4: Input to the AI ​​model

[0835] Specific behavior:

[0836] The server inputs the converted data into an AI model trained using TensorFlow to learn the features and sentiment data of the ideas.

[0837] input:

[0838] Idea details and sentiment data converted into JSON format.

[0839] output:

[0840] AI model parameters generated as a result of learning.

[0841] Step 5: Matching with patent databases

[0842] Specific behavior:

[0843] The AI ​​model matches the learned ideas with existing patent information stored in a patent database by sending queries to the patent database to retrieve relevant patent information and generate a similarity score.

[0844] input:

[0845] Parameters of the trained AI model, patent information from the patent database.

[0846] output:

[0847] Similarity score.

[0848] Step 6: Evaluate and communicate results

[0849] Specific behavior:

[0850] The server integrates and analyzes the similarity scores and emotion data analysis results received from the AI ​​model to create a list of ideas that may be infringing patents, and notifies the administrator terminal of the list.

[0851] input:

[0852] Similarity scores, emotional data analysis results.

[0853] output:

[0854] A list of ideas that may be infringing patents is sent to the administrator's terminal.

[0855] Step 7: View the list

[0856] Specific behavior:

[0857] The terminal (for administrator) displays a list of ideas that may be infringing patents on a user interface. The administrator can check the list and individually review the details of each idea.

[0858] input:

[0859] A list of ideas that may be subject to patent infringement.

[0860] output:

[0861] The idea list and detailed information displayed in the user interface.

[0862] Step 8: Consider and decide on countermeasures

[0863] Specific behavior:

[0864] The administrator will work with the legal department to determine the best course of action to address the risk of patent infringement, including joint development, seeking compensation, and collecting usage fees.

[0865] input:

[0866] A list of potentially infringing ideas and detailed information about them.

[0867] output:

[0868] Determining countermeasures to patent infringement risks.

[0869] Step 9: Manage and monetize your contracts

[0870] Specific behavior:

[0871] Based on the selected action, the server will launch the contract management system to generate a draft contract, track the contract's progress, and notify the administrator of important milestones and deadlines.

[0872] input:

[0873] The decided response measures.

[0874] output:

[0875] Draft contract, progress notification.

[0876] (Application example 2)

[0877] 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."

[0878] Conventional business idea management systems lack a means to efficiently assess the patent infringement risk of generated ideas, often hindering the monetization of inventions. There is also a need for an idea evaluation system that takes user emotional data into account and for improved accuracy in assessing patent infringement risk. Furthermore, idea management in the advertising industry poses unique challenges, necessitating a means to evaluate advertising effectiveness in real time using emotional data.

[0879] The identification process by the identification 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 generated business ideas and storing them in a database; means for identifying and learning the ideas stored in the database; means for comparing the learned ideas with a patent database to screen for possible patent infringement; means for outputting and notifying the ideas that may be patent infringing as a list; means for collecting and analyzing emotional data; and means for inputting the emotional data into a generative AI model and generating prompt sentences. This enables efficient management of patent infringement risk and highly accurate idea evaluation by taking user emotional data into account. Particularly in the advertising industry, emotional data can be used to evaluate advertising effectiveness in real time, thereby maximizing profits.

[0880] "Generated business ideas" refer to new business concepts and plans generated using AI models.

[0881] A "database" is a system for systematically storing and managing digital information.

[0882] "Means of learning" refers to the process of using an AI model to analyze data, understand its patterns and characteristics, and learn from them.

[0883] A "patent database" is a digital system that stores and manages existing patent information.

[0884] A "potential patent infringement screening process" is a process for determining whether a newly generated business idea infringes an existing patent.

[0885] The "means of outputting and notifying as a list" refers to a system for listing ideas that may constitute patent infringement and notifying relevant parties.

[0886] "Emotional data" is data that quantifies and analyzes the user's emotional state.

[0887] A "generative AI model" is an artificial intelligence model that analyzes generated business ideas and emotional data to generate new business ideas.

[0888] A "prompt" is text that contains instructions or questions that are input to a generative AI model.

[0889] This invention is a system for efficiently managing business ideas created using generative AI, reducing the risk of patent infringement, and ultimately monetizing them. This system also collects and analyzes user emotional data to more accurately assess the risk of patent infringement. It is particularly intended for use in the advertising industry, with the system intended for use on smartphones, smart glasses, and head-mounted displays.

[0890] Hardware and software used

[0891] 1. Hardware:

[0892] Smartphone

[0893] Smart Glasses

[0894] head-mounted display

[0895] 2. Software:

[0896] Emotion Engine Module (EmotionEngine)

[0897] Generative AI Models

[0898] Database System

[0899] Server API

[0900] System program description

[0901] The server operates the system through the following steps:

[0902] 1. Idea Submission Phase:

[0903] Users use their smartphones, smart glasses, or head-mounted displays to input new advertising ideas that are then used by generative AI models, and the submitted data is sent to a server.

[0904] At the same time, the emotion engine module is used to collect the user's emotion data in real time, which is also sent to the server.

[0905] 2. Database storage:

[0906] The server receives the submitted idea and emotion data and stores them in a database along with a unique idea ID.

[0907] 3. Idea learning phase:

[0908] The server converts the ideas and emotion data stored in the database into an appropriate data format and inputs it into a generative AI model for learning.

[0909] 4. Patent database matching:

[0910] The learned ideas are compared with a patent database to evaluate the possibility of patent infringement. Here, a similarity score is generated between the idea and existing information in the patent database, and analyzed together with sentiment data.

[0911] 5. Patent Infringement Determination Phase:

[0912] Based on the analysis results of the similarity score and emotion data, ideas that pose a risk of patent infringement are selected and listed.

[0913] The listed ideas are notified to the administrator and displayed through the user interface.

[0914] 6. Countermeasure decision phase:

[0915] The administrator reviews the list and works with the legal department to determine the best course of action, such as joint development or a claim for compensation.

[0916] 7. Monetization Phase:

[0917] Contract management is performed and the server tracks progress, providing notifications about important milestones and deadlines.

[0918] Specific examples

[0919] When a user submits an idea for a new advertising campaign, they enter a prompt like this:

[0920] "Submit your new ad campaign idea. Use generative AI to fill in your idea and allow the sentiment engine to collect data."

[0921] Based on this input, the server collects and analyzes ideas and emotional data. The analysis results are compared with a patent database to assess the risk of patent infringement. If there is a risk, an administrator is notified and appropriate countermeasures are taken. Emotional data can also be used to evaluate the effectiveness of advertising in real time, allowing for the development of optimal monetization strategies.

[0922] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0923] Step 1:

[0924] A user uses a smartphone, smart glasses, or head-mounted display to input ideas for a new advertising campaign using a generative AI model. The input prompt is, "Submit a new advertising campaign idea. Please enter your idea using generative AI and allow the emotion engine to collect data." The emotion engine analyzes the user's facial expressions and voice while they are inputting their idea and generates emotion data. The generated idea and emotion data are sent to the server. The input is the idea and emotion data entered by the user, and the output is the idea and emotion data sent to the server.

[0925] Step 2:

[0926] The server receives the submitted idea and emotion data and stores them in a database with a unique idea ID, ensuring the data is available for reference in later processing steps. The input is the idea and emotion data submitted in step 1, and the output is the idea and emotion data stored in the database.

[0927] Step 3:

[0928] The server converts the idea and emotion data stored in the database into an appropriate format and inputs it into the generative AI model. The generative AI model then learns new ideas based on the received data. The input here is the idea and emotion data stored in the database, and the output is the data converted into the format input into the generative AI model.

[0929] Step 4:

[0930] The server compares the learned ideas with a patent database to assess their potential for patent infringement. This process involves comparing the ideas with existing patent information stored in the patent database and calculating a similarity score. Sentiment data is also taken into account. The input is the idea learned by the generative AI model, and the output is a similarity score indicating the potential for patent infringement.

[0931] Step 5:

[0932] The server lists ideas that may be at risk of patent infringement and notifies the administrator. The list is displayed on the administrator's terminal, and detailed information about ideas that are at risk of patent infringement is provided. The input is a similarity score indicating the possibility of patent infringement, and the output is a risk list displayed on the administrator's terminal.

[0933] Step 6:

[0934] The administrator checks the risk list through a terminal and works with the legal department to consider the best course of action. Specific countermeasures are considered, such as joint development, claims for compensation, and the collection of usage fees. The input is the risk list and detailed information displayed on the terminal, and the output is the decision on the countermeasure.

[0935] Step 7:

[0936] Based on the determined action, the server manages the contract and tracks the progress. It notifies the administrator about important milestones and deadlines of the contract. The input is the determined action, and the output is progress tracking and notification.

[0937] The above are the specific processing steps in the embodiment of the invention.

[0938] 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.

[0939] 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.

[0940] 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.

[0941] [Third embodiment]

[0942] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0943] 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.

[0944] 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).

[0945] 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.

[0946] 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.

[0947] 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).

[0948] 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.

[0949] 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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."

[0954] The present invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and ultimately monetizing them. The system includes the following major components:

[0955] 1. Idea submission phase

[0956] Idea Submission

[0957] Users submit new business ideas using generative AI to the system by accessing a web form and providing a summary of the idea, details, and potential use cases. This information is then sent to the server.

[0958] Database storage

[0959] The server receives the submitted ideas and stores them in a database, with each idea being assigned a unique idea ID.

[0960] 2. Idea learning phase

[0961] Data format conversion

[0962] The server converts new ideas stored in the database into the appropriate format to feed to the AI ​​model.

[0963] Input to the AI ​​model

[0964] The server then feeds the converted data into an AI model to train it, which then learns and analyzes specific features of the ideas.

[0965] 3. Patent database matching

[0966] Data Verification

[0967] The AI ​​model compares the ideas it learns with existing patent information stored in a patent database, thereby detecting whether the submitted idea infringes on an existing patent.

[0968] Generating Similarity Scores

[0969] The AI ​​model generates a similarity score between the idea and the patent information and returns the results to the server.

[0970] 4. Patent infringement determination phase

[0971] Evaluating the results

[0972] The server analyzes the similarity scores received from the AI ​​model and marks ideas that exceed a certain score threshold as potentially patent infringing.

[0973] List Generation and Notifications

[0974] The server generates a list of ideas that may be patent infringing and notifies the administrator's terminal.

[0975] 5. Countermeasure decision phase

[0976] Viewing the list

[0977] The terminal displays to the user a list of ideas that may be patent infringing, including details of the ideas and information on similar patents.

[0978] Consideration of countermeasures

[0979] The administrator will evaluate the list and consider countermeasures, which may include proposing joint development, seeking compensation, or collecting usage fees, etc. Countermeasures will be implemented in coordination with the legal department.

[0980] 6. Monetization Phase

[0981] Contract Management

[0982] The server starts the contract management system based on the selected countermeasure. A draft contract is generated and presented to the administrator. Progress management after the contract is concluded is also performed.

[0983] Progress display

[0984] The terminal displays the contract's progress in a user interface and notifies the administrator, along with alerts about the progress of each phase and key milestones.

[0985] Specific examples

[0986] Example 1: A new voice assistant idea

[0987] User A submits an idea for a new voice assistant using generative AI to a contest. The server receives the idea and stores it in a database. The AI ​​model learns from the idea and compares it with existing patent databases. If Patent B is determined to be similar, the server adds it to a patent infringement list and notifies the administrator. The administrator evaluates the infringement risk and enters into a joint development agreement with the holder of Patent B to monetize it.

[0988] As described above, the system of the present invention can efficiently manage the risk of patent infringement for business ideas of generative AI and maximize profits.

[0989] The processing flow will be explained below.

[0990] Step 1:

[0991] The user enters the generated AI business idea into the system's web form, fills in the necessary details, and then presses the submit button.

