System

Abstract

To provide a system which reduces load on a person in charge and reduces a time.SOLUTION: A system includes: means for collecting free answers of a questionnaire; means for totalizing and summarizing the collected free answers, using prompt statements instructing generation of a tabulation result and a summary result as a written report and a generative AI model, and generating the written report; means for using a prompt statement instructing generation of a content on the basis of the generated written report and the generative AI model and generating the content; and means for previewing the generated content and receiving editing operation.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Since the free-form responses to the questionnaire are compiled and summarized manually, this places a heavy burden on the person in charge and takes a lot of time. [Means for solving the problem]

[0005] We provide a system that collects free-form responses to questionnaires, automatically tallys and summarizes them using natural language processing technology, and outputs the results in a visually easy-to-understand format, reducing the burden on staff and saving time. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16] FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17]FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

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

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (e.g., voice and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

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

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

[0023] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0027] "Example 1"

[0028] The system of the present invention includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, and a means for outputting the aggregation and summary results as a report. Specifically, free responses to the questionnaire are collected using an online questionnaire tool such as Google (registered trademark) Forms. The collected free responses are automatically aggregated and summarized using natural language processing technology. This natural language processing technology uses such techniques as keyword extraction, topic modeling, and sentiment analysis. The aggregation and summary results are presented in a visually easy-to-understand format,

[0029] For example, the data can be output in the form of graphs or charts, which can reduce the burden on staff and shorten time.

[0030] The process flow of each embodiment will be described below.

[0031] "Example 1"

[0032] Step 1: Collect free responses to the survey. Specifically, use an online survey tool such as Google Forms to collect free responses from users.

[0033] Step 2: Automatically aggregate and summarize the collected free responses. In this step, natural language processing technology is used to analyze the content of the free responses. Specifically, techniques such as keyword extraction, topic modeling, and sentiment analysis are used to extract key keywords and phrases from the free responses, and the free responses are aggregated and summarized based on these.

[0034] Step 3: Output the tabulation and summary results as a report. In this step, the tabulation and summary results obtained in the previous step are output in a format that is visually easy to understand. Specifically, the tabulation and summary results are displayed in graph and chart format, and this is output as a report. This allows the person in charge to get an overview of the free responses just by looking at the report, thereby reducing the person in charge's workload and saving time.

[0035] Example 1

[0036] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0037] Conventional systems required users to have specialized knowledge when generating programs, and a high level of technical understanding was required to understand the processing content of the generated programs. Furthermore, there were often a lack of concrete examples to understand the specific operation of the generated programs, making it unclear to users how they should actually use the programs.

[0038] 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.

[0039] In this invention, the server includes a means for a user to input a prompt sentence, a means for generating a program using a generative AI model, a means for explaining the processing of the generated program in natural language, and a means for adding a concrete example to the explanation. This allows a user to generate a program without specialized knowledge, to easily understand the processing content, and to clearly grasp the actual usage method through the concrete example.

[0040] A "user" is an entity that utilizes the system to input prompt statements and receive program generation and explanations of those programs.

[0041] A "prompt sentence" is an instruction sentence that a user inputs into the system, and is the text that serves as the basis for the generative AI model to generate a program.

[0042] A "generative AI model" is an artificial intelligence model that generates program code based on a prompt sentence entered by a user, such as a model that uses natural language processing technology.

[0043] A “program” is the code generated by a generative AI model; that is, a set of instructions to perform a specific task.

[0044] A "server" is a computer system that receives a prompt sentence from a user, generates a program using a generative AI model, and explains the processing content.

[0045] "Natural language" is a language that humans use on a daily basis, and is used to explain the processing content of a generated program to a user in an easily understandable manner.

[0046] A "specific example" is a concrete example showing how to actually use the generated program, and is useful for helping the user understand the operation of the program.

[0047] This invention relates to a system in which a user inputs a prompt sentence, generates a program using a generative AI model, explains the processing content in natural language, and adds concrete examples.

[0048] First, a user uses a terminal to access the system interface and inputs a prompt sentence, for example, "Please generate a program that analyzes the text entered by the user and determines the emotion."

[0049] The server then receives the prompt and generates a program using a generative AI model, such as a model using natural language processing techniques, that generates the appropriate program code based on the prompt entered by the user.

[0050] The generated program is written in a programming language such as Python. The server analyzes the processing content of this generated program and explains the processing in natural language. Specifically, it clearly explains what kind of data processing and data calculation the program performs. For example, if the nltk library is used to analyze the sentiment of text, the server will explain the details.

[0051] In addition, the server adds concrete examples to the explanation. For example, if the user inputs "Today is a very good day," the server shows how the program determines the emotion. This makes it easier for the user to understand the actual behavior of the generated program.

[0052] This system enables users to generate programs without specialist knowledge, easily understand the processing content, and clearly grasp how to use them in practice through concrete examples.

[0053] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0054] Step 1:

[0055] The user enters a prompt sentence.

[0056] A user uses a terminal to access the system interface and input a prompt sentence, for example, "Please generate a program that analyzes the text entered by the user and judges the emotion." The input prompt sentence is sent to the server.

[0057] Step 2:

[0058] The server generates the program using the generative AI model.

[0059] The server inputs the prompt sentence received from the user into the generative AI model. The generative AI model generates appropriate program code based on the prompt sentence. For example, a model using natural language processing technology generates Python code. The generated program code is stored on the server.

[0060] Step 3:

[0061] The server explains in natural language the processing of the generated program.

[0062] The server analyzes the generated program code and explains the processing in natural language. Specifically, it clearly explains what kind of data processing and data calculation the program performs. For example, if the program uses the nltk library to analyze the sentiment of text, it explains the details. The explanation is provided to the user.

[0063] Step 4:

[0064] The server adds specific examples to the description.

[0065] The server adds concrete examples to the generated program description. For example, when a user inputs "Today is a very good day," the server concretely shows how the program determines the emotion. The concrete examples are important to show the actual operation of the program. The description including the concrete examples is provided to the user.

[0066] The above is the flow of processing of the program of this system.

[0067] (Application example 1)

[0068] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0069] In conventional survey systems, the collection and aggregation of free responses was often done manually, which was a time-consuming and labor-intensive process. In addition, when outputting the aggregation and summary results as a report, it was sometimes difficult to output them in a format that was easy to understand visually. Furthermore, specialized knowledge was required to create ad copy, making it difficult to quickly generate high-quality ad copy. To solve these issues, there is a need for a system that can efficiently and automatically aggregate and summarize free responses, generate reports that are easy to understand visually, and generate ad copy based on prompt text using a generative AI model.

[0070] 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.

[0071] In this invention, the server includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for generating advertising copy based on a prompt text using a generative AI model, and a means for displaying the generated advertising copy. This enables efficient aggregation and summarization of free responses and the generation of visually easy-to-understand reports, and further enables the generation of advertising copy with high speed and quality.

[0072] The "means of collecting free responses to the questionnaire" is a function for collecting opinions and comments freely written by respondents in the questionnaire as data.

[0073] "Means of automatically aggregating and summarizing collected free responses" refers to a function that analyzes the collected free responses, extracts key keywords and phrases, and succinctly summarizes the overall content.

[0074] "Means of outputting the results of aggregation and summary as a report" refers to a function for generating and outputting the aggregated and summarized data as a report in a format that is visually easy to understand.

[0075] "Means for generating ad copy based on a prompt text using a generative AI model" refers to a function for automatically creating ad copy based on a prompt text entered by a user, using a generative AI model.

[0076] "Means for displaying generated ad copy" refers to a function for visually displaying to a user the ad copy created by the generative AI model.

[0077] The system for carrying out this invention has the functions of collecting free-form responses to a questionnaire, automatically aggregating and summarizing the collected free-form responses, and outputting the aggregation and summary results as a report. It also has the functions of generating advertising copy based on prompt text using a generative AI model, and displaying the generated advertising copy.

[0078] Hardware and Software Configuration

[0079] Hardware:

[0080] server

[0081] User device (smartphone, smart glasses, etc.)

[0082] software:

[0083] Analysis program using natural language processing technology

[0084] Generative AI models using OpenAI® API

[0085] Database Management Systems

[0086] A visualization tool for generating graphs and charts

[0087] Data processing and calculation

[0088] server:

[0089] 1. The server collects free-form responses to the questionnaire sent from the user terminal into a database.

[0090] 2. Use natural language processing techniques to analyze the collected free responses and extract key keywords and phrases.

[0091] 3. The free-response responses are automatically compiled and summarized based on the extracted keywords and phrases.

[0092] 4. The aggregation and summary results are generated as a report in a visually easy-to-understand format (e.g., graphs or charts) and sent to the user's terminal.

[0093] User device:

[0094] 1. The user enters a prompt sentence and sends it to the generative AI model.

[0095] 2. The server uses the OpenAI API to generate ad copy based on the input prompt text.

[0096] 3. The generated advertising copy is displayed on the user's device.

[0097] Examples

[0098] Example prompt:

[0099] "Write an ad copy for a new smartphone. Its features include a high-resolution camera and a long battery life."

[0100] Example of generated ad copy:

[0101] "A new smartphone has arrived! The high-resolution camera lets you capture every beautiful moment. Plus, the long battery life means you can use it all day without worry. Get yours now and enjoy the best experience!"

[0102] In this way, users can easily input prompt text and use the generative AI model to quickly generate high-quality ad copy. In addition, free-form responses to questionnaires are efficiently collected and summarized, and output as visually easy-to-understand reports, improving work efficiency.

[0103] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0104] Step 1:

[0105] The user inputs free responses to the questionnaire and transmits them from the terminal to the server.

[0106] Input: Free response entered by the user

[0107] Output: Free response data sent to the server

[0108] Specific operation: When a user enters free-form responses into the questionnaire form and presses the send button, the terminal sends the data to the server.

[0109] Step 2:

[0110] The server stores the received free responses in a database.

[0111] Input: Free response data sent from the device

[0112] Output: Free response data stored in a database

[0113] Specific operation: The server stores and preserves the received free response data in a database.

[0114] Step 3:

[0115] The server uses natural language processing technology to analyze the free-form responses and extract key keywords and phrases.

[0116] Input: Free response data stored in a database

[0117] Output: Extracted keywords and phrases

[0118] What it does: The server runs natural language processing algorithms to extract key keywords and phrases from the open-ended text data.

[0119] Step 4:

[0120] The server automatically aggregates and summarizes the content of the free-form responses based on the extracted keywords and phrases.

[0121] Input: Extracted keywords or phrases

[0122] Output: Aggregated and summarized data

[0123] Specific operation: The server aggregates the extracted keywords and phrases and uses a summarization algorithm to concisely summarize the content of the free-form responses.

[0124] Step 5:

[0125] The server generates a report of the aggregation and summary results in a visually easy-to-understand format and sends it to the user's terminal.

[0126] Input: Aggregated and summarized data

[0127] Output: Visually easy-to-understand reports (graphs and charts)

[0128] Specific operation: The server uses a visualization tool to generate a report in the form of graphs and charts based on the aggregation and summary results, and sends it to the user's terminal.

[0129] Step 6:

[0130] The user enters a prompt sentence and sends it to the generative AI model.

[0131] Input: The prompt text entered by the user.

[0132] Output: The prompt text sent to the server.

[0133] Specific operation: When the user inputs a prompt sentence and presses the send button, the terminal sends the data to the server.

[0134] Step 7:

[0135] The server uses the OpenAI API to generate ad copy based on the input prompt text.

[0136] Input: The prompt text sent by the user

[0137] Output: Generated ad copy

[0138] Specific operation: The server calls the OpenAI API, inputs the prompt text into the generative AI model, and obtains the generated ad copy.

[0139] Step 8:

[0140] The server transmits the generated advertising copy to the user terminal, which displays it.

[0141] Input: Generated ad copy

[0142] Output: Ad copy displayed on user device

[0143] Specific operation: The server transmits the generated advertising copy to the user terminal, and the user terminal displays the received advertising copy on the screen.

[0144] Example 2

[0145] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0146] In conventional systems, it has been difficult to quickly and accurately provide an appropriate response to a prompt sentence input by a user. In addition, there has been a lack of means for displaying the generated response in a visually easy-to-understand format, resulting in low user convenience. The object of the present invention is to solve these problems and provide a system that provides a quick and accurate response to a prompt sentence input by a user, and further displays the response in a visually easy-to-understand format.

[0147] 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.

[0148] In this invention, the server includes a means for a user to input a prompt sentence, a means for the terminal to send the prompt sentence to the server, a means for the server to pass the prompt sentence to the generative AI model, a means for the generative AI model to analyze the prompt sentence and generate a response, a means for the server to return the generated response to the terminal, and a means for the terminal to display the response to the user. This makes it possible to provide a quick and accurate response to the prompt sentence input by the user and to display the response in a visually easy-to-understand format.

[0149] A "user" is an individual or entity that utilizes the system to enter prompts and receive generated responses.

[0150] A "prompt sentence" is a question or instruction sentence that a user inputs to a system.

[0151] A "terminal" is a device on which a user inputs a prompt and displays the generated response, such as a personal computer or a smartphone.

[0152] The "server" is a computer system whose role is to receive prompt text sent from a terminal, pass it to a generative AI model, and return the generated response to the terminal.

[0153] A "generative AI model" is an artificial intelligence model that analyzes prompt sentences and generates appropriate responses. For example, this includes models that use natural language processing techniques.

[0154] "Natural language processing technology" is a technology that allows a computer to understand and analyze human language, making it possible to analyze the content of a prompt and generate an appropriate response.

[0155] A "response" is the answer or information that a generative AI model generates by analyzing a prompt.

[0156] A "visually understandable format" is a format in which the generated response is displayed in a manner that is easily understandable to the user, including, for example, text, graphs, charts, etc.

[0157] The present invention is a system that provides a prompt and accurate response to a prompt sentence input by a user and displays the response in a visually easy-to-understand format. The system includes a user, a terminal, a server, and a generative AI model.

[0158] The user inputs a prompt into the input field of the terminal. For example, the prompt might be "What's the weather like today?" or "What's the latest news?". The terminal sends the prompt input by the user to the server. At this time, the prompt is packaged in JSON format.

[0159] The server analyzes the received prompt sentence and sends a request to the API endpoint of the generative AI model. For example, an advanced model using natural language processing technology (e.g., GPT-3 (registered trademark) or GPT-4 (registered trademark)) is used as the generative AI model. The server analyzes the response received from the generative AI model and returns it to the terminal. At this time, the response is packaged again in JSON format.

[0160] The terminal analyzes the response received from the server and displays it to the user. The display format is a visually easy-to-understand format such as text, graphs, and charts. For example, a response such as "Today's weather is sunny" or "The latest news is as follows" may be displayed.

[0161] The system allows users to utilize generative AI models to generate appropriate responses to their input prompts, and the responses are displayed in a visually easy-to-understand format for the user's convenience.

[0162] As a concrete example, consider a case where a user enters the prompt sentence "Please tell me how to write a Hello World program in Python." In this case, the generative AI model generates a response "Here is how to write a Hello World program in Python. python print('Hello, World!')" and displays it on the terminal.

[0163] In this manner, the present invention allows a user to provide a fast and accurate response to a prompt sentence entered by the user, and to display the response in a visually easy to understand format.

[0164] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0165] Step 1:

[0166] The user enters a prompt sentence.

[0167] The user inputs a prompt into an input field on the terminal, for example, "What's the weather like today?" The input prompt is temporarily stored in the terminal's memory.

[0168] Step 2:

[0169] The terminal sends a prompt to the server.

[0170] The terminal sends the prompt text entered by the user as an HTTP request to the server. At this time, the prompt text is packaged in JSON format. The input is the prompt text, and the output is an HTTP request to the server.

[0171] Step 3:

[0172] The server passes the prompt to the generative AI model.

[0173] The server parses the received prompt sentence and sends a request to the API endpoint of the generative AI model. For example, it sends the prompt sentence along with the API key as a POST request. The input is the prompt sentence, and the output is the API request to the generative AI model.

[0174] Step 4:

[0175] A generative AI model parses the prompt and generates a response.

[0176] A generative AI model analyzes the received prompt sentence and generates an appropriate response. For example, in response to the prompt sentence "What's the weather today?", it generates the response "The weather today is sunny." The input is the prompt sentence, and the output is the generated response.

[0177] Step 5:

[0178] The server returns the generated response to the terminal.

[0179] The server parses the response received from the generative AI model and returns it to the device, again packaged in JSON. The input is the generated response, and the output is the HTTP response to the device.

[0180] Step 6:

[0181] The terminal displays the response to the user.

[0182] The terminal analyzes the response received from the server and displays it to the user. For example, it might display "The weather today is sunny" on the screen. The input is the generated response, and the output is what is displayed to the user.

[0183] (Application example 2)

[0184] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0185] In conventional survey systems, the collection of free responses and their compilation and summarization were often done manually, which was problematic in that it was time-consuming and labor-intensive. In addition, when outputting the compilation and summary results as a report, it was difficult to output them in a format that was visually easy to understand. Furthermore, when it came to automatically generating advertisements, even if the user entered a prompt sentence, there was a lack of a means to generate an appropriate advertisement based on the contents of that sentence. This made the creation of advertisements cumbersome, and made it difficult to quickly create effective advertisements.

[0186] 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.

[0187] In this invention, the server includes a means for collecting free responses to a survey, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for automatically generating an advertisement based on a prompt sentence using a generative AI model, a means for previewing the generated advertisement, a means for customizing the generated advertisement, and a means for saving and sharing the generated advertisement. This enables efficient collection, aggregation, and summarization of free responses to a survey, and realizes report output in a visually easy-to-understand format. In addition, effective advertisements can be quickly and automatically generated based on prompt sentences, and users can easily customize and share them.

[0188] A "means for collecting free-form responses to a survey" is a function for collecting free-form responses from users.

[0189] "Means for automatically aggregating and summarizing collected free responses" refers to a function that automatically analyzes collected free responses, extracts key information, and summarizes it.

[0190] "Means for outputting the results of aggregation and summary as a report" refers to a function for outputting the results of aggregation and summary in report format.

[0191] "Means for automatically generating advertisements based on prompt text using a generative AI model" refers to a function that uses a generative AI model to automatically generate advertisements based on prompt text entered by a user.

[0192] The "means for displaying a preview of a generated advertisement" is a function for displaying a preview of the generated advertisement to the user.

[0193] A "means for customizing generated advertisements" is a feature that allows a user to edit and customize a generated advertisement.

[0194] "Means for saving and sharing generated advertisements" refers to a function for saving generated advertisements and sharing them with other users and platforms.

[0195] A system for carrying out the present invention has the following configuration: the system includes means for collecting free responses to a questionnaire, means for automatically aggregating and summarizing the collected free responses, means for outputting the aggregation and summary results as a report, means for automatically generating advertisements based on prompt text using a generative AI model, means for previewing the generated advertisements, means for customizing the generated advertisements, and means for saving and sharing the generated advertisements.

[0196] Hardware and Software Configuration

[0197] Hardware: Smartphones, servers

[0198] Software: Generative AI models (e.g., OpenAI's GPT-4), mobile app development frameworks (e.g., React Native), cloud storage services (e.g., AWS S3)

[0199] Data processing and calculation

[0200] 1. Collecting free-form responses to a survey: Users enter free-form responses through a smartphone app. These responses are sent to a server and stored in a database.

[0201] 2. Automatic aggregation and summarization of free responses: The server uses natural language processing technology to analyze the free responses and extract key keywords and phrases, which are then aggregated and summarized.

[0202] 3. Report output: The aggregated and summarized results are output as a report in a visually easy-to-understand format (e.g., graphs and charts). Users can view this report on their smartphones.

[0203] 4. Automatic ad generation: When a user enters a prompt text, the prompt text is sent to the server, and the generative AI model generates ad text and images based on the prompt text.

[0204] 5. Previewing the ad: The generated ad is previewed to the user on the smartphone app.

[0205] 6. Ad Customization: Users can edit and customize generated ads within the app.

[0206] 7. Save and share your ad: Your completed ad will be saved to cloud storage and a link will be generated that can be shared on social media or email.

[0207] Examples

[0208] For example, if a user wants to create an advertisement to promote the opening of a new cafe, they might enter the following prompt text:

[0209] Example prompt: "Create an ad to promote the opening of a new cafe. The target audience is young people in their 20s and 30s. The cafe features organic coffee and a relaxing atmosphere."

[0210] When this prompt is fed into the generative AI model, the model generates advertising text and images like the following:

[0211] Example ad text: "Relax Cafe is now open! Organic coffee and a relaxing atmosphere perfect for people in their 20s and 30s. Come and visit us!"

[0212] Example of advertising image: A generative AI model generates images of cafe interiors and organic coffee to be incorporated into the ad.

[0213] In this way, users can easily create attractive advertisements and effectively reach their target audience.

[0214] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0215] Step 1:

[0216] Users enter free-form responses to the survey via a smartphone app.

[0217] Input: The user enters free-form text.

[0218] Output: The entered free response is sent to the server.

[0219] Specific behavior: When a user enters a free-form response into the app's text input field and presses the "Submit" button, the data is sent to the server.

[0220] Step 2:

[0221] The server stores the collected free responses in a database.

[0222] Input: Free text response data submitted by the user.

[0223] Output: Free responses stored in a database.

[0224] Specific operation: The server stores the received free response in a database and generates a confirmation message that the response has been saved.

[0225] Step 3:

[0226] The server uses natural language processing technology to analyze the free-form responses and extract key keywords and phrases.

[0227] Input: Free-form text data stored in a database.

[0228] Output: Extracted key keywords and phrases.

[0229] What happens: The server runs natural language processing algorithms to extract key keywords and phrases from the open-ended text.

[0230] Step 4:

[0231] The server performs aggregation and summarization based on the extracted keywords and phrases.

[0232] Input: The extracted main keywords or phrases.

[0233] Output: Aggregated and summarized data.

[0234] How it works: The server aggregates the extracted keywords and phrases and uses a summarization algorithm to generate summary data.

[0235] Step 5:

[0236] The server outputs the aggregation and summary results as a report in a visually easy-to-understand format.

[0237] Input: Aggregated and summarized data.

[0238] Output: Reports in the form of graphs and charts.

