System

Abstract

A system is provided.SOLUTION: This system is provided with a server means for generating an idea by a generation AI, a database means for storing the generated idea, a server means for accepting the submission of the idea, an evaluation AI means for evaluating the submitted idea, a server means for providing an evaluated result, and a AI means for proposing prize money distribution.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] Idea contests held in the business and education sectors are time-consuming and difficult to evaluate fairly and efficiently when there are a large number of submissions. To solve this problem, automated idea generation and evaluation systems are needed. Other challenges include increasing the number, quality, fairness, and transparency of idea evaluation. [Means for solving the problem]

[0005] The present invention provides a system including a terminal means for inputting topics and constraints, a server means for generating ideas using a generation AI, a database means for storing the generated ideas, a server means for accepting idea submissions, an evaluation AI means for evaluating the submitted ideas, a server means for providing the evaluation results, and an AI means for proposing prize allocations. The system is characterized by using evaluation criteria of creativity, feasibility, and marketability when evaluating submitted ideas, thereby improving the fairness and transparency of the evaluation process. Furthermore, the system has a server means for generating insights based on the evaluation results and reporting them to users, thereby supporting operators in efficiently selecting the best ideas.

[0006] A "topic" is a specific theme or subject that will be the subject of a contest or discussion.

[0007] "Constraints" are specific requirements or criteria that must be met when generating or evaluating ideas.

[0008] "Terminal means" refers to a device or interface through which a user inputs information.

[0009] "Generative AI" is an artificial intelligence mechanism that automatically generates new ideas based on given topics and constraints.

[0010] "Server means" refers to a computer server for processing and managing data.

[0011] "Database means" refers to a database system for storing generated and submitted ideas.

[0012] The "evaluation AI method" is an artificial intelligence system that evaluates submitted ideas based on multiple evaluation criteria.

[0013] "Evaluation criteria" are specific indicators or factors used to measure the value of an idea.

[0014] "Creativity" is a criterion for evaluating the originality and novelty of an idea.

[0015] "Feasibility" is a criterion for assessing the likelihood that an idea will be realized.

[0016] "Marketability" is a criterion for assessing the likelihood that an idea will be accepted in the market.

[0017] "Insights" are useful information or opinions generated based on the results of an evaluation.

[0018] "Prize distribution" refers to the method of distributing rewards for outstanding ideas in an idea contest. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The present invention is a system that includes a terminal for inputting topics and constraints, a server that generates ideas using a generation AI, a database that stores the generated ideas, a server that accepts idea submissions, an evaluation AI that evaluates the submitted ideas, a server that provides the evaluation results, and an AI that proposes prize distribution.

[0041] Program processing

[0042] 1. Idea generation

[0043] User: The user inputs the contest topic and constraints into the terminal and submits them.

[0044] Terminal: Sends topics and constraints to the server.

[0045] Server: The server launches a generative AI to generate new ideas based on the received topic and constraints.

[0046] Server: Stores the generated ideas in a database.

[0047] Examples:

[0048] A user inputs the topic "new environmentally friendly products" into a device and sends it. The device sends the topic to the server. The server launches a generative AI to generate ideas for bioplastic containers made from reusable materials. The server stores the generated ideas in a database.

[0049] 2. Idea submission and evaluation

[0050] User: The user inputs and sends his / her own ideas through the terminal.

[0051] Terminal: Sends submitted ideas to the server.

[0052] Server: The server receives submitted ideas and stores them in a database.

[0053] Server: The server runs an evaluation AI and evaluates all submitted ideas.

[0054] Evaluation AI: Score each idea based on criteria such as creativity, feasibility, and marketability.

[0055] Server: Stores the evaluation results in a database.

[0056] Examples:

[0057] A user submits an "idea for a recyclable water bottle" via their device. The device sends the idea to the server. The server receives the submitted idea and stores it in a database. The server then launches an evaluation AI, which evaluates all submitted ideas from the perspectives of "reusability," "cost-effectiveness," and "environmental protection." The server then stores the evaluation results in a database.

[0058] 3. Providing insights and suggesting prize allocations

[0059] Server: The server generates insights based on the evaluation results, including the top-rated ideas and analysis of the evaluations.

[0060] Server: The AI ​​proposes optimal prize distribution, including the evaluation points for each idea and distribution based on the contest budget.

[0061] Server: Sends the proposal results to the terminal so that the user can check them.

[0062] Examples:

[0063] The server generates insights based on the evaluation results and reports that "the idea for a recyclable water bottle received the highest rating." The server uses AI to suggest that the highest prize money be allocated to the idea for a recyclable water bottle. The server sends the proposal results to the device, where the user can confirm them.

[0064] Summary

[0065] The system of the present invention automates a series of processes, from topic input to idea generation, idea submission and evaluation, insight generation, and prize distribution proposals, thereby significantly improving the efficiency of idea contest management, increasing the number and quality of ideas, while ensuring fairness and transparency in evaluation.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] The user inputs the contest topic and constraints into the terminal and submits them.

[0069] The terminal sends the topic and constraints to the server.

[0070] Step 2:

[0071] The server launches a generation AI based on the received topic and constraints.

[0072] Generative AI generates new ideas based on topics and constraints.

[0073] Step 3:

[0074] The server stores the generated ideas in a database.

[0075] Step 4:

[0076] Users input their ideas directly into the terminal and send them.

[0077] The device sends the submitted idea to the server.

[0078] Step 5:

[0079] The server receives the submitted ideas and stores them in a database.

[0080] Step 6:

[0081] The server launches an evaluation AI to evaluate all submitted ideas.

[0082] The evaluation AI scores each idea based on criteria such as creativity, feasibility, and marketability.

[0083] Step 7:

[0084] The server stores the evaluation results in a database.

[0085] Step 8:

[0086] The server generates insights based on the evaluation results.

[0087] The insights generated include the top-rated ideas and an overall ranking analysis.

[0088] Step 9:

[0089] The server will use AI to suggest optimal prize distribution.

[0090] The proposals will include evaluation points for each idea and allocation based on the competition budget.

[0091] Step 10:

[0092] The server sends the proposal results to the terminal so that the user can check them.

[0093] Example 1

[0094] 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."

[0095] Modern idea contests are required to improve the number and quality of ideas and to ensure fair and transparent evaluation. Traditional systems rely on manual processes, from idea generation to evaluation, insight provision, and prize distribution, which are often inefficient and time-consuming. Furthermore, it is difficult to ensure fairness and transparency in evaluation. To solve these challenges, it is necessary to automate the entire process and improve efficiency and fairness.

[0096] 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.

[0097] In this invention, the server includes terminal means for inputting topics and constraints, server means for generating ideas using a generation AI, database means for saving the generated ideas, terminal means for accepting idea submissions, database means for saving submitted ideas, evaluation AI means for evaluating the submitted ideas, database means for saving the evaluation results, server means for providing the evaluation results, server means for generating insights based on the evaluation results and reporting them to users, and AI means for proposing prize distribution. This makes it possible to automate the entire process of an idea contest, not only improving the efficiency of the series of tasks from idea generation to submission and evaluation, provision of insights, and prize distribution, but also ensuring fairness and transparency of the evaluation.

[0098] "Topic" refers to the theme or issue that the User wishes to address in the Contest.

[0099] "Constraints" refer to specific requirements or limitations that must be met in idea generation and evaluation.

[0100] "Terminal" refers to an electronic device that allows a user to input and transmit information such as topics, constraints, and ideas.

[0101] "Server" refers to the central processing unit that performs various processes such as generation AI and evaluation AI.

[0102] "Generative AI" refers to artificial intelligence that automatically generates ideas based on topics and constraints.

[0103] "Database" refers to a system for storing and managing data such as generated ideas and evaluation results.

[0104] "Evaluation AI" refers to artificial intelligence that evaluates submitted ideas based on criteria such as creativity, feasibility, and marketability.

[0105] "Insights" refers to the analysis and key information generated from the assessment results.

[0106] "Reporting" refers to the act of providing information such as insights or evaluation results to users.

[0107] "Prize Share" means the share of the prize awarded to each Idea in the Contest.

[0108] "HTTP POST request" refers to the protocol used by a device to send data to a server.

[0109] "Evaluation criteria" refers to the specific indicators or measures used to evaluate ideas.

[0110] The present invention relates to a system that generates and evaluates ideas based on topics and constraints entered by users, and proposes prize allocations based on the results. The system includes the following hardware and software configurations.

[0111] Hardware and Software Configuration

[0112] Device: This refers to the device that users use to input topics and ideas, such as a computer, smartphone, or tablet.

[0113] Server: Refers to the central processing unit that operates the generation AI, evaluation AI, database, etc.

[0114] Database: This refers to a system for storing data such as ideas and evaluation results, and uses MySQL or SQL Server.

[0115] What the program does

[0116] 1. Idea generation

[0117] 1. The user uses a terminal to input the contest topic (e.g., "New environmentally friendly product") and constraints, and submits it.

[0118] 2. The device sends the entered information to the server via an HTTP POST request.

[0119] 3. The server analyzes the received topic and constraints and passes a prompt to the generation AI (e.g., GPT-4). An example of a prompt is, "Please come up with a new environmentally friendly product, with a budget of less than 1 million yen and using reusable materials."

[0120] 4. The generative AI generates ideas based on the prompt text, for example, "bioplastic containers made from reusable materials."

[0121] 5. The server stores the generated ideas in a MySQL database.

[0122] 2. Idea submission and evaluation

[0123] 1. A user uses a terminal to input and submit their idea (e.g., "An idea for a recyclable water bottle").

[0124] 2. The device sends the idea to the server via an HTTP POST request.

[0125] 3. The server parses the received ideas and stores them in a SQL Server database.

[0126] 4. The server launches an evaluation AI (e.g., Scikit-learn model) to evaluate ideas based on the evaluation criteria (creativity, feasibility, marketability). For example, the idea for a recyclable water bottle is evaluated.

[0127] 5. The server stores the evaluation results in a SQL Server database.

[0128] 3. Providing insights and suggesting prize allocations

[0129] 1. The server generates insights based on the evaluation results, for example, analyzing that "the idea for a recyclable water bottle received high marks for creativity and marketability."

[0130] 2. The server uses a prize allocation AI (e.g., TensorFlow model) to propose an optimal prize allocation. For example, allocate 1 million yen to the idea for a recyclable water bottle.

[0131] 3. The server sends the proposal results to the device as an HTTP response so that the user can check them.

[0132] In this way, the system of the present invention allows users to easily specify a topic, automatically generate and evaluate ideas based on that topic, and provide the results, thereby streamlining the entire idea contest process and ensuring fairness and transparency in the evaluation.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] System program processing flow

[0135] Idea generation process

[0136] Step 1:

[0137] The user uses a terminal to input the contest topic "Environmentally Friendly New Product" and constraints (for example, budget of 1 million yen or less, use of recyclable materials) and submit the input.

[0138] Input: Topic: "New eco-friendly product", Constraints: "Budget: 1 million yen or less, Use recyclable materials"

[0139] Output: Topics and constraints entered in the terminal

[0140] Step 2:

[0141] The terminal sends the entered topic and constraints to the server via an HTTP POST request.

[0142] Input: User-entered topics and constraints

[0143] Output: HTTP POST request with topic and constraints sent to the server

[0144] Step 3:

[0145] The server analyzes the received request and extracts the topic and constraints.

[0146] Input: Topic and constraints received in the HTTP POST request

[0147] Output: Parsed topic "New eco-friendly product" and constraints "budget within 1 million yen, use reusable materials"

[0148] Step 4:

[0149] The server launches a generative AI (e.g., GPT-4) and passes it a prompt: "Please come up with a new environmentally friendly product. The budget should be within 1 million yen and it should use reusable materials."

[0150] Input: Parsed topics and constraints

[0151] Output: Prompt given to the generation AI: "Invent a new eco-friendly product. The budget should be within 1 million yen and it should be made from recyclable materials."

[0152] Step 5:

[0153] The generative AI generates ideas based on a prompt, such as a bioplastic container made from reusable materials.

[0154] Input: Prompt: "Invent a new eco-friendly product. Budget should be under 1 million yen, and it should be made from recyclable materials."

[0155] Output: Generated idea: "Bioplastic container made from reusable materials"

[0156] Step 6:

[0157] The server stores the generated ideas in a MySQL database.

[0158] Input: Generated idea: "Bioplastic container made from reusable materials"

[0159] Output: Ideas stored in a MySQL database

[0160] Idea submission and evaluation process

[0161] Step 1:

[0162] A user uses a terminal to input and submit his / her idea, "An idea for a recyclable water bottle."

[0163] Input: Idea "Recyclable water bottle idea"

[0164] Output: Ideas typed into the terminal

[0165] Step 2:

[0166] The device sends the input idea to the server via an HTTP POST request.

[0167] Input: User-entered ideas

[0168] Output: HTTP POST request for ideas sent to the server

[0169] Step 3:

[0170] The server analyzes the received request and extracts ideas.

[0171] Input: Ideas received in an HTTP POST request

[0172] Output: Parsed idea "Recyclable water bottle idea"

[0173] Step 4:

[0174] The server stores the extracted ideas in a SQL Server database.

[0175] Input: Parsed idea "Recyclable water bottle idea"

[0176] Output: Ideas stored in a SQL Server database

[0177] Step 5:

[0178] The server launches an evaluation AI (e.g., a Scikit-learn model) and passes all ideas in the database to be evaluated based on the following criteria: creativity, feasibility, and marketability.

[0179] Input: All ideas in a SQL Server database

[0180] Output: A list of ideas to be passed to the evaluation AI

[0181] Step 6:

[0182] The AI ​​will score each idea based on the criteria of creativity, feasibility, and marketability. For example, an idea for a recyclable water bottle will be evaluated and given a score.

[0183] Input: List of ideas based on evaluation criteria

[0184] Output: Each idea given a rating score

[0185] Step 7:

[0186] The server stores the evaluation results in a SQL Server database.

[0187] Input: Each idea given a rating score

[0188] Output: Evaluation results stored in a SQL Server database

[0189] Providing insights and processing prize allocation recommendations

[0190] Step 1:

[0191] The server generates insights based on the evaluation results, such as "The idea for a recyclable water bottle received high marks for creativity and marketability."

[0192] Input: Evaluation results in a SQL Server database

[0193] Output: Generated insights

[0194] Step 2:

[0195] The server uses a prize allocation AI (e.g., TensorFlow model) to propose the optimal prize allocation for each idea. For example, it allocates 1 million yen to the idea for a recyclable water bottle.

[0196] Input: Generated insights

[0197] Output: Proposed bounty distribution

[0198] Step 3:

[0199] The server sends the proposal results to the terminal in an HTTP response, allowing the user to check the results.

[0200] Input: Proposed Prize Distribution

[0201] Output: Suggestion results sent to the device

[0202] In this way, the system of the present invention allows users to easily specify a topic, automatically generate and evaluate ideas based on that topic, and provide the results, thereby streamlining the entire idea contest process and ensuring fairness and transparency in the evaluation.

[0203] (Application example 1)

[0204] 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."

[0205] When users propose new product designs or concepts in a virtual store, it is necessary to evaluate the ideas and distribute rewards fairly and efficiently. Currently, there is no system that provides prompt and appropriate feedback and rewards for user-generated ideas. Therefore, it is a challenge to provide a mechanism that increases user motivation and generates many high-quality ideas.

[0206] 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.

[0207] In this invention, the server includes a terminal means for inputting topics and constraints, a server means for generating ideas using a generation AI, and a database means for storing the generated ideas. This allows the generation AI to efficiently create new product ideas based on information input by users and store the evaluation results of those ideas in the database. Furthermore, the evaluation AI means performs fair and detailed evaluations of the proposed ideas, thereby generating high-quality ideas and allocating appropriate rewards. By providing a means for quickly notifying users of the results via a smartphone, smart glasses, or head-mounted display, immediate feedback can be provided, increasing users' motivation to participate.

[0208] "Terminal means" refers to a device that allows a user to input topics and constraints, such as a smartphone, smart glasses, or a head-mounted display.

[0209] "Generative AI" refers to artificial intelligence that generates new ideas based on topics and constraints entered by the user.

[0210] "Server means" refers to a computer system that runs the generative AI to generate ideas and manage them.

[0211] "Database Means" refers to a data management system for storing generated ideas and accessing and managing them as needed.

[0212] "Evaluation AI means" refers to artificial intelligence that evaluates submitted ideas based on criteria such as creativity, feasibility, and marketability.

[0213] "Server means for providing evaluation results" refers to a server for notifying users and systems of the results generated by the evaluation AI means.

[0214] "Means for making reward suggestions" refers to artificial intelligence that suggests optimal reward allocation based on the evaluation results.

[0215] A "virtual store" refers to a virtual shopping space developed on the Internet, where users can propose product designs and concepts.

[0216] "Means for notifying results" refers to a system for notifying users of feedback and evaluation results through devices such as smartphones, smart glasses, and head-mounted displays.

[0217] The system of the present invention allows users to propose new product designs and concepts in a virtual store, and the ideas are evaluated and rewards are distributed.

[0218] System Configuration

[0219] The system consists of the following components:

[0220] Terminal means: A device through which a user inputs topics and constraints. This includes smartphones, smart glasses, head-mounted displays, etc.

[0221] Generative AI: Artificial intelligence that generates new ideas based on user-entered topics and constraints.

[0222] Server means: A computer system for running the generative AI and generating and managing ideas.

[0223] Database means: A database for storing generated ideas and accessing and managing them as needed.

[0224] Evaluation AI method: An artificial intelligence that evaluates submitted ideas based on criteria such as creativity, feasibility, and marketability.

[0225] Server means for providing evaluation results: A server for notifying users and systems of the results generated by the evaluation AI means.

[0226] Means for making reward suggestions: Artificial intelligence that suggests optimal reward allocation based on evaluation results.

[0227] Means of notifying results: A system for notifying users of feedback and evaluation results through devices such as smartphones, smart glasses, and head-mounted displays.

[0228] Program processing

[0229] 1. Topic input and idea generation

[0230] Users access the virtual store via their smartphone and input new product designs and concepts. This input information is sent from the device to a server, where a generative AI generates new ideas. This generative AI uses an open-source generative AI model (e.g., GPT-4). The server stores the generated ideas in a database.

[0231] 2. Submitting and Saving Ideas

[0232] When a user submits a self-generated product idea, the information is sent to the server, which receives the information and stores it in a database.

[0233] 3. Evaluate ideas

[0234] The server launches an evaluation AI to evaluate submitted ideas. The evaluation AI uses TensorFlow and PyTorch to score ideas based on criteria such as creativity, feasibility, and marketability.

[0235] 4. Reward proposals and notifications

[0236] Based on the evaluation results, the server proposes rewards. The server notifies the user of the evaluation results and offers rewards such as points or discount coupons. This notification is done via a smartphone, smart glasses, or a head-mounted display.

[0237] Examples of concrete examples and prompts

[0238] Users enter the topic of creating a "sustainable fashion item" through a smartphone app.

[0239] Example prompt sentence:

[0240] Topic: Sustainable fashion items

[0241] Constraints: Use of recyclable materials and environmentally friendly processes

[0242] Given this prompt, the generative AI generated the following ideas:

[0243] The idea: a recycled cotton t-shirt made from renewable materials, with a design featuring an environmental message.

[0244] The evaluation AI generates the following evaluation results and provides feedback to the user:

[0245] Creativity: 9 / 10

[0246] Feasibility: 8 / 10

[0247] Marketability: 7 / 10

[0248] The idea ultimately involves providing discount coupons and users can view the results on their smartphones.

[0249] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0250] Step 1:

[0251] User inputs topic and constraints

[0252] The user launches the smartphone app and inputs a topic and constraints. For example, the topic is "sustainable fashion items" and the constraints are "use of reusable materials and environmentally friendly processes."

[0253] Input: Topics and constraints from the user

[0254] Output: Topic and constraint data is sent from the terminal to the server.

[0255] Step 2:

[0256] The server receives the topic and constraints

[0257] The server receives the topic and constraints sent from the device, and prepares to pass this data to the generation AI.

[0258] Input: Topic and constraint data sent from the device

[0259] Output: Topic and constraint data to be passed to the generative AI

[0260] Step 3:

[0261] Generative AI generates ideas

[0262] The generative AI in the server generates new product ideas based on the received topic and constraints. The generative AI model used here is GPT-4. The model analyzes the prompt sentence and generates appropriate ideas.

[0263] Input: Topic and constraint data

[0264] Output: Generated ideas (e.g., recycled cotton T-shirts made from renewable materials)

[0265] Step 4:

[0266] Save your ideas in a database

[0267] The generated ideas are stored in a database on the server, making it easy to retrieve the ideas later.

[0268] Input: Generated ideas

[0269] Output: Idea records stored in a database

[0270] Step 5:

[0271] User submits idea

[0272] Users submit their own product ideas through a smartphone app. For example, they can submit an idea for a recycled cotton T-shirt.

[0273] Input: Submitted Idea

[0274] Output: Ideas submitted by users are sent from the device to the server.

[0275] Step 6:

[0276] The server receives and stores submitted ideas.

[0277] The server receives the submitted ideas sent from the terminals and stores them in a database.

[0278] Input: Submitted Idea

[0279] Output: A record of the submitted idea stored in a database

[0280] Step 7:

[0281] Evaluation AI evaluates ideas

[0282] The server runs an evaluation AI model that evaluates ideas in the database based on criteria such as creativity, feasibility, and marketability. The evaluation model uses TensorFlow and PyTorch.

[0283] Input: Each idea in the database

[0284] Output: Evaluation results (creativity, feasibility, marketability scores)

[0285] Step 8:

[0286] Evaluation results are saved in a database

[0287] The server stores the generated evaluation results in a database, and this information is later communicated to the user.

[0288] Input: Evaluation result

[0289] Output: Records of the evaluation results stored in a database

[0290] Step 9:

[0291] Reward proposal

[0292] The server will propose rewards based on the evaluation results, which may include points or discount coupons.

[0293] Input: Evaluation result

[0294] Output: Reward proposal data

[0295] Step 10:

[0296] Notify the user of the results

[0297] The server notifies the user of the evaluation results and reward proposals via a smartphone, smart glasses, or head-mounted display.

[0298] Input: Reward proposal data

[0299] Output: Evaluation results and reward notification displayed on the user's device

[0300] 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.

[0301] The present invention is a system that includes a terminal for inputting topics and constraints, a server that generates ideas using a generation AI, a database that stores the generated ideas, a server that accepts idea submissions, an evaluation AI that evaluates the submitted ideas, a server that provides the evaluation results, an AI that proposes prize allocations, and an emotion engine that recognizes the user's emotions.

[0302] Program processing

[0303] 1. Idea generation

[0304] User: The user inputs the contest topic and constraints into the terminal and submits them.