[0992] Step 2:

[0993] The server receives the submitted ideas and stores them in a database, where each idea is assigned a unique idea ID.

[0994] Step 3:

[0995] The server identifies newly saved ideas and converts them from the database into the appropriate data format to feed to the AI ​​model.

[0996] Step 4:

[0997] The server inputs the converted data into an AI model to train it, which then learns the characteristics of the ideas.

[0998] Step 5:

[0999] The server compares the ideas learned by the AI ​​model with a patent database and calculates a similarity score to existing patent information.

[1000] Step 6:

[1001] The server analyzes the similarity scores received from the AI ​​model and, if the score exceeds a certain threshold, marks the idea as potentially patent infringing.

[1002] Step 7:

[1003] The server compiles a list of ideas that may be patent infringing and notifies the administrator terminal.

[1004] Step 8:

[1005] The terminal displays a list of ideas that may be infringing patents on the user interface, and the administrator checks the list and considers countermeasures.

[1006] Step 9:

[1007] The administrator works with the legal department to determine the best course of action to address the risk of patent infringement, which may include joint development, seeking compensation, or collecting royalties.

[1008] Step 10:

[1009] The server launches a contract management system based on the selected solution, generates a draft contract, and tracks the progress of the contract.

[1010] Step 11:

[1011] The terminal displays the contract's progress in a user interface and sends notifications to administrators about important milestones and deadlines.

[1012] Through these steps, the system can efficiently manage the patent infringement risk of generated AI business ideas and maximize profits.

[1013] Example 1

[1014] 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."

[1015] Currently, there is a lack of systems that can quickly and efficiently assess the risk of patent infringement for business ideas created using generative AI and take appropriate countermeasures. In particular, there is a need for automation and improved accuracy in the process of detecting the patent infringement risk of ideas. Another issue is the lack of effective management of the process of presenting countermeasures and monetizing ideas after assessing the risk of patent infringement.

[1016] 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.

[1017] In this invention, the server includes means for receiving generated business ideas and storing them in a database, means for identifying the ideas stored in the database and learning them using a generative AI model, means for comparing the learned ideas with a patent information database to determine whether they may infringe a patent, and means for analyzing the similarity scores generated by the AI ​​model and outputting and notifying a list of ideas that may infringe a patent. This makes it possible to accurately and quickly evaluate the patent infringement risk of business ideas generated by generative AI, present appropriate countermeasures, and efficiently manage the monetization process.

[1018] A "generated business idea" is a newly devised commercial concept or plan using a generative AI model.

[1019] The "database" is an information storage system for centrally storing and managing generated business ideas and other related information.

[1020] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to process and learn from data.

[1021] The "Patent Information Database" is a database system that aggregates patent information registered to date and allows for searching and matching.

[1022] A "similarity score" is a numerical representation of the similarity between a business idea and existing patent information, and is an indicator for assessing the possibility of patent infringement based on the level of the similarity.

[1023] A "list" is a list of ideas or information selected based on specific criteria.

[1024] "User interface" refers to the screen or operation panel that administrators and users use to interact with the system.

[1025] "Administrator" means an individual or organization that has the authority to operate the system, evaluate patents, and decide on countermeasures.

[1026] The present invention is a system for efficiently managing business ideas, reducing the risk of patent infringement, and ultimately monetizing them. A specific implementation method for this system is described below.

[1027] System configuration

[1028] The system includes the following main components:

[1029] 1. Client terminal

[1030] 2. Server

[1031] 3. Database

[1032] 4. Generative AI Models

[1033] 5. Patent Information Database

[1034] 6. User Interface

[1035] System Operation

[1036] Idea Submission Phase

[1037] A user accesses a web page using a client device and submits a new business idea using a generative AI model. The submitted idea is sent to the server via an HTTP request. The server stores the received idea information in a database, and each idea is assigned a unique idea ID.

[1038] As a specific example, a user can enter a "suggestion for a new voice assistant" and click the submit button, which will save the suggestion to the database.

[1039] Example prompt: "Voice assistants are a new way to manage IoT devices in the home."

[1040] Idea Learning Phase

[1041] The server takes the ideas stored in the database and converts them into a format that the generative AI model can process. The converted data is then fed into the generative AI model to train it, during which it learns specific characteristics of the ideas.

[1042] Patent database matching

[1043] The generative AI model compares the learned ideas with a patent information database to verify the possibility of patent infringement. The AI ​​model generates a similarity score between the idea and the patent information and returns the result to the server.

[1044] Patent infringement determination phase

[1045] The server analyzes the similarity scores and adds ideas that exceed a certain score threshold to a list of potential patent infringements. This list is then sent to an administrator's terminal.

[1046] Countermeasure decision phase

[1047] The administrator's terminal displays a list of ideas that may be infringing patents and performs a detailed analysis. The administrator reviews the list and decides on countermeasures, such as proposing joint development, requesting compensation, or collecting usage fees. These countermeasures are implemented in cooperation with the legal department.

[1048] Monetization Phase

[1049] The server launches the contract management system based on the selected solution, generates a draft contract, and presents it to the administrator. Progress is also monitored after the contract is signed, and alerts are displayed regarding important milestones. The progress of the contract is displayed on the administrator's terminal.

[1050] Specific examples

[1051] As a concrete example, consider the process in which User A submits an idea for a new voice assistant. The server receives the idea and stores it in a database. The generative AI model learns from the idea and compares it with existing patent information databases. If Patent B is determined to be similar, the server adds it to a patent infringement list and notifies the administrator. The administrator evaluates the infringement risk and enters into a joint development agreement with the holder of Patent B to monetize it.

[1052] In this way, the system utilizes generative AI models to efficiently manage patent infringement risks for business ideas and maximize profits.

[1053] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1054] Step 1:

[1055] Idea Submission

[1056] A user accesses a web page using a client device and submits a new business idea using a generative AI model. When the user fills out the form with a summary of the idea, details, and potential use cases, and clicks the submit button, the information is sent to the server via an HTTP request.

[1057] Input: Idea information from users

[1058] Output: Send to server

[1059] Specific behavior: The user enters a "Suggestion for a new voice assistant" and clicks the submit button.

[1060] Step 2:

[1061] Receiving and storing data

[1062] The server processes the received idea information and stores it in a database, where each idea is assigned a unique idea ID.

[1063] Input: Idea information submitted by the user

[1064] Output: Idea information stored in the database and a unique idea ID

[1065] Specific operation: The server saves the "new voice assistant proposal" in the database and assigns it the ID "AI1234."

[1066] Step 3:

[1067] Data format conversion

[1068] The server takes the idea information stored in the database and converts it into a format that can be processed by the generative AI model. This conversion operation involves preprocessing and shaping the data.

[1069] Input: Idea information stored in the database

[1070] Output: Data in a format that can be processed by the generative AI model (e.g., JSON format)

[1071] Specific operation: The server converts "New voice assistant proposal (ID: AI1234)" into JSON format.

[1072] Step 4:

[1073] Input to generative AI models

[1074] The server loads a pre-trained generative AI model and inputs the transformed data into the model, where the model analyzes the idea information and extracts specific features.

[1075] Input: Data in a format that the generative AI model can process.

[1076] Output: Data with extracted features

[1077] Specific operation: The server inputs the "new voice assistant proposal" in JSON format into the generative AI model and extracts features.

[1078] Step 5:

[1079] Matching with patent information database

[1080] The server uses the features extracted by the generative AI model to access the patent information database, retrieve existing patent information from the patent information database, and compare it with the generative AI model.

[1081] Input: Data from which features have been extracted, information from patent information database

[1082] Output: Similarity score indicating the likelihood of patent infringement

[1083] Specific operation: The server compares the features of the "new voice assistant proposal" with the patent data and generates a similarity score.

[1084] Step 6:

[1085] Analysis of similarity scores

[1086] The server receives and analyzes the similarity scores generated by the generative AI model to determine whether they exceed a certain score threshold.

[1087] Input: Similarity score from the generative AI model

[1088] Output: A list of potentially infringing ideas

[1089] Specific operation: The server analyzes the similarity score "0.85" and adds "New voice assistant proposal" to the patent infringement list.

[1090] Step 7:

[1091] Notifications and List Generation

[1092] The server creates a list of ideas that may be patent infringing and notifies the administrator of this list, which includes a summary of the idea and a similarity score.

[1093] Input: List of potentially infringing ideas

[1094] Output: Notification to administrator

[1095] Specific operation: The server adds the "new voice assistant proposal" to the "patent infringement list" and notifies the administrator.

[1096] Step 8:

[1097] Evaluation by the administrator and consideration of countermeasures

[1098] A list of ideas that may be patent infringing is displayed on the administrator's terminal, and the administrator evaluates the list and considers countermeasures, such as proposing joint development, requesting compensation, or collecting usage fees.

[1099] Input: List of potentially infringing ideas

[1100] Output: Administrator decides what to do

[1101] Specific actions: The administrator evaluates the "New Voice Assistant Proposal" and proposes collaboration with the patent holder.

[1102] Step 9:

[1103] Contract management and progress display

[1104] The server starts the contract management system based on the selected countermeasure, generates a draft contract, manages progress, and displays the progress on the terminal.

[1105] Input: Administrator's decision on how to proceed

[1106] Output: Contract draft, progress display

[1107] Specific operation: The server generates a draft contract "Joint Development Agreement" and presents it to the administrator. The progress is displayed on the terminal and an alert is sent if necessary.

[1108] (Application example 1)

[1109] 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."

[1110] It is extremely important to efficiently manage patent infringement risks for generated business ideas and maximize their potential for monetization. However, conventional systems have the problem of requiring a great deal of time and effort to verify novelty, assess patent infringement risks, and monetize ideas. Furthermore, the process from idea submission to patent infringement checks and monetization is often inconsistent and inefficient. To address these issues, the present invention provides a system for centrally managing these processes and making them run efficiently and quickly.

[1111] 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.

[1112] In this invention, the server includes means for receiving the generated business ideas and storing them in a database, means for identifying and learning the ideas stored in the database, means for comparing the learned ideas with a patent database to screen for possible patent infringement, means for outputting and notifying a list of the ideas that may be infringing, and means for evaluating the possibility of patent infringement regarding the user's idea and presenting countermeasures and monetization means. This makes it possible to quickly and efficiently evaluate the patent infringement risk of the generated business ideas and present appropriate countermeasures and monetization possibilities.

[1113] A "generated business idea" is a new business idea or plan created using a generative AI model.

[1114] A "database" is a collection of data managed on a computer system, and is a place to store generated business ideas and patent information.

[1115] "Identification" refers to properly recognizing the information stored in the database and identifying which idea it is.

[1116] "Learning" is the process by which an AI model analyzes and understands the specific characteristics of the ideas it receives as input.

[1117] A "patent database" is a database system for storing and managing existing patent information.

[1118] "Matching" is the act of comparing learned ideas with information in a patent database to find matches or similarities.

[1119] "Patent infringement" refers to a situation in which a newly generated business idea may infringe an existing patent right.

[1120] A "list" is a list of items organized based on specific criteria.

[1121] "Notification" is the act of informing relevant parties of specific information.

[1122] "Countermeasures" are specific actions or methods taken in response to problems or issues that arise.

[1123] "Monetization" is the process of implementing a business idea and generating revenue.

[1124] A "user interface" is software or hardware that serves as a window through which a user interacts with a system.

[1125] An "administrator" is a person responsible for operating and maintaining a system.

[1126] This invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and ultimately monetizing them. The main components of this system include a server for receiving, storing, learning from, and analyzing generated ideas, a terminal for users to submit ideas, and a function for checking against patent databases and notifying the results.