[0239] What happens: The server converts the aggregated and summarized data into graphs and charts and generates a report.

[0240] Step 6:

[0241] The user enters a prompt and sends it to the generative AI model.

[0242] Input: The prompt text entered by the user.

[0243] Output: The prompt sent to the generative AI model.

[0244] How it works: A user enters a prompt sentence into the app's text input field and presses the "Generate" button, and that data is sent to the generative AI model.

[0245] Step 7:

[0246] A generative AI model generates ad text and images based on the prompt.

[0247] Input: The prompt sent to the generative AI model.

[0248] Output: The generated ad text and images.

[0249] How it works: The generative AI model analyzes the prompt and generates ad text and images.

[0250] Step 8:

[0251] The server previews the generated advertisement to the user.

[0252] Input: Generated ad text and images.

[0253] Output: The ad preview that is displayed on the user's smartphone.

[0254] Specific operation: The server sends the generated ad to the user's smartphone and displays a preview in the app.

[0255] Step 9:

[0256] Customize user-generated ads.

[0257] Input: The previewed ad.

[0258] Output: A customized ad.

[0259] What happens: Users use the app's editing features to edit and customize ad text and images.

[0260] Step 10:

[0261] The server stores the customized advertisement in cloud storage and generates a sharing link.

[0262] Enter: your personalized ads.

[0263] Output: Advertisements stored in cloud storage and share links.

[0264] What it does: The server saves the customized ad in cloud storage and generates a link that can be shared on social media or via email.

[0265] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0266] "Example 1"

[0267] The present invention is a system that collects free responses to a questionnaire, automatically tabulates and summarizes the collected free responses, and outputs the tabulation and summary results as a report. As a means for automatically tabulating and summarizing the free responses, the system uses natural language processing technology to analyze the content of the free responses and extracts key keywords and phrases. Furthermore, the system analyzes the content of the free responses using an emotion engine that recognizes the user's emotions. The emotion engine extracts the user's emotions from the text data of the free responses and reflects the emotions in the report. Specifically, if a user gives a free response such as "This product is very good," the emotion engine extracts the emotion "good" from the free response and reflects the emotion in the report. The report output means outputs the emotions extracted by the emotion engine together with the tabulation and summary results in a visually easy-to-understand format, such as a graph or chart format. This allows the person in charge to grasp the outline of the free responses and the user's emotions just by looking at the report, which reduces the person in charge's burden and shortens the time required.

[0268] The process flow of each embodiment will be described below.

[0269] "Example 1"

[0270] Step 1: Collect free responses to the survey. Specifically, use a survey tool such as Google Forms to collect free responses from users.

[0271] Step 2: Automatically aggregate and summarize the collected free responses. Specifically, natural language processing technology is used to analyze the content of the free responses and extract key keywords and phrases.

[0272] Step 3: Recognize the user's emotions. Specifically, an emotion engine is used to analyze the content of the free responses and extract the user's emotions.

[0273] Step 4: Reflect the extracted emotions in the report. Specifically, the emotions extracted by the emotion engine are reflected in the report, and the emotions are output in a format that is easy to understand visually, such as a graph or chart.

[0274] Step 5: The results of the aggregation and summary and the sentiment analysis are output as a report. Specifically, the results of the aggregation and summary and the sentiment analysis are integrated and output as a single report. This allows the person in charge

[0275] By simply looking at the report, you can get an overview of the free responses and users' feelings.

[0276] Example 1

[0277] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0278] In conventional systems, the process of generating an appropriate response to a prompt input by the user was complex and difficult to perform efficiently. In addition, the quality and display format of the generated results were inconsistent, making them difficult for users to understand. This resulted in a poor user experience and limited the usefulness of the system.

[0279] 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.

[0280] In this invention, the server includes a means for receiving a prompt sentence and performing pre-processing, a means for inputting the prompt sentence into a generative AI model and performing inference, and a means for post-processing the generated results, which makes it possible to consistently and efficiently perform the processes from pre-processing of the prompt sentence to post-processing of the generated results.

[0281] A "user" is an entity that operates the system and inputs prompt sentences.

[0282] A "terminal" is a device operated by a user, including a PC, a smartphone, etc.

[0283] A "prompt" is text that a user enters into a generative AI model as an instruction or question.

[0284] A "server" is a computer system that receives prompt sentences, pre-processes them, inputs them into the generative AI model, performs inference, post-processes them, and sends the results.

[0285] "Preprocessing" refers to the process of normalizing and tokenizing the text of the prompt sentence.

[0286] A "generative AI model" is an artificial intelligence model that makes inferences based on input prompt sentences and generates results.

[0287] "Inference" is the process by which a generative AI model generates a response or outcome based on a prompt.

[0288] "Post-processing" refers to formatting and removing unnecessary information from the generated results.

[0289] "Results" are the responses or information that a generative AI model generates based on a prompt.

[0290] "Display" refers to the act of visually presenting the results received by the terminal from the server to the user.

[0291] The present invention is a system in which a user inputs a prompt sentence, a generative AI model is used to generate a response or result based on the prompt sentence, and the response or result is provided to the user. A specific embodiment of the system will be described below.

[0292] First, the user inputs a prompt sentence using a terminal (such as a PC or smartphone). An example of a prompt sentence is "Please use AI to come up with a new recipe."

[0293] The terminal sends the input prompt text to the server. The communication is performed using an HTTP request. The server is a computer system with high-performance computing capabilities, specifically, a server equipped with a high-performance GPU.

[0294] The server receives the prompt sent from the terminal and performs preprocessing, including text normalization (e.g., converting full-width characters to half-width characters) and tokenization (e.g., dividing a sentence into words or phrases). Python libraries (e.g., pandas, numpy) are used for preprocessing.

[0295] Next, the server inputs the preprocessed prompt sentence into a generative AI model. For example, GPT-4 is used as the generative AI model. The model performs inference based on the input prompt sentence and generates a result. TENSORFLOW (registered trademark) and PyTorch are used for model inference.

[0296] The generated results are then post-processed on the server, which may include formatting the results (e.g. converting them to JSON) and removing unnecessary information.

[0297] The post-processed results are sent from the server to the terminal. HTTP responses are used for communication. The terminal displays the received results to the user. HTML and CSS are used for display.

[0298] As a concrete example, if a user inputs a prompt sentence such as "Please use AI to come up with a new recipe," the server receives this prompt sentence, performs preprocessing, and inputs it into a generative AI model. The model generates a new recipe, and the server post-processes the result and sends it to the terminal. The terminal displays the generated recipe to the user.

[0299] In this way, users can obtain new recipes using generative AI models. This system can efficiently perform everything from pre-processing prompts to post-processing generated results, and can provide high-quality responses to users.

[0300] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0301] Step 1:

[0302] The user enters a prompt sentence.

[0303] The user enters "Please use AI to come up with a new recipe" into the input field of the device. The input prompt is stored in the device's memory.

[0304] Step 2:

[0305] The terminal sends a prompt to the server.

[0306] The terminal sends the entered prompt text to the server as an HTTP request. The input is the prompt text, and the output is the request sent to the server.

[0307] Step 3:

[0308] The server receives the prompt text and performs preprocessing.

[0309] The server receives the prompt text sent from the terminal. It normalizes the text (e.g. converts full-width characters to half-width characters) and tokenizes (e.g. splits a sentence into words and phrases) for the received prompt text. The input is the prompt text, and the output is the preprocessed text data.

[0310] Step 4:

[0311] The server inputs a prompt sentence into the generative AI model and performs inference.

[0312] The server inputs the preprocessed prompt sentences into a generative AI model (e.g., GPT-4). The model makes inferences based on the input prompt sentences and generates results. The input is the preprocessed text data, and the output is the generated result (e.g., a new recipe).

[0313] Step 5:

[0314] The server post-processes the generated results.

[0315] The server performs post-processing on the results obtained from the generative AI model, including formatting the results (e.g., converting them to JSON) and removing unnecessary information. The input is the generated results, and the output is the post-processed data.

[0316] Step 6:

[0317] The server sends the result to the terminal.

[0318] The server sends the post-processed result to the terminal as an HTTP response. The input is the post-processed data, and the output is the response sent to the terminal.

[0319] Step 7:

[0320] The terminal displays the results to the user.

[0321] The terminal displays the results received from the server to the user. HTML and CSS are used for display. The input is the response data from the server, and the output is the information provided visually to the user.

[0322] (Application example 1)

[0323] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0324] In conventional survey systems, the collection, compilation, and summary of free responses, as well as the creation of reports, are often done manually, which is time-consuming and labor-intensive. In addition, the generation of advertising content requires specialized knowledge, making it difficult to reach the target audience quickly and effectively. To solve these problems, a system that integrates the automatic compilation and summary of free responses with the automatic generation of advertising content is required.

[0325] 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.

[0326] In this invention, the server includes a means for collecting free responses to a questionnaire, a prompt for instructing to tally and summarize the collected free responses and generate a report with the tally and summary results, a means for generating the report using a generative AI model, a prompt for instructing to generate content based on the generated report, a means for generating the content using the generative AI model, a means for previewing the generated content and accepting editing operations, a means for publishing the edited content, and a means for analyzing the performance of the published content. This enables efficient tallying and summarizing of free responses and automatic generation and publication of advertising content based on the prompt.

[0327] "Means of collecting free-form survey responses" refers to the interface or functionality for collecting free-form responses from users.

[0328] "Means of automatically aggregating and summarizing collected free-response responses" refers to algorithms or software for analysing the collected free-response data and extracting and summarising key information.

[0329] "Means of outputting the results of aggregation and summary as a report" refers to the functionality for generating and outputting the aggregated and summarized data as a report in a visually easy-to-understand format.

[0330] "Means for inputting prompt text" refers to a text input interface that allows a user to provide instructions to a generative AI model.

[0331] "Means of generating content based on a prompt sentence using a generative AI model" refers to algorithms or software that enable a generative AI model to automatically generate content such as text and images based on an input prompt sentence.

[0332] "Means for previewing and editing the generated content" means an interface or functionality that allows a user to review and, if necessary, edit the generated content.

[0333] "Means of publishing the generated content" refers to functions for publishing the generated and edited content on online platforms, social media, etc.

[0334] "Means for analyzing the performance of Published Content" means tools or software used to collect and analyze performance data, such as the number of views and clicks on Published Content.

[0335] A system for implementing the present invention has the following configuration: First, a user uses a terminal such as a smartphone to input free responses to a questionnaire about a specific product. The terminal has a means for collecting free responses to the questionnaire and transmits the free responses input by the user to a server.

[0336] The server is equipped with a means for automatically aggregating and summarizing the collected free responses. This means uses natural language processing technology to analyze the content of the free responses and extract key keywords and phrases. Specifically, natural language processing libraries (e.g., NLTK, spaCy) are used to analyze text data and extract important information. The server is also equipped with a means for outputting the aggregation and summary results as a report. This means outputs the aggregation and summary results in a format that is visually easy to understand, such as a graph or chart. For this purpose, a data visualization tool (e.g., Matplotlib, D3.js) is used. In this embodiment, the server generates a report using a generative AI model and a prompt sentence that instructs the server to tally and summarize the collected free responses and generate a report based on the tally and summary results.

[0337] Furthermore, the user generates content by providing instructions to the generative AI model using a prompt sentence that instructs the model to generate content based on the generated report. An example of a prompt sentence is, "Please use this report as a reference to create an advertisement for a new eco-friendly detergent for housewives in their 30s" along with the generated report. In this example, the specific product is detergent.

[0338] The server includes a means for generating content based on the prompt using a generative AI model, the means using a generative AI model (e.g., OpenAI GPT-4) to generate advertising text and images based on the input prompt.

[0339] The generated content is provided to the user with a means for previewing and editing on the terminal. The user can check the generated content and edit it as necessary. Once the editing is complete, the content is published on an internet platform or SNS via the server.

[0340] Finally, the server includes means for analyzing the performance of the published content, such as using tools (e.g., Google Analytics) to collect and analyze performance data, such as the number of views and clicks on the published content.

[0341] In this way, it becomes possible to efficiently collect and summarize free responses, and to automatically generate and publish advertising content based on prompt text. Additionally, the content may be modified using prompts to modify the content based on the analyzed performance and generative AI models, thereby modifying the content to improve its performance.

[0342] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0343] Step 1:

[0344] A user inputs free-form responses to a questionnaire using a terminal such as a smartphone. The input free-form responses are sent from the terminal to a server. The input data is the free-form responses in text format, and the output is the free-form response data sent to the server.

[0345] Step 2:

[0346] The server automatically aggregates and summarizes the collected free responses. It uses natural language processing technology to analyze the content of the free responses and extract key keywords and phrases. The input data is the free response text, and the output is summary data including the key keywords and phrases. Specifically, it analyzes the text data using a natural language processing library (e.g., NLTK, spaCy). Alternatively, it uses a prompt sentence that instructs the server to aggregate and summarize the collected free responses and a generative AI model to generate the aggregated and summarized results of the collected free responses.

[0347] Step 3:

[0348] The server outputs the aggregation and summary results as a report. The aggregation and summary results are output in a format that is visually easy to understand, such as a graph or chart. The input data is summary data, and the output is a visually represented report. A data visualization tool (e.g., Matplotlib, D3.js) is used. Specifically, the report is generated using a prompt statement that instructs the system to generate a report based on the generated aggregation and summary results, and a generative AI model.

[0349] Step 4:

[0350] A user gives instructions to the generative AI model using a means for inputting a prompt sentence. The input data is a report and a prompt sentence, and the output is the report and the prompt sentence sent to the server. An example of a prompt sentence is, along with inputting the generated report, "Please use this report as a reference to create an advertisement to promote a new eco-friendly detergent to housewives in their 30s."

[0351] Step 5:

[0352] The server uses a generative AI model to generate content based on the prompt sentence. The input data is the prompt sentence, and the output is the generated ad text and image. A generative AI model (e.g. OpenAI GPT-4) is used to generate content based on the input prompt sentence.

[0353] Step 6:

[0354] The generated content is provided to the user with means for previewing and editing on the terminal. The user reviews the generated content and edits it if necessary. The input data is the generated content and the output is the edited content.

[0355] Step 7:

[0356] Once the editing is complete, the content is published on an internet platform or social networking site via the server. The input data is the edited content, and the output is the published content.

[0357] Step 8:

[0358] The server analyzes the performance of the published content. It collects and analyzes performance data, such as the number of views and clicks on the published content. The input data is the performance data, and the output is the analysis result. It uses a data analysis tool (e.g., Google Analytics).

[0359] Example 2

[0360] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0361] In conventional survey systems, the collection, tabulation, and summarization of free responses were often done manually, which was time-consuming and labor-intensive. In addition, when users wanted to generate a specific program, they needed programming knowledge, which was difficult for users without specialized knowledge. Furthermore, the quality and efficiency of the generated program code was often not guaranteed.

[0362] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for a user to input a prompt sentence, a means for a generative AI model to generate program code based on the prompt sentence, and a means for returning the generated program code to the user. This enables efficient aggregation and summarization of free responses and allows the user to easily generate high-quality program code.

[0363] A "survey" is a collection of questions designed to gather specific information.

[0364] "Free responses" are responses that the respondent writes freely in their own words.

[0365] "Collection means" refers to the methods or devices used to collect data.

[0366] "Automatic aggregation and summarization means" refers to methods or devices for mechanically organizing collected data and extracting significant information.

[0367] "Means for outputting as a report" refers to a method or device for providing the aggregated or summarized results in a written format.

[0368] "User" refers to an individual or organization that uses the System.

[0369] A "prompt" is text that is used to input specific instructions or questions to a generative AI model.

[0370] A "generative AI model" is an artificial intelligence model that generates program code or other output based on input prompts.

[0371] "Program code" is a set of instructions for a computer to execute.

[0372] A "server" is a computer system that provides data and services over a network.

[0373] The present invention relates to a system for efficiently collecting, tabulating and summarizing free responses to a questionnaire, and further enabling a user to easily generate program code. A specific embodiment of this system will be described below.

[0374] First, the user inputs free-form responses to the survey through the system interface. The free-form responses entered by the user are collected by the server. The server uses natural language processing technology to automatically compile and summarize the collected free-form responses. Specifically, the server extracts key keywords and phrases, and compiles and summarizes them based on those.

[0375] The server then outputs the results of the aggregation and summary in a report, which is presented in a visually easy-to-understand format, such as graphs and charts, allowing users to intuitively grasp the content of the free-response responses.

[0376] Additionally, the system provides a means for users to input prompts, which are specific instructions or questions to the generative AI model, such as "Generate a program to count specific words in a text file."

[0377] The server receives the prompt sentence sent by the user and passes it to the generative AI model. The generative AI model generates a program code based on the prompt sentence. The generated program code is returned to the user through the server. The user can then execute this program code on their own device.

[0378] The following hardware and software are used to implement this system. The server is a computer system equipped with a high-performance processor and sufficient memory, and is equipped with an NVIDIA GPU. For software, deep learning frameworks such as TensorFlow and PyTorch are used. This allows the generative AI model to operate efficiently and process and calculate data based on the prompt sentence entered by the user.

[0379] As a concrete example, consider the case where a user inputs the following prompt into a generative AI model:

[0380] Example prompt:

[0381] "Create a program to count specific words in a text file."

[0382] Based on this prompt, the generative AI model generates Python code and returns it to the user through the server, who can then run this code on their own device to check the number of occurrences of a particular word.

[0383] As described above, this system provides efficient processing of free responses to questionnaires and a means for users to easily generate program code.

[0384] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0385] Step 1:

[0386] The user enters a prompt sentence.

[0387] The user inputs a prompt for the generative AI model through the system interface. For example, the user might input, "Generate a program that counts specific words from a text file." The input prompt is then sent to the server.

[0388] Step 2:

[0389] The server receives the prompt text and passes it to the generative AI model.

[0390] The server receives the prompt text sent by the user. It makes the appropriate API calls to pass the received prompt text to the generative AI model. The server may also convert the prompt text into a format that the generative AI model can understand. The input is the prompt text, and the output is the request to the generative AI model.

[0391] Step 3:

[0392] The generative AI model generates program code based on the prompt sentence.

[0393] The generative AI model analyzes the received prompt sentence and generates program code based on it. For example, if a user inputs "Generate a program to count specific words in a text file," the generative AI model generates Python code. The input is the prompt sentence, and the output is the generated program code.

[0394] Step 4:

[0395] The server returns the generated program code to the user.

[0396] The server receives the program code returned by the generative AI model and returns it to the user in a format that is easy for the user to use (e.g., a text file or text in a code editor). The input is the generated program code, and the output is the transmission of the program code to the user.

[0397] Step 5:

[0398] The user executes the generated program code.

[0399] The user executes the program code received from the server on his / her own terminal. For example, if a Python code is received, the user executes the code in his / her own Python environment and checks the result. The input is the generated program code, and the output is the result of the program execution. Specifically, the user opens the Python environment, copies the received code, and executes it. As a result, the number of occurrences of a particular word is displayed.

[0400] (Application example 2)

[0401] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0402] Conventional survey systems had the problem that collecting free responses and then aggregating and summarizing them required a lot of time and effort. In addition, despite the advances being made in content generation technology using generative AI models, there was no established method for applying this to survey systems. Furthermore, there was a lack of functionality to automatically generate content based on prompts entered by users and display it.

[0403] 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.

[0404] In this invention, the server includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for inputting a prompt sentence, a means for generating content based on the prompt sentence using a generative AI model, and a means for displaying the generated content. This enables efficient collection, aggregation, and summarization of free responses to a questionnaire, and further realizes content generation and display using a generative AI model.

[0405] A "means for collecting free-form responses to a questionnaire" is a device or software that allows users to enter responses in free form and collects them as data.

[0406] "Means for automatically aggregating and summarizing collected free-response responses" refers to a device or software that analyzes the collected free-response data, extracts key keywords and phrases, and succinctly summarizes the overall content.

[0407] "Means for outputting the results of aggregation and summary as a report" refers to a device or software for generating and outputting the aggregated and summarized data as a report in a visually easy-to-understand format.

[0408] A "means for inputting a prompt sentence" is a device or software that allows a user to input text to provide instructions to a generative AI model.

[0409] A "means for generating content based on a prompt sentence using a generative AI model" refers to a device or software that enables a generative AI model to automatically generate content such as text and images based on an input prompt sentence.

[0410] A “means for displaying generated content” is a device or software for visually displaying to a user the content generated by a generative AI model.

[0411] A system for carrying out this invention has the following configuration. First, a user inputs free-form responses to a questionnaire using a terminal such as a smartphone. The terminal then transmits the input free-form responses to a server. The server then uses natural language processing technology to automatically tally and summarize the collected free-form responses. Specifically, it extracts key keywords and phrases and summarizes the overall content concisely.

[0412] Next, the server generates a report of the aggregated and summarized results and outputs them in a visually easy-to-understand format, such as graphs or charts, allowing users to intuitively grasp the survey results.

[0413] Furthermore, the user gives instructions to the generative AI model using a means for inputting a prompt sentence. An example of a prompt sentence is "A story about a brave knight fighting a dragon." The server uses the generative AI model to generate content based on this prompt sentence. The generated content is in the form of text, images, etc., and is visually displayed to the user.

[0414] The following hardware and software are used to realize this system. For hardware, a smartphone and a server are required. For software, Python and OpenAI API are used. Python is a programming language for implementing natural language processing technology, and the OpenAI API is an interface for using generative AI models.

[0415] As a concrete example, if a user enters the prompt "A story about a brave knight battling a dragon," the server uses a generative AI model to generate the following story:

[0416] Once upon a time, there was a brave knight. His name was Arthur. To protect his kingdom, Arthur decided to fight a terrible dragon. After reaching the dragon's nest, Arthur drew his sword and faced the dragon. After a fierce battle, Arthur defeated the dragon and brought peace to the kingdom.

[0417] In this way, users can enjoy content generation and display using generative AI models.

[0418] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0419] Step 1:

[0420] A user inputs free-form responses to a questionnaire using a terminal such as a smartphone. The input free-form responses are sent from the terminal to a server. The input data is in text format and is sent to the server.

