System

Abstract

To provide a system.SOLUTION: A system is provided, comprising means of making a user think about content of questions, means of having the user input answers to the questions from a WEB questionnaire screen, means of storing the answers in a database, means of inputting the stored answers to a generative AI model, means of allowing the generative AI model to analyze content of the answers, means of storing an analysis result of the analysis means in the database, and means of providing a company with a report with a summary of the analysis result stored in the database.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional methods of collecting opinions for product improvement mainly consist of multiple choice questionnaires, which make it difficult to obtain users' free opinions. In addition, there is a lack of concrete incentives to increase response rates, making it difficult to collect a large amount of useful opinions. [Means for solving the problem]

[0005] The present invention solves the above problems by providing a means for users to think of questions, a means for inputting written answers on a web questionnaire screen, a means for instantly scoring the answers and offering high-value prizes according to the scores, and a means for later summarizing and providing the report to the company. In particular, by having users think of the questions in written form, it is possible to obtain free opinions from users, and by offering high-value prizes, it is possible to increase the response rate. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12]FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16] FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. [Figure 19] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system according to the embodiment 2 of the second embodiment in the case where an emotion engine is combined. [Figure 20] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when combined with an emotion engine. [Figure 21] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the embodiment 3 of the form example 3 in the case where an emotion engine is combined. [Figure 22] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0011] 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.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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 wide area network (WAN) and / or a local area network (LAN).

[0018] 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.

[0019] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] 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 (e.g., voice and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0024] 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.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] In one embodiment of the present invention, a user accesses a web questionnaire screen, thinks about the content of the questions, and answers in a descriptive format. The content of the questions may be, for example, "Which part of the product are you dissatisfied with?" or "How do you feel about the product?"

[0029] The survey asks for open opinions on improving products, such as "Which features do you think could be improved?"

[0030] "Example 2"

[0031] Next, the answer entered by the user is instantly scored by the system of the present invention. The scoring is performed based on, for example, the specificity, novelty, and feasibility of the answer. Then, depending on the scoring result, the user is presented with a high-value prize. The high-value prize may be, for example, an electronic gift or PayPay that can be received via the web.

[0032] "Example 3"

[0033] Finally, the system of the present invention summarizes the responses collected from users and provides them to the company as a report, which may include, for example, trends in opinions and specific improvement proposals for product improvement.

[0034] The process flow of each embodiment will be described below.

[0035] "Example 1"

[0036] Step 1: The user accesses the web survey screen. This screen is provided, for example, on a dedicated website or application.

[0037] Step 2: The user thinks of the question and enters a written answer. The questions ask for open-minded opinions on how to improve the product, such as "Which part of the product are you dissatisfied with?" or "Which feature of the product do you think could be improved?"

[0038] "Example 2"

[0039] Step 1: The answer entered by the user is instantly scored by the system of the present invention. The scoring is based on, for example, the specificity, novelty, and feasibility of the answer.

[0040] Step 2: Depending on the results of the scoring, users will be presented with high-value prizes, such as digital gifts or PayPay that can be received online.

[0041] "Example 3"

[0042] Step 1: The system of the present invention summarizes the responses collected from users, for example using natural language processing techniques.

[0043] Step 2: The summarized responses are provided to the company in the form of a report, which may include, for example, trends in opinions and specific improvement proposals for product improvement.

[0044] Example 1

[0045] Next, a description will be given of Example 1 of the embodiment 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".

[0046] With conventional web survey systems, it was difficult to efficiently analyze users' free-form responses and gather information that would be useful for improving products. In addition, there was a lack of a way to instantly analyze users' responses and provide the results to companies.

[0047] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a means for having a user think about the content of a question, a means for inputting a descriptive answer from a web questionnaire screen, a means for saving the answer in a database, a means for inputting the saved answer to a generative AI model, a means for the generative AI model to analyze the content of the answer, a means for saving the analysis result in a database, and a means for summarizing and providing a report to a company at a later date. This makes it possible to efficiently analyze the user's free-form answers, quickly collect information useful for improving products, and provide it to a company.

[0048] "User" means an individual or organization that uses the system to answer online surveys.

[0049] "Question content" refers to the survey questions presented to the user, soliciting their opinions on improving the product.

[0050] The "WEB survey screen" is an interface that users can access and use to enter written responses to questions.

[0051] A "descriptive response" is a survey response in which the user responds by freely entering text.

[0052] A "database" is an information management system for storing user responses and analysis results.

[0053] A "generative AI model" is an artificial intelligence model that analyzes users' written responses and extracts important keywords and opinions.

[0054] The "analysis results" are important keywords and opinions extracted by the generative AI model by analyzing user responses.

[0055] A "report" is a document summarizing the analysis results and providing them to companies.

[0056] The "company" is the organization that receives user responses and analysis results and uses them to improve products.

[0057] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0058] The purpose of this system is to allow users to access a web questionnaire screen and respond to questions in a written format. A specific embodiment of this system will be described below.

[0059] Hardware and Software Used

[0060] Server: Receives user access, generates the web survey screen, and stores the user's responses in a database. It also inputs the responses into the generative AI model, obtains the analysis results, and stores them in a database.

[0061] Terminal: The device (PC, smartphone, tablet, etc.) that the user uses to access the web survey screen and enter written responses to the questions.

[0062] Database: An information management system (e.g. MySQL (registered trademark)) for storing user responses and analysis results.

[0063] Generative AI model: An artificial intelligence model that analyzes users' written responses and extracts important keywords and opinions (e.g., GPT-4(registered trademark) by OpenAI(registered trademark)).

[0064] Program Processing

[0065] When a user accesses the web survey screen, the server displays a survey form, and the user can freely answer questions such as "Which part of the product are you dissatisfied with?" or "Which feature of the product do you think could be improved?"

[0066] When a user submits an answer, the server stores the answer in a database. The server then inputs the saved answer into a generative AI model and analyzes the answer. The generative AI model extracts important keywords and opinions from the user's answer and stores them in a database.

[0067] Examples

[0068] As a concrete example, consider the case where a user answers the question "Which part of the product are you dissatisfied with?" with "The battery life is poor." This answer is sent to the server and stored in a database. The server then inputs this answer into a generative AI model and obtains the analysis results. The generative AI model extracts the opinion that "the battery life is poor" and stores it in the database.

[0069] Here is an example of a prompt to be input to the generative AI model:

[0070] User Answer: "Battery life is poor"

[0071] Extract important keywords and opinions from these answers.

[0072] In this way, the server can efficiently analyze the users' free-form responses and gather information that will be useful in improving the product.

[0073] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0074] Step 1:

[0075] The user accesses the web survey screen.

[0076] Specifically, a user accesses a specified URL using his / her own device (PC, smartphone, tablet, etc.). The input is the user's access request, and the output is an access request to the server.

[0077] Step 2:

[0078] The server displays the survey form.

[0079] The server receives the user's access request, generates a questionnaire form using HTML and CSS, and sends it to the user's terminal. The input is the user's access request, and the output is the display of the questionnaire form.

[0080] Step 3:

[0081] The user answers the questions in a written format.

[0082] The user freely inputs text in response to the questions displayed on the questionnaire screen. For example, in response to the question "Which part of the product are you dissatisfied with?", the user inputs "The battery life is poor." The input is the user's text response, and the output is the input text data.

[0083] Step 4:

[0084] The user submits the answer.

[0085] The user checks the answer they entered and clicks the send button. This sends the answer to the server. The input is the user's send operation, and the output is the sending of the answer data to the server.

[0086] Step 5:

[0087] The server stores the answer in a database.

[0088] The server receives the answers sent by the user and stores them in a database. The stored data includes the user ID, question ID, answer content, etc. The input is the user's answer data, and the output is the save operation to the database.

[0089] Step 6:

[0090] The server inputs the stored answers into a generative AI model.

[0091] The server retrieves the answers stored in the database and inputs them to the generative AI model. Specifically, the answer content is sent to the generative AI model as a prompt sentence. The input is the answer data retrieved from the database, and the output is the sending of the prompt sentence to the generative AI model.

[0092] Step 7:

[0093] A generative AI model analyzes the answers.

[0094] A generative AI model (e.g., OpenAI's GPT-4) analyzes the input prompt sentence and extracts important keywords and opinions. For example, it extracts the keyword "Battery life is poor" from the answer "Battery life is poor." The input is the prompt sentence, and the output is the analyzed keywords and opinions.

[0095] Step 8:

[0096] The server stores the analysis results in a database.

[0097] The server receives the analysis results returned from the generative AI model and stores them in the database again. The stored data includes the analyzed keywords and opinions. The input is the analysis results from the generative AI model, and the output is the save operation to the database.

[0098] In this way, the user's free-form responses can be efficiently analyzed and information useful for improving the product can be collected.

[0099] (Application example 1)

[0100] Next, a description will be given of Application Example 1 of Form 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."

[0101] Conventional web survey systems have difficulty efficiently collecting and analyzing feedback from users. In addition, there is a lack of a way to automatically generate specific proposals to reflect users' free opinions in product improvements. This has led to problems in which companies are unable to effectively utilize user opinions, leading to a decrease in the speed and accuracy of product improvements.

[0102] 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.

[0103] In this invention, the server includes a means for having the user think of questions, a means for inputting written answers from a web questionnaire screen, a means for instantly scoring the answers and presenting high-value prizes according to the scores, a means for later summarizing and providing a report to the company, and a means for inputting the user's written answers into a generative AI model and displaying the analysis results. This makes it possible to efficiently collect feedback from users and automatically generate specific product improvement proposals based on the analysis results.

[0104] "User" means an individual or organization that uses the system to respond to surveys.

[0105] "Question content" refers to the survey questions presented to the user, soliciting their opinions on improving the product.

[0106] The "WEB survey screen" is an interface that users can access via the Internet and use to input responses to questions in written form.

[0107] "Written response" is a response format in which users can freely enter their opinions or feedback in text format.

[0108] "Scoring" is the process of assigning a score to a user's written response, evaluating the response based on its quality and content.

[0109] "High-value prizes" are rewards offered to users for their answers, and refer to high-value products such as electronic gifts or electronic payment services.

[0110] A "Report" is a document that summarizes feedback collected from users and provides it to a company.

[0111] A "generative AI model" is an artificial intelligence model that analyzes users' written responses and automatically generates specific suggestions for product improvement.

[0112] "Analysis results" are the information and suggestions obtained after the generative AI model analyzes the user's written responses.

[0113] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings.

[0114] System Programming

[0115] The system for realizing the present invention includes a program for a user to access a web survey screen and input a response in a written format, and also includes a program for inputting the user's written response to a generative AI model and displaying the analysis results.

[0116] Hardware and Software Used

[0117] Hardware: Smartphones, servers

[0118] Software: Python, OpenAI API

[0119] Data processing and calculation

[0120] 1. Data collection: The user accesses the web survey screen using a smartphone and enters answers in a written format. The server receives these answers and stores them in a database.

[0121] 2. Data analysis: The server inputs the saved user's written responses into a generative AI model (OpenAI's GPT-3 (registered trademark)). The generative AI model analyzes the input text and automatically generates specific suggestions for improving the product.

[0122] 3. Displaying the results: The server displays the analysis results obtained from the generative AI model to the user, allowing the user to see how their feedback can help improve the product.

[0123] Examples

[0124] For example, if a user enters feedback such as "This product has a short battery life," the generative AI model will generate specific improvement suggestions such as "Consider increasing the battery capacity or adding a power-saving mode."

[0125] Examples of prompt statements

[0126] User feedback: This product has a short battery life

[0127] Please provide suggestions for product improvement based on this feedback.

[0128] In this way, it is possible to efficiently collect feedback from users and automatically generate specific product improvement proposals based on the analysis results.

[0129] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0130] Step 1:

[0131] Users access the web survey screen using their smartphone and enter their answers in written form.

[0132] Input: User's written response (e.g. "This product has a short battery life")

[0133] Output: Short answer data

[0134] Specific operation: The user accesses the web survey screen and inputs answers to the questions in text format. The input answers are sent to the server.

[0135] Step 2:

[0136] The server receives the user's written responses and stores them in a database.

[0137] Input: Written response data

[0138] Output: Answer data stored in a database

[0139] Specific operation: The server stores the received user's written answers in a database. The stored data is used for subsequent analysis processing.

[0140] Step 3:

[0141] The server inputs the user's stored written answers into a generative AI model.

[0142] Input: Answer data stored in the database

[0143] Output: Data input to the generative AI model

[0144] How it works: The server retrieves the user's written answers from the database and converts them into a format that can be input into a generative AI model (OpenAI's GPT-3).

[0145] Step 4:

[0146] A generative AI model analyzes users' written responses and automatically generates specific suggestions for improving products.

[0147] Input: Data to be input to the generative AI model (e.g., "This product has a short battery life")

[0148] Output: Analysis results (e.g. "Consider increasing battery capacity or adding a power saving mode")

[0149] Specific operation: The generative AI model analyzes the user's written responses and generates specific suggestions for improving the product.

[0150] Step 5:

[0151] The server displays the analysis results obtained from the generative AI model to the user.

[0152] Input: Analysis results

[0153] Output: Analysis results that are displayed to the user

[0154] What it does: The server displays the analysis results from the generative AI model to the user, who can see how their feedback can help improve the product.

[0155] Example 2

[0156] Next, a second embodiment of the second embodiment will be described. 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."

[0157] In conventional web survey systems, it was difficult to instantly evaluate users' answers and offer high-value prizes based on the results. In addition, the evaluation criteria were vague, making it impossible to properly evaluate the specificity, novelty, and feasibility of users' answers. Furthermore, the process of quickly collating the evaluation results and notifying users was cumbersome.

[0158] 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.

[0159] In this invention, the server includes a means for having a user think of a question, a means for inputting a descriptive answer from a web survey screen, a means for using a generative AI model to instantly score the answer, a means for generating a prompt sentence and inputting it to the generative AI model, a means for tallying up the evaluation results from the generative AI model, a means for offering a high-value prize according to the score, and a means for later summarizing and providing a report to a company. This makes it possible to instantly evaluate a user's answers and offer a high-value prize based on the results.

[0160] A "user" is an individual or entity that accesses the system, formulates a question, and enters an answer.

[0161] "Questions" are themes or topics that the user should think about and are presented by the system.

[0162] The "web survey screen" is an interface for users to input written responses and is accessible via a web browser.

[0163] A "descriptive response" is a response format in which the user freely enters text.

[0164] A "generative AI model" is an artificial intelligence model used to evaluate user responses, for example using natural language processing techniques.

[0165] A "prompt" is a text sentence that is generated to provide input to a generative AI model, containing the user's answer and evaluation criteria.

[0166] "Evaluation results" refer to the scores and feedback that the generative AI model generates by analyzing the user's answers.

[0167] A "high-value prize" is a valuable reward provided based on the user's evaluation results, such as an electronic gift or credits for an electronic payment service.

[0168] A "report" is a document that summarizes user responses and evaluation results and is provided to companies.

[0169] "Company" refers to a legal entity or organization that uses the system to collect responses from users and receive the analysis results.

[0170] This invention is a system that instantly evaluates answers entered by users and offers high-value prizes based on the results. The system is implemented using a server, a terminal, and a generative AI model.

[0171] First, a user accesses the system using a terminal and inputs a descriptive answer into the web survey screen. For example, a user inputs "solar-powered portable charger" as an "idea for a new eco-friendly product."

[0172] The server receives the answers entered by the user and stores them in text format. The server then converts the answers into a suitable format for input to the generative AI model, specifically by removing unnecessary whitespace and special characters and cleaning up the text.

[0173] The server generates a prompt sentence to be input to the generative AI model. The prompt sentence includes the user's answer and evaluation criteria (concreteness, novelty, feasibility). For example, the following prompt sentence is generated:

[0174] Enter your answer below:

[0175] Answer: A portable solar charger

[0176] Evaluation criteria: Specificity, novelty, feasibility

[0177] The server inputs the prompt sentence into a generative AI model (e.g., a model using natural language processing technology). The generative AI model analyzes the prompt sentence and generates a score based on each evaluation criterion. For example, it may generate a score of 8 points for concreteness, 7 points for novelty, and 9 points for feasibility.

[0178] The server receives the scores returned by the generative AI model and sums up the scores for each evaluation criterion, for example a total of 24 points. The server then notifies the user of the final evaluation result. Depending on the evaluation result, the user is given a large prize, for example an electronic gift or credits for an electronic payment service are sent to the user's account.

[0179] This system makes it possible to instantly evaluate users' answers and offer high-value prizes based on the results. In addition, the evaluation criteria are clear, and the concreteness, novelty, and feasibility of users' answers can be appropriately evaluated. Furthermore, the process of quickly collating evaluation results and notifying users is streamlined.

[0180] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0181] Step 1:

[0182] The user accesses the system using a terminal and inputs descriptive responses via the web survey screen.

[0183] Input: The question and answer the user thought of (e.g., "Portable charger powered by solar power")

[0184] Output: The user's answer is sent to the system.

[0185] Step 2:

[0186] The server receives the answers entered by the user and stores them in text format.

[0187] Input: Answer data submitted by the user

[0188] Output: Saved text data

[0189] Step 3:

[0190] The server converts the received answers into a suitable format for input to the generative AI model, specifically cleaning up the text by removing unnecessary whitespace and special characters.

[0191] Input: Saved text data

[0192] Output: Cleaned text data

[0193] Step 4:

[0194] The server generates a prompt sentence to be input to the generative AI model. The prompt sentence includes the user's answer and evaluation criteria (concreteness, novelty, feasibility).

[0195] Input: Cleaned text data

[0196] Output: Generated prompt (e.g., "Please enter the user's answer below: Answer: Portable solar charger Evaluation criteria: Specificity, novelty, feasibility")

[0197] Step 5:

[0198] The server inputs this prompt into a generative AI model, which analyzes the prompt and generates a score based on each evaluation criterion.

[0199] Input: Generated prompt text

[0200] Output: Evaluation score from the generative AI model (e.g. concreteness 8 points, novelty 7 points, feasibility 9 points)

[0201] Step 6:

[0202] The server receives the scores returned by the generative AI model and aggregates the scores for each evaluation criterion.

[0203] Input: Evaluation scores from a generative AI model

[0204] Output: Aggregated final score (e.g. 24 points total)

[0205] Step 7:

[0206] The server notifies the user of the final evaluation result. Depending on the evaluation result, the user is given a large prize, for example, an electronic gift or credits for an electronic payment service are sent to the user's account.

[0207] Input: Final scores tallied

[0208] Output: Notify the user and provide a high-value prize (e.g., send an e-gift)

[0209] (Application example 2)

[0210] Next, a description will be given of Application Example 2 of Form 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."

[0211] In conventional survey systems, it was difficult to instantly evaluate users' responses and provide high-value prizes based on the results. In addition, the evaluation criteria for responses were vague, and feedback to users was often delayed. Furthermore, creating reports to be provided to companies was time-consuming and inefficient. To solve these problems, a system that can instantly evaluate users' responses and provide high-value prizes based on the scores is needed.

[0212] 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.

[0213] In this invention, the server includes a means for having the user think of questions, a means for inputting descriptive answers from a web questionnaire screen, a means for instantly scoring the answers using a generative AI model, a means for presenting a high-value prize according to the score, and a means for later summarizing and providing the report to the company. This makes it possible to instantly evaluate the user's answers and provide a high-value prize according to the score. In addition, the report can be efficiently provided to the company.

[0214] "User" means an individual or entity that uses the system to answer questions.

[0215] "Question content" refers to questions thought up by the user or questions in a survey or quiz provided by the system.

[0216] The "WEB survey screen" is an interface that allows users to answer questions via the Internet.

[0217] "Written response" refers to a format in which users can freely enter text to answer questions.

[0218] A "generative AI model" is an algorithm or program that uses artificial intelligence to evaluate and score user responses.

[0219] "Instant scoring" is the process of evaluating and scoring answers immediately after a user enters the answer.

[0220] A "high value prize" is a valuable reward provided depending on the score of a user's answer.

[0221] An "e-gift" is a gift in digital form that can be received over the Internet.

[0222] An "electronic payment service" is a service for conducting monetary transactions over the Internet.

[0223] A "Report" is a report summarizing the results of users' responses and is provided to companies.

[0224] "Company" refers to a legal entity or organization that uses the system to receive survey results and reports.

[0225] The system for implementing this invention allows users to think up questions, input descriptive answers on a web survey screen, and instantly score the answers using a generative AI model. High-value prizes are awarded according to the scores, and a summary report is later provided to the company.

[0226] Hardware and Software Used

[0227] Hardware: Smartphones, servers

[0228] Software: Python, OpenAI API, Internet connection

[0229] Data processing and calculation

[0230] User Actions

[0231] Users access the online survey screen using their smartphones, think about the question, and enter their answer in a descriptive format. For example, to the question, "What is the diameter of the Earth?", the user answers, "About 12,742 kilometers."

[0232] Server Processing

[0233] 1. Getting the answer: The server receives the written answer submitted by the user.

[0234] 2. Scoring by the generative AI model: The server uses the OpenAI API to input the user's answer into the generative AI model and instantly scores it. For example, the server sends a prompt message to the generative AI model: "Please score the following answer: Approximately 12,742 kilometers."

[0235] 3. Score determination: Determine the user's score based on the score returned by the generative AI model.

[0236] 4. Offering prizes: Offering high-value prizes to users depending on their scores. For example, if the score is 90 or above, they will receive electronic gifts, and if the score is 80 or below, they will receive points for electronic payment services.

[0237] Report creation and delivery

[0238] The server summarizes the users' answers and provides the company with a report that includes information such as the answers the users gave, their scores, and the types of prizes they received.

[0239] Examples

[0240] If a user answers the question "What is the diameter of the Earth in kilometers?" with "About 12,742 kilometers," the server sends this answer to a generative AI model, which scores it using the following prompt:

[0241] Examples of prompt statements

[0242] Please rate the following answer: Approximately 12,742 kilometers

[0243] The generative AI model scores the answers, and if it scores 90 points, for example, the server provides the user with an e-gift. It also summarizes the results and provides them to the company in a report.

[0244] In this way, a system is realized that instantly evaluates users' answers and offers high-value prizes according to the scores.

[0245] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0246] Step 1:

[0247] Users access the web survey screen using their smartphone, think about the questions they want to ask, and enter their answers in descriptive format.

[0248] Input: User's written answer

[0249] Output: Answer data sent to the server

[0250] Specific behavior: In response to the question "What is the diameter of the Earth in kilometers?", the user enters "About 12,742 kilometers."

[0251] Step 2:

[0252] The server receives the written response submitted by the user.

[0253] Input: User's written response data

[0254] Output: Answer data stored in the answer database on the server

[0255] What happens: The server stores the user's answer in a database and prepares it for further processing.

[0256] Step 3:

[0257] The server uses a generative AI model to instantly score the user's answers.

[0258] Input: User written answers stored in the database

[0259] Output: The score returned by the generative AI model.

[0260] What happens: The server calls the OpenAI API, sends the prompt "Please score the following answer: Approximately 12,742 kilometers" to the generative AI model, and gets a score.

[0261] Step 4:

[0262] The server determines the user's score based on the score returned by the generative AI model.

[0263] Input: The score returned by the generative AI model

[0264] Output: User score

[0265] Specific operation: The server analyzes the score returned by the generative AI model and determines it to be, for example, 90 points.

[0266] Step 5:

[0267] The server will award users with large prizes depending on their scores.

[0268] Input: User's score

[0269] Output: Large prize given to the user

[0270] Specific operation: The server provides electronic gifts to users with a score of 90 or above, and points for electronic payment services to users with a score of 80 or below.