[1127] 1. Idea submission phase

[1128] Idea Submission

[1129] Users submit their generated business ideas to the system using a client device (smartphone, tablet, etc.). They are provided with a form to enter a summary of their idea, details, and potential use cases. For example, if you have an idea for an "AI-based movie recommendation system," you might use the following prompt:

[1130] Example prompt sentence:

[1131] Title: "AI-based movie recommendation system"

[1132] Summary: "An AI-based movie recommendation system based on a user's viewing history and preferences. It analyzes a user's past viewing data and recommends similar movies."

[1133] 2. Idea learning phase

[1134] The server receives the submitted ideas and stores them in a database. Each saved idea is assigned a unique idea ID. This data is converted into an appropriate format for input into an AI model, which then learns from it. For example, TensorFlow or PyTorch are used as AI models.

[1135] 3. Patent database matching

[1136] The learned ideas are then matched against a patent database. The server compares the ideas with existing patent information to assess the likelihood of patent infringement. This process uses an AI model to extract specific features and calculate a similarity score.

[1137] 4. Patent infringement determination phase

[1138] The server analyzes the similarity scores received from the AI ​​model and marks ideas that exceed a certain threshold as potentially infringing. These ideas are then listed and notified to the administrator, who then displays the notification on their device.

[1139] 5. Countermeasures Consideration Phase

[1140] Countermeasures include proposing joint development, requesting compensation, collecting usage fees, etc. The server manages these countermeasures and generates the necessary draft contracts.

[1141] 6. Monetization Phase

[1142] The server launches the contract management system based on the selected countermeasure. The progress of the contract is displayed to the administrator through a user interface, allowing the administrator to grasp important milestones and progress in real time.

[1143] This system quickly and efficiently evaluates the patent infringement risk of generated business ideas and proposes appropriate countermeasures and monetization possibilities. The hardware used is AWS EC2 instances, and the database is AWS RDS (MySQL). The AI ​​models used are TensorFlow and PyTorch.

[1144] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1145] Step 1:

[1146] A user enters the generated business idea using the idea submission form and clicks the submit button. The user enters a title and a summary, such as "AI-based movie recommendation system." The entered data is sent from the client device to the server as an HTTP POST request. The input data (idea title and summary) is sent, and the server processes the received data based on it.

[1147] Step 2:

[1148] The server stores the received business ideas in a database. When stored, each idea is assigned a unique idea ID. This idea ID is used to identify the idea in subsequent processing. The server stores the input data (idea title and summary) and generates a unique idea ID as its output.

[1149] Step 3:

[1150] The server takes ideas stored in the database and converts them into the appropriate format for input to the AI ​​model. This format conversion prepares the data in a format that the AI ​​model can accurately parse. The server converts the input data (idea details based on idea ID) and generates a data format that is compatible with the AI ​​model as output.

[1151] Step 4:

[1152] The AI ​​model receives the converted idea data and begins the learning process. The AI ​​model analyzes the features of the ideas using TensorFlow or PyTorch and learns specific patterns. The AI ​​model processes the input data (the format-converted idea data) and generates learned feature data as its output.

[1153] Step 5:

[1154] The server compares the trained idea data with a patent database, retrieves related patent information from the patent database, and calculates a similarity score using the AI ​​model. The server performs data matching using input data (trained feature data and patent database information) and generates a similarity score as its output.

[1155] Step 6:

[1156] The server analyzes the similarity scores received from the AI ​​model and evaluates the possibility of patent infringement. It lists ideas that exceed a certain threshold as being at risk of patent infringement and generates information for notification. The server evaluates based on the input data (similarity scores) and generates a list of ideas at risk of patent infringement as its output.

[1157] Step 7:

[1158] The server notifies the administrator's terminal of the generated list of ideas that pose a risk of patent infringement. The terminal receives the information and visually displays it to the administrator. Patent information and idea details are provided to make it easier for the administrator to consider specific countermeasures. The server notifies the administrator of the input data (the list of ideas that pose a risk of patent infringement), and the terminal displays the information to the administrator as output.

[1159] Step 8:

[1160] The administrator uses a terminal to check the displayed list of ideas that pose a risk of patent infringement and consider specific countermeasures, such as proposing joint development, requesting compensation, or collecting usage fees. The administrator decides on specific countermeasures for the ideas and takes concrete action to monetize them.

[1161] Step 9:

[1162] The server starts the contract management system based on the countermeasures decided by the administrator. It generates the necessary draft contracts and displays the progress in real time. The administrator uses a terminal to monitor the contract management system and manage the progress of the contract. The server processes the input data (details of the countermeasures) and generates contract management progress information as its output.

[1163] 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.

[1164] The present invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and ultimately monetizing them, and further combines an emotion engine that recognizes user emotions. The system includes the following main components and processing flow:

[1165] 1. Idea submission phase

[1166] Idea Submission

[1167] Users submit their new business ideas using generative AI by entering details into the system's web form. This information is sent to the server, and the emotion engine simultaneously collects and analyzes the user's emotional data.

[1168] Database storage

[1169] The server receives submitted ideas and emotion data and stores them in a database, where each idea is assigned a unique idea ID.

[1170] 2. Idea learning phase

[1171] Data format conversion

[1172] The server converts new ideas and corresponding emotional data stored in the database into an appropriate data format to provide to the AI ​​model.

[1173] Input to the AI ​​model

[1174] The server then inputs the converted data into an AI model to train it, which then learns the specific characteristics of the idea and the user's emotional state.

[1175] 3. Patent database matching

[1176] Data Verification

[1177] The AI ​​model compares the ideas it learns with existing patent information stored in a patent database, thereby detecting whether the submitted idea infringes on an existing patent.

[1178] Generating Similarity Scores

[1179] The AI ​​model generates a similarity score between the idea and the patent information and returns it to the server along with analysis results that take into account the user's emotional data.

[1180] 4. Patent infringement determination phase

[1181] Evaluating the results

[1182] The server analyzes the similarity scores and sentiment data analysis results received from the AI ​​model, and if they exceed a certain score threshold, marks the idea as potentially patent infringing.

[1183] List Generation and Notifications

[1184] The server compiles a list of ideas that may be patent infringing and notifies the administrator terminal.

[1185] 5. Countermeasure decision phase

[1186] Viewing the list

[1187] The terminal displays a list of ideas that may be infringing patents on the user interface. The administrator checks the list and considers countermeasures.

[1188] Consideration of countermeasures

[1189] The administrator works with the legal department to determine the best course of action to address the risk of patent infringement, which may include joint development, seeking compensation, or collecting royalties.

[1190] 6. Monetization Phase

[1191] Contract Management

[1192] The server starts a contract management system based on the selected solution, generates a draft contract, and tracks the progress of the contract.

[1193] Progress display

[1194] The terminal displays the contract's progress in a user interface and sends notifications to administrators about important milestones and deadlines.

[1195] Specific examples

[1196] Example 1: A new voice assistant idea

[1197] User A submits an idea for a new voice assistant using generative AI to a contest. The server's emotion engine collects and analyzes the idea along with User A's emotion data. The idea and emotion data stored in the database are learned by an AI model and compared with the patent database. If Patent B is determined to be similar, the server adds it to a patent infringement list and notifies the administrator. The administrator considers the risk of infringement and the emotion data, and enters into a joint development agreement with the holder of Patent B to monetize it.

[1198] In this way, the system of the present invention, which combines an emotion engine, can more accurately manage the patent infringement risk of generated AI business ideas and maximize profits.

[1199] The processing flow will be explained below.

[1200] Step 1:

[1201] The user enters the generated AI business idea into the system's web form, provides detailed information (summary, use case, etc.), and presses the submit button. At the same time as submission, the emotion engine collects the user's facial expression and voice data.

[1202] Step 2:

[1203] The server receives the submitted ideas and stores them in a database. A unique idea ID is assigned when the ideas are saved. The server also stores the user's emotional data collected by the emotion engine.

[1204] Step 3:

[1205] The server extracts new ideas and corresponding emotional data stored in the database and converts them into an appropriate data format to provide to the AI ​​model.

[1206] Step 4:

[1207] The server inputs the converted data into the AI ​​model and starts training the model, which learns the content of the idea and the user's emotional state.

[1208] Step 5:

[1209] The AI ​​model uses the training data to match the submitted idea with existing patent information in the database, and also incorporates user sentiment data into the analysis.

[1210] Step 6:

[1211] Based on the matching results, the AI ​​model generates a similarity score that reflects not only the degree of match with the idea content but also the confidence level based on the user's emotional state.

[1212] Step 7:

[1213] The server evaluates the similarity scores received from the AI ​​model and the results of the sentiment data analysis, and marks ideas with scores above a certain threshold as having a high probability of patent infringement.

[1214] Step 8:

[1215] The server creates a list of ideas that are likely to infringe patents and notifies the administrator's terminal, along with user emotional data.

[1216] Step 9:

[1217] The terminal displays a list of ideas that may be patent infringing on the user interface. The administrator checks the list and considers countermeasures based on the idea details and user emotion data.

[1218] Step 10:

[1219] The administrator works with the legal department to determine the best course of action to address patent infringement risk, which may include joint development, seeking compensation, or collecting royalties.

[1220] Step 11:

[1221] The server starts the contract management system based on the selected solution, generates the necessary contract drafts, and tracks the progress of the contract and provides it to the administrator.

[1222] Step 12:

[1223] The terminal displays the contract's progress in a user interface, and administrators can receive notifications based on important milestones and deadlines.

[1224] As a concrete example, when User A submits an idea for a new voice assistant, the server stores the idea and emotional data in a database, and uses an AI model to compare it with a patent database and finds that it is similar to Patent B. The server notifies this information to the administrator, who, taking User A's emotional data into account, decides to enter into a joint development agreement with the holder of Patent B. Through this process, the risk of patent infringement of the idea can be reduced while monetizing it.

[1225] Example 2

[1226] 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."

[1227] In today's business environment, generating and managing new business ideas is an important challenge. However, generated business ideas may infringe existing patents, which can hinder business development. Furthermore, systems for quickly detecting potential patent infringement and taking appropriate countermeasures are often lacking. The present invention aims to efficiently manage generated business ideas, reduce patent infringement risks, and ultimately monetize them. In particular, the present invention aims to provide a system that uses user emotion data to make more accurate judgments and provide appropriate notifications and countermeasures to administrators.

[1228] The identification process by the identification 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 generated business ideas and storing them in a database; means for converting the ideas and emotion data stored in the database into an appropriate data format; means for inputting the converted idea data and emotion data into an AI model and training the model; means for comparing the learned ideas with a patent database to screen for possible patent infringement; and means for outputting and notifying the ideas with the possibility of patent infringement as a list. This enables the patent infringement risk of generated business ideas to be quickly and accurately determined and notified to an administrator in a timely manner. Furthermore, by including means for displaying the list on a user interface and allowing an administrator to identify ideas with the possibility of patent infringement, the administrator can easily perform detailed analysis and consider countermeasures. Furthermore, by including means for presenting optimal countermeasures for the ideas with the possibility of patent infringement and promoting monetization, the system can realize appropriate utilization of business ideas and maximize profits.

[1229] A "generated business idea" is a new business concept or plan devised by a user using a generative AI model.

[1230] A "database" is a digital recording device that structures and stores information such as business ideas and emotional data, allowing it to be searched and retrieved efficiently later.

[1231] "Emotional Data" refers to data that represents the user's emotional state and is collected and analyzed by the Emotion Engine.

[1232] A "data format" is a set of standards or rules for arranging digital data into a particular structure or form.

[1233] An "AI model" is an artificial intelligence system built using machine learning algorithms that learns specific data and makes predictions and judgments.

[1234] A "patent database" is a digital recording device that aggregates existing patent information and allows for searching and matching.