[0421] Step 2:

[0422] The server uses natural language processing technology to analyze the received free-response data. Specifically, it extracts key keywords and phrases and summarizes the overall content concisely. The input data is the free-response text, and the output data is a summary text that includes the key keywords and phrases.

[0423] Step 3:

[0424] The server generates a report based on the results of the aggregation and summary. The report is output in a visually understandable format, such as a graph or chart. The input data is the summary text, and the output data is a visual report.

[0425] Step 4:

[0426] The user gives instructions to the generative AI model using a means for inputting a prompt sentence. The prompt sentence is input in text format and sent from the terminal to the server. The input data is the text of the prompt sentence.

[0427] Step 5:

[0428] The server generates content based on the prompt sentence using a generative AI model. The generative AI model automatically generates content such as text and images based on the input prompt sentence. The input data is the text of the prompt sentence, and the output data is the generated content.

[0429] Step 6:

[0430] The server visually displays the generated content to the user. The generated content is sent to the terminal and displayed on the user's screen. The input data is the generated content and the output data is the content displayed on the user's screen.

[0431] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0432] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0433] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

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

[0435] [Second embodiment]

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

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

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

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

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

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

[0442] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

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

[0444] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0447] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0448] "Example 1"

[0449] The system of the present invention includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, and a means for outputting the aggregation and summary results as a report. Specifically, free responses to the questionnaire are collected using an online questionnaire tool such as Google Forms. The collected free responses are automatically aggregated and summarized using natural language processing technology. For example, keyword extraction, topic modeling, sentiment analysis, and other techniques are used for this natural language processing technology. The aggregation and summary results are output in a visually easy-to-understand format, such as a graph or chart format. This reduces the burden on the person in charge and saves time.

[0450] The process flow of each embodiment will be described below.

[0451] "Example 1"

[0452] Step 1: Collect free responses to the survey. Specifically, use an online survey tool such as Google Forms to collect free responses from users.

[0453] Step 2: Automatically aggregate and summarize the collected free responses. In this step, natural language processing technology is used to analyze the content of the free responses. Specifically, techniques such as keyword extraction, topic modeling, and sentiment analysis are used to extract key keywords and phrases from the free responses, and the free responses are aggregated and summarized based on these.

[0454] Step 3: Output the tabulation and summary results as a report. In this step, the tabulation and summary results obtained in the previous step are output in a format that is visually easy to understand. Specifically, the tabulation and summary results are displayed in graph and chart format, and this is output as a report. This allows the person in charge to get an overview of the free responses just by looking at the report, thereby reducing the person in charge's workload and saving time.

[0455] Example 1

[0456] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0457] Conventional systems required users to have specialized knowledge when generating programs, and a high level of technical understanding was required to understand the processing content of the generated programs. Furthermore, there were often a lack of concrete examples to understand the specific operation of the generated programs, making it unclear to users how they should actually use the programs.

[0458] 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.

[0459] In this invention, the server includes a means for a user to input a prompt sentence, a means for generating a program using a generative AI model, a means for explaining the processing of the generated program in natural language, and a means for adding a concrete example to the explanation. This allows a user to generate a program without specialized knowledge, to easily understand the processing content, and to clearly grasp the actual usage method through the concrete example.

[0460] A "user" is an entity that utilizes the system to input prompt statements and receive program generation and explanations of those programs.

[0461] A "prompt sentence" is an instruction sentence that a user inputs into the system, and is the text that serves as the basis for the generative AI model to generate a program.

[0462] A "generative AI model" is an artificial intelligence model that generates program code based on a prompt sentence entered by a user, such as a model that uses natural language processing technology.

[0463] A “program” is the code generated by a generative AI model; that is, a set of instructions to perform a specific task.

[0464] A "server" is a computer system that receives a prompt sentence from a user, generates a program using a generative AI model, and explains the processing content.

[0465] "Natural language" is a language that humans use on a daily basis, and is used to explain the processing content of a generated program to a user in an easily understandable manner.

[0466] A "specific example" is a concrete example showing how to actually use the generated program, and is useful for helping the user understand the operation of the program.

[0467] This invention relates to a system in which a user inputs a prompt sentence, generates a program using a generative AI model, explains the processing content in natural language, and adds concrete examples.

[0468] First, a user uses a terminal to access the system interface and inputs a prompt sentence, for example, "Please generate a program that analyzes the text entered by the user and determines the emotion."

[0469] The server then receives the prompt and generates a program using a generative AI model, such as a model using natural language processing techniques, that generates the appropriate program code based on the prompt entered by the user.

[0470] The generated program is written in a programming language such as Python. The server analyzes the processing content of this generated program and explains the processing in natural language. Specifically, it clearly explains what kind of data processing and data calculation the program performs. For example, if the nltk library is used to analyze the sentiment of text, the server will explain the details.

[0471] In addition, the server adds concrete examples to the explanation. For example, if the user inputs "Today is a very good day," the server shows how the program determines the emotion. This makes it easier for the user to understand the actual behavior of the generated program.

[0472] This system enables users to generate programs without specialist knowledge, easily understand the processing content, and clearly grasp how to use them in practice through concrete examples.

[0473] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0474] Step 1:

[0475] The user enters a prompt sentence.

[0476] A user uses a terminal to access the system interface and input a prompt sentence, for example, "Please generate a program that analyzes the text entered by the user and judges the emotion." The input prompt sentence is sent to the server.

[0477] Step 2:

[0478] The server generates the program using the generative AI model.

[0479] The server inputs the prompt sentence received from the user into the generative AI model. The generative AI model generates appropriate program code based on the prompt sentence. For example, a model using natural language processing technology generates Python code. The generated program code is stored on the server.

[0480] Step 3:

[0481] The server explains in natural language the processing of the generated program.

[0482] The server analyzes the generated program code and explains the processing in natural language. Specifically, it clearly explains what kind of data processing and data calculation the program performs. For example, if the program uses the nltk library to analyze the sentiment of text, it explains the details. The explanation is provided to the user.

[0483] Step 4:

[0484] The server adds specific examples to the description.

[0485] The server adds concrete examples to the generated program description. For example, when a user inputs "Today is a very good day," the server concretely shows how the program determines the emotion. The concrete examples are important to show the actual operation of the program. The description including the concrete examples is provided to the user.

[0486] The above is the flow of processing of the program of this system.

[0487] (Application example 1)

[0488] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0489] In conventional survey systems, the collection and aggregation of free responses was often done manually, which was a time-consuming and labor-intensive process. In addition, when outputting the aggregation and summary results as a report, it was sometimes difficult to output them in a format that was easy to understand visually. Furthermore, specialized knowledge was required to create ad copy, making it difficult to quickly generate high-quality ad copy. To solve these issues, there is a need for a system that can efficiently and automatically aggregate and summarize free responses, generate reports that are easy to understand visually, and generate ad copy based on prompt text using a generative AI model.

[0490] 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.

[0491] In this invention, the server includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for generating advertising copy based on a prompt text using a generative AI model, and a means for displaying the generated advertising copy. This enables efficient aggregation and summarization of free responses and the generation of visually easy-to-understand reports, and further enables the generation of advertising copy with high speed and quality.

[0492] The "means of collecting free responses to the questionnaire" is a function for collecting opinions and comments freely written by respondents in the questionnaire as data.

[0493] "Means of automatically aggregating and summarizing collected free responses" refers to a function that analyzes the collected free responses, extracts key keywords and phrases, and succinctly summarizes the overall content.

[0494] "Means of outputting the results of aggregation and summary as a report" refers to a function for generating and outputting the aggregated and summarized data as a report in a format that is visually easy to understand.

[0495] "Means for generating ad copy based on a prompt text using a generative AI model" refers to a function for automatically creating ad copy based on a prompt text entered by a user, using a generative AI model.

[0496] "Means for displaying generated ad copy" refers to a function for visually displaying to a user the ad copy created by the generative AI model.

[0497] The system for carrying out this invention has the functions of collecting free-form responses to a questionnaire, automatically aggregating and summarizing the collected free-form responses, and outputting the aggregation and summary results as a report. It also has the functions of generating advertising copy based on prompt text using a generative AI model, and displaying the generated advertising copy.

[0498] Hardware and Software Configuration

[0499] Hardware:

[0500] server

[0501] User device (smartphone, smart glasses, etc.)

[0502] software:

[0503] Analysis program using natural language processing technology

[0504] Generative AI models using the OpenAI API

[0505] Database Management Systems

[0506] A visualization tool for generating graphs and charts

[0507] Data processing and calculation

[0508] server:

[0509] 1. The server collects free-form responses to the questionnaire sent from the user terminal into a database.

[0510] 2. Use natural language processing techniques to analyze the collected free responses and extract key keywords and phrases.

[0511] 3. The free-response responses are automatically compiled and summarized based on the extracted keywords and phrases.

[0512] 4. The aggregation and summary results are generated as a report in a visually easy-to-understand format (e.g., graphs or charts) and sent to the user's terminal.

[0513] User device:

[0514] 1. The user enters a prompt sentence and sends it to the generative AI model.

[0515] 2. The server uses the OpenAI API to generate ad copy based on the input prompt text.

[0516] 3. The generated advertising copy is displayed on the user's device.

[0517] Examples

[0518] Example prompt:

[0519] "Write an ad copy for a new smartphone. Its features include a high-resolution camera and a long battery life."

[0520] Example of generated ad copy:

[0521] "A new smartphone has arrived! The high-resolution camera lets you capture every beautiful moment. Plus, the long battery life means you can use it all day without worry. Get yours now and enjoy the best experience!"

[0522] In this way, users can easily input prompt text and use the generative AI model to quickly generate high-quality ad copy. In addition, free-form responses to questionnaires are efficiently collected and summarized, and output as visually easy-to-understand reports, improving work efficiency.

[0523] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0524] Step 1:

[0525] The user inputs free responses to the questionnaire and transmits them from the terminal to the server.

[0526] Input: Free response entered by the user

[0527] Output: Free response data sent to the server

[0528] Specific operation: When a user enters free-form responses into the questionnaire form and presses the send button, the terminal sends the data to the server.

[0529] Step 2:

[0530] The server stores the received free responses in a database.

[0531] Input: Free response data sent from the device

[0532] Output: Free response data stored in a database

[0533] Specific operation: The server stores and preserves the received free response data in a database.

[0534] Step 3:

[0535] The server uses natural language processing technology to analyze the free-form responses and extract key keywords and phrases.

[0536] Input: Free response data stored in a database

[0537] Output: Extracted keywords and phrases

[0538] What it does: The server runs natural language processing algorithms to extract key keywords and phrases from the open-ended text data.

[0539] Step 4:

[0540] The server automatically aggregates and summarizes the content of the free-form responses based on the extracted keywords and phrases.

[0541] Input: Extracted keywords or phrases

[0542] Output: Aggregated and summarized data

[0543] Specific operation: The server aggregates the extracted keywords and phrases and uses a summarization algorithm to concisely summarize the content of the free-form responses.

[0544] Step 5:

[0545] The server generates a report of the aggregation and summary results in a visually easy-to-understand format and sends it to the user's terminal.

[0546] Input: Aggregated and summarized data

[0547] Output: Visually easy-to-understand reports (graphs and charts)

[0548] Specific operation: The server uses a visualization tool to generate a report in the form of graphs and charts based on the aggregation and summary results, and sends it to the user's terminal.

[0549] Step 6:

[0550] The user enters a prompt sentence and sends it to the generative AI model.

[0551] Input: The prompt text entered by the user.

[0552] Output: The prompt text sent to the server.

[0553] Specific operation: When the user inputs a prompt sentence and presses the send button, the terminal sends the data to the server.

[0554] Step 7:

[0555] The server uses the OpenAI API to generate ad copy based on the input prompt text.

[0556] Input: The prompt text sent by the user

[0557] Output: Generated ad copy

[0558] Specific operation: The server calls the OpenAI API, inputs the prompt text into the generative AI model, and obtains the generated ad copy.

[0559] Step 8:

[0560] The server transmits the generated advertising copy to the user terminal, which displays it.

[0561] Input: Generated ad copy

[0562] Output: Ad copy displayed on user device

[0563] Specific operation: The server transmits the generated advertising copy to the user terminal, and the user terminal displays the received advertising copy on the screen.

[0564] Example 2

[0565] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0566] In conventional systems, it has been difficult to quickly and accurately provide an appropriate response to a prompt sentence input by a user. In addition, there has been a lack of means for displaying the generated response in a visually easy-to-understand format, resulting in low user convenience. The object of the present invention is to solve these problems and provide a system that provides a quick and accurate response to a prompt sentence input by a user, and further displays the response in a visually easy-to-understand format.

[0567] 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.

[0568] In this invention, the server includes a means for a user to input a prompt sentence, a means for the terminal to send the prompt sentence to the server, a means for the server to pass the prompt sentence to the generative AI model, a means for the generative AI model to analyze the prompt sentence and generate a response, a means for the server to return the generated response to the terminal, and a means for the terminal to display the response to the user. This makes it possible to provide a quick and accurate response to the prompt sentence input by the user and to display the response in a visually easy-to-understand format.

[0569] A "user" is an individual or entity that utilizes the system to enter prompts and receive generated responses.

[0570] A "prompt sentence" is a question or instruction sentence that a user inputs to a system.

[0571] A "terminal" is a device on which a user inputs a prompt and displays the generated response, such as a personal computer or a smartphone.

[0572] The "server" is a computer system whose role is to receive prompt text sent from a terminal, pass it to a generative AI model, and return the generated response to the terminal.

[0573] A "generative AI model" is an artificial intelligence model that analyzes prompt sentences and generates appropriate responses. For example, this includes models that use natural language processing techniques.

[0574] "Natural language processing technology" is a technology that allows a computer to understand and analyze human language, making it possible to analyze the content of a prompt and generate an appropriate response.

[0575] A "response" is the answer or information that a generative AI model generates by analyzing a prompt.

[0576] A "visually understandable format" is a format in which the generated response is displayed in a manner that is easily understandable to the user, including, for example, text, graphs, charts, etc.

[0577] The present invention is a system that provides a prompt and accurate response to a prompt sentence input by a user and displays the response in a visually easy-to-understand format. The system includes a user, a terminal, a server, and a generative AI model.

[0578] The user inputs a prompt into the input field of the terminal. For example, the prompt might be "What's the weather like today?" or "What's the latest news?". The terminal sends the prompt input by the user to the server. At this time, the prompt is packaged in JSON format.

[0579] The server analyzes the received prompt sentence and sends a request to the API endpoint of the generative AI model. For example, an advanced model using natural language processing technology (e.g., GPT-3 or GPT-4) is used as the generative AI model. The server analyzes the response received from the generative AI model and returns it to the terminal. At this time, the response is packaged again in JSON format.

[0580] The terminal analyzes the response received from the server and displays it to the user. The display format is a visually easy-to-understand format such as text, graphs, and charts. For example, a response such as "Today's weather is sunny" or "The latest news is as follows" may be displayed.

[0581] The system allows users to utilize generative AI models to generate appropriate responses to their input prompts, and the responses are displayed in a visually easy-to-understand format for the user's convenience.

[0582] As a concrete example, consider a case where a user enters the prompt sentence "Please tell me how to write a Hello World program in Python." In this case, the generative AI model generates a response "Here is how to write a Hello World program in Python. python print('Hello, World!')" and displays it on the terminal.

[0583] In this manner, the present invention allows a user to provide a fast and accurate response to a prompt sentence entered by the user, and to display the response in a visually easy to understand format.

[0584] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0585] Step 1:

[0586] The user enters a prompt sentence.

[0587] The user inputs a prompt into an input field on the terminal, for example, "What's the weather like today?" The input prompt is temporarily stored in the terminal's memory.

[0588] Step 2:

[0589] The terminal sends a prompt to the server.

[0590] The terminal sends the prompt text entered by the user as an HTTP request to the server. At this time, the prompt text is packaged in JSON format. The input is the prompt text, and the output is an HTTP request to the server.

[0591] Step 3:

[0592] The server passes the prompt to the generative AI model.

[0593] The server parses the received prompt sentence and sends a request to the API endpoint of the generative AI model. For example, it sends the prompt sentence along with the API key as a POST request. The input is the prompt sentence, and the output is the API request to the generative AI model.

[0594] Step 4:

[0595] A generative AI model parses the prompt and generates a response.

[0596] A generative AI model analyzes the received prompt sentence and generates an appropriate response. For example, in response to the prompt sentence "What's the weather today?", it generates the response "The weather today is sunny." The input is the prompt sentence, and the output is the generated response.

[0597] Step 5:

[0598] The server returns the generated response to the terminal.

[0599] The server parses the response received from the generative AI model and returns it to the device, again packaged in JSON. The input is the generated response, and the output is the HTTP response to the device.

[0600] Step 6:

[0601] The terminal displays the response to the user.

[0602] The terminal analyzes the response received from the server and displays it to the user. For example, it might display "The weather today is sunny" on the screen. The input is the generated response, and the output is what is displayed to the user.

[0603] (Application example 2)

[0604] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[0605] In conventional survey systems, the collection of free responses and their compilation and summarization were often done manually, which was problematic in that it was time-consuming and labor-intensive. In addition, when outputting the compilation and summary results as a report, it was difficult to output them in a format that was visually easy to understand. Furthermore, when it came to automatically generating advertisements, even if the user entered a prompt sentence, there was a lack of a means to generate an appropriate advertisement based on the contents of that sentence. This made the creation of advertisements cumbersome, and made it difficult to quickly create effective advertisements.

[0606] 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.

[0607] In this invention, the server includes a means for collecting free responses to a survey, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for automatically generating an advertisement based on a prompt sentence using a generative AI model, a means for previewing the generated advertisement, a means for customizing the generated advertisement, and a means for saving and sharing the generated advertisement. This enables efficient collection, aggregation, and summarization of free responses to a survey, and realizes report output in a visually easy-to-understand format. In addition, effective advertisements can be quickly and automatically generated based on prompt sentences, and users can easily customize and share them.

[0608] A "means for collecting free-form responses to a survey" is a function for collecting free-form responses from users.

[0609] "Means for automatically aggregating and summarizing collected free responses" refers to a function that automatically analyzes collected free responses, extracts key information, and summarizes it.

[0610] "Means for outputting the results of aggregation and summary as a report" refers to a function for outputting the results of aggregation and summary in report format.

[0611] "Means for automatically generating advertisements based on prompt text using a generative AI model" refers to a function that uses a generative AI model to automatically generate advertisements based on prompt text entered by a user.

[0612] The "means for displaying a preview of a generated advertisement" is a function for displaying a preview of the generated advertisement to the user.

[0613] A "means for customizing generated advertisements" is a feature that allows a user to edit and customize a generated advertisement.

[0614] "Means for saving and sharing generated advertisements" refers to a function for saving generated advertisements and sharing them with other users and platforms.

[0615] A system for carrying out the present invention has the following configuration: the system includes means for collecting free responses to a questionnaire, means for automatically aggregating and summarizing the collected free responses, means for outputting the aggregation and summary results as a report, means for automatically generating advertisements based on prompt text using a generative AI model, means for previewing the generated advertisements, means for customizing the generated advertisements, and means for saving and sharing the generated advertisements.

[0616] Hardware and Software Configuration

[0617] Hardware: Smartphones, servers

[0618] Software: Generative AI models (e.g., OpenAI's GPT-4), mobile app development frameworks (e.g., React Native), cloud storage services (e.g., AWS S3)

[0619] Data processing and calculation

[0620] 1. Collecting free-form responses to a survey: Users enter free-form responses through a smartphone app. These responses are sent to a server and stored in a database.

[0621] 2. Automatic aggregation and summarization of free responses: The server uses natural language processing technology to analyze the free responses and extract key keywords and phrases, which are then aggregated and summarized.

[0622] 3. Report output: The aggregated and summarized results are output as a report in a visually easy-to-understand format (e.g., graphs and charts). Users can view this report on their smartphones.

[0623] 4. Automatic ad generation: When a user enters a prompt text, the prompt text is sent to the server, and the generative AI model generates ad text and images based on the prompt text.

[0624] 5. Previewing the ad: The generated ad is previewed to the user on the smartphone app.

[0625] 6. Ad Customization: Users can edit and customize generated ads within the app.

[0626] 7. Save and share your ad: Your completed ad will be saved to cloud storage and a link will be generated that can be shared on social media or email.

[0627] Examples

[0628] For example, if a user wants to create an advertisement to promote the opening of a new cafe, they might enter the following prompt text:

[0629] Example prompt: "Create an ad to promote the opening of a new cafe. The target audience is young people in their 20s and 30s. The cafe features organic coffee and a relaxing atmosphere."

[0630] When this prompt is fed into the generative AI model, the model generates advertising text and images like the following:

[0631] Example ad text: "Relax Cafe is now open! Organic coffee and a relaxing atmosphere perfect for people in their 20s and 30s. Come and visit us!"

[0632] Example of advertising image: A generative AI model generates images of cafe interiors and organic coffee to be incorporated into the ad.

[0633] In this way, users can easily create attractive advertisements and effectively reach their target audience.

[0634] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0635] Step 1:

[0636] Users enter free-form responses to the survey via a smartphone app.

[0637] Input: The user enters free-form text.

[0638] Output: The entered free response is sent to the server.

[0639] Specific behavior: When a user enters a free-form response into the app's text input field and presses the "Submit" button, the data is sent to the server.

[0640] Step 2:

[0641] The server stores the collected free responses in a database.

[0642] Input: Free text response data submitted by the user.

[0643] Output: Free responses stored in a database.

[0644] Specific operation: The server stores the received free response in a database and generates a confirmation message that the response has been saved.

[0645] Step 3:

[0646] The server uses natural language processing technology to analyze the free-form responses and extract key keywords and phrases.

[0647] Input: Free-form text data stored in a database.

[0648] Output: Extracted key keywords and phrases.

[0649] What happens: The server runs natural language processing algorithms to extract key keywords and phrases from the open-ended text.

[0650] Step 4:

[0651] The server performs aggregation and summarization based on the extracted keywords and phrases.

[0652] Input: The extracted main keywords or phrases.

[0653] Output: Aggregated and summarized data.

[0654] How it works: The server aggregates the extracted keywords and phrases and uses a summarization algorithm to generate summary data.