[0271] Step 6:

[0272] The server summarizes the users' responses and provides a report to the company.

[0273] Input: User's answer, points, prize type

[0274] Output: Reports provided to companies

[0275] What it does: The server summarizes the users' answers and provides them to the company as a report, including information about the users' answers, their scores, and the types of prizes awarded.

[0276] Example 3

[0277] Next, a third embodiment of the third embodiment will be described. 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."

[0278] Conventional survey systems have difficulty efficiently summarizing responses collected from users and providing them to companies. In addition, there is a lack of incentives to improve the quality of responses, and a method to increase users' willingness to participate was needed. Furthermore, preprocessing and summarizing response data takes a lot of time and effort, making it difficult to quickly create reports.

[0279] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0280] In this invention, the server includes a means for having a user think about the content of a question, a means for inputting a descriptive answer from a web survey screen, a means for instantly scoring the answers and presenting a high-value prize according to the score, a means for saving the answers in a database, a means for preprocessing the saved answer data, a means for sending a prompt sentence to the generative AI model, a means for compiling the summary returned from the generative AI model into a report, and a means for providing the report to the company. This makes it possible to efficiently summarize the user's answers and quickly provide them to the company. Also, by providing a high-value prize, it is possible to increase the user's motivation to participate.

[0281] "User" means an individual or organization that uses the system to respond to surveys.

[0282] "Question content" refers to the questions in the survey presented to the user.

[0283] The "web survey screen" is an interface that users can access via the Internet to answer questions.

[0284] A "descriptive response" is a survey response in which the user responds by freely entering text.

[0285] "Instant scoring" is the process of rating and scoring an answer immediately after the user enters the answer.

[0286] A "Big Prize" is a high value reward offered to a user for their answer.

[0287] "Database" refers to an information management system for storing user response data.

[0288] "Preprocessing" is the process of tokenizing, removing stop words, stemming, and other processing of answer data before inputting it into a generative AI model.

[0289] A "generative AI model" is an artificial intelligence model used to summarize a user's answers.

[0290] A "prompt sentence" is an input sentence that gives instructions to a generative AI model.

[0291] A "summary" is a text that briefly summarizes the user's response.

[0292] A "Report" is a document that compiles summarized User responses and provides them to the Company.

[0293] A "company" is an organization that uses user response data to improve products and services.

[0294] This invention is a system that efficiently summarizes responses collected from users and provides them to companies. The system is composed of users, terminals, and a server.

[0295] Users access the survey form using a web browser or a mobile app and enter descriptive responses, such as, "The design of this product is great, but the battery life is poor. I'd like you to improve that."

[0296] The device sends the entered answer to the server as an HTTP POST request, with the answer data sent in JSON format.

[0297] The server saves the received response data in a database. When saving, metadata such as user ID and timestamp are also recorded. The database is a common database management system such as MySQL or PostgreSQL.

[0298] The server then takes the stored answer data and pre-processes it using spaCy, an NLP library. This pre-processing includes tokenization, stop word removal, stemming, etc. For example, it converts "The design of this product is great, but the battery life is poor. I hope they improve it" into "Great design, poor battery life, improved."

[0299] The preprocessed data is input to GPT-3, a generative AI model. The server generates a prompt for GPT-3 and instructs it to summarize the user's answer. For example, the prompt can be "Summarize the following user's opinion: The design of this product is great, but the battery life is poor. I would like you to improve it."

[0300] The summary results returned by GPT-3 are collected by the server and formatted into a report. This report includes opinion trends and specific improvement suggestions for product improvement. Finally, the server provides this report to companies. Companies can use this report to improve their products and services.

[0301] This system makes it possible to efficiently summarize the user's answers and quickly provide them to the company. In addition, by offering high-value prizes, it is possible to increase the user's motivation to participate. The flow of the identification process in the third embodiment will be described with reference to FIG. 15.

[0302] Step 1:

[0303] The user enters the answer

[0304] A user accesses the survey form using a web browser or a mobile app and enters a descriptive response. For example, the user might enter, "The design of this product is great, but the battery life is poor. I hope they can improve it." The input data is in text format.

[0305] Step 2:

[0306] The device sends the answer to the server

[0307] The terminal sends the entered answer to the server as an HTTP POST request. At this time, the answer data is sent in JSON format. For example, the following data is sent: {"user_id": 123, "response": "The design of this product is great, but the battery life is poor. I hope they will improve this."}. The input is JSON format data, and the output is a request sent to the server.

[0308] Step 3:

[0309] The server stores the answer in a database

[0310] The server saves the received response data in a database. When saving, metadata such as the user ID and timestamp are also recorded. For example, execute the following query in a MySQL table: INSERT INTO responses (user_id, response, timestamp) VALUES (123, 'The design of this product is great, but the battery life is poor. I hope they improve it.', '2023-10-01 12:34:56'). The input is JSON format data, and the output is saved in the database.

[0311] Step 4:

[0312] The server preprocesses the answer data

[0313] The server retrieves answer data from the database and performs preprocessing using spaCy, an NLP library. This preprocessing includes tokenization, stop word removal, stemming, etc. For example, "The design of this product is great, but the battery life is poor. I hope it will be improved." is converted to "Great design, poor battery life, improvement." The input is text data retrieved from the database, and the output is the preprocessed text data.

[0314] Step 5:

[0315] The server sends a prompt to the generative AI model.

[0316] The server generates a prompt sentence based on the preprocessed data and sends it to the generative AI model (GPT-3). For example, it generates a prompt sentence such as "Summarize the following user opinions: Design is great Battery life is bad Improve." The input is the preprocessed text data, and the output is the prompt sentence sent to the generative AI model.

[0317] Step 6:

[0318] Generative AI model returns summary

[0319] The generative AI model (GPT-3) generates a summary based on the prompt sentence and returns it to the server. For example, it generates a summary such as "The design is good, but the battery life is poor." The input is the prompt sentence, and the output is the summarized text data.

[0320] Step 7:

[0321] The server summarizes the results into a report

[0322] The server collects the summaries returned by the generative AI model and compiles them into a report along with the opinions of other users. The report includes trends in opinions and specific suggestions for improvement. For example, it shows a trend that "many users appreciate the design, but are dissatisfied with the battery life." The input is summarized text data, and the output is a document in the form of a report.

[0323] Step 8:

[0324] The server provides the report to the company.

[0325] The server generates the completed report in PDF or Excel format and sends it to the company by email. The company uses this report to improve its products. The input is a document in report format, and the output is the provision of the report to the company.

[0326] (Application example 3)

[0327] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0328] With conventional survey systems, it was difficult to efficiently collect feedback from users and provide it in a form that was useful to the company. In addition, there was a problem that it was difficult to quickly improve products because it took a lot of time and effort to summarize and analyze the feedback. Furthermore, there was a need to generate reports that accurately reflected user opinions, but there was a problem that it was difficult to achieve this.

[0329] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0330] In this invention, the server includes means for having users think of questions, means for inputting written answers from a web questionnaire screen, means for instantly scoring answers and presenting high-value prizes according to scores, means for later summarizing and providing a report to companies, means for summarizing feedback collected from users using a generative AI model, means for generating a report including opinion trends and specific improvement proposals based on the summarized feedback, and means for providing the generated report to companies. This makes it possible to efficiently collect user feedback and quickly and accurately summarize and analyze it before providing it to companies.

[0331] A "User" is an individual or organization that uses the system to formulate questions and provide feedback.

[0332] "Question content" refers to the questions that the user thinks of in the survey, and is entered in descriptive form.

[0333] The "WEB survey screen" is an interface that allows users to enter questions online and write their answers.

[0334] A "descriptive answer" is an answer in which the user freely enters the answer in text.

[0335] "Scoring" is the process of assigning a score to a user's answer and is done instantly.

[0336] "High-value prizes" are rewards given to users for their answers, and can include electronic gifts and electronic payment services.

[0337] "Summarization" is the process of concisely summarizing the feedback collected from users.

[0338] A "report" is a document prepared based on summarized feedback, including trends in opinions and specific suggestions for improvement.

[0339] "Company" means a legal entity or organization that receives User Feedback and uses it to improve its products and services.

[0340] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate and summarize text data.

[0341] "Opinion trends" are common opinions or patterns that emerge based on collected feedback from users.

[0342] "Specific improvement proposals" are specific improvements to products or services proposed based on user feedback.

[0343] As an embodiment of the present invention, the following system is constructed.

[0344] System Configuration

[0345] The system includes a user terminal, a server, and a generative AI model. The user terminal is a device such as a smartphone or PC, and is used by users to think of questions and input descriptive answers on a web survey screen. The server is responsible for processing the feedback collected from users, summarizing it using the generative AI model, and generating a report.

[0346] Hardware and Software Used

[0347] Hardware: Smartphones, PCs, servers

[0348] Software: Python, OpenAI API, WEB survey system

[0349] Data processing and calculation

[0350] 1. Data collection: The user enters descriptive responses into the web survey screen from their device. This sends the user's feedback to the server.

[0351] 2. Data Summarization: The server summarizes the collected feedback using a generative AI model (e.g., GPT-3) by using the OpenAI API to concisely summarize the feedback.

[0352] 3. Report generation: Based on the summarized feedback, the server generates a report containing opinion trends and specific improvement suggestions. This report is provided to the company.

[0353] Examples

[0354] For example, if a user enters feedback such as "This product is very easy to use," the server collects this along with other feedback and summarizes it using a generative AI model. The summarized feedback is compiled into a report in the form of "Many users would like to see improvements in price and design."

[0355] Examples of prompt statements

[0356] Below are examples of prompts to be input to the generative AI model.

[0357] Please summarize your feedback below:

[0358] This product is very easy to use.

[0359] I think it would be better if the design was a little better.

[0360] The price is high, so I wish it was a little cheaper.

[0361] There are too many functions and it's hard to understand how to use it.

[0362] In this way, user feedback can be collected efficiently, quickly and accurately summarized, analyzed, and provided to companies.

[0363] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0364] Step 1:

[0365] The user accesses the web survey screen, thinks about the questions to be asked, and enters a descriptive answer.

[0366] Input: The question and short answer entered by the user.

[0367] Output: User feedback data sent to the server.

[0368] Specific operation: The user accesses the web survey screen using a smartphone or PC, thinks about the questions, and enters descriptive answers. The entered data is sent to the server.

[0369] Step 2:

[0370] The server receives the feedback data sent by the user and stores it in a database.

[0371] Input: Feedback data submitted by the user.

[0372] Output: Feedback data stored in a database.

[0373] Specific operation: The server receives the feedback data sent by the user and stores it in a database for later processing.

[0374] Step 3:

[0375] The server retrieves feedback data from the database and generates prompt sentences to input into the generative AI model.

[0376] Input: Feedback data stored in the database.

[0377] Output: A prompt to be fed into the generative AI model.

[0378] Specific behavior: The server retrieves feedback data from the database and generates a prompt sentence like the following: "Please summarize the following feedback: This product is very easy to use. It would be even better if it was better designed. It is expensive, so I wish it was a little cheaper. There are too many functions and it is difficult to understand how to use it."

[0379] Step 4:

[0380] The server uses a generative AI model (e.g., GPT-3) to input prompts and summarize the feedback data.

[0381] Input: The generated prompt statement.

[0382] Output: Summarised feedback data.

[0383] Specific operation: The server inputs a prompt sentence into the generative AI model and summarizes the feedback data. The generative AI model summarizes the feedback data concisely based on the input prompt sentence.

[0384] Step 5:

[0385] The server generates a report based on the summarized feedback data, including trends in opinions and specific suggestions for improvement.

[0386] Input: Summarised feedback data.

[0387] Output: A report containing opinion trends and concrete improvement suggestions.

[0388] Specific actions: The server analyzes the summarized feedback data, extracts opinion trends and specific improvement suggestions, and generates a report to provide to the company.

[0389] Step 6:

[0390] The server provides the generated reports to the enterprise.

[0391] Input: A report containing opinion trends and specific suggestions for improvement.

[0392] Output: Reports provided to the company.

[0393] Specific operation: The server provides the generated report to the company, which uses the report to improve its products and services.

[0394] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0395] "Example 1"

[0396] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system receives a written response from a user and recognizes the emotion contained in the response. The emotion engine uses natural language processing technology to extract emotion from the user's written response. Specifically, if the user responds that "the product's design feels outdated," the emotion engine recognizes the negative emotion of "dissatisfaction" from this response.

[0397] "Example 2"

[0398] The emotion engine also has a function of measuring the intensity of a user's emotion. The intensity of emotion is calculated based on, for example, the intensity of vocabulary indicating emotion contained in the written answer, the proportion of the vocabulary in the entire answer, etc. For example, the expression "very dissatisfied" is determined to have a higher intensity of emotion than the expression "slightly dissatisfied."

[0399] "Example 3"

[0400] Furthermore, the results of the emotion engine are reflected in reports generated by the system. The reports provide companies with information such as the trends and strength of users' emotions, as well as the factors that trigger specific emotions. This allows companies to obtain specific guidelines for improving their products. For example, if negative emotions such as "the design is outdated" are detected from many users, companies can consider revising the product design.

[0401] The process flow of each embodiment will be described below.

[0402] "Example 1"

[0403] Step 1: User enters written responses into a web survey.

[0404] Step 2: The system receives the user's written response and sends it to the emotion engine.

[0405] Step 3: The emotion engine extracts emotions from the written responses and returns the results to the system.

[0406] Step 1: The sentiment engine extracts sentiment from written responses.

[0407] Step 2: The sentiment engine calculates the intensity of the sentiment, based on factors such as the strength of the sentiment vocabulary and its proportion in the overall response.

[0408] Step 3: The emotion engine calculates the emotion intensity and returns it to the system.

[0409] "Example 3"

[0410] Step 1: The system receives the results from the emotion engine.

[0411] Step 2: The system compiles a report on the trends and intensity of emotions, as well as factors that trigger specific emotions.

[0412] Step 3: Provide the system-generated reports to the business.

[0413] Example 1

[0414] Next, a description will be given of Example 1 of the embodiment 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".

[0415] Conventional web survey systems have difficulty extracting emotions from users' free-form responses and conducting analysis based on those emotions. They also lack a mechanism for instantly scoring users' responses and offering high-value prizes according to the scores. This has led to problems such as a decline in user satisfaction and the quality of feedback for companies' product improvements.

[0416] 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.

[0417] In this invention, the server includes a means for having a user think about the content of a question, a means for inputting a written response from a web questionnaire screen, a means for instantly scoring the response and presenting a high-value prize according to the score, a means for later summarizing and providing a report to the company, a means for receiving the user's written response and using an emotion engine for recognizing the emotion contained in the response, a means for the emotion engine to extract emotion from the user's response using natural language processing technology, and a means for storing the extracted emotion in a database. This makes it possible to extract emotion from the user's free-form response and perform analysis based on the emotion, thereby improving user satisfaction and improving the quality of feedback for company product improvements.

[0418] "User" refers to an individual or organization that accesses the web survey screen and enters written responses to the questions.

[0419] "WEB survey screen" refers to a web page that users can access via a browser and that provides an interface for entering written responses to questions.

[0420] "Written answers" refers to answers in which the user freely enters text.

[0421] "Sentiment Engine" refers to a software module for extracting emotions from a user's written responses.

[0422] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate human language.

[0423] "Database" refers to an information management system for storing extracted emotions and user response data.

[0424] "Big Prizes" refers to valuable rewards that are awarded according to the score that is instantly scored for a user's answers.

[0425] An "electronic gift" refers to a digital gift that can be received online.

[0426] "Electronic payment service" refers to a service that enables monetary transactions to be conducted over the Internet.

[0427] This invention is a system in which users access a web survey screen and input written responses to questions. This system has the function of instantly scoring the user's responses and presenting high-value prizes according to the scores. It also has the function of extracting emotions from the user's written responses and storing those emotions in a database.

[0428] The server generates a program that allows the user to access the web survey screen. This program includes an interface that allows the user to enter answers in a descriptive format. The interface is built using web technologies such as HTML and JavaScript (registered trademark).

[0429] When the user enters a response in written form, the device sends the response to the server. The server passes the received response to the emotion engine. The emotion engine uses natural language processing technology to extract emotions from the user's response. This emotion engine is implemented using, for example, Python's NLTK library or the Google (registered trademark) Cloud Natural Language API.

[0430] For example, if a user answers, "The design of the product feels old-fashioned," the emotion engine extracts the emotion of "dissatisfaction" from the answer. This recognized emotion is returned to the server, which stores the result in a database.

[0431] As a concrete example, consider a case where a user answers the question, "Which part of the product are you dissatisfied with?" with, "The design of the product feels old-fashioned." In this case, the emotion engine extracts the emotion, "dissatisfied."

[0432] Examples of prompts to be input to a generative AI model include:

[0433] User response: "The product design seems outdated"

[0434] Sentiment engine output: "Unhappy"

[0435] This prompt sentence can be used to provide training data for the generative AI model to improve the accuracy of emotion recognition.

[0436] This system can extract emotions from users' free-form responses and perform analysis based on those emotions, which can improve user satisfaction and the quality of feedback for companies to improve their products.

[0437] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0438] Step 1:

[0439] The user accesses the web survey screen.

[0440] The user accesses the specified URL using a browser. This URL is a link to the web survey page hosted by the server. The input is the access request from the user's browser, and the output is the display of the web survey page.

[0441] Step 2:

[0442] The user inputs a written response to the question.

[0443] A user freely enters their opinion in a text box in response to questions displayed on a web survey screen. For example, in response to the question "Which part of the product are you dissatisfied with?", the user enters "The design of the product seems old-fashioned." The input is the user's response text, and the output is the click event of the send button.

[0444] Step 3:

[0445] The terminal transmits the user's answer to the server.

[0446] When the user enters an answer and clicks the send button, the terminal sends the answer data to the server as an HTTP POST request, which includes the user's answer text. The input is the user's answer text, and the output is an HTTP POST request to the server.

[0447] Step 4:

[0448] The server passes the received answer to the emotion engine.

[0449] The server passes the user's answer received from the terminal to the emotion engine for analysis. The emotion engine is a module that runs in the server and extracts emotions using natural language processing technology. The input is the user's answer text, and the output is the data transfer to the emotion engine.

[0450] Step 5:

[0451] The sentiment engine extracts sentiment from user responses.

[0452] The emotion engine analyzes the user's response text and extracts emotions. For example, it extracts the emotion "dissatisfied" from the user's response "The product's design feels outdated." This process uses Python's NLTK library and Google Cloud Natural Language API. The input is the user's response text, and the output is the extracted emotion data.

[0453] Step 6:

[0454] The server stores the extracted emotions in a database.

[0455] The server receives the emotion data returned by the emotion engine and stores it in a database. The stored data includes the user's answer text and the extracted emotion. The input is the extracted emotion data, and the output is the save operation to the database.

[0456] (Application example 1)

[0457] Next, a description will be given of Application Example 1 of Form 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."

[0458] With conventional web survey systems, it was difficult to accurately grasp the nuances of emotions when collecting user feedback. There was also a lack of means to instantly analyze user feedback and provide it as useful information for companies. Furthermore, incentives for users were limited, making it difficult to improve the quality and quantity of feedback.

[0459] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for asking the user to think of the question content, a means for inputting a descriptive answer from a web questionnaire screen, a means for using an emotion engine for recognizing the emotion contained in the answer, a means for instantly scoring the answer and presenting a high-value prize according to the score, and a means for later summarizing and providing a report to the company. This makes it possible to accurately extract emotions from the user's feedback, analyze it instantly, and provide it to the company as useful information. Furthermore, by providing a high-value prize, the quality and quantity of the user's feedback can be improved.

[0460] A "user" is an individual or group that uses the system to formulate questions and enter written responses.

[0461] "Question content" refers to a theme or topic on which a user can write their opinion or impression regarding a product or service.

[0462] The "WEB survey screen" is an interface that users can access via the Internet and use to enter written responses.

[0463] A "descriptive answer" is an answer in which the user freely enters text to express their opinions or thoughts.

[0464] An "emotion engine" is software or a system for extracting and recognizing emotions from a user's written responses using natural language processing techniques.

[0465] "Scoring" is the process of evaluating and assigning a score to a user's written response.

[0466] A "high-value prize" is a valuable reward offered to a user in return for their answer, and may include electronic gifts or electronic payment services.

[0467] "Report" means a document or data summarizing User feedback and providing it to Company.

[0468] "Company" means an organization or legal entity that receives User Feedback and uses it to improve its products and services.

[0469] A system for implementing the present invention allows a user to access a web survey screen and input opinions about products and services in a written format. The system includes an emotion engine that extracts and analyzes emotions from the user's written responses. A specific embodiment of this system will be described below.

[0470] First, the user accesses the online survey screen using a device such as a smartphone or tablet. The user thinks of questions to ask about the product or service and enters their answers in a descriptive format. The answers entered by the user are then sent to the server in real time.

[0471] The server passes the received written response to the emotion engine, which uses natural language processing technology to extract the emotion. The emotion engine uses the TextBlob library to recognize positive, negative, or neutral emotions from the user's response. For example, if a user responds, "The design of this product feels old-fashioned," the emotion engine extracts the emotion "negative" from the response.

[0472] The server then scores the answers based on the extracted emotions and offers high-value prizes based on the scores. The high-value prizes include electronic gifts and electronic payment services. Users can receive these prizes online.

[0473] In addition, the server summarizes the user feedback and provides it to the company as a report, which includes the results of the user's sentiment analysis and the specific feedback content. The company can use this report to improve its products and services.

[0474] As a concrete example, consider the case where a user enters the following feedback:

[0475] User ID: 12345

[0476] Product ID: 67890

[0477] Feedback: The design of this product seems outdated.

[0478] This feedback is sent to a server, where it is recognized as "negative" by the sentiment engine. The server then scores the feedback and awards the user with a high-value prize. It also provides the company with a report summarizing the feedback and sentiment analysis results.

[0479] In this way, it is possible to accurately extract emotions from user feedback, analyze them in real time, and provide the information to companies as useful information. In addition, by offering high-value prizes, the quality and quantity of user feedback can be improved.

[0480] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0481] Step 1:

[0482] The user uses a terminal to access the web survey screen. The user thinks of questions to ask about products and services, and enters answers in descriptive format. The entered answers are sent from the terminal to the server. The input data includes the user ID, product ID, and feedback content.

[0483] Step 2:

[0484] The server passes the received written responses to the emotion engine. The emotion engine uses the TextBlob library to extract emotions from the user's responses. Specifically, it uses natural language processing techniques to recognize positive, negative, and neutral emotions in the feedback content. The input data is the feedback content, and the output data is the extracted emotions.

[0485] Step 3:

[0486] The server scores the answers based on the extracted emotions. The scoring algorithm assigns scores according to the type of emotion and the content of the feedback. For example, positive emotions are given high scores and negative emotions are given low scores. The input data are the extracted emotions and the output data are the scoring results.

[0487] Step 4:

[0488] The server provides high-value prizes according to the scoring results. The high-value prizes include electronic gifts and electronic payment services. Users can receive these prizes on the web. The input data is the scoring results, and the output data is the prizes provided.

[0489] Step 5:

[0490] The server summarizes the user feedback and provides it to the company as a report. This report includes the results of the user's sentiment analysis and the specific feedback content. The company can use this report to improve its products and services. The input data is the feedback content and the sentiment analysis results, and the output data is the summarized report.

[0491] In this way, it is possible to accurately extract emotions from user feedback, analyze them in real time, and provide the information to companies as useful information. In addition, by offering high-value prizes, the quality and quantity of user feedback can be improved.