[1235] The "similarity score" is a numerical indicator that shows the degree of similarity when the AI ​​model compares the generated idea with existing patent information.

[1236] "User interface" refers to the screen and input device that a user uses to operate and exchange information with a system.

[1237] The "administrator" is a person whose role is to supervise and operate the entire system, assess the risk of patent infringement, and decide on countermeasures.

[1238] "Countermeasures" refer to specific actions and methods to be taken when a risk of patent infringement is discovered, and include joint development, claims for compensation, and collection of usage fees.

[1239] "Monetization" is the process of obtaining economic benefits from generated business ideas and associated patents.

[1240] The present invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and monetizing them. This system includes multiple components that are operated mainly by a server, a terminal, and a user. Details for specifically implementing the present invention will now be described.

[1241] System Overview

[1242] In this system, users input new business ideas using a generative AI model, which are then processed by a server. Specifically, the server receives the ideas, stores them in a database, and then converts them into the appropriate format and inputs them into the AI ​​model. The AI ​​model compares the learned ideas with an existing patent database to evaluate the possibility of patent infringement. This information is sent to an administrator's terminal, which determines appropriate countermeasures and monetizes the ideas, forming a series of steps.

[1243] Hardware and software used

[1244] Server: The server is the central component that stores data, transforms it, inputs it into the AI ​​model, and checks it against the patent database. The server runs a MySQL database, TensorFlow, and other applications.

[1245] Terminal: The administrator terminal has a user interface and uses front-end technologies such as React to support list display, notifications, and detailed analysis.

[1246] Generative AI models: Generative AI models such as GPT-4 are used to support the generation of new business ideas.

[1247] Emotion engine: Emotion recognition uses Azure Cognitive Services to analyze the user's emotional state in real time.

[1248] Processing flow

[1249] 1. Idea Submission:

[1250] The user uses the generative AI to generate new business ideas and enter them into the system's web form. For example, the user enters the following prompt into the generative AI:

[1251] "We are thinking of an idea for a new voice assistant. Please propose an assistant that can not only control home appliances with voice commands, but also detect the user's emotional state and change its response accordingly."

[1252] This idea and emotion data is sent to the server when the user submits it in a web form.

[1253] 2. Database storage:

[1254] The server stores the received ideas and emotion data in a MySQL database and assigns each idea a unique ID.

[1255] 3. Data format conversion:

[1256] The server converts the stored ideas and emotional data into a format such as JSON, making it recognizable to the AI ​​model.

[1257] 4. Input to the AI ​​model:

[1258] The server inputs the converted data into an AI model trained using TensorFlow for learning.

[1259] 5. Patent database matching:

[1260] The AI ​​model compares the learned ideas with a patent database to assess the likelihood of patent infringement, and the matching results are returned to the server as a similarity score.

[1261] 6. Evaluation and Notification of Results:

[1262] Based on the similarity score and the results of the emotion data analysis, the server generates a list of ideas that may be patent infringing and notifies the administrator terminal.

[1263] Specific examples

[1264] User A inputs an idea for a new voice assistant, and the server collects and analyzes the idea along with emotional data. The idea and emotional data stored in the database are converted into an appropriate format by the server and input into the AI ​​model. The AI ​​model compares the idea with a patent database, and if it is determined to be similar to patent B, the server adds it to a patent infringement list and notifies the administrator. The administrator checks this list on the user interface on their device, and, taking into consideration the risk of patent infringement and the emotional data, seeks to monetize the idea by entering into a joint development agreement with the holder of patent B.

[1265] In this way, the system of the present invention can accurately manage the patent infringement risk of generated business ideas and maximize profitability.

[1266] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1267] System processing flow

[1268] Step 1: Submit your idea

[1269] Specific behavior:

[1270] Using a generative AI model (such as GPT-4), users input their new business idea into a web form, and the emotion engine collects and analyzes the user's emotional data. For example, the following prompt sentence can be input into the generative AI model:

[1271] "We are thinking of an idea for a new voice assistant. Please propose an assistant that can not only control home appliances with voice commands, but also detect the user's emotional state and change its response accordingly."

[1272] input:

[1273] Ideas from users and idea details and sentiment data generated by the generative AI model.

[1274] output:

[1275] Idea details and sentiment data are submitted to the server through a web form.

[1276] Step 2: Save to database

[1277] Specific behavior:

[1278] The server stores idea details and sentiment data received through the web form in a MySQL database, and assigns each idea a unique idea ID.

[1279] input:

[1280] Idea details and sentiment data submitted via web forms.

[1281] output:

[1282] Idea details and sentiment data, idea ID stored in the database.

[1283] Step 3: Convert data formats

[1284] Specific behavior:

[1285] The server converts the idea details and emotion data stored in the database into a data format (e.g., JSON) that the AI ​​model can recognize.

[1286] input:

[1287] Idea details and sentiment data stored in a database.

[1288] output:

[1289] The converted idea details and sentiment data in JSON format.

[1290] Step 4: Input to the AI ​​model

[1291] Specific behavior:

[1292] The server inputs the converted data into an AI model trained using TensorFlow to learn the features and sentiment data of the ideas.

[1293] input:

[1294] Idea details and sentiment data converted into JSON format.

[1295] output:

[1296] AI model parameters generated as a result of learning.

[1297] Step 5: Matching with patent databases

[1298] Specific behavior:

[1299] The AI ​​model matches the learned ideas with existing patent information stored in a patent database by sending queries to the patent database to retrieve relevant patent information and generate a similarity score.

[1300] input:

[1301] Parameters of the trained AI model, patent information from the patent database.

[1302] output:

[1303] Similarity score.

[1304] Step 6: Evaluate and communicate results

[1305] Specific behavior:

[1306] The server integrates and analyzes the similarity scores and emotion data analysis results received from the AI ​​model to create a list of ideas that may be infringing patents, and notifies the administrator terminal of the list.

[1307] input:

[1308] Similarity scores, emotional data analysis results.

[1309] output:

[1310] A list of ideas that may be infringing patents is sent to the administrator's terminal.

[1311] Step 7: View the list

[1312] Specific behavior:

[1313] The terminal (for administrator) displays a list of ideas that may be infringing patents on a user interface. The administrator can check the list and individually review the details of each idea.

[1314] input:

[1315] A list of ideas that may be subject to patent infringement.

[1316] output:

[1317] The idea list and detailed information displayed in the user interface.

[1318] Step 8: Consider and decide on countermeasures

[1319] Specific behavior:

[1320] The administrator will work with the legal department to determine the best course of action to address the risk of patent infringement, including joint development, seeking compensation, and collecting usage fees.

[1321] input:

[1322] A list of potentially infringing ideas and detailed information about them.

[1323] output:

[1324] Determining countermeasures to patent infringement risks.

[1325] Step 9: Manage and monetize your contracts

[1326] Specific behavior:

[1327] Based on the selected action, the server will launch the contract management system to generate a draft contract, track the contract's progress, and notify the administrator of important milestones and deadlines.

[1328] input:

[1329] The decided response measures.

[1330] output:

[1331] Draft contract, progress notification.

[1332] (Application example 2)

[1333] 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."

[1334] Conventional business idea management systems lack a means to efficiently assess the patent infringement risk of generated ideas, often hindering the monetization of inventions. There is also a need for an idea evaluation system that takes user emotional data into account and for improved accuracy in assessing patent infringement risk. Furthermore, idea management in the advertising industry poses unique challenges, necessitating a means to evaluate advertising effectiveness in real time using emotional data.

[1335] The identification process by the identification 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 generated business ideas and storing them in a database; means for identifying and learning the ideas stored in the database; means for comparing the learned ideas with a patent database to screen for possible patent infringement; means for outputting and notifying the ideas that may be patent infringing as a list; means for collecting and analyzing emotional data; and means for inputting the emotional data into a generative AI model and generating prompt sentences. This enables efficient management of patent infringement risk and highly accurate idea evaluation by taking user emotional data into account. Particularly in the advertising industry, emotional data can be used to evaluate advertising effectiveness in real time, thereby maximizing profits.

[1336] "Generated business ideas" refer to new business concepts and plans generated using AI models.

[1337] A "database" is a system for systematically storing and managing digital information.

[1338] "Means of learning" refers to the process of using an AI model to analyze data, understand its patterns and characteristics, and learn from them.

[1339] A "patent database" is a digital system that stores and manages existing patent information.

[1340] A "potential patent infringement screening process" is a process for determining whether a newly generated business idea infringes an existing patent.

[1341] The "means of outputting and notifying as a list" refers to a system for listing ideas that may constitute patent infringement and notifying relevant parties.

[1342] "Emotional data" is data that quantifies and analyzes the user's emotional state.

[1343] A "generative AI model" is an artificial intelligence model that analyzes generated business ideas and emotional data to generate new business ideas.

[1344] A "prompt" is text that contains instructions or questions that are input to a generative AI model.

[1345] This invention is a system for efficiently managing business ideas created using generative AI, reducing the risk of patent infringement, and ultimately monetizing them. This system also collects and analyzes user emotional data to more accurately assess the risk of patent infringement. It is particularly intended for use in the advertising industry, with the system intended for use on smartphones, smart glasses, and head-mounted displays.

[1346] Hardware and software used

[1347] 1. Hardware:

[1348] Smartphone

[1349] Smart Glasses

[1350] head-mounted display

[1351] 2. Software:

[1352] Emotion Engine Module (EmotionEngine)

[1353] Generative AI Models

[1354] Database System

[1355] Server API

[1356] System program description

[1357] The server operates the system through the following steps:

[1358] 1. Idea Submission Phase:

[1359] Users use their smartphones, smart glasses, or head-mounted displays to input new advertising ideas that are then used by generative AI models, and the submitted data is sent to a server.

[1360] At the same time, the emotion engine module is used to collect the user's emotion data in real time, which is also sent to the server.

[1361] 2. Database storage:

[1362] The server receives the submitted idea and emotion data and stores them in a database along with a unique idea ID.

[1363] 3. Idea learning phase:

[1364] The server converts the ideas and emotion data stored in the database into an appropriate data format and inputs it into a generative AI model for learning.

[1365] 4. Patent database matching:

[1366] The learned ideas are compared with a patent database to evaluate the possibility of patent infringement. Here, a similarity score is generated between the idea and existing information in the patent database, and analyzed together with sentiment data.

[1367] 5. Patent Infringement Determination Phase:

[1368] Based on the analysis results of the similarity score and emotion data, ideas that pose a risk of patent infringement are selected and listed.

[1369] The listed ideas are notified to the administrator and displayed through the user interface.

[1370] 6. Countermeasure decision phase:

[1371] The administrator reviews the list and works with the legal department to determine the best course of action, such as joint development or a claim for compensation.

[1372] 7. Monetization Phase:

[1373] Contract management is performed and the server tracks progress, providing notifications about important milestones and deadlines.

[1374] Specific examples

[1375] When a user submits an idea for a new advertising campaign, they enter a prompt like this:

[1376] "Submit your new ad campaign idea. Use generative AI to fill in your idea and allow the sentiment engine to collect data."

[1377] Based on this input, the server collects and analyzes ideas and emotional data. The analysis results are compared with a patent database to assess the risk of patent infringement. If there is a risk, an administrator is notified and appropriate countermeasures are taken. Emotional data can also be used to evaluate the effectiveness of advertising in real time, allowing for the development of optimal monetization strategies.

[1378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1379] Step 1:

[1380] A user uses a smartphone, smart glasses, or head-mounted display to input ideas for a new advertising campaign using a generative AI model. The input prompt is, "Submit a new advertising campaign idea. Please enter your idea using generative AI and allow the emotion engine to collect data." The emotion engine analyzes the user's facial expressions and voice while they are inputting their idea and generates emotion data. The generated idea and emotion data are sent to the server. The input is the idea and emotion data entered by the user, and the output is the idea and emotion data sent to the server.