[0305] Terminal: Sends topics and constraints to the server.

[0306] Server: The server activates the generation AI and generates new ideas based on the received topics and constraints. The emotion engine also analyzes the user's emotion data and reflects it in idea generation.

[0307] Server: Stores the generated ideas in a database.

[0308] Examples:

[0309] The user inputs the topic "new environmentally friendly products" into the device and sends it. The device sends the topic to the server. The server activates the generative AI to generate ideas for bioplastic containers made from reusable materials. The emotion engine analyzes the user's emotions and reflects this in the generation process to prioritize ideas that show a positive emotional response. The server then stores the generated ideas in a database.

[0310] 2. Idea submission and evaluation

[0311] User: The user inputs and sends his / her own ideas through the terminal.

[0312] Terminal: Sends submitted ideas to the server.

[0313] Server: The server receives submitted ideas and stores them in a database.

[0314] Server: Launches the evaluation AI and evaluates all submitted ideas. The evaluation AI scores each idea based on the criteria of creativity, feasibility, and marketability. In addition, the emotion engine analyzes the user's emotional data and reflects it in the evaluation.

[0315] Server: Stores the evaluation results in a database.

[0316] Examples:

[0317] A user submits an "idea for a recyclable water bottle" via their device. The device sends the idea to the server. The server receives the submitted idea and stores it in a database. The server then activates an evaluation AI, which evaluates all submitted ideas from the perspectives of "reusability," "cost-effectiveness," and "environmental protection." At this time, an emotion engine analyzes the user's emotions and gives high ratings to ideas that elicit a positive response. The server then stores the evaluation results in a database.

[0318] 3. Providing insights and suggesting prize allocations

[0319] Server: The server generates insights based on the evaluation results, including the top-rated ideas and an analysis of the evaluations.

[0320] Server: The AI ​​proposes optimal prize distribution. The proposal includes allocation based on the evaluation points of each idea and the contest budget. In addition, the emotion engine analyzes users' emotional data and reflects it in the proposal.

[0321] Server: Sends the proposal results to the terminal so that the user can check them.

[0322] Examples:

[0323] The server generates an insight based on the evaluation results and reports that "the idea for a recyclable water bottle received the highest rating." The emotion engine analyzes the user's emotions and reflects them in the insight. The server uses AI to suggest allocating the highest prize money to the idea for a recyclable water bottle. In this case, the emotion engine analyzes the user's emotional data and adds a small bonus to ideas that show a positive emotional response. The server sends the proposal results to the device, where the user can confirm them.

[0324] Summary

[0325] The system of this invention not only handles a series of processes from topic input to idea generation, idea submission and evaluation, insight generation, and prize distribution proposals, but also recognizes user emotions and reflects them in the generation, evaluation, and proposals. This significantly improves the efficiency of idea contest management and, by taking user emotions into consideration, enables contest management that is more user-friendly.

[0326] The processing flow will be explained below.

[0327] Program processing

[0328] 1. Idea generation

[0329] Step 1:

[0330] The user inputs the contest topic and constraints into the terminal and submits them.

[0331] Step 2:

[0332] The terminal sends the topic and constraints to the server.

[0333] Step 3:

[0334] The server sends the received topic and constraints to the generation AI and starts the generation AI.

[0335] Step 4:

[0336] Generative AI generates new ideas based on topics and constraints.

[0337] Step 5:

[0338] The emotion engine acquires the user's emotional data and reflects it in the generated ideas. In this case, the emotion engine analyzes the user's positive emotions and adjusts the idea generation process accordingly.

[0339] Step 6:

[0340] The server stores the generated ideas in a database.

[0341] Examples:

[0342] The user inputs the topic "new environmentally friendly products" into the device and sends it. The device sends the topic to the server. The server activates the generative AI to generate ideas for bioplastic containers made from reusable materials. The emotion engine analyzes the user's emotions and reflects this in the generation process to prioritize ideas that show a positive emotional response. The server then stores the generated ideas in a database.

[0343] 2. Idea submission and evaluation

[0344] Step 1:

[0345] Users input and send their ideas through a terminal.

[0346] Step 2:

[0347] The device sends the submitted idea to the server.

[0348] Step 3:

[0349] The server receives the submitted ideas and stores them in a database.

[0350] Step 4:

[0351] The server launches an evaluation AI to evaluate all submitted ideas.

[0352] Step 5:

[0353] The evaluation AI scores each idea based on the criteria of "creativity," "feasibility," and "marketability."

[0354] Step 6:

[0355] The emotion engine analyzes the user's emotional data and reflects it in the evaluation AI. At this time, the emotion engine analyzes the user's positive emotional reactions and adjusts the evaluation process to give a high rating.

[0356] Step 7:

[0357] The server stores the evaluation results in a database.

[0358] Examples:

[0359] A user submits an "idea for a recyclable water bottle" via their device. The device sends the idea to the server. The server receives the submitted idea and stores it in a database. The server then activates an evaluation AI, which evaluates all submitted ideas from the perspectives of "reusability," "cost-effectiveness," and "environmental protection." At this time, an emotion engine analyzes the user's emotions and gives high ratings to ideas that elicit a positive response. The server then stores the evaluation results in a database.

[0360] 3. Providing insights and suggesting prize allocations

[0361] Step 1:

[0362] The server generates insights based on the evaluation results, including the top-rated ideas and an overall evaluation analysis.

[0363] Step 2:

[0364] The emotion engine analyzes the user's emotions and reflects them in the generated insights. In this case, the emotion engine analyzes the user's emotional data and generates advantageous insights for ideas that show a positive reaction.

[0365] Step 3:

[0366] The server uses AI to propose optimal prize distribution, including the evaluation points for each idea and distribution based on the contest budget.

[0367] Step 4:

[0368] The emotion engine analyzes users' emotional data and reflects it in the prize distribution proposals, adding a little extra weight to ideas that show positive emotions.

[0369] Step 5:

[0370] The server sends the proposal results to the terminal so that the user can check them.

[0371] Examples:

[0372] The server generates an insight based on the evaluation results and reports that "the idea for a recyclable water bottle received the highest rating." The emotion engine analyzes the user's emotions and reflects them in the insight. The server uses AI to suggest allocating the highest prize money to the idea for a recyclable water bottle. In this case, the emotion engine analyzes the user's emotional data and adds a small bonus to ideas that show a positive emotional response. The server sends the proposal results to the device, where the user can confirm them.

[0373] Summary

[0374] The system of this invention not only handles a series of processes from topic input to idea generation, idea submission and evaluation, insight generation, and prize distribution proposals, but also recognizes user emotions and reflects them in the generation, evaluation, and proposals. This significantly improves the efficiency of idea contest management and, by taking user emotions into consideration, enables contest management that is more user-friendly.

[0375] Example 2

[0376] 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."

[0377] Conventional idea contest management systems have a complicated process from idea generation to evaluation, reporting, and prize distribution, making it difficult to manage the contest efficiently and with consideration for users' emotions.In addition, it is not possible to evaluate or propose ideas with consideration for users' emotions, making it difficult to manage a contest that provides high user satisfaction.

[0378] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0379] In this invention, the server includes terminal means for inputting topics and constraints, server means for generating ideas using a generative AI model, database means for saving the generated ideas, emotion engine means for analyzing emotion data, server means for activating evaluation AI means to evaluate the generated ideas, server means for accepting submitted ideas, server means for activating the evaluation AI means to provide evaluation results, AI means for generating insights based on the evaluation results and proposing optimal prize distribution, and emotion engine means for recognizing user emotions and reflecting them in evaluations and proposals. This streamlines the process from idea generation to evaluation, insight generation, and prize distribution proposals, and enables the operation of a more satisfying contest that takes user emotions into consideration.

[0380] "Terminal means" is a device that allows a user to input topics and constraints and send them to the system.

[0381] A "generative AI model" is an artificial intelligence that generates new ideas based on received topics and constraints.

[0382] The "server means" is a server device for processing and storing various data within the system.

[0383] The "database means" is a database device for storing and managing data such as generated ideas and evaluation results.

[0384] The "emotion engine means" is a system that analyzes the user's emotional data and reflects it in idea generation, evaluation, and proposals.

[0385] The "evaluation AI method" is an artificial intelligence that evaluates submitted ideas based on the criteria of creativity, feasibility, and marketability.

[0386] "Insights" refers to the analytical results and important information gained from the evaluation results.

[0387] The "AI method for proposing prize distribution" is an artificial intelligence that proposes optimal prize distribution based on evaluation results and budget.

[0388] This invention relates to a system that streamlines the process from topic input to idea generation, submission, evaluation, insight generation, and prize allocation proposals. This system includes a terminal where users input topics and constraints, a server where new ideas are generated using a generative AI model, a database where the generated ideas and evaluation results are stored, and an emotion engine that analyzes user emotion data and reflects this in idea generation and evaluation. The main components of this system and their operation are described in detail below.

[0389] Entering and sending topics and constraints

[0390] Users input the contest topic and constraints using a terminal. This terminal can be a general PC, tablet, or smartphone. The input information is sent from the terminal to the server.

[0391] Example: A user inputs the topic "New eco-friendly products" and performs a send operation. At this time, the terminal sends the input information to the server.

[0392] Idea generation

[0393] The server generates new ideas by launching a generative AI model based on the topics and constraints received from the device. This generative AI model uses machine learning technology to generate new ideas from a variety of data.

[0394] The server uses an emotion engine to analyze the user's emotion data during the generation process and reflects the data in the generated ideas.

[0395] Example: Generative AI generates ideas for "bioplastic containers made from reusable materials," while the emotion engine reflects the user's positive emotions.

[0396] Saving ideas

[0397] The server stores the generated ideas in a database.

[0398] Example: The generated ideas for bioplastic containers are stored in the "Ideas" table of the database.

[0399] Submitting and Receiving Ideas

[0400] Users input and transmit their ideas through a terminal.

[0401] The device sends the input ideas to the server, and this information is also stored and managed in a database.

[0402] Example: A user submits an idea for a recyclable water bottle via their device. This data is then sent to the server and stored in a database.

[0403] Idea Evaluation

[0404] The server launches an evaluation AI to evaluate all submitted ideas based on the criteria of creativity, feasibility, and marketability, and also reflects the user's emotional data using an emotion engine.

[0405] Example: An evaluation AI evaluates ideas for recyclable water bottles from the perspectives of "reusability," "cost-effectiveness," and "environmental protection," giving higher ratings to ideas that express positive emotions.

[0406] Providing insights and suggesting prize allocations

[0407] The server generates insights based on the evaluation results and reports them to the user. It also activates an AI that proposes optimal prize distribution based on the evaluation results. This proposal also reflects the analysis results of the emotion engine.

[0408] Example: The server reports that "the idea for a recyclable water bottle received the highest votes" and suggests optimal prize distribution taking sentiment data into account.

[0409] Prompt Sentence Examples

[0410] "Please tell me your ideas for new eco-friendly products. I'd like specific ideas for bioplastic containers made from reusable materials."

[0411] This system will significantly improve the efficiency of idea contest management and will also enable more satisfying management that takes into account user emotions.

[0412] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0413] Step 1: Enter topic and constraints and submit

[0414] User: The user inputs the contest topic and constraints into the terminal and presses the send button. This input data includes the topic and constraints in text format. Based on the input data, the terminal sends the data to the server.

[0415] Input: Topic (e.g., "New eco-friendly product"), Constraint (e.g., "Use reusable materials")

[0416] Output: A data packet containing topics and constraints

[0417] Specific operation: The user enters a topic in the text field of the terminal and clicks the send button. The terminal converts the input data into a packet format and sends it to the server as an HTTP request.

[0418] Step 2: Receive topics and constraints and launch the generation AI model

[0419] Server: The server receives the topics and constraints sent from the device. The received data contains the topics and constraints in text format. The server then launches the generative AI model based on this.

[0420] Input: Data packets of topics and constraints sent from the device

[0421] Output: Ready for idea generation

[0422] How it works: The server receives an HTTP request, extracts topics and constraints, then calls the API of the generative AI model to start the idea generation process.

[0423] Step 3: Generate ideas and reflect on sentiment data

[0424] Server: The server uses the generative AI model to generate ideas based on the received topic and constraints. It also activates the emotion engine to analyze the user's emotion data and reflect it in the generation process.

[0425] Input: Generative AI model, emotion data

[0426] Output: Generated ideas

[0427] How it works: The generative AI model generates several ideas based on a given topic. In parallel, the emotion engine analyzes the user's emotion data and filters the ideas to elicit positive emotions. Once the final idea is determined, it is output.

[0428] Step 4: Saving generated ideas

[0429] Server: Stores the generated ideas in a database, which stores the idea content and associated metadata.

[0430] Input: Generated ideas

[0431] Output: Ideas stored in a database

[0432] What happens: The server converts the generated ideas into JSON format and executes a query to insert them into the "ideas" table in the database, where they are permanently stored.

[0433] Step 5: Submit and submit your idea

[0434] User: The user inputs their idea into the terminal and presses the send button. This information is sent from the terminal to the server.

[0435] Input: User-entered ideas

[0436] Output: Data packet to be sent

[0437] Specific operation: A user fills out an idea submission form on the device and clicks the submit button. The device converts the input data into packets and sends them to the server as an HTTP request.

[0438] Step 6: Receiving and storing submissions

[0439] Server: Receives submitted ideas and stores them in a database. The received data includes the ideas entered by users.

[0440] Input: Ideas sent from your device

[0441] Output: User ideas stored in a database

[0442] What happens: The server receives the HTTP request, extracts the ideas, converts them to JSON format, and executes a query to insert them into the "Submitted Ideas" table in the database.

[0443] Step 7: Launch the evaluation AI and evaluate your ideas

[0444] Server: The server launches the evaluation AI and evaluates all ideas stored in the database. Evaluation criteria include creativity, feasibility, and marketability. In addition, it uses an emotion engine to reflect user emotional data in the evaluation.

[0445] Input: Submitted ideas, sentiment data

[0446] Output: Idea rating score

[0447] Specific operation: The server invokes the evaluation AI model and starts evaluating each idea. The generated score and emotional data are combined to calculate the final evaluation. The evaluation results are then stored in the database.

[0448] Step 8: Save the evaluation results

[0449] Server: Stores the evaluation results in a database. For each evaluated idea, a score and associated data are stored.

[0450] Input: Idea rating score

[0451] Output: Evaluation results stored in a database

[0452] Specific operation: The server converts the evaluation results into JSON format and executes a query to insert them into the "Evaluation Results" table in the database, thereby permanently storing the evaluation results.

[0453] Step 9: Analyze assessment results and generate insights

[0454] Server: Generates insights based on the evaluation results, including the top-rated ideas and an overall evaluation analysis.

[0455] Input: Evaluation result

[0456] Output: Generated insights

[0457] Specific operation: The server analyzes the evaluation results and generates insights in text format, which are temporarily stored on the server.

[0458] Step 10: Proposal for Prize Distribution

[0459] Server: Proposes optimal prize distribution using prize distribution AI. Proposals include distribution based on evaluation points and budget. It also uses an emotion engine to reflect user emotion data.

[0460] Input: Evaluation results, budget, sentiment data

[0461] Output: Prize allocation proposal

[0462] Specific operation: The server calls the prize allocation AI model and calculates the optimal prize allocation using the evaluation points and budget as input. It then modifies the proposal based on data from the emotion engine and generates the final prize allocation proposal.

[0463] Step 11: Submit your proposal

[0464] Server: Sends the prize distribution proposal to the user's device so that the user can confirm it.

[0465] Input: Prize allocation proposal

[0466] Output: Notification of proposal results

[0467] Specific operation: The server sends the generated prize distribution proposal to the terminal. The user can check the proposal result on the terminal screen. Notifications are sent to the user via email, push notifications, etc.

[0468] (Application example 2)

[0469] 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."

[0470] Conventional content delivery systems have the problem of not efficiently generating personalized content based on specific topics and constraints desired by users. Furthermore, content suggestions do not take user emotions into consideration, which may reduce user satisfaction. The objective of this invention is to provide a system that combines generation AI, evaluation AI, and an emotion engine to effectively generate, evaluate, and provide content that satisfies users.

[0471] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0472] In this invention, the server includes terminal means for inputting topics and constraints, server means for generating ideas using a generation AI, database means for saving the generated ideas, server means for accepting submissions of ideas, evaluation AI means for evaluating the submitted ideas, server means for providing the evaluation results, AI means for proposing prize distribution, emotion engine means for recognizing user emotions, and means for proposing content that matches the user's preferences based on the content ideas generated by the evaluation AI. This makes it possible to generate novel content ideas based on the user's topics and constraints, and to make personalized suggestions that reflect the user's emotions.

[0473] "Terminal means" is a device that allows a user to input topics and constraints and send them to the server.

[0474] "Generative AI" is an artificial intelligence technology that generates new ideas based on topics and constraints entered by the user.

[0475] "Server means" refers to a device that processes and manages data and on which generation AI, evaluation AI, emotion engine, etc. operate.

[0476] "Database means" is a data management system for storing generated ideas.

[0477] "Evaluation AI methods" are artificial intelligence technologies that evaluate submitted ideas based on evaluation criteria such as creativity, feasibility, and marketability.

[0478] The "emotion engine means" is a technology for recognizing and analyzing the user's emotions.

[0479] "AI method for proposing prize distribution" is an artificial intelligence technology that proposes optimal prize distribution based on the evaluation results.

[0480] "Means for suggesting content that matches the user's preferences" refers to a device or technology that suggests personalized content based on content ideas generated by evaluation AI, taking into account the user's emotional state.

[0481] A system for implementing this invention is configured as follows: A user uses a terminal (such as a smartphone) to input topics and constraints. The terminal is equipped with an interface for transmitting the topics and constraints input by the user to a server. The server activates a generation AI based on the received topics and constraints to generate new ideas. At this time, an emotion engine analyzes the user's emotion data and reflects it in the generation process. The generated ideas are stored in a database.

[0482] Next, when a user submits their own idea, they send it to the server via their device. The server receives the submitted idea and evaluates it using evaluation AI. The evaluation AI scores the idea using evaluation criteria of creativity, feasibility, and marketability. At that time, an emotion engine analyzes the user's emotional data and reflects it in the evaluation. The evaluation results are stored in a database.

[0483] The server then generates insights based on the evaluation results and reports them to the user. Furthermore, the AI ​​that proposes prize distribution proposes optimal prize distribution, and the emotion engine analyzes the user's emotional data and reflects it in the proposal. The proposal results are sent to the device so that the user can check them. There is also a means to suggest content that suits the user's preferences based on the content ideas generated by the evaluation AI, allowing the user to receive personalized content.

[0484] Hardware and software used

[0485] Hardware: Smartphone (iOS or Android device)

[0486] software:

[0487] Python 3.x

[0488] The transformers library (for generative AI and sentiment analysis)

[0489] SQLite (for storing and managing ideas)

[0490] Specific examples

[0491] Suppose a user enters the following topic and constraints into a terminal:

[0492] Topic: "The Future of Travel"

[0493] Constraint: "Consider sustainability and environmental protection"

[0494] This input is sent to the server, which then triggers a generative AI to generate ideas based on the prompt.

[0495] Example prompt sentence:

[0496] Topic: Future of Travel

[0497] Constraints: Sustainability and environmental protection

[0498] Generate ideas:

[0499] The server stores the generated ideas in a database for users to review. The emotion engine analyzes the user's emotions and reflects them in the creation and evaluation process, thereby providing content that matches the user's preferences.

[0500] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0501] Step 1:

[0502] The user inputs the topic and constraints using a terminal.

[0503] Input: The user inputs the topics and constraints "future travel" and "consider sustainability and environmental protection."

[0504] Specific operation: A screen for entering the topic and constraints appears on the terminal interface. The user enters them and presses the send button.

[0505] Output: The entered data is sent to the server.

[0506] Step 2:

[0507] The server launches a generative AI based on the received topic and constraints to generate new ideas.

[0508] Input: Topic "Future of Travel" and Constraint "Consider sustainability and environmental protection".

[0509] What it does: The server passes these inputs as prompts to the generative AI, which then generates new ideas based on these prompts (e.g., the gpt-3 model).

[0510] Output: Generated ideas (e.g. "Ecological Tours Using Sustainable Energy").

[0511] Step 3:

[0512] The emotion engine analyzes the user's emotion data and reflects it in the generation process.

[0513] Input: Generated ideas and user sentiment data.

[0514] What it does: The emotion engine analyzes the user's emotional response to the generated ideas, for example, evaluating positive or negative reactions to the text content.

[0515] Output: The sentiment analysis results are fed into the generation process.

[0516] Step 4:

[0517] The server stores the generated ideas in a database.

[0518] Input: Generated ideas and sentiment analysis results.

[0519] Specific operation: The server connects to the database means and records the generated ideas and their sentiment analysis results.

[0520] Output: The saved ideas and their sentiment analysis results are stored in a database.

[0521] Step 5:

[0522] Users input their ideas through their terminals and send them to the server.

[0523] Input: A user-generated idea (e.g., "An idea for a recyclable water bottle").

[0524] Specific operation: The idea submission screen appears on the device, and the user enters their idea and presses the submit button.

[0525] Output: The user's idea is sent to the server.

[0526] Step 6:

[0527] The server receives the submitted ideas and evaluates them using an evaluation AI.

[0528] Input: Submitted idea and evaluation criteria (creativity, feasibility, marketability).

[0529] Specific operation: The server launches the evaluation AI and scores ideas based on the evaluation criteria. The analysis results of the emotion engine are also reflected in the evaluation.

[0530] Output: The evaluated ideas and their evaluation scores.

[0531] Step 7:

[0532] The server stores the evaluation results in a database.

[0533] Input: The rated ideas and their rating scores.

[0534] What it does: The server connects to a database and records the ideas that have been rated and their rating scores.

[0535] Output: The saved rated ideas and their rating scores.

[0536] Step 8:

[0537] The server generates insights based on the evaluation results and reports them to the user.

[0538] Input: Saved evaluation results.

[0539] Specific operation: The server analyzes the evaluation results and generates insights based on the most highly rated ideas and the analysis of the evaluations.

[0540] Output: An insights report is generated and provided to the user.

[0541] Step 9:

[0542] AI proposes optimal prize distribution.

[0543] Input: Evaluation results and contest budget.

[0544] Specific operation: The proposed AI calculates the optimal prize distribution based on the evaluation results, taking into account the analysis results of the emotion engine.

[0545] Output: An optimal prize distribution proposal is generated and provided to the user.

[0546] Step 10:

[0547] Based on content ideas generated by the evaluation AI, content that matches the user's preferences is suggested.

[0548] Input: Content ideas generated by the evaluation AI and analysis data from the sentiment engine.

[0549] Specific operation: The server proposes personalized content based on the ideas generated by the evaluation AI and the user's emotional data.

