System

Abstract

A system is provided.SOLUTION: A system comprising: means for taking attribute data such as an age, a region, a family structure, and an income into a database in a form excluding personal information and causing artificial intelligence to learn the attribute data; means for allowing a user to input or select a question of a questionnaire; means for designating an attribute of a response and the number of responses desired by the user; and means for providing the generated response to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional market research and surveys are costly, time-consuming, and labor-intensive, making them difficult for small businesses and those with budget constraints. Increasing the number of respondent samples also poses a problem of high costs. The present invention aims to solve these problems and enable market research to be conducted at low cost and in a short period of time. [Means for solving the problem]

[0005] The present invention aims to solve the above problems by providing a system that includes: means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and having an artificial intelligence learn the data; means for a user to input or select survey questions; means for the user to specify the attributes and number of answers desired by the user; means for the artificial intelligence to generate answers based on the specified attributes and number of answers; and means for providing the generated answers to the user. Furthermore, the present invention also provides means for charging a metered fee based on the income of the user, and means for calculating fees based on the number of answers generated and sending an invoice to the user.

[0006] "Age" refers to the number of years that have passed since a person was born.

[0007] "Region" refers to a particular geographic area or location.

[0008] "Family structure" refers to the composition of family members in a household.

[0009] "Income" refers to the total amount of economic benefits or rewards received within a certain period of time.

[0010] "Attribute data" refers to data that indicates characteristics or conditions related to an object or person.

[0011] "Personal information" refers to information that can identify a specific individual.

[0012] A "database" is a collection of information that is organized so that large amounts of data can be efficiently stored, managed, and searched.

[0013] "Artificial intelligence" refers to technologies and systems that allow computers to mimic human intellectual functions and learn and make decisions autonomously.

[0014] "Questions" refer to the questions asked to subjects in a questionnaire or survey.

[0015] An "answer" is an answer or reaction to a question.

[0016] "Pay-as-you-go" refers to a billing method in which fees are determined according to the amount of use or usage.

[0017] An "invoice" is a document that lists the amount due and payment details and requests payment from a specific customer. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] 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.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] 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.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] 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.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The system of the present invention automatically generates responses to market research and questionnaires. To implement this system, it is important that the server, terminals, and users work together. The detailed configuration and operation of this system will now be described.

[0040] Basic configuration

[0041] Server: A central computer system that hosts the artificial intelligence (AI) model and manages the database. The server handles key processes such as attribute data capture, AI training, answer generation, and billing processing.

[0042] Terminal: A device that provides an interface for users to access. Users enter survey questions, specify response attributes, and receive generated responses through the terminal.

[0043] User: An individual or organization who wants to conduct market research or a questionnaire. The user operates a terminal to input questions and specify the desired response attributes and number.

[0044] Program processing

[0045] 1. User enters and selects survey questions:

[0046] The user opens the input form on the device and enters or selects the survey questions. The device receives the user's input and temporarily saves the questions.

[0047] example:

[0048] Question 1: What is your age?

[0049] Question 2: What area do you live in?

[0050] Question 3: What is your income?

[0051] 2. User specifies the attributes and number of answers:

[0052] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through the device. The device collects and temporarily stores this information.

[0053] example:

[0054] Number of answers: 100

[0055] attribute:

[0056] Age range: [20-30, 30-40]

[0057] Region: [Tokyo, Osaka]

[0058] Income: [3 million to 5 million yen, 5 million to 7 million yen]

[0059] 3. AI generates answers based on learning data:

[0060] The device sends the user's input data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates answers based on the specified attributes and number of answers, using data previously learned from a database.

[0061] 4. Provide the generated answer to the user:

[0062] The server sends the generated answer data to the device, which visually displays the generated answers to the user, who can then download the results or perform further analysis as needed.

[0063] example:

[0064] json

[0065] {

[0066] 'Question 1': 'What is your age?',

[0067] 'Answer': [

[0068] '28',

[0069] '32',

[0070] 'twenty two',

[0071] '35',

[0072] ...

[0073] ],

[0074] 'Question 2': 'Which area do you live in?'

[0075] 'Answer': [

[0076] 'Tokyo',

[0077] 'Osaka',

[0078] 'Tokyo',

[0079] 'Osaka',

[0080] ...

[0081] ],

[0082] ...

[0083] }

[0084] 5. Pay-per-use system:

[0085] The server issues a bill to the user based on the number of responses generated. The server calculates the fee based on the number of responses and automatically notifies the user of the amount due, realizing pay-per-use billing.

[0086] This allows users to conduct market research quickly and cost-effectively. Through specific embodiments of the invention, users can intuitively operate the system and obtain the required data in a short period of time.

[0087] The processing flow will be explained below.

[0088] Step 1:

[0089] The user opens the input form on the device, and the device displays the input form for creating a questionnaire to the user.

[0090] Step 2:

[0091] The user inputs or selects a question. The terminal receives the input from the user and temporarily stores the question data.

[0092] Step 3:

[0093] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate. The terminal provides the user with attribute options, collects the attribute data selected by the user, and also inputs the number of answers they want to generate.

[0094] Step 4:

[0095] The device sends the user's input data (question, attributes, number of answers) to the server, which receives the data, formats it, and converts it into a format suitable for the AI ​​model.

[0096] Step 5:

[0097] The server inputs the formatted data into the AI ​​model, which generates answers based on the specified attributes and number of answers, using attribute data previously learned from a database.

[0098] Step 6:

[0099] The server transmits the generated answer data to the terminal, which visually displays the generated answer to the user.

[0100] Step 7:

[0101] The user can check the response data generated on the device and download or perform additional analysis as needed.

[0102] Step 8:

[0103] The server calculates the fee based on the number of generated answers. The server calculates the fee for the user based on the number of answers and their attributes, and generates a bill.

[0104] Step 9:

[0105] The server sends the bill to the user, who receives the bill through the terminal and pays based on a pay-as-you-go system.

[0106] This allows users to quickly conduct market research at low cost and in a short period of time.

[0107] Example 1

[0108] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0109] When conducting market research or questionnaires, it is difficult to generate a large number of responses based on various attributes quickly and accurately. It is also important to effectively implement a pay-per-use system and charge users appropriate fees based on the responses generated. Furthermore, a method is required for visually displaying the results and allowing users to easily receive data while ensuring data security.

[0110] 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.

[0111] In this invention, the server includes: means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and training the AI ​​to learn from the data; means for the user to input or select survey questions; means for the user to specify the desired answer attributes and number of answers; means for the AI ​​to generate answers based on the specified attributes and number of answers; means for providing the generated answers to the user; means for calculating fees based on the number of generated answers and automatically generating an invoice; communication means for ensuring data security; and means for visually displaying answer results and making data downloadable. This enables market research and surveys to be conducted quickly and accurately, enabling effective pay-per-use billing. It also enables users to easily receive data while ensuring data security.

[0112] "Age" refers to the number of years that have passed since an individual was born.

[0113] "Region" means a geographic or administrative division.

[0114] "Family structure" refers to the number of people in a household and their relationships.

[0115] "Income" refers to the total amount of money earned through work, investments, etc.

[0116] "Attribute data" refers to data that contains specific characteristics or traits about an individual or object.

[0117] "Personal information" is information that can be used to identify a specific individual.

[0118] A "database" refers to a system for efficiently storing, searching, and managing large amounts of data.

[0119] "Artificial intelligence" refers to technology that aims to reproduce intelligent behavior using computers.

[0120] "Learning" refers to the process by which an algorithm acquires the ability to analyze data patterns and make predictions or classifications based on them.

[0121] "User" refers to any individual or entity that uses this system.

[0122] A "survey" refers to a collection of questions designed to collect data for a specific purpose.

[0123] "Question" refers to a question that is asked in a questionnaire to request an answer.

[0124] "Number of responses" refers to the number of survey responses that a user wishes to generate.

[0125] "Generation" refers to the process of creating new data or information based on specified conditions.

[0126] "Visually displaying" refers to presenting data or information graphically on a user interface.

[0127] "Pay-as-you-go" refers to a billing method in which fees are calculated based on the amount of service used.

[0128] "Invoice" means a document requesting payment for goods or services provided.

[0129] "Communication means" refers to the technologies, protocols, and infrastructure used to send and receive data.

[0130] "Downloading" refers to the process of obtaining data from a remote system, such as via the Internet.

[0131] The system of the present invention automatically generates market research and questionnaire responses, and it is important that the server, terminals, and users work in cooperation with each other. The detailed configuration and operation of this system will now be described.

[0132] Basic configuration

[0133] Server: A central computer system that hosts artificial intelligence (AI) models and manages databases. The server performs key processes such as attribute data collection, AI learning, answer generation, and billing processing. Specifically, the server uses a server machine (e.g., a high-performance general-purpose server) with a high-performance CPU and large memory capacity. Machine learning frameworks such as TensorFlow and PyTorch run on the server to execute AI models. A database management system (e.g., MySQL, PostgreSQL) stores and manages data.

[0134] Terminal: A device that provides an interface for users to access the survey. Users use the terminal to enter survey questions, specify response attributes, and receive generated responses. The terminals used vary widely, including personal computers (PCs), tablets, and smartphones. A dedicated application or a general web browser (e.g., Chrome, Safari) is installed on the terminal.

[0135] User: An individual or organization who wants to conduct market research or a questionnaire. The user operates a terminal to input questions and specify the desired response attributes and number.

[0136] Detailed program processing

[0137] 1. User enters and selects survey questions:

[0138] The user opens the input form on the device and enters or selects survey questions. The device receives the user's input and temporarily stores the questions. For example, questions such as "Question 1: How old are you?", "Question 2: Where do you live?", and "Question 3: How much do you earn?" are entered.

[0139] 2. User specifies the attributes and number of answers:

[0140] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through their device. The device collects this information and temporarily stores it. For example, they might specify "Number of answers: 100," "Attributes: Age group: [20-30, 30-40], Region: [Tokyo, Osaka], Income: [3 million yen - 5 million yen, 5 million yen - 7 million yen]."

[0141] 3. AI generates answers based on learning data:

[0142] The device sends the user's input data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates answers based on the specified attributes and number of answers, using data previously learned from a database.

[0143] 4. Provide the generated answer to the user:

[0144] The generated answer data is sent from the server to the terminal. The terminal visually displays the generated answer to the user. The user can download the results or perform additional analysis as needed. For example, the generated answer is provided with the following prompt sentence:

[0145] Please generate your survey answers. The questions are as follows:

[0146] 1. How old are you?

[0147] 2. What area do you live in?

[0148] 3. What is your income?

[0149] Generate 100 answers based on the following attributes:

[0150] Age range: [20-30, 30-40]

[0151] Region: [Tokyo, Osaka]

[0152] Income: [3 million to 5 million yen, 5 million to 7 million yen]

[0153] 5. Pay-per-use system:

[0154] The server issues an invoice to the user based on the number of responses generated. The server calculates the fee based on the number of responses and automatically notifies the user of the invoice amount. For example, the server calculates a fixed fee for generating 100 responses and sends an invoice to the user. This enables users to conduct market research quickly and cost-effectively. In addition, to ensure data security, the HTTPS protocol is used, and data transmission and reception is encrypted to protect user information.

[0155] With the above-described configuration and operation, the present invention efficiently realizes market research and automatic generation of questionnaire responses.

[0156] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0157] Step 1:

[0158] User enters or selects survey question:

[0159] The user opens the input form on the device and enters or selects survey questions. For example, the user might enter "Question 1: How old are you?", "Question 2: Where do you live?", and "Question 3: How much do you earn?" The device receives this input data and temporarily stores it in local storage or memory.

[0160] Input: Survey question entered by the user

[0161] Output: Temporarily saved question data

[0162] Step 2:

[0163] User specifies attributes and number of answers:

[0164] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through their device. For example, the user might specify "Number of answers: 100," "Attributes: Age group: [20-30, 30-40]," "Region: [Tokyo, Osaka]," and "Income: [3 million yen - 5 million yen, 5 million yen - 7 million yen]." The device receives this information and temporarily stores it again in local storage or memory.

[0165] Input: User-specified answer attributes and number of answers

[0166] Output: Temporarily saved answer attributes and number of answers

[0167] Step 3:

[0168] The device sends data to the server:

[0169] The device sends the saved question data, answer attributes, and number of answers to the server. The HTTPS protocol is used to ensure data security. The server analyzes the received data, formats it, and stores it in a database.

[0170] Input: Saved question data, answer attributes, and number of answers

[0171] Output: Data sent to the server

[0172] Step 4:

[0173] The server uses an AI model to generate the answer:

[0174] The server inputs the formatted data into the AI ​​model, which generates answers based on the specified attributes and number of answers based on a pre-trained dataset. The generated answer data is output in JSON format or similar.

[0175] Input: Formatted question data, answer attributes, and number of answers

[0176] Output: Answer data generated by the AI ​​model

[0177] Step 5:

[0178] The server generates the answer and sends it to the device:

[0179] The server receives the generated response data, formats it in an appropriate format (e.g., JSON), and sends it to the device. This information is also transmitted securely using the HTTPS protocol. The device receives this data, analyzes it, and displays it visually to the user.

[0180] Input: Answer data generated by the AI ​​model

[0181] Output: Formatted answer data sent to the device

[0182] Step 6:

[0183] User reviews and downloads results:

[0184] The device visually displays the generated answers to the user in tabular and graphical formats, and the user can review the results and download the data in formats such as CSV and PDF if desired.

[0185] Input: Answer data displayed on the terminal

[0186] Output: Generated answers for user review, and downloadable data

[0187] Step 7:

[0188] The server issues a pay-as-you-go bill:

[0189] The server calculates the usage fee based on the number of generated answers. Based on the calculation result, an invoice is automatically generated and sent to the user via email, etc. For example, a specific fee is calculated for generating 100 answers.

[0190] Input: Number of generated answers

[0191] Output: Invoice sent to user

[0192] (Application example 1)

[0193] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0194] Market research and collecting survey responses require a great deal of time and effort, and it is particularly difficult to collect data specific to target users in a short amount of time for advertising campaigns. Conventional methods are unable to quickly collect accurate attribute-based data and analyze it in real time, making it difficult to maximize advertising effectiveness.

[0195] 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.

[0196] In this invention, the server includes means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and for training an artificial intelligence to learn the data, means for a user to input or select survey questions, means for a user to specify the desired answer attributes and number of answers, means for the artificial intelligence to generate answers based on the specified attributes and number of answers, means for generating market research data specific to target users of an advertising campaign in real time, and means for providing the generated answers to users. This enables advertising agencies to collect accurate target market data in a short amount of time and analyze it in real time.

[0197] "Age" is an attribute that indicates the length of time that has passed since a particular individual was born.

[0198] "Region" is an attribute that indicates a specific geographical location or area.

[0199] "Family structure" is an attribute that indicates the family relationships of a particular individual.

[0200] "Income" is an attribute that indicates the monetary rewards or benefits that a particular individual or household receives.

[0201] "Personal information" refers to information that can identify a specific individual, including attributes that can be linked to a specific person.

[0202] A "database" is a system for efficiently managing, searching, and manipulating an organized collection of data.

[0203] "Artificial intelligence" is a general term for technology that allows computers to perform processes similar to human intelligence.

[0204] "Learning" is the act of teaching artificial intelligence to acquire patterns and knowledge using specific data.

[0205] A "survey" is a research method that asks people to respond through questions in order to gather specific information.

[0206] A "question" is a question set in a questionnaire to solicit an answer.

[0207] "Response attributes" refer to characteristics of the respondent, such as specific age, region, family structure, income, etc.

[0208] "Number of responses" refers to the total number of responses received in response to the questionnaire.

[0209] An "advertising campaign" is a planned promotional activity carried out to promote a particular product or service and appeal to consumers.

[0210] "Target users" refer to consumers who may be particularly interested in or concerned with a particular advertisement or product.

[0211] "Market research data" refers to the results of collecting and analyzing information on consumer opinions, behavior, purchasing trends, etc.

[0212] "Real-time" refers to information processing and operations being carried out almost instantly, with almost no delay.

[0213] "Providing" is the act of handing over the generated answers and data to the user.

[0214] The system of the present invention generates market research data specific to target users of advertising campaigns in real time. It is important that the system operates in cooperation with the server, terminals, and users.

[0215] Basic configuration

[0216] Server: The server is a central computer system that hosts the artificial intelligence (AI) model and manages the database. The server handles key processes such as attribute data ingestion, AI training, answer generation, and billing processing. For example, it uses Python-based AI models (TensorFlow or PyTorch) and databases (PostgreSQL, MySQL).

[0217] Terminal: A terminal is a device that provides an interface for users to access the survey. Users enter survey questions, specify response attributes, and receive generated responses through the terminal. Specific examples include smartphones (iPhone 13, Samsung Galaxy S21).

[0218] User: The user is a marketing person at an advertising agency. He operates a terminal to input questions and specify the desired answer attributes and number.

[0219] Program processing

[0220] 1. Entering survey questions: The user opens the input form on the device and enters or selects the survey questions. The device receives the user's input and temporarily saves the questions.

[0221] 2. Specifying answer attributes and number: The user specifies the answer attributes (e.g., age group, region, income, etc.) and the number of answers they want to generate through the terminal. The terminal collects and temporarily stores this information.

[0222] 3. Answer generation: The device sends the user's input data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates an answer based on the specified attributes and number of answers, using data previously learned from the database.

[0223] 4. Providing the generated answer: The server sends the generated answer data to the device. The device visually displays the generated answer to the user. The user can download the results or perform further analysis as needed.

[0224] 5. Pay-per-use system: The server issues a bill to the user based on the number of responses generated. The server calculates the fee based on the number of responses and automatically notifies the user of the amount due, realizing pay-per-use billing.

[0225] Specific examples of programs

[0226] The server inputs attribute data such as age, region, family composition, and income into a database, excluding personal information, and trains this data using a Python AI model (TensorFlow or PyTorch). Users enter survey questions on their smartphones (iPhone 13, Samsung Galaxy S21), specifying the attributes of the answers and the number of answers.

[0227] The server uses an AI model to generate answers based on the specified attributes and the number of answers, which are then sent to the device and visually displayed to the user, and finally, the user is charged based on the number of answers generated.

[0228] Examples of prompt statements

[0229] Here is an example of a prompt that can be input to a generative AI model:

[0230] plaintext

[0231] Prompt: For market research purposes, please generate answers to the following questions that match the following demographics: age 20-30, living in Tokyo, income 3-5 million yen.

[0232] Question 1: What is your age?

[0233] Question 2: What is your gender?

[0234] Question 3: What social media do you usually use?

[0235] This allows advertising agencies to quickly and efficiently obtain market research data specific to their target users.

[0236] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0237] Step 1:

[0238] The user opens the input form on the terminal and enters or selects the survey questions.

[0239] Input: Survey questions entered by the user.

[0240] Output: Temporarily saved survey question data.

[0241] Specific operation: The user opens the application on their smartphone and enters questions into the question input screen. The device records these in a temporary database.

[0242] Step 2:

[0243] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through their terminal.

[0244] Input: Attributes of the answer entered by the user and the number of desired answers.

[0245] Output: Temporarily saved attribute and response count data.

[0246] Specific operation: The user enters the desired answer attributes (e.g., age 20-30, region: Tokyo, Osaka, income: 3 million to 5 million yen) and the number of answers (e.g., 100) on the attribute specification screen. The device temporarily stores this information.

[0247] Step 3:

[0248] The terminal transmits the user's input data to the server, which receives it.

[0249] Input: Survey question data, response attribute data, and number of responses.

[0250] Output: User input data sent to the server.

[0251] Specific operation: The device converts the temporarily saved survey question data, answer attribute data, and answer count data into JSON format and sends it to the server using an HTTP POST request. The server receives this request and formats the data.

