System

Abstract

A system is provided.SOLUTION: This system includes a means for receiving application information, a means for analyzing the application information and performing evaluation by a generated AI model, and a means for outputting evaluation results.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the selection of applications, there are problems such as differences in the knowledge and experience of the judges, unfairness due to the order in which the judges view the applications, and time constraints. These problems make it difficult to conduct a fair and prompt selection process. The purpose of this invention is to solve these problems and judge all applications from the same perspective, thereby conducting a fair and prompt selection process. [Means for solving the problem]

[0005] The present invention is a system that includes a means for receiving application data, a means for analyzing the application data and evaluating it using a generative AI model, and a means for outputting the evaluation results. Specifically, the system reads the application data from a text file, and verbalizes the reasons for the evaluation based on the evaluation by the generative AI model, thereby achieving fair and prompt selection.

[0006] "Application data" refers to the information and content that is the subject of selection for a contest, etc.

[0007] "Means of receiving" refers to the functions and methods for inputting application data into the system.

[0008] "Analyzing" means extracting and organizing the information necessary to evaluate the application data.

[0009] A "generative AI model" refers to a system or program that uses artificial intelligence to automatically make judgments and evaluations.

[0010] "Means for evaluation" refers to the function of using a generative AI model to make judgments and evaluations based on application data.

[0011] "Evaluation results" refers to the conclusions and insights obtained as a result of analysis and evaluation by the generative AI model.

[0012] "Means for outputting" refers to a function for displaying and saving the evaluation results in a format that can be confirmed by the user.

[0013] A "text file" is a type of file format that stores textual information.

[0014] "Verbalizing" means expressing information such as the reasons for evaluation in natural language and making it explainable. [Brief explanation of the drawings]

[0015] [Figure 1]1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] 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.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The system of the present invention uses a generative AI model to efficiently analyze application data and conduct fair and rapid evaluations, thereby eliminating issues such as unfair selection and time constraints, and providing applicants with a highly transparent evaluation.

[0037] Program processing

[0038] User:

[0039] First, the user prepares application data in a text file called "applications.txt" through the terminal's GUI, and then uploads this file to the system.

[0040] server:

[0041] The server receives the uploaded "applications.txt" and reads its contents. When reading, each line of the file is retrieved as application data and processed in text format. At this time, the application data is managed in list format.

[0042] The server then analyzes the application data, using a generative AI model to evaluate each application. The generative AI model is a pre-trained artificial intelligence that provides an appropriate evaluation based on the content of the application data.

[0043] The evaluation results are returned in text format from the generative AI model. The server collects these evaluation results and performs evaluation on all application data. The evaluation results are compiled in list format and finally output to a file called "evaluations.txt."

[0044] The server uses a means to verbalize the evaluation results and clearly describes the reasons for the evaluation results, which improves the transparency of the reasons for the judgment and allows users to understand the evaluation.

[0045] Device:

[0046] The terminal provides an option to download the "evaluations.txt" file output from the server, allowing users to check the evaluations and reasons for each application.

[0047] Specific examples

[0048] For example, suppose a user submits 50 entries to a creative contest. First, the user puts all the application data into a file called "applications.txt" and uploads this file to the system.

[0049] The server reads this file and evaluates each work using a generative AI model. For example, the generative AI model evaluates the work based on factors such as originality, quality, and suitability for the theme, and obtains the evaluation results for each.

[0050] The server saves the evaluation results in a text file called "evaluations.txt" and makes it available for users to download. Users can download this file and check the evaluation results and reasons for their submissions.

[0051] This allows for fair and prompt evaluation without being influenced by the judges' subjectivity or order. In addition, the reasons for the evaluation are clearly stated, allowing users to accept the results in a satisfactory manner.

[0052] In this way, the present invention automates the analysis and evaluation of application data, realizing a fair and transparent selection process.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] User: The user selects the "applications.txt" file through the terminal GUI and uploads it to the server.

[0056] Step 2:

[0057] Server: The server receives the uploaded "applications.txt" file, which contains the application data, one line per file.

[0058] Step 3:

[0059] Server: The server reads the received "applications.txt" file and parses each line sequentially. The contents of the file are stored internally in list format.

[0060] Step 4:

[0061] Server: The server passes each application data to the generative AI model for evaluation. The generative AI model is a pre-trained artificial intelligence that makes an appropriate evaluation based on the application data.

[0062] Step 5:

[0063] Server: Obtains the evaluation results from the generative AI model and stores them in a single list. The evaluation results include the score for each application and the reason for the evaluation.

[0064] Step 6:

[0065] Server: The server outputs the evaluation results for all application data in a file called "evaluations.txt." This file contains evaluation comments and scores for each application.