[0655] Step 5:

[0656] The server outputs the aggregation and summary results as a report in a visually easy-to-understand format.

[0657] Input: Aggregated and summarized data.

[0658] Output: Reports in the form of graphs and charts.

[0659] What happens: The server converts the aggregated and summarized data into graphs and charts and generates a report.

[0660] Step 6:

[0661] The user enters a prompt and sends it to the generative AI model.

[0662] Input: The prompt text entered by the user.

[0663] Output: The prompt sent to the generative AI model.

[0664] How it works: A user enters a prompt sentence into the app's text input field and presses the "Generate" button, and that data is sent to the generative AI model.

[0665] Step 7:

[0666] A generative AI model generates ad text and images based on the prompt.

[0667] Input: The prompt sent to the generative AI model.

[0668] Output: The generated ad text and images.

[0669] How it works: The generative AI model analyzes the prompt and generates ad text and images.

[0670] Step 8:

[0671] The server previews the generated advertisement to the user.

[0672] Input: Generated ad text and images.

[0673] Output: The ad preview that is displayed on the user's smartphone.

[0674] Specific operation: The server sends the generated ad to the user's smartphone and displays a preview in the app.

[0675] Step 9:

[0676] Customize user-generated ads.

[0677] Input: The previewed ad.

[0678] Output: A customized ad.

[0679] What happens: Users use the app's editing features to edit and customize ad text and images.

[0680] Step 10:

[0681] The server stores the customized advertisement in cloud storage and generates a sharing link.

[0682] Enter: your personalized ads.

[0683] Output: Advertisements stored in cloud storage and share links.

[0684] What it does: The server saves the customized ad in cloud storage and generates a link that can be shared on social media or via email.

[0685] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0686] "Example 1"

[0687] The present invention is a system that collects free responses to a questionnaire, automatically tabulates and summarizes the collected free responses, and outputs the tabulation and summary results as a report. As a means for automatically tabulating and summarizing the free responses, the system uses natural language processing technology to analyze the content of the free responses and extracts key keywords and phrases. Furthermore, the system analyzes the content of the free responses using an emotion engine that recognizes the user's emotions. The emotion engine extracts the user's emotions from the text data of the free responses and reflects the emotions in the report. Specifically, if a user gives a free response such as "This product is very good," the emotion engine extracts the emotion "good" from the free response and reflects the emotion in the report. The report output means outputs the emotions extracted by the emotion engine together with the tabulation and summary results in a visually easy-to-understand format, such as a graph or chart format. This allows the person in charge to grasp the outline of the free responses and the user's emotions just by looking at the report, which reduces the person in charge's burden and shortens the time required.

[0688] The process flow of each embodiment will be described below.

[0689] "Example 1"

[0690] Step 1: Collect free responses to the survey. Specifically, use a survey tool such as Google Forms to collect free responses from users.

[0691] Step 2: Automatically aggregate and summarize the collected free responses. Specifically, natural language processing technology is used to analyze the content of the free responses and extract key keywords and phrases.

[0692] Step 3: Recognize the user's emotions. Specifically, an emotion engine is used to analyze the content of the free responses and extract the user's emotions.

[0693] Step 4: Reflect the extracted emotions in the report. Specifically, the emotions extracted by the emotion engine are reflected in the report, and the emotions are output in a format that is easy to understand visually, such as a graph or chart.

[0694] Step 5: The aggregation / summary results and the sentiment analysis results are output as a report. Specifically, the aggregation / summary results and the sentiment analysis results are integrated and output as a single report. This allows the person in charge to grasp the outline of the free responses and the users' sentiment just by looking at the report.

[0695] Example 1

[0696] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0697] In conventional systems, the process of generating an appropriate response to a prompt input by the user was complex and difficult to perform efficiently. In addition, the quality and display format of the generated results were inconsistent, making them difficult for users to understand. This resulted in a poor user experience and limited the usefulness of the system.

[0698] 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.

[0699] In this invention, the server includes a means for receiving a prompt sentence and performing pre-processing, a means for inputting the prompt sentence into a generative AI model and performing inference, and a means for post-processing the generated results, which makes it possible to consistently and efficiently perform the processes from pre-processing of the prompt sentence to post-processing of the generated results.

[0700] A "user" is an entity that operates the system and inputs prompt sentences.

[0701] A "terminal" is a device operated by a user, including a PC, a smartphone, etc.

[0702] A "prompt" is text that a user enters into a generative AI model as an instruction or question.

[0703] A "server" is a computer system that receives prompt sentences, pre-processes them, inputs them into the generative AI model, performs inference, post-processes them, and sends the results.

[0704] "Preprocessing" refers to the process of normalizing and tokenizing the text of the prompt sentence.

[0705] A "generative AI model" is an artificial intelligence model that makes inferences based on input prompt sentences and generates results.

[0706] "Inference" is the process by which a generative AI model generates a response or outcome based on a prompt.

[0707] "Post-processing" refers to formatting and removing unnecessary information from the generated results.

[0708] "Results" are the responses or information that a generative AI model generates based on a prompt.

[0709] "Display" refers to the act of visually presenting the results received by the terminal from the server to the user.

[0710] The present invention is a system in which a user inputs a prompt sentence, a generative AI model is used to generate a response or result based on the prompt sentence, and the response or result is provided to the user. A specific embodiment of the system will be described below.

[0711] First, the user inputs a prompt sentence using a terminal (such as a PC or smartphone). An example of a prompt sentence is "Please use AI to come up with a new recipe."

[0712] The terminal sends the input prompt text to the server. The communication is performed using an HTTP request. The server is a computer system with high-performance computing capabilities, specifically, a server equipped with a high-performance GPU.

[0713] The server receives the prompt sent from the terminal and performs preprocessing, including text normalization (e.g., converting full-width characters to half-width characters) and tokenization (e.g., dividing a sentence into words or phrases). Python libraries (e.g., pandas, numpy) are used for preprocessing.

[0714] Next, the server inputs the preprocessed prompt sentence into a generative AI model. For example, GPT-4 is used as the generative AI model. The model performs inference based on the input prompt sentence and generates a result. TensorFlow and PyTorch are used for model inference.

[0715] The generated results are then post-processed on the server, which may include formatting the results (e.g. converting them to JSON) and removing unnecessary information.

[0716] The post-processed results are sent from the server to the terminal. HTTP responses are used for communication. The terminal displays the received results to the user. HTML and CSS are used for display.

[0717] As a concrete example, if a user inputs a prompt sentence such as "Please use AI to come up with a new recipe," the server receives this prompt sentence, performs preprocessing, and inputs it into a generative AI model. The model generates a new recipe, and the server post-processes the result and sends it to the terminal. The terminal displays the generated recipe to the user.

[0718] In this way, users can obtain new recipes using generative AI models. This system can efficiently perform everything from pre-processing prompts to post-processing generated results, and can provide high-quality responses to users.

[0719] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0720] Step 1:

[0721] The user enters a prompt sentence.

[0722] The user enters "Please use AI to come up with a new recipe" into the input field of the device. The input prompt is stored in the device's memory.

[0723] Step 2:

[0724] The terminal sends a prompt to the server.

[0725] The terminal sends the entered prompt text to the server as an HTTP request. The input is the prompt text, and the output is the request sent to the server.

[0726] Step 3:

[0727] The server receives the prompt text and performs preprocessing.

[0728] The server receives the prompt text sent from the terminal. It normalizes the text (e.g. converts full-width characters to half-width characters) and tokenizes (e.g. splits a sentence into words and phrases) for the received prompt text. The input is the prompt text, and the output is the preprocessed text data.

[0729] Step 4:

[0730] The server inputs a prompt sentence into the generative AI model and performs inference.

[0731] The server inputs the preprocessed prompt sentences into a generative AI model (e.g., GPT-4). The model makes inferences based on the input prompt sentences and generates results. The input is the preprocessed text data, and the output is the generated result (e.g., a new recipe).

[0732] Step 5:

[0733] The server post-processes the generated results.

[0734] The server performs post-processing on the results obtained from the generative AI model, including formatting the results (e.g., converting them to JSON) and removing unnecessary information. The input is the generated results, and the output is the post-processed data.

[0735] Step 6:

[0736] The server sends the result to the terminal.

[0737] The server sends the post-processed result to the terminal as an HTTP response. The input is the post-processed data, and the output is the response sent to the terminal.

[0738] Step 7:

[0739] The terminal displays the results to the user.

[0740] The terminal displays the results received from the server to the user. HTML and CSS are used for display. The input is the response data from the server, and the output is the information provided visually to the user.

[0741] (Application example 1)

[0742] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0743] In conventional survey systems, the collection, compilation, and summary of free responses, as well as the creation of reports, are often done manually, which is time-consuming and labor-intensive. In addition, the generation of advertising content requires specialized knowledge, making it difficult to reach the target audience quickly and effectively. To solve these problems, a system that integrates the automatic compilation and summary of free responses with the automatic generation of advertising content is required.

[0744] 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.

[0745] In this invention, the server includes a means for collecting free responses to a questionnaire, a prompt for instructing to tally and summarize the collected free responses and generate a report with the tally and summary results, a means for generating the report using a generative AI model, a prompt for instructing to generate content based on the generated report, a means for generating the content using the generative AI model, a means for previewing the generated content and accepting editing operations, a means for publishing the edited content, and a means for analyzing the performance of the published content. This enables efficient tallying and summarizing of free responses and automatic generation and publication of advertising content based on the prompt.

[0746] "Means of collecting free-form survey responses" refers to the interface or functionality for collecting free-form responses from users.

[0747] "Means of automatically aggregating and summarizing collected free-response responses" refers to algorithms or software for analysing the collected free-response data and extracting and summarising key information.

[0748] "Means of outputting the results of aggregation and summary as a report" refers to the functionality for generating and outputting the aggregated and summarized data as a report in a visually easy-to-understand format.

[0749] "Means for inputting prompt text" refers to a text input interface that allows a user to provide instructions to a generative AI model.

[0750] "Means of generating content based on a prompt sentence using a generative AI model" refers to algorithms or software that enable a generative AI model to automatically generate content such as text and images based on an input prompt sentence.

[0751] "Means for previewing and editing the generated content" means an interface or functionality that allows a user to review and, if necessary, edit the generated content.

[0752] "Means of publishing the generated content" refers to functions for publishing the generated and edited content on online platforms, social media, etc.

[0753] "Means for analyzing the performance of Published Content" means tools or software used to collect and analyze performance data, such as the number of views and clicks on Published Content.

[0754] A system for implementing the present invention has the following configuration: First, a user uses a terminal such as a smartphone to input free responses to a questionnaire about a specific product. The terminal has a means for collecting free responses to the questionnaire and transmits the free responses input by the user to a server.

[0755] The server is equipped with a means for automatically aggregating and summarizing the collected free responses. This means uses natural language processing technology to analyze the content of the free responses and extract key keywords and phrases. Specifically, natural language processing libraries (e.g., NLTK, spaCy) are used to analyze text data and extract important information. The server is also equipped with a means for outputting the aggregation and summary results as a report. This means outputs the aggregation and summary results in a format that is visually easy to understand, such as a graph or chart. For this purpose, a data visualization tool (e.g., Matplotlib, D3.js) is used. In this embodiment, the server generates a report using a generative AI model and a prompt sentence that instructs the server to tally and summarize the collected free responses and generate a report based on the tally and summary results.

[0756] Furthermore, the user generates content by providing instructions to the generative AI model using a prompt sentence that instructs the model to generate content based on the generated report. An example of a prompt sentence is, "Please use this report as a reference to create an advertisement for a new eco-friendly detergent for housewives in their 30s" along with the generated report. In this example, the specific product is detergent.

[0757] The server includes a means for generating content based on the prompt using a generative AI model, the means using a generative AI model (e.g., OpenAI GPT-4) to generate advertising text and images based on the input prompt.

[0758] The generated content is provided to the user with a means for previewing and editing on the terminal. The user can check the generated content and edit it as necessary. Once the editing is complete, the content is published on an internet platform or SNS via the server.

[0759] Finally, the server includes means for analyzing the performance of the published content, such as using tools (e.g., Google Analytics) to collect and analyze performance data, such as the number of views and clicks on the published content.

[0760] In this way, it becomes possible to efficiently collect and summarize free responses, and to automatically generate and publish advertising content based on prompt text. Additionally, the content may be modified using prompts to modify the content based on the analyzed performance and generative AI models, thereby modifying the content to improve its performance.

[0761] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0762] Step 1:

[0763] A user inputs free-form responses to a questionnaire using a terminal such as a smartphone. The input free-form responses are sent from the terminal to a server. The input data is the free-form responses in text format, and the output is the free-form response data sent to the server.

[0764] Step 2:

[0765] The server automatically aggregates and summarizes the collected free responses. It uses natural language processing technology to analyze the content of the free responses and extract key keywords and phrases. The input data is the free response text, and the output is summary data including the key keywords and phrases. Specifically, it analyzes the text data using a natural language processing library (e.g., NLTK, spaCy). Alternatively, it uses a prompt sentence that instructs the server to aggregate and summarize the collected free responses and a generative AI model to generate the aggregated and summarized results of the collected free responses.

[0766] Step 3:

[0767] The server outputs the aggregation and summary results as a report. The aggregation and summary results are output in a format that is visually easy to understand, such as a graph or chart. The input data is summary data, and the output is a visually represented report. A data visualization tool (e.g., Matplotlib, D3.js) is used. Specifically, the report is generated using a prompt statement that instructs the system to generate a report based on the generated aggregation and summary results, and a generative AI model.

[0768] Step 4:

[0769] A user gives instructions to the generative AI model using a means for inputting a prompt sentence. The input data is a report and a prompt sentence, and the output is the report and the prompt sentence sent to the server. An example of a prompt sentence is, along with inputting the generated report, "Please use this report as a reference to create an advertisement to promote a new eco-friendly detergent to housewives in their 30s."

[0770] Step 5:

[0771] The server uses a generative AI model to generate content based on the prompt sentence. The input data is the prompt sentence, and the output is the generated ad text and image. A generative AI model (e.g. OpenAI GPT-4) is used to generate content based on the input prompt sentence.

[0772] Step 6:

[0773] The generated content is provided to the user with means for previewing and editing on the terminal. The user reviews the generated content and edits it if necessary. The input data is the generated content and the output is the edited content.

[0774] Step 7:

[0775] Once the editing is complete, the content is published on an internet platform or social networking site via the server. The input data is the edited content, and the output is the published content.

[0776] Step 8:

[0777] The server analyzes the performance of the published content. It collects and analyzes performance data, such as the number of views and clicks on the published content. The input data is the performance data, and the output is the analysis result. It uses a data analysis tool (e.g., Google Analytics).

[0778] Example 2

[0779] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0780] In conventional survey systems, the collection, tabulation, and summarization of free responses were often done manually, which was time-consuming and labor-intensive. In addition, when users wanted to generate a specific program, they needed programming knowledge, which was difficult for users without specialized knowledge. Furthermore, the quality and efficiency of the generated program code was often not guaranteed.

[0781] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for a user to input a prompt sentence, a means for a generative AI model to generate program code based on the prompt sentence, and a means for returning the generated program code to the user. This enables efficient aggregation and summarization of free responses and allows the user to easily generate high-quality program code.

[0782] A "survey" is a collection of questions designed to gather specific information.

[0783] "Free responses" are responses that the respondent writes freely in their own words.

[0784] "Collection means" refers to the methods or devices used to collect data.

[0785] "Automatic aggregation and summarization means" refers to methods or devices for mechanically organizing collected data and extracting significant information.

[0786] "Means for outputting as a report" refers to a method or device for providing the aggregated or summarized results in a written format.

[0787] "User" refers to an individual or organization that uses the System.

[0788] A "prompt" is text that is used to input specific instructions or questions to a generative AI model.

[0789] A "generative AI model" is an artificial intelligence model that generates program code or other output based on input prompts.

[0790] "Program code" is a set of instructions for a computer to execute.

[0791] A "server" is a computer system that provides data and services over a network.

[0792] The present invention relates to a system for efficiently collecting, tabulating and summarizing free responses to a questionnaire, and further enabling a user to easily generate program code. A specific embodiment of this system will be described below.

[0793] First, the user inputs free-form responses to the survey through the system interface. The free-form responses entered by the user are collected by the server. The server uses natural language processing technology to automatically compile and summarize the collected free-form responses. Specifically, the server extracts key keywords and phrases, and compiles and summarizes them based on those.

[0794] The server then outputs the results of the aggregation and summary in a report, which is presented in a visually easy-to-understand format, such as graphs and charts, allowing users to intuitively grasp the content of the free-response responses.

[0795] Additionally, the system provides a means for users to input prompts, which are specific instructions or questions to the generative AI model, such as "Generate a program to count specific words in a text file."

[0796] The server receives the prompt sentence sent by the user and passes it to the generative AI model. The generative AI model generates a program code based on the prompt sentence. The generated program code is returned to the user through the server. The user can then execute this program code on their own device.

[0797] The following hardware and software are used to implement this system. The server is a computer system equipped with a high-performance processor and sufficient memory, and is equipped with an NVIDIA GPU. For software, deep learning frameworks such as TensorFlow and PyTorch are used. This allows the generative AI model to operate efficiently and process and calculate data based on the prompt sentence entered by the user.

[0798] As a concrete example, consider the case where a user inputs the following prompt into a generative AI model:

[0799] Example prompt:

[0800] "Create a program to count specific words in a text file."

[0801] Based on this prompt, the generative AI model generates Python code and returns it to the user through the server, who can then run this code on their own device to check the number of occurrences of a particular word.

[0802] As described above, this system provides efficient processing of free responses to questionnaires and a means for users to easily generate program code.

[0803] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0804] Step 1:

[0805] The user enters a prompt sentence.

[0806] The user inputs a prompt for the generative AI model through the system interface. For example, the user might input, "Generate a program that counts specific words from a text file." The input prompt is then sent to the server.

[0807] Step 2:

[0808] The server receives the prompt text and passes it to the generative AI model.

[0809] The server receives the prompt text sent by the user. It makes the appropriate API calls to pass the received prompt text to the generative AI model. The server may also convert the prompt text into a format that the generative AI model can understand. The input is the prompt text, and the output is the request to the generative AI model.

[0810] Step 3:

[0811] The generative AI model generates program code based on the prompt sentence.

[0812] The generative AI model analyzes the received prompt sentence and generates program code based on it. For example, if a user inputs "Generate a program to count specific words in a text file," the generative AI model generates Python code. The input is the prompt sentence, and the output is the generated program code.

[0813] Step 4:

[0814] The server returns the generated program code to the user.

[0815] The server receives the program code returned by the generative AI model and returns it to the user in a format that is easy for the user to use (e.g., a text file or text in a code editor). The input is the generated program code, and the output is the transmission of the program code to the user.

[0816] Step 5:

[0817] The user executes the generated program code.

[0818] The user executes the program code received from the server on his / her own terminal. For example, if a Python code is received, the user executes the code in his / her own Python environment and checks the result. The input is the generated program code, and the output is the result of the program execution. Specifically, the user opens the Python environment, copies the received code, and executes it. As a result, the number of occurrences of a particular word is displayed.

[0819] (Application example 2)

[0820] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[0821] Conventional survey systems had the problem that collecting free responses and then aggregating and summarizing them required a lot of time and effort. In addition, despite the advances being made in content generation technology using generative AI models, there was no established method for applying this to survey systems. Furthermore, there was a lack of functionality to automatically generate content based on prompts entered by users and display it.

[0822] 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.

[0823] In this invention, the server includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for inputting a prompt sentence, a means for generating content based on the prompt sentence using a generative AI model, and a means for displaying the generated content. This enables efficient collection, aggregation, and summarization of free responses to a questionnaire, and further realizes content generation and display using a generative AI model.

[0824] A "means for collecting free-form responses to a questionnaire" is a device or software that allows users to enter responses in free form and collects them as data.

[0825] "Means for automatically aggregating and summarizing collected free-response responses" refers to a device or software that analyzes the collected free-response data, extracts key keywords and phrases, and succinctly summarizes the overall content.

[0826] "Means for outputting the results of aggregation and summary as a report" refers to a device or software for generating and outputting the aggregated and summarized data as a report in a visually easy-to-understand format.

[0827] A "means for inputting a prompt sentence" is a device or software that allows a user to input text to provide instructions to a generative AI model.

[0828] A "means for generating content based on a prompt sentence using a generative AI model" refers to a device or software that enables a generative AI model to automatically generate content such as text and images based on an input prompt sentence.

[0829] A “means for displaying generated content” is a device or software for visually displaying to a user the content generated by a generative AI model.

[0830] A system for carrying out this invention has the following configuration. First, a user inputs free-form responses to a questionnaire using a terminal such as a smartphone. The terminal then transmits the input free-form responses to a server. The server then uses natural language processing technology to automatically tally and summarize the collected free-form responses. Specifically, it extracts key keywords and phrases and summarizes the overall content concisely.

[0831] Next, the server generates a report of the aggregated and summarized results and outputs them in a visually easy-to-understand format, such as graphs or charts, allowing users to intuitively grasp the survey results.

[0832] Furthermore, the user gives instructions to the generative AI model using a means for inputting a prompt sentence. An example of a prompt sentence is "A story about a brave knight fighting a dragon." The server uses the generative AI model to generate content based on this prompt sentence. The generated content is in the form of text, images, etc., and is visually displayed to the user.

[0833] The following hardware and software are used to realize this system. For hardware, a smartphone and a server are required. For software, Python and OpenAI API are used. Python is a programming language for implementing natural language processing technology, and the OpenAI API is an interface for using generative AI models.

[0834] As a concrete example, if a user enters the prompt "A story about a brave knight battling a dragon," the server uses a generative AI model to generate the following story:

[0835] Once upon a time, there was a brave knight. His name was Arthur. To protect his kingdom, Arthur decided to fight a terrible dragon. After reaching the dragon's nest, Arthur drew his sword and faced the dragon. After a fierce battle, Arthur defeated the dragon and brought peace to the kingdom.

[0836] In this way, users can enjoy content generation and display using generative AI models.

[0837] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0838] Step 1:

[0839] A user inputs free-form responses to a questionnaire using a terminal such as a smartphone. The input free-form responses are sent from the terminal to a server. The input data is in text format and is sent to the server.