[0252] Step 4:

[0253] The server formats the received data and inputs it into the AI ​​model.

[0254] Input: The raw data received by the server.

[0255] Output: The training data input into the AI ​​model.

[0256] Specific operation: The server formats the received data into the required format (e.g., encoding and normalizing the data as needed) and applies it to the AI ​​model.

[0257] Step 5:

[0258] The AI ​​model generates answers based on the specified attributes and number of answers, based on data previously learned from a database.

[0259] Input: The training data input into the AI ​​model.

[0260] Output: Generated survey response data.

[0261] Specific operation: The AI ​​model (implemented in TensorFlow or PyTorch) uses the received training data to generate answers that meet the specified attributes and number of answers.

[0262] Step 6:

[0263] The generated response data is transmitted from the server to the terminal.

[0264] Input: Generated survey response data.

[0265] Output: The generated response data sent back to the device.

[0266] Specific operation: The server converts the generated response data into JSON format and returns it to the terminal as an HTTP response. The terminal receives this data.

[0267] Step 7:

[0268] The terminal visually displays the generated answers to the user.

[0269] Input: Generated response data received at the terminal.

[0270] Output: The generated answer displayed to the user.

[0271] What it does: The device analyzes the data it receives and displays it in a user-friendly format. The user can then download the results or perform further analysis as needed.

[0272] Step 8:

[0273] The server bills the user based on the number of responses generated.

[0274] Input: Generated response count data.

[0275] Output: The invoice sent to the customer.

[0276] Specific operation: The server calculates the fee based on the number of generated answers, automatically generates an invoice, and sends it to the user through the notification system.

[0277] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0278] The system of the present invention automatically generates market research and questionnaire responses, and also combines them with an emotion engine that recognizes the user's emotions. To implement this system, it is important that the server, terminals, users, and emotion engine work in coordination. The detailed configuration and operation of this system will now be described.

[0279] Basic configuration

[0280] Server: A central computer system that hosts the artificial intelligence (AI) model and manages the database. The server handles key processes such as attribute data capture, AI training, answer generation, and billing processing.

[0281] Terminal: A device that provides an interface for users to access. Through the terminal, users input survey questions, specify response attributes, receive generated responses, and collect emotion data.

[0282] User: An individual or organization who wants to conduct market research or a questionnaire. The user operates a terminal to input questions and specify the desired response attributes and number.

[0283] Emotion engine: A system that recognizes the user's emotions and adjusts them according to the survey questions and answer generation.

[0284] Program processing

[0285] 1. User enters and selects survey questions:

[0286] The user opens the input form on the device and enters or selects the survey questions. The device receives the user's input and temporarily saves the questions.

[0287] example:

[0288] Question 1: What is your age?

[0289] Question 2: What area do you live in?

[0290] Question 3: What is your income?

[0291] 2. User specifies the attributes and number of answers:

[0292] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through the device. The device collects and temporarily stores this information.

[0293] example:

[0294] Number of answers: 100

[0295] attribute:

[0296] Age range: [20-30, 30-40]

[0297] Region: [Tokyo, Osaka]

[0298] Income: [3 million to 5 million yen, 5 million to 7 million yen]

[0299] 3. Collecting and analyzing emotional data:

[0300] The device collects the user's emotional data, and the emotion engine recognizes emotions from the user's facial expressions, voice, input speed, etc. and analyzes this data.

[0301] example:

[0302] User emotions: nervousness, joy, excitement

[0303] 4. Emotion-based survey adjustment:

[0304] The emotion engine automatically adjusts the survey questions based on the user's emotions, such as simplifying the questions for users who are highly stressed.

[0305] 5. AI generates answers based on learning data:

[0306] The device sends the user's input data and emotional data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates answers based on the specified attributes and number of answers, using data previously learned from a database.

[0307] 6. Provide the generated answer to the user:

[0308] The server sends the generated answer data to the terminal, which visually displays the generated answer to the user.

[0309] example:

[0310] json

[0311] {

[0312] 'Question 1': 'What is your age?',

[0313] 'Answer': [

[0314] '28',

[0315] '32',

[0316] 'twenty two',

[0317] '35',

[0318] ...

[0319] ],

[0320] 'Question 2': 'Which area do you live in?'

[0321] 'Answer': [

[0322] 'Tokyo',

[0323] 'Osaka',

[0324] 'Tokyo',

[0325] 'Osaka',

[0326] ...

[0327] ],

[0328] ...

[0329] }

[0330] 7. Pay-per-use system:

[0331] The server calculates the fee based on the number of generated answers. The server calculates the user's fee based on the number of answers and their attributes, and automatically notifies the user of the bill.

[0332] This allows users to conduct market research quickly and cost-effectively, and enables them to generate questions and answers that take emotion into consideration.Through specific embodiments of the invention, users can intuitively operate the system and obtain the necessary data in a short period of time.

[0333] The processing flow will be explained below.

[0334] Step 1:

[0335] The user opens the input form on the device, and the device displays the input form for creating a questionnaire to the user.

[0336] Step 2:

[0337] The user inputs or selects a question. The terminal receives the input from the user and temporarily stores the question data.

[0338] Step 3:

[0339] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate. The device provides the user with attribute options, collects and temporarily stores the attribute data selected by the user, and also inputs the number of answers they want to generate.

[0340] Step 4:

[0341] The device sends the user's input data (question, attributes, number of answers) to the server, which receives the data, formats it, and converts it into a format suitable for the AI ​​model.

[0342] Step 5:

[0343] The device runs an emotion engine to collect user emotion data, which is then used to recognize the user's emotions based on facial expression analysis, voice recognition, input speed, etc.

[0344] Step 6:

[0345] The emotion engine analyzes the user's emotions and automatically adjusts the survey questions based on the results, for example, simplifying the questions if the user is nervous.

[0346] Step 7:

[0347] The server generates answers using an AI model based on the formatted data and the analysis results of the emotion engine. The AI ​​model generates answers based on the specified attributes and number of answers.

[0348] Step 8:

[0349] The server transmits the generated answer data to the terminal, which visually displays the generated answer to the user.

[0350] Step 9:

[0351] The user can check the response data generated on the device and download or perform additional analysis as needed.

[0352] Step 10:

[0353] The server calculates the fee based on the number of generated answers. The server calculates the fee for the user based on the number of answers and their attributes, and generates a bill.

[0354] Step 11:

[0355] The server sends the bill to the user, who receives the bill through the terminal and pays based on a pay-as-you-go system.

[0356] This allows users to conduct market research quickly and at low cost in a short period of time, and also enables questions and answers to be generated that take emotions into consideration.

[0357] Example 2

[0358] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0359] Conventional market research and questionnaire systems require users to manually create numerous questions and analyze the response data, requiring a great deal of time and effort. Furthermore, the systems do not reflect the user's emotional state, which can lead to stress and discomfort. Therefore, there is a need for systems that allow users to conduct market research intuitively and efficiently and quickly obtain high-quality data. Furthermore, there is a growing demand for systems that appropriately charge users based on the number of responses generated and automatically notify them of the charges.

[0360] 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.

[0361] In this invention, the server includes a means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and for training a machine learning algorithm, a means for users to input or select survey questions, and a means for users to specify the attributes of the answers and the number of answers they want. This allows users to intuitively operate the system and obtain the data they need in a short period of time.

[0362] "Descriptive data" is information that identifies or characterizes a particular individual or group, such as age, region, family structure, or income.

[0363] A "database" is a structured collection of data and a system for efficiently storing, retrieving, and managing data.

[0364] A "machine learning algorithm" is a computational method for learning regularities and patterns from large amounts of data and making predictions and classifications for new data.

[0365] "User" means any individual or organization wishing to conduct market research or surveys.

[0366] A "question" is a question or problem presented to a user for response in a questionnaire or survey.

[0367] "Emotional data" is information about the user's emotional state that can be inferred from the user's facial expression, voice, input speed, etc.

[0368] An "emotion recognition algorithm" is a computational method for analyzing collected emotional data and recognizing the user's emotional state.

[0369] A "pay-as-you-go system" is a system for calculating and charging fees according to the amount of service used by the user.

[0370] "Charge notification" means the act or means of informing a user of the charges for the services they use.

[0371] The system of the present invention combines the functionality of automatically generating market research and questionnaire responses with the functionality of recognizing user emotions. It is important that this system operates in cooperation with the server, terminals, users, and emotion recognition algorithms.

[0372] Basic configuration

[0373] server:

[0374] The server is a central computer system that hosts the machine learning algorithm and manages the entire database. The server's main processes are attribute data collection, machine learning, generating survey responses, and calculating usage fees. Specifically, it formats the data and inputs it into the algorithm.

[0375] Device:

[0376] A terminal is a device that provides an interface for users to access and operate. Examples include PCs, smartphones, and tablets. Through the terminal, users can enter survey questions, specify response attributes, collect emotional data, and receive the final response results.

[0377] User:

[0378] A user is an individual or organization who wants to conduct market research or a survey. The user inputs survey questions and specifies the desired response attributes and number. In addition, the user provides emotion data through a terminal.

[0379] Emotion Recognition Algorithm:

[0380] The emotion recognition algorithm analyzes the user's emotions in real time and reflects them in the generation of survey questions and answers. Specifically, it analyzes the user's facial expressions, voice, input speed, etc.

[0381] Example of operation

[0382] 1. Attribute data capture:

[0383] The server then takes attribute data such as age, region, family structure, and income and stores it in a database, excluding personal information. This data is then used as training data for machine learning algorithms.

[0384] 2. Enter and select survey questions:

[0385] The user opens the input form on the device and enters or selects survey questions, such as "How old are you?", "Where do you live?", and "How much do you earn?"

[0386] 3. Specify the attributes and number of answers:

[0387] The user specifies the answer attributes (age group, region, income, etc.) and the number of answers they want to generate through their terminal. For example, they could specify "Number of answers: 100," "Age group: [20-30, 30-40]," "Region: [Tokyo, Osaka]," and "Income: [3 million to 5 million yen, 5 million to 7 million yen]."

[0388] 4. Emotional data collection and analysis:

[0389] The device collects data such as facial expressions, voice, and input speed while the user is typing, and analyzes it using an emotion recognition algorithm to determine the user's emotional state, such as whether they are nervous, happy, or excited.

[0390] 5. AI model generates answers:

[0391] The device sends the collected data to a server, which formats it and inputs it into a machine learning algorithm. The AI ​​model generates an answer based on the specified attributes and number of answers. An example prompt is, "Generate an answer based on the following attributes: age group [20-30, 30-40], region [Tokyo, Osaka], income [¥3 million-¥5 million, ¥5 million-¥7 million], number of answers: 100."

[0392] 6. Providing answers to users:

[0393] The server sends the generated answers to the device, which then visually displays them to the user in a table, list, or other format. For example, "Question 1: What is your age? Answers: 28, 32, 22, 35, ..."

[0394] 7. Calculation and Notification:

[0395] The server calculates the fee based on the number of generated responses and their attributes, and automatically notifies the user of the fee. For example, the fee may be something like "Usage fee: ¥5,000, Details: Number of responses: 100, Unit cost: ¥50, Total: ¥5,000."

[0396] This invention allows users to intuitively operate the system and obtain high-quality market research results in a short time. It also improves the user experience through emotion recognition and enables appropriate fee collection through a pay-per-use system.

[0397] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0398] System program processing steps

[0399] Step 1:

[0400] The user opens an input form on the device and enters or selects questions to be used for market research or questionnaires. The device receives the information in real time and temporarily stores it.

[0401] Input: Questions entered by the user via keyboard or touch interface.

[0402] Output: Temporarily saved question data.

[0403] Specifically, the user inputs questions such as "How old are you?" and "Where do you live?"

[0404] Step 2:

[0405] The user uses the device to specify the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate. The device collects this information in real time and temporarily stores it.

[0406] Input: User-specified answer attributes and number of answers (e.g., "Number of answers: 100", "Age range: [20-30, 30-40]", "Region: [Tokyo, Osaka]", "Income: [3 million yen - 5 million yen, 5 million yen - 7 million yen]").

[0407] Output: Temporarily saved answer attributes and answer count data.

[0408] Specific actions include the user selecting an attribute using a drop-down menu or text field and entering a numeric value.

[0409] Step 3:

[0410] The device collects the user's emotional data by using the device's camera and microphone to record the user's facial expressions and voice, and then passes this data to an emotion recognition algorithm for analysis.

[0411] Input: User's facial expression and voice data.

[0412] Output: Parsed emotion data (e.g., "tension", "joy", "excitement").

[0413] Specifically, the system automatically activates the camera and microphone to collect data while the user is using the device.

[0414] Step 4:

[0415] An emotion recognition algorithm analyzes the user's emotional data and automatically adjusts the questions based on the results. For example, it may change the questions to simpler ones for users who are feeling stressed.

[0416] Input: Parsed sentiment data and original question data.

[0417] Output: Adjusted question data.

[0418] Specifically, the system simplifies or refines questions depending on the user's emotional state.

[0419] Step 5:

[0420] The device sends the collected data to a server, which receives it, formats it, and feeds it into a machine learning algorithm. The AI ​​model generates an answer based on the specified attributes and number of answers.

[0421] Input: Adjusted question data, answer attribute data, and emotion data.

[0422] Output: The generated response data.

[0423] Specifically, a prompt is created and instructions are given to the AI ​​model such as, "Generate answers based on the following attributes: age group [20-30, 30-40], region [Tokyo, Osaka], income [3 million to 5 million yen, 5 million to 7 million yen], number of answers: 100."

[0424] Step 6:

[0425] The server sends the generated answer data to the terminal, which then visually displays it to the user.

[0426] Input: Generated response data.

[0427] Output: The answer results displayed to the user.

[0428] Specifically, the display format is provided as a table or list so that the user can easily check the data.

[0429] Step 7:

[0430] The server calculates the fee based on the number of generated answers and automatically notifies the user of the result. A pay-per-use system is used to prepare for fee collection.

[0431] Input: Number of responses generated and attribute data.

[0432] Output: Calculated charges and automatic notifications.

[0433] Specifically, the user is notified in the form of "Usage fee: ¥5000, details: number of responses 100, unit cost ¥50, total ¥5000."

[0434] (Application example 2)

[0435] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0436] Modern market research and content distribution services require more personalized services that adapt to user emotions. However, conventional systems have difficulty understanding a user's emotional state in real time and generating appropriate content based on that information. This makes it difficult to improve the user experience and limits the quality of the service.

[0437] 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.

[0438] In this invention, the server includes means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and for training an artificial intelligence to learn the data, means for a user to input or select survey questions, means for a user to specify the attributes and number of answers desired by the user, means for the artificial intelligence to generate answers based on the specified attributes and number of answers, means for providing the generated answers to the user, means for collecting user emotional data, means for analyzing the emotional data and generating prompt sentences based on the results, means for generating content based on the prompt sentences, and means for visually displaying the generated content. This makes it possible to generate and provide content optimized for the user's emotional state in real time.

[0439] "Attribute data" refers to data that is recorded and managed excluding personal information such as age, region, family structure, and income.

[0440] "Artificial intelligence" is a computer system that automatically makes decisions and generates information based on previously learned data.

[0441] "Emotion data" is data that indicates the user's emotional state, collected from the user's facial expression, voice, input speed, and the like.

[0442] A "prompt sentence" is an input sentence that instructs the artificial intelligence to generate content.

[0443] "Content" is a set of data such as information, messages, articles, videos, etc., provided to users.

[0444] "Pay-as-you-go" is a billing method in which a user is charged according to the amount of service used.

[0445] A "database" is a system that can structure, manage, and search large amounts of data.

[0446] A "server" is a central computer system that processes and stores data and provides services to other computers.

[0447] A "user interface" is a screen or device that allows a user to operate or input data into a system.

[0448] "Visual display" is the act of visually showing information to a user through a screen.

[0449] In order to implement this invention, it is important that the server, terminal, user, and emotion engine work in cooperation. In this configuration, the following hardware and software are used.

[0450] The server has the function of inputting attribute data such as age, region, family structure, and income into a database without personal information and training it into artificial intelligence (AI). The server efficiently manages the data using a database management system (DBMS) and analyzes the collected data using an AI module. This AI module uses machine learning frameworks such as TensorFlow and PyTorch.

[0451] The terminal is a device that provides an interface for users to access the survey, and includes smartphones, smart glasses, head-mounted displays, etc. The terminal provides a UI (user interface) that allows users to input or select survey questions and specify the desired response attributes and number of responses. The terminal also has various sensors (cameras, microphones, etc.) that collect emotion data.

[0452] The emotion engine analyzes the user's emotion data collected through the device. This emotion engine uses facial expression recognition and voice analysis technologies to determine the user's emotional state. For example, it uses OpenCV and Dlib libraries to analyze the facial expression data and generate the optimal prompt sentence according to the user's emotion.

[0453] The server generates a prompt based on the collected emotional data and inputs the prompt into the AI ​​model. The prompt generation module then generates an appropriate instruction based on the emotional data. The prompt has the following format:

[0454] "Write a cheerful blog article about the benefits of outdoor activities."

[0455] "Create a comforting video message for someone having a tough day."

[0456] The AI ​​model generates content based on the prompt sentence. The generated content can be a variety of content, such as text, images, and videos. The generated content is sent from the server to the device, and the device visually displays it to the user through the user interface.

[0457] This makes it possible to generate and provide optimal content in real time according to the user's emotional state. As a specific example, if the user is happy, the AI ​​will provide a prompt to generate a "cheerful blog post about the benefits of outdoor activities," and the AI ​​will automatically generate and display that content to the user. If the user is sad, the AI ​​will generate a "comforting video message for people having a tough day," and the AI ​​will similarly generate and display that content. In this way, the present invention realizes the provision of personalized content that is in line with the user's emotions.

[0458] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0459] Step 1:

[0460] The user opens the input form on the terminal and enters or selects questions for the survey.

[0461] This is done through an HTML form or application UI that provides the user interface.

[0462] The input data (question content) will be temporarily saved on the device.

[0463] For example, questions such as "How old are you?" are entered into the terminal.

[0464] Step 2:

[0465] The user specifies the desired answer attributes and the number of answers through the terminal.

[0466] For example, the number of responses is specified as 100, and the response attributes are specified as "age group: 20-30, region: Tokyo."

[0467] The terminal temporarily stores this specification information and prepares to send it to the server.

[0468] Step 3:

[0469] The terminal collects emotion data of the user.

[0470] Using cameras and microphones, facial expressions, voice, input speed, etc. are acquired as sensor input.

[0471] The collected emotion data is sent to the emotion engine in real time.

[0472] Step 4:

[0473] The emotion engine analyzes the emotion data sent from the device.

[0474] It uses OpenCV and Dlib libraries to analyze facial expression and voice data to determine the user's emotional state.

[0475] For example, the analysis result may be "The user's emotional state is tense."

[0476] Step 5:

[0477] The terminal transmits the analyzed emotion data to the server.

[0478] The server combines this emotion data with question data and answer attribute data collected in advance and stores them in a database.

[0479] The saved data is used to generate subsequent prompt sentences.

[0480] Step 6:

[0481] The server generates prompt sentences according to the user's emotional state.

[0482] Based on the emotion data, instructions for generating content that reflects a specific emotion are created.

[0483] For example, if the user is in a tense state, the prompt sentence generated is "Please generate an article about ways to relax."

[0484] Step 7:

[0485] The server inputs the generated prompt sentence into the AI ​​model to generate content.

[0486] This AI model uses generative AI such as GPT-3.

[0487] Based on the input prompt, AI generates content such as text and videos.

[0488] The generated content might be something like "blog posts about how to relax."

[0489] Step 8:

[0490] The server transmits the generated content to the terminal.

[0491] The terminal visually displays this content in a user interface.

[0492] The user can check and use the generated content.