[1381] Step 2:

[1382] The server receives the submitted idea and emotion data and stores them in a database with a unique idea ID, ensuring the data is available for reference in later processing steps. The input is the idea and emotion data submitted in step 1, and the output is the idea and emotion data stored in the database.

[1383] Step 3:

[1384] The server converts the idea and emotion data stored in the database into an appropriate format and inputs it into the generative AI model. The generative AI model then learns new ideas based on the received data. The input here is the idea and emotion data stored in the database, and the output is the data converted into the format input into the generative AI model.

[1385] Step 4:

[1386] The server compares the learned ideas with a patent database to assess their potential for patent infringement. This process involves comparing the ideas with existing patent information stored in the patent database and calculating a similarity score. Sentiment data is also taken into account. The input is the idea learned by the generative AI model, and the output is a similarity score indicating the potential for patent infringement.

[1387] Step 5:

[1388] The server lists ideas that may be at risk of patent infringement and notifies the administrator. The list is displayed on the administrator's terminal, and detailed information about ideas that are at risk of patent infringement is provided. The input is a similarity score indicating the possibility of patent infringement, and the output is a risk list displayed on the administrator's terminal.

[1389] Step 6:

[1390] The administrator checks the risk list through a terminal and works with the legal department to consider the best course of action. Specific countermeasures are considered, such as joint development, claims for compensation, and the collection of usage fees. The input is the risk list and detailed information displayed on the terminal, and the output is the decision on the countermeasure.

[1391] Step 7:

[1392] Based on the determined action, the server manages the contract and tracks the progress. It notifies the administrator about important milestones and deadlines of the contract. The input is the determined action, and the output is progress tracking and notification.

[1393] The above are the specific processing steps in the embodiment of the invention.

[1394] 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.

[1395] 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.

[1396] 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.

[1397] [Fourth embodiment]

[1398] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1399] 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.

[1400] 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).

[1401] 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.

[1402] 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.

[1403] 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).

[1404] 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.

[1405] 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.

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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.

[1410] 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."

[1411] The present invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and ultimately monetizing them. The system includes the following major components:

[1412] 1. Idea submission phase

[1413] Idea Submission

[1414] Users submit new business ideas using generative AI to the system by accessing a web form and providing a summary of the idea, details, and potential use cases. This information is then sent to the server.

[1415] Database storage

[1416] The server receives the submitted ideas and stores them in a database, with each idea being assigned a unique idea ID.

[1417] 2. Idea learning phase

[1418] Data format conversion

[1419] The server converts new ideas stored in the database into the appropriate format to feed to the AI ​​model.

[1420] Input to the AI ​​model

[1421] The server then feeds the converted data into an AI model to train it, which then learns and analyzes specific features of the ideas.

[1422] 3. Patent database matching

[1423] Data Verification

[1424] The AI ​​model compares the ideas it learns with existing patent information stored in a patent database, thereby detecting whether the submitted idea infringes on an existing patent.

[1425] Generating Similarity Scores

[1426] The AI ​​model generates a similarity score between the idea and the patent information and returns the results to the server.

[1427] 4. Patent infringement determination phase

[1428] Evaluating the results

[1429] The server analyzes the similarity scores received from the AI ​​model and marks ideas that exceed a certain score threshold as potentially patent infringing.

[1430] List Generation and Notifications

[1431] The server generates a list of ideas that may be patent infringing and notifies the administrator's terminal.

[1432] 5. Countermeasure decision phase

[1433] Viewing the list

[1434] The terminal displays to the user a list of ideas that may be patent infringing, including details of the ideas and information on similar patents.

[1435] Consideration of countermeasures

[1436] The administrator will evaluate the list and consider countermeasures, which may include proposing joint development, seeking compensation, or collecting usage fees, etc. Countermeasures will be implemented in coordination with the legal department.

[1437] 6. Monetization Phase

[1438] Contract Management

[1439] The server starts the contract management system based on the selected countermeasure. A draft contract is generated and presented to the administrator. Progress management after the contract is concluded is also performed.

[1440] Progress display

[1441] The terminal displays the contract's progress in a user interface and notifies the administrator, along with alerts about the progress of each phase and key milestones.

[1442] Specific examples

[1443] Example 1: A new voice assistant idea

[1444] User A submits an idea for a new voice assistant using generative AI to a contest. The server receives the idea and stores it in a database. The AI ​​model learns from the idea and compares it with existing patent databases. If Patent B is determined to be similar, the server adds it to a patent infringement list and notifies the administrator. The administrator evaluates the infringement risk and enters into a joint development agreement with the holder of Patent B to monetize it.

[1445] As described above, the system of the present invention can efficiently manage the risk of patent infringement for business ideas of generative AI and maximize profits.

[1446] The processing flow will be explained below.

[1447] Step 1:

[1448] The user enters the generated AI business idea into the system's web form, fills in the necessary details, and then presses the submit button.

[1449] Step 2:

[1450] The server receives the submitted ideas and stores them in a database, where each idea is assigned a unique idea ID.

[1451] Step 3:

[1452] The server identifies newly saved ideas and converts them from the database into the appropriate data format to feed to the AI ​​model.

[1453] Step 4:

[1454] The server inputs the converted data into an AI model to train it, which then learns the characteristics of the ideas.

[1455] Step 5:

[1456] The server compares the ideas learned by the AI ​​model with a patent database and calculates a similarity score to existing patent information.

[1457] Step 6:

[1458] The server analyzes the similarity scores received from the AI ​​model and, if the score exceeds a certain threshold, marks the idea as potentially patent infringing.

[1459] Step 7:

[1460] The server compiles a list of ideas that may be patent infringing and notifies the administrator terminal.

[1461] Step 8:

[1462] The terminal displays a list of ideas that may be infringing patents on the user interface, and the administrator checks the list and considers countermeasures.

[1463] Step 9:

[1464] The administrator works with the legal department to determine the best course of action to address the risk of patent infringement, which may include joint development, seeking compensation, or collecting royalties.

[1465] Step 10:

[1466] The server launches a contract management system based on the selected solution, generates a draft contract, and tracks the progress of the contract.

[1467] Step 11:

[1468] The terminal displays the contract's progress in a user interface and sends notifications to administrators about important milestones and deadlines.

[1469] Through these steps, the system can efficiently manage the patent infringement risk of generated AI business ideas and maximize profits.

[1470] Example 1

[1471] 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."

[1472] Currently, there is a lack of systems that can quickly and efficiently assess the risk of patent infringement for business ideas created using generative AI and take appropriate countermeasures. In particular, there is a need for automation and improved accuracy in the process of detecting the patent infringement risk of ideas. Another issue is the lack of effective management of the process of presenting countermeasures and monetizing ideas after assessing the risk of patent infringement.

[1473] 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.

[1474] In this invention, the server includes means for receiving generated business ideas and storing them in a database, means for identifying the ideas stored in the database and learning them using a generative AI model, means for comparing the learned ideas with a patent information database to determine whether they may infringe a patent, and means for analyzing the similarity scores generated by the AI ​​model and outputting and notifying a list of ideas that may infringe a patent. This makes it possible to accurately and quickly evaluate the patent infringement risk of business ideas generated by generative AI, present appropriate countermeasures, and efficiently manage the monetization process.

[1475] A "generated business idea" is a newly devised commercial concept or plan using a generative AI model.

[1476] The "database" is an information storage system for centrally storing and managing generated business ideas and other related information.

[1477] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to process and learn from data.

[1478] The "Patent Information Database" is a database system that aggregates patent information registered to date and allows for searching and matching.

[1479] A "similarity score" is a numerical representation of the similarity between a business idea and existing patent information, and is an indicator for assessing the possibility of patent infringement based on the level of the similarity.

[1480] A "list" is a list of ideas or information selected based on specific criteria.

[1481] "User interface" refers to the screen or operation panel that administrators and users use to interact with the system.

[1482] "Administrator" means an individual or organization that has the authority to operate the system, evaluate patents, and decide on countermeasures.

[1483] The present invention is a system for efficiently managing business ideas, reducing the risk of patent infringement, and ultimately monetizing them. A specific implementation method for this system is described below.

[1484] System configuration

[1485] The system includes the following main components:

[1486] 1. Client terminal

[1487] 2. Server

[1488] 3. Database

[1489] 4. Generative AI Models

[1490] 5. Patent Information Database

[1491] 6. User Interface

[1492] System Operation

[1493] Idea Submission Phase

[1494] A user accesses a web page using a client device and submits a new business idea using a generative AI model. The submitted idea is sent to the server via an HTTP request. The server stores the received idea information in a database, and each idea is assigned a unique idea ID.

[1495] As a specific example, a user can enter a "suggestion for a new voice assistant" and click the submit button, which will save the suggestion to the database.

[1496] Example prompt: "Voice assistants are a new way to manage IoT devices in the home."

[1497] Idea Learning Phase

[1498] The server takes the ideas stored in the database and converts them into a format that the generative AI model can process. The converted data is then fed into the generative AI model to train it, during which it learns specific characteristics of the ideas.

[1499] Patent database matching

[1500] The generative AI model compares the learned ideas with a patent information database to verify the possibility of patent infringement. The AI ​​model generates a similarity score between the idea and the patent information and returns the result to the server.

[1501] Patent infringement determination phase

[1502] The server analyzes the similarity scores and adds ideas that exceed a certain score threshold to a list of potential patent infringements. This list is then sent to an administrator's terminal.

[1503] Countermeasure decision phase

[1504] The administrator's terminal displays a list of ideas that may be infringing patents and performs a detailed analysis. The administrator reviews the list and decides on countermeasures, such as proposing joint development, requesting compensation, or collecting usage fees. These countermeasures are implemented in cooperation with the legal department.

[1505] Monetization Phase

[1506] The server launches the contract management system based on the selected solution, generates a draft contract, and presents it to the administrator. Progress is also monitored after the contract is signed, and alerts are displayed regarding important milestones. The progress of the contract is displayed on the administrator's terminal.

[1507] Specific examples

[1508] As a concrete example, consider the process in which User A submits an idea for a new voice assistant. The server receives the idea and stores it in a database. The generative AI model learns from the idea and compares it with existing patent information databases. If Patent B is determined to be similar, the server adds it to a patent infringement list and notifies the administrator. The administrator evaluates the infringement risk and enters into a joint development agreement with the holder of Patent B to monetize it.

[1509] In this way, the system utilizes generative AI models to efficiently manage patent infringement risks for business ideas and maximize profits.

[1510] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1511] Step 1:

[1512] Idea Submission

[1513] A user accesses a web page using a client device and submits a new business idea using a generative AI model. When the user fills out the form with a summary of the idea, details, and potential use cases, and clicks the submit button, the information is sent to the server via an HTTP request.

[1514] Input: Idea information from users

[1515] Output: Send to server

[1516] Specific behavior: The user enters a "Suggestion for a new voice assistant" and clicks the submit button.

[1517] Step 2:

[1518] Receiving and storing data

[1519] The server processes the received idea information and stores it in a database, where each idea is assigned a unique idea ID.

[1520] Input: Idea information submitted by the user

[1521] Output: Idea information stored in the database and a unique idea ID

[1522] Specific operation: The server saves the "new voice assistant proposal" in the database and assigns it the ID "AI1234."

[1523] Step 3:

[1524] Data format conversion

[1525] The server takes the idea information stored in the database and converts it into a format that can be processed by the generative AI model. This conversion operation involves preprocessing and shaping the data.

[1526] Input: Idea information stored in the database

[1527] Output: Data in a format that can be processed by the generative AI model (e.g., JSON format)

[1528] Specific operation: The server converts "New voice assistant proposal (ID: AI1234)" into JSON format.