[0840] Step 2:

[0841] The server uses natural language processing technology to analyze the received free-response data. Specifically, it extracts key keywords and phrases and summarizes the overall content concisely. The input data is the free-response text, and the output data is a summary text that includes the key keywords and phrases.

[0842] Step 3:

[0843] The server generates a report based on the results of the aggregation and summary. The report is output in a visually understandable format, such as a graph or chart. The input data is the summary text, and the output data is a visual report.

[0844] Step 4:

[0845] The user gives instructions to the generative AI model using a means for inputting a prompt sentence. The prompt sentence is input in text format and sent from the terminal to the server. The input data is the text of the prompt sentence.

[0846] Step 5:

[0847] The server generates content based on the prompt sentence using a generative AI model. The generative AI model automatically generates content such as text and images based on the input prompt sentence. The input data is the text of the prompt sentence, and the output data is the generated content.

[0848] Step 6:

[0849] The server visually displays the generated content to the user. The generated content is sent to the terminal and displayed on the user's screen. The input data is the generated content and the output data is the content displayed on the user's screen.

[0850] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0851] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0852] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0853] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0854] [Third embodiment]

[0855] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0856] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0857] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0858] 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.

[0859] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0860] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0861] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0862] Fig. 6 shows an example of main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0863] 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.

[0864] 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.

[0865] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0866] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0867] "Example 1"

[0868] The system of the present invention includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, and a means for outputting the aggregation and summary results as a report. Specifically, free responses to the questionnaire are collected using an online questionnaire tool such as Google Forms. The collected free responses are automatically aggregated and summarized using natural language processing technology. For example, keyword extraction, topic modeling, sentiment analysis, and other techniques are used for this natural language processing technology. The aggregation and summary results are output in a visually easy-to-understand format, such as a graph or chart format. This reduces the burden on the person in charge and saves time.

[0869] The process flow of each embodiment will be described below.

[0870] "Example 1"

[0871] Step 1: Collect free responses to the survey. Specifically, use an online survey tool such as Google Forms to collect free responses from users.

[0872] Step 2: Automatically aggregate and summarize the collected free responses. In this step, natural language processing technology is used to analyze the content of the free responses. Specifically, techniques such as keyword extraction, topic modeling, and sentiment analysis are used to extract key keywords and phrases from the free responses, and the free responses are aggregated and summarized based on these.

[0873] Step 3: Output the tabulation and summary results as a report. In this step, the tabulation and summary results obtained in the previous step are output in a format that is visually easy to understand. Specifically, the tabulation and summary results are displayed in graph and chart format, and this is output as a report. This allows the person in charge to get an overview of the free responses just by looking at the report, thereby reducing the person in charge's workload and saving time.

[0874] Example 1

[0875] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[0876] Conventional systems required users to have specialized knowledge when generating programs, and a high level of technical understanding was required to understand the processing content of the generated programs. Furthermore, there were often a lack of concrete examples to understand the specific operation of the generated programs, making it unclear to users how they should actually use the programs.

[0877] 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.

[0878] In this invention, the server includes a means for a user to input a prompt sentence, a means for generating a program using a generative AI model, a means for explaining the processing of the generated program in natural language, and a means for adding a concrete example to the explanation. This allows a user to generate a program without specialized knowledge, to easily understand the processing content, and to clearly grasp the actual usage method through the concrete example.

[0879] A "user" is an entity that utilizes the system to input prompt statements and receive program generation and explanations of those programs.

[0880] A "prompt sentence" is an instruction sentence that a user inputs into the system, and is the text that serves as the basis for the generative AI model to generate a program.

[0881] A "generative AI model" is an artificial intelligence model that generates program code based on a prompt sentence entered by a user, such as a model that uses natural language processing technology.

[0882] A “program” is the code generated by a generative AI model; that is, a set of instructions to perform a specific task.

[0883] A "server" is a computer system that receives a prompt sentence from a user, generates a program using a generative AI model, and explains the processing content.

[0884] "Natural language" is a language that humans use on a daily basis, and is used to explain the processing content of a generated program to a user in an easily understandable manner.

[0885] A "specific example" is a concrete example showing how to actually use the generated program, and is useful for helping the user understand the operation of the program.

[0886] This invention relates to a system in which a user inputs a prompt sentence, generates a program using a generative AI model, explains the processing content in natural language, and adds concrete examples.

[0887] First, a user uses a terminal to access the system interface and inputs a prompt sentence, for example, "Please generate a program that analyzes the text entered by the user and determines the emotion."

[0888] The server then receives the prompt and generates a program using a generative AI model, such as a model using natural language processing techniques, that generates the appropriate program code based on the prompt entered by the user.

[0889] The generated program is written in a programming language such as Python. The server analyzes the processing content of this generated program and explains the processing in natural language. Specifically, it clearly explains what kind of data processing and data calculation the program performs. For example, if the nltk library is used to analyze the sentiment of text, the server will explain the details.

[0890] In addition, the server adds concrete examples to the explanation. For example, if the user inputs "Today is a very good day," the server shows how the program determines the emotion. This makes it easier for the user to understand the actual behavior of the generated program.

[0891] This system enables users to generate programs without specialist knowledge, easily understand the processing content, and clearly grasp how to use them in practice through concrete examples.

[0892] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0893] Step 1:

[0894] The user enters a prompt sentence.

[0895] A user uses a terminal to access the system interface and input a prompt sentence, for example, "Please generate a program that analyzes the text entered by the user and judges the emotion." The input prompt sentence is sent to the server.

[0896] Step 2:

[0897] The server generates the program using the generative AI model.

[0898] The server inputs the prompt sentence received from the user into the generative AI model. The generative AI model generates appropriate program code based on the prompt sentence. For example, a model using natural language processing technology generates Python code. The generated program code is stored on the server.

[0899] Step 3:

[0900] The server explains in natural language the processing of the generated program.

[0901] The server analyzes the generated program code and explains the processing in natural language. Specifically, it clearly explains what kind of data processing and data calculation the program performs. For example, if the program uses the nltk library to analyze the sentiment of text, it explains the details. The explanation is provided to the user.

[0902] Step 4:

[0903] The server adds specific examples to the description.

[0904] The server adds concrete examples to the generated program description. For example, when a user inputs "Today is a very good day," the server concretely shows how the program determines the emotion. The concrete examples are important to show the actual operation of the program. The description including the concrete examples is provided to the user.

[0905] The above is the flow of processing of the program of this system.

[0906] (Application example 1)

[0907] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[0908] In conventional survey systems, the collection and aggregation of free responses was often done manually, which was a time-consuming and labor-intensive process. In addition, when outputting the aggregation and summary results as a report, it was sometimes difficult to output them in a format that was easy to understand visually. Furthermore, specialized knowledge was required to create ad copy, making it difficult to quickly generate high-quality ad copy. To solve these issues, there is a need for a system that can efficiently and automatically aggregate and summarize free responses, generate reports that are easy to understand visually, and generate ad copy based on prompt text using a generative AI model.

[0909] 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.

[0910] In this invention, the server includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for generating advertising copy based on a prompt text using a generative AI model, and a means for displaying the generated advertising copy. This enables efficient aggregation and summarization of free responses and the generation of visually easy-to-understand reports, and further enables the generation of advertising copy with high speed and quality.

[0911] The "means of collecting free responses to the questionnaire" is a function for collecting opinions and comments freely written by respondents in the questionnaire as data.

[0912] "Means of automatically aggregating and summarizing collected free responses" refers to a function that analyzes the collected free responses, extracts key keywords and phrases, and succinctly summarizes the overall content.

[0913] "Means of outputting the results of aggregation and summary as a report" refers to a function for generating and outputting the aggregated and summarized data as a report in a format that is visually easy to understand.

[0914] "Means for generating ad copy based on a prompt text using a generative AI model" refers to a function for automatically creating ad copy based on a prompt text entered by a user, using a generative AI model.

[0915] "Means for displaying generated ad copy" refers to a function for visually displaying to a user the ad copy created by the generative AI model.

[0916] The system for carrying out this invention has the functions of collecting free-form responses to a questionnaire, automatically aggregating and summarizing the collected free-form responses, and outputting the aggregation and summary results as a report. It also has the functions of generating advertising copy based on prompt text using a generative AI model, and displaying the generated advertising copy.

[0917] Hardware and Software Configuration

[0918] Hardware:

[0919] server

[0920] User device (smartphone, smart glasses, etc.)

[0921] software:

[0922] Analysis program using natural language processing technology

[0923] Generative AI models using the OpenAI API

[0924] Database Management Systems

[0925] A visualization tool for generating graphs and charts

[0926] Data processing and calculation

[0927] server:

[0928] 1. The server collects free-form responses to the questionnaire sent from the user terminal into a database.

[0929] 2. Use natural language processing techniques to analyze the collected free responses and extract key keywords and phrases.

[0930] 3. The free-response responses are automatically compiled and summarized based on the extracted keywords and phrases.

[0931] 4. The aggregation and summary results are generated as a report in a visually easy-to-understand format (e.g., graphs or charts) and sent to the user's terminal.

[0932] User device:

[0933] 1. The user enters a prompt sentence and sends it to the generative AI model.

[0934] 2. The server uses the OpenAI API to generate ad copy based on the input prompt text.

[0935] 3. The generated advertising copy is displayed on the user's device.

[0936] Examples

[0937] Example prompt:

[0938] "Write an ad copy for a new smartphone. Its features include a high-resolution camera and a long battery life."

[0939] Example of generated ad copy:

[0940] "A new smartphone has arrived! The high-resolution camera lets you capture every beautiful moment. Plus, the long battery life means you can use it all day without worry. Get yours now and enjoy the best experience!"

[0941] In this way, users can easily input prompt text and use the generative AI model to quickly generate high-quality ad copy. In addition, free-form responses to questionnaires are efficiently collected and summarized, and output as visually easy-to-understand reports, improving work efficiency.

[0942] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0943] Step 1:

[0944] The user inputs free responses to the questionnaire and transmits them from the terminal to the server.

[0945] Input: Free response entered by the user

[0946] Output: Free response data sent to the server

[0947] Specific operation: When a user enters free-form responses into the questionnaire form and presses the send button, the terminal sends the data to the server.

[0948] Step 2:

[0949] The server stores the received free responses in a database.

[0950] Input: Free response data sent from the device

[0951] Output: Free response data stored in a database

[0952] Specific operation: The server stores and preserves the received free response data in a database.

[0953] Step 3:

[0954] The server uses natural language processing technology to analyze the free-form responses and extract key keywords and phrases.

[0955] Input: Free response data stored in a database

[0956] Output: Extracted keywords and phrases

[0957] What it does: The server runs natural language processing algorithms to extract key keywords and phrases from the open-ended text data.

[0958] Step 4:

[0959] The server automatically aggregates and summarizes the content of the free-form responses based on the extracted keywords and phrases.

[0960] Input: Extracted keywords or phrases

[0961] Output: Aggregated and summarized data

[0962] Specific operation: The server aggregates the extracted keywords and phrases and uses a summarization algorithm to concisely summarize the content of the free-form responses.

[0963] Step 5:

[0964] The server generates a report of the aggregation and summary results in a visually easy-to-understand format and sends it to the user's terminal.

[0965] Input: Aggregated and summarized data

[0966] Output: Visually easy-to-understand reports (graphs and charts)

[0967] Specific operation: The server uses a visualization tool to generate a report in the form of graphs and charts based on the aggregation and summary results, and sends it to the user's terminal.

[0968] Step 6:

[0969] The user enters a prompt sentence and sends it to the generative AI model.

[0970] Input: The prompt text entered by the user.

[0971] Output: The prompt text sent to the server.

[0972] Specific operation: When the user inputs a prompt sentence and presses the send button, the terminal sends the data to the server.

[0973] Step 7:

[0974] The server uses the OpenAI API to generate ad copy based on the input prompt text.

[0975] Input: The prompt text sent by the user

[0976] Output: Generated ad copy

[0977] Specific operation: The server calls the OpenAI API, inputs the prompt text into the generative AI model, and obtains the generated ad copy.

[0978] Step 8:

[0979] The server transmits the generated advertising copy to the user terminal, which displays it.

[0980] Input: Generated ad copy

[0981] Output: Ad copy displayed on user device

[0982] Specific operation: The server transmits the generated advertising copy to the user terminal, and the user terminal displays the received advertising copy on the screen.

[0983] Example 2

[0984] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[0985] In conventional systems, it has been difficult to quickly and accurately provide an appropriate response to a prompt sentence input by a user. In addition, there has been a lack of means for displaying the generated response in a visually easy-to-understand format, resulting in low user convenience. The object of the present invention is to solve these problems and provide a system that provides a quick and accurate response to a prompt sentence input by a user, and further displays the response in a visually easy-to-understand format.

[0986] 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.

[0987] In this invention, the server includes a means for a user to input a prompt sentence, a means for the terminal to send the prompt sentence to the server, a means for the server to pass the prompt sentence to the generative AI model, a means for the generative AI model to analyze the prompt sentence and generate a response, a means for the server to return the generated response to the terminal, and a means for the terminal to display the response to the user. This makes it possible to provide a quick and accurate response to the prompt sentence input by the user and to display the response in a visually easy-to-understand format.

[0988] A "user" is an individual or entity that utilizes the system to enter prompts and receive generated responses.

[0989] A "prompt sentence" is a question or instruction sentence that a user inputs to a system.

[0990] A "terminal" is a device on which a user inputs a prompt and displays the generated response, such as a personal computer or a smartphone.

[0991] The "server" is a computer system whose role is to receive prompt text sent from a terminal, pass it to a generative AI model, and return the generated response to the terminal.

[0992] A "generative AI model" is an artificial intelligence model that analyzes prompt sentences and generates appropriate responses. For example, this includes models that use natural language processing techniques.

[0993] "Natural language processing technology" is a technology that allows a computer to understand and analyze human language, making it possible to analyze the content of a prompt and generate an appropriate response.

[0994] A "response" is the answer or information that a generative AI model generates by analyzing a prompt.

[0995] A "visually understandable format" is a format in which the generated response is displayed in a manner that is easily understandable to the user, including, for example, text, graphs, charts, etc.

[0996] The present invention is a system that provides a prompt and accurate response to a prompt sentence input by a user and displays the response in a visually easy-to-understand format. The system includes a user, a terminal, a server, and a generative AI model.

[0997] The user inputs a prompt into the input field of the terminal. For example, the prompt might be "What's the weather like today?" or "What's the latest news?". The terminal sends the prompt input by the user to the server. At this time, the prompt is packaged in JSON format.

[0998] The server analyzes the received prompt sentence and sends a request to the API endpoint of the generative AI model. For example, an advanced model using natural language processing technology (e.g., GPT-3 or GPT-4) is used as the generative AI model. The server analyzes the response received from the generative AI model and returns it to the terminal. At this time, the response is packaged again in JSON format.

[0999] The terminal analyzes the response received from the server and displays it to the user. The display format is a visually easy-to-understand format such as text, graphs, and charts. For example, a response such as "Today's weather is sunny" or "The latest news is as follows" may be displayed.

[1000] The system allows users to utilize generative AI models to generate appropriate responses to their input prompts, and the responses are displayed in a visually easy-to-understand format for the user's convenience.

[1001] As a concrete example, consider a case where a user enters the prompt sentence "Please tell me how to write a Hello World program in Python." In this case, the generative AI model generates a response "Here is how to write a Hello World program in Python. python print('Hello, World!')" and displays it on the terminal.

[1002] In this manner, the present invention allows a user to provide a fast and accurate response to a prompt sentence entered by the user, and to display the response in a visually easy to understand format.

[1003] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1004] Step 1:

[1005] The user enters a prompt sentence.

[1006] The user inputs a prompt into an input field on the terminal, for example, "What's the weather like today?" The input prompt is temporarily stored in the terminal's memory.

[1007] Step 2:

[1008] The terminal sends a prompt to the server.

[1009] The terminal sends the prompt text entered by the user as an HTTP request to the server. At this time, the prompt text is packaged in JSON format. The input is the prompt text, and the output is an HTTP request to the server.

[1010] Step 3:

[1011] The server passes the prompt to the generative AI model.

[1012] The server parses the received prompt sentence and sends a request to the API endpoint of the generative AI model. For example, it sends the prompt sentence along with the API key as a POST request. The input is the prompt sentence, and the output is the API request to the generative AI model.

[1013] Step 4:

[1014] A generative AI model parses the prompt and generates a response.

[1015] A generative AI model analyzes the received prompt sentence and generates an appropriate response. For example, in response to the prompt sentence "What's the weather today?", it generates the response "The weather today is sunny." The input is the prompt sentence, and the output is the generated response.

[1016] Step 5:

[1017] The server returns the generated response to the terminal.

[1018] The server parses the response received from the generative AI model and returns it to the device, again packaged in JSON. The input is the generated response, and the output is the HTTP response to the device.

[1019] Step 6:

[1020] The terminal displays the response to the user.

[1021] The terminal analyzes the response received from the server and displays it to the user. For example, it might display "The weather today is sunny" on the screen. The input is the generated response, and the output is what is displayed to the user.

[1022] (Application example 2)

[1023] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1024] In conventional survey systems, the collection of free responses and their compilation and summarization were often done manually, which was problematic in that it was time-consuming and labor-intensive. In addition, when outputting the compilation and summary results as a report, it was difficult to output them in a format that was visually easy to understand. Furthermore, when it came to automatically generating advertisements, even if the user entered a prompt sentence, there was a lack of a means to generate an appropriate advertisement based on the contents of that sentence. This made the creation of advertisements cumbersome, and made it difficult to quickly create effective advertisements.

[1025] 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.

[1026] In this invention, the server includes a means for collecting free responses to a survey, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for automatically generating an advertisement based on a prompt sentence using a generative AI model, a means for previewing the generated advertisement, a means for customizing the generated advertisement, and a means for saving and sharing the generated advertisement. This enables efficient collection, aggregation, and summarization of free responses to a survey, and realizes report output in a visually easy-to-understand format. In addition, effective advertisements can be quickly and automatically generated based on prompt sentences, and users can easily customize and share them.

[1027] A "means for collecting free-form responses to a survey" is a function for collecting free-form responses from users.

[1028] "Means for automatically aggregating and summarizing collected free responses" refers to a function that automatically analyzes collected free responses, extracts key information, and summarizes it.

[1029] "Means for outputting the results of aggregation and summary as a report" refers to a function for outputting the results of aggregation and summary in report format.

[1030] "Means for automatically generating advertisements based on prompt text using a generative AI model" refers to a function that uses a generative AI model to automatically generate advertisements based on prompt text entered by a user.

[1031] The "means for displaying a preview of a generated advertisement" is a function for displaying a preview of the generated advertisement to the user.

[1032] A "means for customizing generated advertisements" is a feature that allows a user to edit and customize a generated advertisement.

[1033] "Means for saving and sharing generated advertisements" refers to a function for saving generated advertisements and sharing them with other users and platforms.

[1034] A system for carrying out the present invention has the following configuration: the system includes means for collecting free responses to a questionnaire, means for automatically aggregating and summarizing the collected free responses, means for outputting the aggregation and summary results as a report, means for automatically generating advertisements based on prompt text using a generative AI model, means for previewing the generated advertisements, means for customizing the generated advertisements, and means for saving and sharing the generated advertisements.

[1035] Hardware and Software Configuration

[1036] Hardware: Smartphones, servers

[1037] Software: Generative AI models (e.g., OpenAI's GPT-4), mobile app development frameworks (e.g., React Native), cloud storage services (e.g., AWS S3)

[1038] Data processing and calculation

[1039] 1. Collecting free-form responses to a survey: Users enter free-form responses through a smartphone app. These responses are sent to a server and stored in a database.

[1040] 2. Automatic aggregation and summarization of free responses: The server uses natural language processing technology to analyze the free responses and extract key keywords and phrases, which are then aggregated and summarized.

[1041] 3. Report output: The aggregated and summarized results are output as a report in a visually easy-to-understand format (e.g., graphs and charts). Users can view this report on their smartphones.

[1042] 4. Automatic ad generation: When a user enters a prompt text, the prompt text is sent to the server, and the generative AI model generates ad text and images based on the prompt text.

[1043] 5. Previewing the ad: The generated ad is previewed to the user on the smartphone app.

[1044] 6. Ad Customization: Users can edit and customize generated ads within the app.

[1045] 7. Save and share your ad: Your completed ad will be saved to cloud storage and a link will be generated that can be shared on social media or email.

[1046] Examples

[1047] For example, if a user wants to create an advertisement to promote the opening of a new cafe, they might enter the following prompt text:

[1048] Example prompt: "Create an ad to promote the opening of a new cafe. The target audience is young people in their 20s and 30s. The cafe features organic coffee and a relaxing atmosphere."

[1049] When this prompt is fed into the generative AI model, the model generates advertising text and images like the following:

[1050] Example ad text: "Relax Cafe is now open! Organic coffee and a relaxing atmosphere perfect for people in their 20s and 30s. Come and visit us!"

[1051] Example of advertising image: A generative AI model generates images of cafe interiors and organic coffee to be incorporated into the ad.

[1052] In this way, users can easily create attractive advertisements and effectively reach their target audience.

[1053] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1054] Step 1:

[1055] Users enter free-form responses to the survey via a smartphone app.

[1056] Input: The user enters free-form text.

[1057] Output: The entered free response is sent to the server.

[1058] Specific behavior: When a user enters a free-form response into the app's text input field and presses the "Submit" button, the data is sent to the server.

[1059] Step 2:

[1060] The server stores the collected free responses in a database.

[1061] Input: Free text response data submitted by the user.

[1062] Output: Free responses stored in a database.

[1063] Specific operation: The server stores the received free response in a database and generates a confirmation message that the response has been saved.

[1064] Step 3:

[1065] The server uses natural language processing technology to analyze the free-form responses and extract key keywords and phrases.

[1066] Input: Free-form text data stored in a database.

[1067] Output: Extracted key keywords and phrases.

[1068] What happens: The server runs natural language processing algorithms to extract key keywords and phrases from the open-ended text.