[0493] By following these steps in order, we will realize a system that generates and provides personalized content in real time according to the user's emotional state, which is expected to significantly improve the user experience.

[0494] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0495] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0496] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0497] [Second embodiment]

[0498] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0499] 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.

[0500] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0501] 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.

[0502] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0503] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0504] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0505] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0506] 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.

[0507] 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.

[0508] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0509] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0510] The system of the present invention automatically generates responses to market research and questionnaires. To implement this system, it is important that the server, terminals, and users work together. The detailed configuration and operation of this system will now be described.

[0511] Basic configuration

[0512] Server: A central computer system that hosts the artificial intelligence (AI) model and manages the database. The server handles key processes such as attribute data capture, AI training, answer generation, and billing processing.

[0513] Terminal: A device that provides an interface for users to access. Users enter survey questions, specify response attributes, and receive generated responses through the terminal.

[0514] User: An individual or organization who wants to conduct market research or a questionnaire. The user operates a terminal to input questions and specify the desired response attributes and number.

[0515] Program processing

[0516] 1. User enters and selects survey questions:

[0517] The user opens the input form on the device and enters or selects the survey questions. The device receives the user's input and temporarily saves the questions.

[0518] example:

[0519] Question 1: What is your age?

[0520] Question 2: What area do you live in?

[0521] Question 3: What is your income?

[0522] 2. User specifies the attributes and number of answers:

[0523] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through the device. The device collects and temporarily stores this information.

[0524] example:

[0525] Number of answers: 100

[0526] attribute:

[0527] Age range: [20-30, 30-40]

[0528] Region: [Tokyo, Osaka]

[0529] Income: [3 million to 5 million yen, 5 million to 7 million yen]

[0530] 3. AI generates answers based on learning data:

[0531] The device sends the user's input data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates answers based on the specified attributes and number of answers, using data previously learned from a database.

[0532] 4. Provide the generated answer to the user:

[0533] The server sends the generated answer data to the device, which visually displays the generated answers to the user, who can then download the results or perform further analysis as needed.

[0534] example:

[0535] json

[0536] {

[0537] 'Question 1': 'What is your age?',

[0538] 'Answer': [

[0539] '28',

[0540] '32',

[0541] 'twenty two',

[0542] '35',

[0543] ...

[0544] ],

[0545] 'Question 2': 'Which area do you live in?'

[0546] 'Answer': [

[0547] 'Tokyo',

[0548] 'Osaka',

[0549] 'Tokyo',

[0550] 'Osaka',

[0551] ...

[0552] ],

[0553] ...

[0554] }

[0555] 5. Pay-per-use system:

[0556] The server issues a bill to the user based on the number of responses generated. The server calculates the fee based on the number of responses and automatically notifies the user of the amount due, realizing pay-per-use billing.

[0557] This allows users to conduct market research quickly and cost-effectively. Through specific embodiments of the invention, users can intuitively operate the system and obtain the required data in a short period of time.

[0558] The processing flow will be explained below.

[0559] Step 1:

[0560] The user opens the input form on the device, and the device displays the input form for creating a questionnaire to the user.

[0561] Step 2:

[0562] The user inputs or selects a question. The terminal receives the input from the user and temporarily stores the question data.

[0563] Step 3:

[0564] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate. The terminal provides the user with attribute options, collects the attribute data selected by the user, and also inputs the number of answers they want to generate.

[0565] Step 4:

[0566] The device sends the user's input data (question, attributes, number of answers) to the server, which receives the data, formats it, and converts it into a format suitable for the AI ​​model.

[0567] Step 5:

[0568] The server inputs the formatted data into the AI ​​model, which generates answers based on the specified attributes and number of answers, using attribute data previously learned from a database.

[0569] Step 6:

[0570] The server transmits the generated answer data to the terminal, which visually displays the generated answer to the user.

[0571] Step 7:

[0572] The user can check the response data generated on the device and download or perform additional analysis as needed.

[0573] Step 8:

[0574] The server calculates the fee based on the number of generated answers. The server calculates the fee for the user based on the number of answers and their attributes, and generates a bill.

[0575] Step 9:

[0576] The server sends the bill to the user, who receives the bill through the terminal and pays based on a pay-as-you-go system.

[0577] This allows users to quickly conduct market research at low cost and in a short period of time.

[0578] Example 1

[0579] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0580] When conducting market research or questionnaires, it is difficult to generate a large number of responses based on various attributes quickly and accurately. It is also important to effectively implement a pay-per-use system and charge users appropriate fees based on the responses generated. Furthermore, a method is required for visually displaying the results and allowing users to easily receive data while ensuring data security.

[0581] 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.

[0582] In this invention, the server includes: means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and training the AI ​​to learn from the data; means for the user to input or select survey questions; means for the user to specify the desired answer attributes and number of answers; means for the AI ​​to generate answers based on the specified attributes and number of answers; means for providing the generated answers to the user; means for calculating fees based on the number of generated answers and automatically generating an invoice; communication means for ensuring data security; and means for visually displaying answer results and making data downloadable. This enables market research and surveys to be conducted quickly and accurately, enabling effective pay-per-use billing. It also enables users to easily receive data while ensuring data security.

[0583] "Age" refers to the number of years that have passed since an individual was born.

[0584] "Region" means a geographic or administrative division.

[0585] "Family structure" refers to the number of people in a household and their relationships.

[0586] "Income" refers to the total amount of money earned through work, investments, etc.

[0587] "Attribute data" refers to data that contains specific characteristics or traits about an individual or object.

[0588] "Personal information" is information that can be used to identify a specific individual.

[0589] A "database" refers to a system for efficiently storing, searching, and managing large amounts of data.

[0590] "Artificial intelligence" refers to technology that aims to reproduce intelligent behavior using computers.

[0591] "Learning" refers to the process by which an algorithm acquires the ability to analyze data patterns and make predictions or classifications based on them.

[0592] "User" refers to any individual or entity that uses this system.

[0593] A "survey" refers to a collection of questions designed to collect data for a specific purpose.

[0594] "Question" refers to a question that is asked in a questionnaire to request an answer.

[0595] "Number of responses" refers to the number of survey responses that a user wishes to generate.

[0596] "Generation" refers to the process of creating new data or information based on specified conditions.

[0597] "Visually displaying" refers to presenting data or information graphically on a user interface.

[0598] "Pay-as-you-go" refers to a billing method in which fees are calculated based on the amount of service used.

[0599] "Invoice" means a document requesting payment for goods or services provided.

[0600] "Communication means" refers to the technologies, protocols, and infrastructure used to send and receive data.

[0601] "Downloading" refers to the process of obtaining data from a remote system, such as via the Internet.

[0602] The system of the present invention automatically generates market research and questionnaire responses, and it is important that the server, terminals, and users work in cooperation with each other. The detailed configuration and operation of this system will now be described.

[0603] Basic configuration

[0604] Server: A central computer system that hosts artificial intelligence (AI) models and manages databases. The server performs key processes such as attribute data collection, AI learning, answer generation, and billing processing. Specifically, the server uses a server machine (e.g., a high-performance general-purpose server) with a high-performance CPU and large memory capacity. Machine learning frameworks such as TensorFlow and PyTorch run on the server to execute AI models. A database management system (e.g., MySQL, PostgreSQL) stores and manages data.

[0605] Terminal: A device that provides an interface for users to access the survey. Users use the terminal to enter survey questions, specify response attributes, and receive generated responses. The terminals used vary widely, including personal computers (PCs), tablets, and smartphones. A dedicated application or a general web browser (e.g., Chrome, Safari) is installed on the terminal.

[0606] User: An individual or organization who wants to conduct market research or a questionnaire. The user operates a terminal to input questions and specify the desired response attributes and number.

[0607] Detailed program processing

[0608] 1. User enters and selects survey questions:

[0609] The user opens the input form on the device and enters or selects survey questions. The device receives the user's input and temporarily stores the questions. For example, questions such as "Question 1: How old are you?", "Question 2: Where do you live?", and "Question 3: How much do you earn?" are entered.

[0610] 2. User specifies the attributes and number of answers:

[0611] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through their device. The device collects this information and temporarily stores it. For example, they might specify "Number of answers: 100," "Attributes: Age group: [20-30, 30-40], Region: [Tokyo, Osaka], Income: [3 million yen - 5 million yen, 5 million yen - 7 million yen]."

[0612] 3. AI generates answers based on learning data:

[0613] The device sends the user's input data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates answers based on the specified attributes and number of answers, using data previously learned from a database.

[0614] 4. Provide the generated answer to the user:

[0615] The generated answer data is sent from the server to the terminal. The terminal visually displays the generated answer to the user. The user can download the results or perform additional analysis as needed. For example, the generated answer is provided with the following prompt sentence:

[0616] Please generate your survey answers. The questions are as follows:

[0617] 1. How old are you?

[0618] 2. What area do you live in?

[0619] 3. What is your income?

[0620] Generate 100 answers based on the following attributes:

[0621] Age range: [20-30, 30-40]

[0622] Region: [Tokyo, Osaka]

[0623] Income: [3 million to 5 million yen, 5 million to 7 million yen]

[0624] 5. Pay-per-use system:

[0625] The server issues an invoice to the user based on the number of responses generated. The server calculates the fee based on the number of responses and automatically notifies the user of the invoice amount. For example, the server calculates a fixed fee for generating 100 responses and sends an invoice to the user. This enables users to conduct market research quickly and cost-effectively. In addition, to ensure data security, the HTTPS protocol is used, and data transmission and reception is encrypted to protect user information.

[0626] With the above-described configuration and operation, the present invention efficiently realizes market research and automatic generation of questionnaire responses.

[0627] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0628] Step 1:

[0629] User enters or selects survey question:

[0630] The user opens the input form on the device and enters or selects survey questions. For example, the user might enter "Question 1: How old are you?", "Question 2: Where do you live?", and "Question 3: How much do you earn?" The device receives this input data and temporarily stores it in local storage or memory.

[0631] Input: Survey question entered by the user

[0632] Output: Temporarily saved question data

[0633] Step 2:

[0634] User specifies attributes and number of answers:

[0635] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through their device. For example, the user might specify "Number of answers: 100," "Attributes: Age group: [20-30, 30-40]," "Region: [Tokyo, Osaka]," and "Income: [3 million yen - 5 million yen, 5 million yen - 7 million yen]." The device receives this information and temporarily stores it again in local storage or memory.

[0636] Input: User-specified answer attributes and number of answers

[0637] Output: Temporarily saved answer attributes and number of answers

[0638] Step 3:

[0639] The device sends data to the server:

[0640] The device sends the saved question data, answer attributes, and number of answers to the server. The HTTPS protocol is used to ensure data security. The server analyzes the received data, formats it, and stores it in a database.

[0641] Input: Saved question data, answer attributes, and number of answers

[0642] Output: Data sent to the server

[0643] Step 4:

[0644] The server uses an AI model to generate the answer:

[0645] The server inputs the formatted data into the AI ​​model, which generates answers based on the specified attributes and number of answers based on a pre-trained dataset. The generated answer data is output in JSON format or similar.

[0646] Input: Formatted question data, answer attributes, and number of answers

[0647] Output: Answer data generated by the AI ​​model

[0648] Step 5:

[0649] The server generates the answer and sends it to the device:

[0650] The server receives the generated response data, formats it in an appropriate format (e.g., JSON), and sends it to the device. This information is also transmitted securely using the HTTPS protocol. The device receives this data, analyzes it, and displays it visually to the user.

[0651] Input: Answer data generated by the AI ​​model

[0652] Output: Formatted answer data sent to the device

[0653] Step 6:

[0654] User reviews and downloads results:

[0655] The device visually displays the generated answers to the user in tabular and graphical formats, and the user can review the results and download the data in formats such as CSV and PDF if desired.

[0656] Input: Answer data displayed on the terminal

[0657] Output: Generated answers for user review, and downloadable data

[0658] Step 7:

[0659] The server issues a pay-as-you-go bill:

[0660] The server calculates the usage fee based on the number of generated answers. Based on the calculation result, an invoice is automatically generated and sent to the user via email, etc. For example, a specific fee is calculated for generating 100 answers.

[0661] Input: Number of generated answers

[0662] Output: Invoice sent to user

[0663] (Application example 1)

[0664] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0665] Market research and collecting survey responses require a great deal of time and effort, and it is particularly difficult to collect data specific to target users in a short amount of time for advertising campaigns. Conventional methods are unable to quickly collect accurate attribute-based data and analyze it in real time, making it difficult to maximize advertising effectiveness.

[0666] 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.

[0667] In this invention, the server includes means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and for training an artificial intelligence to learn the data, means for a user to input or select survey questions, means for a user to specify the desired answer attributes and number of answers, means for the artificial intelligence to generate answers based on the specified attributes and number of answers, means for generating market research data specific to target users of an advertising campaign in real time, and means for providing the generated answers to users. This enables advertising agencies to collect accurate target market data in a short amount of time and analyze it in real time.

[0668] "Age" is an attribute that indicates the length of time that has passed since a particular individual was born.

[0669] "Region" is an attribute that indicates a specific geographical location or area.

[0670] "Family structure" is an attribute that indicates the family relationships of a particular individual.

[0671] "Income" is an attribute that indicates the monetary rewards or benefits that a particular individual or household receives.

[0672] "Personal information" refers to information that can identify a specific individual, including attributes that can be linked to a specific person.

[0673] A "database" is a system for efficiently managing, searching, and manipulating an organized collection of data.

[0674] "Artificial intelligence" is a general term for technology that allows computers to perform processes similar to human intelligence.

[0675] "Learning" is the act of teaching artificial intelligence to acquire patterns and knowledge using specific data.

[0676] A "survey" is a research method that asks people to respond through questions in order to gather specific information.

[0677] A "question" is a question set in a questionnaire to solicit an answer.

[0678] "Response attributes" refer to characteristics of the respondent, such as specific age, region, family structure, income, etc.

[0679] "Number of responses" refers to the total number of responses received in response to the questionnaire.

[0680] An "advertising campaign" is a planned promotional activity carried out to promote a particular product or service and appeal to consumers.

[0681] "Target users" refer to consumers who may be particularly interested in or concerned with a particular advertisement or product.

[0682] "Market research data" refers to the results of collecting and analyzing information on consumer opinions, behavior, purchasing trends, etc.

[0683] "Real-time" refers to information processing and operations being carried out almost instantly, with almost no delay.

[0684] "Providing" is the act of handing over the generated answers and data to the user.

[0685] The system of the present invention generates market research data specific to target users of advertising campaigns in real time. It is important that the system operates in cooperation with the server, terminals, and users.

[0686] Basic configuration

[0687] Server: The server is a central computer system that hosts the artificial intelligence (AI) model and manages the database. The server handles key processes such as attribute data ingestion, AI training, answer generation, and billing processing. For example, it uses Python-based AI models (TensorFlow or PyTorch) and databases (PostgreSQL, MySQL).

[0688] Terminal: A terminal is a device that provides an interface for users to access the survey. Users enter survey questions, specify response attributes, and receive generated responses through the terminal. Specific examples include smartphones (iPhone 13, Samsung Galaxy S21).

[0689] User: The user is a marketing person at an advertising agency. He operates a terminal to input questions and specify the desired answer attributes and number.

[0690] Program processing

[0691] 1. Entering survey questions: The user opens the input form on the device and enters or selects the survey questions. The device receives the user's input and temporarily saves the questions.

[0692] 2. Specifying answer attributes and number: The user specifies the answer attributes (e.g., age group, region, income, etc.) and the number of answers they want to generate through the terminal. The terminal collects and temporarily stores this information.

[0693] 3. Answer generation: The device sends the user's input data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates an answer based on the specified attributes and number of answers, using data previously learned from the database.

[0694] 4. Providing the generated answer: The server sends the generated answer data to the device. The device visually displays the generated answer to the user. The user can download the results or perform further analysis as needed.

[0695] 5. Pay-per-use system: The server issues a bill to the user based on the number of responses generated. The server calculates the fee based on the number of responses and automatically notifies the user of the amount due, realizing pay-per-use billing.

[0696] Specific examples of programs

[0697] The server inputs attribute data such as age, region, family composition, and income into a database, excluding personal information, and trains this data using a Python AI model (TensorFlow or PyTorch). Users enter survey questions on their smartphones (iPhone 13, Samsung Galaxy S21), specifying the attributes of the answers and the number of answers.

[0698] The server uses an AI model to generate answers based on the specified attributes and the number of answers, which are then sent to the device and visually displayed to the user, and finally, the user is charged based on the number of answers generated.

[0699] Examples of prompt statements

[0700] Here is an example of a prompt that can be input to a generative AI model:

[0701] plaintext

[0702] Prompt: For market research purposes, please generate answers to the following questions that match the following demographics: age 20-30, living in Tokyo, income 3-5 million yen.

[0703] Question 1: What is your age?

[0704] Question 2: What is your gender?

[0705] Question 3: What social media do you usually use?

[0706] This allows advertising agencies to quickly and efficiently obtain market research data specific to their target users.

[0707] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0708] Step 1:

[0709] The user opens the input form on the terminal and enters or selects the survey questions.

[0710] Input: Survey questions entered by the user.

[0711] Output: Temporarily saved survey question data.

[0712] Specific operation: The user opens the application on their smartphone and enters questions into the question input screen. The device records these in a temporary database.

[0713] Step 2:

[0714] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through their terminal.

[0715] Input: Attributes of the answer entered by the user and the number of desired answers.

[0716] Output: Temporarily saved attribute and response count data.

[0717] Specific operation: The user enters the desired answer attributes (e.g., age 20-30, region: Tokyo, Osaka, income: 3 million to 5 million yen) and the number of answers (e.g., 100) on the attribute specification screen. The device temporarily stores this information.

[0718] Step 3:

[0719] The terminal transmits the user's input data to the server, which receives it.

[0720] Input: Survey question data, response attribute data, and number of responses.

[0721] Output: User input data sent to the server.

[0722] Specific operation: The device converts the temporarily saved survey question data, answer attribute data, and answer count data into JSON format and sends it to the server using an HTTP POST request. The server receives this request and formats the data.

[0723] Step 4:

[0724] The server formats the received data and inputs it into the AI ​​model.

[0725] Input: The raw data received by the server.

[0726] Output: The training data input into the AI ​​model.

[0727] Specific operation: The server formats the received data into the required format (e.g., encoding and normalizing the data as needed) and applies it to the AI ​​model.

[0728] Step 5:

[0729] The AI ​​model generates answers based on the specified attributes and number of answers, based on data previously learned from a database.

[0730] Input: The training data input into the AI ​​model.

[0731] Output: Generated survey response data.

[0732] Specific operation: The AI ​​model (implemented in TensorFlow or PyTorch) uses the received training data to generate answers that meet the specified attributes and number of answers.

[0733] Step 6:

[0734] The generated response data is transmitted from the server to the terminal.

[0735] Input: Generated survey response data.

[0736] Output: The generated response data sent back to the device.

[0737] Specific operation: The server converts the generated response data into JSON format and returns it to the terminal as an HTTP response. The terminal receives this data.

[0738] Step 7:

[0739] The terminal visually displays the generated answers to the user.

[0740] Input: Generated response data received at the terminal.

[0741] Output: The generated answer displayed to the user.

[0742] What it does: The device analyzes the data it receives and displays it in a user-friendly format. The user can then download the results or perform further analysis as needed.

[0743] Step 8:

[0744] The server bills the user based on the number of responses generated.

[0745] Input: Generated response count data.

[0746] Output: The invoice sent to the customer.

[0747] Specific operation: The server calculates the fee based on the number of generated answers, automatically generates an invoice, and sends it to the user through the notification system.

[0748] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0749] The system of the present invention automatically generates market research and questionnaire responses, and also combines them with an emotion engine that recognizes the user's emotions. To implement this system, it is important that the server, terminals, users, and emotion engine work in coordination. The detailed configuration and operation of this system will now be described.