[0492] Example 2

[0493] Next, a second embodiment of the second embodiment will be described. 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."

[0494] In conventional survey systems, it was difficult to instantly evaluate users' answers and provide appropriate feedback and rewards. In addition, there was a lack of automated means to evaluate the concreteness, novelty, and feasibility of answers, and the ability to measure the intensity of emotions was also lacking. This made it difficult to motivate users and was unable to provide useful data to companies.

[0495] 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.

[0496] In this invention, the server includes a means for having a user think about the content of a question, a means for inputting a descriptive answer from a web questionnaire screen, a means for instantly scoring the answers and offering a high-value prize according to the score, a means for analyzing the answers using natural language processing technology, a means for evaluating the concreteness, novelty, and feasibility of the answers using a generative AI model, a means for measuring the intensity of emotions using an emotion engine, a means for generating a scoring result and notifying the user, a means for offering a high-value prize, and a means for later summarizing and providing a report to a company. This makes it possible to instantly evaluate the user's answers and provide appropriate feedback and rewards.

[0497] "User" means an individual or entity that uses the system to answer questions.

[0498] The "question content" is the question or challenge presented to the user.

[0499] The "WEB survey screen" is an interface that users can access via the Internet to answer questions.

[0500] "Descriptive response" is a format in which the user answers by freely entering text.

[0501] "Scoring" is the process of evaluating and assigning a score to a user's answer.

[0502] A "high value prize" is a valuable reward that is provided based on the evaluation of a user's answer.

[0503] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0504] A "generative AI model" is a model that uses artificial intelligence to evaluate user answers.

[0505] "Specificity" is a rating scale that indicates how detailed and clear a user's answer is.

[0506] "Novelty" is a criterion that indicates how new ideas or perspectives a user's answer contains.

[0507] "Feasibility" is a criterion that indicates how realistically possible the user's answer is.

[0508] An "emotion engine" is a technology for measuring the intensity of emotions contained in a user's responses.

[0509] "Emotional intensity" is an indicator of how strong the emotion contained in a user's response.

[0510] The "scoring result" is the overall evaluation result of the user's answers.

[0511] "Notification" is the process of communicating information about scoring results and rewards to users.

[0512] A "report" is a report summarizing users' responses and their evaluation results.

[0513] "Company" means the organization that receives the Report for use in utilizing the user response data.

[0514] The present invention provides a system for instantly evaluating answers entered by a user and providing appropriate feedback and rewards. A specific embodiment of this system will be described below.

[0515] System Configuration

[0516] This system mainly consists of three elements: a server, a terminal, and a user. The server plays a central role in receiving, analyzing, and evaluating users' answers. The terminal provides an interface for users to input their answers. Users input answers to questions and receive feedback and rewards from the system.

[0517] Hardware and Software Used

[0518] Hardware: High-performance servers (e.g. virtual servers from a cloud service provider)

[0519] Software: Natural language processing libraries (e.g. spaCy, NLTK), generative AI models (e.g. OpenAI's GPT-4), sentiment engines

[0520] Data processing and calculation

[0521] The server receives the user's response from the device and performs the following operations:

[0522] 1. Natural language processing: The server uses a natural language processing library to analyze the grammar of the answer and extract keywords, thereby understanding the structure and important elements of the answer.

[0523] 2. Evaluation by generative AI model: The server uses the generative AI model to evaluate the concreteness, novelty, and feasibility of the answer. For example, the server inputs a prompt sentence such as "Please evaluate the new idea proposed by the user. The evaluation criteria are concreteness, novelty, and feasibility" into the generative AI model and obtains the evaluation result from the model.

[0524] 3. Measuring the intensity of emotions using an emotion engine: The server uses an emotion engine to analyze the emotion vocabulary in the answer and calculate the intensity of the emotion. For example, if the answer contains the expression "very dissatisfied," the intensity of the emotion is evaluated as high.

[0525] Examples

[0526] The user enters the following into the web browser form: "I would like to propose a new smartphone app. This app will monitor the user's health condition in real time and automatically notify the doctor if an abnormality is detected." and clicks the "Submit" button. The device sends this response in JSON format to the server.

[0527] The server parses the received JSON data and extracts text data. It then uses a natural language processing library to perform grammar analysis and keyword extraction, inputs the prompt and answer into a generative AI model to obtain an evaluation result. It then uses an emotion engine to analyze the emotional vocabulary in the answer and calculates the intensity of the emotion.

[0528] Finally, the server generates a score based on the evaluation results and notifies the user's device. For example, a message such as "Your proposal received a high score!" is displayed on the web browser. The server generates a link to a high-value prize based on the score and provides it to the user. The user can receive the prize by clicking the provided link.

[0529] In this way, the system can instantly evaluate users' answers and provide appropriate feedback and rewards.

[0530] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0531] Step 1:

[0532] The user enters the answer

[0533] The user inputs answers to the questions in the form on the web browser. For example, the user inputs, "I propose a new smartphone app. This app will monitor the user's health condition in real time and automatically notify a doctor if an abnormality is detected," and clicks the "Submit" button. The input data is in text format.

[0534] Step 2:

[0535] The device sends the answer to the server

[0536] The terminal converts the answers entered by the user into JSON format and sends it to the server using the HTTPS protocol. The input is the user's text answer and the output is the JSON data sent to the server.

[0537] Step 3:

[0538] The server receives the answer and begins parsing it.

[0539] The server receives JSON data sent from the terminal. It parses the received data and extracts text data. The input is JSON data and the output is text data. Specifically, the server parses the data using a JSON parser.

[0540] Step 4:

[0541] The server analyzes the answers using natural language processing technology.

[0542] The server uses a natural language processing library (e.g., spaCy, NLTK) to perform grammatical analysis of the answer and extract keywords. The input is text data, and the output is the analysis results (grammatical structures and keywords). In concrete terms, the server inputs the text data into the NLP library and obtains the analysis results.

[0543] Step 5:

[0544] The server evaluates the answers using a generative AI model

[0545] The server uses a generative AI model (e.g., OpenAI's GPT-4) to evaluate the concreteness, novelty, and feasibility of the answer. The input is the analysis result and the prompt text, and the output is the evaluation result. Specifically, the server inputs the prompt text "Please evaluate the new idea proposed by the user. The evaluation criteria are concreteness, novelty, and feasibility" and the analysis result into the generative AI model and obtains the evaluation result.

[0546] Step 6:

[0547] The server uses an emotion engine to measure the intensity of emotions.

[0548] The server uses an emotion engine to analyze the emotion vocabulary in the answer and calculate the intensity of the emotion. The input is text data, and the output is an emotion intensity evaluation. In concrete terms, the server inputs text data to the emotion engine and calculates the intensity and ratio of the emotion vocabulary.

[0549] Step 7:

[0550] The server generates the score and notifies the user.

[0551] The server generates the final scoring result based on the evaluation results obtained from the generative AI model and the emotion engine. The input is the evaluation result and the emotion intensity evaluation, and the output is the scoring result. In concrete terms, the server integrates the evaluation result and the emotion intensity evaluation to generate the scoring result. The generated scoring result is notified to the user's device. For example, a message such as "Your suggestion received a high score!" may be displayed on the web browser.

[0552] Step 8:

[0553] The server provides users with high-value prizes

[0554] The server provides a large prize to the user based on the score. The input is the score and the output is a prize link. In concrete terms, the server generates a prize link according to the score and provides it to the user. The user can receive the prize by clicking on the provided link.

[0555] (Application example 2)

[0556] Next, a description will be given of Application Example 2 of Form 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."

[0557] In conventional survey systems, it was difficult to instantly evaluate users' responses and provide rewards based on those evaluations. There was also no mechanism to measure the intensity of users' emotions and provide rewards accordingly. This made it difficult to motivate users, leading to problems with the quality of surveys and the response rate declining.

[0558] 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.

[0559] In this invention, the server includes a means for having the user think about the content of the question, a means for inputting a written answer from a web questionnaire screen, a means for instantly scoring the answer and presenting a high-value prize according to the score, a means for measuring the intensity of the emotion based on the intensity and ratio of vocabulary indicating the emotion included in the written answer, a means for providing a reward to the user according to the scoring result and the intensity of the emotion, and a means for later summarizing and providing a report to the company. This makes it possible to instantly evaluate the user's answer and provide a reward according to the intensity of the emotion.

[0560] "User" means an individual or entity that uses the system to answer questions.

[0561] The "question content" is the question or challenge presented to the user.

[0562] The "WEB survey screen" is an interface that allows users to answer questions via the Internet.

[0563] A "descriptive answer" is an answer in which the user freely enters text.

[0564] "Instant grading" means that an answer is evaluated immediately after the user enters it.

[0565] A "score" is a numerical evaluation given to a user's answer.

[0566] A "Big Prize" is a valuable reward provided based on the rating of a user's answer.

[0567] "Emotion vocabulary" refers to words that express emotions contained in users' responses.

[0568] "Emotional intensity" is an indicator that shows the strength of the emotions contained in a user's response.

[0569] "Vocabulary strength" refers to the degree of emotional strength contained in the vocabulary expressing an emotion.

[0570] The "proportion" is the ratio of emotion-indicating words to the total number of responses.

[0571] "Reward" means the consideration provided to a user for their answer.

[0572] A "Report" is a report summarizing users' responses.

[0573] "Company" means the legal entity or organization receiving the survey results.

[0574] The system for implementing this invention has a function to instantly score a product review and measure the intensity of the emotion when the user posts the review. Depending on the content of the review and the intensity of the emotion, the user is given an electronic gift or points.

[0575] Hardware and Software Used

[0576] Hardware:

[0577] Smartphone

[0578] server

[0579] software:

[0580] Python

[0581] Django (web framework)

[0582] Hugging Face Transformers (generative AI model)

[0583] NLTK (Natural Language Processing Library)

[0584] Explanation of data processing and data calculation

[0585] 1. User Action:

[0586] Users post product reviews through a smartphone app, and the review content is entered in text format.

[0587] 2. Server process:

[0588] Submitted reviews are sent to a server and processed through the Django framework. The review content is fed into a generative AI model using Hugging Face Transformers and is instantly scored based on criteria such as the review's specificity, novelty, and feasibility.

[0589] 3. Emotion Intensity Measurement:

[0590] At the same time, we use NLTK to calculate the intensity and ratio of sentiment vocabulary in the reviews to measure the intensity of sentiment. For example, the expression "very nice" indicates high sentiment intensity.

[0591] 4. Providing Rewards:

[0592] Depending on the score and the intensity of the emotion, users will be given electronic gifts and points, which will motivate users and encourage them to write high-quality reviews.

[0593] Examples

[0594] If a user writes a review saying, "This product is very good. It's easy to use and I like the design," the generative AI model will instantly score the review, and the emotion engine will measure the high emotion intensity from the expression "very good." As a result, the user will be presented with an e-gift as a positive review.

[0595] Examples of prompt statements

[0596] Review: "This product is amazing. Easy to use and I love the design."

[0597] Scoring criteria: Specificity, novelty, feasibility

[0598] Emotion Intensity Measurement: The intensity and proportion of emotion-indicating words

[0599] In this way, it is possible to instantly rate users' reviews and provide rewards according to the intensity of their emotions, which can motivate users and encourage them to write quality reviews.

[0600] The flow of the identification process in the application example 2 will be described with reference to FIG.

[0601] Step 1:

[0602] A user inputs a product review through a smartphone app. The user inputs the review content in text format and presses the send button. The input data is the review content in text format, and the output is the review content sent to the server.

[0603] Step 2:

[0604] The server processes the received review content through the Django framework. The server converts the review content into a prompt text to be input to the generative AI model. The input data is the review content submitted by the user, and the output is the prompt text to be input to the generative AI model.

[0605] Step 3:

[0606] The server uses a generative AI model (Hugging Face Transformers) to instantly score the review content. The generative AI model evaluates the review's specificity, novelty, and feasibility based on the prompt text, and assigns a score. The input data is the prompt text, and the output is the review score.

[0607] Step 4:

[0608] The server uses NLTK to calculate the strength and ratio of emotion vocabulary contained in the review. The server analyzes the review content, extracts emotion vocabulary, and calculates its strength and ratio. The input data is the review content, and the output is the emotion intensity.

[0609] Step 5:

[0610] The server provides rewards to users based on the score and emotional intensity. The server evaluates the score and emotional intensity and presents appropriate electronic gifts or points to users. The input data are the review score and emotional intensity, and the output is the reward provided to users.

[0611] Step 6:

[0612] The server later summarizes and provides a report to the company. The server summarizes the collected review content and its evaluation results and creates a report for the company. The input data is the review content and its evaluation results, and the output is the report provided to the company.

[0613] Example 3

[0614] Next, a third embodiment of the third embodiment will be described. 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."

[0615] Conventional survey systems only collected feedback from users, but it was difficult to effectively analyze the data and provide it as useful information for companies. In addition, because the analysis did not take into account user emotions, it was difficult for companies to find specific improvement measures. Furthermore, there was a lack of a way to instantly process the collected data and quickly provide it as a report.

[0616] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0617] In this invention, the server includes means for having users think of questions, means for inputting written answers from a web survey screen, means for instantly scoring the answers and presenting high-value prizes according to the scores, means for preprocessing collected answer data, means for analyzing the answer data using an emotion engine, means for summarizing the answer data based on the analysis results, means for integrating the summarized data and the analysis results to generate a report, and means for providing the generated report to the company. This makes it possible to effectively analyze user feedback and provide the company with specific improvement measures that take emotions into consideration.

[0618] A "user" is an individual or group that uses the system to think up questions and enter descriptive answers on the web survey screen.

[0619] The "web survey screen" is an interface that allows users to answer questions online.

[0620] "Descriptive response" refers to a format in which the user answers by freely entering text.

[0621] "Scoring" is the process of assigning points to users' answers.

[0622] "Big Prizes" are high-value rewards that are provided to users when they achieve a high score.

[0623] "Preprocessing" refers to the process of data cleaning and formatting to make collected data easier to analyze.

[0624] An "emotion engine" is software or algorithm that analyzes user response data and extracts emotional trends and intensity.

[0625] "Summarization" is the process of concisely summarizing user response data based on the results of sentiment analysis.

[0626] A "Report" is a document that consolidates the summarized data and the results of the sentiment analysis and provides it to the Company.

[0627] An "enterprise" is an organization that receives feedback from users and uses it to improve its products and services.

[0628] This invention is a system that effectively collects and analyzes feedback from users and provides useful information to companies. A specific embodiment of this system is described below.

[0629] System Overview

[0630] The server asks users to think of questions to ask and then has them enter descriptive answers on a web survey screen. The collected response data is instantly scored, and high-value prizes are awarded according to the scores. The data is then pre-processed and analyzed using an emotion engine. The response data is summarized based on the analysis results, and the summarized data is integrated with the analysis results to generate a report. The generated report is provided to the company.

[0631] Hardware and Software Used

[0632] Server: Collects, pre-processes, analyzes, summarizes, and generates reports on data.

[0633] User device: Access the web survey screen and enter descriptive answers.

[0634] Database: Stores collected data and analysis results.

[0635] Sentiment engine: Analyzes user response data and extracts emotional trends and strengths using natural language processing APIs.

[0636] Generative AI model: Summarize the response data. Specifically, we use a generative AI model.

[0637] Data collection and preprocessing

[0638] Users access the web survey screen and enter their answers in a written format. The server collects these answer data in real time and stores them in a database. The server then preprocesses the data using Python's Pandas library. Preprocessing includes filling in missing values ​​and standardizing the data format.

[0639] sentiment analysis

[0640] The server sends the pre-processed data to the emotion engine, which uses natural language processing APIs to analyze the user's response text and extract the trend and intensity of emotions. For example, the feedback "the design is outdated" is returned as an emotion result of "negative."

[0641] Data Summarization and Report Generation

[0642] The server generates a summary using a generative AI model based on the results of the sentiment analysis. The generated summary is stored in a database. The server then integrates the summary with the results of the sentiment analysis and generates a report. The report is automatically created using the APIs of Microsoft (registered trademark) Word or Google Docs.

[0643] Examples and prompts

[0644] As a concrete example, the following prompt sentence is input into the generative AI model.

[0645] Example prompt:

[0646] The response data collected from users is given below: Summarize these data and generate a report with the results of sentiment analysis.

[0647] Response data:

[0648] 1. "The product design seems old-fashioned. I'd like it to have a more modern design."

[0649] 2. "Easy to use, but too expensive."

[0650] 3. "The customer support was very helpful."

[0651] Sentiment analysis results:

[0652] 1. Negative (outdated design)

[0653] 2. Negative (expensive)

[0654] 3. Positive (Friendly customer support)

[0655] Summary and Report:

[0656] Many users have negative feelings about the product design, especially feeling that it is "outdated." In addition, while there is dissatisfaction with the price, there are many positive evaluations of customer support. Based on these results, it is recommended that the product design be modernized and the pricing be reviewed.

[0657] In this way, the server collects and analyzes the user's response data and generates a report that is useful to the company. The flow of the specific process in the third embodiment will be described with reference to FIG.

[0658] Step 1: Data collection

[0659] The server collects the response data from the users. The users access the web survey screen and input their responses in the form of written answers. The input data is sent to the server and stored in a database.

[0660] Input: A descriptive response entered by the user into the web survey screen

[0661] Output: Answer data stored in a database

[0662] Specific operation: A user accesses the survey form, enters answers to questions, and clicks the submit button. The server receives the submitted data in real time and stores it in a database.

[0663] Step 2: Data Preprocessing

[0664] The server preprocesses the collected data, including cleaning the data and standardizing the format. Specifically, the server uses the Python Pandas library to process the data.

[0665] Input: Answer data stored in the database

[0666] Output: Preprocessed data

[0667] Specific operation: The server retrieves answer data from the database, and uses the Pandas library to fill in missing values ​​and standardize the data format (for example, standardizing date formats and removing unnecessary spaces in text data).

[0668] Step 3: Sentiment analysis

[0669] The server sends the pre-processed data to the emotion engine, which uses a natural language processing API to analyze the user's response text and extract the trend and intensity of emotions.

[0670] Input: Preprocessed data

[0671] Output: Sentiment analysis results

[0672] What happens: The server sends the pre-processed data to a natural language processing API to perform sentiment analysis. The API analyzes the sentiment (positive, negative, neutral) in the text and returns the result to the server. For example, the feedback "the design is outdated" will return a sentiment result of "negative."

[0673] Step 4: Data Summarization

[0674] Based on the results of the sentiment analysis, the server uses a generative AI model to generate a summary.

[0675] Input: Sentiment analysis results

[0676] Output: Summary

[0677] Specific operation: The server inputs the results of the sentiment analysis into the generative AI model and generates a summary sentence. For example, a summary sentence such as "Many users have negative feelings toward the design" is generated.

[0678] Step 5: Generate reports

[0679] The server integrates the summary sentences and the results of the sentiment analysis to generate a report, which is then provided to the company.

[0680] Input: Summary and sentiment analysis results

[0681] Output: The generated report

[0682] Specific operation: The server integrates the summary sentences and the results of sentiment analysis, and creates a report using APIs for Microsoft Word or Google Docs. The report includes trends in user opinions and specific improvement suggestions. The generated report is sent to the company via email.

[0683] In this way, the server collects and analyzes user response data and generates useful reports for the company.

[0684] (Application example 3)

[0685] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0686] Conventional survey systems only collected feedback from users, but it was difficult to analyze their emotions and specific improvements in detail. In addition, when providing collected feedback to companies, there was a lack of a way to generate detailed reports including the trends and strength of emotions. This made it difficult for companies to grasp the true opinions and emotions of users, and to obtain specific guidelines for product improvement.

[0687] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0688] In this invention, the server includes a means for having the user think of questions, a means for inputting written answers from a web questionnaire screen, a means for instantly scoring the answers and presenting high-value prizes according to the scores, a means for later summarizing and providing a report to the company, a means for analyzing the user's emotions using an emotion engine, a means for generating a report based on the analysis results, and a means for providing the generated report to the company. This makes it possible to analyze user feedback in detail and provide a specific report including the tendency and strength of emotions to the company.

[0689] "User" means any individual or entity that uses the System to provide Feedback.

[0690] "Question content" refers to questions or questionnaire items presented to the user.

[0691] The "WEB survey screen" is an interface that users can access via the Internet and use to input answers to questions.

[0692] "Written response" refers to a format in which users can freely enter text to answer questions.

[0693] "Scoring" is the process of evaluating and assigning a score to a user's answer.

[0694] A "Big Prize" is a valuable reward or prize offered to a user in return for their answer.

[0695] A "report" is a document that summarizes the feedback collected and the results of its analysis.

[0696] "Company" means a legal entity or organization that receives User Feedback and uses it to improve its products and services.

[0697] An "emotion engine" is software or algorithms used to analyze emotions from user feedback and evaluate their tendency and intensity.

[0698] "Analysis results" refers to data and evaluations regarding the user's emotions obtained by the emotion engine.

[0699] "Generation" is the process of creating new documents or reports based on data or information.

[0700] "Providing" refers to the act of handing over the generated reports and information to a company.

[0701] As an embodiment of the present invention, the following system is constructed.

[0702] System Configuration

[0703] The system consists of a user terminal, a server, an emotion engine, and a database. The user terminal is a device such as a smartphone or PC, and provides an interface for displaying the web survey screen. The server is responsible for collecting feedback from users, analyzing it using the emotion engine, and generating reports. The database is used to store the collected feedback and analysis results.

[0704] Hardware and Software Used

[0705] Hardware: Smartphones, PCs, servers

[0706] Software: Python, TextBlob (sentiment analysis library), web survey system

[0707] Data processing and calculation

[0708] 1. Feedback collection: Users input descriptive responses via a web survey screen from their terminal. The server collects these in real time and stores them in a database.

[0709] 2. Sentiment Analysis: The server analyzes the collected feedback using TextBlob to determine whether each feedback has a positive, negative, or neutral sentiment and to evaluate its tendency and intensity.

[0710] 3. Report Generation: The server generates a report based on the results of the sentiment analysis, including the total number of feedbacks, the number of positive, negative, and neutral feedbacks, and common phrases.

[0711] 4. Report provision: The generated report is provided to the company, who uses the report to improve their products and services.

[0712] Examples

[0713] For example, if a user enters feedback such as "The design of this product is outdated," the server collects this feedback and uses TextBlob to determine whether it is a negative sentiment. The server generates a report containing this feedback and provides it to the company. The company can then review the design of the product based on this report.

[0714] Examples of prompt statements

[0715] "Collect user feedback, perform sentiment analysis, and generate a report. The report should include the total number of feedback comments, the number of positive, negative, and neutral feedback comments, and common phrases."

[0716] In this way, user feedback can be analysed in detail and businesses can be provided with specific reports including sentiment trends and intensity.

[0717] The flow of the specific process in the application example 3 will be described with reference to FIG.

[0718] Step 1:

[0719] The user accesses the web survey screen and enters descriptive answers.

[0720] Input: User feedback (in text format)

[0721] Output: User feedback data

[0722] Specific operation: The user accesses the online survey screen using a smartphone or PC and enters answers to the questions in a descriptive format. The entered feedback is sent to the server.

[0723] Step 2:

[0724] The server collects user feedback and stores it in a database.

[0725] Input: User feedback data

[0726] Output: Feedback data stored in a database

[0727] Specific operation: The server collects the received user feedback in real time and stores it in a database, which will be used for later processing.

[0728] Step 3:

[0729] The server performs sentiment analysis of the feedback using TextBlob.