[0550] Output: Personalized content suggestions sent to the user.

[0551] 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.

[0552] 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.

[0553] 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.

[0554] [Second embodiment]

[0555] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0556] 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.

[0557] 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).

[0558] 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.

[0559] 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.

[0560] 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).

[0561] 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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."

[0567] The present invention is a system that includes a terminal for inputting topics and constraints, a server that generates ideas using a generation AI, a database that stores the generated ideas, a server that accepts idea submissions, an evaluation AI that evaluates the submitted ideas, a server that provides the evaluation results, and an AI that proposes prize distribution.

[0568] Program processing

[0569] 1. Idea generation

[0570] User: The user inputs the contest topic and constraints into the terminal and submits them.

[0571] Terminal: Sends topics and constraints to the server.

[0572] Server: The server launches a generative AI to generate new ideas based on the received topic and constraints.

[0573] Server: Stores the generated ideas in a database.

[0574] Examples:

[0575] A user inputs the topic "new environmentally friendly products" into a device and sends it. The device sends the topic to the server. The server launches a generative AI to generate ideas for bioplastic containers made from reusable materials. The server stores the generated ideas in a database.

[0576] 2. Idea submission and evaluation

[0577] User: The user inputs and sends his / her own ideas through the terminal.

[0578] Terminal: Sends submitted ideas to the server.

[0579] Server: The server receives submitted ideas and stores them in a database.

[0580] Server: The server runs an evaluation AI and evaluates all submitted ideas.

[0581] Evaluation AI: Score each idea based on criteria such as creativity, feasibility, and marketability.

[0582] Server: Stores the evaluation results in a database.

[0583] Examples:

[0584] A user submits an "idea for a recyclable water bottle" via their device. The device sends the idea to the server. The server receives the submitted idea and stores it in a database. The server then launches an evaluation AI, which evaluates all submitted ideas from the perspectives of "reusability," "cost-effectiveness," and "environmental protection." The server then stores the evaluation results in a database.

[0585] 3. Providing insights and suggesting prize allocations

[0586] Server: The server generates insights based on the evaluation results, including the top-rated ideas and analysis of the evaluations.

[0587] Server: The AI ​​proposes optimal prize distribution, including the evaluation points for each idea and distribution based on the contest budget.

[0588] Server: Sends the proposal results to the terminal so that the user can check them.

[0589] Examples:

[0590] The server generates insights based on the evaluation results and reports that "the idea for a recyclable water bottle received the highest rating." The server uses AI to suggest that the highest prize money be allocated to the idea for a recyclable water bottle. The server sends the proposal results to the device, where the user can confirm them.

[0591] Summary

[0592] The system of the present invention automates a series of processes, from topic input to idea generation, idea submission and evaluation, insight generation, and prize distribution proposals, thereby significantly improving the efficiency of idea contest management, increasing the number and quality of ideas, while ensuring fairness and transparency in evaluation.

[0593] The processing flow will be explained below.

[0594] Step 1:

[0595] The user inputs the contest topic and constraints into the terminal and submits them.

[0596] The terminal sends the topic and constraints to the server.

[0597] Step 2:

[0598] The server launches a generation AI based on the received topic and constraints.

[0599] Generative AI generates new ideas based on topics and constraints.

[0600] Step 3:

[0601] The server stores the generated ideas in a database.

[0602] Step 4:

[0603] Users input their ideas directly into the terminal and send them.

[0604] The device sends the submitted idea to the server.

[0605] Step 5:

[0606] The server receives the submitted ideas and stores them in a database.

[0607] Step 6:

[0608] The server launches an evaluation AI to evaluate all submitted ideas.

[0609] The evaluation AI scores each idea based on criteria such as creativity, feasibility, and marketability.

[0610] Step 7:

[0611] The server stores the evaluation results in a database.

[0612] Step 8:

[0613] The server generates insights based on the evaluation results.

[0614] The insights generated include the top-rated ideas and an overall ranking analysis.

[0615] Step 9:

[0616] The server will use AI to suggest optimal prize distribution.

[0617] The proposals will include evaluation points for each idea and allocation based on the competition budget.

[0618] Step 10:

[0619] The server sends the proposal results to the terminal so that the user can check them.

[0620] Example 1

[0621] 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."

[0622] Modern idea contests are required to improve the number and quality of ideas and to ensure fair and transparent evaluation. Traditional systems rely on manual processes, from idea generation to evaluation, insight provision, and prize distribution, which are often inefficient and time-consuming. Furthermore, it is difficult to ensure fairness and transparency in evaluation. To solve these challenges, it is necessary to automate the entire process and improve efficiency and fairness.

[0623] 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.

[0624] In this invention, the server includes terminal means for inputting topics and constraints, server means for generating ideas using a generation AI, database means for saving the generated ideas, terminal means for accepting idea submissions, database means for saving submitted ideas, evaluation AI means for evaluating the submitted ideas, database means for saving the evaluation results, server means for providing the evaluation results, server means for generating insights based on the evaluation results and reporting them to users, and AI means for proposing prize distribution. This makes it possible to automate the entire process of an idea contest, not only improving the efficiency of the series of tasks from idea generation to submission and evaluation, provision of insights, and prize distribution, but also ensuring fairness and transparency of the evaluation.

[0625] "Topic" refers to the theme or issue that the User wishes to address in the Contest.

[0626] "Constraints" refer to specific requirements or limitations that must be met in idea generation and evaluation.

[0627] "Terminal" refers to an electronic device that allows a user to input and transmit information such as topics, constraints, and ideas.

[0628] "Server" refers to the central processing unit that performs various processes such as generation AI and evaluation AI.

[0629] "Generative AI" refers to artificial intelligence that automatically generates ideas based on topics and constraints.

[0630] "Database" refers to a system for storing and managing data such as generated ideas and evaluation results.

[0631] "Evaluation AI" refers to artificial intelligence that evaluates submitted ideas based on criteria such as creativity, feasibility, and marketability.

[0632] "Insights" refers to the analysis and key information generated from the assessment results.

[0633] "Reporting" refers to the act of providing information such as insights or evaluation results to users.

[0634] "Prize Share" means the share of the prize awarded to each Idea in the Contest.

[0635] "HTTP POST request" refers to the protocol used by a device to send data to a server.

[0636] "Evaluation criteria" refers to the specific indicators or measures used to evaluate ideas.

[0637] The present invention relates to a system that generates and evaluates ideas based on topics and constraints entered by users, and proposes prize allocations based on the results. The system includes the following hardware and software configurations.

[0638] Hardware and Software Configuration

[0639] Device: This refers to the device that users use to input topics and ideas, such as a computer, smartphone, or tablet.

[0640] Server: Refers to the central processing unit that operates the generation AI, evaluation AI, database, etc.

[0641] Database: This refers to a system for storing data such as ideas and evaluation results, and uses MySQL or SQL Server.

[0642] What the program does

[0643] 1. Idea generation

[0644] 1. The user uses a terminal to input the contest topic (e.g., "New environmentally friendly product") and constraints, and submits it.

[0645] 2. The device sends the entered information to the server via an HTTP POST request.

[0646] 3. The server analyzes the received topic and constraints and passes a prompt to the generation AI (e.g., GPT-4). An example of a prompt is, "Please come up with a new environmentally friendly product, with a budget of less than 1 million yen and using reusable materials."

[0647] 4. The generative AI generates ideas based on the prompt text, for example, "bioplastic containers made from reusable materials."

[0648] 5. The server stores the generated ideas in a MySQL database.

[0649] 2. Idea submission and evaluation

[0650] 1. A user uses a terminal to input and submit their idea (e.g., "An idea for a recyclable water bottle").

[0651] 2. The device sends the idea to the server via an HTTP POST request.

[0652] 3. The server parses the received ideas and stores them in a SQL Server database.

[0653] 4. The server launches an evaluation AI (e.g., Scikit-learn model) to evaluate ideas based on the evaluation criteria (creativity, feasibility, marketability). For example, the idea for a recyclable water bottle is evaluated.

[0654] 5. The server stores the evaluation results in a SQL Server database.

[0655] 3. Providing insights and suggesting prize allocations

[0656] 1. The server generates insights based on the evaluation results, for example, analyzing that "the idea for a recyclable water bottle received high marks for creativity and marketability."

[0657] 2. The server uses a prize allocation AI (e.g., TensorFlow model) to propose an optimal prize allocation. For example, allocate 1 million yen to the idea for a recyclable water bottle.

[0658] 3. The server sends the proposal results to the device as an HTTP response so that the user can check them.

[0659] In this way, the system of the present invention allows users to easily specify a topic, automatically generate and evaluate ideas based on that topic, and provide the results, thereby streamlining the entire idea contest process and ensuring fairness and transparency in the evaluation.

[0660] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0661] System program processing flow

[0662] Idea generation process

[0663] Step 1:

[0664] The user uses a terminal to input the contest topic "Environmentally Friendly New Product" and constraints (for example, budget of 1 million yen or less, use of recyclable materials) and submit the input.

[0665] Input: Topic: "New eco-friendly product", Constraints: "Budget: 1 million yen or less, Use recyclable materials"

[0666] Output: Topics and constraints entered in the terminal

[0667] Step 2:

[0668] The terminal sends the entered topic and constraints to the server via an HTTP POST request.

[0669] Input: User-entered topics and constraints

[0670] Output: HTTP POST request with topic and constraints sent to the server

[0671] Step 3:

[0672] The server analyzes the received request and extracts the topic and constraints.

[0673] Input: Topic and constraints received in the HTTP POST request

[0674] Output: Parsed topic "New eco-friendly product" and constraints "budget within 1 million yen, use reusable materials"

[0675] Step 4:

[0676] The server launches a generative AI (e.g., GPT-4) and passes it a prompt: "Please come up with a new environmentally friendly product. The budget should be within 1 million yen and it should use reusable materials."

[0677] Input: Parsed topics and constraints

[0678] Output: Prompt given to the generation AI: "Invent a new eco-friendly product. The budget should be within 1 million yen and it should be made from recyclable materials."

[0679] Step 5:

[0680] The generative AI generates ideas based on a prompt, such as a bioplastic container made from reusable materials.

[0681] Input: Prompt: "Invent a new eco-friendly product. Budget should be under 1 million yen, and it should be made from recyclable materials."

[0682] Output: Generated idea: "Bioplastic container made from reusable materials"

[0683] Step 6:

[0684] The server stores the generated ideas in a MySQL database.

[0685] Input: Generated idea: "Bioplastic container made from reusable materials"

[0686] Output: Ideas stored in a MySQL database

[0687] Idea submission and evaluation process

[0688] Step 1:

[0689] A user uses a terminal to input and submit his / her idea, "An idea for a recyclable water bottle."

[0690] Input: Idea "Recyclable water bottle idea"

[0691] Output: Ideas typed into the terminal

[0692] Step 2:

[0693] The device sends the input idea to the server via an HTTP POST request.

[0694] Input: User-entered ideas

[0695] Output: HTTP POST request for ideas sent to the server

[0696] Step 3:

[0697] The server analyzes the received request and extracts ideas.

[0698] Input: Ideas received in an HTTP POST request

[0699] Output: Parsed idea "Recyclable water bottle idea"

[0700] Step 4:

[0701] The server stores the extracted ideas in a SQL Server database.

[0702] Input: Parsed idea "Recyclable water bottle idea"

[0703] Output: Ideas stored in a SQL Server database

[0704] Step 5:

[0705] The server launches an evaluation AI (e.g., a Scikit-learn model) and passes all ideas in the database to be evaluated based on the following criteria: creativity, feasibility, and marketability.

[0706] Input: All ideas in a SQL Server database

[0707] Output: A list of ideas to be passed to the evaluation AI

[0708] Step 6:

[0709] The AI ​​will score each idea based on the criteria of creativity, feasibility, and marketability. For example, an idea for a recyclable water bottle will be evaluated and given a score.

[0710] Input: List of ideas based on evaluation criteria

[0711] Output: Each idea given a rating score

[0712] Step 7:

[0713] The server stores the evaluation results in a SQL Server database.

[0714] Input: Each idea given a rating score

[0715] Output: Evaluation results stored in a SQL Server database

[0716] Providing insights and processing prize allocation recommendations

[0717] Step 1:

[0718] The server generates insights based on the evaluation results, such as "The idea for a recyclable water bottle received high marks for creativity and marketability."

[0719] Input: Evaluation results in a SQL Server database

[0720] Output: Generated insights

[0721] Step 2:

[0722] The server uses a prize allocation AI (e.g., TensorFlow model) to propose the optimal prize allocation for each idea. For example, it allocates 1 million yen to the idea for a recyclable water bottle.

[0723] Input: Generated insights

[0724] Output: Proposed bounty distribution

[0725] Step 3:

[0726] The server sends the proposal results to the terminal in an HTTP response, allowing the user to check the results.

[0727] Input: Proposed Prize Distribution

[0728] Output: Suggestion results sent to the device

[0729] In this way, the system of the present invention allows users to easily specify a topic, automatically generate and evaluate ideas based on that topic, and provide the results, thereby streamlining the entire idea contest process and ensuring fairness and transparency in the evaluation.

[0730] (Application example 1)

[0731] 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."

[0732] When users propose new product designs or concepts in a virtual store, it is necessary to evaluate the ideas and distribute rewards fairly and efficiently. Currently, there is no system that provides prompt and appropriate feedback and rewards for user-generated ideas. Therefore, it is a challenge to provide a mechanism that increases user motivation and generates many high-quality ideas.

[0733] 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.

[0734] In this invention, the server includes a terminal means for inputting topics and constraints, a server means for generating ideas using a generation AI, and a database means for storing the generated ideas. This allows the generation AI to efficiently create new product ideas based on information input by users and store the evaluation results of those ideas in the database. Furthermore, the evaluation AI means performs fair and detailed evaluations of the proposed ideas, thereby generating high-quality ideas and allocating appropriate rewards. By providing a means for quickly notifying users of the results via a smartphone, smart glasses, or head-mounted display, immediate feedback can be provided, increasing users' motivation to participate.

[0735] "Terminal means" refers to a device that allows a user to input topics and constraints, such as a smartphone, smart glasses, or a head-mounted display.

[0736] "Generative AI" refers to artificial intelligence that generates new ideas based on topics and constraints entered by the user.

[0737] "Server means" refers to a computer system that runs the generative AI to generate ideas and manage them.

[0738] "Database Means" refers to a data management system for storing generated ideas and accessing and managing them as needed.

[0739] "Evaluation AI means" refers to artificial intelligence that evaluates submitted ideas based on criteria such as creativity, feasibility, and marketability.

[0740] "Server means for providing evaluation results" refers to a server for notifying users and systems of the results generated by the evaluation AI means.

[0741] "Means for making reward suggestions" refers to artificial intelligence that suggests optimal reward allocation based on the evaluation results.

[0742] A "virtual store" refers to a virtual shopping space developed on the Internet, where users can propose product designs and concepts.

[0743] "Means for notifying results" refers to a system for notifying users of feedback and evaluation results through devices such as smartphones, smart glasses, and head-mounted displays.

[0744] The system of the present invention allows users to propose new product designs and concepts in a virtual store, and the ideas are evaluated and rewards are distributed.

[0745] System Configuration

[0746] The system consists of the following components:

[0747] Terminal means: A device through which a user inputs topics and constraints. This includes smartphones, smart glasses, head-mounted displays, etc.

[0748] Generative AI: Artificial intelligence that generates new ideas based on user-entered topics and constraints.

[0749] Server means: A computer system for running the generative AI and generating and managing ideas.

[0750] Database means: A database for storing generated ideas and accessing and managing them as needed.

[0751] Evaluation AI method: An artificial intelligence that evaluates submitted ideas based on criteria such as creativity, feasibility, and marketability.

[0752] Server means for providing evaluation results: A server for notifying users and systems of the results generated by the evaluation AI means.

[0753] Means for making reward suggestions: Artificial intelligence that suggests optimal reward allocation based on evaluation results.

[0754] Means of notifying results: A system for notifying users of feedback and evaluation results through devices such as smartphones, smart glasses, and head-mounted displays.

[0755] Program processing

[0756] 1. Topic input and idea generation

[0757] Users access the virtual store via their smartphone and input new product designs and concepts. This input information is sent from the device to a server, where a generative AI generates new ideas. This generative AI uses an open-source generative AI model (e.g., GPT-4). The server stores the generated ideas in a database.

[0758] 2. Submitting and Saving Ideas

[0759] When a user submits a self-generated product idea, the information is sent to the server, which receives the information and stores it in a database.

[0760] 3. Evaluate ideas

[0761] The server launches an evaluation AI to evaluate submitted ideas. The evaluation AI uses TensorFlow and PyTorch to score ideas based on criteria such as creativity, feasibility, and marketability.

[0762] 4. Reward proposals and notifications

[0763] Based on the evaluation results, the server proposes rewards. The server notifies the user of the evaluation results and offers rewards such as points or discount coupons. This notification is done via a smartphone, smart glasses, or a head-mounted display.

[0764] Examples of concrete examples and prompts

[0765] Users enter the topic of creating a "sustainable fashion item" through a smartphone app.

[0766] Example prompt sentence:

[0767] Topic: Sustainable fashion items

[0768] Constraints: Use of recyclable materials and environmentally friendly processes

[0769] Given this prompt, the generative AI generated the following ideas:

[0770] The idea: a recycled cotton t-shirt made from renewable materials, with a design featuring an environmental message.

[0771] The evaluation AI generates the following evaluation results and provides feedback to the user:

[0772] Creativity: 9 / 10

[0773] Feasibility: 8 / 10

[0774] Marketability: 7 / 10

[0775] The idea ultimately involves providing discount coupons and users can view the results on their smartphones.

[0776] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0777] Step 1:

[0778] User inputs topic and constraints

[0779] The user launches the smartphone app and inputs a topic and constraints. For example, the topic is "sustainable fashion items" and the constraints are "use of reusable materials and environmentally friendly processes."

[0780] Input: Topics and constraints from the user

[0781] Output: Topic and constraint data is sent from the terminal to the server.

[0782] Step 2:

[0783] The server receives the topic and constraints

[0784] The server receives the topic and constraints sent from the device, and prepares to pass this data to the generation AI.

[0785] Input: Topic and constraint data sent from the device

[0786] Output: Topic and constraint data to be passed to the generative AI

[0787] Step 3:

[0788] Generative AI generates ideas

[0789] The generative AI in the server generates new product ideas based on the received topic and constraints. The generative AI model used here is GPT-4. The model analyzes the prompt sentence and generates appropriate ideas.

[0790] Input: Topic and constraint data

[0791] Output: Generated ideas (e.g., recycled cotton T-shirts made from renewable materials)

[0792] Step 4:

[0793] Save your ideas in a database

[0794] The generated ideas are stored in a database on the server, making it easy to retrieve the ideas later.

[0795] Input: Generated ideas

[0796] Output: Idea records stored in a database

[0797] Step 5:

[0798] User submits idea

[0799] Users submit their own product ideas through a smartphone app. For example, they can submit an idea for a recycled cotton T-shirt.

[0800] Input: Submitted Idea

[0801] Output: Ideas submitted by users are sent from the device to the server.

[0802] Step 6:

[0803] The server receives and stores submitted ideas.

[0804] The server receives the submitted ideas sent from the terminals and stores them in a database.

[0805] Input: Submitted Idea

[0806] Output: A record of the submitted idea stored in a database

[0807] Step 7:

[0808] Evaluation AI evaluates ideas

[0809] The server runs an evaluation AI model that evaluates ideas in the database based on criteria such as creativity, feasibility, and marketability. The evaluation model uses TensorFlow and PyTorch.

[0810] Input: Each idea in the database

[0811] Output: Evaluation results (creativity, feasibility, marketability scores)

[0812] Step 8:

[0813] Evaluation results are saved in a database

[0814] The server stores the generated evaluation results in a database, and this information is later communicated to the user.

[0815] Input: Evaluation result

[0816] Output: Records of the evaluation results stored in a database

[0817] Step 9:

[0818] Reward proposal

[0819] The server will propose rewards based on the evaluation results, which may include points or discount coupons.

[0820] Input: Evaluation result

[0821] Output: Reward proposal data

[0822] Step 10:

[0823] Notify the user of the results

[0824] The server notifies the user of the evaluation results and reward proposals via a smartphone, smart glasses, or head-mounted display.

[0825] Input: Reward proposal data

[0826] Output: Evaluation results and reward notification displayed on the user's device

[0827] 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.

[0828] The present invention is a system that includes a terminal for inputting topics and constraints, a server that generates ideas using a generation AI, a database that stores the generated ideas, a server that accepts idea submissions, an evaluation AI that evaluates the submitted ideas, a server that provides the evaluation results, an AI that proposes prize allocations, and an emotion engine that recognizes the user's emotions.

[0829] Program processing

[0830] 1. Idea generation

[0831] User: The user inputs the contest topic and constraints into the terminal and submits them.

[0832] Terminal: Sends topics and constraints to the server.

[0833] Server: The server activates the generation AI and generates new ideas based on the received topics and constraints. The emotion engine also analyzes the user's emotion data and reflects it in idea generation.

[0834] Server: Stores the generated ideas in a database.

[0835] Examples:

[0836] The user inputs the topic "new environmentally friendly products" into the device and sends it. The device sends the topic to the server. The server activates the generative AI to generate ideas for bioplastic containers made from reusable materials. The emotion engine analyzes the user's emotions and reflects this in the generation process to prioritize ideas that show a positive emotional response. The server then stores the generated ideas in a database.

[0837] 2. Idea submission and evaluation

[0838] User: The user inputs and sends his / her own ideas through the terminal.

[0839] Terminal: Sends submitted ideas to the server.

[0840] Server: The server receives submitted ideas and stores them in a database.

[0841] Server: Launches the evaluation AI and evaluates all submitted ideas. The evaluation AI scores each idea based on the criteria of creativity, feasibility, and marketability. In addition, the emotion engine analyzes the user's emotional data and reflects it in the evaluation.

[0842] Server: Stores the evaluation results in a database.

[0843] Examples:

[0844] A user submits an "idea for a recyclable water bottle" via their device. The device sends the idea to the server. The server receives the submitted idea and stores it in a database. The server then activates an evaluation AI, which evaluates all submitted ideas from the perspectives of "reusability," "cost-effectiveness," and "environmental protection." At this time, an emotion engine analyzes the user's emotions and gives high ratings to ideas that elicit a positive response. The server then stores the evaluation results in a database.

[0845] 3. Providing insights and suggesting prize allocations

[0846] Server: The server generates insights based on the evaluation results, including the top-rated ideas and an analysis of the evaluations.

[0847] Server: The AI ​​proposes optimal prize distribution. The proposal includes allocation based on the evaluation points of each idea and the contest budget. In addition, the emotion engine analyzes users' emotional data and reflects it in the proposal.