[0750] Basic configuration

[0751] Server: A central computer system that hosts the artificial intelligence (AI) model and manages the database. The server handles key processes such as attribute data capture, AI training, answer generation, and billing processing.

[0752] Terminal: A device that provides an interface for users to access. Through the terminal, users input survey questions, specify response attributes, receive generated responses, and collect emotion data.

[0753] User: An individual or organization who wants to conduct market research or a questionnaire. The user operates a terminal to input questions and specify the desired response attributes and number.

[0754] Emotion engine: A system that recognizes the user's emotions and adjusts them according to the survey questions and answer generation.

[0755] Program processing

[0756] 1. User enters and selects survey questions:

[0757] The user opens the input form on the device and enters or selects the survey questions. The device receives the user's input and temporarily saves the questions.

[0758] example:

[0759] Question 1: What is your age?

[0760] Question 2: What area do you live in?

[0761] Question 3: What is your income?

[0762] 2. User specifies the attributes and number of answers:

[0763] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through the device. The device collects and temporarily stores this information.

[0764] example:

[0765] Number of answers: 100

[0766] attribute:

[0767] Age range: [20-30, 30-40]

[0768] Region: [Tokyo, Osaka]

[0769] Income: [3 million to 5 million yen, 5 million to 7 million yen]

[0770] 3. Collecting and analyzing emotional data:

[0771] The device collects the user's emotional data, and the emotion engine recognizes emotions from the user's facial expressions, voice, input speed, etc. and analyzes this data.

[0772] example:

[0773] User emotions: nervousness, joy, excitement

[0774] 4. Emotion-based survey adjustment:

[0775] The emotion engine automatically adjusts the survey questions based on the user's emotions, such as simplifying the questions for users who are highly stressed.

[0776] 5. AI generates answers based on learning data:

[0777] The device sends the user's input data and emotional data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates answers based on the specified attributes and number of answers, using data previously learned from a database.

[0778] 6. Provide the generated answer to the user:

[0779] The server sends the generated answer data to the terminal, which visually displays the generated answer to the user.

[0780] example:

[0781] json

[0782] {

[0783] 'Question 1': 'What is your age?',

[0784] 'Answer': [

[0785] '28',

[0786] '32',

[0787] 'twenty two',

[0788] '35',

[0789] ...

[0790] ],

[0791] 'Question 2': 'Which area do you live in?'

[0792] 'Answer': [

[0793] 'Tokyo',

[0794] 'Osaka',

[0795] 'Tokyo',

[0796] 'Osaka',

[0797] ...

[0798] ],

[0799] ...

[0800] }

[0801] 7. Pay-per-use system:

[0802] The server calculates the fee based on the number of generated answers. The server calculates the user's fee based on the number of answers and their attributes, and automatically notifies the user of the bill.

[0803] This allows users to conduct market research quickly and cost-effectively, and enables them to generate questions and answers that take emotion into consideration.Through specific embodiments of the invention, users can intuitively operate the system and obtain the necessary data in a short period of time.

[0804] The processing flow will be explained below.

[0805] Step 1:

[0806] The user opens the input form on the device, and the device displays the input form for creating a questionnaire to the user.

[0807] Step 2:

[0808] The user inputs or selects a question. The terminal receives the input from the user and temporarily stores the question data.

[0809] Step 3:

[0810] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate. The device provides the user with attribute options, collects and temporarily stores the attribute data selected by the user, and also inputs the number of answers they want to generate.

[0811] Step 4:

[0812] The device sends the user's input data (question, attributes, number of answers) to the server, which receives the data, formats it, and converts it into a format suitable for the AI ​​model.

[0813] Step 5:

[0814] The device runs an emotion engine to collect user emotion data, which is then used to recognize the user's emotions based on facial expression analysis, voice recognition, input speed, etc.

[0815] Step 6:

[0816] The emotion engine analyzes the user's emotions and automatically adjusts the survey questions based on the results, for example, simplifying the questions if the user is nervous.

[0817] Step 7:

[0818] The server generates answers using an AI model based on the formatted data and the analysis results of the emotion engine. The AI ​​model generates answers based on the specified attributes and number of answers.

[0819] Step 8:

[0820] The server transmits the generated answer data to the terminal, which visually displays the generated answer to the user.

[0821] Step 9:

[0822] The user can check the response data generated on the device and download or perform additional analysis as needed.

[0823] Step 10:

[0824] The server calculates the fee based on the number of generated answers. The server calculates the fee for the user based on the number of answers and their attributes, and generates a bill.

[0825] Step 11:

[0826] The server sends the bill to the user, who receives the bill through the terminal and pays based on a pay-as-you-go system.

[0827] This allows users to conduct market research quickly and at low cost in a short period of time, and also enables questions and answers to be generated that take emotions into consideration.

[0828] Example 2

[0829] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0830] Conventional market research and questionnaire systems require users to manually create numerous questions and analyze the response data, requiring a great deal of time and effort. Furthermore, the systems do not reflect the user's emotional state, which can lead to stress and discomfort. Therefore, there is a need for systems that allow users to conduct market research intuitively and efficiently and quickly obtain high-quality data. Furthermore, there is a growing demand for systems that appropriately charge users based on the number of responses generated and automatically notify them of the charges.

[0831] 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.

[0832] In this invention, the server includes a means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and for training a machine learning algorithm, a means for users to input or select survey questions, and a means for users to specify the attributes of the answers and the number of answers they want. This allows users to intuitively operate the system and obtain the data they need in a short period of time.

[0833] "Descriptive data" is information that identifies or characterizes a particular individual or group, such as age, region, family structure, or income.

[0834] A "database" is a structured collection of data and a system for efficiently storing, retrieving, and managing data.

[0835] A "machine learning algorithm" is a computational method for learning regularities and patterns from large amounts of data and making predictions and classifications for new data.

[0836] "User" means any individual or organization wishing to conduct market research or surveys.

[0837] A "question" is a question or problem presented to a user for response in a questionnaire or survey.

[0838] "Emotional data" is information about the user's emotional state that can be inferred from the user's facial expression, voice, input speed, etc.

[0839] An "emotion recognition algorithm" is a computational method for analyzing collected emotional data and recognizing the user's emotional state.

[0840] A "pay-as-you-go system" is a system for calculating and charging fees according to the amount of service used by the user.

[0841] "Charge notification" means the act or means of informing a user of the charges for the services they use.

[0842] The system of the present invention combines the functionality of automatically generating market research and questionnaire responses with the functionality of recognizing user emotions. It is important that this system operates in cooperation with the server, terminals, users, and emotion recognition algorithms.

[0843] Basic configuration

[0844] server:

[0845] The server is a central computer system that hosts the machine learning algorithm and manages the entire database. The server's main processes are attribute data collection, machine learning, generating survey responses, and calculating usage fees. Specifically, it formats the data and inputs it into the algorithm.

[0846] Device:

[0847] A terminal is a device that provides an interface for users to access and operate. Examples include PCs, smartphones, and tablets. Through the terminal, users can enter survey questions, specify response attributes, collect emotional data, and receive the final response results.

[0848] User:

[0849] A user is an individual or organization who wants to conduct market research or a survey. The user inputs survey questions and specifies the desired response attributes and number. In addition, the user provides emotion data through a terminal.

[0850] Emotion Recognition Algorithm:

[0851] The emotion recognition algorithm analyzes the user's emotions in real time and reflects them in the generation of survey questions and answers. Specifically, it analyzes the user's facial expressions, voice, input speed, etc.

[0852] Example of operation

[0853] 1. Attribute data capture:

[0854] The server then takes attribute data such as age, region, family structure, and income and stores it in a database, excluding personal information. This data is then used as training data for machine learning algorithms.

[0855] 2. Enter and select survey questions:

[0856] The user opens the input form on the device and enters or selects survey questions, such as "How old are you?", "Where do you live?", and "How much do you earn?"

[0857] 3. Specify the attributes and number of answers:

[0858] The user specifies the answer attributes (age group, region, income, etc.) and the number of answers they want to generate through their terminal. For example, they could specify "Number of answers: 100," "Age group: [20-30, 30-40]," "Region: [Tokyo, Osaka]," and "Income: [3 million to 5 million yen, 5 million to 7 million yen]."

[0859] 4. Emotional data collection and analysis:

[0860] The device collects data such as facial expressions, voice, and input speed while the user is typing, and analyzes it using an emotion recognition algorithm to determine the user's emotional state, such as whether they are nervous, happy, or excited.

[0861] 5. AI model generates answers:

[0862] The device sends the collected data to a server, which formats it and inputs it into a machine learning algorithm. The AI ​​model generates an answer based on the specified attributes and number of answers. An example prompt is, "Generate an answer based on the following attributes: age group [20-30, 30-40], region [Tokyo, Osaka], income [¥3 million-¥5 million, ¥5 million-¥7 million], number of answers: 100."

[0863] 6. Providing answers to users:

[0864] The server sends the generated answers to the device, which then visually displays them to the user in a table, list, or other format. For example, "Question 1: What is your age? Answers: 28, 32, 22, 35, ..."

[0865] 7. Calculation and Notification:

[0866] The server calculates the fee based on the number of generated responses and their attributes, and automatically notifies the user of the fee. For example, the fee may be something like "Usage fee: ¥5,000, Details: Number of responses: 100, Unit cost: ¥50, Total: ¥5,000."

[0867] This invention allows users to intuitively operate the system and obtain high-quality market research results in a short time. It also improves the user experience through emotion recognition and enables appropriate fee collection through a pay-per-use system.

[0868] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0869] System program processing steps

[0870] Step 1:

[0871] The user opens an input form on the device and enters or selects questions to be used for market research or questionnaires. The device receives the information in real time and temporarily stores it.

[0872] Input: Questions entered by the user via keyboard or touch interface.

[0873] Output: Temporarily saved question data.

[0874] Specifically, the user inputs questions such as "How old are you?" and "Where do you live?"

[0875] Step 2:

[0876] The user uses the device to specify the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate. The device collects this information in real time and temporarily stores it.

[0877] Input: User-specified answer attributes and number of answers (e.g., "Number of answers: 100", "Age range: [20-30, 30-40]", "Region: [Tokyo, Osaka]", "Income: [3 million yen - 5 million yen, 5 million yen - 7 million yen]").

[0878] Output: Temporarily saved answer attributes and answer count data.

[0879] Specific actions include the user selecting an attribute using a drop-down menu or text field and entering a numeric value.

[0880] Step 3:

[0881] The device collects the user's emotional data by using the device's camera and microphone to record the user's facial expressions and voice, and then passes this data to an emotion recognition algorithm for analysis.

[0882] Input: User's facial expression and voice data.

[0883] Output: Parsed emotion data (e.g., "tension", "joy", "excitement").

[0884] Specifically, the system automatically activates the camera and microphone to collect data while the user is using the device.

[0885] Step 4:

[0886] An emotion recognition algorithm analyzes the user's emotional data and automatically adjusts the questions based on the results. For example, it may change the questions to simpler ones for users who are feeling stressed.

[0887] Input: Parsed sentiment data and original question data.

[0888] Output: Adjusted question data.

[0889] Specifically, the system simplifies or refines questions depending on the user's emotional state.

[0890] Step 5:

[0891] The device sends the collected data to a server, which receives it, formats it, and feeds it into a machine learning algorithm. The AI ​​model generates an answer based on the specified attributes and number of answers.

[0892] Input: Adjusted question data, answer attribute data, and emotion data.

[0893] Output: The generated response data.

[0894] Specifically, a prompt is created and instructions are given to the AI ​​model such as, "Generate answers based on the following attributes: age group [20-30, 30-40], region [Tokyo, Osaka], income [3 million to 5 million yen, 5 million to 7 million yen], number of answers: 100."

[0895] Step 6:

[0896] The server sends the generated answer data to the terminal, which then visually displays it to the user.

[0897] Input: Generated response data.

[0898] Output: The answer results displayed to the user.

[0899] Specifically, the display format is provided as a table or list so that the user can easily check the data.

[0900] Step 7:

[0901] The server calculates the fee based on the number of generated answers and automatically notifies the user of the result. A pay-per-use system is used to prepare for fee collection.

[0902] Input: Number of responses generated and attribute data.

[0903] Output: Calculated charges and automatic notifications.

[0904] Specifically, the user is notified in the form of "Usage fee: ¥5000, details: number of responses 100, unit cost ¥50, total ¥5000."

[0905] (Application example 2)

[0906] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0907] Modern market research and content distribution services require more personalized services that adapt to user emotions. However, conventional systems have difficulty understanding a user's emotional state in real time and generating appropriate content based on that information. This makes it difficult to improve the user experience and limits the quality of the service.

[0908] 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.

[0909] In this invention, the server includes means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and for training an artificial intelligence to learn the data, means for a user to input or select survey questions, means for a user to specify the attributes and number of answers desired by the user, means for the artificial intelligence to generate answers based on the specified attributes and number of answers, means for providing the generated answers to the user, means for collecting user emotional data, means for analyzing the emotional data and generating prompt sentences based on the results, means for generating content based on the prompt sentences, and means for visually displaying the generated content. This makes it possible to generate and provide content optimized for the user's emotional state in real time.

[0910] "Attribute data" refers to data that is recorded and managed excluding personal information such as age, region, family structure, and income.

[0911] "Artificial intelligence" is a computer system that automatically makes decisions and generates information based on previously learned data.

[0912] "Emotion data" is data that indicates the user's emotional state, collected from the user's facial expression, voice, input speed, and the like.

[0913] A "prompt sentence" is an input sentence that instructs the artificial intelligence to generate content.

[0914] "Content" is a set of data such as information, messages, articles, videos, etc., provided to users.

[0915] "Pay-as-you-go" is a billing method in which a user is charged according to the amount of service used.

[0916] A "database" is a system that can structure, manage, and search large amounts of data.

[0917] A "server" is a central computer system that processes and stores data and provides services to other computers.

[0918] A "user interface" is a screen or device that allows a user to operate or input data into a system.

[0919] "Visual display" is the act of visually showing information to a user through a screen.

[0920] In order to implement this invention, it is important that the server, terminal, user, and emotion engine work in cooperation. In this configuration, the following hardware and software are used.

[0921] The server has the function of inputting attribute data such as age, region, family structure, and income into a database without personal information and training it into artificial intelligence (AI). The server efficiently manages the data using a database management system (DBMS) and analyzes the collected data using an AI module. This AI module uses machine learning frameworks such as TensorFlow and PyTorch.

[0922] The terminal is a device that provides an interface for users to access the survey, and includes smartphones, smart glasses, head-mounted displays, etc. The terminal provides a UI (user interface) that allows users to input or select survey questions and specify the desired response attributes and number of responses. The terminal also has various sensors (cameras, microphones, etc.) that collect emotion data.

[0923] The emotion engine analyzes the user's emotion data collected through the device. This emotion engine uses facial expression recognition and voice analysis technologies to determine the user's emotional state. For example, it uses OpenCV and Dlib libraries to analyze the facial expression data and generate the optimal prompt sentence according to the user's emotion.

[0924] The server generates a prompt based on the collected emotional data and inputs the prompt into the AI ​​model. The prompt generation module then generates an appropriate instruction based on the emotional data. The prompt has the following format:

[0925] "Write a cheerful blog article about the benefits of outdoor activities."

[0926] "Create a comforting video message for someone having a tough day."

[0927] The AI ​​model generates content based on the prompt sentence. The generated content can be a variety of content, such as text, images, and videos. The generated content is sent from the server to the device, and the device visually displays it to the user through the user interface.

[0928] This makes it possible to generate and provide optimal content in real time according to the user's emotional state. As a specific example, if the user is happy, the AI ​​will provide a prompt to generate a "cheerful blog post about the benefits of outdoor activities," and the AI ​​will automatically generate and display that content to the user. If the user is sad, the AI ​​will generate a "comforting video message for people having a tough day," and the AI ​​will similarly generate and display that content. In this way, the present invention realizes the provision of personalized content that is in line with the user's emotions.

[0929] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0930] Step 1:

[0931] The user opens the input form on the terminal and enters or selects questions for the survey.

[0932] This is done through an HTML form or application UI that provides the user interface.

[0933] The input data (question content) will be temporarily saved on the device.

[0934] For example, questions such as "How old are you?" are entered into the terminal.

[0935] Step 2:

[0936] The user specifies the desired answer attributes and the number of answers through the terminal.

[0937] For example, the number of responses is specified as 100, and the response attributes are specified as "age group: 20-30, region: Tokyo."

[0938] The terminal temporarily stores this specification information and prepares to send it to the server.

[0939] Step 3:

[0940] The terminal collects emotion data of the user.

[0941] Using cameras and microphones, facial expressions, voice, input speed, etc. are acquired as sensor input.

[0942] The collected emotion data is sent to the emotion engine in real time.

[0943] Step 4:

[0944] The emotion engine analyzes the emotion data sent from the device.

[0945] It uses OpenCV and Dlib libraries to analyze facial expression and voice data to determine the user's emotional state.

[0946] For example, the analysis result may be "The user's emotional state is tense."

[0947] Step 5:

[0948] The terminal transmits the analyzed emotion data to the server.

[0949] The server combines this emotion data with question data and answer attribute data collected in advance and stores them in a database.

[0950] The saved data is used to generate subsequent prompt sentences.

[0951] Step 6:

[0952] The server generates prompt sentences according to the user's emotional state.

[0953] Based on the emotion data, instructions for generating content that reflects a specific emotion are created.

[0954] For example, if the user is in a tense state, the prompt sentence generated is "Please generate an article about ways to relax."

[0955] Step 7:

[0956] The server inputs the generated prompt sentence into the AI ​​model to generate content.

[0957] This AI model uses generative AI such as GPT-3.

[0958] Based on the input prompt, AI generates content such as text and videos.

[0959] The generated content might be something like "blog posts about how to relax."

[0960] Step 8:

[0961] The server transmits the generated content to the terminal.

[0962] The terminal visually displays this content in a user interface.

[0963] The user can check and use the generated content.

[0964] By following these steps in order, we will realize a system that generates and provides personalized content in real time according to the user's emotional state, which is expected to significantly improve the user experience.

[0965] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0966] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0967] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0968] [Third embodiment]

[0969] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0970] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0971] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0972] 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.

[0973] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0974] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0975] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0976] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0977] 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.

[0978] 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.

[0979] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0980] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0981] The system of the present invention automatically generates responses to market research and questionnaires. To implement this system, it is important that the server, terminals, and users work together. The detailed configuration and operation of this system will now be described.

[0982] Basic configuration

[0983] Server: A central computer system that hosts the artificial intelligence (AI) model and manages the database. The server handles key processes such as attribute data capture, AI training, answer generation, and billing processing.

[0984] Terminal: A device that provides an interface for users to access. Users enter survey questions, specify response attributes, and receive generated responses through the terminal.

[0985] User: An individual or organization who wants to conduct market research or a questionnaire. The user operates a terminal to input questions and specify the desired response attributes and number.

[0986] Program processing

[0987] 1. User enters and selects survey questions:

[0988] The user opens the input form on the device and enters or selects the survey questions. The device receives the user's input and temporarily saves the questions.

[0989] example:

[0990] Question 1: What is your age?

[0991] Question 2: What area do you live in?

[0992] Question 3: What is your income?

[0993] 2. User specifies the attributes and number of answers:

[0994] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through the device. The device collects and temporarily stores this information.

[0995] example:

[0996] Number of answers: 100

[0997] attribute:

[0998] Age range: [20-30, 30-40]

[0999] Region: [Tokyo, Osaka]

[1000] Income: [3 million to 5 million yen, 5 million to 7 million yen]

[1001] 3. AI generates answers based on learning data:

[1002] The device sends the user's input data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates answers based on the specified attributes and number of answers, using data previously learned from a database.