[0730] Input: Feedback data stored in the database

[0731] Output: Sentiment analysis results (positive, negative, neutral judgement)

[0732] What it does: The server retrieves feedback data from the database and uses the TextBlob library to analyze the sentiment of each piece of feedback, which is then classified as positive, negative, or neutral.

[0733] Step 4:

[0734] The server generates a report based on the sentiment analysis results.

[0735] Input: Sentiment analysis results

[0736] Output: Generated reports (total number of feedbacks, sentiment trends, common phrases, etc.)

[0737] What it does: The server aggregates the sentiment analysis results and generates a report that includes the total number of feedbacks, the number of positive, negative, and neutral feedbacks, and common phrases.

[0738] Step 5:

[0739] The server provides the generated reports to the enterprise.

[0740] Input: Generated report

[0741] Output: Report provided to the company

[0742] Specific operation: The server transmits the generated report to the company, which can then use the report to improve its products and services.

[0743] In this way, user feedback can be analysed in detail and businesses can be provided with specific reports including sentiment trends and intensity.

[0744] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0745] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0746] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0747] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0748] [Second embodiment]

[0749] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0750] 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.

[0751] 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 wide area network (WAN) and / or a local area network (LAN).

[0752] 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.

[0753] 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 the voice according to instructions from the processor 46.

[0754] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0755] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0756] Fig. 4 shows an example of 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0761] "Example 1"

[0762] In one embodiment of the present invention, a user accesses a web questionnaire screen, thinks about the questions, and answers in a descriptive format. These questions ask for free opinions on how to improve a product, such as "Which part of the product are you dissatisfied with?" or "Which function of the product do you think could be improved?"

[0763] "Example 2"

[0764] Next, the answer entered by the user is instantly scored by the system of the present invention. The scoring is performed based on, for example, the specificity, novelty, and feasibility of the answer. Then, depending on the scoring result, the user is presented with a high-value prize. The high-value prize may be, for example, an electronic gift or PayPay that can be received via the web.

[0765] "Example 3"

[0766] Finally, the system of the present invention summarizes the responses collected from users and provides them to the company as a report, which may include, for example, trends in opinions and specific improvement proposals for product improvement.

[0767] The process flow of each embodiment will be described below.

[0768] "Example 1"

[0769] Step 1: The user accesses the web survey screen. This screen is provided, for example, on a dedicated website or application.

[0770] Step 2: The user thinks of the question and enters a written answer. The questions ask for open-minded opinions on how to improve the product, such as "Which part of the product are you dissatisfied with?" or "Which feature of the product do you think could be improved?"

[0771] "Example 2"

[0772] Step 1: The answer entered by the user is instantly scored by the system of the present invention. The scoring is based on, for example, the specificity, novelty, and feasibility of the answer.

[0773] Step 2: Depending on the results of the scoring, users will be presented with high-value prizes, such as digital gifts or PayPay that can be received online.

[0774] "Example 3"

[0775] Step 1: The system of the present invention summarizes the responses collected from users, for example using natural language processing techniques.

[0776] Step 2: The summarized responses are provided to the company in the form of a report, which may include, for example, trends in opinions and specific improvement proposals for product improvement.

[0777] Example 1

[0778] Next, a description will be given of Example 1 of Form 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".

[0779] With conventional web survey systems, it was difficult to efficiently analyze users' free-form responses and gather information that would be useful for improving products. In addition, there was a lack of a way to instantly analyze users' responses and provide the results to companies.

[0780] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a means for having a user think about the content of a question, a means for inputting a descriptive answer from a web questionnaire screen, a means for saving the answer in a database, a means for inputting the saved answer to a generative AI model, a means for the generative AI model to analyze the content of the answer, a means for saving the analysis result in a database, and a means for summarizing and providing a report to a company at a later date. This makes it possible to efficiently analyze the user's free-form answers, quickly collect information useful for improving products, and provide it to a company.

[0781] "User" means an individual or organization that uses the system to answer online surveys.

[0782] "Question content" refers to the survey questions presented to the user, soliciting their opinions on improving the product.

[0783] The "WEB survey screen" is an interface that users can access and use to enter written responses to questions.

[0784] A "descriptive response" is a survey response in which the user responds by freely entering text.

[0785] A "database" is an information management system for storing user responses and analysis results.

[0786] A "generative AI model" is an artificial intelligence model that analyzes users' written responses and extracts important keywords and opinions.

[0787] The "analysis results" are important keywords and opinions extracted by the generative AI model by analyzing user responses.

[0788] A "report" is a document summarizing the analysis results and providing them to companies.

[0789] The "company" is the organization that receives user responses and analysis results and uses them to improve products.

[0790] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0791] The purpose of this system is to allow users to access a web questionnaire screen and respond to questions in a written format. A specific embodiment of this system will be described below.

[0792] Hardware and Software Used

[0793] Server: Receives user access, generates the web survey screen, and stores the user's responses in a database. It also inputs the responses into the generative AI model, obtains the analysis results, and stores them in a database.

[0794] Terminal: The device (PC, smartphone, tablet, etc.) that the user uses to access the web survey screen and enter written responses to the questions.

[0795] Database: An information management system (e.g. MySQL) to store user answers and analysis results.

[0796] Generative AI model: An artificial intelligence model that analyzes users' written responses and extracts important keywords and opinions (e.g., OpenAI's GPT-4).

[0797] Program Processing

[0798] When a user accesses the web survey screen, the server displays a survey form, and the user can freely answer questions such as "Which part of the product are you dissatisfied with?" or "Which feature of the product do you think could be improved?"

[0799] When a user submits an answer, the server stores the answer in a database. The server then inputs the saved answer into a generative AI model and analyzes the answer. The generative AI model extracts important keywords and opinions from the user's answer and stores them in a database.

[0800] Examples

[0801] As a concrete example, consider the case where a user answers the question "Which part of the product are you dissatisfied with?" with "The battery life is poor." This answer is sent to the server and stored in a database. The server then inputs this answer into a generative AI model and obtains the analysis results. The generative AI model extracts the opinion that "the battery life is poor" and stores it in the database.

[0802] Here is an example of a prompt to be input to the generative AI model:

[0803] User Answer: "Battery life is poor"

[0804] Extract important keywords and opinions from these answers.

[0805] In this way, the server can efficiently analyze the user's free-form responses and gather information that can be used to improve the product.

[0806] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0807] Step 1:

[0808] The user accesses the web survey screen.

[0809] Specifically, a user accesses a specified URL using his / her own device (PC, smartphone, tablet, etc.). The input is the user's access request, and the output is an access request to the server.

[0810] Step 2:

[0811] The server displays the survey form.

[0812] The server receives the user's access request, generates a questionnaire form using HTML and CSS, and sends it to the user's terminal. The input is the user's access request, and the output is the display of the questionnaire form.

[0813] Step 3:

[0814] The user answers the questions in a written format.

[0815] The user freely inputs text in response to the questions displayed on the questionnaire screen. For example, in response to the question "Which part of the product are you dissatisfied with?", the user inputs "The battery life is poor." The input is the user's text response, and the output is the input text data.

[0816] Step 4:

[0817] The user submits the answer.

[0818] The user checks the answer they entered and clicks the send button. This sends the answer to the server. The input is the user's send operation, and the output is the sending of the answer data to the server.

[0819] Step 5:

[0820] The server stores the answer in a database.

[0821] The server receives the answers sent by the user and stores them in a database. The stored data includes the user ID, question ID, answer content, etc. The input is the user's answer data, and the output is the save operation to the database.

[0822] Step 6:

[0823] The server inputs the stored answers into a generative AI model.

[0824] The server retrieves the answers stored in the database and inputs them to the generative AI model. Specifically, it sends the answer content to the generative AI model as a prompt sentence. The input is the answer data retrieved from the database, and the output is the prompt sentence sent to the generative AI model.

[0825] Step 7:

[0826] A generative AI model analyzes the answers.

[0827] A generative AI model (e.g., OpenAI's GPT-4) analyzes the input prompt sentence and extracts important keywords and opinions. For example, it extracts the keyword "battery life is poor" from the answer "battery life is poor." The input is the prompt sentence, and the output is the analyzed keywords and opinions.

[0828] Step 8:

[0829] The server stores the analysis results in a database.

[0830] The server receives the analysis results returned from the generative AI model and stores them in the database again. The stored data includes the analyzed keywords and opinions. The input is the analysis results from the generative AI model, and the output is the save operation to the database.

[0831] In this way, the user's free-form responses can be efficiently analyzed and information useful for improving the product can be collected.

[0832] (Application example 1)

[0833] Next, a description will be given of Application Example 1 of Form 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".

[0834] Conventional web survey systems have difficulty efficiently collecting and analyzing feedback from users. In addition, there is a lack of a way to automatically generate specific proposals to reflect users' free opinions in product improvements. This has led to problems in which companies are unable to effectively utilize user opinions, leading to a decrease in the speed and accuracy of product improvements.

[0835] 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.

[0836] In this invention, the server includes a means for having the user think of questions, a means for inputting written answers from a web questionnaire screen, a means for instantly scoring the answers and presenting high-value prizes according to the scores, a means for later summarizing and providing a report to the company, and a means for inputting the user's written answers into a generative AI model and displaying the analysis results. This makes it possible to efficiently collect feedback from users and automatically generate specific product improvement proposals based on the analysis results.

[0837] "User" means an individual or organization that uses the system to respond to surveys.

[0838] "Question content" refers to the survey questions presented to the user, soliciting their opinions on improving the product.

[0839] The "WEB survey screen" is an interface that users can access via the Internet and use to enter written responses to questions.

[0840] "Written response" is a response format in which users can freely enter their opinions or feedback in text format.

[0841] "Scoring" is the process of assigning a score to a user's written response, evaluating the response based on its quality and content.

[0842] "High-value prizes" are rewards offered to users for their answers, and refer to high-value products such as electronic gifts or electronic payment services.

[0843] A "Report" is a document that summarizes feedback collected from users and provides it to a company.

[0844] A "generative AI model" is an artificial intelligence model that analyzes users' written responses and automatically generates specific suggestions for product improvement.

[0845] "Analysis results" are the information and suggestions obtained after the generative AI model analyzes the user's written responses.

[0846] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings.

[0847] System Programming

[0848] The system for realizing the present invention includes a program for a user to access a web survey screen and input a response in a written format, and also includes a program for inputting the user's written response to a generative AI model and displaying the analysis results.

[0849] Hardware and Software Used

[0850] Hardware: Smartphones, servers

[0851] Software: Python, OpenAI API

[0852] Data processing and calculation

[0853] 1. Data collection: The user accesses the web survey screen using a smartphone and enters answers in a written format. The server receives these answers and stores them in a database.

[0854] 2. Data analysis: The server inputs the saved user's written responses into a generative AI model (OpenAI's GPT-3). The generative AI model analyzes the input text and automatically generates specific suggestions for improving the product.

[0855] 3. Displaying the results: The server displays the analysis results obtained from the generative AI model to the user, allowing the user to see how their feedback can help improve the product.

[0856] Examples

[0857] For example, if a user enters feedback such as "This product has a short battery life," the generative AI model will generate specific improvement suggestions such as "Consider increasing the battery capacity or adding a power-saving mode."

[0858] Examples of prompt statements

[0859] User feedback: This product has a short battery life

[0860] Please provide suggestions for product improvement based on this feedback.

[0861] In this way, it is possible to efficiently collect feedback from users and automatically generate specific product improvement proposals based on the analysis results.

[0862] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0863] Step 1:

[0864] Users access the web survey screen using their smartphone and enter their answers in written form.

[0865] Input: User's written response (e.g. "This product has a short battery life")

[0866] Output: Short answer data

[0867] Specific operation: The user accesses the web survey screen and inputs answers to the questions in text format. The input answers are sent to the server.

[0868] Step 2:

[0869] The server receives the user's written responses and stores them in a database.

[0870] Input: Written response data

[0871] Output: Answer data stored in a database

[0872] Specific operation: The server stores the received user's written answers in a database. The stored data is used for subsequent analysis processing.

[0873] Step 3:

[0874] The server inputs the user's stored written answers into a generative AI model.

[0875] Input: Answer data stored in the database

[0876] Output: Data input to the generative AI model

[0877] How it works: The server retrieves the user's written answers from the database and converts them into a format that can be input into a generative AI model (OpenAI's GPT-3).

[0878] Step 4:

[0879] A generative AI model analyzes users' written responses and automatically generates specific suggestions for improving products.

[0880] Input: Data to be input to the generative AI model (e.g., "This product has a short battery life")

[0881] Output: Analysis results (e.g. "Consider increasing battery capacity or adding a power saving mode")

[0882] Specific operation: The generative AI model analyzes the user's written responses and generates specific suggestions for improving the product.

[0883] Step 5:

[0884] The server displays the analysis results obtained from the generative AI model to the user.

[0885] Input: Analysis results

[0886] Output: Analysis results that are displayed to the user

[0887] What it does: The server displays the analysis results from the generative AI model to the user, who can see how their feedback can help improve the product.

[0888] Example 2

[0889] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0890] In conventional web survey systems, it was difficult to instantly evaluate users' answers and offer high-value prizes based on the results. In addition, the evaluation criteria were vague, making it impossible to properly evaluate the specificity, novelty, and feasibility of users' answers. Furthermore, the process of quickly collating the evaluation results and notifying users was cumbersome.

[0891] 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.

[0892] In this invention, the server includes a means for having a user think of a question, a means for inputting a descriptive answer from a web survey screen, a means for using a generative AI model to instantly score the answer, a means for generating a prompt sentence and inputting it to the generative AI model, a means for tallying up the evaluation results from the generative AI model, a means for offering a high-value prize according to the score, and a means for later summarizing and providing a report to a company. This makes it possible to instantly evaluate a user's answers and offer a high-value prize based on the results.

[0893] A "user" is an individual or entity that accesses the system, formulates a question, and enters an answer.

[0894] "Questions" are themes or topics that the user should think about and are presented by the system.

[0895] The "web survey screen" is an interface for users to input written responses and is accessible via a web browser.

[0896] A "descriptive response" is a response format in which the user freely enters text.

[0897] A "generative AI model" is an artificial intelligence model used to evaluate user responses, for example using natural language processing techniques.

[0898] A "prompt" is a text sentence that is generated to provide input to a generative AI model, containing the user's answer and evaluation criteria.

[0899] "Evaluation results" refer to the scores and feedback that the generative AI model generates by analyzing the user's answers.

[0900] A "high-value prize" is a valuable reward provided based on the user's evaluation results, such as an electronic gift or credits for an electronic payment service.

[0901] A "report" is a document that summarizes user responses and evaluation results and is provided to companies.

[0902] "Company" refers to a legal entity or organization that uses the system to collect responses from users and receive the analysis results.

[0903] This invention is a system that instantly evaluates answers entered by users and offers high-value prizes based on the results. The system is implemented using a server, a terminal, and a generative AI model.

[0904] First, a user accesses the system using a terminal and inputs a descriptive answer into the web survey screen. For example, a user inputs "solar-powered portable charger" as an "idea for a new eco-friendly product."

[0905] The server receives the answers entered by the user and stores them in text format. The server then converts the answers into a suitable format for input to the generative AI model, specifically by removing unnecessary whitespace and special characters and cleaning up the text.

[0906] The server generates a prompt sentence to be input to the generative AI model. The prompt sentence includes the user's answer and evaluation criteria (concreteness, novelty, feasibility). For example, the following prompt sentence is generated:

[0907] Enter your answer below:

[0908] Answer: A portable solar charger

[0909] Evaluation criteria: Specificity, novelty, feasibility

[0910] The server inputs the prompt sentence into a generative AI model (e.g., a model using natural language processing technology). The generative AI model analyzes the prompt sentence and generates a score based on each evaluation criterion. For example, it may generate a score of 8 points for concreteness, 7 points for novelty, and 9 points for feasibility.

[0911] The server receives the scores returned by the generative AI model and sums up the scores for each evaluation criterion, for example a total of 24 points. The server then notifies the user of the final evaluation result. Depending on the evaluation result, the user is given a large prize, for example an electronic gift or credits for an electronic payment service are sent to the user's account.

[0912] This system makes it possible to instantly evaluate users' answers and offer high-value prizes based on the results. In addition, the evaluation criteria are clear, and the concreteness, novelty, and feasibility of users' answers can be appropriately evaluated. Furthermore, the process of quickly collating evaluation results and notifying users is streamlined.

[0913] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0914] Step 1:

[0915] The user accesses the system using a terminal and inputs descriptive responses via the web survey screen.

[0916] Input: The question and answer the user thought of (e.g., "Portable charger powered by solar power")

[0917] Output: The user's answer is sent to the system.

[0918] Step 2:

[0919] The server receives the answers entered by the user and stores them in text format.

[0920] Input: Answer data submitted by the user

[0921] Output: Saved text data

[0922] Step 3:

[0923] The server converts the received answers into a suitable format for input to the generative AI model, specifically cleaning up the text by removing unnecessary whitespace and special characters.

[0924] Input: Saved text data

[0925] Output: Cleaned text data

[0926] Step 4:

[0927] The server generates a prompt sentence to be input to the generative AI model. The prompt sentence includes the user's answer and evaluation criteria (concreteness, novelty, feasibility).

[0928] Input: Cleaned text data

[0929] Output: Generated prompt (e.g., "Please enter the user's answer below: Answer: Portable solar charger Evaluation criteria: Specificity, novelty, feasibility")

[0930] Step 5:

[0931] The server inputs this prompt into a generative AI model, which analyzes the prompt and generates a score based on each evaluation criterion.

[0932] Input: Generated prompt text

[0933] Output: Evaluation score from the generative AI model (e.g. concreteness 8 points, novelty 7 points, feasibility 9 points)

[0934] Step 6:

[0935] The server receives the scores returned by the generative AI model and aggregates the scores for each evaluation criterion.

[0936] Input: Evaluation scores from a generative AI model

[0937] Output: Aggregated final score (e.g. 24 points total)

[0938] Step 7:

[0939] The server notifies the user of the final evaluation result. Depending on the evaluation result, the user is given a large prize, for example, an electronic gift or credits for an electronic payment service are sent to the user's account.

[0940] Input: Final scores tallied

[0941] Output: Notify the user and provide a high-value prize (e.g., send an e-gift)

[0942] (Application example 2)

[0943] Next, a description will be given of Application Example 2 of Form 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".

[0944] In conventional survey systems, it was difficult to instantly evaluate users' responses and provide high-value prizes based on the results. In addition, the evaluation criteria for responses were vague, and feedback to users was often delayed. Furthermore, creating reports to be provided to companies was time-consuming and inefficient. To solve these problems, a system that can instantly evaluate users' responses and provide high-value prizes based on the scores is needed.

[0945] 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.

[0946] In this invention, the server includes a means for having the user think of questions, a means for inputting descriptive answers from a web questionnaire screen, a means for instantly scoring the answers using a generative AI model, a means for presenting a high-value prize according to the score, and a means for later summarizing and providing the report to the company. This makes it possible to instantly evaluate the user's answers and provide a high-value prize according to the score. In addition, the report can be efficiently provided to the company.

[0947] "User" means an individual or entity that uses the system to answer questions.

[0948] "Question content" refers to questions thought up by the user or questions in a survey or quiz provided by the system.

[0949] The "WEB survey screen" is an interface that allows users to answer questions via the Internet.

[0950] "Written response" refers to a format in which users can freely enter text to answer questions.

[0951] A "generative AI model" is an algorithm or program that uses artificial intelligence to evaluate and score user responses.

[0952] "Instant scoring" is the process of evaluating and scoring answers immediately after a user enters the answer.

[0953] A "high value prize" is a valuable reward provided depending on the score of a user's answer.

[0954] An "e-gift" is a gift in digital form that can be received over the Internet.

[0955] An "electronic payment service" is a service for conducting monetary transactions over the Internet.

[0956] A "Report" is a report summarizing the results of users' responses and is provided to companies.

[0957] "Company" refers to a legal entity or organization that uses the system to receive survey results and reports.

[0958] The system for implementing this invention allows users to think up questions, input descriptive answers on a web survey screen, and instantly score the answers using a generative AI model. High-value prizes are awarded according to the scores, and a summary report is later provided to the company.

[0959] Hardware and Software Used

[0960] Hardware: Smartphones, servers

[0961] Software: Python, OpenAI API, Internet connection

[0962] Data processing and calculation

[0963] User Actions

[0964] Users access the online survey screen using their smartphones, think about the question, and enter their answer in a descriptive format. For example, to the question, "What is the diameter of the Earth?", the user answers, "About 12,742 kilometers."

[0965] Server Processing

[0966] 1. Getting the answer: The server receives the written answer submitted by the user.

[0967] 2. Scoring by the generative AI model: The server uses the OpenAI API to input the user's answer into the generative AI model and instantly scores it. For example, the server sends a prompt message to the generative AI model: "Please score the following answer: Approximately 12,742 kilometers."

[0968] 3. Score determination: Determine the user's score based on the score returned by the generative AI model.

[0969] 4. Offering prizes: Offering high-value prizes to users depending on their scores. For example, if the score is 90 or above, they will receive electronic gifts, and if the score is 80 or below, they will receive points for electronic payment services.

[0970] Report creation and delivery

[0971] The server summarizes the users' answers and provides the company with a report that includes information such as the answers the users gave, their scores, and the types of prizes they received.

[0972] Examples

[0973] If a user answers the question "What is the diameter of the Earth in kilometers?" with "About 12,742 kilometers," the server sends this answer to a generative AI model, which scores it using the following prompt:

[0974] Examples of prompt statements

[0975] Please rate the following answer: Approximately 12,742 kilometers

[0976] The generative AI model scores the answers, and if it scores 90 points, for example, the server provides the user with an e-gift. It also summarizes the results and provides them to the company in a report.

[0977] In this way, a system is realized that instantly evaluates users' answers and offers high-value prizes according to the scores.

[0978] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0979] Step 1:

[0980] Users access the web survey screen using their smartphone, think about the questions they want to ask, and enter their answers in descriptive format.

[0981] Input: User's written answer

[0982] Output: Answer data sent to the server

[0983] Specific behavior: In response to the question "What is the diameter of the Earth in kilometers?", the user enters "About 12,742 kilometers."

[0984] Step 2:

[0985] The server receives the written response submitted by the user.

[0986] Input: User's written response data

[0987] Output: Answer data stored in the answer database on the server

[0988] What happens: The server stores the user's answer in a database and prepares it for further processing.

[0989] Step 3:

[0990] The server uses a generative AI model to instantly score the user's answers.

[0991] Input: User written answers stored in the database

[0992] Output: The score returned by the generative AI model.

[0993] What happens: The server calls the OpenAI API, sends the prompt "Please score the following answer: Approximately 12,742 kilometers" to the generative AI model, and gets a score.

[0994] Step 4:

[0995] The server determines the user's score based on the score returned by the generative AI model.

[0996] Input: The score returned by the generative AI model

[0997] Output: User score

[0998] Specific operation: The server analyzes the score returned by the generative AI model and determines it to be, for example, 90 points.

[0999] Step 5:

[1000] The server will award users with large prizes depending on their scores.

[1001] Input: User's score

[1002] Output: Large prize given to the user

[1003] Specific operation: The server provides electronic gifts to users with a score of 90 or above, and points for electronic payment services to users with a score of 80 or below.

[1004] Step 6:

[1005] The server summarizes the users' responses and provides a report to the company.

[1006] Input: User's answer, points, prize type

[1007] Output: Reports provided to companies

[1008] What it does: The server summarizes the users' answers and provides them to the company as a report, including information about the users' answers, their scores, and the types of prizes awarded.

[1009] Example 3

[1010] Next, a third embodiment of the third embodiment will be described. 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".