[1529] Step 4:

[1530] Input to generative AI models

[1531] The server loads a pre-trained generative AI model and inputs the transformed data into the model, where the model analyzes the idea information and extracts specific features.

[1532] Input: Data in a format that the generative AI model can process.

[1533] Output: Data with extracted features

[1534] Specific operation: The server inputs the "new voice assistant proposal" in JSON format into the generative AI model and extracts features.

[1535] Step 5:

[1536] Matching with patent information database

[1537] The server uses the features extracted by the generative AI model to access the patent information database, retrieve existing patent information from the patent information database, and compare it with the generative AI model.

[1538] Input: Data from which features have been extracted, information from patent information database

[1539] Output: Similarity score indicating the likelihood of patent infringement

[1540] Specific operation: The server compares the features of the "new voice assistant proposal" with the patent data and generates a similarity score.

[1541] Step 6:

[1542] Analysis of similarity scores

[1543] The server receives and analyzes the similarity scores generated by the generative AI model to determine whether they exceed a certain score threshold.

[1544] Input: Similarity score from the generative AI model

[1545] Output: A list of potentially infringing ideas

[1546] Specific operation: The server analyzes the similarity score "0.85" and adds "New voice assistant proposal" to the patent infringement list.

[1547] Step 7:

[1548] Notifications and List Generation

[1549] The server creates a list of ideas that may be patent infringing and notifies the administrator of this list, which includes a summary of the idea and a similarity score.

[1550] Input: List of potentially infringing ideas

[1551] Output: Notification to administrator

[1552] Specific operation: The server adds the "new voice assistant proposal" to the "patent infringement list" and notifies the administrator.

[1553] Step 8:

[1554] Evaluation by the administrator and consideration of countermeasures

[1555] A list of ideas that may be patent infringing is displayed on the administrator's terminal, and the administrator evaluates the list and considers countermeasures, such as proposing joint development, requesting compensation, or collecting usage fees.

[1556] Input: List of potentially infringing ideas

[1557] Output: Administrator decides what to do

[1558] Specific actions: The administrator evaluates the "New Voice Assistant Proposal" and proposes collaboration with the patent holder.

[1559] Step 9:

[1560] Contract management and progress display

[1561] The server starts the contract management system based on the selected countermeasure, generates a draft contract, manages progress, and displays the progress on the terminal.

[1562] Input: Administrator's decision on how to proceed

[1563] Output: Contract draft, progress display

[1564] Specific operation: The server generates a draft contract "Joint Development Agreement" and presents it to the administrator. The progress is displayed on the terminal and an alert is sent if necessary.

[1565] (Application example 1)

[1566] 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."

[1567] It is extremely important to efficiently manage patent infringement risks for generated business ideas and maximize their potential for monetization. However, conventional systems have the problem of requiring a great deal of time and effort to verify novelty, assess patent infringement risks, and monetize ideas. Furthermore, the process from idea submission to patent infringement checks and monetization is often inconsistent and inefficient. To address these issues, the present invention provides a system for centrally managing these processes and making them run efficiently and quickly.

[1568] 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.

[1569] In this invention, the server includes means for receiving the generated business ideas and storing them in a database, means for identifying and learning the ideas stored in the database, means for comparing the learned ideas with a patent database to screen for possible patent infringement, means for outputting and notifying a list of the ideas that may be infringing, and means for evaluating the possibility of patent infringement regarding the user's idea and presenting countermeasures and monetization means. This makes it possible to quickly and efficiently evaluate the patent infringement risk of the generated business ideas and present appropriate countermeasures and monetization possibilities.

[1570] A "generated business idea" is a new business idea or plan created using a generative AI model.

[1571] A "database" is a collection of data managed on a computer system, and is a place to store generated business ideas and patent information.

[1572] "Identification" refers to properly recognizing the information stored in the database and identifying which idea it is.

[1573] "Learning" is the process by which an AI model analyzes and understands the specific characteristics of the ideas it receives as input.

[1574] A "patent database" is a database system for storing and managing existing patent information.

[1575] "Matching" is the act of comparing learned ideas with information in a patent database to find matches or similarities.

[1576] "Patent infringement" refers to a situation in which a newly generated business idea may infringe an existing patent right.

[1577] A "list" is a list of items organized based on specific criteria.

[1578] "Notification" is the act of informing relevant parties of specific information.

[1579] "Countermeasures" are specific actions or methods taken in response to problems or issues that arise.

[1580] "Monetization" is the process of implementing a business idea and generating revenue.

[1581] A "user interface" is software or hardware that serves as a window through which a user interacts with a system.

[1582] An "administrator" is a person responsible for operating and maintaining a system.

[1583] This invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and ultimately monetizing them. The main components of this system include a server for receiving, storing, learning from, and analyzing generated ideas, a terminal for users to submit ideas, and a function for checking against patent databases and notifying the results.

[1584] 1. Idea submission phase

[1585] Idea Submission

[1586] Users submit their generated business ideas to the system using a client device (smartphone, tablet, etc.). They are provided with a form to enter a summary of their idea, details, and potential use cases. For example, if you have an idea for an "AI-based movie recommendation system," you might use the following prompt:

[1587] Example prompt sentence:

[1588] Title: "AI-based movie recommendation system"

[1589] Summary: "An AI-based movie recommendation system based on a user's viewing history and preferences. It analyzes a user's past viewing data and recommends similar movies."

[1590] 2. Idea learning phase

[1591] The server receives the submitted ideas and stores them in a database. Each saved idea is assigned a unique idea ID. This data is converted into an appropriate format for input into an AI model, which then learns from it. For example, TensorFlow or PyTorch are used as AI models.

[1592] 3. Patent database matching

[1593] The learned ideas are then matched against a patent database. The server compares the ideas with existing patent information to assess the likelihood of patent infringement. This process uses an AI model to extract specific features and calculate a similarity score.

[1594] 4. Patent infringement determination phase

[1595] The server analyzes the similarity scores received from the AI ​​model and marks ideas that exceed a certain threshold as potentially infringing. These ideas are then listed and notified to the administrator, who then displays the notification on their device.

[1596] 5. Countermeasures Consideration Phase

[1597] Countermeasures include proposing joint development, requesting compensation, collecting usage fees, etc. The server manages these countermeasures and generates the necessary draft contracts.

[1598] 6. Monetization Phase

[1599] The server launches the contract management system based on the selected countermeasure. The progress of the contract is displayed to the administrator through a user interface, allowing the administrator to grasp important milestones and progress in real time.

[1600] This system quickly and efficiently evaluates the patent infringement risk of generated business ideas and proposes appropriate countermeasures and monetization possibilities. The hardware used is AWS EC2 instances, and the database is AWS RDS (MySQL). The AI ​​models used are TensorFlow and PyTorch.

[1601] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1602] Step 1:

[1603] A user enters the generated business idea using the idea submission form and clicks the submit button. The user enters a title and a summary, such as "AI-based movie recommendation system." The entered data is sent from the client device to the server as an HTTP POST request. The input data (idea title and summary) is sent, and the server processes the received data based on it.

[1604] Step 2:

[1605] The server stores the received business ideas in a database. When stored, each idea is assigned a unique idea ID. This idea ID is used to identify the idea in subsequent processing. The server stores the input data (idea title and summary) and generates a unique idea ID as its output.

[1606] Step 3:

[1607] The server takes ideas stored in the database and converts them into the appropriate format for input to the AI ​​model. This format conversion prepares the data in a format that the AI ​​model can accurately parse. The server converts the input data (idea details based on idea ID) and generates a data format that is compatible with the AI ​​model as output.

[1608] Step 4:

[1609] The AI ​​model receives the converted idea data and begins the learning process. The AI ​​model analyzes the features of the ideas using TensorFlow or PyTorch and learns specific patterns. The AI ​​model processes the input data (the format-converted idea data) and generates learned feature data as its output.

[1610] Step 5:

[1611] The server compares the trained idea data with a patent database, retrieves related patent information from the patent database, and calculates a similarity score using the AI ​​model. The server performs data matching using input data (trained feature data and patent database information) and generates a similarity score as its output.

[1612] Step 6:

[1613] The server analyzes the similarity scores received from the AI ​​model and evaluates the possibility of patent infringement. It lists ideas that exceed a certain threshold as being at risk of patent infringement and generates information for notification. The server evaluates based on the input data (similarity scores) and generates a list of ideas at risk of patent infringement as its output.

[1614] Step 7:

[1615] The server notifies the administrator's terminal of the generated list of ideas that pose a risk of patent infringement. The terminal receives the information and visually displays it to the administrator. Patent information and idea details are provided to make it easier for the administrator to consider specific countermeasures. The server notifies the administrator of the input data (the list of ideas that pose a risk of patent infringement), and the terminal displays the information to the administrator as output.

[1616] Step 8:

[1617] The administrator uses a terminal to check the displayed list of ideas that pose a risk of patent infringement and consider specific countermeasures, such as proposing joint development, requesting compensation, or collecting usage fees. The administrator decides on specific countermeasures for the ideas and takes concrete action to monetize them.

[1618] Step 9:

[1619] The server starts the contract management system based on the countermeasures decided by the administrator. It generates the necessary draft contracts and displays the progress in real time. The administrator uses a terminal to monitor the contract management system and manage the progress of the contract. The server processes the input data (details of the countermeasures) and generates contract management progress information as its output.

[1620] 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.

[1621] The present invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and ultimately monetizing them, and further combines an emotion engine that recognizes user emotions. The system includes the following main components and processing flow:

[1622] 1. Idea submission phase

[1623] Idea Submission

[1624] Users submit their new business ideas using generative AI by entering details into the system's web form. This information is sent to the server, and the emotion engine simultaneously collects and analyzes the user's emotional data.

[1625] Database storage

[1626] The server receives submitted ideas and emotion data and stores them in a database, where each idea is assigned a unique idea ID.

[1627] 2. Idea learning phase

[1628] Data format conversion

[1629] The server converts new ideas and corresponding emotional data stored in the database into an appropriate data format to provide to the AI ​​model.

[1630] Input to the AI ​​model

[1631] The server then inputs the converted data into an AI model to train it, which then learns the specific characteristics of the idea and the user's emotional state.

[1632] 3. Patent database matching

[1633] Data Verification

[1634] The AI ​​model compares the ideas it learns with existing patent information stored in a patent database, thereby detecting whether the submitted idea infringes on an existing patent.

[1635] Generating Similarity Scores

[1636] The AI ​​model generates a similarity score between the idea and the patent information and returns it to the server along with analysis results that take into account the user's emotional data.

[1637] 4. Patent infringement determination phase

[1638] Evaluating the results

[1639] The server analyzes the similarity scores and sentiment data analysis results received from the AI ​​model, and if they exceed a certain score threshold, marks the idea as potentially patent infringing.

[1640] List Generation and Notifications

[1641] The server compiles a list of ideas that may be patent infringing and notifies the administrator terminal.

[1642] 5. Countermeasure decision phase

[1643] Viewing the list

[1644] The terminal displays a list of ideas that may be infringing patents on the user interface. The administrator checks the list and considers countermeasures.

[1645] Consideration of countermeasures

[1646] The administrator works with the legal department to determine the best course of action to address the risk of patent infringement, which may include joint development, seeking compensation, or collecting royalties.

[1647] 6. Monetization Phase

[1648] Contract Management

[1649] The server starts a contract management system based on the selected solution, generates a draft contract, and tracks the progress of the contract.

[1650] Progress display

[1651] The terminal displays the contract's progress in a user interface and sends notifications to administrators about important milestones and deadlines.