[0848] Server: Sends the proposal results to the terminal so that the user can check them.

[0849] Examples:

[0850] The server generates an insight based on the evaluation results and reports that "the idea for a recyclable water bottle received the highest rating." The emotion engine analyzes the user's emotions and reflects them in the insight. The server uses AI to suggest allocating the highest prize money to the idea for a recyclable water bottle. In this case, the emotion engine analyzes the user's emotional data and adds a small bonus to ideas that show a positive emotional response. The server sends the proposal results to the device, where the user can confirm them.

[0851] Summary

[0852] The system of this invention not only handles a series of processes from topic input to idea generation, idea submission and evaluation, insight generation, and prize distribution proposals, but also recognizes user emotions and reflects them in the generation, evaluation, and proposals. This significantly improves the efficiency of idea contest management and, by taking user emotions into consideration, enables contest management that is more user-friendly.

[0853] The processing flow will be explained below.

[0854] Program processing

[0855] 1. Idea generation

[0856] Step 1:

[0857] The user inputs the contest topic and constraints into the terminal and submits them.

[0858] Step 2:

[0859] The terminal sends the topic and constraints to the server.

[0860] Step 3:

[0861] The server sends the received topic and constraints to the generation AI and starts the generation AI.

[0862] Step 4:

[0863] Generative AI generates new ideas based on topics and constraints.

[0864] Step 5:

[0865] The emotion engine acquires the user's emotional data and reflects it in the generated ideas. In this case, the emotion engine analyzes the user's positive emotions and adjusts the idea generation process accordingly.

[0866] Step 6:

[0867] The server stores the generated ideas in a database.

[0868] Examples:

[0869] The user inputs the topic "new environmentally friendly products" into the device and sends it. The device sends the topic to the server. The server activates the generative AI to generate ideas for bioplastic containers made from reusable materials. The emotion engine analyzes the user's emotions and reflects this in the generation process to prioritize ideas that show a positive emotional response. The server then stores the generated ideas in a database.

[0870] 2. Idea submission and evaluation

[0871] Step 1:

[0872] Users input and send their ideas through a terminal.

[0873] Step 2:

[0874] The device sends the submitted idea to the server.

[0875] Step 3:

[0876] The server receives the submitted ideas and stores them in a database.

[0877] Step 4:

[0878] The server launches an evaluation AI to evaluate all submitted ideas.

[0879] Step 5:

[0880] The evaluation AI scores each idea based on the criteria of "creativity," "feasibility," and "marketability."

[0881] Step 6:

[0882] The emotion engine analyzes the user's emotional data and reflects it in the evaluation AI. At this time, the emotion engine analyzes the user's positive emotional reactions and adjusts the evaluation process to give a high rating.

[0883] Step 7:

[0884] The server stores the evaluation results in a database.

[0885] Examples:

[0886] A user submits an "idea for a recyclable water bottle" via their device. The device sends the idea to the server. The server receives the submitted idea and stores it in a database. The server then activates an evaluation AI, which evaluates all submitted ideas from the perspectives of "reusability," "cost-effectiveness," and "environmental protection." At this time, an emotion engine analyzes the user's emotions and gives high ratings to ideas that elicit a positive response. The server then stores the evaluation results in a database.

[0887] 3. Providing insights and suggesting prize allocations

[0888] Step 1:

[0889] The server generates insights based on the evaluation results, including the top-rated ideas and an overall evaluation analysis.

[0890] Step 2:

[0891] The emotion engine analyzes the user's emotions and reflects them in the generated insights. In this case, the emotion engine analyzes the user's emotional data and generates advantageous insights for ideas that show a positive reaction.

[0892] Step 3:

[0893] The server uses AI to propose optimal prize distribution, including the evaluation points for each idea and distribution based on the contest budget.

[0894] Step 4:

[0895] The emotion engine analyzes users' emotional data and reflects it in the prize distribution proposals, adding a little extra weight to ideas that show positive emotions.

[0896] Step 5:

[0897] The server sends the proposal results to the terminal so that the user can check them.

[0898] Examples:

[0899] The server generates an insight based on the evaluation results and reports that "the idea for a recyclable water bottle received the highest rating." The emotion engine analyzes the user's emotions and reflects them in the insight. The server uses AI to suggest allocating the highest prize money to the idea for a recyclable water bottle. In this case, the emotion engine analyzes the user's emotional data and adds a small bonus to ideas that show a positive emotional response. The server sends the proposal results to the device, where the user can confirm them.

[0900] Summary

[0901] The system of this invention not only handles a series of processes from topic input to idea generation, idea submission and evaluation, insight generation, and prize distribution proposals, but also recognizes user emotions and reflects them in the generation, evaluation, and proposals. This significantly improves the efficiency of idea contest management and, by taking user emotions into consideration, enables contest management that is more user-friendly.

[0902] Example 2

[0903] 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."

[0904] Conventional idea contest management systems have a complicated process from idea generation to evaluation, reporting, and prize distribution, making it difficult to manage the contest efficiently and with consideration for users' emotions.In addition, it is not possible to evaluate or propose ideas with consideration for users' emotions, making it difficult to manage a contest that provides high user satisfaction.

[0905] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0906] In this invention, the server includes terminal means for inputting topics and constraints, server means for generating ideas using a generative AI model, database means for saving the generated ideas, emotion engine means for analyzing emotion data, server means for activating evaluation AI means to evaluate the generated ideas, server means for accepting submitted ideas, server means for activating the evaluation AI means to provide evaluation results, AI means for generating insights based on the evaluation results and proposing optimal prize distribution, and emotion engine means for recognizing user emotions and reflecting them in evaluations and proposals. This streamlines the process from idea generation to evaluation, insight generation, and prize distribution proposals, and enables the operation of a more satisfying contest that takes user emotions into consideration.

[0907] "Terminal means" is a device that allows a user to input topics and constraints and send them to the system.

[0908] A "generative AI model" is an artificial intelligence that generates new ideas based on received topics and constraints.

[0909] The "server means" is a server device for processing and storing various data within the system.

[0910] The "database means" is a database device for storing and managing data such as generated ideas and evaluation results.

[0911] The "emotion engine means" is a system that analyzes the user's emotional data and reflects it in idea generation, evaluation, and proposals.

[0912] The "evaluation AI method" is an artificial intelligence that evaluates submitted ideas based on the criteria of creativity, feasibility, and marketability.

[0913] "Insights" refers to the analytical results and important information gained from the evaluation results.

[0914] The "AI method for proposing prize distribution" is an artificial intelligence that proposes optimal prize distribution based on evaluation results and budget.

[0915] This invention relates to a system that streamlines the process from topic input to idea generation, submission, evaluation, insight generation, and prize allocation proposals. This system includes a terminal where users input topics and constraints, a server where new ideas are generated using a generative AI model, a database where the generated ideas and evaluation results are stored, and an emotion engine that analyzes user emotion data and reflects this in idea generation and evaluation. The main components of this system and their operation are described in detail below.

[0916] Entering and sending topics and constraints

[0917] Users input the contest topic and constraints using a terminal. This terminal can be a general PC, tablet, or smartphone. The input information is sent from the terminal to the server.

[0918] Example: A user inputs the topic "New eco-friendly products" and performs a send operation. At this time, the terminal sends the input information to the server.

[0919] Idea generation

[0920] The server generates new ideas by launching a generative AI model based on the topics and constraints received from the device. This generative AI model uses machine learning technology to generate new ideas from a variety of data.

[0921] The server uses an emotion engine to analyze the user's emotion data during the generation process and reflects the data in the generated ideas.

[0922] Example: Generative AI generates ideas for "bioplastic containers made from reusable materials," while the emotion engine reflects the user's positive emotions.

[0923] Saving ideas

[0924] The server stores the generated ideas in a database.

[0925] Example: The generated ideas for bioplastic containers are stored in the "Ideas" table of the database.

[0926] Submitting and Receiving Ideas

[0927] Users input and transmit their ideas through a terminal.

[0928] The device sends the input ideas to the server, and this information is also stored and managed in a database.

[0929] Example: A user submits an idea for a recyclable water bottle via their device. This data is then sent to the server and stored in a database.

[0930] Idea Evaluation

[0931] The server launches an evaluation AI to evaluate all submitted ideas based on the criteria of creativity, feasibility, and marketability, and also reflects the user's emotional data using an emotion engine.

[0932] Example: An evaluation AI evaluates ideas for recyclable water bottles from the perspectives of "reusability," "cost-effectiveness," and "environmental protection," giving higher ratings to ideas that express positive emotions.

[0933] Providing insights and suggesting prize allocations

[0934] The server generates insights based on the evaluation results and reports them to the user. It also activates an AI that proposes optimal prize distribution based on the evaluation results. This proposal also reflects the analysis results of the emotion engine.

[0935] Example: The server reports that "the idea for a recyclable water bottle received the highest votes" and suggests optimal prize distribution taking sentiment data into account.

[0936] Prompt Sentence Examples

[0937] "Please tell me your ideas for new eco-friendly products. I'd like specific ideas for bioplastic containers made from reusable materials."

[0938] This system will significantly improve the efficiency of idea contest management and will also enable more satisfying management that takes into account user emotions.

[0939] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0940] Step 1: Enter topic and constraints and submit

[0941] User: The user inputs the contest topic and constraints into the terminal and presses the send button. This input data includes the topic and constraints in text format. Based on the input data, the terminal sends the data to the server.

[0942] Input: Topic (e.g., "New eco-friendly product"), Constraint (e.g., "Use reusable materials")

[0943] Output: A data packet containing topics and constraints

[0944] Specific operation: The user enters a topic in the text field of the terminal and clicks the send button. The terminal converts the input data into a packet format and sends it to the server as an HTTP request.

[0945] Step 2: Receive topics and constraints and launch the generation AI model

[0946] Server: The server receives the topics and constraints sent from the device. The received data contains the topics and constraints in text format. The server then launches the generative AI model based on this.

[0947] Input: Data packets of topics and constraints sent from the device

[0948] Output: Ready for idea generation

[0949] How it works: The server receives an HTTP request, extracts topics and constraints, then calls the API of the generative AI model to start the idea generation process.

[0950] Step 3: Generate ideas and reflect on sentiment data

[0951] Server: The server uses the generative AI model to generate ideas based on the received topic and constraints. It also activates the emotion engine to analyze the user's emotion data and reflect it in the generation process.

[0952] Input: Generative AI model, emotion data

[0953] Output: Generated ideas

[0954] How it works: The generative AI model generates several ideas based on a given topic. In parallel, the emotion engine analyzes the user's emotion data and filters the ideas to elicit positive emotions. Once the final idea is determined, it is output.

[0955] Step 4: Saving generated ideas

[0956] Server: Stores the generated ideas in a database, which stores the idea content and associated metadata.

[0957] Input: Generated ideas

[0958] Output: Ideas stored in a database

[0959] What happens: The server converts the generated ideas into JSON format and executes a query to insert them into the "ideas" table in the database, where they are permanently stored.

[0960] Step 5: Submit and submit your idea

[0961] User: The user inputs their idea into the terminal and presses the send button. This information is sent from the terminal to the server.

[0962] Input: User-entered ideas

[0963] Output: Data packet to be sent

[0964] Specific operation: A user fills out an idea submission form on the device and clicks the submit button. The device converts the input data into packets and sends them to the server as an HTTP request.

[0965] Step 6: Receiving and storing submissions

[0966] Server: Receives submitted ideas and stores them in a database. The received data includes the ideas entered by users.

[0967] Input: Ideas sent from your device

[0968] Output: User ideas stored in a database

[0969] What happens: The server receives the HTTP request, extracts the ideas, converts them to JSON format, and executes a query to insert them into the "Submitted Ideas" table in the database.

[0970] Step 7: Launch the evaluation AI and evaluate your ideas

[0971] Server: The server launches the evaluation AI and evaluates all ideas stored in the database. Evaluation criteria include creativity, feasibility, and marketability. In addition, it uses an emotion engine to reflect user emotional data in the evaluation.

[0972] Input: Submitted ideas, sentiment data

[0973] Output: Idea rating score

[0974] Specific operation: The server invokes the evaluation AI model and starts evaluating each idea. The generated score and emotional data are combined to calculate the final evaluation. The evaluation results are then stored in the database.

[0975] Step 8: Save the evaluation results

[0976] Server: Stores the evaluation results in a database. For each evaluated idea, a score and associated data are stored.

[0977] Input: Idea rating score

[0978] Output: Evaluation results stored in a database

[0979] Specific operation: The server converts the evaluation results into JSON format and executes a query to insert them into the "Evaluation Results" table in the database, thereby permanently storing the evaluation results.

[0980] Step 9: Analyze assessment results and generate insights

[0981] Server: Generates insights based on the evaluation results, including the top-rated ideas and an overall evaluation analysis.

[0982] Input: Evaluation result

[0983] Output: Generated insights

[0984] Specific operation: The server analyzes the evaluation results and generates insights in text format, which are temporarily stored on the server.

[0985] Step 10: Proposal for Prize Distribution

[0986] Server: Proposes optimal prize distribution using prize distribution AI. Proposals include distribution based on evaluation points and budget. It also uses an emotion engine to reflect user emotion data.

[0987] Input: Evaluation results, budget, sentiment data

[0988] Output: Prize allocation proposal

[0989] Specific operation: The server calls the prize allocation AI model and calculates the optimal prize allocation using the evaluation points and budget as input. It then modifies the proposal based on data from the emotion engine and generates the final prize allocation proposal.

[0990] Step 11: Submit your proposal

[0991] Server: Sends the prize distribution proposal to the user's device so that the user can confirm it.

[0992] Input: Prize allocation proposal

[0993] Output: Notification of proposal results

[0994] Specific operation: The server sends the generated prize distribution proposal to the terminal. The user can check the proposal result on the terminal screen. Notifications are sent to the user via email, push notifications, etc.

[0995] (Application example 2)

[0996] 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."

[0997] Conventional content delivery systems have the problem of not efficiently generating personalized content based on specific topics and constraints desired by users. Furthermore, content suggestions do not take user emotions into consideration, which may reduce user satisfaction. The objective of this invention is to provide a system that combines generation AI, evaluation AI, and an emotion engine to effectively generate, evaluate, and provide content that satisfies users.

[0998] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0999] In this invention, the server includes terminal means for inputting topics and constraints, server means for generating ideas using a generation AI, database means for saving the generated ideas, server means for accepting submissions of ideas, evaluation AI means for evaluating the submitted ideas, server means for providing the evaluation results, AI means for proposing prize distribution, emotion engine means for recognizing user emotions, and means for proposing content that matches the user's preferences based on the content ideas generated by the evaluation AI. This makes it possible to generate novel content ideas based on the user's topics and constraints, and to make personalized suggestions that reflect the user's emotions.

[1000] "Terminal means" is a device that allows a user to input topics and constraints and send them to the server.

[1001] "Generative AI" is an artificial intelligence technology that generates new ideas based on topics and constraints entered by the user.

[1002] "Server means" refers to a device that processes and manages data and on which generation AI, evaluation AI, emotion engine, etc. operate.

[1003] "Database means" is a data management system for storing generated ideas.

[1004] "Evaluation AI methods" are artificial intelligence technologies that evaluate submitted ideas based on evaluation criteria such as creativity, feasibility, and marketability.

[1005] The "emotion engine means" is a technology for recognizing and analyzing the user's emotions.

[1006] "AI method for proposing prize distribution" is an artificial intelligence technology that proposes optimal prize distribution based on the evaluation results.

[1007] "Means for suggesting content that matches the user's preferences" refers to a device or technology that suggests personalized content based on content ideas generated by evaluation AI, taking into account the user's emotional state.

[1008] A system for implementing this invention is configured as follows: A user uses a terminal (such as a smartphone) to input topics and constraints. The terminal is equipped with an interface for transmitting the topics and constraints input by the user to a server. The server activates a generation AI based on the received topics and constraints to generate new ideas. At this time, an emotion engine analyzes the user's emotion data and reflects it in the generation process. The generated ideas are stored in a database.

[1009] Next, when a user submits their own idea, they send it to the server via their device. The server receives the submitted idea and evaluates it using evaluation AI. The evaluation AI scores the idea using evaluation criteria of creativity, feasibility, and marketability. At that time, an emotion engine analyzes the user's emotional data and reflects it in the evaluation. The evaluation results are stored in a database.

[1010] The server then generates insights based on the evaluation results and reports them to the user. Furthermore, the AI ​​that proposes prize distribution proposes optimal prize distribution, and the emotion engine analyzes the user's emotional data and reflects it in the proposal. The proposal results are sent to the device so that the user can check them. There is also a means to suggest content that suits the user's preferences based on the content ideas generated by the evaluation AI, allowing the user to receive personalized content.

[1011] Hardware and software used

[1012] Hardware: Smartphone (iOS or Android device)

[1013] software:

[1014] Python 3.x

[1015] The transformers library (for generative AI and sentiment analysis)

[1016] SQLite (for storing and managing ideas)

[1017] Specific examples

[1018] Suppose a user enters the following topic and constraints into a terminal:

[1019] Topic: "The Future of Travel"

[1020] Constraint: "Consider sustainability and environmental protection"

[1021] This input is sent to the server, which then triggers a generative AI to generate ideas based on the prompt.

[1022] Example prompt sentence:

[1023] Topic: Future of Travel

[1024] Constraints: Sustainability and environmental protection

[1025] Generate ideas:

[1026] The server stores the generated ideas in a database for users to review. The emotion engine analyzes the user's emotions and reflects them in the creation and evaluation process, thereby providing content that matches the user's preferences.

[1027] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1028] Step 1:

[1029] The user inputs the topic and constraints using a terminal.

[1030] Input: The user inputs the topics and constraints "future travel" and "consider sustainability and environmental protection."

[1031] Specific operation: A screen for entering the topic and constraints appears on the terminal interface. The user enters them and presses the send button.

[1032] Output: The entered data is sent to the server.

[1033] Step 2:

[1034] The server launches a generative AI based on the received topic and constraints to generate new ideas.

[1035] Input: Topic "Future of Travel" and Constraint "Consider sustainability and environmental protection".

[1036] What it does: The server passes these inputs as prompts to the generative AI, which then generates new ideas based on these prompts (e.g., the gpt-3 model).

[1037] Output: Generated ideas (e.g. "Ecological Tours Using Sustainable Energy").

[1038] Step 3:

[1039] The emotion engine analyzes the user's emotion data and reflects it in the generation process.

[1040] Input: Generated ideas and user sentiment data.

[1041] What it does: The emotion engine analyzes the user's emotional response to the generated ideas, for example, evaluating positive or negative reactions to the text content.

[1042] Output: The sentiment analysis results are fed into the generation process.

[1043] Step 4:

[1044] The server stores the generated ideas in a database.

[1045] Input: Generated ideas and sentiment analysis results.

[1046] Specific operation: The server connects to the database means and records the generated ideas and their sentiment analysis results.

[1047] Output: The saved ideas and their sentiment analysis results are stored in a database.

[1048] Step 5:

[1049] Users input their ideas through their terminals and send them to the server.

[1050] Input: A user-generated idea (e.g., "An idea for a recyclable water bottle").

[1051] Specific operation: The idea submission screen appears on the device, and the user enters their idea and presses the submit button.

[1052] Output: The user's idea is sent to the server.

[1053] Step 6:

[1054] The server receives the submitted ideas and evaluates them using an evaluation AI.

[1055] Input: Submitted idea and evaluation criteria (creativity, feasibility, marketability).

[1056] Specific operation: The server launches the evaluation AI and scores ideas based on the evaluation criteria. The analysis results of the emotion engine are also reflected in the evaluation.

[1057] Output: The evaluated ideas and their evaluation scores.

[1058] Step 7:

[1059] The server stores the evaluation results in a database.

[1060] Input: The rated ideas and their rating scores.

[1061] What it does: The server connects to a database and records the ideas that have been rated and their rating scores.

[1062] Output: The saved rated ideas and their rating scores.

[1063] Step 8:

[1064] The server generates insights based on the evaluation results and reports them to the user.

[1065] Input: Saved evaluation results.

[1066] Specific operation: The server analyzes the evaluation results and generates insights based on the most highly rated ideas and the analysis of the evaluations.

[1067] Output: An insights report is generated and provided to the user.

[1068] Step 9:

[1069] AI proposes optimal prize distribution.

[1070] Input: Evaluation results and contest budget.

[1071] Specific operation: The proposed AI calculates the optimal prize distribution based on the evaluation results, taking into account the analysis results of the emotion engine.

[1072] Output: An optimal prize distribution proposal is generated and provided to the user.

[1073] Step 10:

[1074] Based on content ideas generated by the evaluation AI, content that matches the user's preferences is suggested.

[1075] Input: Content ideas generated by the evaluation AI and analysis data from the sentiment engine.

[1076] Specific operation: The server proposes personalized content based on the ideas generated by the evaluation AI and the user's emotional data.

[1077] Output: Personalized content suggestions sent to the user.

[1078] 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.

[1079] 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.

[1080] 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.

[1081] [Third embodiment]

[1082] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1083] 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.

[1084] 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).

[1085] 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.

[1086] 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.

[1087] 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).

[1088] 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.

[1089] 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.

[1090] 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.

[1091] 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.

[1092] 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.

[1093] 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."

[1094] The present invention is a system that includes a terminal for inputting topics and constraints, a server that generates ideas using a generation AI, a database that stores the generated ideas, a server that accepts idea submissions, an evaluation AI that evaluates the submitted ideas, a server that provides the evaluation results, and an AI that proposes prize distribution.

[1095] Program processing

[1096] 1. Idea generation

[1097] User: The user inputs the contest topic and constraints into the terminal and submits them.

[1098] Terminal: Sends topics and constraints to the server.

[1099] Server: The server launches a generative AI to generate new ideas based on the received topic and constraints.

[1100] Server: Stores the generated ideas in a database.

[1101] Examples:

[1102] A user inputs the topic "new environmentally friendly products" into a device and sends it. The device sends the topic to the server. The server launches a generative AI to generate ideas for bioplastic containers made from reusable materials. The server stores the generated ideas in a database.

[1103] 2. Idea submission and evaluation

[1104] User: The user inputs and sends his / her own ideas through the terminal.

[1105] Terminal: Sends submitted ideas to the server.

[1106] Server: The server receives submitted ideas and stores them in a database.

[1107] Server: The server runs an evaluation AI and evaluates all submitted ideas.

[1108] Evaluation AI: Score each idea based on criteria such as creativity, feasibility, and marketability.

[1109] Server: Stores the evaluation results in a database.

[1110] Examples:

[1111] A user submits an "idea for a recyclable water bottle" via their device. The device sends the idea to the server. The server receives the submitted idea and stores it in a database. The server then launches an evaluation AI, which evaluates all submitted ideas from the perspectives of "reusability," "cost-effectiveness," and "environmental protection." The server then stores the evaluation results in a database.