[1003] 4. Provide the generated answer to the user:

[1004] The server sends the generated answer data to the device, which visually displays the generated answers to the user, who can then download the results or perform further analysis as needed.

[1005] example:

[1006] json

[1007] {

[1008] 'Question 1': 'What is your age?',

[1009] 'Answer': [

[1010] '28',

[1011] '32',

[1012] 'twenty two',

[1013] '35',

[1014] ...

[1015] ],

[1016] 'Question 2': 'Which area do you live in?'

[1017] 'Answer': [

[1018] 'Tokyo',

[1019] 'Osaka',

[1020] 'Tokyo',

[1021] 'Osaka',

[1022] ...

[1023] ],

[1024] ...

[1025] }

[1026] 5. Pay-per-use system:

[1027] The server issues a bill to the user based on the number of responses generated. The server calculates the fee based on the number of responses and automatically notifies the user of the amount due, realizing pay-per-use billing.

[1028] This allows users to conduct market research quickly and cost-effectively. Through specific embodiments of the invention, users can intuitively operate the system and obtain the required data in a short period of time.

[1029] The processing flow will be explained below.

[1030] Step 1:

[1031] The user opens the input form on the device, and the device displays the input form for creating a questionnaire to the user.

[1032] Step 2:

[1033] The user inputs or selects a question. The terminal receives the input from the user and temporarily stores the question data.

[1034] Step 3:

[1035] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate. The terminal provides the user with attribute options, collects the attribute data selected by the user, and also inputs the number of answers they want to generate.

[1036] Step 4:

[1037] The device sends the user's input data (question, attributes, number of answers) to the server, which receives the data, formats it, and converts it into a format suitable for the AI ​​model.

[1038] Step 5:

[1039] The server inputs the formatted data into the AI ​​model, which generates answers based on the specified attributes and number of answers, using attribute data previously learned from a database.

[1040] Step 6:

[1041] The server transmits the generated answer data to the terminal, which visually displays the generated answer to the user.

[1042] Step 7:

[1043] The user can check the response data generated on the device and download or perform additional analysis as needed.

[1044] Step 8:

[1045] The server calculates the fee based on the number of generated answers. The server calculates the fee for the user based on the number of answers and their attributes, and generates a bill.

[1046] Step 9:

[1047] The server sends the bill to the user, who receives the bill through the terminal and pays based on a pay-as-you-go system.

[1048] This allows users to quickly conduct market research at low cost and in a short period of time.

[1049] Example 1

[1050] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1051] When conducting market research or questionnaires, it is difficult to generate a large number of responses based on various attributes quickly and accurately. It is also important to effectively implement a pay-per-use system and charge users appropriate fees based on the responses generated. Furthermore, a method is required for visually displaying the results and allowing users to easily receive data while ensuring data security.

[1052] 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.

[1053] In this invention, the server includes: means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and training the AI ​​to learn from the data; means for the user to input or select survey questions; means for the user to specify the desired answer attributes and number of answers; means for the AI ​​to generate answers based on the specified attributes and number of answers; means for providing the generated answers to the user; means for calculating fees based on the number of generated answers and automatically generating an invoice; communication means for ensuring data security; and means for visually displaying answer results and making data downloadable. This enables market research and surveys to be conducted quickly and accurately, enabling effective pay-per-use billing. It also enables users to easily receive data while ensuring data security.

[1054] "Age" refers to the number of years that have passed since an individual was born.

[1055] "Region" means a geographic or administrative division.

[1056] "Family structure" refers to the number of people in a household and their relationships.

[1057] "Income" refers to the total amount of money earned through work, investments, etc.

[1058] "Attribute data" refers to data that contains specific characteristics or traits about an individual or object.

[1059] "Personal information" is information that can be used to identify a specific individual.

[1060] A "database" refers to a system for efficiently storing, searching, and managing large amounts of data.

[1061] "Artificial intelligence" refers to technology that aims to reproduce intelligent behavior using computers.

[1062] "Learning" refers to the process by which an algorithm acquires the ability to analyze data patterns and make predictions or classifications based on them.

[1063] "User" refers to any individual or entity that uses this system.

[1064] A "survey" refers to a collection of questions designed to collect data for a specific purpose.

[1065] "Question" refers to a question that is asked in a questionnaire to request an answer.

[1066] "Number of responses" refers to the number of survey responses that a user wishes to generate.

[1067] "Generation" refers to the process of creating new data or information based on specified conditions.

[1068] "Visually displaying" refers to presenting data or information graphically on a user interface.

[1069] "Pay-as-you-go" refers to a billing method in which fees are calculated based on the amount of service used.

[1070] "Invoice" means a document requesting payment for goods or services provided.

[1071] "Communication means" refers to the technologies, protocols, and infrastructure used to send and receive data.

[1072] "Downloading" refers to the process of obtaining data from a remote system, such as via the Internet.

[1073] The system of the present invention automatically generates market research and questionnaire responses, and it is important that the server, terminals, and users work in cooperation with each other. The detailed configuration and operation of this system will now be described.

[1074] Basic configuration

[1075] Server: A central computer system that hosts artificial intelligence (AI) models and manages databases. The server performs key processes such as attribute data collection, AI learning, answer generation, and billing processing. Specifically, the server uses a server machine (e.g., a high-performance general-purpose server) with a high-performance CPU and large memory capacity. Machine learning frameworks such as TensorFlow and PyTorch run on the server to execute AI models. A database management system (e.g., MySQL, PostgreSQL) stores and manages data.

[1076] Terminal: A device that provides an interface for users to access the survey. Users use the terminal to enter survey questions, specify response attributes, and receive generated responses. The terminals used vary widely, including personal computers (PCs), tablets, and smartphones. A dedicated application or a general web browser (e.g., Chrome, Safari) is installed on the terminal.

[1077] User: An individual or organization who wants to conduct market research or a questionnaire. The user operates a terminal to input questions and specify the desired response attributes and number.

[1078] Detailed program processing

[1079] 1. User enters and selects survey questions:

[1080] The user opens the input form on the device and enters or selects survey questions. The device receives the user's input and temporarily stores the questions. For example, questions such as "Question 1: How old are you?", "Question 2: Where do you live?", and "Question 3: How much do you earn?" are entered.

[1081] 2. User specifies the attributes and number of answers:

[1082] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through their device. The device collects this information and temporarily stores it. For example, they might specify "Number of answers: 100," "Attributes: Age group: [20-30, 30-40], Region: [Tokyo, Osaka], Income: [3 million yen - 5 million yen, 5 million yen - 7 million yen]."

[1083] 3. AI generates answers based on learning data:

[1084] The device sends the user's input data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates answers based on the specified attributes and number of answers, using data previously learned from a database.

[1085] 4. Provide the generated answer to the user:

[1086] The generated answer data is sent from the server to the terminal. The terminal visually displays the generated answer to the user. The user can download the results or perform additional analysis as needed. For example, the generated answer is provided with the following prompt sentence:

[1087] Please generate your survey answers. The questions are as follows:

[1088] 1. How old are you?

[1089] 2. What area do you live in?

[1090] 3. What is your income?

[1091] Generate 100 answers based on the following attributes:

[1092] Age range: [20-30, 30-40]

[1093] Region: [Tokyo, Osaka]

[1094] Income: [3 million to 5 million yen, 5 million to 7 million yen]

[1095] 5. Pay-per-use system:

[1096] The server issues an invoice to the user based on the number of responses generated. The server calculates the fee based on the number of responses and automatically notifies the user of the invoice amount. For example, the server calculates a fixed fee for generating 100 responses and sends an invoice to the user. This enables users to conduct market research quickly and cost-effectively. In addition, to ensure data security, the HTTPS protocol is used, and data transmission and reception is encrypted to protect user information.

[1097] With the above-described configuration and operation, the present invention efficiently realizes market research and automatic generation of questionnaire responses.

[1098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1099] Step 1:

[1100] User enters or selects survey question:

[1101] The user opens the input form on the device and enters or selects survey questions. For example, the user might enter "Question 1: How old are you?", "Question 2: Where do you live?", and "Question 3: How much do you earn?" The device receives this input data and temporarily stores it in local storage or memory.

[1102] Input: Survey question entered by the user

[1103] Output: Temporarily saved question data

[1104] Step 2:

[1105] User specifies attributes and number of answers:

[1106] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through their device. For example, the user might specify "Number of answers: 100," "Attributes: Age group: [20-30, 30-40]," "Region: [Tokyo, Osaka]," and "Income: [3 million yen - 5 million yen, 5 million yen - 7 million yen]." The device receives this information and temporarily stores it again in local storage or memory.

[1107] Input: User-specified answer attributes and number of answers

[1108] Output: Temporarily saved answer attributes and number of answers

[1109] Step 3:

[1110] The device sends data to the server:

[1111] The device sends the saved question data, answer attributes, and number of answers to the server. The HTTPS protocol is used to ensure data security. The server analyzes the received data, formats it, and stores it in a database.

[1112] Input: Saved question data, answer attributes, and number of answers

[1113] Output: Data sent to the server

[1114] Step 4:

[1115] The server uses an AI model to generate the answer:

[1116] The server inputs the formatted data into the AI ​​model, which generates answers based on the specified attributes and number of answers based on a pre-trained dataset. The generated answer data is output in JSON format or similar.

[1117] Input: Formatted question data, answer attributes, and number of answers

[1118] Output: Answer data generated by the AI ​​model

[1119] Step 5:

[1120] The server generates the answer and sends it to the device:

[1121] The server receives the generated response data, formats it in an appropriate format (e.g., JSON), and sends it to the device. This information is also transmitted securely using the HTTPS protocol. The device receives this data, analyzes it, and displays it visually to the user.

[1122] Input: Answer data generated by the AI ​​model

[1123] Output: Formatted answer data sent to the device

[1124] Step 6:

[1125] User reviews and downloads results:

[1126] The device visually displays the generated answers to the user in tabular and graphical formats, and the user can review the results and download the data in formats such as CSV and PDF if desired.

[1127] Input: Answer data displayed on the terminal

[1128] Output: Generated answers for user review, and downloadable data

[1129] Step 7:

[1130] The server issues a pay-as-you-go bill:

[1131] The server calculates the usage fee based on the number of generated answers. Based on the calculation result, an invoice is automatically generated and sent to the user via email, etc. For example, a specific fee is calculated for generating 100 answers.

[1132] Input: Number of generated answers

[1133] Output: Invoice sent to user

[1134] (Application example 1)

[1135] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1136] Market research and collecting survey responses require a great deal of time and effort, and it is particularly difficult to collect data specific to target users in a short amount of time for advertising campaigns. Conventional methods are unable to quickly collect accurate attribute-based data and analyze it in real time, making it difficult to maximize advertising effectiveness.

[1137] 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.

[1138] In this invention, the server includes means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and for training an artificial intelligence to learn the data, means for a user to input or select survey questions, means for a user to specify the desired answer attributes and number of answers, means for the artificial intelligence to generate answers based on the specified attributes and number of answers, means for generating market research data specific to target users of an advertising campaign in real time, and means for providing the generated answers to users. This enables advertising agencies to collect accurate target market data in a short amount of time and analyze it in real time.

[1139] "Age" is an attribute that indicates the length of time that has passed since a particular individual was born.

[1140] "Region" is an attribute that indicates a specific geographical location or area.

[1141] "Family structure" is an attribute that indicates the family relationships of a particular individual.

[1142] "Income" is an attribute that indicates the monetary rewards or benefits that a particular individual or household receives.

[1143] "Personal information" refers to information that can identify a specific individual, including attributes that can be linked to a specific person.

[1144] A "database" is a system for efficiently managing, searching, and manipulating an organized collection of data.

[1145] "Artificial intelligence" is a general term for technology that allows computers to perform processes similar to human intelligence.

[1146] "Learning" is the act of teaching artificial intelligence to acquire patterns and knowledge using specific data.

[1147] A "survey" is a research method that asks people to respond through questions in order to gather specific information.

[1148] A "question" is a question set in a questionnaire to solicit an answer.

[1149] "Response attributes" refer to characteristics of the respondent, such as specific age, region, family structure, income, etc.

[1150] "Number of responses" refers to the total number of responses received in response to the questionnaire.

[1151] An "advertising campaign" is a planned promotional activity carried out to promote a particular product or service and appeal to consumers.

[1152] "Target users" refer to consumers who may be particularly interested in or concerned with a particular advertisement or product.

[1153] "Market research data" refers to the results of collecting and analyzing information on consumer opinions, behavior, purchasing trends, etc.

[1154] "Real-time" refers to information processing and operations being carried out almost instantly, with almost no delay.

[1155] "Providing" is the act of handing over the generated answers and data to the user.

[1156] The system of the present invention generates market research data specific to target users of advertising campaigns in real time. It is important that the system operates in cooperation with the server, terminals, and users.

[1157] Basic configuration

[1158] Server: The server is a central computer system that hosts the artificial intelligence (AI) model and manages the database. The server handles key processes such as attribute data ingestion, AI training, answer generation, and billing processing. For example, it uses Python-based AI models (TensorFlow or PyTorch) and databases (PostgreSQL, MySQL).

[1159] Terminal: A terminal is a device that provides an interface for users to access the survey. Users enter survey questions, specify response attributes, and receive generated responses through the terminal. Specific examples include smartphones (iPhone 13, Samsung Galaxy S21).

[1160] User: The user is a marketing person at an advertising agency. He operates a terminal to input questions and specify the desired answer attributes and number.

[1161] Program processing

[1162] 1. Entering survey questions: The user opens the input form on the device and enters or selects the survey questions. The device receives the user's input and temporarily saves the questions.

[1163] 2. Specifying answer attributes and number: The user specifies the answer attributes (e.g., age group, region, income, etc.) and the number of answers they want to generate through the terminal. The terminal collects and temporarily stores this information.

[1164] 3. Answer generation: The device sends the user's input data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates an answer based on the specified attributes and number of answers, using data previously learned from the database.

[1165] 4. Providing the generated answer: The server sends the generated answer data to the device. The device visually displays the generated answer to the user. The user can download the results or perform further analysis as needed.

[1166] 5. Pay-per-use system: The server issues a bill to the user based on the number of responses generated. The server calculates the fee based on the number of responses and automatically notifies the user of the amount due, realizing pay-per-use billing.

[1167] Specific examples of programs

[1168] The server inputs attribute data such as age, region, family composition, and income into a database, excluding personal information, and trains this data using a Python AI model (TensorFlow or PyTorch). Users enter survey questions on their smartphones (iPhone 13, Samsung Galaxy S21), specifying the attributes of the answers and the number of answers.

[1169] The server uses an AI model to generate answers based on the specified attributes and the number of answers, which are then sent to the device and visually displayed to the user, and finally, the user is charged based on the number of answers generated.

[1170] Examples of prompt statements

[1171] Here is an example of a prompt that can be input to a generative AI model:

[1172] plaintext

[1173] Prompt: For market research purposes, please generate answers to the following questions that match the following demographics: age 20-30, living in Tokyo, income 3-5 million yen.

[1174] Question 1: What is your age?

[1175] Question 2: What is your gender?

[1176] Question 3: What social media do you usually use?

[1177] This allows advertising agencies to quickly and efficiently obtain market research data specific to their target users.

[1178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1179] Step 1:

[1180] The user opens the input form on the terminal and enters or selects the survey questions.

[1181] Input: Survey questions entered by the user.

[1182] Output: Temporarily saved survey question data.

[1183] Specific operation: The user opens the application on their smartphone and enters questions into the question input screen. The device records these in a temporary database.

[1184] Step 2:

[1185] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through their terminal.

[1186] Input: Attributes of the answer entered by the user and the number of desired answers.

[1187] Output: Temporarily saved attribute and response count data.

[1188] Specific operation: The user enters the desired answer attributes (e.g., age 20-30, region: Tokyo, Osaka, income: 3 million to 5 million yen) and the number of answers (e.g., 100) on the attribute specification screen. The device temporarily stores this information.

[1189] Step 3:

[1190] The terminal transmits the user's input data to the server, which receives it.

[1191] Input: Survey question data, response attribute data, and number of responses.

[1192] Output: User input data sent to the server.

[1193] Specific operation: The device converts the temporarily saved survey question data, answer attribute data, and answer count data into JSON format and sends it to the server using an HTTP POST request. The server receives this request and formats the data.

[1194] Step 4:

[1195] The server formats the received data and inputs it into the AI ​​model.

[1196] Input: The raw data received by the server.

[1197] Output: The training data input into the AI ​​model.

[1198] Specific operation: The server formats the received data into the required format (e.g., encoding and normalizing the data as needed) and applies it to the AI ​​model.

[1199] Step 5:

[1200] The AI ​​model generates answers based on the specified attributes and number of answers, based on data previously learned from a database.

[1201] Input: The training data input into the AI ​​model.

[1202] Output: Generated survey response data.

[1203] Specific operation: The AI ​​model (implemented in TensorFlow or PyTorch) uses the received training data to generate answers that meet the specified attributes and number of answers.

[1204] Step 6:

[1205] The generated response data is transmitted from the server to the terminal.

[1206] Input: Generated survey response data.

[1207] Output: The generated response data sent back to the device.

[1208] Specific operation: The server converts the generated response data into JSON format and returns it to the terminal as an HTTP response. The terminal receives this data.

[1209] Step 7:

[1210] The terminal visually displays the generated answers to the user.

[1211] Input: Generated response data received at the terminal.

[1212] Output: The generated answer displayed to the user.

[1213] What it does: The device analyzes the data it receives and displays it in a user-friendly format. The user can then download the results or perform further analysis as needed.

[1214] Step 8:

[1215] The server bills the user based on the number of responses generated.

[1216] Input: Generated response count data.

[1217] Output: The invoice sent to the customer.

[1218] Specific operation: The server calculates the fee based on the number of generated answers, automatically generates an invoice, and sends it to the user through the notification system.

[1219] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1220] The system of the present invention automatically generates market research and questionnaire responses, and also combines them with an emotion engine that recognizes the user's emotions. To implement this system, it is important that the server, terminals, users, and emotion engine work in coordination. The detailed configuration and operation of this system will now be described.

[1221] Basic configuration

[1222] Server: A central computer system that hosts the artificial intelligence (AI) model and manages the database. The server handles key processes such as attribute data capture, AI training, answer generation, and billing processing.

[1223] Terminal: A device that provides an interface for users to access. Through the terminal, users input survey questions, specify response attributes, receive generated responses, and collect emotion data.

[1224] User: An individual or organization who wants to conduct market research or a questionnaire. The user operates a terminal to input questions and specify the desired response attributes and number.

[1225] Emotion engine: A system that recognizes the user's emotions and adjusts them according to the survey questions and answer generation.

[1226] Program processing

[1227] 1. User enters and selects survey questions:

[1228] The user opens the input form on the device and enters or selects the survey questions. The device receives the user's input and temporarily saves the questions.

[1229] example:

[1230] Question 1: What is your age?

[1231] Question 2: What area do you live in?

[1232] Question 3: What is your income?

[1233] 2. User specifies the attributes and number of answers:

[1234] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through the device. The device collects and temporarily stores this information.

[1235] example:

[1236] Number of answers: 100

[1237] attribute:

[1238] Age range: [20-30, 30-40]

[1239] Region: [Tokyo, Osaka]

[1240] Income: [3 million to 5 million yen, 5 million to 7 million yen]

[1241] 3. Collecting and analyzing emotional data:

[1242] The device collects the user's emotional data, and the emotion engine recognizes emotions from the user's facial expressions, voice, input speed, etc. and analyzes this data.

[1243] example:

[1244] User emotions: nervousness, joy, excitement

[1245] 4. Emotion-based survey adjustment:

[1246] The emotion engine automatically adjusts the survey questions based on the user's emotions, such as simplifying the questions for users who are highly stressed.