[1011] Conventional survey systems have difficulty efficiently summarizing responses collected from users and providing them to companies. In addition, there is a lack of incentives to improve the quality of responses, and a method to increase users' willingness to participate was needed. Furthermore, preprocessing and summarizing response data takes a lot of time and effort, making it difficult to quickly create reports.

[1012] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1013] In this invention, the server includes a means for having a user think about the content of a question, a means for inputting a descriptive answer from a web survey screen, a means for instantly scoring the answers and presenting a high-value prize according to the score, a means for saving the answers in a database, a means for preprocessing the saved answer data, a means for sending a prompt sentence to the generative AI model, a means for compiling the summary returned from the generative AI model into a report, and a means for providing the report to the company. This makes it possible to efficiently summarize the user's answers and quickly provide them to the company. Also, by providing a high-value prize, it is possible to increase the user's motivation to participate.

[1014] "User" means an individual or organization that uses the system to respond to surveys.

[1015] "Question content" refers to the questions in the survey presented to the user.

[1016] The "web survey screen" is an interface that users can access via the Internet to answer questions.

[1017] A "descriptive response" is a survey response in which the user responds by freely entering text.

[1018] "Instant scoring" is the process of rating and scoring an answer immediately after the user enters the answer.

[1019] A "Big Prize" is a high value reward offered to a user for their answer.

[1020] "Database" refers to an information management system for storing user response data.

[1021] "Preprocessing" is the process of tokenizing, removing stop words, stemming, and other processing of answer data before inputting it into a generative AI model.

[1022] A "generative AI model" is an artificial intelligence model used to summarize a user's answers.

[1023] A "prompt sentence" is an input sentence that gives instructions to a generative AI model.

[1024] A "summary" is a text that briefly summarizes the user's response.

[1025] A "Report" is a document that compiles summarized User responses and provides them to the Company.

[1026] A "company" is an organization that uses user response data to improve products and services.

[1027] This invention is a system that efficiently summarizes responses collected from users and provides them to companies. The system is composed of users, terminals, and a server.

[1028] Users access the survey form using a web browser or a mobile app and enter descriptive responses, such as, "The design of this product is great, but the battery life is poor. I'd like you to improve that."

[1029] The device sends the entered answer to the server as an HTTP POST request, with the answer data sent in JSON format.

[1030] The server saves the received response data in a database. When saving, metadata such as user ID and timestamp are also recorded. The database is a common database management system such as MySQL or PostgreSQL.

[1031] The server then takes the stored answer data and pre-processes it using spaCy, an NLP library. This pre-processing includes tokenization, stop word removal, stemming, etc. For example, it converts "The design of this product is great, but the battery life is poor. I hope they improve it" into "Great design, poor battery life, improved."

[1032] The preprocessed data is input to GPT-3, a generative AI model. The server generates a prompt for GPT-3 and instructs it to summarize the user's answer. For example, the prompt can be "Summarize the following user's opinion: The design of this product is great, but the battery life is poor. I would like you to improve it."

[1033] The summary results returned by GPT-3 are collected by the server and formatted into a report. This report includes opinion trends and specific improvement suggestions for product improvement. Finally, the server provides this report to companies. Companies can use this report to improve their products and services.

[1034] This system makes it possible to efficiently summarize the user's answers and quickly provide them to the company. In addition, by offering high-value prizes, it is possible to increase the user's motivation to participate. The flow of the identification process in the third embodiment will be described with reference to FIG. 15.

[1035] Step 1:

[1036] The user enters the answer

[1037] A user accesses the survey form using a web browser or a mobile app and enters a descriptive response. For example, the user might enter, "The design of this product is great, but the battery life is poor. I hope they can improve it." The input data is in text format.

[1038] Step 2:

[1039] The device sends the answer to the server

[1040] The terminal sends the entered answer to the server as an HTTP POST request. At this time, the answer data is sent in JSON format. For example, the following data is sent: {"user_id": 123, "response": "The design of this product is great, but the battery life is poor. I hope they will improve this."}. The input is JSON format data, and the output is a request sent to the server.

[1041] Step 3:

[1042] The server stores the answer in a database

[1043] The server saves the received response data in a database. When saving, metadata such as the user ID and timestamp are also recorded. For example, execute the following query in a MySQL table: INSERT INTO responses (user_id, response, timestamp) VALUES (123, 'The design of this product is great, but the battery life is poor. I hope they improve it.', '2023-10-01 12:34:56'). The input is JSON format data, and the output is saved in the database.

[1044] Step 4:

[1045] The server preprocesses the answer data

[1046] The server retrieves answer data from the database and performs preprocessing using spaCy, an NLP library. This preprocessing includes tokenization, stop word removal, stemming, etc. For example, "The design of this product is great, but the battery life is poor. I hope it will be improved." is converted to "Great design, poor battery life, improvement." The input is text data retrieved from the database, and the output is the preprocessed text data.

[1047] Step 5:

[1048] The server sends a prompt to the generative AI model.

[1049] The server generates a prompt sentence based on the preprocessed data and sends it to the generative AI model (GPT-3). For example, it generates a prompt sentence such as "Summarize the following user opinions: Design is great Battery life is bad Improve." The input is the preprocessed text data, and the output is the prompt sentence sent to the generative AI model.

[1050] Step 6:

[1051] Generative AI model returns summary

[1052] The generative AI model (GPT-3) generates a summary based on the prompt sentence and returns it to the server. For example, it generates a summary such as "The design is good, but the battery life is poor." The input is the prompt sentence, and the output is the summarized text data.

[1053] Step 7:

[1054] The server summarizes the results into a report

[1055] The server collects the summaries returned by the generative AI model and compiles them into a report along with the opinions of other users. The report includes trends in opinions and specific suggestions for improvement. For example, it shows a trend that "many users appreciate the design, but are dissatisfied with the battery life." The input is summarized text data, and the output is a document in the form of a report.

[1056] Step 8:

[1057] The server provides the report to the company.

[1058] The server generates the completed report in PDF or Excel format and sends it to the company by email. The company uses this report to improve its products. The input is a document in report format, and the output is the provision of the report to the company.

[1059] (Application example 3)

[1060] Next, a description will be given of Application Example 3 of Form Example 3. 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".

[1061] With conventional survey systems, it was difficult to efficiently collect feedback from users and provide it in a form that was useful to the company. In addition, there was a problem that it was difficult to quickly improve products because it took a lot of time and effort to summarize and analyze the feedback. Furthermore, there was a need to generate reports that accurately reflected user opinions, but there was a problem that it was difficult to achieve this.

[1062] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1063] In this invention, the server includes means for having users think of questions, means for inputting written answers from a web questionnaire screen, means for instantly scoring answers and presenting high-value prizes according to scores, means for later summarizing and providing a report to companies, means for summarizing feedback collected from users using a generative AI model, means for generating a report including opinion trends and specific improvement proposals based on the summarized feedback, and means for providing the generated report to companies. This makes it possible to efficiently collect user feedback and quickly and accurately summarize and analyze it before providing it to companies.

[1064] A "User" is an individual or organization that uses the system to formulate questions and provide feedback.

[1065] "Question content" refers to the questions that the user thinks of in the survey, and is entered in descriptive form.

[1066] The "WEB survey screen" is an interface that allows users to enter questions online and write their answers.

[1067] A "descriptive answer" is an answer in which the user freely enters the answer in text.

[1068] "Scoring" is the process of assigning a score to a user's answer and is done instantly.

[1069] "High-value prizes" are rewards given to users for their answers, and can include electronic gifts and electronic payment services.

[1070] "Summarization" is the process of concisely summarizing the feedback collected from users.

[1071] A "report" is a document prepared based on summarized feedback, including trends in opinions and specific suggestions for improvement.

[1072] "Company" means a legal entity or organization that receives User Feedback and uses it to improve its products and services.

[1073] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate and summarize text data.

[1074] "Opinion trends" are common opinions or patterns that emerge based on collected feedback from users.

[1075] "Specific improvement proposals" are specific improvements to products or services proposed based on user feedback.

[1076] As an embodiment of the present invention, the following system is constructed.

[1077] System Configuration

[1078] The system includes a user terminal, a server, and a generative AI model. The user terminal is a device such as a smartphone or PC, and is used by users to think of questions and input descriptive answers on a web survey screen. The server is responsible for processing the feedback collected from users, summarizing it using the generative AI model, and generating a report.

[1079] Hardware and Software Used

[1080] Hardware: Smartphones, PCs, servers

[1081] Software: Python, OpenAI API, WEB survey system

[1082] Data processing and calculation

[1083] 1. Data collection: The user enters descriptive responses into the web survey screen from their device. This sends the user's feedback to the server.

[1084] 2. Data Summarization: The server summarizes the collected feedback using a generative AI model (e.g., GPT-3) by using the OpenAI API to concisely summarize the feedback.

[1085] 3. Report generation: Based on the summarized feedback, the server generates a report containing opinion trends and specific improvement suggestions. This report is provided to the company.

[1086] Examples

[1087] For example, if a user enters feedback such as "This product is very easy to use," the server collects this along with other feedback and summarizes it using a generative AI model. The summarized feedback is compiled into a report in the form of "Many users would like to see improvements in price and design."

[1088] Examples of prompt statements

[1089] Below are examples of prompts to be input to the generative AI model.

[1090] Please summarize your feedback below:

[1091] This product is very easy to use.

[1092] I think it would be better if the design was a little better.

[1093] The price is high, so I wish it was a little cheaper.

[1094] There are too many functions and it's hard to understand how to use it.

[1095] In this way, user feedback can be collected efficiently, quickly and accurately summarized, analyzed, and provided to companies.

[1096] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1097] Step 1:

[1098] The user accesses the web survey screen, thinks about the questions to be asked, and enters a descriptive answer.

[1099] Input: The question and short answer entered by the user.

[1100] Output: User feedback data sent to the server.

[1101] Specific operation: The user accesses the web survey screen using a smartphone or PC, thinks about the questions, and enters descriptive answers. The entered data is sent to the server.

[1102] Step 2:

[1103] The server receives the feedback data sent by the user and stores it in a database.

[1104] Input: Feedback data submitted by the user.

[1105] Output: Feedback data stored in a database.

[1106] Specific operation: The server receives the feedback data sent by the user and stores it in a database for later processing.

[1107] Step 3:

[1108] The server retrieves feedback data from the database and generates prompt sentences to input into the generative AI model.

[1109] Input: Feedback data stored in the database.

[1110] Output: A prompt to be fed into the generative AI model.

[1111] Specific behavior: The server retrieves feedback data from the database and generates a prompt sentence like the following: "Please summarize the following feedback: This product is very easy to use. It would be even better if it was better designed. It is expensive, so I wish it was a little cheaper. There are too many functions and it is difficult to understand how to use it."

[1112] Step 4:

[1113] The server uses a generative AI model (e.g., GPT-3) to input prompts and summarize the feedback data.

[1114] Input: The generated prompt statement.

[1115] Output: Summarised feedback data.

[1116] Specific operation: The server inputs a prompt sentence into the generative AI model and summarizes the feedback data. The generative AI model summarizes the feedback data concisely based on the input prompt sentence.

[1117] Step 5:

[1118] The server generates a report based on the summarized feedback data, including trends in opinions and specific suggestions for improvement.

[1119] Input: Summarised feedback data.

[1120] Output: A report containing opinion trends and concrete improvement suggestions.

[1121] Specific actions: The server analyzes the summarized feedback data, extracts opinion trends and specific improvement suggestions, and generates a report to provide to the company.

[1122] Step 6:

[1123] The server provides the generated reports to the enterprise.

[1124] Input: A report containing opinion trends and specific suggestions for improvement.

[1125] Output: Reports provided to the company.

[1126] Specific operation: The server provides the generated report to the company, which uses the report to improve its products and services.

[1127] In addition, 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.

[1128] "Example 1"

[1129] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system receives a written response from a user and recognizes the emotion contained in the response. The emotion engine uses natural language processing technology to extract emotion from the user's written response. Specifically, if the user responds that "the product's design feels outdated," the emotion engine recognizes the negative emotion of "dissatisfaction" from this response.

[1130] "Example 2"

[1131] The emotion engine also has a function of measuring the intensity of a user's emotion. The intensity of emotion is calculated based on, for example, the intensity of vocabulary indicating emotion contained in the written answer, the proportion of the vocabulary in the entire answer, etc. For example, the expression "very dissatisfied" is determined to have a higher intensity of emotion than the expression "slightly dissatisfied."

[1132] "Example 3"

[1133] Furthermore, the results of the emotion engine are reflected in reports generated by the system. The reports provide companies with information such as the trends and strength of users' emotions, as well as the factors that trigger specific emotions. This allows companies to obtain specific guidelines for improving their products. For example, if negative emotions such as "the design is outdated" are detected from many users, companies can consider revising the product design.

[1134] The process flow of each embodiment will be described below.

[1135] "Example 1"

[1136] Step 1: User enters written responses into a web survey.

[1137] Step 2: The system receives the user's written response and sends it to the emotion engine.

[1138] Step 3: The emotion engine extracts emotions from the written responses and returns the results to the system.

[1139] Step 1: The sentiment engine extracts sentiment from written responses.

[1140] Step 2: The sentiment engine calculates the intensity of the sentiment, based on factors such as the strength of the sentiment vocabulary and its proportion in the overall response.

[1141] Step 3: The emotion engine calculates the emotion intensity and returns it to the system.

[1142] "Example 3"

[1143] Step 1: The system receives the results from the emotion engine.

[1144] Step 2: The system compiles a report on the trends and intensity of emotions, as well as factors that trigger specific emotions.

[1145] Step 3: Provide the system-generated reports to the business.

[1146] Example 1

[1147] Next, a description will be given of Example 1 of Form 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".

[1148] Conventional web survey systems have difficulty extracting emotions from users' free-form responses and conducting analysis based on those emotions. They also lack a mechanism for instantly scoring users' responses and offering high-value prizes according to the scores. This has led to problems such as a decline in user satisfaction and the quality of feedback for companies' product improvements.

[1149] 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.

[1150] In this invention, the server includes a means for having a user think about the content of a question, a means for inputting a written response from a web questionnaire screen, a means for instantly scoring the response and presenting a high-value prize according to the score, a means for later summarizing and providing a report to the company, a means for receiving the user's written response and using an emotion engine for recognizing the emotion contained in the response, a means for the emotion engine to extract emotion from the user's response using natural language processing technology, and a means for storing the extracted emotion in a database. This makes it possible to extract emotion from the user's free-form response and perform analysis based on the emotion, thereby improving user satisfaction and improving the quality of feedback for company product improvements.

[1151] "User" refers to an individual or organization that accesses the web survey screen and enters written responses to the questions.

[1152] "WEB survey screen" refers to a web page that users can access via a browser and that provides an interface for entering written responses to questions.

[1153] "Written answers" refers to answers in which the user freely enters text.

[1154] "Sentiment Engine" refers to a software module for extracting emotions from a user's written responses.

[1155] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate human language.

[1156] "Database" refers to an information management system for storing extracted emotions and user response data.

[1157] "Big Prizes" refers to valuable rewards that are awarded according to the score that is instantly scored for a user's answers.

[1158] An "electronic gift" refers to a digital gift that can be received online.

[1159] "Electronic payment service" refers to a service that enables monetary transactions to be conducted over the Internet.

[1160] This invention is a system in which users access a web survey screen and input written responses to questions. This system has the function of instantly scoring the user's responses and presenting high-value prizes according to the scores. It also has the function of extracting emotions from the user's written responses and storing those emotions in a database.

[1161] The server generates a program that allows the user to access the web survey screen. This program includes an interface that allows the user to enter answers in a written format. The interface is built using web technologies such as HTML and JavaScript.

[1162] When the user enters a response in written form, the device sends the response to the server. The server passes the received response to the emotion engine. The emotion engine uses natural language processing technology to extract emotions from the user's response. This emotion engine is implemented using, for example, Python's NLTK library or the Google Cloud Natural Language API.

[1163] For example, if a user answers, "The design of the product feels old-fashioned," the emotion engine extracts the emotion of "dissatisfaction" from the answer. This recognized emotion is returned to the server, which stores the result in a database.

[1164] As a concrete example, consider a case where a user answers the question, "Which part of the product are you dissatisfied with?" with, "The design of the product feels old-fashioned." In this case, the emotion engine extracts the emotion, "dissatisfied."

[1165] Examples of prompts to be input to a generative AI model include:

[1166] User response: "The product design seems outdated"

[1167] Sentiment engine output: "Unhappy"

[1168] This prompt sentence can be used to provide training data for the generative AI model to improve the accuracy of emotion recognition.

[1169] This system can extract emotions from users' free-form responses and perform analysis based on those emotions, which can improve user satisfaction and the quality of feedback for companies to improve their products.

[1170] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1171] Step 1:

[1172] The user accesses the web survey screen.

[1173] The user accesses the specified URL using a browser. This URL is a link to the web survey page hosted by the server. The input is the access request from the user's browser, and the output is the display of the web survey page.

[1174] Step 2:

[1175] The user inputs a written response to the question.

[1176] A user freely enters their opinion in a text box in response to questions displayed on a web survey screen. For example, in response to the question "Which part of the product are you dissatisfied with?", the user enters "The design of the product seems old-fashioned." The input is the user's response text, and the output is the click event of the send button.

[1177] Step 3:

[1178] The terminal transmits the user's answer to the server.

[1179] When the user enters an answer and clicks the send button, the terminal sends the answer data to the server as an HTTP POST request, which includes the user's answer text. The input is the user's answer text, and the output is an HTTP POST request to the server.

[1180] Step 4:

[1181] The server passes the received answer to the emotion engine.

[1182] The server passes the user's answer received from the terminal to the emotion engine for analysis. The emotion engine is a module that runs in the server and extracts emotions using natural language processing technology. The input is the user's answer text, and the output is the data transfer to the emotion engine.

[1183] Step 5:

[1184] The sentiment engine extracts sentiment from user responses.

[1185] The emotion engine analyzes the user's response text and extracts emotions. For example, it extracts the emotion "dissatisfied" from the user's response "The product's design feels outdated." This process uses Python's NLTK library and Google Cloud Natural Language API. The input is the user's response text, and the output is the extracted emotion data.

[1186] Step 6:

[1187] The server stores the extracted emotions in a database.

[1188] The server receives the emotion data returned by the emotion engine and stores it in a database. The stored data includes the user's answer text and the extracted emotion. The input is the extracted emotion data, and the output is the save operation to the database.

[1189] (Application example 1)

[1190] Next, a description will be given of Application Example 1 of Form 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".

[1191] With conventional web survey systems, it was difficult to accurately grasp the nuances of emotions when collecting user feedback. There was also a lack of means to instantly analyze user feedback and provide it as useful information for companies. Furthermore, incentives for users were limited, making it difficult to improve the quality and quantity of feedback.

[1192] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for asking the user to think of the question content, a means for inputting a descriptive answer from a web questionnaire screen, a means for using an emotion engine for recognizing the emotion contained in the answer, a means for instantly scoring the answer and presenting a high-value prize according to the score, and a means for later summarizing and providing a report to the company. This makes it possible to accurately extract emotions from the user's feedback, analyze it instantly, and provide it to the company as useful information. Furthermore, by providing a high-value prize, the quality and quantity of the user's feedback can be improved.

[1193] A "user" is an individual or group that uses the system to formulate questions and enter written responses.

[1194] "Question content" refers to a theme or topic on which a user can write their opinion or impression regarding a product or service.

[1195] The "WEB survey screen" is an interface that users can access via the Internet and use to enter written responses.

[1196] A "descriptive answer" is an answer in which the user freely enters text to express their opinions or thoughts.

[1197] An "emotion engine" is software or a system for extracting and recognizing emotions from a user's written responses using natural language processing techniques.

[1198] "Scoring" is the process of evaluating and assigning a score to a user's written response.

[1199] A "high-value prize" is a valuable reward offered to a user in return for their answer, and may include electronic gifts or electronic payment services.

[1200] "Report" means a document or data summarizing User feedback and providing it to Company.

[1201] "Company" means an organization or legal entity that receives User Feedback and uses it to improve its products and services.

[1202] A system for implementing the present invention allows a user to access a web survey screen and input opinions about products and services in a written format. The system includes an emotion engine that extracts and analyzes emotions from the user's written responses. A specific embodiment of this system will be described below.

[1203] First, the user accesses the online survey screen using a device such as a smartphone or tablet. The user thinks of questions to ask about the product or service and enters their answers in a descriptive format. The answers entered by the user are then sent to the server in real time.

[1204] The server passes the received written response to the emotion engine, which uses natural language processing technology to extract the emotion. The emotion engine uses the TextBlob library to recognize positive, negative, or neutral emotions from the user's response. For example, if a user responds, "The design of this product feels old-fashioned," the emotion engine extracts the emotion "negative" from the response.

[1205] The server then scores the answers based on the extracted emotions and offers high-value prizes based on the scores. The high-value prizes include electronic gifts and electronic payment services. Users can receive these prizes online.

[1206] In addition, the server summarizes the user feedback and provides it to the company as a report, which includes the results of the user's sentiment analysis and the specific feedback content. The company can use this report to improve its products and services.

[1207] As a concrete example, consider the case where a user enters the following feedback:

[1208] User ID: 12345

[1209] Product ID: 67890

[1210] Feedback: The design of this product seems outdated.

[1211] This feedback is sent to a server, where it is recognized as "negative" by the sentiment engine. The server then scores the feedback and awards the user with a high-value prize. It also provides the company with a report summarizing the feedback and sentiment analysis results.

[1212] In this way, it is possible to accurately extract emotions from user feedback, analyze them in real time, and provide the information to companies as useful information. In addition, by offering high-value prizes, the quality and quantity of user feedback can be improved.

[1213] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1214] Step 1:

[1215] The user uses a terminal to access the web survey screen. The user thinks of questions to ask about products and services, and enters answers in descriptive format. The entered answers are sent from the terminal to the server. The input data includes the user ID, product ID, and feedback content.

[1216] Step 2:

[1217] The server passes the received written answers to the emotion engine. The emotion engine uses the TextBlob library to extract emotions from the user's answers. Specifically, it uses natural language processing techniques to recognize positive, negative, and neutral emotions in the feedback content. The input data is the feedback content, and the output data is the extracted emotions.

[1218] Step 3:

[1219] The server scores the answers based on the extracted emotions. The scoring algorithm assigns scores according to the type of emotion and the content of the feedback. For example, positive emotions are given high scores and negative emotions are given low scores. The input data are the extracted emotions and the output data are the scoring results.

[1220] Step 4:

[1221] The server provides high-value prizes according to the scoring results. The high-value prizes include electronic gifts and electronic payment services. Users can receive these prizes on the web. The input data is the scoring results, and the output data is the prizes provided.

[1222] Step 5:

[1223] The server summarizes the user feedback and provides it to the company as a report. This report includes the results of the user's sentiment analysis and the specific feedback content. The company can use this report to improve its products and services. The input data are the feedback content and the sentiment analysis results, and the output data is the summarized report.

[1224] In this way, it is possible to accurately extract emotions from user feedback, analyze them in real time, and provide the information to companies as useful information. In addition, by offering high-value prizes, the quality and quantity of user feedback can be improved.

[1225] Example 2

[1226] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[1227] In conventional survey systems, it was difficult to instantly evaluate users' answers and provide appropriate feedback and rewards. In addition, there was a lack of automated means to evaluate the concreteness, novelty, and feasibility of answers, and the ability to measure the intensity of emotions was also lacking. This made it difficult to motivate users and was unable to provide useful data to companies.