[1112] 3. Providing insights and suggesting prize allocations

[1113] Server: The server generates insights based on the evaluation results, including the top-rated ideas and analysis of the evaluations.

[1114] Server: The AI ​​proposes optimal prize distribution, including the evaluation points for each idea and distribution based on the contest budget.

[1115] Server: Sends the proposal results to the terminal so that the user can check them.

[1116] Examples:

[1117] The server generates insights based on the evaluation results and reports that "the idea for a recyclable water bottle received the highest rating." The server uses AI to suggest that the highest prize money be allocated to the idea for a recyclable water bottle. The server sends the proposal results to the device, where the user can confirm them.

[1118] Summary

[1119] The system of the present invention automates a series of processes, from topic input to idea generation, idea submission and evaluation, insight generation, and prize distribution proposals, thereby significantly improving the efficiency of idea contest management, increasing the number and quality of ideas, while ensuring fairness and transparency in evaluation.

[1120] The processing flow will be explained below.

[1121] Step 1:

[1122] The user inputs the contest topic and constraints into the terminal and submits them.

[1123] The terminal sends the topic and constraints to the server.

[1124] Step 2:

[1125] The server launches a generation AI based on the received topic and constraints.

[1126] Generative AI generates new ideas based on topics and constraints.

[1127] Step 3:

[1128] The server stores the generated ideas in a database.

[1129] Step 4:

[1130] Users input their ideas directly into the terminal and send them.

[1131] The device sends the submitted idea to the server.

[1132] Step 5:

[1133] The server receives the submitted ideas and stores them in a database.

[1134] Step 6:

[1135] The server launches an evaluation AI to evaluate all submitted ideas.

[1136] The evaluation AI scores each idea based on criteria such as creativity, feasibility, and marketability.

[1137] Step 7:

[1138] The server stores the evaluation results in a database.

[1139] Step 8:

[1140] The server generates insights based on the evaluation results.

[1141] The insights generated include the top-rated ideas and an overall ranking analysis.

[1142] Step 9:

[1143] The server will use AI to suggest optimal prize distribution.

[1144] The proposals will include evaluation points for each idea and allocation based on the competition budget.

[1145] Step 10:

[1146] The server sends the proposal results to the terminal so that the user can check them.

[1147] Example 1

[1148] 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."

[1149] Modern idea contests are required to improve the number and quality of ideas and to ensure fair and transparent evaluation. Traditional systems rely on manual processes, from idea generation to evaluation, insight provision, and prize distribution, which are often inefficient and time-consuming. Furthermore, it is difficult to ensure fairness and transparency in evaluation. To solve these challenges, it is necessary to automate the entire process and improve efficiency and fairness.

[1150] 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.

[1151] In this invention, the server includes terminal means for inputting topics and constraints, server means for generating ideas using a generation AI, database means for saving the generated ideas, terminal means for accepting idea submissions, database means for saving submitted ideas, evaluation AI means for evaluating the submitted ideas, database means for saving the evaluation results, server means for providing the evaluation results, server means for generating insights based on the evaluation results and reporting them to users, and AI means for proposing prize distribution. This makes it possible to automate the entire process of an idea contest, not only improving the efficiency of the series of tasks from idea generation to submission and evaluation, provision of insights, and prize distribution, but also ensuring fairness and transparency of the evaluation.

[1152] "Topic" refers to the theme or issue that the User wishes to address in the Contest.

[1153] "Constraints" refer to specific requirements or limitations that must be met in idea generation and evaluation.

[1154] "Terminal" refers to an electronic device that allows a user to input and transmit information such as topics, constraints, and ideas.

[1155] "Server" refers to the central processing unit that performs various processes such as generation AI and evaluation AI.

[1156] "Generative AI" refers to artificial intelligence that automatically generates ideas based on topics and constraints.

[1157] "Database" refers to a system for storing and managing data such as generated ideas and evaluation results.

[1158] "Evaluation AI" refers to artificial intelligence that evaluates submitted ideas based on criteria such as creativity, feasibility, and marketability.

[1159] "Insights" refers to the analysis and key information generated from the assessment results.

[1160] "Reporting" refers to the act of providing information such as insights or evaluation results to users.

[1161] "Prize Share" means the share of the prize awarded to each Idea in the Contest.

[1162] "HTTP POST request" refers to the protocol used by a device to send data to a server.

[1163] "Evaluation criteria" refers to the specific indicators or measures used to evaluate ideas.

[1164] The present invention relates to a system that generates and evaluates ideas based on topics and constraints entered by users, and proposes prize allocations based on the results. The system includes the following hardware and software configurations.

[1165] Hardware and Software Configuration

[1166] Device: This refers to the device that users use to input topics and ideas, such as a computer, smartphone, or tablet.

[1167] Server: Refers to the central processing unit that operates the generation AI, evaluation AI, database, etc.

[1168] Database: This refers to a system for storing data such as ideas and evaluation results, and uses MySQL or SQL Server.

[1169] What the program does

[1170] 1. Idea generation

[1171] 1. The user uses a terminal to input the contest topic (e.g., "New environmentally friendly product") and constraints, and submits it.

[1172] 2. The device sends the entered information to the server via an HTTP POST request.

[1173] 3. The server analyzes the received topic and constraints and passes a prompt to the generation AI (e.g., GPT-4). An example of a prompt is, "Please come up with a new environmentally friendly product, with a budget of less than 1 million yen and using reusable materials."

[1174] 4. The generative AI generates ideas based on the prompt text, for example, "bioplastic containers made from reusable materials."

[1175] 5. The server stores the generated ideas in a MySQL database.

[1176] 2. Idea submission and evaluation

[1177] 1. A user uses a terminal to input and submit their idea (e.g., "An idea for a recyclable water bottle").

[1178] 2. The device sends the idea to the server via an HTTP POST request.

[1179] 3. The server parses the received ideas and stores them in a SQL Server database.

[1180] 4. The server launches an evaluation AI (e.g., Scikit-learn model) to evaluate ideas based on the evaluation criteria (creativity, feasibility, marketability). For example, the idea for a recyclable water bottle is evaluated.

[1181] 5. The server stores the evaluation results in a SQL Server database.

[1182] 3. Providing insights and suggesting prize allocations

[1183] 1. The server generates insights based on the evaluation results, for example, analyzing that "the idea for a recyclable water bottle received high marks for creativity and marketability."

[1184] 2. The server uses a prize allocation AI (e.g., TensorFlow model) to propose an optimal prize allocation. For example, allocate 1 million yen to the idea for a recyclable water bottle.

[1185] 3. The server sends the proposal results to the device as an HTTP response so that the user can check them.

[1186] In this way, the system of the present invention allows users to easily specify a topic, automatically generate and evaluate ideas based on that topic, and provide the results, thereby streamlining the entire idea contest process and ensuring fairness and transparency in the evaluation.

[1187] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1188] System program processing flow

[1189] Idea generation process

[1190] Step 1:

[1191] The user uses a terminal to input the contest topic "Environmentally Friendly New Product" and constraints (for example, budget of 1 million yen or less, use of recyclable materials) and submit the input.

[1192] Input: Topic: "New eco-friendly product", Constraints: "Budget: 1 million yen or less, Use recyclable materials"

[1193] Output: Topics and constraints entered in the terminal

[1194] Step 2:

[1195] The terminal sends the entered topic and constraints to the server via an HTTP POST request.

[1196] Input: User-entered topics and constraints

[1197] Output: HTTP POST request with topic and constraints sent to the server

[1198] Step 3:

[1199] The server analyzes the received request and extracts the topic and constraints.

[1200] Input: Topic and constraints received in the HTTP POST request

[1201] Output: Parsed topic "New eco-friendly product" and constraints "budget within 1 million yen, use reusable materials"

[1202] Step 4:

[1203] The server launches a generative AI (e.g., GPT-4) and passes it a prompt: "Please come up with a new environmentally friendly product. The budget should be within 1 million yen and it should use reusable materials."

[1204] Input: Parsed topics and constraints

[1205] Output: Prompt given to the generation AI: "Invent a new eco-friendly product. The budget should be within 1 million yen and it should be made from recyclable materials."

[1206] Step 5:

[1207] The generative AI generates ideas based on a prompt, such as a bioplastic container made from reusable materials.

[1208] Input: Prompt: "Invent a new eco-friendly product. Budget should be under 1 million yen, and it should be made from recyclable materials."

[1209] Output: Generated idea: "Bioplastic container made from reusable materials"

[1210] Step 6:

[1211] The server stores the generated ideas in a MySQL database.

[1212] Input: Generated idea: "Bioplastic container made from reusable materials"

[1213] Output: Ideas stored in a MySQL database

[1214] Idea submission and evaluation process

[1215] Step 1:

[1216] A user uses a terminal to input and submit his / her idea, "An idea for a recyclable water bottle."

[1217] Input: Idea "Recyclable water bottle idea"

[1218] Output: Ideas typed into the terminal

[1219] Step 2:

[1220] The device sends the input idea to the server via an HTTP POST request.

[1221] Input: User-entered ideas

[1222] Output: HTTP POST request for ideas sent to the server

[1223] Step 3:

[1224] The server analyzes the received request and extracts ideas.

[1225] Input: Ideas received in an HTTP POST request

[1226] Output: Parsed idea "Recyclable water bottle idea"

[1227] Step 4:

[1228] The server stores the extracted ideas in a SQL Server database.

[1229] Input: Parsed idea "Recyclable water bottle idea"

[1230] Output: Ideas stored in a SQL Server database

[1231] Step 5:

[1232] The server launches an evaluation AI (e.g., a Scikit-learn model) and passes all ideas in the database to be evaluated based on the following criteria: creativity, feasibility, and marketability.

[1233] Input: All ideas in a SQL Server database

[1234] Output: A list of ideas to be passed to the evaluation AI

[1235] Step 6:

[1236] The AI ​​will score each idea based on the criteria of creativity, feasibility, and marketability. For example, an idea for a recyclable water bottle will be evaluated and given a score.

[1237] Input: List of ideas based on evaluation criteria

[1238] Output: Each idea given a rating score

[1239] Step 7:

[1240] The server stores the evaluation results in a SQL Server database.

[1241] Input: Each idea given a rating score

[1242] Output: Evaluation results stored in a SQL Server database

[1243] Providing insights and processing prize allocation recommendations

[1244] Step 1:

[1245] The server generates insights based on the evaluation results, such as "The idea for a recyclable water bottle received high marks for creativity and marketability."

[1246] Input: Evaluation results in a SQL Server database

[1247] Output: Generated insights

[1248] Step 2:

[1249] The server uses a prize allocation AI (e.g., TensorFlow model) to propose the optimal prize allocation for each idea. For example, it allocates 1 million yen to the idea for a recyclable water bottle.

[1250] Input: Generated insights

[1251] Output: Proposed bounty distribution

[1252] Step 3:

[1253] The server sends the proposal results to the terminal in an HTTP response, allowing the user to check the results.

[1254] Input: Proposed Prize Distribution

[1255] Output: Suggestion results sent to the device

[1256] In this way, the system of the present invention allows users to easily specify a topic, automatically generate and evaluate ideas based on that topic, and provide the results, thereby streamlining the entire idea contest process and ensuring fairness and transparency in the evaluation.

[1257] (Application example 1)

[1258] 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."

[1259] When users propose new product designs or concepts in a virtual store, it is necessary to evaluate the ideas and distribute rewards fairly and efficiently. Currently, there is no system that provides prompt and appropriate feedback and rewards for user-generated ideas. Therefore, it is a challenge to provide a mechanism that increases user motivation and generates many high-quality ideas.

[1260] 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.

[1261] In this invention, the server includes a terminal means for inputting topics and constraints, a server means for generating ideas using a generation AI, and a database means for storing the generated ideas. This allows the generation AI to efficiently create new product ideas based on information input by users and store the evaluation results of those ideas in the database. Furthermore, the evaluation AI means performs fair and detailed evaluations of the proposed ideas, thereby generating high-quality ideas and allocating appropriate rewards. By providing a means for quickly notifying users of the results via a smartphone, smart glasses, or head-mounted display, immediate feedback can be provided, increasing users' motivation to participate.

[1262] "Terminal means" refers to a device that allows a user to input topics and constraints, such as a smartphone, smart glasses, or a head-mounted display.

[1263] "Generative AI" refers to artificial intelligence that generates new ideas based on topics and constraints entered by the user.

[1264] "Server means" refers to a computer system that runs the generative AI to generate ideas and manage them.

[1265] "Database Means" refers to a data management system for storing generated ideas and accessing and managing them as needed.

[1266] "Evaluation AI means" refers to artificial intelligence that evaluates submitted ideas based on criteria such as creativity, feasibility, and marketability.

[1267] "Server means for providing evaluation results" refers to a server for notifying users and systems of the results generated by the evaluation AI means.

[1268] "Means for making reward suggestions" refers to artificial intelligence that suggests optimal reward allocation based on the evaluation results.

[1269] A "virtual store" refers to a virtual shopping space developed on the Internet, where users can propose product designs and concepts.

[1270] "Means for notifying results" refers to a system for notifying users of feedback and evaluation results through devices such as smartphones, smart glasses, and head-mounted displays.

[1271] The system of the present invention allows users to propose new product designs and concepts in a virtual store, and the ideas are evaluated and rewards are distributed.

[1272] System Configuration

[1273] The system consists of the following components:

[1274] Terminal means: A device through which a user inputs topics and constraints. This includes smartphones, smart glasses, head-mounted displays, etc.

[1275] Generative AI: Artificial intelligence that generates new ideas based on user-entered topics and constraints.

[1276] Server means: A computer system for running the generative AI and generating and managing ideas.

[1277] Database means: A database for storing generated ideas and accessing and managing them as needed.

[1278] Evaluation AI method: An artificial intelligence that evaluates submitted ideas based on criteria such as creativity, feasibility, and marketability.

[1279] Server means for providing evaluation results: A server for notifying users and systems of the results generated by the evaluation AI means.

[1280] Means for making reward suggestions: Artificial intelligence that suggests optimal reward allocation based on evaluation results.

[1281] Means of notifying results: A system for notifying users of feedback and evaluation results through devices such as smartphones, smart glasses, and head-mounted displays.

[1282] Program processing

[1283] 1. Topic input and idea generation

[1284] Users access the virtual store via their smartphone and input new product designs and concepts. This input information is sent from the device to a server, where a generative AI generates new ideas. This generative AI uses an open-source generative AI model (e.g., GPT-4). The server stores the generated ideas in a database.

[1285] 2. Submitting and Saving Ideas

[1286] When a user submits a self-generated product idea, the information is sent to the server, which receives the information and stores it in a database.

[1287] 3. Evaluate ideas

[1288] The server launches an evaluation AI to evaluate submitted ideas. The evaluation AI uses TensorFlow and PyTorch to score ideas based on criteria such as creativity, feasibility, and marketability.

[1289] 4. Reward proposals and notifications

[1290] Based on the evaluation results, the server proposes rewards. The server notifies the user of the evaluation results and offers rewards such as points or discount coupons. This notification is done via a smartphone, smart glasses, or a head-mounted display.

[1291] Examples of concrete examples and prompts

[1292] Users enter the topic of creating a "sustainable fashion item" through a smartphone app.

[1293] Example prompt sentence:

[1294] Topic: Sustainable fashion items

[1295] Constraints: Use of recyclable materials and environmentally friendly processes

[1296] Given this prompt, the generative AI generated the following ideas:

[1297] The idea: a recycled cotton t-shirt made from renewable materials, with a design featuring an environmental message.

[1298] The evaluation AI generates the following evaluation results and provides feedback to the user:

[1299] Creativity: 9 / 10

[1300] Feasibility: 8 / 10

[1301] Marketability: 7 / 10

[1302] The idea ultimately involves providing discount coupons and users can view the results on their smartphones.

[1303] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1304] Step 1:

[1305] User inputs topic and constraints

[1306] The user launches the smartphone app and inputs a topic and constraints. For example, the topic is "sustainable fashion items" and the constraints are "use of reusable materials and environmentally friendly processes."

[1307] Input: Topics and constraints from the user

[1308] Output: Topic and constraint data is sent from the terminal to the server.

[1309] Step 2:

[1310] The server receives the topic and constraints

[1311] The server receives the topic and constraints sent from the device, and prepares to pass this data to the generation AI.

[1312] Input: Topic and constraint data sent from the device

[1313] Output: Topic and constraint data to be passed to the generative AI

[1314] Step 3:

[1315] Generative AI generates ideas

[1316] The generative AI in the server generates new product ideas based on the received topic and constraints. The generative AI model used here is GPT-4. The model analyzes the prompt sentence and generates appropriate ideas.

[1317] Input: Topic and constraint data

[1318] Output: Generated ideas (e.g., recycled cotton T-shirts made from renewable materials)

[1319] Step 4:

[1320] Save your ideas in a database

[1321] The generated ideas are stored in a database on the server, making it easy to retrieve the ideas later.

[1322] Input: Generated ideas

[1323] Output: Idea records stored in a database

[1324] Step 5:

[1325] User submits idea

[1326] Users submit their own product ideas through a smartphone app. For example, they can submit an idea for a recycled cotton T-shirt.

[1327] Input: Submitted Idea

[1328] Output: Ideas submitted by users are sent from the device to the server.

[1329] Step 6:

[1330] The server receives and stores submitted ideas.

[1331] The server receives the submitted ideas sent from the terminals and stores them in a database.

[1332] Input: Submitted Idea

[1333] Output: A record of the submitted idea stored in a database

[1334] Step 7:

[1335] Evaluation AI evaluates ideas

[1336] The server runs an evaluation AI model that evaluates ideas in the database based on criteria such as creativity, feasibility, and marketability. The evaluation model uses TensorFlow and PyTorch.

[1337] Input: Each idea in the database

[1338] Output: Evaluation results (creativity, feasibility, marketability scores)

[1339] Step 8:

[1340] Evaluation results are saved in a database

[1341] The server stores the generated evaluation results in a database, and this information is later communicated to the user.

[1342] Input: Evaluation result

[1343] Output: Records of the evaluation results stored in a database

[1344] Step 9:

[1345] Reward proposal

[1346] The server will propose rewards based on the evaluation results, which may include points or discount coupons.

[1347] Input: Evaluation result

[1348] Output: Reward proposal data

[1349] Step 10:

[1350] Notify the user of the results

[1351] The server notifies the user of the evaluation results and reward proposals via a smartphone, smart glasses, or head-mounted display.

[1352] Input: Reward proposal data

[1353] Output: Evaluation results and reward notification displayed on the user's device

[1354] 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.

[1355] The present invention is a system that includes a terminal for inputting topics and constraints, a server that generates ideas using a generation AI, a database that stores the generated ideas, a server that accepts idea submissions, an evaluation AI that evaluates the submitted ideas, a server that provides the evaluation results, an AI that proposes prize allocations, and an emotion engine that recognizes the user's emotions.

[1356] Program processing

[1357] 1. Idea generation

[1358] User: The user inputs the contest topic and constraints into the terminal and submits them.

[1359] Terminal: Sends topics and constraints to the server.

[1360] Server: The server activates the generation AI and generates new ideas based on the received topics and constraints. The emotion engine also analyzes the user's emotion data and reflects it in idea generation.

[1361] Server: Stores the generated ideas in a database.

[1362] Examples:

[1363] The user inputs the topic "new environmentally friendly products" into the device and sends it. The device sends the topic to the server. The server activates the generative AI to generate ideas for bioplastic containers made from reusable materials. The emotion engine analyzes the user's emotions and reflects this in the generation process to prioritize ideas that show a positive emotional response. The server then stores the generated ideas in a database.

[1364] 2. Idea submission and evaluation

[1365] User: The user inputs and sends his / her own ideas through the terminal.

[1366] Terminal: Sends submitted ideas to the server.

[1367] Server: The server receives submitted ideas and stores them in a database.

[1368] Server: Launches the evaluation AI and evaluates all submitted ideas. The evaluation AI scores each idea based on the criteria of creativity, feasibility, and marketability. In addition, the emotion engine analyzes the user's emotional data and reflects it in the evaluation.

[1369] Server: Stores the evaluation results in a database.

[1370] Examples:

[1371] A user submits an "idea for a recyclable water bottle" via their device. The device sends the idea to the server. The server receives the submitted idea and stores it in a database. The server then activates an evaluation AI, which evaluates all submitted ideas from the perspectives of "reusability," "cost-effectiveness," and "environmental protection." At this time, an emotion engine analyzes the user's emotions and gives high ratings to ideas that elicit a positive response. The server then stores the evaluation results in a database.

[1372] 3. Providing insights and suggesting prize allocations

[1373] Server: The server generates insights based on the evaluation results, including the top-rated ideas and an analysis of the evaluations.

[1374] Server: The AI ​​proposes optimal prize distribution. The proposal includes allocation based on the evaluation points of each idea and the contest budget. In addition, the emotion engine analyzes users' emotional data and reflects it in the proposal.

[1375] Server: Sends the proposal results to the terminal so that the user can check them.

[1376] Examples:

[1377] The server generates an insight based on the evaluation results and reports that "the idea for a recyclable water bottle received the highest rating." The emotion engine analyzes the user's emotions and reflects them in the insight. The server uses AI to suggest allocating the highest prize money to the idea for a recyclable water bottle. In this case, the emotion engine analyzes the user's emotional data and adds a small bonus to ideas that show a positive emotional response. The server sends the proposal results to the device, where the user can confirm them.

[1378] Summary

[1379] The system of this invention not only handles a series of processes from topic input to idea generation, idea submission and evaluation, insight generation, and prize distribution proposals, but also recognizes user emotions and reflects them in the generation, evaluation, and proposals. This significantly improves the efficiency of idea contest management and, by taking user emotions into consideration, enables contest management that is more user-friendly.

[1380] The processing flow will be explained below.

[1381] Program processing

[1382] 1. Idea generation

[1383] Step 1:

[1384] The user inputs the contest topic and constraints into the terminal and submits them.

[1385] Step 2:

[1386] The terminal sends the topic and constraints to the server.

[1387] Step 3:

[1388] The server sends the received topic and constraints to the generation AI and starts the generation AI.

[1389] Step 4:

[1390] Generative AI generates new ideas based on topics and constraints.

[1391] Step 5:

[1392] The emotion engine acquires the user's emotional data and reflects it in the generated ideas. In this case, the emotion engine analyzes the user's positive emotions and adjusts the idea generation process accordingly.

[1393] Step 6:

[1394] The server stores the generated ideas in a database.

[1395] Examples:

[1396] The user inputs the topic "new environmentally friendly products" into the device and sends it. The device sends the topic to the server. The server activates the generative AI to generate ideas for bioplastic containers made from reusable materials. The emotion engine analyzes the user's emotions and reflects this in the generation process to prioritize ideas that show a positive emotional response. The server then stores the generated ideas in a database.