[0066] Step 7:

[0067] Terminal: The terminal provides the user with a link or button to download the "evaluations.txt" file output from the server.

[0068] Step 8:

[0069] User: The user downloads the "evaluations.txt" file via their device. After downloading, they can check the evaluation results and reasons for each application data.

[0070] By going through the above steps, the system of the present invention efficiently analyzes application data and achieves fair and prompt evaluation.

[0071] Example 1

[0072] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0073] The conventional application data evaluation process lacked fairness and speed because it relied on the evaluator's subjectivity and order. Furthermore, the evaluation process lacked transparency, making it difficult for applicants to accept the evaluation results. These issues reduced the reliability of the evaluation process and had a significant impact on applicants.

[0074] 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.

[0075] In this invention, the server includes means for a user to prepare application data in text file format and upload it via the terminal's GUI, means for the server to receive the uploaded application data and read it in text format, means for managing the application data read by the server in list format and analyzing and evaluating each data using a generative AI model, means for evaluating the application data using prompt sentences from the generative AI model and obtaining the evaluation results in list format, means for aggregating the evaluation results for all application data and verbalizing the reasons for the evaluation and outputting them as a text file, and means for the terminal to download the evaluation result file. This improves the fairness and speed of the evaluation process and enables highly transparent evaluation.

[0076] A "user" is an individual or organization that uses the system to prepare application data and obtain evaluation results.

[0077] A "terminal" is an electronic device such as a computer device or a smartphone that is operated by a user.

[0078] "GUI" is a graphical user interface that allows users to operate a system through a terminal.

[0079] "Application data" refers to a group of data that a user submits to the system for evaluation.

[0080] A "text file" is a file format that stores only character data.

[0081] "Server" means a central processing unit that receives, processes, and evaluates application data.

[0082] "Loading" means that the server holds the application data in file format and makes the contents available for analysis.

[0083] The "list format" is a format in which data is listed, with each data item arranged in order.

[0084] A "generative AI model" is an artificial intelligence model that has been trained in advance with a large amount of data and evaluates applications based on the application data.

[0085] A "prompt" is an input instruction given to a generative AI model that determines how the model evaluates the data.

[0086] "Analysis" is the process of breaking down, converting, and evaluating application data based on a database.

[0087] "Evaluation" refers to the act of analyzing the content of application data using a generative AI model and determining results based on specific criteria.

[0088] "Evaluation results" are judgment results obtained based on analysis by the generative AI model.

[0089] "Verbalization" is the process of converting and describing the evaluation results in a form that users can understand.

[0090] "Output as text file" means saving the evaluation results as a text file.

[0091] "Making the file available for download" means making the file available for the user to obtain through the terminal.

[0092] The present invention is a system that automates the process of a user preparing application data and a server evaluating it. In this system, the user, terminal, and server each play a specific role, and the application data is evaluated with a high degree of fairness and transparency.

[0093] User:

[0094] The user prepares application data in a text file called "applications.txt" using the terminal's GUI. The prepared text file contains multiple lines of application data submitted by the applicant. The user uploads this file to the system through the GUI. For example, the GUI may provide a file selection dialog, and the user can click the upload button to send the file to the system.

[0095] server:

[0096] The server receives the "applications.txt" file uploaded by the user. The server then reads this file in text format and extracts each line as individual application data. The extracted application data is managed in list format. The server then analyzes and evaluates each data using a generative AI model. The generative AI model is a pre-trained artificial intelligence that performs detailed analysis and evaluation of the application data.

[0097] Analysis and evaluation are performed using prompts from the generative AI model. Specific examples of prompts are shown below.

[0098] Example prompt:

[0099] Application Data: [Enter your application data here]

[0100] Evaluation criteria: Entries will be evaluated based on originality, quality, and relevance to the topic.

[0101] Output evaluation result format:

[0102] Originality: [Evaluation Results]

[0103] Quality: [Evaluation Results]

[0104] Theme suitability: [Evaluation result]

[0105] Reason for evaluation: [Describe the reason]

[0106] Please print out the evaluation results and reasons for the following application data.

[0107] The generative AI model evaluates each application data using prompt sentences and obtains the evaluation results in list format.

[0108] The server compiles these evaluation results and outputs them as a text file called "evaluations.txt." The evaluation results clearly state the reasons for the evaluation of each application. The generated "evaluations.txt" file is provided by the server for easy access by users.

[0109] Device:

[0110] The device provides the user with an operation to download the "evaluations.txt" file. The user can click the dedicated download button to download the evaluation result file to the device. The downloaded file contains all the evaluations made by the generative AI model and the reasons for them. The user can review this file to understand the evaluations made to their submission and the reasons for them.