[1228] 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.

[1229] In this invention, the server includes a means for having a user think about the content of a question, a means for inputting a descriptive answer from a web questionnaire screen, a means for instantly scoring the answers and offering a high-value prize according to the score, a means for analyzing the answers using natural language processing technology, a means for evaluating the concreteness, novelty, and feasibility of the answers using a generative AI model, a means for measuring the intensity of emotions using an emotion engine, a means for generating a scoring result and notifying the user, a means for offering a high-value prize, and a means for later summarizing and providing a report to a company. This makes it possible to instantly evaluate the user's answers and provide appropriate feedback and rewards.

[1230] "User" means an individual or entity that uses the system to answer questions.

[1231] The "question content" is the question or challenge presented to the user.

[1232] The "WEB survey screen" is an interface that users can access via the Internet to answer questions.

[1233] "Descriptive response" is a format in which the user answers by freely entering text.

[1234] "Scoring" is the process of evaluating and assigning a score to a user's answer.

[1235] A "high value prize" is a valuable reward that is provided based on the evaluation of a user's answer.

[1236] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[1237] A "generative AI model" is a model that uses artificial intelligence to evaluate user answers.

[1238] "Specificity" is a rating scale that indicates how detailed and clear a user's answer is.

[1239] "Novelty" is a criterion that indicates how new ideas or perspectives a user's answer contains.

[1240] "Feasibility" is a criterion that indicates how realistically possible the user's answer is.

[1241] An "emotion engine" is a technology for measuring the intensity of emotions contained in a user's responses.

[1242] "Emotional intensity" is an indicator of how strong the emotion contained in a user's response.

[1243] The "scoring result" is the overall evaluation result of the user's answers.

[1244] "Notification" is the process of communicating information about scoring results and rewards to users.

[1245] A "report" is a report summarizing users' responses and their evaluation results.

[1246] "Company" means the organization that receives the Report for use in utilizing the user response data.

[1247] The present invention provides a system for instantly evaluating answers input by a user and providing appropriate feedback and rewards. A specific embodiment of this system will be described below.

[1248] System Configuration

[1249] This system mainly consists of three elements: a server, a terminal, and a user. The server plays a central role in receiving, analyzing, and evaluating users' answers. The terminal provides an interface for users to input their answers. Users input answers to questions and receive feedback and rewards from the system.

[1250] Hardware and Software Used

[1251] Hardware: High-performance servers (e.g. virtual servers from a cloud service provider)

[1252] Software: Natural language processing libraries (e.g. spaCy, NLTK), generative AI models (e.g. OpenAI's GPT-4), sentiment engines

[1253] Data processing and calculation

[1254] The server receives the user's response from the device and performs the following operations:

[1255] 1. Natural language processing: The server uses a natural language processing library to analyze the grammar of the answer and extract keywords, thereby understanding the structure and important elements of the answer.

[1256] 2. Evaluation by generative AI model: The server uses the generative AI model to evaluate the concreteness, novelty, and feasibility of the answer. For example, the server inputs a prompt sentence such as "Please evaluate the new idea proposed by the user. The evaluation criteria are concreteness, novelty, and feasibility" into the generative AI model and obtains the evaluation result from the model.

[1257] 3. Measuring the intensity of emotions using an emotion engine: The server uses an emotion engine to analyze the emotion vocabulary in the answer and calculate the intensity of the emotion. For example, if the answer contains the expression "very dissatisfied," the intensity of the emotion is evaluated as high.

[1258] Examples

[1259] The user enters the following into the web browser form: "I would like to propose a new smartphone app. This app will monitor the user's health condition in real time and automatically notify the doctor if an abnormality is detected." and clicks the "Submit" button. The device sends this response in JSON format to the server.

[1260] The server parses the received JSON data and extracts text data. It then uses a natural language processing library to perform grammar analysis and keyword extraction, inputs the prompt and answer into a generative AI model to obtain an evaluation result. It then uses an emotion engine to analyze the emotional vocabulary in the answer and calculates the intensity of the emotion.

[1261] Finally, the server generates a score based on the evaluation results and notifies the user's device. For example, a message such as "Your proposal received a high score!" is displayed on the web browser. The server generates a link to a high-value prize based on the score and provides it to the user. The user can receive the prize by clicking the provided link.

[1262] In this way, the system can instantly evaluate users' answers and provide appropriate feedback and rewards.

[1263] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1264] Step 1:

[1265] The user enters the answer

[1266] The user inputs answers to the questions in the form on the web browser. For example, the user inputs, "I propose a new smartphone app. This app will monitor the user's health condition in real time and automatically notify a doctor if an abnormality is detected," and clicks the "Submit" button. The input data is in text format.

[1267] Step 2:

[1268] The device sends the answer to the server

[1269] The terminal converts the answers entered by the user into JSON format and sends it to the server using the HTTPS protocol. The input is the user's text answer and the output is the JSON data sent to the server.

[1270] Step 3:

[1271] The server receives the answer and begins parsing it.

[1272] The server receives JSON data sent from the terminal. It parses the received data and extracts text data. The input is JSON data and the output is text data. Specifically, the server parses the data using a JSON parser.

[1273] Step 4:

[1274] The server analyzes the answers using natural language processing technology.

[1275] The server uses a natural language processing library (e.g., spaCy, NLTK) to perform grammatical analysis of the answer and extract keywords. The input is text data, and the output is the analysis results (grammatical structures and keywords). In concrete terms, the server inputs the text data into the NLP library and obtains the analysis results.

[1276] Step 5:

[1277] The server evaluates the answers using a generative AI model

[1278] The server uses a generative AI model (e.g., OpenAI's GPT-4) to evaluate the concreteness, novelty, and feasibility of the answer. The input is the analysis result and the prompt text, and the output is the evaluation result. Specifically, the server inputs the prompt text "Please evaluate the new idea proposed by the user. The evaluation criteria are concreteness, novelty, and feasibility" and the analysis result into the generative AI model and obtains the evaluation result.

[1279] Step 6:

[1280] The server uses an emotion engine to measure the intensity of emotions.

[1281] The server uses an emotion engine to analyze the emotion vocabulary in the answer and calculate the intensity of the emotion. The input is text data, and the output is an emotion intensity evaluation. In concrete terms, the server inputs text data to the emotion engine and calculates the intensity and ratio of the emotion vocabulary.

[1282] Step 7:

[1283] The server generates the score and notifies the user.

[1284] The server generates the final scoring result based on the evaluation results obtained from the generative AI model and the emotion engine. The input is the evaluation result and the emotion intensity evaluation, and the output is the scoring result. In concrete terms, the server integrates the evaluation result and the emotion intensity evaluation to generate the scoring result. The generated scoring result is notified to the user's device. For example, a message such as "Your suggestion received a high score!" may be displayed on the web browser.

[1285] Step 8:

[1286] The server provides users with high-value prizes

[1287] The server provides a large prize to the user based on the score. The input is the score and the output is a prize link. In concrete terms, the server generates a prize link according to the score and provides it to the user. The user can receive the prize by clicking on the provided link.

[1288] (Application example 2)

[1289] Next, a description will be given of Application Example 2 of Form 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".

[1290] In conventional survey systems, it was difficult to instantly evaluate users' responses and provide rewards based on those evaluations. There was also no mechanism to measure the intensity of users' emotions and provide rewards accordingly. This made it difficult to motivate users, leading to problems with the quality of surveys and the response rate declining.

[1291] 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.

[1292] In this invention, the server includes a means for having the user think about the content of the question, a means for inputting a written answer from a web questionnaire screen, a means for instantly scoring the answer and presenting a high-value prize according to the score, a means for measuring the intensity of the emotion based on the intensity and ratio of vocabulary indicating the emotion included in the written answer, a means for providing a reward to the user according to the scoring result and the intensity of the emotion, and a means for later summarizing and providing a report to the company. This makes it possible to instantly evaluate the user's answer and provide a reward according to the intensity of the emotion.

[1293] "User" means an individual or entity that uses the system to answer questions.

[1294] The "question content" is the question or challenge presented to the user.

[1295] The "WEB survey screen" is an interface that allows users to answer questions via the Internet.

[1296] A "descriptive answer" is an answer in which the user freely enters text.

[1297] "Instant grading" means that an answer is evaluated immediately after the user enters it.

[1298] A "score" is a numerical evaluation given to a user's answer.

[1299] A "Big Prize" is a valuable reward provided based on the rating of a user's answer.

[1300] "Emotion vocabulary" refers to words that express emotions contained in users' responses.

[1301] "Emotional intensity" is an indicator that shows the strength of the emotions contained in a user's response.

[1302] "Vocabulary strength" refers to the degree of emotional strength contained in the vocabulary expressing an emotion.

[1303] The "proportion" is the ratio of emotion-indicating words to the total number of responses.

[1304] "Reward" means the consideration provided to a user for their answer.

[1305] A "Report" is a report summarizing users' responses.

[1306] "Company" means the legal entity or organization receiving the survey results.

[1307] The system for implementing this invention has a function to instantly score a product review and measure the intensity of the emotion when the user posts the review. Depending on the content of the review and the intensity of the emotion, the user is given an electronic gift or points.

[1308] Hardware and Software Used

[1309] Hardware:

[1310] Smartphone

[1311] server

[1312] software:

[1313] Python

[1314] Django (web framework)

[1315] Hugging Face Transformers (generative AI model)

[1316] NLTK (Natural Language Processing Library)

[1317] Explanation of data processing and data calculation

[1318] 1. User Action:

[1319] Users post product reviews through a smartphone app, and the review content is entered in text format.

[1320] 2. Server process:

[1321] Submitted reviews are sent to a server and processed through the Django framework. The review content is fed into a generative AI model using Hugging Face Transformers and is instantly scored based on criteria such as the review's specificity, novelty, and feasibility.

[1322] 3. Emotion Intensity Measurement:

[1323] At the same time, we use NLTK to calculate the intensity and ratio of sentiment vocabulary contained in the reviews to measure the intensity of sentiment. For example, the expression "very nice" indicates high sentiment intensity.

[1324] 4. Providing Rewards:

[1325] Depending on the score and the intensity of the emotion, users will be given electronic gifts and points, which will motivate users and encourage them to write high-quality reviews.

[1326] Examples

[1327] If a user writes a review saying, "This product is very good. It's easy to use and I like the design," the generative AI model will instantly score the review, and the emotion engine will measure the high emotion intensity from the expression "very good." As a result, the user will be presented with an e-gift as a positive review.

[1328] Examples of prompt statements

[1329] Review: "This product is amazing. Easy to use and I love the design."

[1330] Scoring criteria: Specificity, novelty, feasibility

[1331] Emotion Intensity Measurement: The intensity and proportion of emotion-indicating words

[1332] In this way, it is possible to instantly rate users' reviews and provide rewards according to the intensity of their emotions, which can motivate users and encourage them to write quality reviews.

[1333] The flow of the identification process in the application example 2 will be described with reference to FIG.

[1334] Step 1:

[1335] A user inputs a product review through a smartphone app. The user inputs the review content in text format and presses the send button. The input data is the review content in text format, and the output is the review content sent to the server.

[1336] Step 2:

[1337] The server processes the received review content through the Django framework. The server converts the review content into a prompt text to be input to the generative AI model. The input data is the review content submitted by the user, and the output is the prompt text to be input to the generative AI model.

[1338] Step 3:

[1339] The server uses a generative AI model (Hugging Face Transformers) to instantly score the review content. The generative AI model evaluates the review's specificity, novelty, and feasibility based on the prompt text, and assigns a score. The input data is the prompt text, and the output is the review score.

[1340] Step 4:

[1341] The server uses NLTK to calculate the strength and ratio of emotion vocabulary contained in the review. The server analyzes the review content, extracts emotion vocabulary, and calculates its strength and ratio. The input data is the review content, and the output is the emotion intensity.

[1342] Step 5:

[1343] The server provides rewards to users based on the score and emotional intensity. The server evaluates the score and emotional intensity and presents appropriate electronic gifts or points to users. The input data are the review score and emotional intensity, and the output is the reward provided to users.

[1344] Step 6:

[1345] The server later summarizes and provides a report to the company. The server summarizes the collected review content and its evaluation results and creates a report for the company. The input data is the review content and its evaluation results, and the output is the report provided to the company.

[1346] Example 3

[1347] Next, a third embodiment of the third embodiment will be described. 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".

[1348] Conventional survey systems only collected feedback from users, but it was difficult to effectively analyze the data and provide it as useful information for companies. In addition, because the analysis did not take into account user emotions, it was difficult for companies to find specific improvement measures. Furthermore, there was a lack of a way to instantly process the collected data and quickly provide it as a report.

[1349] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1350] In this invention, the server includes means for having users think of questions, means for inputting written answers from a web survey screen, means for instantly scoring the answers and presenting high-value prizes according to the scores, means for preprocessing collected answer data, means for analyzing the answer data using an emotion engine, means for summarizing the answer data based on the analysis results, means for integrating the summarized data and the analysis results to generate a report, and means for providing the generated report to the company. This makes it possible to effectively analyze user feedback and provide the company with specific improvement measures that take emotions into consideration.

[1351] A "user" is an individual or group that uses the system to think up questions and enter descriptive answers on the web survey screen.

[1352] The "web survey screen" is an interface that allows users to answer questions online.

[1353] "Descriptive response" refers to a format in which the user answers by freely entering text.

[1354] "Scoring" is the process of assigning points to users' answers.

[1355] "Big Prizes" are high-value rewards that are provided to users when they achieve a high score.

[1356] "Preprocessing" refers to the process of data cleaning and formatting to make collected data easier to analyze.

[1357] An "emotion engine" is software or algorithm that analyzes user response data and extracts emotional trends and intensity.

[1358] "Summarization" is the process of concisely summarizing user response data based on the results of sentiment analysis.

[1359] A "Report" is a document that consolidates the summarized data and the results of the sentiment analysis and provides it to the Company.

[1360] An "enterprise" is an organization that receives feedback from users and uses it to improve its products and services.

[1361] This invention is a system that effectively collects and analyzes feedback from users and provides useful information to companies. A specific embodiment of this system is described below.

[1362] System Overview

[1363] The server asks users to think of questions to ask and then has them enter descriptive answers on a web survey screen. The collected response data is instantly scored, and high-value prizes are awarded according to the scores. The data is then pre-processed and analyzed using an emotion engine. The response data is summarized based on the analysis results, and the summarized data is integrated with the analysis results to generate a report. The generated report is provided to the company.

[1364] Hardware and Software Used

[1365] Server: Collects, pre-processes, analyzes, summarizes, and generates reports on data.

[1366] User device: Access the web survey screen and enter descriptive answers.

[1367] Database: Stores collected data and analysis results.

[1368] Sentiment engine: Analyzes user response data and extracts emotional trends and strengths using natural language processing APIs.

[1369] Generative AI model: Summarize the response data. Specifically, we use a generative AI model.

[1370] Data collection and preprocessing

[1371] Users access the web survey screen and enter their answers in a written format. The server collects these answer data in real time and stores them in a database. The server then preprocesses the data using Python's Pandas library. Preprocessing includes filling in missing values ​​and standardizing the data format.

[1372] sentiment analysis

[1373] The server sends the pre-processed data to the emotion engine, which uses a natural language processing API to analyze the user's response text and extract the trend and intensity of emotions. For example, the feedback "the design is outdated" is returned as an emotion result of "negative."

[1374] Data Summarization and Report Generation

[1375] The server uses a generative AI model to generate a summary based on the results of the sentiment analysis. The generated summary is stored in a database. The server then integrates the summary with the results of the sentiment analysis to generate a report. The report is automatically created using APIs for Microsoft Word and Google Docs.

[1376] Examples and prompts

[1377] As a concrete example, the following prompt sentence is input into the generative AI model.

[1378] Example prompt:

[1379] The response data collected from users is given below: Summarize these data and generate a report with the results of sentiment analysis.

[1380] Response data:

[1381] 1. "The product design seems old-fashioned. I'd like it to have a more modern design."

[1382] 2. "Easy to use, but too expensive."

[1383] 3. "The customer support was very helpful."

[1384] Sentiment analysis results:

[1385] 1. Negative (outdated design)

[1386] 2. Negative (expensive)

[1387] 3. Positive (Friendly customer support)

[1388] Summary and Report:

[1389] Many users have negative feelings about the product design, especially feeling that it is "outdated." In addition, while there is dissatisfaction with the price, there are many positive evaluations of customer support. Based on these results, it is recommended that the product design be modernized and the pricing be reviewed.

[1390] In this way, the server collects and analyzes the user's response data and generates a report that is useful to the company. The flow of the specific process in the third embodiment will be described with reference to FIG.

[1391] Step 1: Data collection

[1392] The server collects the response data from the users. The users access the web survey screen and input their responses in the form of written answers. The input data is sent to the server and stored in a database.

[1393] Input: A descriptive response entered by the user into the web survey screen

[1394] Output: Answer data stored in a database

[1395] Specific operation: A user accesses the survey form, enters answers to questions, and clicks the submit button. The server receives the submitted data in real time and stores it in a database.

[1396] Step 2: Data Preprocessing

[1397] The server preprocesses the collected data, including cleaning the data and standardizing the format. Specifically, the data is processed using the Python Pandas library.

[1398] Input: Answer data stored in the database

[1399] Output: Preprocessed data

[1400] Specific operation: The server retrieves answer data from the database, and uses the Pandas library to fill in missing values ​​and standardize the data format (for example, standardizing date formats and removing unnecessary spaces in text data).

[1401] Step 3: Sentiment analysis

[1402] The server sends the pre-processed data to the emotion engine, which uses a natural language processing API to analyze the user's response text and extract the trend and intensity of emotions.

[1403] Input: Preprocessed data

[1404] Output: Sentiment analysis results

[1405] What happens: The server sends the pre-processed data to a natural language processing API to perform sentiment analysis. The API analyzes the sentiment (positive, negative, neutral) in the text and returns the result to the server. For example, the feedback "the design is outdated" will return a sentiment result of "negative."

[1406] Step 4: Data Summarization

[1407] Based on the results of the sentiment analysis, the server uses a generative AI model to generate a summary.

[1408] Input: Sentiment analysis results

[1409] Output: Summary

[1410] Specific operation: The server inputs the results of the sentiment analysis into the generative AI model and generates a summary sentence. For example, a summary sentence such as "Many users have negative feelings toward the design" is generated.

[1411] Step 5: Generate reports

[1412] The server integrates the summary sentences and the results of the sentiment analysis to generate a report, which is then provided to the company.

[1413] Input: Summary and sentiment analysis results

[1414] Output: The generated report

[1415] Specific operation: The server integrates the summary sentences and the results of sentiment analysis, and creates a report using APIs for Microsoft Word or Google Docs. The report includes trends in user opinions and specific improvement suggestions. The generated report is sent to the company via email.

[1416] In this way, the server collects and analyzes user response data and generates useful reports for the company.

[1417] (Application example 3)

[1418] Next, a description will be given of Application Example 3 of Form Example 3. 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".

[1419] Conventional survey systems only collected feedback from users, but it was difficult to analyze their emotions and specific improvements in detail. In addition, when providing collected feedback to companies, there was a lack of a way to generate detailed reports including the trends and strength of emotions. This made it difficult for companies to grasp the true opinions and emotions of users, and to obtain specific guidelines for product improvement.

[1420] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1421] In this invention, the server includes a means for having the user think of questions, a means for inputting written answers from a web questionnaire screen, a means for instantly scoring the answers and presenting high-value prizes according to the scores, a means for later summarizing and providing a report to the company, a means for analyzing the user's emotions using an emotion engine, a means for generating a report based on the analysis results, and a means for providing the generated report to the company. This makes it possible to analyze user feedback in detail and provide a specific report including the tendency and strength of emotions to the company.

[1422] "User" means any individual or entity that uses the System to provide Feedback.

[1423] "Question content" refers to questions or questionnaire items presented to the user.

[1424] The "WEB survey screen" is an interface that users can access via the Internet and use to input answers to questions.

[1425] "Written response" refers to a format in which users can freely enter text to answer questions.

[1426] "Scoring" is the process of evaluating and assigning a score to a user's answer.

[1427] A "Big Prize" is a valuable reward or prize offered to a user in return for their answer.

[1428] A "report" is a document that summarizes the feedback collected and the results of its analysis.

[1429] "Company" means a legal entity or organization that receives User Feedback and uses it to improve its products and services.

[1430] An "emotion engine" is software or algorithms used to analyze emotions from user feedback and evaluate their tendency and intensity.

[1431] "Analysis results" refers to data and evaluations regarding the user's emotions obtained by the emotion engine.

[1432] "Generation" is the process of creating new documents or reports based on data or information.

[1433] "Providing" refers to the act of handing over the generated reports and information to a company.

[1434] As an embodiment of the present invention, the following system is constructed.

[1435] System Configuration

[1436] The system consists of a user terminal, a server, an emotion engine, and a database. The user terminal is a device such as a smartphone or PC, and provides an interface for displaying the web survey screen. The server is responsible for collecting feedback from users, analyzing it using the emotion engine, and generating reports. The database is used to store the collected feedback and analysis results.

[1437] Hardware and Software Used

[1438] Hardware: Smartphones, PCs, servers

[1439] Software: Python, TextBlob (sentiment analysis library), web survey system

[1440] Data processing and calculation

[1441] 1. Feedback collection: Users input descriptive responses via a web survey screen from their terminal. The server collects these in real time and stores them in a database.

[1442] 2. Sentiment Analysis: The server analyzes the collected feedback using TextBlob to determine whether each feedback has a positive, negative, or neutral sentiment and to evaluate its tendency and intensity.

[1443] 3. Report Generation: The server generates a report based on the results of the sentiment analysis, including the total number of feedbacks, the number of positive, negative, and neutral feedbacks, and common phrases.

[1444] 4. Report provision: The generated report is provided to the company, who uses the report to improve their products and services.

[1445] Examples

[1446] For example, if a user enters feedback such as "The design of this product is outdated," the server collects this feedback and uses TextBlob to determine whether it is a negative sentiment. The server generates a report containing this feedback and provides it to the company. The company can then review the design of the product based on this report.

[1447] Examples of prompt statements

[1448] "Collect user feedback, perform sentiment analysis, and generate a report. The report should include the total number of feedback comments, the number of positive, negative, and neutral feedback comments, and common phrases."

[1449] In this way, user feedback can be analysed in detail and businesses can be provided with specific reports including sentiment trends and intensity.

[1450] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1451] Step 1:

[1452] The user accesses the web survey screen and enters descriptive answers.

[1453] Input: User feedback (in text format)

[1454] Output: User feedback data

[1455] Specific operation: The user accesses the online survey screen using a smartphone or PC and enters answers to the questions in a descriptive format. The entered feedback is sent to the server.

[1456] Step 2:

[1457] The server collects user feedback and stores it in a database.

[1458] Input: User feedback data

[1459] Output: Feedback data stored in a database

[1460] Specific operation: The server collects the received user feedback in real time and stores it in a database, which will be used for later processing.

[1461] Step 3:

[1462] The server performs sentiment analysis of the feedback using TextBlob.

[1463] Input: Feedback data stored in the database

[1464] Output: Sentiment analysis results (positive, negative, neutral judgement)

[1465] What it does: The server retrieves feedback data from the database and uses the TextBlob library to analyze the sentiment of each piece of feedback, which is then classified as positive, negative, or neutral.

[1466] Step 4:

[1467] The server generates a report based on the sentiment analysis results.

[1468] Input: Sentiment analysis results

[1469] Output: Generated reports (total number of feedbacks, sentiment trends, common phrases, etc.)