[1069] Step 4:

[1070] The server performs aggregation and summarization based on the extracted keywords and phrases.

[1071] Input: The extracted main keywords or phrases.

[1072] Output: Aggregated and summarized data.

[1073] How it works: The server aggregates the extracted keywords and phrases and uses a summarization algorithm to generate summary data.

[1074] Step 5:

[1075] The server outputs the aggregation and summary results as a report in a visually easy-to-understand format.

[1076] Input: Aggregated and summarized data.

[1077] Output: Reports in the form of graphs and charts.

[1078] What happens: The server converts the aggregated and summarized data into graphs and charts and generates a report.

[1079] Step 6:

[1080] The user enters a prompt and sends it to the generative AI model.

[1081] Input: The prompt text entered by the user.

[1082] Output: The prompt sent to the generative AI model.

[1083] How it works: A user enters a prompt sentence into the app's text input field and presses the "Generate" button, and that data is sent to the generative AI model.

[1084] Step 7:

[1085] A generative AI model generates ad text and images based on the prompt.

[1086] Input: The prompt sent to the generative AI model.

[1087] Output: The generated ad text and images.

[1088] How it works: The generative AI model analyzes the prompt and generates ad text and images.

[1089] Step 8:

[1090] The server previews the generated advertisement to the user.

[1091] Input: Generated ad text and images.

[1092] Output: The ad preview that is displayed on the user's smartphone.

[1093] Specific operation: The server sends the generated ad to the user's smartphone and displays a preview in the app.

[1094] Step 9:

[1095] Customize user-generated ads.

[1096] Input: The previewed ad.

[1097] Output: A customized ad.

[1098] What happens: Users use the app's editing features to edit and customize ad text and images.

[1099] Step 10:

[1100] The server stores the customized advertisement in cloud storage and generates a sharing link.

[1101] Enter: your personalized ads.

[1102] Output: Advertisements stored in cloud storage and share links.

[1103] What it does: The server saves the customized ad in cloud storage and generates a link that can be shared on social media or via email.

[1104] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1105] "Example 1"

[1106] The present invention is a system that collects free responses to a questionnaire, automatically tabulates and summarizes the collected free responses, and outputs the tabulation and summary results as a report. As a means for automatically tabulating and summarizing the free responses, the system uses natural language processing technology to analyze the content of the free responses and extracts key keywords and phrases. Furthermore, the system analyzes the content of the free responses using an emotion engine that recognizes the user's emotions. The emotion engine extracts the user's emotions from the text data of the free responses and reflects the emotions in the report. Specifically, if a user gives a free response such as "This product is very good," the emotion engine extracts the emotion "good" from the free response and reflects the emotion in the report. The report output means outputs the emotions extracted by the emotion engine together with the tabulation and summary results in a visually easy-to-understand format, such as a graph or chart format. This allows the person in charge to grasp the outline of the free responses and the user's emotions just by looking at the report, which reduces the person in charge's burden and shortens the time required.

[1107] The process flow of each embodiment will be described below.

[1108] "Example 1"

[1109] Step 1: Collect free responses to the survey. Specifically, use a survey tool such as Google Forms to collect free responses from users.

[1110] Step 2: Automatically aggregate and summarize the collected free responses. Specifically, natural language processing technology is used to analyze the content of the free responses and extract key keywords and phrases.

[1111] Step 3: Recognize the user's emotions. Specifically, an emotion engine is used to analyze the content of the free responses and extract the user's emotions.

[1112] Step 4: Reflect the extracted emotions in the report. Specifically, the emotions extracted by the emotion engine are reflected in the report, and the emotions are output in a format that is easy to understand visually, such as a graph or chart.

[1113] Step 5: The aggregation / summary results and the sentiment analysis results are output as a report. Specifically, the aggregation / summary results and the sentiment analysis results are integrated and output as a single report. This allows the person in charge to grasp the outline of the free responses and the users' sentiment just by looking at the report.

[1114] Example 1

[1115] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1116] In conventional systems, the process of generating an appropriate response to a prompt input by the user was complex and difficult to perform efficiently. In addition, the quality and display format of the generated results were inconsistent, making them difficult for users to understand. This resulted in a poor user experience and limited the usefulness of the system.

[1117] 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.

[1118] In this invention, the server includes a means for receiving a prompt sentence and performing pre-processing, a means for inputting the prompt sentence into a generative AI model and performing inference, and a means for post-processing the generated results, which makes it possible to consistently and efficiently perform the processes from pre-processing of the prompt sentence to post-processing of the generated results.

[1119] A "user" is an entity that operates the system and inputs prompt sentences.

[1120] A "terminal" is a device operated by a user, including a PC, a smartphone, etc.

[1121] A "prompt" is text that a user enters into a generative AI model as an instruction or question.

[1122] A "server" is a computer system that receives prompt sentences, pre-processes them, inputs them into the generative AI model, performs inference, post-processes them, and sends the results.

[1123] "Preprocessing" refers to the process of normalizing and tokenizing the text of the prompt sentence.

[1124] A "generative AI model" is an artificial intelligence model that makes inferences based on input prompt sentences and generates results.

[1125] "Inference" is the process by which a generative AI model generates a response or outcome based on a prompt.

[1126] "Post-processing" refers to formatting and removing unnecessary information from the generated results.

[1127] "Results" are the responses or information that a generative AI model generates based on a prompt.

[1128] "Display" refers to the act of visually presenting the results received by the terminal from the server to the user.

[1129] The present invention is a system in which a user inputs a prompt sentence, a generative AI model is used to generate a response or result based on the prompt sentence, and the response or result is provided to the user. A specific embodiment of the system will be described below.

[1130] First, the user inputs a prompt sentence using a terminal (such as a PC or smartphone). An example of a prompt sentence is "Please use AI to come up with a new recipe."

[1131] The terminal sends the input prompt text to the server. The communication is performed using an HTTP request. The server is a computer system with high-performance computing capabilities, specifically, a server equipped with a high-performance GPU.

[1132] The server receives the prompt sent from the terminal and performs preprocessing, including text normalization (e.g., converting full-width characters to half-width characters) and tokenization (e.g., dividing a sentence into words or phrases). Python libraries (e.g., pandas, numpy) are used for preprocessing.

[1133] Next, the server inputs the preprocessed prompt sentence into a generative AI model. For example, GPT-4 is used as the generative AI model. The model performs inference based on the input prompt sentence and generates a result. TensorFlow and PyTorch are used for model inference.

[1134] The generated results are then post-processed on the server, which may include formatting the results (e.g. converting them to JSON) and removing unnecessary information.

[1135] The post-processed results are sent from the server to the terminal. HTTP responses are used for communication. The terminal displays the received results to the user. HTML and CSS are used for display.

[1136] As a concrete example, if a user inputs a prompt sentence such as "Please use AI to come up with a new recipe," the server receives this prompt sentence, performs preprocessing, and inputs it into a generative AI model. The model generates a new recipe, and the server post-processes the result and sends it to the terminal. The terminal displays the generated recipe to the user.

[1137] In this way, users can obtain new recipes using generative AI models. This system can efficiently perform everything from pre-processing prompts to post-processing generated results, and can provide high-quality responses to users.

[1138] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1139] Step 1:

[1140] The user enters a prompt sentence.

[1141] The user enters "Please use AI to come up with a new recipe" into the input field of the device. The input prompt is stored in the device's memory.

[1142] Step 2:

[1143] The terminal sends a prompt to the server.

[1144] The terminal sends the entered prompt text to the server as an HTTP request. The input is the prompt text, and the output is the request sent to the server.

[1145] Step 3:

[1146] The server receives the prompt text and performs preprocessing.

[1147] The server receives the prompt text sent from the terminal. It normalizes the text (e.g. converts full-width characters to half-width characters) and tokenizes (e.g. splits a sentence into words and phrases) for the received prompt text. The input is the prompt text, and the output is the preprocessed text data.

[1148] Step 4:

[1149] The server inputs a prompt sentence into the generative AI model and performs inference.

[1150] The server inputs the preprocessed prompt sentences into a generative AI model (e.g., GPT-4). The model makes inferences based on the input prompt sentences and generates results. The input is the preprocessed text data, and the output is the generated result (e.g., a new recipe).

[1151] Step 5:

[1152] The server post-processes the generated results.

[1153] The server performs post-processing on the results obtained from the generative AI model, including formatting the results (e.g., converting them to JSON) and removing unnecessary information. The input is the generated results, and the output is the post-processed data.

[1154] Step 6:

[1155] The server sends the result to the terminal.

[1156] The server sends the post-processed result to the terminal as an HTTP response. The input is the post-processed data, and the output is the response sent to the terminal.

[1157] Step 7:

[1158] The terminal displays the results to the user.

[1159] The terminal displays the results received from the server to the user. HTML and CSS are used for display. The input is the response data from the server, and the output is the information provided visually to the user.

[1160] (Application example 1)

[1161] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1162] In conventional survey systems, the collection, compilation, and summary of free responses, as well as the creation of reports, are often done manually, which is time-consuming and labor-intensive. In addition, the generation of advertising content requires specialized knowledge, making it difficult to reach the target audience quickly and effectively. To solve these problems, a system that integrates the automatic compilation and summary of free responses with the automatic generation of advertising content is required.

[1163] 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.

[1164] In this invention, the server includes a means for collecting free responses to a questionnaire, a prompt for instructing to tally and summarize the collected free responses and generate a report with the tally and summary results, a means for generating the report using a generative AI model, a prompt for instructing to generate content based on the generated report, a means for generating the content using the generative AI model, a means for previewing the generated content and accepting editing operations, a means for publishing the edited content, and a means for analyzing the performance of the published content. This enables efficient tallying and summarizing of free responses and automatic generation and publication of advertising content based on the prompt.

[1165] "Means of collecting free-form survey responses" refers to the interface or functionality for collecting free-form responses from users.

[1166] "Means of automatically aggregating and summarizing collected free-response responses" refers to algorithms or software for analysing the collected free-response data and extracting and summarising key information.

[1167] "Means of outputting the results of aggregation and summary as a report" refers to the functionality for generating and outputting the aggregated and summarized data as a report in a visually easy-to-understand format.

[1168] "Means for inputting prompt text" refers to a text input interface that allows a user to provide instructions to a generative AI model.

[1169] "Means of generating content based on a prompt sentence using a generative AI model" refers to algorithms or software that enable a generative AI model to automatically generate content such as text and images based on an input prompt sentence.

[1170] "Means for previewing and editing the generated content" means an interface or functionality that allows a user to review and, if necessary, edit the generated content.

[1171] "Means of publishing the generated content" refers to functions for publishing the generated and edited content on online platforms, social media, etc.

[1172] "Means for analyzing the performance of Published Content" means tools or software used to collect and analyze performance data, such as the number of views and clicks on Published Content.

[1173] A system for implementing the present invention has the following configuration: First, a user uses a terminal such as a smartphone to input free responses to a questionnaire about a specific product. The terminal has a means for collecting free responses to the questionnaire and transmits the free responses input by the user to a server.

[1174] The server is equipped with a means for automatically aggregating and summarizing the collected free responses. This means uses natural language processing technology to analyze the content of the free responses and extract key keywords and phrases. Specifically, natural language processing libraries (e.g., NLTK, spaCy) are used to analyze text data and extract important information. The server is also equipped with a means for outputting the aggregation and summary results as a report. This means outputs the aggregation and summary results in a format that is visually easy to understand, such as a graph or chart. For this purpose, a data visualization tool (e.g., Matplotlib, D3.js) is used. In this embodiment, the server generates a report using a generative AI model and a prompt sentence that instructs the server to tally and summarize the collected free responses and generate a report based on the tally and summary results.

[1175] Furthermore, the user generates content by providing instructions to the generative AI model using a prompt sentence that instructs the model to generate content based on the generated report. An example of a prompt sentence is, "Please use this report as a reference to create an advertisement for a new eco-friendly detergent for housewives in their 30s" along with the generated report. In this example, the specific product is detergent.

[1176] The server includes a means for generating content based on the prompt using a generative AI model, the means using a generative AI model (e.g., OpenAI GPT-4) to generate advertising text and images based on the input prompt.

[1177] The generated content is provided to the user with a means for previewing and editing on the terminal. The user can check the generated content and edit it as necessary. Once the editing is complete, the content is published on an internet platform or SNS via the server.

[1178] Finally, the server includes means for analyzing the performance of the published content, such as using tools (e.g., Google Analytics) to collect and analyze performance data, such as the number of views and clicks on the published content.

[1179] In this way, it becomes possible to efficiently collect and summarize free responses, and to automatically generate and publish advertising content based on prompt text. Additionally, the content may be modified using prompts to modify the content based on the analyzed performance and generative AI models, thereby modifying the content to improve its performance.

[1180] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1181] Step 1:

[1182] A user inputs free-form responses to a questionnaire using a terminal such as a smartphone. The input free-form responses are sent from the terminal to a server. The input data is the free-form responses in text format, and the output is the free-form response data sent to the server.

[1183] Step 2:

[1184] The server automatically aggregates and summarizes the collected free responses. It uses natural language processing technology to analyze the content of the free responses and extract key keywords and phrases. The input data is the free response text, and the output is summary data including the key keywords and phrases. Specifically, it analyzes the text data using a natural language processing library (e.g., NLTK, spaCy). Alternatively, it uses a prompt sentence that instructs the server to aggregate and summarize the collected free responses and a generative AI model to generate the aggregated and summarized results of the collected free responses.

[1185] Step 3:

[1186] The server outputs the aggregation and summary results as a report. The aggregation and summary results are output in a format that is visually easy to understand, such as a graph or chart. The input data is summary data, and the output is a visually represented report. A data visualization tool (e.g., Matplotlib, D3.js) is used. Specifically, the report is generated using a prompt statement that instructs the system to generate a report based on the generated aggregation and summary results, and a generative AI model.

[1187] Step 4:

[1188] A user gives instructions to the generative AI model using a means for inputting a prompt sentence. The input data is a report and a prompt sentence, and the output is the report and the prompt sentence sent to the server. An example of a prompt sentence is, along with inputting the generated report, "Please use this report as a reference to create an advertisement to promote a new eco-friendly detergent to housewives in their 30s."

[1189] Step 5:

[1190] The server uses a generative AI model to generate content based on the prompt sentence. The input data is the prompt sentence, and the output is the generated ad text and image. A generative AI model (e.g. OpenAI GPT-4) is used to generate content based on the input prompt sentence.

[1191] Step 6:

[1192] The generated content is provided to the user with means for previewing and editing on the terminal. The user reviews the generated content and edits it if necessary. The input data is the generated content and the output is the edited content.

[1193] Step 7:

[1194] Once the editing is complete, the content is published on an internet platform or social networking site via the server. The input data is the edited content, and the output is the published content.

[1195] Step 8:

[1196] The server analyzes the performance of the published content. It collects and analyzes performance data, such as the number of views and clicks on the published content. The input data is the performance data, and the output is the analysis result. It uses a data analysis tool (e.g., Google Analytics).

[1197] Example 2

[1198] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1199] In conventional survey systems, the collection, tabulation, and summarization of free responses were often done manually, which was time-consuming and labor-intensive. In addition, when users wanted to generate a specific program, they needed programming knowledge, which was difficult for users without specialized knowledge. Furthermore, the quality and efficiency of the generated program code was often not guaranteed.

[1200] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for a user to input a prompt sentence, a means for a generative AI model to generate program code based on the prompt sentence, and a means for returning the generated program code to the user. This enables efficient aggregation and summarization of free responses and allows the user to easily generate high-quality program code.

[1201] A "survey" is a collection of questions designed to gather specific information.

[1202] "Free responses" are responses that the respondent writes freely in their own words.

[1203] "Collection means" refers to the methods or devices used to collect data.

[1204] "Automatic aggregation and summarization means" refers to methods or devices for mechanically organizing collected data and extracting significant information.

[1205] "Means for outputting as a report" refers to a method or device for providing the aggregated or summarized results in a written format.

[1206] "User" refers to an individual or organization that uses the System.

[1207] A "prompt" is text that is used to input specific instructions or questions to a generative AI model.

[1208] A "generative AI model" is an artificial intelligence model that generates program code or other output based on input prompts.

[1209] "Program code" is a set of instructions for a computer to execute.

[1210] A "server" is a computer system that provides data and services over a network.

[1211] The present invention relates to a system for efficiently collecting, tabulating and summarizing free responses to a questionnaire, and further enabling a user to easily generate program code. A specific embodiment of this system will be described below.

[1212] First, the user inputs free-form responses to the survey through the system interface. The free-form responses entered by the user are collected by the server. The server uses natural language processing technology to automatically compile and summarize the collected free-form responses. Specifically, the server extracts key keywords and phrases, and compiles and summarizes them based on those.

[1213] The server then outputs the results of the aggregation and summary in a report, which is presented in a visually easy-to-understand format, such as graphs and charts, allowing users to intuitively grasp the content of the free-response responses.

[1214] Additionally, the system provides a means for users to input prompts, which are specific instructions or questions to the generative AI model, such as "Generate a program to count specific words in a text file."

[1215] The server receives the prompt sentence sent by the user and passes it to the generative AI model. The generative AI model generates a program code based on the prompt sentence. The generated program code is returned to the user through the server. The user can then execute this program code on their own device.

[1216] The following hardware and software are used to implement this system. The server is a computer system equipped with a high-performance processor and sufficient memory, and is equipped with an NVIDIA GPU. For software, deep learning frameworks such as TensorFlow and PyTorch are used. This allows the generative AI model to operate efficiently and process and calculate data based on the prompt sentence entered by the user.

[1217] As a concrete example, consider the case where a user inputs the following prompt into a generative AI model:

[1218] Example prompt:

[1219] "Create a program to count specific words in a text file."

[1220] Based on this prompt, the generative AI model generates Python code and returns it to the user through the server, who can then run this code on their own device to check the number of occurrences of a particular word.

[1221] As described above, this system provides efficient processing of free responses to questionnaires and a means for users to easily generate program code.

[1222] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1223] Step 1:

[1224] The user enters a prompt sentence.

[1225] The user inputs a prompt for the generative AI model through the system interface. For example, the user might input, "Generate a program that counts specific words from a text file." The input prompt is then sent to the server.

[1226] Step 2:

[1227] The server receives the prompt text and passes it to the generative AI model.

[1228] The server receives the prompt text sent by the user. It makes the appropriate API calls to pass the received prompt text to the generative AI model. The server may also convert the prompt text into a format that the generative AI model can understand. The input is the prompt text, and the output is the request to the generative AI model.

[1229] Step 3:

[1230] The generative AI model generates program code based on the prompt sentence.

[1231] The generative AI model analyzes the received prompt sentence and generates program code based on it. For example, if a user inputs "Generate a program to count specific words in a text file," the generative AI model generates Python code. The input is the prompt sentence, and the output is the generated program code.

[1232] Step 4:

[1233] The server returns the generated program code to the user.

[1234] The server receives the program code returned by the generative AI model and returns it to the user in a format that is easy for the user to use (e.g., a text file or text in a code editor). The input is the generated program code, and the output is the transmission of the program code to the user.

[1235] Step 5:

[1236] The user executes the generated program code.

[1237] The user executes the program code received from the server on his / her own terminal. For example, if a Python code is received, the user executes the code in his / her own Python environment and checks the result. The input is the generated program code, and the output is the result of the program execution. Specifically, the user opens the Python environment, copies the received code, and executes it. As a result, the number of occurrences of a particular word is displayed.

[1238] (Application example 2)

[1239] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1240] Conventional survey systems had the problem that collecting free responses and then aggregating and summarizing them required a lot of time and effort. In addition, despite the advances being made in content generation technology using generative AI models, there was no established method for applying this to survey systems. Furthermore, there was a lack of functionality to automatically generate content based on prompts entered by users and display it.

[1241] 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.

[1242] In this invention, the server includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for inputting a prompt sentence, a means for generating content based on the prompt sentence using a generative AI model, and a means for displaying the generated content. This enables efficient collection, aggregation, and summarization of free responses to a questionnaire, and further realizes content generation and display using a generative AI model.

[1243] A "means for collecting free-form responses to a questionnaire" is a device or software that allows users to enter responses in free form and collects them as data.

[1244] "Means for automatically aggregating and summarizing collected free-response responses" refers to a device or software that analyzes the collected free-response data, extracts key keywords and phrases, and succinctly summarizes the overall content.

[1245] "Means for outputting the results of aggregation and summary as a report" refers to a device or software for generating and outputting the aggregated and summarized data as a report in a visually easy-to-understand format.

[1246] A "means for inputting a prompt sentence" is a device or software that allows a user to input text to provide instructions to a generative AI model.

[1247] A "means for generating content based on a prompt sentence using a generative AI model" refers to a device or software that enables a generative AI model to automatically generate content such as text and images based on an input prompt sentence.

[1248] A “means for displaying generated content” is a device or software for visually displaying to a user the content generated by a generative AI model.

[1249] A system for carrying out this invention has the following configuration. First, a user inputs free-form responses to a questionnaire using a terminal such as a smartphone. The terminal then transmits the input free-form responses to a server. The server then uses natural language processing technology to automatically tally and summarize the collected free-form responses. Specifically, it extracts key keywords and phrases and summarizes the overall content concisely.

[1250] Next, the server generates a report of the aggregated and summarized results and outputs them in a visually easy-to-understand format, such as graphs or charts, allowing users to intuitively grasp the survey results.

[1251] Furthermore, the user gives instructions to the generative AI model using a means for inputting a prompt sentence. An example of a prompt sentence is "A story about a brave knight fighting a dragon." The server uses the generative AI model to generate content based on this prompt sentence. The generated content is in the form of text, images, etc., and is visually displayed to the user.

[1252] The following hardware and software are used to realize this system. For hardware, a smartphone and a server are required. For software, Python and OpenAI API are used. Python is a programming language for implementing natural language processing technology, and the OpenAI API is an interface for using generative AI models.

[1253] As a concrete example, if a user enters the prompt "A story about a brave knight battling a dragon," the server uses a generative AI model to generate the following story:

[1254] Once upon a time, there was a brave knight. His name was Arthur. To protect his kingdom, Arthur decided to fight a terrible dragon. After reaching the dragon's nest, Arthur drew his sword and faced the dragon. After a fierce battle, Arthur defeated the dragon and brought peace to the kingdom.