[1652] Specific examples

[1653] Example 1: A new voice assistant idea

[1654] User A submits an idea for a new voice assistant using generative AI to a contest. The server's emotion engine collects and analyzes the idea along with User A's emotion data. The idea and emotion data stored in the database are learned by an AI model and compared with the patent database. If Patent B is determined to be similar, the server adds it to a patent infringement list and notifies the administrator. The administrator considers the risk of infringement and the emotion data, and enters into a joint development agreement with the holder of Patent B to monetize it.

[1655] In this way, the system of the present invention, which combines an emotion engine, can more accurately manage the patent infringement risk of generated AI business ideas and maximize profits.

[1656] The processing flow will be explained below.

[1657] Step 1:

[1658] The user enters the generated AI business idea into the system's web form, provides detailed information (summary, use case, etc.), and presses the submit button. At the same time as submission, the emotion engine collects the user's facial expression and voice data.

[1659] Step 2:

[1660] The server receives the submitted ideas and stores them in a database. A unique idea ID is assigned when the ideas are saved. The server also stores the user's emotional data collected by the emotion engine.

[1661] Step 3:

[1662] The server extracts new ideas and corresponding emotional data stored in the database and converts them into an appropriate data format to provide to the AI ​​model.

[1663] Step 4:

[1664] The server inputs the converted data into the AI ​​model and starts training the model, which learns the content of the idea and the user's emotional state.

[1665] Step 5:

[1666] The AI ​​model uses the training data to match the submitted idea with existing patent information in the database, and also incorporates user sentiment data into the analysis.

[1667] Step 6:

[1668] Based on the matching results, the AI ​​model generates a similarity score that reflects not only the degree of match with the idea content but also the confidence level based on the user's emotional state.

[1669] Step 7:

[1670] The server evaluates the similarity scores received from the AI ​​model and the results of the sentiment data analysis, and marks ideas with scores above a certain threshold as having a high probability of patent infringement.

[1671] Step 8:

[1672] The server creates a list of ideas that are likely to infringe patents and notifies the administrator's terminal, along with user emotional data.

[1673] Step 9:

[1674] The terminal displays a list of ideas that may be patent infringing on the user interface. The administrator checks the list and considers countermeasures based on the idea details and user emotion data.

[1675] Step 10:

[1676] The administrator works with the legal department to determine the best course of action to address patent infringement risk, which may include joint development, seeking compensation, or collecting royalties.

[1677] Step 11:

[1678] The server starts the contract management system based on the selected solution, generates the necessary contract drafts, and tracks the progress of the contract and provides it to the administrator.

[1679] Step 12:

[1680] The terminal displays the contract's progress in a user interface, and administrators can receive notifications based on important milestones and deadlines.

[1681] As a concrete example, when User A submits an idea for a new voice assistant, the server stores the idea and emotional data in a database, and uses an AI model to compare it with a patent database and finds that it is similar to Patent B. The server notifies this information to the administrator, who, taking User A's emotional data into account, decides to enter into a joint development agreement with the holder of Patent B. Through this process, the risk of patent infringement of the idea can be reduced while monetizing it.

[1682] Example 2

[1683] 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."

[1684] In today's business environment, generating and managing new business ideas is an important challenge. However, generated business ideas may infringe existing patents, which can hinder business development. Furthermore, systems for quickly detecting potential patent infringement and taking appropriate countermeasures are often lacking. The present invention aims to efficiently manage generated business ideas, reduce patent infringement risks, and ultimately monetize them. In particular, the present invention aims to provide a system that uses user emotion data to make more accurate judgments and provide appropriate notifications and countermeasures to administrators.

[1685] The identification process by the identification 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 generated business ideas and storing them in a database; means for converting the ideas and emotion data stored in the database into an appropriate data format; means for inputting the converted idea data and emotion data into an AI model and training the model; means for comparing the learned ideas with a patent database to screen for possible patent infringement; and means for outputting and notifying the ideas with the possibility of patent infringement as a list. This enables the patent infringement risk of generated business ideas to be quickly and accurately determined and notified to an administrator in a timely manner. Furthermore, by including means for displaying the list on a user interface and allowing an administrator to identify ideas with the possibility of patent infringement, the administrator can easily perform detailed analysis and consider countermeasures. Furthermore, by including means for presenting optimal countermeasures for the ideas with the possibility of patent infringement and promoting monetization, the system can realize appropriate utilization of business ideas and maximize profits.

[1686] A "generated business idea" is a new business concept or plan devised by a user using a generative AI model.

[1687] A "database" is a digital recording device that structures and stores information such as business ideas and emotional data, allowing it to be searched and retrieved efficiently later.

[1688] "Emotional Data" refers to data that represents the user's emotional state and is collected and analyzed by the Emotion Engine.

[1689] A "data format" is a set of standards or rules for arranging digital data into a particular structure or form.

[1690] An "AI model" is an artificial intelligence system built using machine learning algorithms that learns specific data and makes predictions and judgments.

[1691] A "patent database" is a digital recording device that aggregates existing patent information and allows for searching and matching.

[1692] The "similarity score" is a numerical indicator that shows the degree of similarity when the AI ​​model compares the generated idea with existing patent information.

[1693] "User interface" refers to the screen and input device that a user uses to operate and exchange information with a system.

[1694] The "administrator" is a person whose role is to supervise and operate the entire system, assess the risk of patent infringement, and decide on countermeasures.

[1695] "Countermeasures" refer to specific actions and methods to be taken when a risk of patent infringement is discovered, and include joint development, claims for compensation, and collection of usage fees.

[1696] "Monetization" is the process of obtaining economic benefits from generated business ideas and associated patents.

[1697] The present invention is a system for efficiently managing generated business ideas, reducing the risk of patent infringement, and monetizing them. This system includes multiple components that are operated mainly by a server, a terminal, and a user. Details for specifically implementing the present invention will now be described.

[1698] System Overview

[1699] In this system, users input new business ideas using a generative AI model, which are then processed by a server. Specifically, the server receives the ideas, stores them in a database, and then converts them into the appropriate format and inputs them into the AI ​​model. The AI ​​model compares the learned ideas with an existing patent database to evaluate the possibility of patent infringement. This information is sent to an administrator's terminal, which determines appropriate countermeasures and monetizes the ideas, forming a series of steps.

[1700] Hardware and software used

[1701] Server: The server is the central component that stores data, transforms it, inputs it into the AI ​​model, and checks it against the patent database. The server runs a MySQL database, TensorFlow, and other applications.

[1702] Terminal: The administrator terminal has a user interface and uses front-end technologies such as React to support list display, notifications, and detailed analysis.

[1703] Generative AI models: Generative AI models such as GPT-4 are used to support the generation of new business ideas.

[1704] Emotion engine: Emotion recognition uses Azure Cognitive Services to analyze the user's emotional state in real time.

[1705] Processing flow

[1706] 1. Idea Submission:

[1707] The user uses the generative AI to generate new business ideas and enter them into the system's web form. For example, the user enters the following prompt into the generative AI:

[1708] "We are thinking of an idea for a new voice assistant. Please propose an assistant that can not only control home appliances with voice commands, but also detect the user's emotional state and change its response accordingly."

[1709] This idea and emotion data is sent to the server when the user submits it in a web form.

[1710] 2. Database storage:

[1711] The server stores the received ideas and emotion data in a MySQL database and assigns each idea a unique ID.

[1712] 3. Data format conversion:

[1713] The server converts the stored ideas and emotional data into a format such as JSON, making it recognizable to the AI ​​model.

[1714] 4. Input to the AI ​​model:

[1715] The server inputs the converted data into an AI model trained using TensorFlow for learning.

[1716] 5. Patent database matching:

[1717] The AI ​​model compares the learned ideas with a patent database to assess the likelihood of patent infringement, and the matching results are returned to the server as a similarity score.

[1718] 6. Evaluation and Notification of Results:

[1719] Based on the similarity score and the results of the emotion data analysis, the server generates a list of ideas that may be patent infringing and notifies the administrator terminal.

[1720] Specific examples

[1721] User A inputs an idea for a new voice assistant, and the server collects and analyzes the idea along with emotional data. The idea and emotional data stored in the database are converted into an appropriate format by the server and input into the AI ​​model. The AI ​​model compares the idea with a patent database, and if it is determined to be similar to patent B, the server adds it to a patent infringement list and notifies the administrator. The administrator checks this list on the user interface on their device, and, taking into consideration the risk of patent infringement and the emotional data, seeks to monetize the idea by entering into a joint development agreement with the holder of patent B.

[1722] In this way, the system of the present invention can accurately manage the patent infringement risk of generated business ideas and maximize profitability.

[1723] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1724] System processing flow

[1725] Step 1: Submit your idea

[1726] Specific behavior:

[1727] Using a generative AI model (such as GPT-4), users input their new business idea into a web form, and the emotion engine collects and analyzes the user's emotional data. For example, the following prompt sentence can be input into the generative AI model:

[1728] "We are thinking of an idea for a new voice assistant. Please propose an assistant that can not only control home appliances with voice commands, but also detect the user's emotional state and change its response accordingly."

[1729] input:

[1730] Ideas from users and idea details and sentiment data generated by the generative AI model.

[1731] output:

[1732] Idea details and sentiment data are submitted to the server through a web form.

[1733] Step 2: Save to database

[1734] Specific behavior:

[1735] The server stores idea details and sentiment data received through the web form in a MySQL database, and assigns each idea a unique idea ID.

[1736] input:

[1737] Idea details and sentiment data submitted via web forms.

[1738] output:

[1739] Idea details and sentiment data, idea ID stored in the database.

[1740] Step 3: Convert data formats

[1741] Specific behavior:

[1742] The server converts the idea details and emotion data stored in the database into a data format (e.g., JSON) that the AI ​​model can recognize.

[1743] input:

[1744] Idea details and sentiment data stored in a database.

[1745] output:

[1746] The converted idea details and sentiment data in JSON format.

[1747] Step 4: Input to the AI ​​model

[1748] Specific behavior:

[1749] The server inputs the converted data into an AI model trained using TensorFlow to learn the features and sentiment data of the ideas.

[1750] input:

[1751] Idea details and sentiment data converted into JSON format.

[1752] output:

[1753] AI model parameters generated as a result of learning.

[1754] Step 5: Matching with patent databases

[1755] Specific behavior:

[1756] The AI ​​model matches the learned ideas with existing patent information stored in a patent database by sending queries to the patent database to retrieve relevant patent information and generate a similarity score.

[1757] input:

[1758] Parameters of the trained AI model, patent information from the patent database.

[1759] output:

[1760] Similarity score.

[1761] Step 6: Evaluate and communicate results

[1762] Specific behavior:

[1763] The server integrates and analyzes the similarity scores and emotion data analysis results received from the AI ​​model to create a list of ideas that may be infringing patents, and notifies the administrator terminal of the list.

[1764] input:

[1765] Similarity scores, emotional data analysis results.

[1766] output:

[1767] A list of ideas that may be infringing patents is sent to the administrator's terminal.

[1768] Step 7: View the list

[1769] Specific behavior:

[1770] The terminal (for administrator) displays a list of ideas that may be infringing patents on a user interface. The administrator can check the list and individually review the details of each idea.

[1771] input:

[1772] A list of ideas that may be subject to patent infringement.

[1773] output:

[1774] The idea list and detailed information displayed in the user interface.

[1775] Step 8: Consider and decide on countermeasures

[1776] Specific behavior:

[1777] The administrator will work with the legal department to determine the best course of action to address the risk of patent infringement, including joint development, seeking compensation, and collecting usage fees.

[1778] input:

[1779] A list of potentially infringing ideas and detailed information about them.

[1780] output:

[1781] Determining countermeasures to patent infringement risks.