[1470] What it does: The server aggregates the sentiment analysis results and generates a report that includes the total number of feedbacks, the number of positive, negative, and neutral feedbacks, and common phrases.

[1471] Step 5:

[1472] The server provides the generated reports to the enterprise.

[1473] Input: Generated report

[1474] Output: Report provided to the company

[1475] Specific operation: The server transmits the generated report to the company, which can then use the report to improve its products and services.

[1476] In this way, user feedback can be analysed in detail and businesses can be provided with specific reports including sentiment trends and intensity.

[1477] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[1478] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[1479] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1480] 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 smart glasses 214.

[1481] [Third embodiment]

[1482] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1483] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1484] 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 wide area network (WAN) and / or a local area network (LAN).

[1485] 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.

[1486] 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 the voice according to instructions from the processor 46.

[1487] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1488] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1489] Fig. 6 shows an example of 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.

[1490] 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.

[1491] 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.

[1492] In the headset type terminal 314, 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.

[1493] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1494] "Example 1"

[1495] In one embodiment of the present invention, a user accesses a web questionnaire screen, thinks about the questions, and answers in a descriptive format. These questions ask for free opinions on how to improve a product, such as "Which part of the product are you dissatisfied with?" or "Which function of the product do you think could be improved?"

[1496] "Example 2"

[1497] Next, the answer entered by the user is instantly scored by the system of the present invention. The scoring is performed based on, for example, the specificity, novelty, and feasibility of the answer. Then, depending on the scoring result, the user is presented with a high-value prize. The high-value prize may be, for example, an electronic gift or PayPay that can be received via the web.

[1498] "Example 3"

[1499] Finally, the system of the present invention summarizes the responses collected from users and provides them to the company as a report, which may include, for example, trends in opinions and specific improvement proposals for product improvement.

[1500] The process flow of each embodiment will be described below.

[1501] "Example 1"

[1502] Step 1: The user accesses the web survey screen. This screen is provided, for example, on a dedicated website or application.

[1503] Step 2: The user thinks of the question and enters a written answer. The questions ask for open-minded opinions on how to improve the product, such as "Which part of the product are you dissatisfied with?" or "Which feature of the product do you think could be improved?"

[1504] "Example 2"

[1505] Step 1: The answer entered by the user is instantly scored by the system of the present invention. The scoring is based on, for example, the specificity, novelty, and feasibility of the answer.

[1506] Step 2: Depending on the results of the scoring, users will be presented with high-value prizes, such as digital gifts or PayPay that can be received online.

[1507] "Example 3"

[1508] Step 1: The system of the present invention summarizes the responses collected from users, for example using natural language processing techniques.

[1509] Step 2: The summarized responses are provided to the company in the form of a report, which may include, for example, trends in opinions and specific improvement proposals for product improvement.

[1510] Example 1

[1511] Next, a description will be given of Example 1 of the embodiment 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".

[1512] With conventional web survey systems, it was difficult to efficiently analyze users' free-form responses and gather information that would be useful for improving products. In addition, there was a lack of a way to instantly analyze users' responses and provide the results to companies.

[1513] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a means for having a user think about the content of a question, a means for inputting a descriptive answer from a web questionnaire screen, a means for saving the answer in a database, a means for inputting the saved answer to a generative AI model, a means for the generative AI model to analyze the content of the answer, a means for saving the analysis result in a database, and a means for summarizing and providing a report to a company at a later date. This makes it possible to efficiently analyze the user's free-form answers, quickly collect information useful for improving products, and provide it to a company.

[1514] "User" means an individual or organization that uses the system to answer online surveys.

[1515] "Question content" refers to the survey questions presented to the user, soliciting their opinions on improving the product.

[1516] The "WEB survey screen" is an interface that users can access and use to enter written responses to questions.

[1517] A "descriptive response" is a survey response in which the user responds by freely entering text.

[1518] A "database" is an information management system for storing user responses and analysis results.

[1519] A "generative AI model" is an artificial intelligence model that analyzes users' written responses and extracts important keywords and opinions.

[1520] The "analysis results" are important keywords and opinions extracted by the generative AI model by analyzing user responses.

[1521] A "report" is a document summarizing the analysis results and providing them to companies.

[1522] The "company" is the organization that receives user responses and analysis results and uses them to improve products.

[1523] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1524] The purpose of this system is to allow users to access a web questionnaire screen and respond to questions in a written format. A specific embodiment of this system will be described below.

[1525] Hardware and Software Used

[1526] Server: Receives user access, generates the web survey screen, and stores the user's responses in a database. It also inputs the responses into the generative AI model, obtains the analysis results, and stores them in a database.

[1527] Terminal: The device (PC, smartphone, tablet, etc.) that the user uses to access the web survey screen and enter written responses to the questions.

[1528] Database: An information management system (e.g. MySQL) to store user answers and analysis results.

[1529] Generative AI model: An artificial intelligence model that analyzes users' written responses and extracts important keywords and opinions (e.g., OpenAI's GPT-4).

[1530] Program Processing

[1531] When a user accesses the web survey screen, the server displays a survey form, and the user can freely answer questions such as "Which part of the product are you dissatisfied with?" or "Which feature of the product do you think could be improved?"

[1532] When a user submits an answer, the server stores the answer in a database. The server then inputs the saved answer into a generative AI model and analyzes the answer. The generative AI model extracts important keywords and opinions from the user's answer and stores them in a database.

[1533] Examples

[1534] As a concrete example, consider the case where a user answers the question "Which part of the product are you dissatisfied with?" with "The battery life is poor." This answer is sent to the server and stored in a database. The server then inputs this answer into a generative AI model and obtains the analysis results. The generative AI model extracts the opinion that "the battery life is poor" and stores it in the database.

[1535] Here is an example of a prompt to be input to the generative AI model:

[1536] User Answer: "Battery life is poor"

[1537] Extract important keywords and opinions from these answers.

[1538] In this way, the server can efficiently analyze the user's free-form responses and gather information that can be used to improve the product.

[1539] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1540] Step 1:

[1541] The user accesses the web survey screen.

[1542] Specifically, a user accesses a specified URL using his / her own device (PC, smartphone, tablet, etc.). The input is the user's access request, and the output is an access request to the server.

[1543] Step 2:

[1544] The server displays the survey form.

[1545] The server receives the user's access request, generates a questionnaire form using HTML and CSS, and sends it to the user's terminal. The input is the user's access request, and the output is the display of the questionnaire form.

[1546] Step 3:

[1547] The user answers the questions in a written format.

[1548] The user freely inputs text in response to the questions displayed on the questionnaire screen. For example, in response to the question "Which part of the product are you dissatisfied with?", the user inputs "The battery life is poor." The input is the user's text response, and the output is the input text data.

[1549] Step 4:

[1550] The user submits the answer.

[1551] The user checks the answer they entered and clicks the send button. This sends the answer to the server. The input is the user's send operation, and the output is the sending of the answer data to the server.

[1552] Step 5:

[1553] The server stores the answer in a database.

[1554] The server receives the answers sent by the user and stores them in a database. The stored data includes the user ID, question ID, answer content, etc. The input is the user's answer data, and the output is the save operation to the database.

[1555] Step 6:

[1556] The server inputs the stored answers into a generative AI model.

[1557] The server retrieves the answers stored in the database and inputs them to the generative AI model. Specifically, the answer content is sent to the generative AI model as a prompt sentence. The input is the answer data retrieved from the database, and the output is the sending of the prompt sentence to the generative AI model.

[1558] Step 7:

[1559] A generative AI model analyzes the answers.

[1560] A generative AI model (e.g., OpenAI's GPT-4) analyzes the input prompt sentence and extracts important keywords and opinions. For example, it extracts the keyword "Battery life is poor" from the answer "Battery life is poor." The input is the prompt sentence, and the output is the analyzed keywords and opinions.

[1561] Step 8:

[1562] The server stores the analysis results in a database.

[1563] The server receives the analysis results returned from the generative AI model and stores them in the database again. The stored data includes the analyzed keywords and opinions. The input is the analysis results from the generative AI model, and the output is the save operation to the database.

[1564] In this way, the user's free-form responses can be efficiently analyzed and information useful for improving the product can be collected.

[1565] (Application example 1)

[1566] Next, a description will be given of Application Example 1 of Form 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".

[1567] Conventional web survey systems have difficulty efficiently collecting and analyzing feedback from users. In addition, there is a lack of a way to automatically generate specific proposals to reflect users' free opinions in product improvements. This has led to problems in which companies are unable to effectively utilize user opinions, leading to a decrease in the speed and accuracy of product improvements.

[1568] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1569] In this invention, the server includes a means for having the user think of questions, a means for inputting written answers from a web questionnaire screen, a means for instantly scoring the answers and presenting high-value prizes according to the scores, a means for later summarizing and providing a report to the company, and a means for inputting the user's written answers into a generative AI model and displaying the analysis results. This makes it possible to efficiently collect feedback from users and automatically generate specific product improvement proposals based on the analysis results.

[1570] "User" means an individual or organization that uses the system to respond to surveys.

[1571] "Question content" refers to the survey questions presented to the user, soliciting their opinions on improving the product.

[1572] The "WEB survey screen" is an interface that users can access via the Internet and use to input responses to questions in written form.

[1573] "Written response" is a response format in which users can freely enter their opinions or feedback in text format.

[1574] "Scoring" is the process of assigning a score to a user's written response, evaluating the response based on its quality and content.

[1575] "High-value prizes" are rewards offered to users for their answers, and refer to high-value products such as electronic gifts or electronic payment services.

[1576] A "Report" is a document that summarizes feedback collected from users and provides it to a company.

[1577] A "generative AI model" is an artificial intelligence model that analyzes users' written responses and automatically generates specific suggestions for product improvement.

[1578] "Analysis results" are the information and suggestions obtained after the generative AI model analyzes the user's written responses.

[1579] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the accompanying drawings.

[1580] System Programming

[1581] The system for realizing the present invention includes a program for a user to access a web survey screen and input a response in a written format, and also includes a program for inputting the user's written response to a generative AI model and displaying the analysis results.

[1582] Hardware and Software Used

[1583] Hardware: Smartphones, servers

[1584] Software: Python, OpenAI API

[1585] Data processing and calculation

[1586] 1. Data collection: The user accesses the web survey screen using a smartphone and enters answers in a written format. The server receives these answers and stores them in a database.

[1587] 2. Data analysis: The server inputs the saved user's written responses into a generative AI model (OpenAI's GPT-3). The generative AI model analyzes the input text and automatically generates specific suggestions for improving the product.

[1588] 3. Displaying the results: The server displays the analysis results obtained from the generative AI model to the user, allowing the user to see how their feedback can help improve the product.

[1589] Examples

[1590] For example, if a user enters feedback such as "This product has a short battery life," the generative AI model will generate specific improvement suggestions such as "Consider increasing the battery capacity or adding a power-saving mode."

[1591] Examples of prompt statements

[1592] User feedback: This product has a short battery life

[1593] Please provide suggestions for product improvement based on this feedback.

[1594] In this way, it is possible to efficiently collect feedback from users and automatically generate specific product improvement proposals based on the analysis results.

[1595] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1596] Step 1:

[1597] Users access the web survey screen using their smartphone and enter their answers in written form.

[1598] Input: User's written response (e.g. "This product has a short battery life")

[1599] Output: Short answer data

[1600] Specific operation: The user accesses the web survey screen and inputs answers to the questions in text format. The input answers are sent to the server.

[1601] Step 2:

[1602] The server receives the user's written responses and stores them in a database.

[1603] Input: Written response data

[1604] Output: Answer data stored in a database

[1605] Specific operation: The server stores the received user's written answers in a database. The stored data is used for subsequent analysis processing.

[1606] Step 3:

[1607] The server inputs the user's stored written answers into a generative AI model.

[1608] Input: Answer data stored in the database

[1609] Output: Data input to the generative AI model

[1610] How it works: The server retrieves the user's written answers from the database and converts them into a format that can be input into a generative AI model (OpenAI's GPT-3).

[1611] Step 4:

[1612] A generative AI model analyzes users' written responses and automatically generates specific suggestions for improving products.

[1613] Input: Data to be input to the generative AI model (e.g., "This product has a short battery life")

[1614] Output: Analysis results (e.g. "Consider increasing battery capacity or adding a power saving mode")

[1615] Specific operation: The generative AI model analyzes the user's written responses and generates specific suggestions for improving the product.

[1616] Step 5:

[1617] The server displays the analysis results obtained from the generative AI model to the user.

[1618] Input: Analysis results

[1619] Output: Analysis results that are displayed to the user

[1620] What it does: The server displays the analysis results from the generative AI model to the user, who can see how their feedback can help improve the product.

[1621] Example 2

[1622] Next, a second embodiment of the second form will be described. 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".

[1623] In conventional web survey systems, it was difficult to instantly evaluate users' answers and offer high-value prizes based on the results. In addition, the evaluation criteria were vague, making it impossible to properly evaluate the specificity, novelty, and feasibility of users' answers. Furthermore, the process of quickly collating the evaluation results and notifying users was cumbersome.

[1624] 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.

[1625] In this invention, the server includes a means for having a user think of a question, a means for inputting a descriptive answer from a web survey screen, a means for using a generative AI model to instantly score the answer, a means for generating a prompt sentence and inputting it to the generative AI model, a means for tallying up the evaluation results from the generative AI model, a means for offering a high-value prize according to the score, and a means for later summarizing and providing a report to a company. This makes it possible to instantly evaluate a user's answers and offer a high-value prize based on the results.

[1626] A "user" is an individual or entity that accesses the system, formulates a question, and enters an answer.

[1627] "Questions" are themes or topics that the user should think about and are presented by the system.

[1628] The "web survey screen" is an interface for users to input written responses and is accessible via a web browser.

[1629] A "descriptive response" is a response format in which the user freely enters text.

[1630] A "generative AI model" is an artificial intelligence model used to evaluate user responses, for example using natural language processing techniques.

[1631] A "prompt" is a text sentence that is generated to provide input to a generative AI model, containing the user's answer and evaluation criteria.

[1632] "Evaluation results" refer to the scores and feedback that the generative AI model generates by analyzing the user's answers.

[1633] A "high-value prize" is a valuable reward provided based on the user's evaluation results, such as an electronic gift or credits for an electronic payment service.

[1634] A "report" is a document that summarizes user responses and evaluation results and is provided to companies.

[1635] "Company" refers to a legal entity or organization that uses the system to collect responses from users and receive the analysis results.

[1636] This invention is a system that instantly evaluates answers entered by users and offers high-value prizes based on the results. The system is implemented using a server, a terminal, and a generative AI model.

[1637] First, a user accesses the system using a terminal and inputs a descriptive answer into the web survey screen. For example, a user inputs "solar-powered portable charger" as an "idea for a new eco-friendly product."

[1638] The server receives the answers entered by the user and stores them in text format. The server then converts the answers into a suitable format for input to the generative AI model, specifically by removing unnecessary whitespace and special characters and cleaning up the text.

[1639] The server generates a prompt sentence to be input to the generative AI model. The prompt sentence includes the user's answer and evaluation criteria (concreteness, novelty, feasibility). For example, the following prompt sentence is generated:

[1640] Enter your answer below:

[1641] Answer: A portable solar charger

[1642] Evaluation criteria: Specificity, novelty, feasibility

[1643] The server inputs the prompt sentence into a generative AI model (e.g., a model using natural language processing technology). The generative AI model analyzes the prompt sentence and generates a score based on each evaluation criterion. For example, it may generate a score of 8 points for concreteness, 7 points for novelty, and 9 points for feasibility.

[1644] The server receives the scores returned by the generative AI model and sums up the scores for each evaluation criterion, for example a total of 24 points. The server then notifies the user of the final evaluation result. Depending on the evaluation result, the user is given a large prize, for example an electronic gift or credits for an electronic payment service are sent to the user's account.

[1645] This system makes it possible to instantly evaluate users' answers and offer high-value prizes based on the results. In addition, the evaluation criteria are clear, and the concreteness, novelty, and feasibility of users' answers can be appropriately evaluated. Furthermore, the process of quickly collating evaluation results and notifying users is streamlined.

[1646] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1647] Step 1:

[1648] The user accesses the system using a terminal and inputs descriptive responses via the web survey screen.

[1649] Input: The question and answer the user thought of (e.g., "Portable charger powered by solar power")

[1650] Output: The user's answer is sent to the system.

[1651] Step 2:

[1652] The server receives the answers entered by the user and stores them in text format.

[1653] Input: Answer data submitted by the user

[1654] Output: Saved text data

[1655] Step 3:

[1656] The server converts the received answers into a suitable format for input to the generative AI model, specifically cleaning up the text by removing unnecessary whitespace and special characters.

[1657] Input: Saved text data

[1658] Output: Cleaned text data

[1659] Step 4:

[1660] The server generates a prompt sentence to be input to the generative AI model. The prompt sentence includes the user's answer and evaluation criteria (concreteness, novelty, feasibility).

[1661] Input: Cleaned text data

[1662] Output: Generated prompt (e.g., "Please enter the user's answer below: Answer: Portable solar charger Evaluation criteria: Specificity, novelty, feasibility")

[1663] Step 5:

[1664] The server inputs this prompt into a generative AI model, which analyzes the prompt and generates a score based on each evaluation criterion.

[1665] Input: Generated prompt text

[1666] Output: Evaluation score from the generative AI model (e.g. concreteness 8 points, novelty 7 points, feasibility 9 points)

[1667] Step 6:

[1668] The server receives the scores returned by the generative AI model and aggregates the scores for each evaluation criterion.

[1669] Input: Evaluation scores from a generative AI model

[1670] Output: Aggregated final score (e.g. 24 points total)

[1671] Step 7:

[1672] The server notifies the user of the final evaluation result. Depending on the evaluation result, the user is given a large prize, for example, an electronic gift or credits for an electronic payment service are sent to the user's account.

[1673] Input: Final scores tallied

[1674] Output: Notify the user and provide a high-value prize (e.g., send an e-gift)

[1675] (Application example 2)

[1676] Next, a description will be given of Application Example 2 of Form 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".

[1677] In conventional survey systems, it was difficult to instantly evaluate users' responses and provide high-value prizes based on the results. In addition, the evaluation criteria for responses were vague, and feedback to users was often delayed. Furthermore, creating reports to be provided to companies was time-consuming and inefficient. To solve these problems, a system that can instantly evaluate users' responses and provide high-value prizes based on the scores is needed.

[1678] 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.

[1679] In this invention, the server includes a means for having the user think of questions, a means for inputting descriptive answers from a web questionnaire screen, a means for instantly scoring the answers using a generative AI model, a means for presenting a high-value prize according to the score, and a means for later summarizing and providing the report to the company. This makes it possible to instantly evaluate the user's answers and provide a high-value prize according to the score. In addition, the report can be efficiently provided to the company.

[1680] "User" means an individual or entity that uses the system to answer questions.

[1681] "Question content" refers to questions thought up by the user or questions in a survey or quiz provided by the system.

[1682] The "WEB survey screen" is an interface that allows users to answer questions via the Internet.

[1683] "Written response" refers to a format in which users can freely enter text to answer questions.

[1684] A "generative AI model" is an algorithm or program that uses artificial intelligence to evaluate and score user responses.

[1685] "Instant scoring" is the process of evaluating and scoring answers immediately after a user enters the answer.

[1686] A "high value prize" is a valuable reward provided depending on the score of a user's answer.

[1687] An "e-gift" is a gift in digital form that can be received over the Internet.

[1688] An "electronic payment service" is a service for conducting monetary transactions over the Internet.

[1689] A "Report" is a report summarizing the results of users' responses and is provided to companies.

[1690] "Company" refers to a legal entity or organization that uses the system to receive survey results and reports.

[1691] The system for implementing this invention allows users to think up questions, input descriptive answers on a web survey screen, and instantly score the answers using a generative AI model. High-value prizes are awarded according to the scores, and a summary report is later provided to the company.

[1692] Hardware and Software Used

[1693] Hardware: Smartphones, servers

[1694] Software: Python, OpenAI API, Internet connection

[1695] Data processing and calculation

[1696] User Actions

[1697] Users access the online survey screen using their smartphones, think about the question, and enter their answer in a descriptive format. For example, to the question, "What is the diameter of the Earth?", the user answers, "About 12,742 kilometers."

[1698] Server Processing

[1699] 1. Getting the answer: The server receives the written answer submitted by the user.

[1700] 2. Scoring by the generative AI model: The server uses the OpenAI API to input the user's answer into the generative AI model and instantly scores it. For example, the server sends a prompt message to the generative AI model: "Please score the following answer: Approximately 12,742 kilometers."

[1701] 3. Score determination: Determine the user's score based on the score returned by the generative AI model.

[1702] 4. Offering prizes: Offering high-value prizes to users depending on their scores. For example, if the score is 90 or above, they will receive electronic gifts, and if the score is 80 or below, they will receive points for electronic payment services.

[1703] Report creation and delivery

[1704] The server summarizes the users' answers and provides the company with a report that includes information such as the answers the users gave, their scores, and the types of prizes they received.

[1705] Examples

[1706] If a user answers the question "What is the diameter of the Earth in kilometers?" with "About 12,742 kilometers," the server sends this answer to a generative AI model, which scores it using the following prompt:

[1707] Examples of prompt statements

[1708] Please rate the following answer: Approximately 12,742 kilometers

[1709] The generative AI model scores the answers, and if it scores 90 points, for example, the server provides the user with an e-gift. It also summarizes the results and provides them to the company in a report.

[1710] In this way, a system is realized that instantly evaluates users' answers and offers high-value prizes according to the scores.

[1711] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1712] Step 1:

[1713] Users access the web survey screen using their smartphone, think about the questions they want to ask, and enter their answers in descriptive format.

[1714] Input: User's written answer

[1715] Output: Answer data sent to the server

[1716] Specific behavior: In response to the question "What is the diameter of the Earth in kilometers?", the user enters "About 12,742 kilometers."

[1717] Step 2:

[1718] The server receives the written response submitted by the user.

[1719] Input: User's written response data

[1720] Output: Answer data stored in the answer database on the server

[1721] What happens: The server stores the user's answer in a database and prepares it for further processing.

[1722] Step 3:

[1723] The server uses a generative AI model to instantly score the user's answers.

[1724] Input: User written answers stored in the database

[1725] Output: The score returned by the generative AI model.

[1726] What happens: The server calls the OpenAI API, sends the prompt "Please score the following answer: Approximately 12,742 kilometers" to the generative AI model, and gets a score.

[1727] Step 4:

[1728] The server determines the user's score based on the score returned by the generative AI model.

[1729] Input: The score returned by the generative AI model

[1730] Output: User score

[1731] Specific operation: The server analyzes the score returned by the generative AI model and determines it to be, for example, 90 points.

[1732] Step 5:

[1733] The server will award users with large prizes depending on their scores.

[1734] Input: User's score

[1735] Output: Large prize given to the user

[1736] Specific operation: The server provides electronic gifts to users with a score of 90 or above, and points for electronic payment services to users with a score of 80 or below.

[1737] Step 6:

[1738] The server summarizes the users' responses and provides a report to the company.

[1739] Input: User's answer, points, prize type

[1740] Output: Reports provided to companies

[1741] What it does: The server summarizes the users' answers and provides them to the company as a report, including information about the users' answers, their scores, and the types of prizes awarded.

[1742] Example 3

[1743] Next, a third embodiment of the third embodiment will be described. 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".

[1744] Conventional survey systems have difficulty efficiently summarizing responses collected from users and providing them to companies. In addition, there is a lack of incentives to improve the quality of responses, and a method to increase users' willingness to participate was needed. Furthermore, preprocessing and summarizing response data takes a lot of time and effort, making it difficult to quickly create reports.