[1247] 5. AI generates answers based on learning data:

[1248] The device sends the user's input data and emotional data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates answers based on the specified attributes and number of answers, using data previously learned from a database.

[1249] 6. Provide the generated answer to the user:

[1250] The server sends the generated answer data to the terminal, which visually displays the generated answer to the user.

[1251] example:

[1252] json

[1253] {

[1254] 'Question 1': 'What is your age?',

[1255] 'Answer': [

[1256] '28',

[1257] '32',

[1258] 'twenty two',

[1259] '35',

[1260] ...

[1261] ],

[1262] 'Question 2': 'Which area do you live in?'

[1263] 'Answer': [

[1264] 'Tokyo',

[1265] 'Osaka',

[1266] 'Tokyo',

[1267] 'Osaka',

[1268] ...

[1269] ],

[1270] ...

[1271] }

[1272] 7. Pay-per-use system:

[1273] The server calculates the fee based on the number of generated answers. The server calculates the user's fee based on the number of answers and their attributes, and automatically notifies the user of the bill.

[1274] This allows users to conduct market research quickly and cost-effectively, and enables them to generate questions and answers that take emotion into consideration.Through specific embodiments of the invention, users can intuitively operate the system and obtain the necessary data in a short period of time.

[1275] The processing flow will be explained below.

[1276] Step 1:

[1277] The user opens the input form on the device, and the device displays the input form for creating a questionnaire to the user.

[1278] Step 2:

[1279] The user inputs or selects a question. The terminal receives the input from the user and temporarily stores the question data.

[1280] Step 3:

[1281] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate. The device provides the user with attribute options, collects and temporarily stores the attribute data selected by the user, and also inputs the number of answers they want to generate.

[1282] Step 4:

[1283] The device sends the user's input data (question, attributes, number of answers) to the server, which receives the data, formats it, and converts it into a format suitable for the AI ​​model.

[1284] Step 5:

[1285] The device runs an emotion engine to collect user emotion data, which is then used to recognize the user's emotions based on facial expression analysis, voice recognition, input speed, etc.

[1286] Step 6:

[1287] The emotion engine analyzes the user's emotions and automatically adjusts the survey questions based on the results, for example, simplifying the questions if the user is nervous.

[1288] Step 7:

[1289] The server generates answers using an AI model based on the formatted data and the analysis results of the emotion engine. The AI ​​model generates answers based on the specified attributes and number of answers.

[1290] Step 8:

[1291] The server transmits the generated answer data to the terminal, which visually displays the generated answer to the user.

[1292] Step 9:

[1293] The user can check the response data generated on the device and download or perform additional analysis as needed.

[1294] Step 10:

[1295] The server calculates the fee based on the number of generated answers. The server calculates the fee for the user based on the number of answers and their attributes, and generates a bill.

[1296] Step 11:

[1297] The server sends the bill to the user, who receives the bill through the terminal and pays based on a pay-as-you-go system.

[1298] This allows users to conduct market research quickly and at low cost in a short period of time, and also enables questions and answers to be generated that take emotions into consideration.

[1299] Example 2

[1300] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1301] Conventional market research and questionnaire systems require users to manually create numerous questions and analyze the response data, requiring a great deal of time and effort. Furthermore, the systems do not reflect the user's emotional state, which can lead to stress and discomfort. Therefore, there is a need for systems that allow users to conduct market research intuitively and efficiently and quickly obtain high-quality data. Furthermore, there is a growing demand for systems that appropriately charge users based on the number of responses generated and automatically notify them of the charges.

[1302] 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.

[1303] In this invention, the server includes a means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and for training a machine learning algorithm, a means for users to input or select survey questions, and a means for users to specify the attributes of the answers and the number of answers they want. This allows users to intuitively operate the system and obtain the data they need in a short period of time.

[1304] "Descriptive data" is information that identifies or characterizes a particular individual or group, such as age, region, family structure, or income.

[1305] A "database" is a structured collection of data and a system for efficiently storing, retrieving, and managing data.

[1306] A "machine learning algorithm" is a computational method for learning regularities and patterns from large amounts of data and making predictions and classifications for new data.

[1307] "User" means any individual or organization wishing to conduct market research or surveys.

[1308] A "question" is a question or problem presented to a user for response in a questionnaire or survey.

[1309] "Emotional data" is information about the user's emotional state that can be inferred from the user's facial expression, voice, input speed, etc.

[1310] An "emotion recognition algorithm" is a computational method for analyzing collected emotional data and recognizing the user's emotional state.

[1311] A "pay-as-you-go system" is a system for calculating and charging fees according to the amount of service used by the user.

[1312] "Charge notification" means the act or means of informing a user of the charges for the services they use.

[1313] The system of the present invention combines the functionality of automatically generating market research and questionnaire responses with the functionality of recognizing user emotions. It is important that this system operates in cooperation with the server, terminals, users, and emotion recognition algorithms.

[1314] Basic configuration

[1315] server:

[1316] The server is a central computer system that hosts the machine learning algorithm and manages the entire database. The server's main processes are attribute data collection, machine learning, generating survey responses, and calculating usage fees. Specifically, it formats the data and inputs it into the algorithm.

[1317] Device:

[1318] A terminal is a device that provides an interface for users to access and operate. Examples include PCs, smartphones, and tablets. Through the terminal, users can enter survey questions, specify response attributes, collect emotional data, and receive the final response results.

[1319] User:

[1320] A user is an individual or organization who wants to conduct market research or a survey. The user inputs survey questions and specifies the desired response attributes and number. In addition, the user provides emotion data through a terminal.

[1321] Emotion Recognition Algorithm:

[1322] The emotion recognition algorithm analyzes the user's emotions in real time and reflects them in the generation of survey questions and answers. Specifically, it analyzes the user's facial expressions, voice, input speed, etc.

[1323] Example of operation

[1324] 1. Attribute data capture:

[1325] The server then takes attribute data such as age, region, family structure, and income and stores it in a database, excluding personal information. This data is then used as training data for machine learning algorithms.

[1326] 2. Enter and select survey questions:

[1327] The user opens the input form on the device and enters or selects survey questions, such as "How old are you?", "Where do you live?", and "How much do you earn?"

[1328] 3. Specify the attributes and number of answers:

[1329] The user specifies the answer attributes (age group, region, income, etc.) and the number of answers they want to generate through their terminal. For example, they could specify "Number of answers: 100," "Age group: [20-30, 30-40]," "Region: [Tokyo, Osaka]," and "Income: [3 million to 5 million yen, 5 million to 7 million yen]."

[1330] 4. Emotional data collection and analysis:

[1331] The device collects data such as facial expressions, voice, and input speed while the user is typing, and analyzes it using an emotion recognition algorithm to determine the user's emotional state, such as whether they are nervous, happy, or excited.

[1332] 5. AI model generates answers:

[1333] The device sends the collected data to a server, which formats it and inputs it into a machine learning algorithm. The AI ​​model generates an answer based on the specified attributes and number of answers. An example prompt is, "Generate an answer based on the following attributes: age group [20-30, 30-40], region [Tokyo, Osaka], income [¥3 million-¥5 million, ¥5 million-¥7 million], number of answers: 100."

[1334] 6. Providing answers to users:

[1335] The server sends the generated answers to the device, which then visually displays them to the user in a table, list, or other format. For example, "Question 1: What is your age? Answers: 28, 32, 22, 35, ..."

[1336] 7. Calculation and Notification:

[1337] The server calculates the fee based on the number of generated responses and their attributes, and automatically notifies the user of the fee. For example, the fee may be something like "Usage fee: ¥5,000, Details: Number of responses: 100, Unit cost: ¥50, Total: ¥5,000."

[1338] This invention allows users to intuitively operate the system and obtain high-quality market research results in a short time. It also improves the user experience through emotion recognition and enables appropriate fee collection through a pay-per-use system.

[1339] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1340] System program processing steps

[1341] Step 1:

[1342] The user opens an input form on the device and enters or selects questions to be used for market research or questionnaires. The device receives the information in real time and temporarily stores it.

[1343] Input: Questions entered by the user via keyboard or touch interface.

[1344] Output: Temporarily saved question data.

[1345] Specifically, the user inputs questions such as "How old are you?" and "Where do you live?"

[1346] Step 2:

[1347] The user uses the device to specify the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate. The device collects this information in real time and temporarily stores it.

[1348] Input: User-specified answer attributes and number of answers (e.g., "Number of answers: 100", "Age range: [20-30, 30-40]", "Region: [Tokyo, Osaka]", "Income: [3 million yen - 5 million yen, 5 million yen - 7 million yen]").

[1349] Output: Temporarily saved answer attributes and answer count data.

[1350] Specific actions include the user selecting an attribute using a drop-down menu or text field and entering a numeric value.

[1351] Step 3:

[1352] The device collects the user's emotional data by using the device's camera and microphone to record the user's facial expressions and voice, and then passes this data to an emotion recognition algorithm for analysis.

[1353] Input: User's facial expression and voice data.

[1354] Output: Parsed emotion data (e.g., "tension", "joy", "excitement").

[1355] Specifically, the system automatically activates the camera and microphone to collect data while the user is using the device.

[1356] Step 4:

[1357] An emotion recognition algorithm analyzes the user's emotional data and automatically adjusts the questions based on the results. For example, it may change the questions to simpler ones for users who are feeling stressed.

[1358] Input: Parsed sentiment data and original question data.

[1359] Output: Adjusted question data.

[1360] Specifically, the system simplifies or refines questions depending on the user's emotional state.

[1361] Step 5:

[1362] The device sends the collected data to a server, which receives it, formats it, and feeds it into a machine learning algorithm. The AI ​​model generates an answer based on the specified attributes and number of answers.

[1363] Input: Adjusted question data, answer attribute data, and emotion data.

[1364] Output: The generated response data.

[1365] Specifically, a prompt is created and instructions are given to the AI ​​model such as, "Generate answers based on the following attributes: age group [20-30, 30-40], region [Tokyo, Osaka], income [3 million to 5 million yen, 5 million to 7 million yen], number of answers: 100."

[1366] Step 6:

[1367] The server sends the generated answer data to the terminal, which then visually displays it to the user.

[1368] Input: Generated response data.

[1369] Output: The answer results displayed to the user.

[1370] Specifically, the display format is provided as a table or list so that the user can easily check the data.

[1371] Step 7:

[1372] The server calculates the fee based on the number of generated answers and automatically notifies the user of the result. A pay-per-use system is used to prepare for fee collection.

[1373] Input: Number of responses generated and attribute data.

[1374] Output: Calculated charges and automatic notifications.

[1375] Specifically, the user is notified in the form of "Usage fee: ¥5000, details: number of responses 100, unit cost ¥50, total ¥5000."

[1376] (Application example 2)

[1377] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1378] Modern market research and content distribution services require more personalized services that adapt to user emotions. However, conventional systems have difficulty understanding a user's emotional state in real time and generating appropriate content based on that information. This makes it difficult to improve the user experience and limits the quality of the service.

[1379] 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.

[1380] In this invention, the server includes means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and for training an artificial intelligence to learn the data, means for a user to input or select survey questions, means for a user to specify the attributes and number of answers desired by the user, means for the artificial intelligence to generate answers based on the specified attributes and number of answers, means for providing the generated answers to the user, means for collecting user emotional data, means for analyzing the emotional data and generating prompt sentences based on the results, means for generating content based on the prompt sentences, and means for visually displaying the generated content. This makes it possible to generate and provide content optimized for the user's emotional state in real time.

[1381] "Attribute data" refers to data that is recorded and managed excluding personal information such as age, region, family structure, and income.

[1382] "Artificial intelligence" is a computer system that automatically makes decisions and generates information based on previously learned data.

[1383] "Emotion data" is data that indicates the user's emotional state, collected from the user's facial expression, voice, input speed, and the like.

[1384] A "prompt sentence" is an input sentence that instructs the artificial intelligence to generate content.

[1385] "Content" is a set of data such as information, messages, articles, videos, etc., provided to users.

[1386] "Pay-as-you-go" is a billing method in which a user is charged according to the amount of service used.

[1387] A "database" is a system that can structure, manage, and search large amounts of data.

[1388] A "server" is a central computer system that processes and stores data and provides services to other computers.

[1389] A "user interface" is a screen or device that allows a user to operate or input data into a system.

[1390] "Visual display" is the act of visually showing information to a user through a screen.

[1391] In order to implement this invention, it is important that the server, terminal, user, and emotion engine work in cooperation. In this configuration, the following hardware and software are used.

[1392] The server has the function of inputting attribute data such as age, region, family structure, and income into a database without personal information and training it into artificial intelligence (AI). The server efficiently manages the data using a database management system (DBMS) and analyzes the collected data using an AI module. This AI module uses machine learning frameworks such as TensorFlow and PyTorch.

[1393] The terminal is a device that provides an interface for users to access the survey, and includes smartphones, smart glasses, head-mounted displays, etc. The terminal provides a UI (user interface) that allows users to input or select survey questions and specify the desired response attributes and number of responses. The terminal also has various sensors (cameras, microphones, etc.) that collect emotion data.

[1394] The emotion engine analyzes the user's emotion data collected through the device. This emotion engine uses facial expression recognition and voice analysis technologies to determine the user's emotional state. For example, it uses OpenCV and Dlib libraries to analyze the facial expression data and generate the optimal prompt sentence according to the user's emotion.

[1395] The server generates a prompt based on the collected emotional data and inputs the prompt into the AI ​​model. The prompt generation module then generates an appropriate instruction based on the emotional data. The prompt has the following format:

[1396] "Write a cheerful blog article about the benefits of outdoor activities."

[1397] "Create a comforting video message for someone having a tough day."

[1398] The AI ​​model generates content based on the prompt sentence. The generated content can be a variety of content, such as text, images, and videos. The generated content is sent from the server to the device, and the device visually displays it to the user through the user interface.

[1399] This makes it possible to generate and provide optimal content in real time according to the user's emotional state. As a specific example, if the user is happy, the AI ​​will provide a prompt to generate a "cheerful blog post about the benefits of outdoor activities," and the AI ​​will automatically generate and display that content to the user. If the user is sad, the AI ​​will generate a "comforting video message for people having a tough day," and the AI ​​will similarly generate and display that content. In this way, the present invention realizes the provision of personalized content that is in line with the user's emotions.

[1400] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1401] Step 1:

[1402] The user opens the input form on the terminal and enters or selects questions for the survey.

[1403] This is done through an HTML form or application UI that provides the user interface.

[1404] The input data (question content) will be temporarily saved on the device.

[1405] For example, questions such as "How old are you?" are entered into the terminal.

[1406] Step 2:

[1407] The user specifies the desired answer attributes and the number of answers through the terminal.

[1408] For example, the number of responses is specified as 100, and the response attributes are specified as "age group: 20-30, region: Tokyo."

[1409] The terminal temporarily stores this specification information and prepares to send it to the server.

[1410] Step 3:

[1411] The terminal collects emotion data of the user.

[1412] Using cameras and microphones, facial expressions, voice, input speed, etc. are acquired as sensor input.

[1413] The collected emotion data is sent to the emotion engine in real time.

[1414] Step 4:

[1415] The emotion engine analyzes the emotion data sent from the device.

[1416] It uses OpenCV and Dlib libraries to analyze facial expression and voice data to determine the user's emotional state.

[1417] For example, the analysis result may be "The user's emotional state is tense."

[1418] Step 5:

[1419] The terminal transmits the analyzed emotion data to the server.

[1420] The server combines this emotion data with question data and answer attribute data collected in advance and stores them in a database.

[1421] The saved data is used to generate subsequent prompt sentences.

[1422] Step 6:

[1423] The server generates prompt sentences according to the user's emotional state.

[1424] Based on the emotion data, instructions for generating content that reflects a specific emotion are created.

[1425] For example, if the user is in a tense state, the prompt sentence generated is "Please generate an article about ways to relax."

[1426] Step 7:

[1427] The server inputs the generated prompt sentence into the AI ​​model to generate content.

[1428] This AI model uses generative AI such as GPT-3.

[1429] Based on the input prompt, AI generates content such as text and videos.

[1430] The generated content might be something like "blog posts about how to relax."

[1431] Step 8:

[1432] The server transmits the generated content to the terminal.

[1433] The terminal visually displays this content in a user interface.

[1434] The user can check and use the generated content.

[1435] By following these steps in order, we will realize a system that generates and provides personalized content in real time according to the user's emotional state, which is expected to significantly improve the user experience.

[1436] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1437] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1438] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1439] [Fourth embodiment]

[1440] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1441] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1442] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1443] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1444] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1445] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1446] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1447] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1448] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1449] 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.

[1450] 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.

[1451] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1452] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1453] The system of the present invention automatically generates responses to market research and questionnaires. To implement this system, it is important that the server, terminals, and users work together. The detailed configuration and operation of this system will now be described.

[1454] Basic configuration

[1455] Server: A central computer system that hosts the artificial intelligence (AI) model and manages the database. The server handles key processes such as attribute data capture, AI training, answer generation, and billing processing.

[1456] Terminal: A device that provides an interface for users to access. Users enter survey questions, specify response attributes, and receive generated responses through the terminal.

[1457] User: An individual or organization who wants to conduct market research or a questionnaire. The user operates a terminal to input questions and specify the desired response attributes and number.

[1458] Program processing

[1459] 1. User enters and selects survey questions:

[1460] The user opens the input form on the device and enters or selects the survey questions. The device receives the user's input and temporarily saves the questions.

[1461] example:

[1462] Question 1: What is your age?

[1463] Question 2: What area do you live in?

[1464] Question 3: What is your income?

[1465] 2. User specifies the attributes and number of answers:

[1466] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through the device. The device collects and temporarily stores this information.

[1467] example:

[1468] Number of answers: 100

[1469] attribute:

[1470] Age range: [20-30, 30-40]

[1471] Region: [Tokyo, Osaka]

[1472] Income: [3 million to 5 million yen, 5 million to 7 million yen]

[1473] 3. AI generates answers based on learning data:

[1474] The device sends the user's input data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates answers based on the specified attributes and number of answers, using data previously learned from a database.

[1475] 4. Provide the generated answer to the user:

[1476] The server sends the generated answer data to the device, which visually displays the generated answers to the user, who can then download the results or perform further analysis as needed.

[1477] example:

[1478] json

[1479] {

[1480] 'Question 1': 'What is your age?',

[1481] 'Answer': [

[1482] '28',

[1483] '32',

[1484] 'twenty two',

[1485] '35',

[1486] ...

[1487] ],

[1488] 'Question 2': 'Which area do you live in?'

[1489] 'Answer': [

[1490] 'Tokyo',

[1491] 'Osaka',

[1492] 'Tokyo',

[1493] 'Osaka',

[1494] ...

[1495] ],

[1496] ...

[1497] }

[1498] 5. Pay-per-use system:

[1499] The server issues a bill to the user based on the number of responses generated. The server calculates the fee based on the number of responses and automatically notifies the user of the amount due, realizing pay-per-use billing.

[1500] This allows users to conduct market research quickly and cost-effectively. Through specific embodiments of the invention, users can intuitively operate the system and obtain the required data in a short period of time.

[1501] The processing flow will be explained below.

[1502] Step 1:

[1503] The user opens the input form on the device, and the device displays the input form for creating a questionnaire to the user.

[1504] Step 2:

[1505] The user inputs or selects a question. The terminal receives the input from the user and temporarily stores the question data.

[1506] Step 3:

[1507] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate. The terminal provides the user with attribute options, collects the attribute data selected by the user, and also inputs the number of answers they want to generate.

[1508] Step 4:

[1509] The device sends the user's input data (question, attributes, number of answers) to the server, which receives the data, formats it, and converts it into a format suitable for the AI ​​model.

[1510] Step 5:

[1511] The server inputs the formatted data into the AI ​​model, which generates answers based on the specified attributes and number of answers, using attribute data previously learned from a database.