[1255] In this way, users can enjoy content generation and display using generative AI models.

[1256] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1257] Step 1:

[1258] A user inputs free-form responses to a questionnaire using a terminal such as a smartphone. The input free-form responses are sent from the terminal to a server. The input data is in text format and is sent to the server.

[1259] Step 2:

[1260] The server uses natural language processing technology to analyze the received free-response data. Specifically, it extracts key keywords and phrases and summarizes the overall content concisely. The input data is the free-response text, and the output data is a summary text that includes the key keywords and phrases.

[1261] Step 3:

[1262] The server generates a report based on the results of the aggregation and summary. The report is output in a visually understandable format, such as a graph or chart. The input data is the summary text, and the output data is a visual report.

[1263] Step 4:

[1264] The user gives instructions to the generative AI model using a means for inputting a prompt sentence. The prompt sentence is input in text format and sent from the terminal to the server. The input data is the text of the prompt sentence.

[1265] Step 5:

[1266] The server generates content based on the prompt sentence using a generative AI model. The generative AI model automatically generates content such as text and images based on the input prompt sentence. The input data is the text of the prompt sentence, and the output data is the generated content.

[1267] Step 6:

[1268] The server visually displays the generated content to the user. The generated content is sent to the terminal and displayed on the user's screen. The input data is the generated content and the output data is the content displayed on the user's screen.

[1269] 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 voice indicating a user input for 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.

[1270] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1271] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1272] 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.

[1273] [Fourth embodiment]

[1274] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1275] 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.

[1276] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[1277] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1278] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[1279] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1280] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1281] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. 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.

[1282] FIG. 8 shows an example of the main functions of the data processing device 12 and the robot 414.

[1283] 8, in the data processing device 12, a specific process is performed by a processor 28. In the storage 32, a specific process program 56 is stored.

[1284] 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.

[1285] 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.

[1286] In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1287] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1288] "Example 1"

[1289] The system of the present invention includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, and a means for outputting the aggregation and summary results as a report. Specifically, free responses to the questionnaire are collected using an online questionnaire tool such as Google Forms. The collected free responses are automatically aggregated and summarized using natural language processing technology. For example, keyword extraction, topic modeling, sentiment analysis, and other techniques are used for this natural language processing technology. The aggregation and summary results are output in a visually easy-to-understand format, such as a graph or chart format. This reduces the burden on the person in charge and saves time.

[1290] The process flow of each embodiment will be described below.

[1291] "Example 1"

[1292] Step 1: Collect free responses to the survey. Specifically, use an online survey tool such as Google Forms to collect free responses from users.

[1293] Step 2: Automatically aggregate and summarize the collected free responses. In this step, natural language processing technology is used to analyze the content of the free responses. Specifically, techniques such as keyword extraction, topic modeling, and sentiment analysis are used to extract key keywords and phrases from the free responses, and the free responses are aggregated and summarized based on these.

[1294] Step 3: Output the tabulation and summary results as a report. In this step, the tabulation and summary results obtained in the previous step are output in a format that is visually easy to understand. Specifically, the tabulation and summary results are displayed in graph and chart format, and this is output as a report. This allows the person in charge to get an overview of the free responses just by looking at the report, thereby reducing the person in charge's workload and saving time.

[1295] Example 1

[1296] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1297] Conventional systems required users to have specialized knowledge when generating programs, and a high level of technical understanding was required to understand the processing content of the generated programs. Furthermore, there were often a lack of concrete examples to understand the specific operation of the generated programs, making it unclear to users how they should actually use the programs.

[1298] 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.

[1299] In this invention, the server includes a means for a user to input a prompt sentence, a means for generating a program using a generative AI model, a means for explaining the processing of the generated program in natural language, and a means for adding a concrete example to the explanation. This allows a user to generate a program without specialized knowledge, to easily understand the processing content, and to clearly grasp the actual usage method through the concrete example.

[1300] A "user" is an entity that utilizes the system to input prompt statements and receive program generation and explanations of those programs.

[1301] A "prompt sentence" is an instruction sentence that a user inputs into the system, and is the text that serves as the basis for the generative AI model to generate a program.

[1302] A "generative AI model" is an artificial intelligence model that generates program code based on a prompt sentence entered by a user, such as a model that uses natural language processing technology.

[1303] A “program” is the code generated by a generative AI model; that is, a set of instructions to perform a specific task.

[1304] A "server" is a computer system that receives a prompt sentence from a user, generates a program using a generative AI model, and explains the processing content.

[1305] "Natural language" is a language that humans use on a daily basis, and is used to explain the processing content of a generated program to a user in an easily understandable manner.

[1306] A "specific example" is a concrete example showing how to actually use the generated program, and is useful for helping the user understand the operation of the program.

[1307] This invention relates to a system in which a user inputs a prompt sentence, generates a program using a generative AI model, explains the processing content in natural language, and adds concrete examples.

[1308] First, a user uses a terminal to access the system interface and inputs a prompt sentence, for example, "Please generate a program that analyzes the text entered by the user and determines the emotion."

[1309] The server then receives the prompt and generates a program using a generative AI model, such as a model using natural language processing techniques, that generates the appropriate program code based on the prompt entered by the user.

[1310] The generated program is written in a programming language such as Python. The server analyzes the processing content of this generated program and explains the processing in natural language. Specifically, it clearly explains what kind of data processing and data calculation the program performs. For example, if the nltk library is used to analyze the sentiment of text, the server will explain the details.

[1311] In addition, the server adds concrete examples to the explanation. For example, if the user inputs "Today is a very good day," the server shows how the program determines the emotion. This makes it easier for the user to understand the actual behavior of the generated program.

[1312] This system enables users to generate programs without specialist knowledge, easily understand the processing content, and clearly grasp how to use them in practice through concrete examples.

[1313] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1314] Step 1:

[1315] The user enters a prompt sentence.

[1316] A user uses a terminal to access the system interface and input a prompt sentence, for example, "Please generate a program that analyzes the text entered by the user and judges the emotion." The input prompt sentence is sent to the server.

[1317] Step 2:

[1318] The server generates the program using the generative AI model.

[1319] The server inputs the prompt sentence received from the user into the generative AI model. The generative AI model generates appropriate program code based on the prompt sentence. For example, a model using natural language processing technology generates Python code. The generated program code is stored on the server.

[1320] Step 3:

[1321] The server explains in natural language the processing of the generated program.

[1322] The server analyzes the generated program code and explains the processing in natural language. Specifically, it clearly explains what kind of data processing and data calculation the program performs. For example, if the program uses the nltk library to analyze the sentiment of text, it explains the details. The explanation is provided to the user.

[1323] Step 4:

[1324] The server adds specific examples to the description.

[1325] The server adds concrete examples to the generated program description. For example, when a user inputs "Today is a very good day," the server concretely shows how the program determines the emotion. The concrete examples are important to show the actual operation of the program. The description including the concrete examples is provided to the user.

[1326] The above is the flow of processing of the program of this system.

[1327] (Application example 1)

[1328] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1329] In conventional survey systems, the collection and aggregation of free responses was often done manually, which was a time-consuming and labor-intensive process. In addition, when outputting the aggregation and summary results as a report, it was sometimes difficult to output them in a format that was easy to understand visually. Furthermore, specialized knowledge was required to create ad copy, making it difficult to quickly generate high-quality ad copy. To solve these issues, there is a need for a system that can efficiently and automatically aggregate and summarize free responses, generate reports that are easy to understand visually, and generate ad copy based on prompt text using a generative AI model.

[1330] 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.

[1331] In this invention, the server includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for generating advertising copy based on a prompt text using a generative AI model, and a means for displaying the generated advertising copy. This enables efficient aggregation and summarization of free responses and the generation of visually easy-to-understand reports, and further enables the generation of advertising copy with high speed and quality.

[1332] The "means of collecting free responses to the questionnaire" is a function for collecting opinions and comments freely written by respondents in the questionnaire as data.

[1333] "Means of automatically aggregating and summarizing collected free responses" refers to a function that analyzes the collected free responses, extracts key keywords and phrases, and succinctly summarizes the overall content.

[1334] "Means of outputting the results of aggregation and summary as a report" refers to a function for generating and outputting the aggregated and summarized data as a report in a format that is visually easy to understand.

[1335] "Means for generating ad copy based on a prompt text using a generative AI model" refers to a function for automatically creating ad copy based on a prompt text entered by a user, using a generative AI model.

[1336] "Means for displaying generated ad copy" refers to a function for visually displaying to a user the ad copy created by the generative AI model.

[1337] The system for carrying out this invention has the functions of collecting free-form responses to a questionnaire, automatically aggregating and summarizing the collected free-form responses, and outputting the aggregation and summary results as a report. It also has the functions of generating advertising copy based on prompt text using a generative AI model, and displaying the generated advertising copy.

[1338] Hardware and Software Configuration

[1339] Hardware:

[1340] server

[1341] User device (smartphone, smart glasses, etc.)

[1342] software:

[1343] Analysis program using natural language processing technology

[1344] Generative AI models using the OpenAI API

[1345] Database Management Systems

[1346] A visualization tool for generating graphs and charts

[1347] Data processing and calculation

[1348] server:

[1349] 1. The server collects free-form responses to the questionnaire sent from the user terminal into a database.

[1350] 2. Use natural language processing techniques to analyze the collected free responses and extract key keywords and phrases.

[1351] 3. The free-response responses are automatically compiled and summarized based on the extracted keywords and phrases.

[1352] 4. The aggregation and summary results are generated as a report in a visually easy-to-understand format (e.g., graphs or charts) and sent to the user's terminal.

[1353] User device:

[1354] 1. The user enters a prompt sentence and sends it to the generative AI model.

[1355] 2. The server uses the OpenAI API to generate ad copy based on the input prompt text.

[1356] 3. The generated advertising copy is displayed on the user's device.

[1357] Examples

[1358] Example prompt:

[1359] "Write an ad copy for a new smartphone. Its features include a high-resolution camera and a long battery life."

[1360] Example of generated ad copy:

[1361] "A new smartphone has arrived! The high-resolution camera lets you capture every beautiful moment. Plus, the long battery life means you can use it all day without worry. Get yours now and enjoy the best experience!"

[1362] In this way, users can easily input prompt text and use the generative AI model to quickly generate high-quality ad copy. In addition, free-form responses to questionnaires are efficiently collected and summarized, and output as visually easy-to-understand reports, improving work efficiency.

[1363] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1364] Step 1:

[1365] The user inputs free responses to the questionnaire and transmits them from the terminal to the server.

[1366] Input: Free response entered by the user

[1367] Output: Free response data sent to the server

[1368] Specific operation: When a user enters free-form responses into the questionnaire form and presses the send button, the terminal sends the data to the server.

[1369] Step 2:

[1370] The server stores the received free responses in a database.

[1371] Input: Free response data sent from the device

[1372] Output: Free response data stored in a database

[1373] Specific operation: The server stores and preserves the received free response data in a database.

[1374] Step 3:

[1375] The server uses natural language processing technology to analyze the free-form responses and extract key keywords and phrases.

[1376] Input: Free response data stored in a database

[1377] Output: Extracted keywords and phrases

[1378] What it does: The server runs natural language processing algorithms to extract key keywords and phrases from the open-ended text data.

[1379] Step 4:

[1380] The server automatically aggregates and summarizes the content of the free-form responses based on the extracted keywords and phrases.

[1381] Input: Extracted keywords or phrases

[1382] Output: Aggregated and summarized data

[1383] Specific operation: The server aggregates the extracted keywords and phrases and uses a summarization algorithm to concisely summarize the content of the free-form responses.

[1384] Step 5:

[1385] The server generates a report of the aggregation and summary results in a visually easy-to-understand format and sends it to the user's terminal.

[1386] Input: Aggregated and summarized data

[1387] Output: Visually easy-to-understand reports (graphs and charts)

[1388] Specific operation: The server uses a visualization tool to generate a report in the form of graphs and charts based on the aggregation and summary results, and sends it to the user's terminal.

[1389] Step 6:

[1390] The user enters a prompt sentence and sends it to the generative AI model.

[1391] Input: The prompt text entered by the user.

[1392] Output: The prompt text sent to the server.

[1393] Specific operation: When the user inputs a prompt sentence and presses the send button, the terminal sends the data to the server.

[1394] Step 7:

[1395] The server uses the OpenAI API to generate ad copy based on the input prompt text.

[1396] Input: The prompt text sent by the user

[1397] Output: Generated ad copy

[1398] Specific operation: The server calls the OpenAI API, inputs the prompt text into the generative AI model, and obtains the generated ad copy.

[1399] Step 8:

[1400] The server transmits the generated advertising copy to the user terminal, which displays it.

[1401] Input: Generated ad copy

[1402] Output: Ad copy displayed on user device

[1403] Specific operation: The server transmits the generated advertising copy to the user terminal, and the user terminal displays the received advertising copy on the screen.

[1404] Example 2

[1405] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1406] In conventional systems, it has been difficult to quickly and accurately provide an appropriate response to a prompt sentence input by a user. In addition, there has been a lack of means for displaying the generated response in a visually easy-to-understand format, resulting in low user convenience. The object of the present invention is to solve these problems and provide a system that provides a quick and accurate response to a prompt sentence input by a user, and further displays the response in a visually easy-to-understand format.

[1407] 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.

[1408] In this invention, the server includes a means for a user to input a prompt sentence, a means for the terminal to send the prompt sentence to the server, a means for the server to pass the prompt sentence to the generative AI model, a means for the generative AI model to analyze the prompt sentence and generate a response, a means for the server to return the generated response to the terminal, and a means for the terminal to display the response to the user. This makes it possible to provide a quick and accurate response to the prompt sentence input by the user and to display the response in a visually easy-to-understand format.

[1409] A "user" is an individual or entity that utilizes the system to enter prompts and receive generated responses.

[1410] A "prompt sentence" is a question or instruction sentence that a user inputs to a system.

[1411] A "terminal" is a device on which a user inputs a prompt and displays the generated response, such as a personal computer or a smartphone.

[1412] The "server" is a computer system whose role is to receive prompt text sent from a terminal, pass it to a generative AI model, and return the generated response to the terminal.

[1413] A "generative AI model" is an artificial intelligence model that analyzes prompt sentences and generates appropriate responses. For example, this includes models that use natural language processing techniques.

[1414] "Natural language processing technology" is a technology that allows a computer to understand and analyze human language, making it possible to analyze the content of a prompt and generate an appropriate response.

[1415] A "response" is the answer or information that a generative AI model generates by analyzing a prompt.

[1416] A "visually understandable format" is a format in which the generated response is displayed in a manner that is easily understandable to the user, including, for example, text, graphs, charts, etc.

[1417] The present invention is a system that provides a prompt and accurate response to a prompt sentence input by a user and displays the response in a visually easy-to-understand format. The system includes a user, a terminal, a server, and a generative AI model.

[1418] The user inputs a prompt into the input field of the terminal. For example, the prompt might be "What's the weather like today?" or "What's the latest news?". The terminal sends the prompt input by the user to the server. At this time, the prompt is packaged in JSON format.

[1419] The server analyzes the received prompt sentence and sends a request to the API endpoint of the generative AI model. For example, an advanced model using natural language processing technology (e.g., GPT-3 or GPT-4) is used as the generative AI model. The server analyzes the response received from the generative AI model and returns it to the terminal. At this time, the response is packaged again in JSON format.

[1420] The terminal analyzes the response received from the server and displays it to the user. The display format is a visually easy-to-understand format such as text, graphs, and charts. For example, a response such as "Today's weather is sunny" or "The latest news is as follows" may be displayed.

[1421] The system allows users to utilize generative AI models to generate appropriate responses to their input prompts, and the responses are displayed in a visually easy-to-understand format for the user's convenience.

[1422] As a concrete example, consider a case where a user enters the prompt sentence "Please tell me how to write a Hello World program in Python." In this case, the generative AI model generates a response "Here is how to write a Hello World program in Python. python print('Hello, World!')" and displays it on the terminal.

[1423] In this manner, the present invention allows a user to provide a fast and accurate response to a prompt sentence entered by the user, and to display the response in a visually easy to understand format.

[1424] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1425] Step 1:

[1426] The user enters a prompt sentence.

[1427] The user inputs a prompt into an input field on the terminal, for example, "What's the weather like today?" The input prompt is temporarily stored in the terminal's memory.

[1428] Step 2:

[1429] The terminal sends a prompt to the server.

[1430] The terminal sends the prompt text entered by the user as an HTTP request to the server. At this time, the prompt text is packaged in JSON format. The input is the prompt text, and the output is an HTTP request to the server.

[1431] Step 3:

[1432] The server passes the prompt to the generative AI model.

[1433] The server parses the received prompt sentence and sends a request to the API endpoint of the generative AI model. For example, it sends the prompt sentence along with the API key as a POST request. The input is the prompt sentence, and the output is the API request to the generative AI model.

[1434] Step 4:

[1435] A generative AI model parses the prompt and generates a response.

[1436] A generative AI model analyzes the received prompt sentence and generates an appropriate response. For example, in response to the prompt sentence "What's the weather today?", it generates the response "The weather today is sunny." The input is the prompt sentence, and the output is the generated response.

[1437] Step 5:

[1438] The server returns the generated response to the terminal.

[1439] The server parses the response received from the generative AI model and returns it to the device, again packaged in JSON. The input is the generated response, and the output is the HTTP response to the device.

[1440] Step 6:

[1441] The terminal displays the response to the user.

[1442] The terminal analyzes the response received from the server and displays it to the user. For example, it might display "The weather today is sunny" on the screen. The input is the generated response, and the output is what is displayed to the user.

[1443] (Application example 2)

[1444] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1445] In conventional survey systems, the collection of free responses and their compilation and summarization were often done manually, which was problematic in that it was time-consuming and labor-intensive. In addition, when outputting the compilation and summary results as a report, it was difficult to output them in a format that was visually easy to understand. Furthermore, when it came to automatically generating advertisements, even if the user entered a prompt sentence, there was a lack of a means to generate an appropriate advertisement based on the contents of that sentence. This made the creation of advertisements cumbersome, and made it difficult to quickly create effective advertisements.

[1446] 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.

[1447] In this invention, the server includes a means for collecting free responses to a survey, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for automatically generating an advertisement based on a prompt sentence using a generative AI model, a means for previewing the generated advertisement, a means for customizing the generated advertisement, and a means for saving and sharing the generated advertisement. This enables efficient collection, aggregation, and summarization of free responses to a survey, and realizes report output in a visually easy-to-understand format. In addition, effective advertisements can be quickly and automatically generated based on prompt sentences, and users can easily customize and share them.

[1448] A "means for collecting free-form responses to a survey" is a function for collecting free-form responses from users.

[1449] "Means for automatically aggregating and summarizing collected free responses" refers to a function that automatically analyzes collected free responses, extracts key information, and summarizes it.

[1450] "Means for outputting the results of aggregation and summary as a report" refers to a function for outputting the results of aggregation and summary in report format.

[1451] "Means for automatically generating advertisements based on prompt text using a generative AI model" refers to a function that uses a generative AI model to automatically generate advertisements based on prompt text entered by a user.

[1452] The "means for displaying a preview of a generated advertisement" is a function for displaying a preview of the generated advertisement to the user.

[1453] A "means for customizing generated advertisements" is a feature that allows a user to edit and customize a generated advertisement.

[1454] "Means for saving and sharing generated advertisements" refers to a function for saving generated advertisements and sharing them with other users and platforms.

[1455] A system for carrying out the present invention has the following configuration: the system includes means for collecting free responses to a questionnaire, means for automatically aggregating and summarizing the collected free responses, means for outputting the aggregation and summary results as a report, means for automatically generating advertisements based on prompt text using a generative AI model, means for previewing the generated advertisements, means for customizing the generated advertisements, and means for saving and sharing the generated advertisements.

[1456] Hardware and Software Configuration

[1457] Hardware: Smartphones, servers

[1458] Software: Generative AI models (e.g., OpenAI's GPT-4), mobile app development frameworks (e.g., React Native), cloud storage services (e.g., AWS S3)

[1459] Data processing and calculation

[1460] 1. Collecting free-form responses to a survey: Users enter free-form responses through a smartphone app. These responses are sent to a server and stored in a database.

[1461] 2. Automatic aggregation and summarization of free responses: The server uses natural language processing technology to analyze the free responses and extract key keywords and phrases, which are then aggregated and summarized.

[1462] 3. Report output: The aggregated and summarized results are output as a report in a visually easy-to-understand format (e.g., graphs and charts). Users can view this report on their smartphones.

[1463] 4. Automatic ad generation: When a user enters a prompt text, the prompt text is sent to the server, and the generative AI model generates ad text and images based on the prompt text.

[1464] 5. Previewing the ad: The generated ad is previewed to the user on the smartphone app.

[1465] 6. Ad Customization: Users can edit and customize generated ads within the app.

[1466] 7. Save and share your ad: Your completed ad will be saved to cloud storage and a link will be generated that can be shared on social media or email.

[1467] Examples

[1468] For example, if a user wants to create an advertisement to promote the opening of a new cafe, they might enter the following prompt text:

[1469] Example prompt: "Create an ad to promote the opening of a new cafe. The target audience is young people in their 20s and 30s. The cafe features organic coffee and a relaxing atmosphere."

[1470] When this prompt is fed into the generative AI model, the model generates advertising text and images like the following:

[1471] Example ad text: "Relax Cafe is now open! Organic coffee and a relaxing atmosphere perfect for people in their 20s and 30s. Come and visit us!"

[1472] Example of advertising image: A generative AI model generates images of cafe interiors and organic coffee to be incorporated into the ad.

[1473] In this way, users can easily create attractive advertisements and effectively reach their target audience.

[1474] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1475] Step 1:

[1476] Users enter free-form responses to the survey via a smartphone app.

[1477] Input: The user enters free-form text.

[1478] Output: The entered free response is sent to the server.

[1479] Specific behavior: When a user enters a free-form response into the app's text input field and presses the "Submit" button, the data is sent to the server.

[1480] Step 2:

[1481] The server stores the collected free responses in a database.