[1745] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1746] In this invention, the server includes a means for having a user think about the content of a question, a means for inputting a descriptive answer from a web survey screen, a means for instantly scoring the answers and presenting a high-value prize according to the score, a means for saving the answers in a database, a means for preprocessing the saved answer data, a means for sending a prompt sentence to the generative AI model, a means for compiling the summary returned from the generative AI model into a report, and a means for providing the report to the company. This makes it possible to efficiently summarize the user's answers and quickly provide them to the company. Also, by providing a high-value prize, it is possible to increase the user's motivation to participate.

[1747] "User" means an individual or organization that uses the system to respond to surveys.

[1748] "Question content" refers to the questions in the survey presented to the user.

[1749] The "web survey screen" is an interface that users can access via the Internet to answer questions.

[1750] A "descriptive response" is a survey response in which the user responds by freely entering text.

[1751] "Instant scoring" is the process of rating and scoring an answer immediately after the user enters the answer.

[1752] A "Big Prize" is a high value reward offered to a user for their answer.

[1753] "Database" refers to an information management system for storing user response data.

[1754] "Preprocessing" is the process of tokenizing, removing stop words, stemming, and other processing of answer data before inputting it into a generative AI model.

[1755] A "generative AI model" is an artificial intelligence model used to summarize a user's answers.

[1756] A "prompt sentence" is an input sentence that gives instructions to a generative AI model.

[1757] A "summary" is a text that briefly summarizes the user's response.

[1758] A "Report" is a document that compiles summarized User responses and provides them to the Company.

[1759] A "company" is an organization that uses user response data to improve products and services.

[1760] This invention is a system that efficiently summarizes responses collected from users and provides them to companies. The system is composed of users, terminals, and a server.

[1761] Users access the survey form using a web browser or a mobile app and enter descriptive responses, such as, "The design of this product is great, but the battery life is poor. I'd like you to improve that."

[1762] The device sends the entered answer to the server as an HTTP POST request, with the answer data sent in JSON format.

[1763] The server saves the received response data in a database. When saving, metadata such as user ID and timestamp are also recorded. The database is a common database management system such as MySQL or PostgreSQL.

[1764] The server then takes the stored answer data and pre-processes it using spaCy, an NLP library. This pre-processing includes tokenization, stop word removal, stemming, etc. For example, it converts "The design of this product is great, but the battery life is poor. I hope they improve it" into "Great design, poor battery life, improved."

[1765] The preprocessed data is input to GPT-3, a generative AI model. The server generates a prompt for GPT-3 and instructs it to summarize the user's answer. For example, the prompt can be "Summarize the following user's opinion: The design of this product is great, but the battery life is poor. I would like you to improve it."

[1766] The summary results returned by GPT-3 are collected by the server and formatted into a report. This report includes opinion trends and specific improvement suggestions for product improvement. Finally, the server provides this report to companies. Companies can use this report to improve their products and services.

[1767] This system makes it possible to efficiently summarize the user's answers and quickly provide them to the company. In addition, by offering high-value prizes, it is possible to increase the user's motivation to participate. The flow of the identification process in the third embodiment will be described with reference to FIG. 15.

[1768] Step 1:

[1769] The user enters the answer

[1770] A user accesses the survey form using a web browser or a mobile app and enters a descriptive response. For example, the user might enter, "The design of this product is great, but the battery life is poor. I hope they can improve it." The input data is in text format.

[1771] Step 2:

[1772] The device sends the answer to the server

[1773] The terminal sends the entered answer to the server as an HTTP POST request. At this time, the answer data is sent in JSON format. For example, the following data is sent: {"user_id": 123, "response": "The design of this product is great, but the battery life is poor. I hope they will improve this."}. The input is JSON format data, and the output is a request sent to the server.

[1774] Step 3:

[1775] The server stores the answer in a database

[1776] The server saves the received response data in a database. When saving, metadata such as the user ID and timestamp are also recorded. For example, execute the following query in a MySQL table: INSERT INTO responses (user_id, response, timestamp) VALUES (123, 'The design of this product is great, but the battery life is poor. I hope they improve it.', '2023-10-01 12:34:56'). The input is JSON format data, and the output is saved in the database.

[1777] Step 4:

[1778] The server preprocesses the answer data

[1779] The server retrieves answer data from the database and performs preprocessing using spaCy, an NLP library. This preprocessing includes tokenization, stop word removal, stemming, etc. For example, "The design of this product is great, but the battery life is poor. I hope it will be improved." is converted to "Great design, poor battery life, improvement." The input is text data retrieved from the database, and the output is the preprocessed text data.

[1780] Step 5:

[1781] The server sends a prompt to the generative AI model.

[1782] The server generates a prompt sentence based on the preprocessed data and sends it to the generative AI model (GPT-3). For example, it generates a prompt sentence such as "Summarize the following user opinions: Design is great Battery life is bad Improve." The input is the preprocessed text data, and the output is the prompt sentence sent to the generative AI model.

[1783] Step 6:

[1784] Generative AI model returns summary

[1785] The generative AI model (GPT-3) generates a summary based on the prompt sentence and returns it to the server. For example, it generates a summary such as "The design is good, but the battery life is poor." The input is the prompt sentence, and the output is the summarized text data.

[1786] Step 7:

[1787] The server summarizes the results into a report

[1788] The server collects the summaries returned by the generative AI model and compiles them into a report along with the opinions of other users. The report includes trends in opinions and specific suggestions for improvement. For example, it shows a trend that "many users appreciate the design, but are dissatisfied with the battery life." The input is summarized text data, and the output is a document in the form of a report.

[1789] Step 8:

[1790] The server provides the report to the company.

[1791] The server generates the completed report in PDF or Excel format and sends it to the company by email. The company uses this report to improve its products. The input is a document in report format, and the output is the provision of the report to the company.

[1792] (Application example 3)

[1793] Next, a description will be given of Application Example 3 of Form Example 3. 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".

[1794] With conventional survey systems, it was difficult to efficiently collect feedback from users and provide it in a form that was useful to the company. In addition, there was a problem that it was difficult to quickly improve products because it took a lot of time and effort to summarize and analyze the feedback. Furthermore, there was a need to generate reports that accurately reflected user opinions, but there was a problem that it was difficult to achieve this.

[1795] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1796] In this invention, the server includes means for having users think of questions, means for inputting written answers from a web questionnaire screen, means for instantly scoring answers and presenting high-value prizes according to scores, means for later summarizing and providing a report to companies, means for summarizing feedback collected from users using a generative AI model, means for generating a report including opinion trends and specific improvement proposals based on the summarized feedback, and means for providing the generated report to companies. This makes it possible to efficiently collect user feedback and quickly and accurately summarize and analyze it before providing it to companies.

[1797] A "User" is an individual or organization that uses the system to formulate questions and provide feedback.

[1798] "Question content" refers to the questions that the user thinks of in the survey, and is entered in descriptive form.

[1799] The "WEB survey screen" is an interface that allows users to enter questions online and write their answers.

[1800] A "descriptive answer" is an answer in which the user freely enters the answer in text.

[1801] "Scoring" is the process of assigning a score to a user's answer and is done instantly.

[1802] "High-value prizes" are rewards given to users for their answers, and can include electronic gifts and electronic payment services.

[1803] "Summarization" is the process of concisely summarizing the feedback collected from users.

[1804] A "report" is a document prepared based on summarized feedback, including trends in opinions and specific suggestions for improvement.

[1805] "Company" means a legal entity or organization that receives User Feedback and uses it to improve its products and services.

[1806] A "generative AI model" is an algorithm or program that uses artificial intelligence to generate and summarize text data.

[1807] "Opinion trends" are common opinions or patterns that emerge based on collected feedback from users.

[1808] "Specific improvement proposals" are specific improvements to products or services proposed based on user feedback.

[1809] As an embodiment of the present invention, the following system is constructed.

[1810] System Configuration

[1811] The system includes a user terminal, a server, and a generative AI model. The user terminal is a device such as a smartphone or PC, and is used by users to think of questions and input descriptive answers on a web survey screen. The server is responsible for processing the feedback collected from users, summarizing it using the generative AI model, and generating a report.

[1812] Hardware and Software Used

[1813] Hardware: Smartphones, PCs, servers

[1814] Software: Python, OpenAI API, WEB survey system

[1815] Data processing and calculation

[1816] 1. Data collection: The user enters descriptive responses into the web survey screen from their device. This sends the user's feedback to the server.

[1817] 2. Data Summarization: The server summarizes the collected feedback using a generative AI model (e.g., GPT-3) by using the OpenAI API to concisely summarize the feedback.

[1818] 3. Report generation: Based on the summarized feedback, the server generates a report containing opinion trends and specific improvement suggestions. This report is provided to the company.

[1819] Examples

[1820] For example, if a user enters feedback such as "This product is very easy to use," the server collects this along with other feedback and summarizes it using a generative AI model. The summarized feedback is compiled into a report in the form of "Many users would like to see improvements in price and design."

[1821] Examples of prompt statements

[1822] Below are examples of prompts to be input to the generative AI model.

[1823] Please summarize your feedback below:

[1824] This product is very easy to use.

[1825] I think it would be better if the design was a little better.

[1826] The price is high, so I wish it was a little cheaper.

[1827] There are too many functions and it's hard to understand how to use it.

[1828] In this way, user feedback can be collected efficiently, quickly and accurately summarized, analyzed, and provided to companies.

[1829] The flow of the specific process in the application example 3 will be described with reference to FIG.

[1830] Step 1:

[1831] The user accesses the web survey screen, thinks about the questions to be asked, and enters a descriptive answer.

[1832] Input: The question and short answer entered by the user.

[1833] Output: User feedback data sent to the server.

[1834] Specific operation: The user accesses the web survey screen using a smartphone or PC, thinks about the questions, and enters descriptive answers. The entered data is sent to the server.

[1835] Step 2:

[1836] The server receives the feedback data sent by the user and stores it in a database.

[1837] Input: Feedback data submitted by the user.

[1838] Output: Feedback data stored in a database.

[1839] Specific operation: The server receives the feedback data sent by the user and stores it in a database for later processing.

[1840] Step 3:

[1841] The server retrieves feedback data from the database and generates prompt sentences to input into the generative AI model.

[1842] Input: Feedback data stored in the database.

[1843] Output: A prompt to be fed into the generative AI model.

[1844] Specific behavior: The server retrieves feedback data from the database and generates a prompt sentence like the following: "Please summarize the following feedback: This product is very easy to use. It would be even better if it was better designed. It is expensive, so I wish it was a little cheaper. There are too many functions and it is difficult to understand how to use it."

[1845] Step 4:

[1846] The server uses a generative AI model (e.g., GPT-3) to input prompts and summarize the feedback data.

[1847] Input: The generated prompt statement.

[1848] Output: Summarised feedback data.

[1849] Specific operation: The server inputs a prompt sentence into the generative AI model and summarizes the feedback data. The generative AI model summarizes the feedback data concisely based on the input prompt sentence.

[1850] Step 5:

[1851] The server generates a report based on the summarized feedback data, including trends in opinions and specific suggestions for improvement.

[1852] Input: Summarised feedback data.

[1853] Output: A report containing opinion trends and concrete improvement suggestions.

[1854] Specific actions: The server analyzes the summarized feedback data, extracts opinion trends and specific improvement suggestions, and generates a report to provide to the company.

[1855] Step 6:

[1856] The server provides the generated reports to the enterprise.

[1857] Input: A report containing opinion trends and specific suggestions for improvement.

[1858] Output: Reports provided to the company.

[1859] Specific operation: The server provides the generated report to the company, which uses the report to improve its products and services.

[1860] In addition, 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.

[1861] "Example 1"

[1862] In one embodiment of the present invention, a system is provided incorporating an emotion engine that receives written responses from a user and recognizes emotions contained in the responses. The emotion engine uses natural language processing techniques to extract emotions from the user's written responses.

[1863] Specifically, if a user responds by saying, "The product's design feels outdated," the emotion engine will recognize the negative emotion of "dissatisfaction" from this response.

[1864] "Example 2"

[1865] The emotion engine also has a function of measuring the intensity of a user's emotion. The intensity of emotion is calculated based on, for example, the intensity of vocabulary indicating emotion contained in the written answer, the proportion of the vocabulary in the entire answer, etc. For example, the expression "very dissatisfied" is determined to have a higher intensity of emotion than the expression "slightly dissatisfied."

[1866] "Example 3"

[1867] Furthermore, the results of the emotion engine are reflected in reports generated by the system. The reports provide companies with information such as the trends and strength of users' emotions, as well as the factors that trigger specific emotions. This allows companies to obtain specific guidelines for improving their products. For example, if negative emotions such as "the design is outdated" are detected from many users, companies can consider revising the product design.

[1868] The process flow of each embodiment will be described below.

[1869] "Example 1"

[1870] Step 1: User enters written responses into a web survey.

[1871] Step 2: The system receives the user's written response and sends it to the emotion engine.

[1872] Step 3: The emotion engine extracts emotions from the written responses and returns the results to the system.

[1873] Step 1: The sentiment engine extracts sentiment from written responses.

[1874] Step 2: The sentiment engine calculates the intensity of the sentiment, based on factors such as the strength of the sentiment vocabulary and its proportion in the overall response.

[1875] Step 3: The emotion engine calculates the emotion intensity and returns it to the system.

[1876] "Example 3"

[1877] Step 1: The system receives the results from the emotion engine.

[1878] Step 2: The system compiles a report on the trends and intensity of emotions, as well as factors that trigger specific emotions.

[1879] Step 3: Provide the system-generated reports to the business.

[1880] Example 1

[1881] Next, a description will be given of Example 1 of the embodiment 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".

[1882] Conventional web survey systems have difficulty extracting emotions from users' free-form responses and conducting analysis based on those emotions. They also lack a mechanism for instantly scoring users' responses and offering high-value prizes according to the scores. This has led to problems such as a decline in user satisfaction and the quality of feedback for companies' product improvements.

[1883] 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.

[1884] In this invention, the server includes a means for having a user think about the content of a question, a means for inputting a written response from a web questionnaire screen, a means for instantly scoring the response and presenting a high-value prize according to the score, a means for later summarizing and providing a report to the company, a means for receiving the user's written response and using an emotion engine for recognizing the emotion contained in the response, a means for the emotion engine to extract emotion from the user's response using natural language processing technology, and a means for storing the extracted emotion in a database. This makes it possible to extract emotion from the user's free-form response and perform analysis based on the emotion, thereby improving user satisfaction and improving the quality of feedback for company product improvements.

[1885] "User" refers to an individual or organization that accesses the web survey screen and enters written responses to the questions.

[1886] "WEB survey screen" refers to a web page that users can access via a browser and that provides an interface for entering written responses to questions.

[1887] "Written answers" refers to answers in which the user freely enters text.

[1888] "Sentiment Engine" refers to a software module for extracting emotions from a user's written responses.

[1889] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate human language.

[1890] "Database" refers to an information management system for storing extracted emotions and user response data.

[1891] "Big Prizes" refers to valuable rewards that are awarded according to the score that is instantly scored for a user's answers.

[1892] An "electronic gift" refers to a digital gift that can be received online.

[1893] "Electronic payment service" refers to a service that enables monetary transactions to be conducted over the Internet.

[1894] This invention is a system in which users access a web survey screen and input written responses to questions. This system has the function of instantly scoring the user's responses and presenting high-value prizes according to the scores. It also has the function of extracting emotions from the user's written responses and storing those emotions in a database.

[1895] The server generates a program that allows the user to access the web survey screen. This program includes an interface that allows the user to enter answers in a written format. The interface is built using web technologies such as HTML and JavaScript.

[1896] When the user enters a response in written form, the device sends the response to the server. The server passes the received response to the emotion engine. The emotion engine uses natural language processing technology to extract emotions from the user's response. This emotion engine is implemented using, for example, Python's NLTK library or the Google Cloud Natural Language API.

[1897] For example, if a user answers, "The design of the product feels old-fashioned," the emotion engine extracts the emotion of "dissatisfaction" from the answer. This recognized emotion is returned to the server, which stores the result in a database.

[1898] As a concrete example, consider a case where a user answers the question, "Which part of the product are you dissatisfied with?" with, "The design of the product feels old-fashioned." In this case, the emotion engine extracts the emotion, "dissatisfied."

[1899] Examples of prompts to be input to a generative AI model include:

[1900] User response: "The product design seems outdated"

[1901] Sentiment engine output: "Unhappy"

[1902] This prompt sentence can be used to provide training data for the generative AI model to improve the accuracy of emotion recognition.

[1903] This system can extract emotions from users' free-form responses and perform analysis based on those emotions, which can improve user satisfaction and the quality of feedback for companies to improve their products.

[1904] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1905] Step 1:

[1906] The user accesses the web survey screen.

[1907] The user accesses the specified URL using a browser. This URL is a link to the web survey page hosted by the server. The input is the access request from the user's browser, and the output is the display of the web survey page.

[1908] Step 2:

[1909] The user inputs a written response to the question.

[1910] A user freely enters their opinion in a text box in response to questions displayed on a web survey screen. For example, in response to the question "Which part of the product are you dissatisfied with?", the user enters "The design of the product seems old-fashioned." The input is the user's response text, and the output is the click event of the send button.

[1911] Step 3:

[1912] The terminal transmits the user's answer to the server.

[1913] When the user enters an answer and clicks the send button, the terminal sends the answer data to the server as an HTTP POST request, which includes the user's answer text. The input is the user's answer text, and the output is an HTTP POST request to the server.

[1914] Step 4:

[1915] The server passes the received answer to the emotion engine.

[1916] The server passes the user's answer received from the terminal to the emotion engine for analysis. The emotion engine is a module that runs in the server and extracts emotions using natural language processing technology. The input is the user's answer text, and the output is the data transfer to the emotion engine.

[1917] Step 5:

[1918] The sentiment engine extracts sentiment from user responses.

[1919] The emotion engine analyzes the user's response text and extracts emotions. For example, it extracts the emotion "dissatisfied" from the user's response "The product's design feels outdated." This process uses Python's NLTK library and Google Cloud Natural Language API. The input is the user's response text, and the output is the extracted emotion data.

[1920] Step 6:

[1921] The server stores the extracted emotions in a database.

[1922] The server receives the emotion data returned by the emotion engine and stores it in a database. The stored data includes the user's answer text and the extracted emotion. The input is the extracted emotion data, and the output is the save operation to the database.

[1923] (Application example 1)

[1924] Next, a description will be given of Application Example 1 of Form 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".

[1925] With conventional web survey systems, it was difficult to accurately grasp the nuances of emotions when collecting user feedback. There was also a lack of means to instantly analyze user feedback and provide it as useful information for companies. Furthermore, incentives for users were limited, making it difficult to improve the quality and quantity of feedback.

[1926] The identification process by the identification processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means. In this invention, the server includes a means for asking the user to think of the question content, a means for inputting a descriptive answer from a web questionnaire screen, a means for using an emotion engine for recognizing the emotion contained in the answer, a means for instantly scoring the answer and presenting a high-value prize according to the score, and a means for later summarizing and providing a report to the company. This makes it possible to accurately extract emotions from the user's feedback, analyze it instantly, and provide it to the company as useful information. Furthermore, by providing a high-value prize, the quality and quantity of the user's feedback can be improved.

[1927] A "user" is an individual or group that uses the system to formulate questions and enter written responses.

[1928] "Question content" refers to a theme or topic on which a user can write their opinion or impression regarding a product or service.

[1929] The "WEB survey screen" is an interface that users can access via the Internet and use to enter written responses.

[1930] A "descriptive answer" is an answer in which the user freely enters text to express their opinions or thoughts.

[1931] An "emotion engine" is software or a system for extracting and recognizing emotions from a user's written responses using natural language processing techniques.

[1932] "Scoring" is the process of evaluating and assigning a score to a user's written response.

[1933] A "high-value prize" is a valuable reward offered to a user in return for their answer, and may include electronic gifts or electronic payment services.

[1934] "Report" means a document or data summarizing User feedback and providing it to Company.

[1935] "Company" means an organization or legal entity that receives User Feedback and uses it to improve its products and services.

[1936] A system for implementing the present invention allows a user to access a web survey screen and input opinions about products and services in a written format. The system includes an emotion engine that extracts and analyzes emotions from the user's written responses. A specific embodiment of this system will be described below.

[1937] First, the user accesses the online survey screen using a device such as a smartphone or tablet. The user thinks of questions to ask about the product or service and enters their answers in a descriptive format. The answers entered by the user are then sent to the server in real time.

[1938] The server passes the received written response to the emotion engine, which uses natural language processing technology to extract the emotion. The emotion engine uses the TextBlob library to recognize positive, negative, or neutral emotions from the user's response. For example, if a user responds, "The design of this product feels old-fashioned," the emotion engine extracts the emotion "negative" from the response.

[1939] The server then scores the answers based on the extracted emotions and offers high-value prizes based on the scores. The high-value prizes include electronic gifts and electronic payment services. Users can receive these prizes online.

[1940] In addition, the server summarizes the user feedback and provides it to the company as a report, which includes the results of the user's sentiment analysis and the specific feedback content. The company can use this report to improve its products and services.

[1941] As a concrete example, consider the case where a user enters the following feedback:

[1942] User ID: 12345

[1943] Product ID: 67890

[1944] Feedback: The design of this product seems outdated.

[1945] This feedback is sent to a server, where it is recognized as "negative" by the sentiment engine. The server then scores the feedback and awards the user with a high-value prize. It also provides the company with a report summarizing the feedback and sentiment analysis results.

[1946] In this way, it is possible to accurately extract emotions from user feedback, analyze them in real time, and provide the information to companies as useful information. In addition, by offering high-value prizes, the quality and quantity of user feedback can be improved.

[1947] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1948] Step 1:

[1949] The user uses a terminal to access the web survey screen. The user thinks of questions to ask about products and services, and enters answers in descriptive format. The entered answers are sent from the terminal to the server. The input data includes the user ID, product ID, and feedback content.

[1950] Step 2:

[1951] The server passes the received written responses to the emotion engine. The emotion engine uses the TextBlob library to extract emotions from the user's responses. Specifically, it uses natural language processing techniques to recognize positive, negative, and neutral emotions in the feedback content. The input data is the feedback content, and the output data is the extracted emotions.

[1952] Step 3:

[1953] The server scores the answers based on the extracted emotions. The scoring algorithm assigns scores according to the type of emotion and the content of the feedback. For example, positive emotions are given high scores and negative emotions are given low scores. The input data are the extracted emotions and the output data are the scoring results.

[1954] Step 4:

[1955] The server provides high-value prizes according to the scoring results. The high-value prizes include electronic gifts and electronic payment services. Users can receive these prizes on the web. The input data is the scoring results, and the output data is the prizes provided.

[1956] Step 5:

[1957] The server summarizes the user feedback and provides it to the company as a report. This report includes the results of the user's sentiment analysis and the specific feedback content. The company can use this r...

Claims

1. A way to get users to think about the content of their questions, A means for inputting answers to the questions from a web questionnaire screen; means for storing said responses in a database; means for inputting the stored answers into a generative AI model; A means for the generative AI model to analyze the content of the answer; A means for storing an analysis result by the analyzing means in a database; The system further comprises means for summarizing the analysis results stored in the database and providing a report to the company.

2. 2. The system according to claim 1, wherein the means for prompting the user to consider the content of the question is a descriptive format rather than a multiple choice format.

3. The system of claim 1, wherein the generative AI model extracts important keywords and opinions from user responses.

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