[1512] Step 6:

[1513] The server transmits the generated answer data to the terminal, which visually displays the generated answer to the user.

[1514] Step 7:

[1515] The user can check the response data generated on the device and download or perform additional analysis as needed.

[1516] Step 8:

[1517] The server calculates the fee based on the number of generated answers. The server calculates the fee for the user based on the number of answers and their attributes, and generates a bill.

[1518] Step 9:

[1519] The server sends the bill to the user, who receives the bill through the terminal and pays based on a pay-as-you-go system.

[1520] This allows users to quickly conduct market research at low cost and in a short period of time.

[1521] Example 1

[1522] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1523] When conducting market research or questionnaires, it is difficult to generate a large number of responses based on various attributes quickly and accurately. It is also important to effectively implement a pay-per-use system and charge users appropriate fees based on the responses generated. Furthermore, a method is required for visually displaying the results and allowing users to easily receive data while ensuring data security.

[1524] 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.

[1525] In this invention, the server includes: means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and training the AI ​​to learn from the data; means for the user to input or select survey questions; means for the user to specify the desired answer attributes and number of answers; means for the AI ​​to generate answers based on the specified attributes and number of answers; means for providing the generated answers to the user; means for calculating fees based on the number of generated answers and automatically generating an invoice; communication means for ensuring data security; and means for visually displaying answer results and making data downloadable. This enables market research and surveys to be conducted quickly and accurately, enabling effective pay-per-use billing. It also enables users to easily receive data while ensuring data security.

[1526] "Age" refers to the number of years that have passed since an individual was born.

[1527] "Region" means a geographic or administrative division.

[1528] "Family structure" refers to the number of people in a household and their relationships.

[1529] "Income" refers to the total amount of money earned through work, investments, etc.

[1530] "Attribute data" refers to data that contains specific characteristics or traits about an individual or object.

[1531] "Personal information" is information that can be used to identify a specific individual.

[1532] A "database" refers to a system for efficiently storing, searching, and managing large amounts of data.

[1533] "Artificial intelligence" refers to technology that aims to reproduce intelligent behavior using computers.

[1534] "Learning" refers to the process by which an algorithm acquires the ability to analyze data patterns and make predictions or classifications based on them.

[1535] "User" refers to any individual or entity that uses this system.

[1536] A "survey" refers to a collection of questions designed to collect data for a specific purpose.

[1537] "Question" refers to a question that is asked in a questionnaire to request an answer.

[1538] "Number of responses" refers to the number of survey responses that a user wishes to generate.

[1539] "Generation" refers to the process of creating new data or information based on specified conditions.

[1540] "Visually displaying" refers to presenting data or information graphically on a user interface.

[1541] "Pay-as-you-go" refers to a billing method in which fees are calculated based on the amount of service used.

[1542] "Invoice" means a document requesting payment for goods or services provided.

[1543] "Communication means" refers to the technologies, protocols, and infrastructure used to send and receive data.

[1544] "Downloading" refers to the process of obtaining data from a remote system, such as via the Internet.

[1545] The system of the present invention automatically generates market research and questionnaire responses, and it is important that the server, terminals, and users work in cooperation with each other. The detailed configuration and operation of this system will now be described.

[1546] Basic configuration

[1547] Server: A central computer system that hosts artificial intelligence (AI) models and manages databases. The server performs key processes such as attribute data collection, AI learning, answer generation, and billing processing. Specifically, the server uses a server machine (e.g., a high-performance general-purpose server) with a high-performance CPU and large memory capacity. Machine learning frameworks such as TensorFlow and PyTorch run on the server to execute AI models. A database management system (e.g., MySQL, PostgreSQL) stores and manages data.

[1548] Terminal: A device that provides an interface for users to access the survey. Users use the terminal to enter survey questions, specify response attributes, and receive generated responses. The terminals used vary widely, including personal computers (PCs), tablets, and smartphones. A dedicated application or a general web browser (e.g., Chrome, Safari) is installed on the terminal.

[1549] User: An individual or organization who wants to conduct market research or a questionnaire. The user operates a terminal to input questions and specify the desired response attributes and number.

[1550] Detailed program processing

[1551] 1. User enters and selects survey questions:

[1552] The user opens the input form on the device and enters or selects survey questions. The device receives the user's input and temporarily stores the questions. For example, questions such as "Question 1: How old are you?", "Question 2: Where do you live?", and "Question 3: How much do you earn?" are entered.

[1553] 2. User specifies the attributes and number of answers:

[1554] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through their device. The device collects this information and temporarily stores it. For example, they might specify "Number of answers: 100," "Attributes: Age group: [20-30, 30-40], Region: [Tokyo, Osaka], Income: [3 million yen - 5 million yen, 5 million yen - 7 million yen]."

[1555] 3. AI generates answers based on learning data:

[1556] The device sends the user's input data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates answers based on the specified attributes and number of answers, using data previously learned from a database.

[1557] 4. Provide the generated answer to the user:

[1558] The generated answer data is sent from the server to the terminal. The terminal visually displays the generated answer to the user. The user can download the results or perform additional analysis as needed. For example, the generated answer is provided with the following prompt sentence:

[1559] Please generate your survey answers. The questions are as follows:

[1560] 1. How old are you?

[1561] 2. What area do you live in?

[1562] 3. What is your income?

[1563] Generate 100 answers based on the following attributes:

[1564] Age range: [20-30, 30-40]

[1565] Region: [Tokyo, Osaka]

[1566] Income: [3 million to 5 million yen, 5 million to 7 million yen]

[1567] 5. Pay-per-use system:

[1568] The server issues an invoice to the user based on the number of responses generated. The server calculates the fee based on the number of responses and automatically notifies the user of the invoice amount. For example, the server calculates a fixed fee for generating 100 responses and sends an invoice to the user. This enables users to conduct market research quickly and cost-effectively. In addition, to ensure data security, the HTTPS protocol is used, and data transmission and reception is encrypted to protect user information.

[1569] With the above-described configuration and operation, the present invention efficiently realizes market research and automatic generation of questionnaire responses.

[1570] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1571] Step 1:

[1572] User enters or selects survey question:

[1573] The user opens the input form on the device and enters or selects survey questions. For example, the user might enter "Question 1: How old are you?", "Question 2: Where do you live?", and "Question 3: How much do you earn?" The device receives this input data and temporarily stores it in local storage or memory.

[1574] Input: Survey question entered by the user

[1575] Output: Temporarily saved question data

[1576] Step 2:

[1577] User specifies attributes and number of answers:

[1578] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through their device. For example, the user might specify "Number of answers: 100," "Attributes: Age group: [20-30, 30-40]," "Region: [Tokyo, Osaka]," and "Income: [3 million yen - 5 million yen, 5 million yen - 7 million yen]." The device receives this information and temporarily stores it again in local storage or memory.

[1579] Input: User-specified answer attributes and number of answers

[1580] Output: Temporarily saved answer attributes and number of answers

[1581] Step 3:

[1582] The device sends data to the server:

[1583] The device sends the saved question data, answer attributes, and number of answers to the server. The HTTPS protocol is used to ensure data security. The server analyzes the received data, formats it, and stores it in a database.

[1584] Input: Saved question data, answer attributes, and number of answers

[1585] Output: Data sent to the server

[1586] Step 4:

[1587] The server uses an AI model to generate the answer:

[1588] The server inputs the formatted data into the AI ​​model, which generates answers based on the specified attributes and number of answers based on a pre-trained dataset. The generated answer data is output in JSON format or similar.

[1589] Input: Formatted question data, answer attributes, and number of answers

[1590] Output: Answer data generated by the AI ​​model

[1591] Step 5:

[1592] The server generates the answer and sends it to the device:

[1593] The server receives the generated response data, formats it in an appropriate format (e.g., JSON), and sends it to the device. This information is also transmitted securely using the HTTPS protocol. The device receives this data, analyzes it, and displays it visually to the user.

[1594] Input: Answer data generated by the AI ​​model

[1595] Output: Formatted answer data sent to the device

[1596] Step 6:

[1597] User reviews and downloads results:

[1598] The device visually displays the generated answers to the user in tabular and graphical formats, and the user can review the results and download the data in formats such as CSV and PDF if desired.

[1599] Input: Answer data displayed on the terminal

[1600] Output: Generated answers for user review, and downloadable data

[1601] Step 7:

[1602] The server issues a pay-as-you-go bill:

[1603] The server calculates the usage fee based on the number of generated answers. Based on the calculation result, an invoice is automatically generated and sent to the user via email, etc. For example, a specific fee is calculated for generating 100 answers.

[1604] Input: Number of generated answers

[1605] Output: Invoice sent to user

[1606] (Application example 1)

[1607] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1608] Market research and collecting survey responses require a great deal of time and effort, and it is particularly difficult to collect data specific to target users in a short amount of time for advertising campaigns. Conventional methods are unable to quickly collect accurate attribute-based data and analyze it in real time, making it difficult to maximize advertising effectiveness.

[1609] 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.

[1610] In this invention, the server includes means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and for training an artificial intelligence to learn the data, means for a user to input or select survey questions, means for a user to specify the desired answer attributes and number of answers, means for the artificial intelligence to generate answers based on the specified attributes and number of answers, means for generating market research data specific to target users of an advertising campaign in real time, and means for providing the generated answers to users. This enables advertising agencies to collect accurate target market data in a short amount of time and analyze it in real time.

[1611] "Age" is an attribute that indicates the length of time that has passed since a particular individual was born.

[1612] "Region" is an attribute that indicates a specific geographical location or area.

[1613] "Family structure" is an attribute that indicates the family relationships of a particular individual.

[1614] "Income" is an attribute that indicates the monetary rewards or benefits that a particular individual or household receives.

[1615] "Personal information" refers to information that can identify a specific individual, including attributes that can be linked to a specific person.

[1616] A "database" is a system for efficiently managing, searching, and manipulating an organized collection of data.

[1617] "Artificial intelligence" is a general term for technology that allows computers to perform processes similar to human intelligence.

[1618] "Learning" is the act of teaching artificial intelligence to acquire patterns and knowledge using specific data.

[1619] A "survey" is a research method that asks people to respond through questions in order to gather specific information.

[1620] A "question" is a question set in a questionnaire to solicit an answer.

[1621] "Response attributes" refer to characteristics of the respondent, such as specific age, region, family structure, income, etc.

[1622] "Number of responses" refers to the total number of responses received in response to the questionnaire.

[1623] An "advertising campaign" is a planned promotional activity carried out to promote a particular product or service and appeal to consumers.

[1624] "Target users" refer to consumers who may be particularly interested in or concerned with a particular advertisement or product.

[1625] "Market research data" refers to the results of collecting and analyzing information on consumer opinions, behavior, purchasing trends, etc.

[1626] "Real-time" refers to information processing and operations being carried out almost instantly, with almost no delay.

[1627] "Providing" is the act of handing over the generated answers and data to the user.

[1628] The system of the present invention generates market research data specific to target users of advertising campaigns in real time. It is important that the system operates in cooperation with the server, terminals, and users.

[1629] Basic configuration

[1630] Server: The server is a central computer system that hosts the artificial intelligence (AI) model and manages the database. The server handles key processes such as attribute data ingestion, AI training, answer generation, and billing processing. For example, it uses Python-based AI models (TensorFlow or PyTorch) and databases (PostgreSQL, MySQL).

[1631] Terminal: A terminal is a device that provides an interface for users to access the survey. Users enter survey questions, specify response attributes, and receive generated responses through the terminal. Specific examples include smartphones (iPhone 13, Samsung Galaxy S21).

[1632] User: The user is a marketing person at an advertising agency. He operates a terminal to input questions and specify the desired answer attributes and number.

[1633] Program processing

[1634] 1. Entering survey questions: The user opens the input form on the device and enters or selects the survey questions. The device receives the user's input and temporarily saves the questions.

[1635] 2. Specifying answer attributes and number: The user specifies the answer attributes (e.g., age group, region, income, etc.) and the number of answers they want to generate through the terminal. The terminal collects and temporarily stores this information.

[1636] 3. Answer generation: The device sends the user's input data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates an answer based on the specified attributes and number of answers, using data previously learned from the database.

[1637] 4. Providing the generated answer: The server sends the generated answer data to the device. The device visually displays the generated answer to the user. The user can download the results or perform further analysis as needed.

[1638] 5. Pay-per-use system: The server issues a bill to the user based on the number of responses generated. The server calculates the fee based on the number of responses and automatically notifies the user of the amount due, realizing pay-per-use billing.

[1639] Specific examples of programs

[1640] The server inputs attribute data such as age, region, family composition, and income into a database, excluding personal information, and trains this data using a Python AI model (TensorFlow or PyTorch). Users enter survey questions on their smartphones (iPhone 13, Samsung Galaxy S21), specifying the attributes of the answers and the number of answers.

[1641] The server uses an AI model to generate answers based on the specified attributes and the number of answers, which are then sent to the device and visually displayed to the user, and finally, the user is charged based on the number of answers generated.

[1642] Examples of prompt statements

[1643] Here is an example of a prompt that can be input to a generative AI model:

[1644] plaintext

[1645] Prompt: For market research purposes, please generate answers to the following questions that match the following demographics: age 20-30, living in Tokyo, income 3-5 million yen.

[1646] Question 1: What is your age?

[1647] Question 2: What is your gender?

[1648] Question 3: What social media do you usually use?

[1649] This allows advertising agencies to quickly and efficiently obtain market research data specific to their target users.

[1650] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1651] Step 1:

[1652] The user opens the input form on the terminal and enters or selects the survey questions.

[1653] Input: Survey questions entered by the user.

[1654] Output: Temporarily saved survey question data.

[1655] Specific operation: The user opens the application on their smartphone and enters questions into the question input screen. The device records these in a temporary database.

[1656] Step 2:

[1657] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through their terminal.

[1658] Input: Attributes of the answer entered by the user and the number of desired answers.

[1659] Output: Temporarily saved attribute and response count data.

[1660] Specific operation: The user enters the desired answer attributes (e.g., age 20-30, region: Tokyo, Osaka, income: 3 million to 5 million yen) and the number of answers (e.g., 100) on the attribute specification screen. The device temporarily stores this information.

[1661] Step 3:

[1662] The terminal transmits the user's input data to the server, which receives it.

[1663] Input: Survey question data, response attribute data, and number of responses.

[1664] Output: User input data sent to the server.

[1665] Specific operation: The device converts the temporarily saved survey question data, answer attribute data, and answer count data into JSON format and sends it to the server using an HTTP POST request. The server receives this request and formats the data.

[1666] Step 4:

[1667] The server formats the received data and inputs it into the AI ​​model.

[1668] Input: The raw data received by the server.

[1669] Output: The training data input into the AI ​​model.

[1670] Specific operation: The server formats the received data into the required format (e.g., encoding and normalizing the data as needed) and applies it to the AI ​​model.

[1671] Step 5:

[1672] The AI ​​model generates answers based on the specified attributes and number of answers, based on data previously learned from a database.

[1673] Input: The training data input into the AI ​​model.

[1674] Output: Generated survey response data.

[1675] Specific operation: The AI ​​model (implemented in TensorFlow or PyTorch) uses the received training data to generate answers that meet the specified attributes and number of answers.

[1676] Step 6:

[1677] The generated response data is transmitted from the server to the terminal.

[1678] Input: Generated survey response data.

[1679] Output: The generated response data sent back to the device.

[1680] Specific operation: The server converts the generated response data into JSON format and returns it to the terminal as an HTTP response. The terminal receives this data.

[1681] Step 7:

[1682] The terminal visually displays the generated answers to the user.

[1683] Input: Generated response data received at the terminal.

[1684] Output: The generated answer displayed to the user.

[1685] What it does: The device analyzes the data it receives and displays it in a user-friendly format. The user can then download the results or perform further analysis as needed.

[1686] Step 8:

[1687] The server bills the user based on the number of responses generated.

[1688] Input: Generated response count data.

[1689] Output: The invoice sent to the customer.

[1690] Specific operation: The server calculates the fee based on the number of generated answers, automatically generates an invoice, and sends it to the user through the notification system.

[1691] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1692] The system of the present invention automatically generates market research and questionnaire responses, and also combines them with an emotion engine that recognizes the user's emotions. To implement this system, it is important that the server, terminals, users, and emotion engine work in coordination. The detailed configuration and operation of this system will now be described.

[1693] Basic configuration

[1694] Server: A central computer system that hosts the artificial intelligence (AI) model and manages the database. The server handles key processes such as attribute data capture, AI training, answer generation, and billing processing.

[1695] Terminal: A device that provides an interface for users to access. Through the terminal, users input survey questions, specify response attributes, receive generated responses, and collect emotion data.

[1696] User: An individual or organization who wants to conduct market research or a questionnaire. The user operates a terminal to input questions and specify the desired response attributes and number.

[1697] Emotion engine: A system that recognizes the user's emotions and adjusts them according to the survey questions and answer generation.

[1698] Program processing

[1699] 1. User enters and selects survey questions:

[1700] The user opens the input form on the device and enters or selects the survey questions. The device receives the user's input and temporarily saves the questions.

[1701] example:

[1702] Question 1: What is your age?

[1703] Question 2: What area do you live in?

[1704] Question 3: What is your income?

[1705] 2. User specifies the attributes and number of answers:

[1706] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate through the device. The device collects and temporarily stores this information.

[1707] example:

[1708] Number of answers: 100

[1709] attribute:

[1710] Age range: [20-30, 30-40]

[1711] Region: [Tokyo, Osaka]

[1712] Income: [3 million to 5 million yen, 5 million to 7 million yen]

[1713] 3. Collecting and analyzing emotional data:

[1714] The device collects the user's emotional data, and the emotion engine recognizes emotions from the user's facial expressions, voice, input speed, etc. and analyzes this data.

[1715] example:

[1716] User emotions: nervousness, joy, excitement

[1717] 4. Emotion-based survey adjustment:

[1718] The emotion engine automatically adjusts the survey questions based on the user's emotions, such as simplifying the questions for users who are highly stressed.

[1719] 5. AI generates answers based on learning data:

[1720] The device sends the user's input data and emotional data to the server, which receives it. The server formats the received data and inputs it into the AI ​​model. The AI ​​model generates answers based on the specified attributes and number of answers, using data previously learned from a database.

[1721] 6. Provide the generated answer to the user:

[1722] The server sends the generated answer data to the terminal, which visually displays the generated answer to the user.

[1723] example:

[1724] json

[1725] {

[1726] 'Question 1': 'What is your age?',

[1727] 'Answer': [

[1728] '28',

[1729] '32',

[1730] 'twenty two',

[1731] '35',

[1732] ...

[1733] ],

[1734] 'Question 2': 'Which area do you live in?'

[1735] 'Answer': [

[1736] 'Tokyo',

[1737] 'Osaka',

[1738] 'Tokyo',

[1739] 'Osaka',

[1740] ...

[1741] ],

[1742] ...

[1743] }

[1744] 7. Pay-per-use system:

[1745] The server calculates the fee based on the number of generated answers. The server calculates the user's fee based on the number of answers and their attributes, and automatically notifies the user of the bill.

[1746] This allows users to conduct market research quickly and cost-effectively, and enables them to generate questions and answers that take emotion into consideration.Through specific embodiments of the invention, users can intuitively operate the system and obtain the necessary data in a short period of time.

[1747] The processing flow will be explained below.

[1748] Step 1:

[1749] The user opens the input form on the device, and the device displays the input form for creating a questionnaire to the user.

[1750] Step 2:

[1751] The user inputs or selects a question. The terminal receives the input from the user and temporarily stores the question data.

[1752] Step 3:

[1753] The user specifies the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate. The device provides the user with attribute options, collects and temporarily stores the attribute data selected by the user, and also inputs the number of answers they want to generate.