[1782] Step 9: Manage and monetize your contracts

[1783] Specific behavior:

[1784] Based on the selected action, the server will launch the contract management system to generate a draft contract, track the contract's progress, and notify the administrator of important milestones and deadlines.

[1785] input:

[1786] The decided response measures.

[1787] output:

[1788] Draft contract, progress notification.

[1789] (Application example 2)

[1790] 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."

[1791] Conventional business idea management systems lack a means to efficiently assess the patent infringement risk of generated ideas, often hindering the monetization of inventions. There is also a need for an idea evaluation system that takes user emotional data into account and for improved accuracy in assessing patent infringement risk. Furthermore, idea management in the advertising industry poses unique challenges, necessitating a means to evaluate advertising effectiveness in real time using emotional data.

[1792] The identification process by the identification 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 generated business ideas and storing them in a database; means for identifying and learning the ideas stored in the database; means for comparing the learned ideas with a patent database to screen for possible patent infringement; means for outputting and notifying the ideas that may be patent infringing as a list; means for collecting and analyzing emotional data; and means for inputting the emotional data into a generative AI model and generating prompt sentences. This enables efficient management of patent infringement risk and highly accurate idea evaluation by taking user emotional data into account. Particularly in the advertising industry, emotional data can be used to evaluate advertising effectiveness in real time, thereby maximizing profits.

[1793] "Generated business ideas" refer to new business concepts and plans generated using AI models.

[1794] A "database" is a system for systematically storing and managing digital information.

[1795] "Means of learning" refers to the process of using an AI model to analyze data, understand its patterns and characteristics, and learn from them.

[1796] A "patent database" is a digital system that stores and manages existing patent information.

[1797] A "potential patent infringement screening process" is a process for determining whether a newly generated business idea infringes an existing patent.

[1798] The "means of outputting and notifying as a list" refers to a system for listing ideas that may constitute patent infringement and notifying relevant parties.

[1799] "Emotional data" is data that quantifies and analyzes the user's emotional state.

[1800] A "generative AI model" is an artificial intelligence model that analyzes generated business ideas and emotional data to generate new business ideas.

[1801] A "prompt" is text that contains instructions or questions that are input to a generative AI model.

[1802] This invention is a system for efficiently managing business ideas created using generative AI, reducing the risk of patent infringement, and ultimately monetizing them. This system also collects and analyzes user emotional data to more accurately assess the risk of patent infringement. It is particularly intended for use in the advertising industry, with the system intended for use on smartphones, smart glasses, and head-mounted displays.

[1803] Hardware and software used

[1804] 1. Hardware:

[1805] Smartphone

[1806] Smart Glasses

[1807] head-mounted display

[1808] 2. Software:

[1809] Emotion Engine Module (EmotionEngine)

[1810] Generative AI Models

[1811] Database System

[1812] Server API

[1813] System program description

[1814] The server operates the system through the following steps:

[1815] 1. Idea Submission Phase:

[1816] Users use their smartphones, smart glasses, or head-mounted displays to input new advertising ideas that are then used by generative AI models, and the submitted data is sent to a server.

[1817] At the same time, the emotion engine module is used to collect the user's emotion data in real time, which is also sent to the server.

[1818] 2. Database storage:

[1819] The server receives the submitted idea and emotion data and stores them in a database along with a unique idea ID.

[1820] 3. Idea learning phase:

[1821] The server converts the ideas and emotion data stored in the database into an appropriate data format and inputs it into a generative AI model for learning.

[1822] 4. Patent database matching:

[1823] The learned ideas are compared with a patent database to evaluate the possibility of patent infringement. Here, a similarity score is generated between the idea and existing information in the patent database, and analyzed together with sentiment data.

[1824] 5. Patent Infringement Determination Phase:

[1825] Based on the analysis results of the similarity score and emotion data, ideas that pose a risk of patent infringement are selected and listed.

[1826] The listed ideas are notified to the administrator and displayed through the user interface.

[1827] 6. Countermeasure decision phase:

[1828] The administrator reviews the list and works with the legal department to determine the best course of action, such as joint development or a claim for compensation.

[1829] 7. Monetization Phase:

[1830] Contract management is performed and the server tracks progress, providing notifications about important milestones and deadlines.

[1831] Specific examples

[1832] When a user submits an idea for a new advertising campaign, they enter a prompt like this:

[1833] "Submit your new ad campaign idea. Use generative AI to fill in your idea and allow the sentiment engine to collect data."

[1834] Based on this input, the server collects and analyzes ideas and emotional data. The analysis results are compared with a patent database to assess the risk of patent infringement. If there is a risk, an administrator is notified and appropriate countermeasures are taken. Emotional data can also be used to evaluate the effectiveness of advertising in real time, allowing for the development of optimal monetization strategies.

[1835] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1836] Step 1:

[1837] A user uses a smartphone, smart glasses, or head-mounted display to input ideas for a new advertising campaign using a generative AI model. The input prompt is, "Submit a new advertising campaign idea. Please enter your idea using generative AI and allow the emotion engine to collect data." The emotion engine analyzes the user's facial expressions and voice while they are inputting their idea and generates emotion data. The generated idea and emotion data are sent to the server. The input is the idea and emotion data entered by the user, and the output is the idea and emotion data sent to the server.

[1838] Step 2:

[1839] The server receives the submitted idea and emotion data and stores them in a database with a unique idea ID, ensuring the data is available for reference in later processing steps. The input is the idea and emotion data submitted in step 1, and the output is the idea and emotion data stored in the database.

[1840] Step 3:

[1841] The server converts the idea and emotion data stored in the database into an appropriate format and inputs it into the generative AI model. The generative AI model then learns new ideas based on the received data. The input here is the idea and emotion data stored in the database, and the output is the data converted into the format input into the generative AI model.

[1842] Step 4:

[1843] The server compares the learned ideas with a patent database to assess their potential for patent infringement. This process involves comparing the ideas with existing patent information stored in the patent database and calculating a similarity score. Sentiment data is also taken into account. The input is the idea learned by the generative AI model, and the output is a similarity score indicating the potential for patent infringement.

[1844] Step 5:

[1845] The server lists ideas that may be at risk of patent infringement and notifies the administrator. The list is displayed on the administrator's terminal, and detailed information about ideas that are at risk of patent infringement is provided. The input is a similarity score indicating the possibility of patent infringement, and the output is a risk list displayed on the administrator's terminal.

[1846] Step 6:

[1847] The administrator checks the risk list through a terminal and works with the legal department to consider the best course of action. Specific countermeasures are considered, such as joint development, claims for compensation, and the collection of usage fees. The input is the risk list and detailed information displayed on the terminal, and the output is the decision on the countermeasure.

[1848] Step 7:

[1849] Based on the determined action, the server manages the contract and tracks the progress. It notifies the administrator about important milestones and deadlines of the contract. The input is the determined action, and the output is progress tracking and notification.

[1850] The above are the specific processing steps in the embodiment of the invention.

[1851] 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.

[1852] 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.

[1853] 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.

[1854] 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.

[1855] 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.

[1856] 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.

[1857] 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).

[1858] 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.

[1859] 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."

[1860] 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.

[1861] 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).

[1862] 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.

[1863] 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.

[1864] 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.

[1865] 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.

[1866] 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.

[1867] 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.

[1868] 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.

[1869] 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.

[1870] 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.

[1871] 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.

[1872] The following is further disclosed regarding the above embodiment.

[1873] (Claim 1)

[1874] means for receiving and storing the generated business ideas in a database;

[1875] means for identifying and learning from ideas stored in said database;

[1876] a means for comparing the learned ideas with a patent database to identify potential patent infringements;

[1877] a means for outputting and notifying a list of the ideas that may infringe the patent;

[1878] A system including:

[1879] (Claim 2)

[1880] 10. The system of claim 1, further comprising means for suggesting countermeasures to the potentially patent infringing ideas and for monetizing them.

[1881] (Claim 3)

[1882] 2. The system according to claim 1, further comprising means for displaying a list of ideas that may be infringing a patent to an administrator using the user interface for further analysis.

[1883] "Example 1"

[1884] (Claim 1)

[1885] means for receiving and storing the generated business ideas in a database;

[1886] means for identifying ideas stored in said database and learning from them using a generative AI model;

[1887] a means for comparing the learned ideas with a patent information database to determine whether or not there is a possibility of patent infringement;

[1888] A means to analyze the similarity scores generated by the AI ​​model and output a list of ideas that may be infringing patents and notify them.

[1889] A system including:

[1890] (Claim 2)

[1891] 10. The system of claim 1, further comprising means for suggesting countermeasures to the potentially patent infringing ideas and monetizing them.

[1892] (Claim 3)

[1893] 2. The system according to claim 1, further comprising means for displaying a list of ideas that may be infringing a patent to an administrator using the user interface for further analysis.

[1894] "Application Example 1"

[1895] (Claim 1)

[1896] means for receiving and storing the generated business ideas in a database;

[1897] means for identifying and learning from ideas stored in said database;

[1898] a means for comparing the learned ideas with a patent database to identify potential patent infringements;

[1899] a means for outputting and notifying a list of the ideas that may infringe the patent;

[1900] A means of assessing the possibility of patent infringement regarding the user's ideas and suggesting countermeasures and monetization methods;

[1901] A system including:

[1902] (Claim 2)

[1903] 10. The system of claim 1, further comprising: means for providing recommendations for countermeasures and monetization of the potentially infringing ideas.

[1904] (Claim 3)

[1905] The system of claim 1 further comprising means for providing a list of ideas that may be infringing a patent using the user interface and supporting detailed analysis and implementation of countermeasures.

[1906] "Example 2: Combining Emotion Engines"

[1907] (Claim 1)

[1908] means for receiving and storing the generated business ideas in a database;

[1909] means for converting the idea and emotion data stored in said database into a suitable data format;

[1910] A means of inputting the converted idea data and emotion data into an AI model and training the model;

[1911] a means for comparing the learned ideas with a patent database to identify potential patent infringements;

[1912] a means for outputting and notifying a list of the ideas that may infringe the patent;

[1913] A system including:

[1914] (Claim 2)

[1915] 10. The system of claim 1, further comprising means for displaying the list on a user interface and allowing an administrator to identify ideas that may be patent infringing.

[1916] (Claim 3)

[1917] The system of claim 1, further comprising means for suggesting optimal countermeasures for the potentially patent infringing ideas and for monetizing them.

[1918] "Application example 2 when combining emotion engines"

[1919] (Claim 1)

[1920] means for receiving and storing the generated business ideas in a database;

[1921] means for identifying and learning from ideas stored in said database;

[1922] a means for comparing the learned ideas with a patent database to identify potential patent infringements;

[1923] a means for outputting and notifying a list of the ideas that may infringe the patent;

[1924] a means for collecting and analyzing emotion data;

[1925] a means for inputting the emotion data into a generative AI model to generate a prompt sentence;

[1926] A system including:

[1927] (Claim 2)

[1928] 10. The system of claim 1, further comprising means for suggesting countermeasures to the potentially patent infringing ideas and for monetizing them.

[1929] (Claim 3)

[1930] 2. The system according to claim 1, further comprising means for displaying a list of ideas that may be infringing a patent to an administrator using the user interface for further analysis. [Explanation of symbols]

[1931] 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. means for receiving and storing the generated business ideas in a database; means for identifying and learning from ideas stored in said database; a means for comparing the learned ideas with a patent database to identify potential patent infringements; a means for outputting and notifying a list of the ideas that may infringe the patent; A system including:

2. The system of claim 1 , further comprising means for suggesting countermeasures for the potentially patent infringing ideas and for monetizing them.

3. 2. The system according to claim 1, further comprising means for displaying a list of ideas that may be infringing a patent to an administrator using the user interface for detailed analysis.

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