[1482] Input: Free text response data submitted by the user.

[1483] Output: Free responses stored in a database.

[1484] Specific operation: The server stores the received free response in a database and generates a confirmation message that the response has been saved.

[1485] Step 3:

[1486] The server uses natural language processing technology to analyze the free-form responses and extract key keywords and phrases.

[1487] Input: Free-form text data stored in a database.

[1488] Output: Extracted key keywords and phrases.

[1489] What happens: The server runs natural language processing algorithms to extract key keywords and phrases from the open-ended text.

[1490] Step 4:

[1491] The server performs aggregation and summarization based on the extracted keywords and phrases.

[1492] Input: The extracted main keywords or phrases.

[1493] Output: Aggregated and summarized data.

[1494] How it works: The server aggregates the extracted keywords and phrases and uses a summarization algorithm to generate summary data.

[1495] Step 5:

[1496] The server outputs the aggregation and summary results as a report in a visually easy-to-understand format.

[1497] Input: Aggregated and summarized data.

[1498] Output: Reports in the form of graphs and charts.

[1499] What happens: The server converts the aggregated and summarized data into graphs and charts and generates a report.

[1500] Step 6:

[1501] The user enters a prompt and sends it to the generative AI model.

[1502] Input: The prompt text entered by the user.

[1503] Output: The prompt sent to the generative AI model.

[1504] How it works: A user enters a prompt sentence into the app's text input field and presses the "Generate" button, and that data is sent to the generative AI model.

[1505] Step 7:

[1506] A generative AI model generates ad text and images based on the prompt.

[1507] Input: The prompt sent to the generative AI model.

[1508] Output: The generated ad text and images.

[1509] How it works: The generative AI model analyzes the prompt and generates ad text and images.

[1510] Step 8:

[1511] The server previews the generated advertisement to the user.

[1512] Input: Generated ad text and images.

[1513] Output: The ad preview that is displayed on the user's smartphone.

[1514] Specific operation: The server sends the generated ad to the user's smartphone and displays a preview in the app.

[1515] Step 9:

[1516] Customize user-generated ads.

[1517] Input: The previewed ad.

[1518] Output: A customized ad.

[1519] What happens: Users use the app's editing features to edit and customize ad text and images.

[1520] Step 10:

[1521] The server stores the customized advertisement in cloud storage and generates a sharing link.

[1522] Enter: your personalized ads.

[1523] Output: Advertisements stored in cloud storage and share links.

[1524] What it does: The server saves the customized ad in cloud storage and generates a link that can be shared on social media or via email.

[1525] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1526] "Example 1"

[1527] The present invention is a system that collects free responses to a questionnaire, automatically tabulates and summarizes the collected free responses, and outputs the tabulation and summary results as a report. As a means for automatically tabulating and summarizing the free responses, the system uses natural language processing technology to analyze the content of the free responses and extracts key keywords and phrases. Furthermore, the system analyzes the content of the free responses using an emotion engine that recognizes the user's emotions. The emotion engine extracts the user's emotions from the text data of the free responses and reflects the emotions in the report. Specifically, if a user gives a free response such as "This product is very good," the emotion engine extracts the emotion "good" from the free response and reflects the emotion in the report. The report output means outputs the emotions extracted by the emotion engine together with the tabulation and summary results in a visually easy-to-understand format, such as a graph or chart format. This allows the person in charge to grasp the outline of the free responses and the user's emotions just by looking at the report, which reduces the person in charge's burden and shortens the time required.

[1528] The process flow of each embodiment will be described below.

[1529] "Example 1"

[1530] Step 1: Collect free responses to the survey. Specifically, use a survey tool such as Google Forms to collect free responses from users.

[1531] Step 2: Automatically aggregate and summarize the collected free responses. Specifically, natural language processing technology is used to analyze the content of the free responses and extract key keywords and phrases.

[1532] Step 3: Recognize the user's emotions. Specifically, an emotion engine is used to analyze the content of the free responses and extract the user's emotions.

[1533] Step 4: Reflect the extracted emotions in the report. Specifically, the emotions extracted by the emotion engine are reflected in the report, and the emotions are output in a format that is easy to understand visually, such as a graph or chart.

[1534] Step 5: The aggregation / summary results and the sentiment analysis results are output as a report. Specifically, the aggregation / summary results and the sentiment analysis results are integrated and output as a single report. This allows the person in charge to grasp the outline of the free responses and the users' sentiment just by looking at the report.

[1535] Example 1

[1536] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1537] In conventional systems, the process of generating an appropriate response to a prompt input by the user was complex and difficult to perform efficiently. In addition, the quality and display format of the generated results were inconsistent, making them difficult for users to understand. This resulted in a poor user experience and limited the usefulness of the system.

[1538] 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.

[1539] In this invention, the server includes a means for receiving a prompt sentence and performing pre-processing, a means for inputting the prompt sentence into a generative AI model and performing inference, and a means for post-processing the generated results, which makes it possible to consistently and efficiently perform the processes from pre-processing of the prompt sentence to post-processing of the generated results.

[1540] A "user" is an entity that operates the system and inputs prompt sentences.

[1541] A "terminal" is a device operated by a user, including a PC, a smartphone, etc.

[1542] A "prompt" is text that a user enters into a generative AI model as an instruction or question.

[1543] A "server" is a computer system that receives prompt sentences, pre-processes them, inputs them into the generative AI model, performs inference, post-processes them, and sends the results.

[1544] "Preprocessing" refers to the process of normalizing and tokenizing the text of the prompt sentence.

[1545] A "generative AI model" is an artificial intelligence model that makes inferences based on input prompt sentences and generates results.

[1546] "Inference" is the process by which a generative AI model generates a response or outcome based on a prompt.

[1547] "Post-processing" refers to formatting and removing unnecessary information from the generated results.

[1548] "Results" are the responses or information that a generative AI model generates based on a prompt.

[1549] "Display" refers to the act of visually presenting the results received by the terminal from the server to the user.

[1550] The present invention is a system in which a user inputs a prompt sentence, a generative AI model is used to generate a response or result based on the prompt sentence, and the response or result is provided to the user. A specific embodiment of the system will be described below.

[1551] First, the user inputs a prompt sentence using a terminal (such as a PC or smartphone). An example of a prompt sentence is "Please use AI to come up with a new recipe."

[1552] The terminal sends the input prompt text to the server. The communication is performed using an HTTP request. The server is a computer system with high-performance computing capabilities, specifically, a server equipped with a high-performance GPU.

[1553] The server receives the prompt sent from the terminal and performs preprocessing, including text normalization (e.g., converting full-width characters to half-width characters) and tokenization (e.g., dividing a sentence into words or phrases). Python libraries (e.g., pandas, numpy) are used for preprocessing.

[1554] Next, the server inputs the preprocessed prompt sentence into a generative AI model. For example, GPT-4 is used as the generative AI model. The model performs inference based on the input prompt sentence and generates a result. TensorFlow and PyTorch are used for model inference.

[1555] The generated results are then post-processed on the server, which may include formatting the results (e.g. converting them to JSON) and removing unnecessary information.

[1556] The post-processed results are sent from the server to the terminal. HTTP responses are used for communication. The terminal displays the received results to the user. HTML and CSS are used for display.

[1557] As a concrete example, if a user inputs a prompt sentence such as "Please use AI to come up with a new recipe," the server receives this prompt sentence, performs preprocessing, and inputs it into a generative AI model. The model generates a new recipe, and the server post-processes the result and sends it to the terminal. The terminal displays the generated recipe to the user.

[1558] In this way, users can obtain new recipes using generative AI models. This system can efficiently perform everything from pre-processing prompts to post-processing generated results, and can provide high-quality responses to users.

[1559] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1560] Step 1:

[1561] The user enters a prompt sentence.

[1562] The user enters "Please use AI to come up with a new recipe" into the input field of the device. The input prompt is stored in the device's memory.

[1563] Step 2:

[1564] The terminal sends a prompt to the server.

[1565] The terminal sends the entered prompt text to the server as an HTTP request. The input is the prompt text, and the output is the request sent to the server.

[1566] Step 3:

[1567] The server receives the prompt text and performs preprocessing.

[1568] The server receives the prompt text sent from the terminal. It normalizes the text (e.g. converts full-width characters to half-width characters) and tokenizes (e.g. splits a sentence into words and phrases) for the received prompt text. The input is the prompt text, and the output is the preprocessed text data.

[1569] Step 4:

[1570] The server inputs a prompt sentence into the generative AI model and performs inference.

[1571] The server inputs the preprocessed prompt sentences into a generative AI model (e.g., GPT-4). The model makes inferences based on the input prompt sentences and generates results. The input is the preprocessed text data, and the output is the generated result (e.g., a new recipe).

[1572] Step 5:

[1573] The server post-processes the generated results.

[1574] The server performs post-processing on the results obtained from the generative AI model, including formatting the results (e.g., converting them to JSON) and removing unnecessary information. The input is the generated results, and the output is the post-processed data.

[1575] Step 6:

[1576] The server sends the result to the terminal.

[1577] The server sends the post-processed result to the terminal as an HTTP response. The input is the post-processed data, and the output is the response sent to the terminal.

[1578] Step 7:

[1579] The terminal displays the results to the user.

[1580] The terminal displays the results received from the server to the user. HTML and CSS are used for display. The input is the response data from the server, and the output is the information provided visually to the user.

[1581] (Application example 1)

[1582] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1583] In conventional survey systems, the collection, compilation, and summary of free responses, as well as the creation of reports, are often done manually, which is time-consuming and labor-intensive. In addition, the generation of advertising content requires specialized knowledge, making it difficult to reach the target audience quickly and effectively. To solve these problems, a system that integrates the automatic compilation and summary of free responses with the automatic generation of advertising content is required.

[1584] 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.

[1585] In this invention, the server includes a means for collecting free responses to a questionnaire, a prompt for instructing to tally and summarize the collected free responses and generate a report with the tally and summary results, a means for generating the report using a generative AI model, a prompt for instructing to generate content based on the generated report, a means for generating the content using the generative AI model, a means for previewing the generated content and accepting editing operations, a means for publishing the edited content, and a means for analyzing the performance of the published content. This enables efficient tallying and summarizing of free responses and automatic generation and publication of advertising content based on the prompt.

[1586] "Means of collecting free-form survey responses" refers to the interface or functionality for collecting free-form responses from users.

[1587] "Means of automatically aggregating and summarizing collected free-response responses" refers to algorithms or software for analysing the collected free-response data and extracting and summarising key information.

[1588] "Means of outputting the results of aggregation and summary as a report" refers to the functionality for generating and outputting the aggregated and summarized data as a report in a visually easy-to-understand format.

[1589] "Means for inputting prompt text" refers to a text input interface that allows a user to provide instructions to a generative AI model.

[1590] "Means of generating content based on a prompt sentence using a generative AI model" refers to algorithms or software that enable a generative AI model to automatically generate content such as text and images based on an input prompt sentence.

[1591] "Means for previewing and editing the generated content" means an interface or functionality that allows a user to review and, if necessary, edit the generated content.

[1592] "Means of publishing the generated content" refers to functions for publishing the generated and edited content on online platforms, social media, etc.

[1593] "Means for analyzing the performance of Published Content" means tools or software used to collect and analyze performance data, such as the number of views and clicks on Published Content.

[1594] A system for implementing the present invention has the following configuration: First, a user uses a terminal such as a smartphone to input free responses to a questionnaire about a specific product. The terminal has a means for collecting free responses to the questionnaire and transmits the free responses input by the user to a server.

[1595] The server is equipped with a means for automatically aggregating and summarizing the collected free responses. This means uses natural language processing technology to analyze the content of the free responses and extract key keywords and phrases. Specifically, natural language processing libraries (e.g., NLTK, spaCy) are used to analyze text data and extract important information. The server is also equipped with a means for outputting the aggregation and summary results as a report. This means outputs the aggregation and summary results in a format that is visually easy to understand, such as a graph or chart. For this purpose, a data visualization tool (e.g., Matplotlib, D3.js) is used. In this embodiment, the server generates a report using a generative AI model and a prompt sentence that instructs the server to tally and summarize the collected free responses and generate a report based on the tally and summary results.

[1596] Furthermore, the user generates content by providing instructions to the generative AI model using a prompt sentence that instructs the model to generate content based on the generated report. An example of a prompt sentence is, "Please use this report as a reference to create an advertisement for a new eco-friendly detergent for housewives in their 30s" along with the generated report. In this example, the specific product is detergent.

[1597] The server includes a means for generating content based on the prompt using a generative AI model, the means using a generative AI model (e.g., OpenAI GPT-4) to generate advertising text and images based on the input prompt.

[1598] The generated content is provided to the user with a means for previewing and editing on the terminal. The user can check the generated content and edit it as necessary. Once the editing is complete, the content is published on an internet platform or SNS via the server.

[1599] Finally, the server includes means for analyzing the performance of the published content, such as using tools (e.g., Google Analytics) to collect and analyze performance data, such as the number of views and clicks on the published content.

[1600] In this way, it becomes possible to efficiently collect and summarize free responses, and to automatically generate and publish advertising content based on prompt text. Additionally, the content may be modified using prompts to modify the content based on the analyzed performance and generative AI models, thereby modifying the content to improve its performance.

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

[1602] Step 1:

[1603] A user inputs free-form responses to a questionnaire using a terminal such as a smartphone. The input free-form responses are sent from the terminal to a server. The input data is the free-form responses in text format, and the output is the free-form response data sent to the server.

[1604] Step 2:

[1605] The server automatically aggregates and summarizes the collected free responses. It uses natural language processing technology to analyze the content of the free responses and extract key keywords and phrases. The input data is the free response text, and the output is summary data including the key keywords and phrases. Specifically, it analyzes the text data using a natural language processing library (e.g., NLTK, spaCy). Alternatively, it uses a prompt sentence that instructs the server to aggregate and summarize the collected free responses and a generative AI model to generate the aggregated and summarized results of the collected free responses.

[1606] Step 3:

[1607] The server outputs the aggregation and summary results as a report. The aggregation and summary results are output in a format that is visually easy to understand, such as a graph or chart. The input data is summary data, and the output is a visually represented report. A data visualization tool (e.g., Matplotlib, D3.js) is used. Specifically, the report is generated using a prompt statement that instructs the system to generate a report based on the generated aggregation and summary results, and a generative AI model.

[1608] Step 4:

[1609] A user gives instructions to the generative AI model using a means for inputting a prompt sentence. The input data is a report and a prompt sentence, and the output is the report and the prompt sentence sent to the server. An example of a prompt sentence is, along with inputting the generated report, "Please use this report as a reference to create an advertisement to promote a new eco-friendly detergent to housewives in their 30s."

[1610] Step 5:

[1611] The server uses a generative AI model to generate content based on the prompt sentence. The input data is the prompt sentence, and the output is the generated ad text and image. A generative AI model (e.g. OpenAI GPT-4) is used to generate content based on the input prompt sentence.

[1612] Step 6:

[1613] The generated content is provided to the user with means for previewing and editing on the terminal. The user reviews the generated content and edits it if necessary. The input data is the generated content and the output is the edited content.

[1614] Step 7:

[1615] The edited content is then published via the server on platforms and social media sites on the Internet. The input data is the edited content, and the output is the published content.

[1616] Step 8:

[1617] The server analyzes the performance of the published content. It collects and analyzes performance data, such as the number of views and clicks on the published content. The input data is the performance data, and the output is the analysis result. It uses a data analysis tool (e.g., Google Analytics).

[1618] Example 2

[1619] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1620] In conventional survey systems, the collection, tabulation, and summarization of free responses were often done manually, which was time-consuming and labor-intensive. In addition, when users wanted to generate a specific program, they needed programming knowledge, which was difficult for users without specialized knowledge. Furthermore, the quality and efficiency of the generated program code was often not guaranteed.

[1621] The specification process by the specification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for a user to input a prompt sentence, a means for a generative AI model to generate program code based on the prompt sentence, and a means for returning the generated program code to the user. This enables efficient aggregation and summarization of free responses and allows the user to easily generate high-quality program code.

[1622] A "survey" is a collection of questions designed to gather specific information.

[1623] "Free responses" are responses that the respondent writes freely in their own words.

[1624] "Collection means" refers to the methods or devices used to collect data.

[1625] "Automatic aggregation and summarization means" refers to methods or devices for mechanically organizing collected data and extracting significant information.

[1626] "Means for outputting as a report" refers to a method or device for providing the aggregated or summarized results in a written format.

[1627] "User" refers to an individual or organization that uses the System.

[1628] A "prompt" is text that is used to input specific instructions or questions to a generative AI model.

[1629] A "generative AI model" is an artificial intelligence model that generates program code or other output based on input prompts.

[1630] "Program code" is a set of instructions for a computer to execute.

[1631] A "server" is a computer system that provides data and services over a network.

[1632] The present invention relates to a system for efficiently collecting, tabulating and summarizing free responses to a questionnaire, and further enabling a user to easily generate program code. A specific embodiment of this system will be described below.

[1633] First, the user inputs free-form responses to the survey through the system interface. The free-form responses entered by the user are collected by the server. The server uses natural language processing technology to automatically compile and summarize the collected free-form responses. Specifically, the server extracts key keywords and phrases, and compiles and summarizes them based on those.

[1634] The server then outputs the results of the aggregation and summary in a report, which is presented in a visually easy-to-understand format, such as graphs and charts, allowing users to intuitively grasp the content of the free-response responses.

[1635] Additionally, the system provides a means for users to input prompts, which are specific instructions or questions to the generative AI model, such as "Generate a program to count specific words in a text file."

[1636] The server receives the prompt sentence sent by the user and passes it to the generative AI model. The generative AI model generates a program code based on the prompt sentence. The generated program code is returned to the user through the server. The user can then execute this program code on their own device.

[1637] The following hardware and software are used to implement this system. The server is a computer system equipped with a high-performance processor and sufficient memory, and is equipped with an NVIDIA GPU. For software, deep learning frameworks such as TensorFlow and PyTorch are used. This allows the generative AI model to operate efficiently and process and calculate data based on the prompt sentence entered by the user.

[1638] As a concrete example, consider the case where a user inputs the following prompt into a generative AI model:

[1639] Example prompt:

[1640] "Create a program to count specific words in a text file."

[1641] Based on this prompt, the generative AI model generates Python code and returns it to the user through the server, who can then run this code on their own device to check the number of occurrences of a particular word.

[1642] As described above, this system provides efficient processing of free responses to questionnaires and a means for users to easily generate program code.

[1643] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1644] Step 1:

[1645] The user enters a prompt sentence.

[1646] The user inputs a prompt for the generative AI model through the system interface. For example, the user might input, "Generate a program that counts specific words from a text file." The input prompt is then sent to the server.

[1647] Step 2:

[1648] The server receives the prompt text and passes it to the generative AI model.

[1649] The server receives the prompt text sent by the user. It makes the appropriate API calls to pass the received prompt text to the generative AI model. The server may also convert the prompt text into a format that the generative AI model can understand. The input is the prompt text, and the output is the request to the generative AI model.

[1650] Step 3:

[1651] The generative AI model generates program code based on the prompt sentence.

[1652] The generative AI model analyzes the received prompt sentence and generates program code based on it. For example, if a user inputs "Generate a program to count specific words in a text file," the generative AI model generates Python code. The input is the prompt sentence, and the output is the generated program code.

[1653] Step 4:

[1654] The server returns the generated program code to the user.

[1655] The server receives the program code returned by the generative AI model and returns it to the user in a format that is easy for the user to use (e.g., a text file or text in a code editor). The input is the generated program code, and the output is the transmission of the program code to the user.

[1656] Step 5:

[1657] The user executes the generated program code.

[1658] The user executes the program code received from the server on his / her own terminal. For example, if a Python code is received, the user executes the code in his / her own Python environment and checks the result. The input is the generated program code, and the output is the result of the program execution. Specifically, the user opens the Python environment, copies the received code, and executes it. As a result, the number of occurrences of a particular word is displayed.

[1659] (Application example 2)

[1660] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1661] Conventional survey systems had the problem that collecting free responses and then aggregating and summarizing them required a lot of time and effort. In addition, despite the advances being made in content generation technology using generative AI models, there was no established method for applying this to survey systems. Furthermore, there was a lack of functionality to automatically generate content based on prompts entered by users and display it.

[1662] 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.

[1663] In this invention, the server includes a means for collecting free responses to a questionnaire, a means for automatically aggregating and summarizing the collected free responses, a means for outputting the aggregation and summary results as a report, a means for inputting a prompt sentence, a means for generating content based on the prompt sentence using a generative AI model, and a means for displaying the generated content. This enables efficient collection, aggregation, and summarization of free responses to a questionnaire, and further realizes content generation and display using a generative AI model.

[1664] A "means for collecting free-form responses to a questionnaire" is a device or software that allows users to enter responses in free form and collects them as data.

[1665] "Means for automatically aggregating and summarizing collected free-response responses" refers to a device or software that analyzes the collected free-response data, extracts key keywords and phrases, and succinctly summarizes the overall content.

[1666] "Means for outputting the results of aggregation and summary as a report" refers to a device or software for generating and outputting the aggregated and summarized data as a report in a visually easy-to-understand format.

[1667] A "means for inputting a prompt sentence" is a device or software that allows a user to input text to provide instructions to a generative AI model.

[1668] A "means for generating content based on a prompt sentence using a generative AI model" refers to a device or software that enables a generative AI model to automatically generate content such as text and images based on an input prompt sentence.

[1669] A “means for displaying generated content” is a device or software for visually displaying to a user the content generated by a generative AI model.

[1670] A system for carrying out this invention has the following configuration. First, a us...

Claims

1. A means of collecting open-ended survey responses; A means for generating a report by using a prompt sentence that instructs the user to compile and summarize the collected free responses and generate a report based on the compilation results and the summary results, and a generative AI model; A means for generating the content using a prompt sentence instructing to generate content based on the generated report and the generative AI model; means for previewing the generated content and accepting an editing operation; A system including:

2. means for publishing the edited content; The system of claim 1 , further comprising: means for analyzing performance of the published content.

3. 3. The system of claim 2, further comprising: means for modifying the content using a prompt to modify the content based on the analyzed performance and the generative AI model.

Images