[1754] Step 4:

[1755] The device sends the user's input data (question, attributes, number of answers) to the server, which receives the data, formats it, and converts it into a format suitable for the AI ​​model.

[1756] Step 5:

[1757] The device runs an emotion engine to collect user emotion data, which is then used to recognize the user's emotions based on facial expression analysis, voice recognition, input speed, etc.

[1758] Step 6:

[1759] The emotion engine analyzes the user's emotions and automatically adjusts the survey questions based on the results, for example, simplifying the questions if the user is nervous.

[1760] Step 7:

[1761] The server generates answers using an AI model based on the formatted data and the analysis results of the emotion engine. The AI ​​model generates answers based on the specified attributes and number of answers.

[1762] Step 8:

[1763] The server transmits the generated answer data to the terminal, which visually displays the generated answer to the user.

[1764] Step 9:

[1765] The user can check the response data generated on the device and download or perform additional analysis as needed.

[1766] Step 10:

[1767] The server calculates the fee based on the number of generated answers. The server calculates the fee for the user based on the number of answers and their attributes, and generates a bill.

[1768] Step 11:

[1769] The server sends the bill to the user, who receives the bill through the terminal and pays based on a pay-as-you-go system.

[1770] This allows users to conduct market research quickly and at low cost in a short period of time, and also enables questions and answers to be generated that take emotions into consideration.

[1771] Example 2

[1772] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1773] Conventional market research and questionnaire systems require users to manually create numerous questions and analyze the response data, requiring a great deal of time and effort. Furthermore, the systems do not reflect the user's emotional state, which can lead to stress and discomfort. Therefore, there is a need for systems that allow users to conduct market research intuitively and efficiently and quickly obtain high-quality data. Furthermore, there is a growing demand for systems that appropriately charge users based on the number of responses generated and automatically notify them of the charges.

[1774] 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.

[1775] In this invention, the server includes a means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and for training a machine learning algorithm, a means for users to input or select survey questions, and a means for users to specify the attributes of the answers and the number of answers they want. This allows users to intuitively operate the system and obtain the data they need in a short period of time.

[1776] "Descriptive data" is information that identifies or characterizes a particular individual or group, such as age, region, family structure, or income.

[1777] A "database" is a structured collection of data and a system for efficiently storing, retrieving, and managing data.

[1778] A "machine learning algorithm" is a computational method for learning regularities and patterns from large amounts of data and making predictions and classifications for new data.

[1779] "User" means any individual or organization wishing to conduct market research or surveys.

[1780] A "question" is a question or problem presented to a user for response in a questionnaire or survey.

[1781] "Emotional data" is information about the user's emotional state that can be inferred from the user's facial expression, voice, input speed, etc.

[1782] An "emotion recognition algorithm" is a computational method for analyzing collected emotional data and recognizing the user's emotional state.

[1783] A "pay-as-you-go system" is a system for calculating and charging fees according to the amount of service used by the user.

[1784] "Charge notification" means the act or means of informing a user of the charges for the services they use.

[1785] The system of the present invention combines the functionality of automatically generating market research and questionnaire responses with the functionality of recognizing user emotions. It is important that this system operates in cooperation with the server, terminals, users, and emotion recognition algorithms.

[1786] Basic configuration

[1787] server:

[1788] The server is a central computer system that hosts the machine learning algorithm and manages the entire database. The server's main processes are attribute data collection, machine learning, generating survey responses, and calculating usage fees. Specifically, it formats the data and inputs it into the algorithm.

[1789] Device:

[1790] A terminal is a device that provides an interface for users to access and operate. Examples include PCs, smartphones, and tablets. Through the terminal, users can enter survey questions, specify response attributes, collect emotional data, and receive the final response results.

[1791] User:

[1792] A user is an individual or organization who wants to conduct market research or a survey. The user inputs survey questions and specifies the desired response attributes and number. In addition, the user provides emotion data through a terminal.

[1793] Emotion Recognition Algorithm:

[1794] The emotion recognition algorithm analyzes the user's emotions in real time and reflects them in the generation of survey questions and answers. Specifically, it analyzes the user's facial expressions, voice, input speed, etc.

[1795] Example of operation

[1796] 1. Attribute data capture:

[1797] The server then takes attribute data such as age, region, family structure, and income and stores it in a database, excluding personal information. This data is then used as training data for machine learning algorithms.

[1798] 2. Enter and select survey questions:

[1799] The user opens the input form on the device and enters or selects survey questions, such as "How old are you?", "Where do you live?", and "How much do you earn?"

[1800] 3. Specify the attributes and number of answers:

[1801] The user specifies the answer attributes (age group, region, income, etc.) and the number of answers they want to generate through their terminal. For example, they could specify "Number of answers: 100," "Age group: [20-30, 30-40]," "Region: [Tokyo, Osaka]," and "Income: [3 million to 5 million yen, 5 million to 7 million yen]."

[1802] 4. Emotional data collection and analysis:

[1803] The device collects data such as facial expressions, voice, and input speed while the user is typing, and analyzes it using an emotion recognition algorithm to determine the user's emotional state, such as whether they are nervous, happy, or excited.

[1804] 5. AI model generates answers:

[1805] The device sends the collected data to a server, which formats it and inputs it into a machine learning algorithm. The AI ​​model generates an answer based on the specified attributes and number of answers. An example prompt is, "Generate an answer based on the following attributes: age group [20-30, 30-40], region [Tokyo, Osaka], income [¥3 million-¥5 million, ¥5 million-¥7 million], number of answers: 100."

[1806] 6. Providing answers to users:

[1807] The server sends the generated answers to the device, which then visually displays them to the user in a table, list, or other format. For example, "Question 1: What is your age? Answers: 28, 32, 22, 35, ..."

[1808] 7. Calculation and Notification:

[1809] The server calculates the fee based on the number of generated responses and their attributes, and automatically notifies the user of the fee. For example, the fee may be something like "Usage fee: ¥5,000, Details: Number of responses: 100, Unit cost: ¥50, Total: ¥5,000."

[1810] This invention allows users to intuitively operate the system and obtain high-quality market research results in a short time. It also improves the user experience through emotion recognition and enables appropriate fee collection through a pay-per-use system.

[1811] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1812] System program processing steps

[1813] Step 1:

[1814] The user opens an input form on the device and enters or selects questions to be used for market research or questionnaires. The device receives the information in real time and temporarily stores it.

[1815] Input: Questions entered by the user via keyboard or touch interface.

[1816] Output: Temporarily saved question data.

[1817] Specifically, the user inputs questions such as "How old are you?" and "Where do you live?"

[1818] Step 2:

[1819] The user uses the device to specify the attributes of the answers (e.g., age group, region, income, etc.) and the number of answers they want to generate. The device collects this information in real time and temporarily stores it.

[1820] Input: User-specified answer attributes and number of answers (e.g., "Number of answers: 100", "Age range: [20-30, 30-40]", "Region: [Tokyo, Osaka]", "Income: [3 million yen - 5 million yen, 5 million yen - 7 million yen]").

[1821] Output: Temporarily saved answer attributes and answer count data.

[1822] Specific actions include the user selecting an attribute using a drop-down menu or text field and entering a numeric value.

[1823] Step 3:

[1824] The device collects the user's emotional data by using the device's camera and microphone to record the user's facial expressions and voice, and then passes this data to an emotion recognition algorithm for analysis.

[1825] Input: User's facial expression and voice data.

[1826] Output: Parsed emotion data (e.g., "tension", "joy", "excitement").

[1827] Specifically, the system automatically activates the camera and microphone to collect data while the user is using the device.

[1828] Step 4:

[1829] An emotion recognition algorithm analyzes the user's emotional data and automatically adjusts the questions based on the results. For example, it may change the questions to simpler ones for users who are feeling stressed.

[1830] Input: Parsed sentiment data and original question data.

[1831] Output: Adjusted question data.

[1832] Specifically, the system simplifies or refines questions depending on the user's emotional state.

[1833] Step 5:

[1834] The device sends the collected data to a server, which receives it, formats it, and feeds it into a machine learning algorithm. The AI ​​model generates an answer based on the specified attributes and number of answers.

[1835] Input: Adjusted question data, answer attribute data, and emotion data.

[1836] Output: The generated response data.

[1837] Specifically, a prompt is created and instructions are given to the AI ​​model such as, "Generate answers based on the following attributes: age group [20-30, 30-40], region [Tokyo, Osaka], income [3 million to 5 million yen, 5 million to 7 million yen], number of answers: 100."

[1838] Step 6:

[1839] The server sends the generated answer data to the terminal, which then visually displays it to the user.

[1840] Input: Generated response data.

[1841] Output: The answer results displayed to the user.

[1842] Specifically, the display format is provided as a table or list so that the user can easily check the data.

[1843] Step 7:

[1844] The server calculates the fee based on the number of generated answers and automatically notifies the user of the result. A pay-per-use system is used to prepare for fee collection.

[1845] Input: Number of responses generated and attribute data.

[1846] Output: Calculated charges and automatic notifications.

[1847] Specifically, the user is notified in the form of "Usage fee: ¥5000, details: number of responses 100, unit cost ¥50, total ¥5000."

[1848] (Application example 2)

[1849] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1850] Modern market research and content distribution services require more personalized services that adapt to user emotions. However, conventional systems have difficulty understanding a user's emotional state in real time and generating appropriate content based on that information. This makes it difficult to improve the user experience and limits the quality of the service.

[1851] 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.

[1852] In this invention, the server includes means for inputting attribute data such as age, region, family structure, and income into a database excluding personal information and for training an artificial intelligence to learn the data, means for a user to input or select survey questions, means for a user to specify the attributes and number of answers desired by the user, means for the artificial intelligence to generate answers based on the specified attributes and number of answers, means for providing the generated answers to the user, means for collecting user emotional data, means for analyzing the emotional data and generating prompt sentences based on the results, means for generating content based on the prompt sentences, and means for visually displaying the generated content. This makes it possible to generate and provide content optimized for the user's emotional state in real time.

[1853] "Attribute data" refers to data that is recorded and managed excluding personal information such as age, region, family structure, and income.

[1854] "Artificial intelligence" is a computer system that automatically makes decisions and generates information based on previously learned data.

[1855] "Emotion data" is data that indicates the user's emotional state, collected from the user's facial expression, voice, input speed, and the like.

[1856] A "prompt sentence" is an input sentence that instructs the artificial intelligence to generate content.

[1857] "Content" is a set of data such as information, messages, articles, videos, etc., provided to users.

[1858] "Pay-as-you-go" is a billing method in which a user is charged according to the amount of service used.

[1859] A "database" is a system that can structure, manage, and search large amounts of data.

[1860] A "server" is a central computer system that processes and stores data and provides services to other computers.

[1861] A "user interface" is a screen or device that allows a user to operate or input data into a system.

[1862] "Visual display" is the act of visually showing information to a user through a screen.

[1863] In order to implement this invention, it is important that the server, terminal, user, and emotion engine work in cooperation. In this configuration, the following hardware and software are used.

[1864] The server has the function of inputting attribute data such as age, region, family structure, and income into a database without personal information and training it into artificial intelligence (AI). The server efficiently manages the data using a database management system (DBMS) and analyzes the collected data using an AI module. This AI module uses machine learning frameworks such as TensorFlow and PyTorch.

[1865] The terminal is a device that provides an interface for users to access the survey, and includes smartphones, smart glasses, head-mounted displays, etc. The terminal provides a UI (user interface) that allows users to input or select survey questions and specify the desired response attributes and number of responses. The terminal also has various sensors (cameras, microphones, etc.) that collect emotion data.

[1866] The emotion engine analyzes the user's emotion data collected through the device. This emotion engine uses facial expression recognition and voice analysis technologies to determine the user's emotional state. For example, it uses OpenCV and Dlib libraries to analyze the facial expression data and generate the optimal prompt sentence according to the user's emotion.

[1867] The server generates a prompt based on the collected emotional data and inputs the prompt into the AI ​​model. The prompt generation module then generates an appropriate instruction based on the emotional data. The prompt has the following format:

[1868] "Write a cheerful blog article about the benefits of outdoor activities."

[1869] "Create a comforting video message for someone having a tough day."

[1870] The AI ​​model generates content based on the prompt sentence. The generated content can be a variety of content, such as text, images, and videos. The generated content is sent from the server to the device, and the device visually displays it to the user through the user interface.

[1871] This makes it possible to generate and provide optimal content in real time according to the user's emotional state. As a specific example, if the user is happy, the AI ​​will provide a prompt to generate a "cheerful blog post about the benefits of outdoor activities," and the AI ​​will automatically generate and display that content to the user. If the user is sad, the AI ​​will generate a "comforting video message for people having a tough day," and the AI ​​will similarly generate and display that content. In this way, the present invention realizes the provision of personalized content that is in line with the user's emotions.

[1872] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1873] Step 1:

[1874] The user opens the input form on the terminal and enters or selects questions for the survey.

[1875] This is done through an HTML form or application UI that provides the user interface.

[1876] The input data (question content) will be temporarily saved on the device.

[1877] For example, questions such as "How old are you?" are entered into the terminal.

[1878] Step 2:

[1879] The user specifies the desired answer attributes and the number of answers through the terminal.

[1880] For example, the number of responses is specified as 100, and the response attributes are specified as "age group: 20-30, region: Tokyo."

[1881] The terminal temporarily stores this specification information and prepares to send it to the server.

[1882] Step 3:

[1883] The terminal collects emotion data of the user.

[1884] Using cameras and microphones, facial expressions, voice, input speed, etc. are acquired as sensor input.

[1885] The collected emotion data is sent to the emotion engine in real time.

[1886] Step 4:

[1887] The emotion engine analyzes the emotion data sent from the device.

[1888] It uses OpenCV and Dlib libraries to analyze facial expression and voice data to determine the user's emotional state.

[1889] For example, the analysis result may be "The user's emotional state is tense."

[1890] Step 5:

[1891] The terminal transmits the analyzed emotion data to the server.

[1892] The server combines this emotion data with question data and answer attribute data collected in advance and stores them in a database.

[1893] The saved data is used to generate subsequent prompt sentences.

[1894] Step 6:

[1895] The server generates prompt sentences according to the user's emotional state.

[1896] Based on the emotion data, instructions for generating content that reflects a specific emotion are created.

[1897] For example, if the user is in a tense state, the prompt sentence generated is "Please generate an article about ways to relax."

[1898] Step 7:

[1899] The server inputs the generated prompt sentence into the AI ​​model to generate content.

[1900] This AI model uses generative AI such as GPT-3.

[1901] Based on the input prompt, AI generates content such as text and videos.

[1902] The generated content might be something like "blog posts about how to relax."

[1903] Step 8:

[1904] The server transmits the generated content to the terminal.

[1905] The terminal visually displays this content in a user interface.

[1906] The user can check and use the generated content.

[1907] By following these steps in order, we will realize a system that generates and provides personalized content in real time according to the user's emotional state, which is expected to significantly improve the user experience.

[1908] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1909] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1910] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1911] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1912] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1913] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1914] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1915] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1916] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1917] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1918] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1919] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1920] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1921] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1922] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1923] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1924] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1925] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1926] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1927] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1928] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1929] The following is further disclosed regarding the above embodiment.

[1930] (Claim 1)

[1931] A method of inputting attribute data such as age, region, family structure, and income into a database without personal information and letting the AI ​​learn from it.

[1932] A means for users to input or select survey questions;

[1933] A means for a user to specify the desired answer attributes and number of answers;

[1934] A means for generating answers by artificial intelligence based on the specified attributes and number of answers;

[1935] a means for providing the generated answer to a user;

[1936] A system including:

[1937] (Claim 2)

[1938] 2. The system according to claim 1, wherein pay-per-use charges are made based on revenue from users.

[1939] (Claim 3)

[1940] 10. The system of claim 1, wherein the system calculates a fee based on the number of responses generated and sends an invoice to the user.

[1941] "Example 1"

[1942] (Claim 1)

[1943] A method of inputting attribute data such as age, region, family structure, and income into a database without personal information and letting the AI ​​learn from it.

[1944] A means for users to input or select survey questions;

[1945] A means for a user to specify the desired answer attributes and number of answers;

[1946] A means for generating answers by artificial intelligence based on the specified attributes and number of answers;

[1947] a means for providing the generated answer to a user;

[1948] a means for calculating fees based on the number of responses generated and automatically generating invoices;

[1949] Communication methods to ensure data security;

[1950] A means to visually display the results and make the data downloadable;

[1951] A system including:

[1952] (Claim 2)

[1953] 2. The system according to claim 1, wherein pay-per-use charges are made based on revenue from users.

[1954] (Claim 3)

[1955] 10. The system of claim 1, wherein the system visually displays the generated answers and attributes through a user interface.

[1956] "Application Example 1"

[1957] (Claim 1)

[1958] A method of inputting attribute data such as age, region, family structure, and income into a database without personal information and letting the AI ​​learn from it.

[1959] A means for users to input or select survey questions;

[1960] A means for a user to specify the desired answer attributes and number of answers;

[1961] A means for generating answers by artificial intelligence based on the specified attributes and number of answers;

[1962] a means for generating market research data in real time specific to the target users of an advertising campaign;

[1963] a means for providing the generated answer to a user;

[1964] A system including:

[1965] (Claim 2)

[1966] 2. The system according to claim 1, wherein pay-per-use charges are made based on revenue from users.

[1967] (Claim 3)

[1968] 10. The system of claim 1, wherein the system calculates a fee based on the number of responses generated and sends an invoice to the user.

[1969] "Example 2: Combining Emotion Engines"

[1970] (Claim 1)

[1971] A method of inputting attribute data such as age, region, family structure, and income into a database without personal information and training the machine learning algorithm.

[1972] A means for users to input or select survey questions;

[1973] A means for a user to specify the desired answer attributes and number of answers;

[1974] A means for acquiring user emotion data and analyzing it using an emotion recognition algorithm;

[1975] means for automatically adjusting questions based on the acquired emotion data;

[1976] a means for the machine learning algorithm to generate answers based on the specified attributes and number of answers;

[1977] a means for providing the generated answer to a user;

[1978] A system including:

[1979] (Claim 2)

[1980] 2. The system of claim 1, wherein the system calculates a fee based on the number of responses generated and automatically notifies the user of the fee.

[1981] (Claim 3)

[1982] 10. The system according to claim 1, wherein fees are collected from users through a pay-per-use system.

[1983] "Application example 2 when combining emotion engines"

[1984] (Claim 1)

[1985] A method of inputting attribute data such as age, region, family structure, and income into a database without personal information and letting the AI ​​learn from it.

[1986] A means for users to input or select survey questions;

[1987] A means for a user to specify the desired answer attributes and number of answers;

[1988] A means for generating answers by artificial intelligence based on the specified attributes and number of answers;

[1989] a means for providing the generated answer to a user;

[1990] a means for collecting user emotion data;

[1991] means for analyzing the emotion data and generating a prompt sentence based on the analysis result;

[1992] means for generating content based on a prompt sentence;

[1993] a means for visually displaying the generated content;

[1994] A system including:

[1995] (Claim 2)

[1996] 2. The system according to claim 1, wherein pay-per-use charges are made based on revenue from users.

[1997] (Claim 3)

[1998] 10. The system of claim 1, wherein the system calculates a fee based on the number of responses generated and sends an invoice to the user. [Explanation of symbols]

[1999] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A method of inputting attribute data such as age, region, family structure, and income into a database without personal information and letting the AI ​​learn from it. A means for users to input or select survey questions; A means for a user to specify the desired answer attributes and number of answers; A means for generating answers by artificial intelligence based on the specified attributes and number of answers; a means for providing the generated answer to a user; A system including:

2. 2. The system according to claim 1, wherein pay-per-use charges are made based on revenue from users.

3. 10. The system of claim 1, wherein the system calculates a fee based on the number of responses generated and sends a bill to the user.

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