[1397] 2. Idea submission and evaluation

[1398] Step 1:

[1399] Users input and send their ideas through a terminal.

[1400] Step 2:

[1401] The device sends the submitted idea to the server.

[1402] Step 3:

[1403] The server receives the submitted ideas and stores them in a database.

[1404] Step 4:

[1405] The server launches an evaluation AI to evaluate all submitted ideas.

[1406] Step 5:

[1407] The evaluation AI scores each idea based on the criteria of "creativity," "feasibility," and "marketability."

[1408] Step 6:

[1409] The emotion engine analyzes the user's emotional data and reflects it in the evaluation AI. At this time, the emotion engine analyzes the user's positive emotional reactions and adjusts the evaluation process to give a high rating.

[1410] Step 7:

[1411] The server stores the evaluation results in a database.

[1412] Examples:

[1413] A user submits an "idea for a recyclable water bottle" via their device. The device sends the idea to the server. The server receives the submitted idea and stores it in a database. The server then activates an evaluation AI, which evaluates all submitted ideas from the perspectives of "reusability," "cost-effectiveness," and "environmental protection." At this time, an emotion engine analyzes the user's emotions and gives high ratings to ideas that elicit a positive response. The server then stores the evaluation results in a database.

[1414] 3. Providing insights and suggesting prize allocations

[1415] Step 1:

[1416] The server generates insights based on the evaluation results, including the top-rated ideas and an overall evaluation analysis.

[1417] Step 2:

[1418] The emotion engine analyzes the user's emotions and reflects them in the generated insights. In this case, the emotion engine analyzes the user's emotional data and generates advantageous insights for ideas that show a positive reaction.

[1419] Step 3:

[1420] The server uses AI to propose optimal prize distribution, including the evaluation points for each idea and distribution based on the contest budget.

[1421] Step 4:

[1422] The emotion engine analyzes users' emotional data and reflects it in the prize distribution proposals, adding a little extra weight to ideas that show positive emotions.

[1423] Step 5:

[1424] The server sends the proposal results to the terminal so that the user can check them.

[1425] Examples:

[1426] The server generates an insight based on the evaluation results and reports that "the idea for a recyclable water bottle received the highest rating." The emotion engine analyzes the user's emotions and reflects them in the insight. The server uses AI to suggest allocating the highest prize money to the idea for a recyclable water bottle. In this case, the emotion engine analyzes the user's emotional data and adds a small bonus to ideas that show a positive emotional response. The server sends the proposal results to the device, where the user can confirm them.

[1427] Summary

[1428] The system of this invention not only handles a series of processes from topic input to idea generation, idea submission and evaluation, insight generation, and prize distribution proposals, but also recognizes user emotions and reflects them in the generation, evaluation, and proposals. This significantly improves the efficiency of idea contest management and, by taking user emotions into consideration, enables contest management that is more user-friendly.

[1429] Example 2

[1430] 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."

[1431] Conventional idea contest management systems have a complicated process from idea generation to evaluation, reporting, and prize distribution, making it difficult to manage the contest efficiently and with consideration for users' emotions.In addition, it is not possible to evaluate or propose ideas with consideration for users' emotions, making it difficult to manage a contest that provides high user satisfaction.

[1432] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1433] In this invention, the server includes terminal means for inputting topics and constraints, server means for generating ideas using a generative AI model, database means for saving the generated ideas, emotion engine means for analyzing emotion data, server means for activating evaluation AI means to evaluate the generated ideas, server means for accepting submitted ideas, server means for activating the evaluation AI means to provide evaluation results, AI means for generating insights based on the evaluation results and proposing optimal prize distribution, and emotion engine means for recognizing user emotions and reflecting them in evaluations and proposals. This streamlines the process from idea generation to evaluation, insight generation, and prize distribution proposals, and enables the operation of a more satisfying contest that takes user emotions into consideration.

[1434] "Terminal means" is a device that allows a user to input topics and constraints and send them to the system.

[1435] A "generative AI model" is an artificial intelligence that generates new ideas based on received topics and constraints.

[1436] The "server means" is a server device for processing and storing various data within the system.

[1437] The "database means" is a database device for storing and managing data such as generated ideas and evaluation results.

[1438] The "emotion engine means" is a system that analyzes the user's emotional data and reflects it in idea generation, evaluation, and proposals.

[1439] The "evaluation AI method" is an artificial intelligence that evaluates submitted ideas based on the criteria of creativity, feasibility, and marketability.

[1440] "Insights" refers to the analytical results and important information gained from the evaluation results.

[1441] The "AI method for proposing prize distribution" is an artificial intelligence that proposes optimal prize distribution based on evaluation results and budget.

[1442] This invention relates to a system that streamlines the process from topic input to idea generation, submission, evaluation, insight generation, and prize allocation proposals. This system includes a terminal where users input topics and constraints, a server where new ideas are generated using a generative AI model, a database where the generated ideas and evaluation results are stored, and an emotion engine that analyzes user emotion data and reflects this in idea generation and evaluation. The main components of this system and their operation are described in detail below.

[1443] Entering and sending topics and constraints

[1444] Users input the contest topic and constraints using a terminal. This terminal can be a general PC, tablet, or smartphone. The input information is sent from the terminal to the server.

[1445] Example: A user inputs the topic "New eco-friendly products" and performs a send operation. At this time, the terminal sends the input information to the server.

[1446] Idea generation

[1447] The server generates new ideas by launching a generative AI model based on the topics and constraints received from the device. This generative AI model uses machine learning technology to generate new ideas from a variety of data.

[1448] The server uses an emotion engine to analyze the user's emotion data during the generation process and reflects the data in the generated ideas.

[1449] Example: Generative AI generates ideas for "bioplastic containers made from reusable materials," while the emotion engine reflects the user's positive emotions.

[1450] Saving ideas

[1451] The server stores the generated ideas in a database.

[1452] Example: The generated ideas for bioplastic containers are stored in the "Ideas" table of the database.

[1453] Submitting and Receiving Ideas

[1454] Users input and transmit their ideas through a terminal.

[1455] The device sends the input ideas to the server, and this information is also stored and managed in a database.

[1456] Example: A user submits an idea for a recyclable water bottle via their device. This data is then sent to the server and stored in a database.

[1457] Idea Evaluation

[1458] The server launches an evaluation AI to evaluate all submitted ideas based on the criteria of creativity, feasibility, and marketability, and also reflects the user's emotional data using an emotion engine.

[1459] Example: An evaluation AI evaluates ideas for recyclable water bottles from the perspectives of "reusability," "cost-effectiveness," and "environmental protection," giving higher ratings to ideas that express positive emotions.

[1460] Providing insights and suggesting prize allocations

[1461] The server generates insights based on the evaluation results and reports them to the user. It also activates an AI that proposes optimal prize distribution based on the evaluation results. This proposal also reflects the analysis results of the emotion engine.

[1462] Example: The server reports that "the idea for a recyclable water bottle received the highest votes" and suggests optimal prize distribution taking sentiment data into account.

[1463] Prompt Sentence Examples

[1464] "Please tell me your ideas for new eco-friendly products. I'd like specific ideas for bioplastic containers made from reusable materials."

[1465] This system will significantly improve the efficiency of idea contest management and will also enable more satisfying management that takes into account user emotions.

[1466] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1467] Step 1: Enter topic and constraints and submit

[1468] User: The user inputs the contest topic and constraints into the terminal and presses the send button. This input data includes the topic and constraints in text format. Based on the input data, the terminal sends the data to the server.

[1469] Input: Topic (e.g., "New eco-friendly product"), Constraint (e.g., "Use reusable materials")

[1470] Output: A data packet containing topics and constraints

[1471] Specific operation: The user enters a topic in the text field of the terminal and clicks the send button. The terminal converts the input data into a packet format and sends it to the server as an HTTP request.

[1472] Step 2: Receive topics and constraints and launch the generation AI model

[1473] Server: The server receives the topics and constraints sent from the device. The received data contains the topics and constraints in text format. The server then launches the generative AI model based on this.

[1474] Input: Data packets of topics and constraints sent from the device

[1475] Output: Ready for idea generation

[1476] How it works: The server receives an HTTP request, extracts topics and constraints, then calls the API of the generative AI model to start the idea generation process.

[1477] Step 3: Generate ideas and reflect on sentiment data

[1478] Server: The server uses the generative AI model to generate ideas based on the received topic and constraints. It also activates the emotion engine to analyze the user's emotion data and reflect it in the generation process.

[1479] Input: Generative AI model, emotion data

[1480] Output: Generated ideas

[1481] How it works: The generative AI model generates several ideas based on a given topic. In parallel, the emotion engine analyzes the user's emotion data and filters the ideas to elicit positive emotions. Once the final idea is determined, it is output.

[1482] Step 4: Saving generated ideas

[1483] Server: Stores the generated ideas in a database, which stores the idea content and associated metadata.

[1484] Input: Generated ideas

[1485] Output: Ideas stored in a database

[1486] What happens: The server converts the generated ideas into JSON format and executes a query to insert them into the "ideas" table in the database, where they are permanently stored.

[1487] Step 5: Submit and submit your idea

[1488] User: The user inputs their idea into the terminal and presses the send button. This information is sent from the terminal to the server.

[1489] Input: User-entered ideas

[1490] Output: Data packet to be sent

[1491] Specific operation: A user fills out an idea submission form on the device and clicks the submit button. The device converts the input data into packets and sends them to the server as an HTTP request.

[1492] Step 6: Receiving and storing submissions

[1493] Server: Receives submitted ideas and stores them in a database. The received data includes the ideas entered by users.

[1494] Input: Ideas sent from your device

[1495] Output: User ideas stored in a database

[1496] What happens: The server receives the HTTP request, extracts the ideas, converts them to JSON format, and executes a query to insert them into the "Submitted Ideas" table in the database.

[1497] Step 7: Launch the evaluation AI and evaluate your ideas

[1498] Server: The server launches the evaluation AI and evaluates all ideas stored in the database. Evaluation criteria include creativity, feasibility, and marketability. In addition, it uses an emotion engine to reflect user emotional data in the evaluation.

[1499] Input: Submitted ideas, sentiment data

[1500] Output: Idea rating score

[1501] Specific operation: The server invokes the evaluation AI model and starts evaluating each idea. The generated score and emotional data are combined to calculate the final evaluation. The evaluation results are then stored in the database.

[1502] Step 8: Save the evaluation results

[1503] Server: Stores the evaluation results in a database. For each evaluated idea, a score and associated data are stored.

[1504] Input: Idea rating score

[1505] Output: Evaluation results stored in a database

[1506] Specific operation: The server converts the evaluation results into JSON format and executes a query to insert them into the "Evaluation Results" table in the database, thereby permanently storing the evaluation results.

[1507] Step 9: Analyze assessment results and generate insights

[1508] Server: Generates insights based on the evaluation results, including the top-rated ideas and an overall evaluation analysis.

[1509] Input: Evaluation result

[1510] Output: Generated insights

[1511] Specific operation: The server analyzes the evaluation results and generates insights in text format, which are temporarily stored on the server.

[1512] Step 10: Proposal for Prize Distribution

[1513] Server: Proposes optimal prize distribution using prize distribution AI. Proposals include distribution based on evaluation points and budget. It also uses an emotion engine to reflect user emotion data.

[1514] Input: Evaluation results, budget, sentiment data

[1515] Output: Prize allocation proposal

[1516] Specific operation: The server calls the prize allocation AI model and calculates the optimal prize allocation using the evaluation points and budget as input. It then modifies the proposal based on data from the emotion engine and generates the final prize allocation proposal.

[1517] Step 11: Submit your proposal

[1518] Server: Sends the prize distribution proposal to the user's device so that the user can confirm it.

[1519] Input: Prize allocation proposal

[1520] Output: Notification of proposal results

[1521] Specific operation: The server sends the generated prize distribution proposal to the terminal. The user can check the proposal result on the terminal screen. Notifications are sent to the user via email, push notifications, etc.

[1522] (Application example 2)

[1523] 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."

[1524] Conventional content delivery systems have the problem of not efficiently generating personalized content based on specific topics and constraints desired by users. Furthermore, content suggestions do not take user emotions into consideration, which may reduce user satisfaction. The objective of this invention is to provide a system that combines generation AI, evaluation AI, and an emotion engine to effectively generate, evaluate, and provide content that satisfies users.

[1525] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1526] In this invention, the server includes terminal means for inputting topics and constraints, server means for generating ideas using a generation AI, database means for saving the generated ideas, server means for accepting submissions of ideas, evaluation AI means for evaluating the submitted ideas, server means for providing the evaluation results, AI means for proposing prize distribution, emotion engine means for recognizing user emotions, and means for proposing content that matches the user's preferences based on the content ideas generated by the evaluation AI. This makes it possible to generate novel content ideas based on the user's topics and constraints, and to make personalized suggestions that reflect the user's emotions.

[1527] "Terminal means" is a device that allows a user to input topics and constraints and send them to the server.

[1528] "Generative AI" is an artificial intelligence technology that generates new ideas based on topics and constraints entered by the user.

[1529] "Server means" refers to a device that processes and manages data and on which generation AI, evaluation AI, emotion engine, etc. operate.

[1530] "Database means" is a data management system for storing generated ideas.

[1531] "Evaluation AI methods" are artificial intelligence technologies that evaluate submitted ideas based on evaluation criteria such as creativity, feasibility, and marketability.

[1532] The "emotion engine means" is a technology for recognizing and analyzing the user's emotions.

[1533] "AI method for proposing prize distribution" is an artificial intelligence technology that proposes optimal prize distribution based on the evaluation results.

[1534] "Means for suggesting content that matches the user's preferences" refers to a device or technology that suggests personalized content based on content ideas generated by evaluation AI, taking into account the user's emotional state.

[1535] A system for implementing this invention is configured as follows: A user uses a terminal (such as a smartphone) to input topics and constraints. The terminal is equipped with an interface for transmitting the topics and constraints input by the user to a server. The server activates a generation AI based on the received topics and constraints to generate new ideas. At this time, an emotion engine analyzes the user's emotion data and reflects it in the generation process. The generated ideas are stored in a database.

[1536] Next, when a user submits their own idea, they send it to the server via their device. The server receives the submitted idea and evaluates it using evaluation AI. The evaluation AI scores the idea using evaluation criteria of creativity, feasibility, and marketability. At that time, an emotion engine analyzes the user's emotional data and reflects it in the evaluation. The evaluation results are stored in a database.

[1537] The server then generates insights based on the evaluation results and reports them to the user. Furthermore, the AI ​​that proposes prize distribution proposes optimal prize distribution, and the emotion engine analyzes the user's emotional data and reflects it in the proposal. The proposal results are sent to the device so that the user can check them. There is also a means to suggest content that suits the user's preferences based on the content ideas generated by the evaluation AI, allowing the user to receive personalized content.

[1538] Hardware and software used

[1539] Hardware: Smartphone (iOS or Android device)

[1540] software:

[1541] Python 3.x

[1542] The transformers library (for generative AI and sentiment analysis)

[1543] SQLite (for storing and managing ideas)

[1544] Specific examples

[1545] Suppose a user enters the following topic and constraints into a terminal:

[1546] Topic: "The Future of Travel"

[1547] Constraint: "Consider sustainability and environmental protection"

[1548] This input is sent to the server, which then triggers a generative AI to generate ideas based on the prompt.

[1549] Example prompt sentence:

[1550] Topic: Future of Travel

[1551] Constraints: Sustainability and environmental protection

[1552] Generate ideas:

[1553] The server stores the generated ideas in a database for users to review. The emotion engine analyzes the user's emotions and reflects them in the creation and evaluation process, thereby providing content that matches the user's preferences.

[1554] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1555] Step 1:

[1556] The user inputs the topic and constraints using a terminal.

[1557] Input: The user inputs the topics and constraints "future travel" and "consider sustainability and environmental protection."

[1558] Specific operation: A screen for entering the topic and constraints appears on the terminal interface. The user enters them and presses the send button.

[1559] Output: The entered data is sent to the server.

[1560] Step 2:

[1561] The server launches a generative AI based on the received topic and constraints to generate new ideas.

[1562] Input: Topic "Future of Travel" and Constraint "Consider sustainability and environmental protection".

[1563] What it does: The server passes these inputs as prompts to the generative AI, which then generates new ideas based on these prompts (e.g., the gpt-3 model).

[1564] Output: Generated ideas (e.g. "Ecological Tours Using Sustainable Energy").

[1565] Step 3:

[1566] The emotion engine analyzes the user's emotion data and reflects it in the generation process.

[1567] Input: Generated ideas and user sentiment data.

[1568] What it does: The emotion engine analyzes the user's emotional response to the generated ideas, for example, evaluating positive or negative reactions to the text content.

[1569] Output: The sentiment analysis results are fed into the generation process.

[1570] Step 4:

[1571] The server stores the generated ideas in a database.

[1572] Input: Generated ideas and sentiment analysis results.

[1573] Specific operation: The server connects to the database means and records the generated ideas and their sentiment analysis results.

[1574] Output: The saved ideas and their sentiment analysis results are stored in a database.

[1575] Step 5:

[1576] Users input their ideas through their terminals and send them to the server.

[1577] Input: A user-generated idea (e.g., "An idea for a recyclable water bottle").

[1578] Specific operation: The idea submission screen appears on the device, and the user enters their idea and presses the submit button.

[1579] Output: The user's idea is sent to the server.

[1580] Step 6:

[1581] The server receives the submitted ideas and evaluates them using an evaluation AI.

[1582] Input: Submitted idea and evaluation criteria (creativity, feasibility, marketability).

[1583] Specific operation: The server launches the evaluation AI and scores ideas based on the evaluation criteria. The analysis results of the emotion engine are also reflected in the evaluation.

[1584] Output: The evaluated ideas and their evaluation scores.

[1585] Step 7:

[1586] The server stores the evaluation results in a database.

[1587] Input: The rated ideas and their rating scores.

[1588] What it does: The server connects to a database and records the ideas that have been rated and their rating scores.

[1589] Output: The saved rated ideas and their rating scores.

[1590] Step 8:

[1591] The server generates insights based on the evaluation results and reports them to the user.

[1592] Input: Saved evaluation results.

[1593] Specific operation: The server analyzes the evaluation results and generates insights based on the most highly rated ideas and the analysis of the evaluations.

[1594] Output: An insights report is generated and provided to the user.

[1595] Step 9:

[1596] AI proposes optimal prize distribution.

[1597] Input: Evaluation results and contest budget.

[1598] Specific operation: The proposed AI calculates the optimal prize distribution based on the evaluation results, taking into account the analysis results of the emotion engine.

[1599] Output: An optimal prize distribution proposal is generated and provided to the user.

[1600] Step 10:

[1601] Based on content ideas generated by the evaluation AI, content that matches the user's preferences is suggested.

[1602] Input: Content ideas generated by the evaluation AI and analysis data from the sentiment engine.

[1603] Specific operation: The server proposes personalized content based on the ideas generated by the evaluation AI and the user's emotional data.

[1604] Output: Personalized content suggestions sent to the user.

[1605] 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.

[1606] 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.

[1607] 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.

[1608] [Fourth embodiment]

[1609] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1610] 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.

[1611] 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).

[1612] 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.

[1613] 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.

[1614] 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).

[1615] 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.

[1616] 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.

[1617] 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.

[1618] 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.

[1619] 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.

[1620] 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.

[1621] 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."

[1622] The present invention is a system that includes a terminal for inputting topics and constraints, a server that generates ideas using a generation AI, a database that stores the generated ideas, a server that accepts idea submissions, an evaluation AI that evaluates the submitted ideas, a server that provides the evaluation results, and an AI that proposes prize distribution.

[1623] Program processing

[1624] 1. Idea generation

[1625] User: The user inputs the contest topic and constraints into the terminal and submits them.

[1626] Terminal: Sends topics and constraints to the server.

[1627] Server: The server launches a generative AI to generate new ideas based on the received topic and constraints.

[1628] Server: Stores the generated ideas in a database.

[1629] Examples:

[1630] A user inputs the topic "new environmentally friendly products" into a device and sends it. The device sends the topic to the server. The server launches a generative AI to generate ideas for bioplastic containers made from reusable materials. The server stores the generated ideas in a database.

[1631] 2. Idea submission and evaluation

[1632] User: The user inputs and sends his / her own ideas through the terminal.

[1633] Terminal: Sends submitted ideas to the server.

[1634] Server: The server receives submitted ideas and stores them in a database.

[1635] Server: The server runs an evaluation AI and evaluates all submitted ideas.

[1636] Evaluation AI: Score each idea based on criteria such as creativity, feasibility, and marketability.

[1637] Server: Stores the evaluation results in a database.

[1638] Examples:

[1639] A user submits an "idea for a recyclable water bottle" via their device. The device sends the idea to the server. The server receives the submitted idea and stores it in a database. The server then launches an evaluation AI, which evaluates all submitted ideas from the perspectives of "reusability," "cost-effectiveness," and "environmental protection." The server then stores the evaluation results in a database.

[1640] 3. Providing insights and suggesting prize allocations

[1641] Server: The server generates insights based on the evaluation results, including the top-rated ideas and analysis of the evaluations.

[1642] Server: The AI ​​proposes optimal prize distribution, including the evaluation points for each idea and distribution based on the contest budget.

[1643] Server: Sends the proposal results to the terminal so that the user can check them.

[1644] Examples:

[1645] The server generates insights based on the evaluation results and reports that "the idea for a recyclable water bottle received the highest rating." The server uses AI to suggest that the highest prize money be allocated to the idea for a recyclable water bottle. The server sends the proposal results to the device, where the user can confirm them.

[1646] Summary

[1647] The system of the present invention automates a series of processes, from topic input to idea generation, idea submission and evaluation, insight generation, and prize distribution proposals, thereby significantly improving the efficiency of idea contest management, increasing the number and quality of ideas, while ensuring fairness and transparency in evaluation.

[1648] The processing flow will be explained below.

[1649] Step 1:

[1650] The user inputs the contest topic and constraints into the terminal and submits them.

[1651] The terminal sends the topic and constraints to the server.

[1652] Step 2:

[1653] The server launches a generation AI based on the received topic and constraints.

[1654] Generative AI generates new ideas based on topics and constraints.

[1655] Step 3:

[1656] The server stores the generated ideas in a database.

[1657] Step 4:

[1658] Users input their ideas directly into the terminal and send them.

[1659] The device sends the submitted idea to the server.

[1660] Step 5:

[1661] The server receives the submitted ideas and stores them in a database.

[1662] Step 6:

[1663] The server launches an evaluation AI to evaluate all submitted ideas.

[1664] The evaluation AI scores each idea based on criteria such as creativity, feasibility, and marketability.

[1665] Step 7:

[1666] The server stores the evaluation results in a database.

[1667] Step 8:

[1668] The server generates insights based on the evaluation results.

[1669] The insights generated include the top-rated ideas and an overall ranking analysis.

[1670] Step 9:

[1671] The server will use AI to suggest optimal prize distribution.

[1672] The proposals will include evaluation points for each idea and allocation based on the competition budget.

[1673] Step 10:

[1674] The server sends the proposal results to the terminal so that the user can check them.

[1675] Example 1

[1676] 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."

[1677] Modern idea contests are required to improve the number and quality of ideas and to ensure fair and transparent evaluation. Traditional systems rely on manual processes, from idea generation to evaluation, insight provision, and prize distribution, which are often inefficient and time-consuming. Furthermore, it is difficult to ensure fairness and transparency in evaluation. To solve these challenges, it is necessary to automate the entire process and improve efficiency and fairness.

[1678] 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.

[1679] In this invention, the server includes terminal means for inputting topics and constraints, server means for generating ideas using a generation AI, database means for saving the generated ideas, terminal means for accepting idea submissions, database means for saving submitted ideas, evaluation AI means for evaluating the submitted ideas, database means for saving the evaluation results, server means for providing the evaluation results, server means for generating insights based on the evaluation results and reporting them to users, and AI means for proposing prize distribution. This makes it possible to automate the entire process of an idea contest, not only improving the efficiency of the series of tasks from idea generation to submission and evaluation, provision of insights, and prize distribution, but also ensuring fairness and transparency of the evaluation.

[1680] "Topic" refers to the theme or issue that the User wishes to address in the Contest.

[1681] "Constraints" refer to specific requirements or limitations that must be met in idea generation and evaluation.

[1682] "Terminal" refers to an electronic device that allows a user to input and transmit information such as topics, constraints, and ideas.

[1683] "Server" refers to the central processing unit that performs various processes such as generation AI and evaluation AI.

[1684] "Generative AI" refers to artificial intelligence that automatically generates ideas based on topics and constraints.

[1685] "Database" refers to a system for storing and managing data such as generated ideas and evaluation results.

[1686] "Evaluation AI" refers to artificial intelligence that evaluates submitted ideas based on criteria such as creativity, feasibility, and marketability.

[1687] "Insights" refers to the analysis and key information generated from the assessment results.

[1688] "Reporting" refers to the act of providing information such as insights or evaluation results to users.

[1689] "Prize Share" means the share of the prize awarded to each Idea in the Contest.

[1690] "HTTP POST request" refers to the protocol used by a device to send data to a server.

[1691] "Evaluation criteria" refers to the specific indicators or measures used to evaluate ideas.

[1692] The present invention relates to a system that generates and evaluates ideas based on topics and constraints entered by users, and proposes prize allocations based on the results. The system includes the following hardware and software configurations.

[1693] Hardware and Software Configuration

[1694] Device: This refers to the device that users use to input topics and ideas, such as a computer, smartphone, or tablet.

[1695] Server: Refers to the central processing unit that operates the generation AI, evaluation AI, database, etc.

[1696] Database: This refers to a system for storing data such as ideas and evaluation results, and uses MySQL or SQL Server.

[1697] What the program does

[1698] 1. Idea generation

[1699] 1. The user uses a terminal to input the contest topic (e.g., "New environmentally friendly product") and constraints, and submits it.

[1700] 2. The device sends the entered information to the server via an HTTP POST request.

[1701] 3. The server analyzes the received topic and constraints and passes a prompt to the generation AI (e.g., GPT-4). An example of a prompt is, "Please come up with a new environmentally friendly product, with a budget of less than 1 million yen and using reusable materials."

[1702] 4. The generative AI generates ideas based on the prompt text, for example, "bioplastic containers made from reusable materials."

[1703] 5. The server stores the generated ideas in a MySQL database.

[1704] 2. Idea submission and evaluation

[1705] 1. A user uses a terminal to input and submit their idea (e.g., "An idea for a recyclable water bottle").

[1706] 2. The device sends the idea to the server via an HTTP POST request.

[1707] 3. The server parses the received ideas and stores them in a SQL Server database.

[1708] 4. The server launches an evaluation AI (e.g., Scikit-learn model) to evaluate ideas based on the evaluation criteria (creativity, feasibility, marketability). For example, the idea for a recyclable water bottle is evaluated.

[1709] 5. The server stores the evaluation results in a SQL Server database.

[1710] 3. Providing insights and suggesting prize allocations

[1711] 1. The server generates insights based on the evaluation results, for example, analyzing that "the idea for a recyclable water bottle received high marks for creativity and marketability."

[1712] 2. The server uses a prize allocation AI (e.g., TensorFlow model) to propose an optimal prize allocation. For example, allocate 1 million yen to the idea for a recyclable water bottle.

[1713] 3. The server sends the proposal results to the device as an HTTP response so that the user can check them.

[1714] In this way, the system of the present invention allows users to easily specify a topic, automatically generate and evaluate ideas based on that topic, and provide the results, thereby streamlining the entire idea contest process and ensuring fairness and transparency in the evaluation.

[1715] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1716] System program processing flow

[1717] Idea generation process

[1718] Step 1:

[1719] The user uses a terminal to input the contest topic "Environmentally Friendly New Product" and constraints (for example, budget of 1 million yen or less, use of recyclable materials) and submit the input.

[1720] Input: Topic: "New eco-friendly product", Constraints: "Budget: 1 million yen or less, Use recyclable materials"

[1721] Output: Topics and constraints entered in the terminal

[1722] Step 2:

[1723] The terminal sends the entered topic and constraints to the server via an HTTP POST request.

[1724] Input: User-entered topics and constraints

[1725] Output: HTTP POST request with topic and constraints sent to the server

[1726] Step 3:

[1727] The server analyzes the received request and extracts the topic and constraints.

[1728] Input: Topic and constraints received in the HTTP POST request

[1729] Output: Parsed topic "New eco-friendly product" and constraints "budget within 1 million yen, use reusable materials"

[1730] Step 4:

[1731] The server launches a generative AI (e.g., GPT-4) and passes it a prompt: "Please come up with a new environmentally friendly product. The budget should be within 1 million yen and it should use reusable materials."

[1732] Input: Parsed topics and constraints

[1733] Output: Prompt given to the generation AI: "Invent a new eco-friendly product. The budget should be within 1 million yen and it should be made from recyclable materials."

[1734] Step 5:

[1735] The generative AI generates ideas based on a prompt, such as a bioplastic container made from reusable materials.

[1736] Input: Prompt: "Invent a new eco-friendly product. Budget should be under 1 million yen, and it should be made from recyclable materials."

[1737] Output: Generated idea: "Bioplastic container made from reusable materials"

[1738] Step 6:

[1739] The server stores the generated ideas in a MySQL database.

[1740] Input: Generated idea: "Bioplastic container made from reusable materials"

[1741] Output: Ideas stored in a MySQL database

[1742] Idea submission and evaluation process

[1743] Step 1:

[1744] A user uses a terminal to input and submit his / her idea, "An idea for a recyclable water bottle."

[1745] Input: Idea "Recyclable water bottle idea"

[1746] Output: Ideas typed into the terminal

[1747] Step 2:

[1748] The device sends the input idea to the server via an HTTP POST request.

[1749] Input: User-entered ideas

[1750] Output: HTTP POST request for ideas sent to the server

[1751] Step 3:

[1752] The server analyzes the received request and extracts ideas.

[1753] Input: Ideas received in an HTTP POST request

[1754] Output: Parsed idea "Recyclable water bottle idea"

[1755] Step 4:

[1756] The server stores the extracted ideas in a SQL Server database.

[1757] Input: Parsed idea "Recyclable water bottle idea"

[1758] Output: Ideas stored in a SQL Server database

[1759] Step 5:

[1760] The server launches an evaluation AI (e.g., a Scikit-learn model) and passes all ideas in the database to be evaluated based on the following criteria: creativity, feasibility, and marketability.

[1761] Input: All ideas in a SQL Server database

[1762] Output: A list of ideas to be passed to the evaluation AI

[1763] Step 6:

[1764] The AI ​​will score each idea based on the criteria of creativity, feasibility, and marketability. For example, an idea for a recyclable water bottle will be evaluated and given a score.

[1765] Input: List of ideas based on evaluation criteria

[1766] Output: Each idea given a rating score

[1767] Step 7:

[1768] The server stores the evaluation results in a SQL Server database.

[1769] Input: Each idea given a rating score

[1770] Output: Evaluation results stored in a SQL Server database

[1771] Providing insights and processing prize allocation recommendations

[1772] Step 1:

[1773] The server generates insights based on the evaluation results, such as "The idea for a recyclable water bottle received high marks for creativity and marketability."

[1774] Input: Evaluation results in a SQL Server database

[1775] Output: Generated insights

[1776] Step 2:

[1777] The server uses a prize allocation AI (e.g., TensorFlow model) to propose the optimal prize allocation for each idea. For example, it allocates 1 million yen to the idea for a recyclable water bottle.

[1778] Input: Generated insights

[1779] Output: Proposed bounty distribution

[1780] Step 3:

[1781] The server sends the proposal results to the terminal in an HTTP response, allowing the user to check the results.

[1782] Input: Proposed Prize Distribution

[1783] Output: Suggestion results sent to the device

[1784] In this way, the system of the present invention allows users to easily specify a topic, automatically generate and evaluate ideas based on that topic, and provide the results, thereby streamlining the entire idea contest process and ensuring fairness and transparency in the evaluation.

[1785] (Application example 1)

[1786] 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."

[1787] When users propose new product designs or concepts in a virtual store, it is necessary to evaluate the ideas and distribute rewards fairly and efficiently. Currently, there is no system that provides prompt and appropriate feedback and rewards for user-generated ideas. Therefore, it is a challenge to provide a mechanism that increases user motivation and generates many high-quality ideas.

[1788] 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.

[1789] In this invention, the server includes a terminal means for inputting topics and constraints, a server means for generating ideas using a generation AI, and a database means for storing the generated ideas. This allows the generation AI to efficiently create new product ideas based on information input by users and store the evaluation results of those ideas in the database. Furthermore, the evaluation AI means performs fair and detailed evaluations of the proposed ideas, thereby generating high-quality ideas and allocating appropriate rewards. By providing a means for quickly notifying users of the results via a smartphone, smart glasses, or head-mounted display, immediate feedback can be provided, increasing users' motivation to participate.

[1790] "Terminal means" refers to a device that allows a user to input topics and constraints, such as a smartphone, smart glasses, or a head-mounted display.

[1791] "Generative AI" refers to artificial intelligence that generates new ideas based on topics and constraints entered by the user.

[1792] "Server means" refers to a computer system that runs the generative AI to generate ideas and manage them.

[1793] "Database Means" refers to a data management system for storing generated ideas and accessing and managing them as needed.

[1794] "Evaluation AI means" refers to artificial intelligence that evaluates submitted ideas based on criteria such as creativity, feasibility, and marketability.

[1795] "Server means for providing evaluation results" refers to a server for notifying users and systems of the results generated by the evaluation AI means.

[1796] "Means for making reward suggestions" refers to artificial intelligence that suggests optimal reward allocation based on the evaluation results.

[1797] A "virtual store" refers to a virtual shopping space developed on the Internet, where users can propose product designs and concepts.

[1798] "Means for notifying results" refers to a system for notifying users of feedback and evaluation results through devices such as smartphones, smart glasses, and head-mounted displays.

[1799] The system of the present invention allows users to propose new product designs and concepts in a virtual store, and the ideas are evaluated and rewards are distributed.

[1800] System Configuration

[1801] The system consists of the following components:

[1802] Terminal means: A device through which a user inputs topics and constraints. This includes smartphones, smart glasses, head-mounted displays, etc.

[1803] Generative AI: Artificial intelligence that generates new ideas based on user-entered topics and constraints.

[1804] Server means: A computer system for running the generative AI and generating and managing ideas.

[1805] Database means: A database for storing generated ideas and accessing and managing them as needed.

[1806] Evaluation AI method: An artificial intelligence that evaluates submitted ideas based on criteria such as creativity, feasibility, and marketability.

[1807] Server means for providing evaluation results: A server for notifying users and systems of the results generated by the evaluation AI means.

[1808] Means for making reward suggestions: Artificial intelligence that suggests optimal reward allocation based on evaluation results.

[1809] Means of notifying results: A system for notifying users of feedback and evaluation results through devices such as smartphones, smart glasses, and head-mounted displays.

[1810] Program processing

[1811] 1. Topic input and idea generation

[1812] Users access the virtual store via their smartphone and input new product designs and concepts. This input information is sent from the device to a server, where a generative AI generates new ideas. This generative AI uses an open-source generative AI model (e.g., GPT-4). The server stores the generated ideas in a database.

[1813] 2. Submitting and Saving Ideas

[1814] When a user submits a self-generated product idea, the information is sent to the server, which receives the information and stores it in a database.

[1815] 3. Evaluate ideas

[1816] The server launches an evaluation AI to evaluate submitted ideas. The evaluation AI uses TensorFlow and PyTorch to score ideas based on criteria such as creativity, feasibility, and marketability.

[1817] 4. Reward proposals and notifications

[1818] Based on the evaluation results, the server proposes rewards. The server notifies the user of the evaluation results and offers rewards such as points or discount coupons. This notification is done via a smartphone, smart glasses, or a head-mounted display.

[1819] Examples of concrete examples and prompts

[1820] Users enter the topic of creating a "sustainable fashion item" through a smartphone app.

[1821] Example prompt sentence:

[1822] Topic: Sustainable fashion items

[1823] Constraints: Use of recyclable materials and environmentally friendly processes

[1824] Given this prompt, the generative AI generated the following ideas:

[1825] The idea: a recycled cotton t-shirt made from renewable materials, with a design featuring an environmental message.

[1826] The evaluation AI generates the following evaluation results and provides feedback to the user:

[1827] Creativity: 9 / 10

[1828] Feasibility: 8 / 10

[1829] Marketability: 7 / 10

[1830] The idea ultimately involves providing discount coupons and users can view the results on their smartphones.

[1831] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1832] Step 1:

[1833] User inputs topic and constraints

[1834] The user launches the smartphone app and inputs a topic and constraints. For example, the topic is "sustainable fashion items" and the constraints are "use of reusable materials and environmentally friendly processes."

[1835] Input: Topics and constraints from the user

[1836] Output: Topic and constraint data is sent from the terminal to the server.

[1837] Step 2:

[1838] The server receives the topic and constraints

[1839] The server receives the topic and constraints sent from the device, and prepares to pass this data to the generation AI.

[1840] Input: Topic and constraint data sent from the device

[1841] Output: Topic and constraint data to be passed to the generative AI

[1842] Step 3:

[1843] Generative AI generates ideas

[1844] The generative AI in the server generates new product ideas based on the received topic and constraints. The generative AI model used here is GPT-4. The model analyzes the prompt sentence and generates appropriate ideas.

[1845] Input: Topic and constraint data

[1846] Output: Generated ideas (e.g., recycled cotton T-shirts made from renewable materials)

[1847] Step 4:

[1848] Save your ideas in a database

[1849] The generated ideas are stored in a database on the server, making it easy to retrieve the ideas later.

[1850] Input: Generated ideas

[1851] Output: Idea records stored in a database

[1852] Step 5:

[1853] User submits idea

[1854] Users submit their own product ideas through a smartphone app. For example, they can submit an idea for a recycled cotton T-shirt.

[1855] Input: Submitted Idea

[1856] Output: Ideas submitted by users are sent from the device to the server.

[1857] Step 6:

[1858] The server receives and stores submitted ideas.

[1859] The server receives the submitted ideas sent from the terminals and stores them in a database.

[1860] Input: Submitted Idea

[1861] Output: A record of the submitted idea stored in a database

[1862] Step 7:

[1863] Evaluation AI evaluates ideas

[1864] The server runs an evaluation AI model that evaluates ideas in the database based on criteria such as creativity, feasibility, and marketability. The evaluation model uses TensorFlow and PyTorch.

[1865] Input: Each idea in the database

[1866] Output: Evaluation results (creativity, feasibility, marketability scores)

[1867] Step 8:

[1868] Evaluation results are saved in a database

[1869] The server stores the generated evaluation results in a database, and this information is later communicated to the user.

[1870] Input: Evaluation result

[1871] Output: Records of the evaluation results stored in a database

[1872] Step 9:

[1873] Reward proposal

[1874] The server will propose rewards based on the evaluation results, which may include points or discount coupons.

[1875] Input: Evaluation result

[1876] Output: Reward proposal data

[1877] Step 10:

[1878] Notify the user of the results

[1879] The server notifies the user of the evaluation results and reward proposals via a smartphone, smart glasses, or head-mounted display.

[1880] Input: Reward proposal data

[1881] Output: Evaluation results and reward notification displayed on the user's device

[1882] 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.

[1883] The present invention is a system that includes a terminal for inputting topics and constraints, a server that generates ideas using a generation AI, a database that stores the generated ideas, a server that accepts idea submissions, an evaluation AI that evaluates the submitted ideas, a server that provides the evaluation results, an AI that proposes prize allocations, and an emotion engine that recognizes the user's emotions.

[1884] Program processing

[1885] 1. Idea generation

[1886] User: The user inputs the contest topic and constraints into the terminal and submits them.

[1887] Terminal: Sends topics and constraints to the server.

[1888] Server: The server activates the generation AI and generates new ideas based on the received topics and constraints. The emotion engine also analyzes the user's emotion data and reflects it in idea generation.

[1889] Server: Stores the generated ideas in a database.

[1890] Examples:

[1891] The user inputs the topic "new environmentally friendly products" into the device and sends it. The device sends the topic to the server. The server activates the generative AI to generate ideas for bioplastic containers made from reusable materials. The emotion engine analyzes the user's emotions and reflects this in the generation process to prioritize ideas that show a positive emotional response. The server then stores the generated ideas in a database.

[1892] 2. Idea submission and evaluation

[1893] User: The user inputs and sends his / her own ideas through the terminal.

[1894] Terminal: Sends submitted ideas to the server.

[1895] Server: The server receives submitted ideas and stores them in a database.

[1896] Server: Launches the evaluation AI and evaluates all submitted ideas. The evaluation AI scores each idea based on the criteria of creativity, feasibility, and marketability. In addition, the emotion engine analyzes the user's emotional data and reflects it in the evaluation.

[1897] Server: Stores the evaluation results in a database.

[1898] Examples:

[1899] A user submits an "idea for a recyclable water bottle" via their device. The device sends the idea to the server. The server receives the submitted idea and stores it in a database. The server then activates an evaluation AI, which evaluates all submitted ideas from the perspectives of "reusability," "cost-effectiveness," and "environmental protection." At this time, an emotion engine analyzes the user's emotions and gives high ratings to ideas that elicit a positive response. The server then stores the evaluation results in a database.

[1900] 3. Providing insights and suggesting prize allocations

[1901] Server: The server generates insights based on the evaluation results, including the top-rated ideas and an analysis of the evaluations.

[1902] Server: The AI ​​proposes optimal prize distribution. The proposal includes allocation based on the evaluation points of each idea and the contest budget. In addition, the emotion engine analyzes users' emotional data and reflects it in the proposal.

[1903] Server: Sends the proposal results to the terminal so that the user can check them.

[1904] Examples:

[1905] The server generates an insight based on the evaluation results and reports that "the idea for a recyclable water bottle received the highest rating." The emotion engine analyzes the user's emotions and reflects them in the insight. The server uses AI to suggest allocating the highest prize money to the idea for a recyclable water bottle. In this case, the emotion engine analyzes the user's emotional data and adds a small bonus to ideas that show a positive emotional response. The server sends the proposal results to the device, where the user can confirm them.

[1906] Summary

[1907] The system of this invention not only handles a series of processes from topic input to idea generation, idea submission and evaluation, insight generation, and prize distribution proposals, but also recognizes user emotions and reflects them in the generation, evaluation, and proposals. This significantly improves the efficiency of idea contest management and, by taking user emotions into consideration, enables contest management that is more user-friendly.

[1908] The processing flow will be explained below.

[1909] Program processing

[1910] 1. Idea generation

[1911] Step 1:

[1912] The user inputs the contest topic and constraints into the terminal and submits them.

[1913] Step 2:

[1914] The terminal sends the topic and constraints to the server.

[1915] Step 3:

[1916] The server sends the received topic and constraints to the generation AI and starts the generation AI.

[1917] Step 4:

[1918] Generative AI generates new ideas based on topics and constraints.

[1919] Step 5:

[1920] The emotion engine acquires the user's emotional data and reflects it in the generated ideas. In this case, the emotion engine analyzes the user's positive emotions and adjusts the idea generation process accordingly.

[1921] Step 6:

[1922] The server stores the generated ideas in a database.

[1923] Examples:

[1924] The user inputs the topic "new environmentally friendly products" into the device and sends it. The device sends the topic to the server. The server activates the generative AI to generate ideas for bioplastic containers made from reusable materials. The emotion engine analyzes the user's emotions and reflects this in the generation process to prioritize ideas that show a positive emotional response. The server then stores the generated ideas in a database.

[1925] 2. Idea submission and evaluation

[1926] Step 1:

[1927] Users input and send their ideas through a terminal.

[1928] Step 2:

[1929] The device sends the submitted idea to the server.

[1930] Step 3:

[1931] The server receives the submitted ideas and stores them in a database.

[1932] Step 4:

[1933] The server launches an evaluation AI to evaluate all submitted ideas.

[1934] Step 5:

[1935] The evaluation AI scores each idea based on the criteria of "creativity," "feasibility," and "marketability."

[1936] Step 6:

[1937] The emotion engine analyzes the user's emotional data and reflects it in the evaluation AI. At this time, the emotion engine analyzes the user's positive emotional reactions and adjusts the evaluation process to give a high rating.

[1938] Step 7:

[1939] The server stores the evaluation results in a database.

[1940] Examples:

[1941] A user submits an "idea for a recyclable water bottle" via their device. The device sends the idea to the server. The server receives the submitted idea and stores it in a database. The server then activates an evaluation AI, which evaluates all submitted ideas from the perspectives of "reusability," "cost-effectiveness," and "environmental protection." At this time, an emotion engine analyzes the user's emotions and gives high ratings to ideas that elicit a positive response. The server then stores the evaluation results in a database.

[1942] 3. Providing insights and suggesting prize allocations

[1943] Step 1:

[1944] The server generates insights based on the evaluation results, including the top-rated ideas and an overall evaluation analysis.

[1945] Step 2:

[1946] The emotion engine analyzes the user's emotions and reflects them in the generated insights. In this case, the emotion engine analyzes the user's emotional data and generates advantageous insights for ideas that show a positive reaction.

[1947] Step 3:

[1948] The server uses AI to propose optimal prize distribution, including the evaluation points for each idea and distribution based on the contest budget.

[1949] Step 4:

[1950] The emotion engine analyzes users' emotional data and reflects it in the prize distribution proposals, adding a little extra weight to ideas that show positive emotions.

[1951] Step 5:

[1952] The server sends the proposal results to the terminal so that the user can check them.

[1953] Examples:

[1954] The server generates an insight based on the evaluation results and reports that "the idea for a recyclable water bottle received the highest rating." The emotion engine analyzes the user's emotions and reflects them in the insight. The server uses AI to suggest allocating the highest prize money to the idea for a recyclable water bottle. In this case, the emotion engine analyzes the user's emotional data and adds a small bonus to ideas that show a positive emotional response. The server sends the proposal results to the device, where the user can confirm them.

[1955] Summary

[1956] The system of this invention not only handles a series of processes from topic input to idea generation, idea submission and evaluation, insight generation, and prize distribution proposals, but also recognizes user emotions and reflects them in the generation, evaluation, and proposals. This significantly improves the efficiency of idea contest management and, by taking user emotions into consideration, enables contest management that is more user-friendly.

[1957] Example 2

[1958] 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."

[1959] Conventional idea contest management systems have a complicated process from idea generation to evaluation, reporting, and prize distribution, making it difficult to manage the contest efficiently and with consideration for users' emotions.In addition, it is not possible to evaluate or propose ideas with consideration for users' emotions, making it difficult to manage a contest that provides high user satisfaction.

[1960] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1961] In this invention, the server includes terminal means for inputting topics and constraints, server means for generating ideas using a generative AI model, database means for saving the generated ideas, emotion engine means for analyzing emotion data, server means for activating evaluation AI means to evaluate the generated ideas, server means for accepting submitted ideas, server means for activating the evaluation AI means to provide evaluation results, AI means for generating insights based on the evaluation results and proposing optimal prize distribution, and emotion engine means for recognizing user emotions and reflecting them in evaluations and proposals. This streamlines the process from idea generation to evaluation, insight generation, and prize distribution proposals, and enables the operation of a more satisfying contest that takes user emotions into consideration.

[1962] "Terminal means" is a device that allows a user to input topics and constraints and send them to the system.

[1963] A "generative AI model" is an artificial intelligence that generates new ideas based on received topics and constraints.

[1964] The "server means" is a server device for processing and storing various data within the system.

[1965] The "database means" is a database device for storing and managing data such as generated ideas and evaluation results.

[1966] The "emotion engine means" is a system that analyzes the user's emotional data and reflects it in idea generation, evaluation, and proposals.

[1967] The "evaluation AI method" is an artificial intelligence that evaluates submitted ideas based on the criteria of creativity, feasibility, and marketability.

[1968] "Insights" refers to the analytical results and important information gained from the evaluation results.

[1969] The "AI method for proposing prize distribution" is an artificial intelligence that proposes optimal prize distribution based on evaluation results and budget.

[1970] This invention relates to a system that streamlines the process from topic input to idea generation, submission, evaluation, insight generation, and prize allocation proposals. This system includes a terminal where users input topics and constraints, a server where new ideas are generated using a generative AI model, a database where the generated ideas and evaluation results are stored, and an emotion engine that analyzes user emotion data and reflects this in idea generation and evaluation. The main components of this system and their operation are described in detail below.

[1971] Entering and sending topics and constraints

[1972] Users input the contest topic and constraints using a terminal. This terminal can be a general PC, tablet, or smartphone. The input information is sent from the terminal to the server.

[1973] Example: A user inputs the topic "New eco-friendly products" and performs a send operation. At this time, the terminal sends the input information to the server.

[1974] Idea generation

[1975] The server generates new ideas by launching a generative AI model based on the topics and constraints received from the device. This generative AI model uses machine learning technology to generate new ideas from a variety of data.

[1976] The server uses an emotion engine to analyze the user's emotion data during the generation process and reflects the data in the generated ideas.

[1977] Example: Generative AI generates ideas for "bioplastic containers made from reusable materials," while the emotion engine reflects the user's positive emotions.

[1978] Saving ideas

[1979] The server stores the generated ideas in a database.

[1980] Example: The generated ideas for bioplastic containers are stored in the "Ideas" table of the database.

[1981] Submitting and Receiving Ideas

[1982] Users input and transmit their ideas through a terminal.

[1983] The device sends the input ideas to the server, and this information is also stored and managed in a database.

[1984] Example: A user submits an idea for a recyclable water bottle via their device. This data is then sent to the server and stored in a database.

[1985] Idea Evaluation

[1986] The server launches an evaluation AI to evaluate all submitted ideas based on the criteria of creativity, feasibility, and marketability, and also reflects the user's emotional data using an emotion engine.

[1987] Example: An evaluation AI evaluates ideas for recyclable water bottles from the perspectives of "reusability," "cost-effectiveness," and "environmental protection," giving higher ratings to ideas that express positive emotions.

[1988] Providing insights and suggesting prize allocations

[1989] The server generates insights based on the evaluation results and reports them to the user. It also activates an AI that proposes optimal prize distribution based on the evaluation results. This proposal also reflects the analysis results of the emotion engine.

[1990] Example: The server reports that "the idea for a recyclable water bottle received the highest votes" and suggests optimal prize distribution taking sentiment data into account.

[1991] Prompt Sentence Examples

[1992] "Please tell me your ideas for new eco-friendly products. I'd like specific ideas for bioplastic containers made from reusable materials."

[1993] This system will significantly improve the efficiency of idea contest management and will also enable more satisfying management that takes into account user emotions.

[1994] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1995] Step 1: Enter topic and constraints and submit

[1996] User: The user inputs the contest topic and constraints into the terminal and presses the send button. This input data includes the topic and constraints in text format. Based on the input data, the terminal sends the data to the server.

[1997] Input: Topic (e.g., "New eco-friendly product"), Constraint (e.g., "Use reusable materials")

[1998] Output: A data packet containing topics and constraints

[1999] Specific operation: The user enters a topic in the text field of the terminal and clicks the send button. The terminal converts the input data into a packet format and sends it to the server as an HTTP request.

[2000] Step 2: Receive topics and constraints and launch the generation AI model

[2001] Server: The server receives the topics and constraints sent from the device. The received data contains the topics and constraints in text format. The server then launches the generative AI model based on this.

[2002] Input: Data packets of topics and constraints sent from the device

[2003] Output: Ready for idea generation

[2004] How it works: The server receives an HTTP request, extracts topics and constraints, then calls the API of the generative AI model to start the idea generation process.

[2005] Step 3: Generate ideas and reflect on sentiment data

[2006] Server: The server uses the generative AI model to generate ideas based on the received topic and constraints. It also activates the emotion engine to analyze the user's emotion data and reflect it in the generation process.

[2007] Input: Generative AI model, emotion data

[2008] Output: Generated ideas

[2009] How it works: The generative AI model generates several ideas based on a given topic. In parallel, the emotion engine analyzes the user's emotion data and filters the ideas to elicit positive emotions. Once the final idea is determined, it is output.

[2010] Step 4: Saving generated ideas

[2011] Server: Stores the generated ideas in a database, which stores the idea content and associated metadata.

[2012] Input: Generated ideas

[2013] Output: Ideas stored in a database

[2014] What happens: The server converts the generated ideas into JSON format and executes a query to insert them into the "ideas" table in the database, where they are permanently stored.

[2015] Step 5: Submit and submit your idea

[2016] User: The user inputs their idea into the terminal and presses the send button. This information is sent from the terminal to the server.

[2017] Input: User-entered ideas

[2018] Output: Data packet to be sent

[2019] Specific operation: A user fills out an idea submission form on the device and clicks the submit button. The device converts the input data into packets and sends them to the server as an HTTP request.

[2020] Step 6: Receiving and storing submissions

[2021] Server: Receives submitted ideas and stores them in a database. The received data includes the ideas entered by users.

[2022] Input: Ideas sent from your device

[2023] Output: User ideas stored in a database

[2024] What happens: The server receives the HTTP request, extracts the ideas, converts them to JSON format, and executes a query to insert them into the "Submitted Ideas" table in the database.

[2025] Step 7: Launch the evaluation AI and evaluate your ideas

[2026] Server: The server launches the evaluation AI and evaluates all ideas stored in the database. Evaluation criteria include creativity, feasibility, and marketability. In addition, it uses an emotion engine to reflect user emotional data in the evaluation.

[2027] Input: Submitted ideas, sentiment data

[2028] Output: Idea rating score

[2029] Specific operation: The server invokes the evaluation AI model and starts evaluating each idea. The generated score and emotional data are combined to calculate the final evaluation. The evaluation results are then stored in the database.

[2030] Step 8: Save the evaluation results

[2031] Server: Stores the evaluation results in a database. For each evaluated idea, a score and associated data are stored.

[2032] Input: Idea rating score

[2033] Output: Evaluation results stored in a database

[2034] Specific operation: The server converts the evaluation results into JSON format and executes a query to insert them into the "Evaluation Results" table in the database, thereby permanently storing the evaluation results.

[2035] Step 9: Analyze assessment results and generate insights

[2036] Server: Generates insights based on the evaluation results, including the top-rated ideas and an overall evaluation analysis.

[2037] Input: Evaluation result

[2038] Output: Generated insights

[2039] Specific operation: The server analyzes the evaluation results and generates insights in text format, which are temporarily stored on the server.

[2040] Step 10: Proposal for Prize Distribution

[2041] Server: Proposes optimal prize distribution using prize distribution AI. Proposals include distribution based on evaluation points and budget. It also uses an emotion engine to reflect user emotion data.

[2042] Input: Evaluation results, budget, sentiment data

[2043] Output: Prize allocation proposal

[2044] Specific operation: The server calls the prize allocation AI model and calculates the optimal prize allocation using the evaluation points and budget as input. It then modifies the proposal based on data from the emotion engine and generates the final prize allocation proposal.

[2045] Step 11: Submit your proposal

[2046] Server: Sends the prize distribution proposal to the user's device so that the user can confirm it.

[2047] Input: Prize allocation proposal

[2048] Output: Notification of proposal results

[2049] Specific operation: The server sends the generated prize distribution proposal to the terminal. The user can check the proposal result on the terminal screen. Notifications are sent to the user via email, push notifications, etc.

[2050] (Application example 2)

[2051] 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."

[2052] Conventional content delivery systems have the problem of not efficiently generating personalized content based on specific topics and constraints desired by users. Furthermore, content suggestions do not take user emotions into consideration, which may reduce user satisfaction. The objective of this invention is to provide a system that combines generation AI, evaluation AI, and an emotion engine to effectively generate, evaluate, and provide content that satisfies users.

[2053] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2054] In this invention, the server includes terminal means for inputting topics and constraints, server means for generating ideas using a generation AI, database means for saving the generated ideas, server means for accepting submissions of ideas, evaluation AI means for evaluating the submitted ideas, server means for providing the evaluation results, AI means for proposing prize distribution, emotion engine means for recognizing user emotions, and means for proposing content that matches the user's preferences based on the content ideas generated by the evaluation AI. This makes it possible to generate novel content ideas based on the user's topics and constraints, and to make personalized suggestions that reflect the user's emotions.

[2055] "Terminal means" is a device that allows a user to input topics and constraints and send them to the server.

[2056] "Generative AI" is an artificial intelligence technology that generates new ideas based on topics and constraints entered by the user.

[2057] "Server means" refers to a device that processes and manages data and on which generation AI, evaluation AI, emotion engine, etc. operate.

[2058] "Database means" is a data management system for storing generated ideas.

[2059] "Evaluation AI methods" are artificial intelligence technologies that evaluate submitted ideas based on evaluation criteria such as creativity, feasibility, and marketability.

[2060] The "emotion engine means" is a technology for recognizing and analyzing the user's emotions.

[2061] "AI method for proposing prize distribution" is an artificial intelligence technology that proposes optimal prize distribution based on the evaluation results.

[2062] "Means for suggesting content that matches the user's preferences" refers to a device or technology that suggests personalized content based on content ideas generated by evaluation AI, taking into account the user's emotional state.

[2063] A system for implementing this invention is configured as follows: A user uses a terminal (such as a smartphone) to input topics and constraints. The terminal is equipped with an interface for transmitting the topics and constraints input by the user to a server. The server activates a generation AI based on the received topics and constraints to generate new ideas. At this time, an emotion engine analyzes the user's emotion data and reflects it in the generation process. The generated ideas are stored in a database.

[2064] Next, when a user submits their own idea, they send it to the server via their device. The server receives the submitted idea and evaluates it using evaluation AI. The evaluation AI scores the idea using evaluation criteria of creativity, feasibility, and marketability. At that time, an emotion engine analyzes the user's emotional data and reflects it in the evaluation. The evaluation results are stored in a database.

[2065] The server then generates insights based on the evaluation results and reports them to the user. Furthermore, the AI ​​that proposes prize distribution proposes optimal prize distribution, and the emotion engine analyzes the user's emotional data and reflects it in the proposal. The proposal results are sent to the device so that the user can check them. There is also a means to suggest content that suits the user's preferences based on the content ideas generated by the evaluation AI, allowing the user to receive personalized content.

[2066] Hardware and software used

[2067] Hardware: Smartphone (iOS or Android device)

[2068] software:

[2069] Python 3.x

[2070] The transformers library (for generative AI and sentiment analysis)

[2071] SQLite (for storing and managing ideas)

[2072] Specific examples

[2073] Suppose a user enters the following topic and constraints into a terminal:

[2074] Topic: "The Future of Travel"

[2075] Constraint: "Consider sustainability and environmental protection"

[2076] This input is sent to the server, which then triggers a generative AI to generate ideas based on the prompt.

[2077] Example prompt sentence:

[2078] Topic: Future of Travel

[2079] Constraints: Sustainability and environmental protection

[2080] Generate ideas:

[2081] The server stores the generated ideas in a database for users to review. The emotion engine analyzes the user's emotions and reflects them in the creation and evaluation process, thereby providing content that matches the user's preferences.

[2082] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2083] Step 1:

[2084] The user inputs the topic and constraints using a terminal.

[2085] Input: The user inputs the topics and constraints "future travel" and "consider sustainability and environmental protection."

[2086] Specific operation: A screen for entering the topic and constraints appears on the terminal interface. The user enters them and presses the send button.

[2087] Output: The entered data is sent to the server.

[2088] Step 2:

[2089] The server launches a generative AI based on the received topic and constraints to generate new ideas.

[2090] Input: Topic "Future of Travel" and Constraint "Consider sustainability and environmental protection".

[2091] What it does: The server passes these inputs as prompts to the generative AI, which then generates new ideas based on these prompts (e.g., the gpt-3 model).

[2092] Output: Generated ideas (e.g. "Ecological Tours Using Sustainable Energy").

[2093] Step 3:

[2094] The emotion engine analyzes the user's emotion data and reflects it in the generation process.

[2095] Input: Generated ideas and user sentiment data.

[2096] What it does: The emotion engine analyzes the user's emotional response to the generated ideas, for example, evaluating positive or negative reactions to the text content.

[2097] Output: The sentiment analysis results are fed into the generation process.

[2098] Step 4:

[2099] The server stores the generated ideas in a database.

[2100] Input: Generated ideas and sentiment analysis results.

[2101] Specific operation: The server connects to the database means and records the generated ideas and their sentiment analysis results.

[2102] Output: The saved ideas and their sentiment analysis results are stored in a database.

[2103] Step 5:

[2104] Users input their ideas through their terminals and send them to the server.

[2105] Input: A user-generated idea (e.g., "An idea for a recyclable water bottle").

[2106] Specific operation: The idea submission screen appears on the device, and the user enters their idea and presses the submit button.

[2107] Output: The user's idea is sent to the server.

[2108] Step 6:

[2109] The server receives the submitted ideas and evaluates them using an evaluation AI.

[2110] Input: Submitted idea and evaluation criteria (creativity, feasibility, marketability).

[2111] Specific operation: The server launches the evaluation AI and scores ideas based on the evaluation criteria. The analysis results of the emotion engine are also reflected in the evaluation.

[2112] Output: The evaluated ideas and their evaluation scores.

[2113] Step 7:

[2114] The server stores the evaluation results in a database.

[2115] Input: The rated ideas and their rating scores.

[2116] What it does: The server connects to a database and records the ideas that have been rated and their rating scores.

[2117] Output: The saved rated ideas and their rating scores.

[2118] Step 8:

[2119] The server generates insights based on the evaluation results and reports them to the user.

[2120] Input: Saved evaluation results.

[2121] Specific operation: The server analyzes the evaluation results and generates insights based on the most highly rated ideas and the analysis of the evaluations.

[2122] Output: An insights report is generated and provided to the user.

[2123] Step 9:

[2124] AI proposes optimal prize distribution.

[2125] Input: Evaluation results and contest budget.

[2126] Specific operation: The proposed AI calculates the optimal prize distribution based on the evaluation results, taking into account the analysis results of the emotion engine.

[2127] Output: An optimal prize distribution proposal is generated and provided to the user.

[2128] Step 10:

[2129] Based on content ideas generated by the evaluation AI, content that matches the user's preferences is suggested.

[2130] Input: Content ideas generated by the evaluation AI and analysis data from the sentiment engine.

[2131] Specific operation: The server proposes personalized content based on the ideas generated by the evaluation AI and the user's emotional data.

[2132] Output: Personalized content suggestions sent to the user.

[2133] 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.

[2134] 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.

[2135] 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.

[2136] 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.

[2137] 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.

[2138] 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.

[2139] 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).

[2140] 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.

[2141] 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."

[2142] 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.

[2143] 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).

[2144] 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.

[2145] 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.

[2146] 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.

[2147] 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.

[2148] 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.

[2149] 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.

[2150] 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.

[2151] 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.

[2152] 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.

[2153] 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.

[2154] The following is further disclosed regarding the above embodiment.

[2155] (Claim 1)

[2156] a terminal means for inputting topics and constraints;

[2157] A server means for generating ideas by a generation AI;

[2158] a database means for storing the generated ideas;

[2159] a server means for accepting idea submissions;

[2160] an evaluation AI means for evaluating the submitted ideas;

[2161] a server means for providing an evaluation result;

[2162] AI means to propose prize distribution;

[2163] A system including:

[2164] (Claim 2)

[2165] 10. The system of claim 1, wherein the system uses the following evaluation criteria when evaluating submitted ideas: creativity, feasibility, and marketability.

[2166] (Claim 3)

[2167] 2. The system according to claim 1, further comprising server means for generating insights based on the evaluation results and reporting the insights to the user.

[2168] "Example 1"

[2169] (Claim 1)

[2170] a terminal means for inputting topics and constraints;

[2171] A server means for generating ideas by a generation AI;

[2172] a database means for storing the generated ideas;

[2173] a terminal means for accepting submissions of ideas;

[2174] a database means for storing submitted ideas;

[2175] an evaluation AI means for evaluating the submitted ideas;

[2176] a database means for storing the evaluation results;

[2177] a server means for providing an evaluation result;

[2178] a server means for generating insights based on the evaluation results and reporting the insights to the user;

[2179] AI means to propose prize distribution;

[2180] A system including:

[2181] (Claim 2)

[2182] 10. The system of claim 1, wherein the system uses the following evaluation criteria when evaluating submitted ideas: creativity, feasibility, and marketability.

[2183] (Claim 3)

[2184] 2. The system according to claim 1, further comprising server means for generating insights based on the evaluation results and reporting the insights to the user.

[2185] "Application Example 1"

[2186] (Claim 1)

[2187] a terminal means for inputting topics and constraints;

[2188] A server means for generating ideas by a generation AI;

[2189] a database means for storing the generated ideas;

[2190] a server means for accepting idea submissions;

[2191] an evaluation AI means for evaluating the submitted ideas;

[2192] a server means for providing an evaluation result;

[2193] a means for making compensation offers;

[2194] A means for users to propose product designs and concepts within the virtual store;

[2195] a means for delivering points or rewards based on the ideas generated and the evaluation results;

[2196] a means for notifying the user of the results via a smartphone, smart glasses, or head-mounted display;

[2197] A system including:

[2198] (Claim 2)

[2199] 10. The system of claim 1, wherein the system uses the following evaluation criteria when evaluating submitted ideas: creativity, feasibility, and marketability.

[2200] (Claim 3)

[2201] 2. The system according to claim 1, further comprising server means for generating insights based on the evaluation results and reporting the insights to the user.

[2202] "Example 2: Combining Emotion Engines"

[2203] (Claim 1)

[2204] A terminal means for inputting topics and constraints

[2205] A server means for generating ideas using a generative AI model;

[2206] A database means for storing generated ideas;

[2207] An emotion engine means for analyzing emotion data;

[2208] a server means for launching an evaluation AI means for evaluating the generated ideas;

[2209] a server means for accepting submitted ideas;

[2210] a server means for launching the evaluation AI means to provide the evaluation results;

[2211] AI means to generate insights based on the evaluation results and propose optimal prize allocations.

[2212] An emotion engine that recognizes user emotions and reflects them in evaluations and suggestions.

[2213] A system including:

[2214] (Claim 2)

[2215] 10. The system of claim 1, wherein the submitted ideas are evaluated using evaluation criteria of creativity, feasibility, and marketability, as well as user emotional data.

[2216] (Claim 3)

[2217] 2. The system according to claim 1, further comprising server means for generating insights based on the evaluation results and reporting the insights to the user.

[2218] "Application example 2 when combining emotion engines"

[2219] (Claim 1)

[2220] a terminal means for inputting topics and constraints;

[2221] A server means for generating ideas by a generation AI;

[2222] a database means for storing the generated ideas;

[2223] a server means for accepting idea submissions;

[2224] an evaluation AI means for evaluating the submitted ideas;

[2225] a server means for providing an evaluation result;

[2226] AI means to propose prize distribution;

[2227] emotion engine means for recognizing the emotion of a user;

[2228] A method to suggest content that matches user preferences based on content ideas generated by evaluation AI, and

[2229] A system including:

[2230] (Claim 2)

[2231] 10. The system of claim 1, wherein the system uses the following evaluation criteria when evaluating submitted ideas: creativity, feasibility, and marketability.

[2232] (Claim 3)

[2233] 2. The system according to claim 1, further comprising a server means for generating insights based on the evaluation results and reporting them to the user, wherein the evaluation and generation process reflects the emotional state of the user. [Explanation of symbols]

[2234] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a terminal means for inputting topics and constraints; A server means for generating ideas by a generation AI; a database means for storing the generated ideas; a server means for accepting idea submissions; an evaluation AI means for evaluating the submitted ideas; a server means for providing an evaluation result; AI means to propose prize distribution; A system including:

2. 10. The system of claim 1, wherein the system uses the following criteria to evaluate submitted ideas: creativity, feasibility, and marketability.

3. 2. The system according to claim 1, further comprising server means for generating insights based on the evaluation results and reporting the insights to the user.

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