[0111] As described above, the system of the present invention automates the preparation, uploading, analysis, evaluation, and provision of results of application data, realizing a fair and transparent selection process, allowing users to receive evaluation results in a satisfactory manner.

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Step 1:

[0114] User

[0115] The user uses the GUI on their device to prepare a text file called "applications.txt." This file contains multiple pieces of application data. The user uploads this file to the system through their device. Specifically, the user selects "applications.txt" from the file selection dialog in the GUI and clicks the upload button. The input data is "applications.txt," and this is sent to the system.

[0116] Step 2:

[0117] server

[0118] The server receives "applications.txt" uploaded by the user. Next, the server reads this file in text format and extracts each line as individual application data. The extracted application data is managed in list format. The input data is the received "applications.txt" and the output is the application data in list format.

[0119] Step 3:

[0120] server

[0121] The server passes the acquired list-format application data to a generative AI model for analysis and evaluation. The generative AI model used here receives a prompt as input and evaluates the application data. The input data is a list of application data and the prompt, and the output is the evaluation result corresponding to each application data.

[0122] Step 4:

[0123] server

[0124] The server collects the evaluation results returned by the generative AI model. Each evaluation result is evaluated based on criteria such as originality, quality, and suitability for the theme, with the reasons for each evaluation clearly stated. The input data is a list of the evaluation results returned by the generative AI model, and the output is a list of the evaluation results and their reasons.

[0125] Step 5:

[0126] server

[0127] The server compiles the evaluation results for all application data and outputs them as a text file called "evaluations.txt." This file contains the evaluation results for each application data and the reasons for each. The input data is a list of evaluation results, and the output is the "evaluations.txt" file.

[0128] Step 6:

[0129] Terminal

[0130] The device provides an operation that allows the user to download the evaluation result file "evaluations.txt." The user clicks a dedicated download button to save the evaluation results to their device. The input data is "evaluations.txt" saved on the server, and the output is the "evaluations.txt" file downloaded to the user's device.

[0131] Step 7:

[0132] User

[0133] Users can review the downloaded "evaluations.txt" and understand the evaluation of their submission and the reasons for it. This ensures fairness and transparency in the evaluation, allowing users to receive evaluation results in a manner that satisfies them. The input data is "evaluations.txt," and the output is the user's understanding and agreement.

[0134] (Application example 1)

[0135] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0136] In conventional design contests, the evaluation of submitted works is often influenced by the judges' subjective opinions and order, resulting in a lack of fairness and transparency. Furthermore, because the evaluation process takes time, there is also the problem of a time lag before the results are notified to the applicants. The present invention aims to solve these problems and achieve fair and prompt evaluation.

[0137] 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.

[0138] In this invention, the server includes means for receiving application data, means for analyzing the application data and evaluating it using a generative AI model, means for outputting the evaluation results, means for generating the reasons for the evaluation in text format, and means for outputting the evaluation results in a list format. This makes it possible to fairly and quickly evaluate submitted designs and provide applicants with highly transparent evaluation results.

[0139] "Application Data" refers to information including designs and ideas submitted for design contests and various selection processes.

[0140] A "generative AI model" is a type of artificial intelligence that is trained in advance using a large amount of data and can evaluate and analyze specific tasks with high accuracy.

[0141] "Evaluation results" are the overall evaluation results of the application data obtained after analysis by the generative AI model.

[0142] The "reason for evaluation" is textual information that clearly explains why the evaluation was made in response to the evaluation result output by the generative AI model.

[0143] A "list format" is a data format in which multiple items are arranged in an orderly manner so that the contents can be understood at a glance.

[0144] The present invention is a system that uses a generative AI model to analyze application data and perform fair and rapid evaluation, and is specifically implemented as follows.

[0145] Generating a Program

[0146] System Configuration

[0147] 1. Server: The server plays a central role in analyzing the application data received from users. It mainly uses the following hardware and software:

[0148] Hardware: A server with a powerful processor and large memory capacity.

[0149] Software: Python programming language and OpenAI GPT-4, CLIP model for image recognition, database system for data management.

[0150] 2. Terminal: Provides an interface for users to upload application data and download evaluation results.

[0151] Hardware: Smartphone, tablet, or personal computer.

[0152] Software: Use a dedicated application or web browser.

[0153] Processing flow

[0154] 1. Receipt of application data

[0155] Users upload their design (text and image files) to a dedicated smartphone app, which the server receives and manages in list format.

[0156] 2. Data analysis and evaluation

[0157] The server analyzes the received submission data using generative AI models (e.g., OpenAI GPT-4 and CLIP models). The models evaluate the designs' creativity, practicality, and theme suitability, and generate the results in text format.

[0158] 3. Output of evaluation results

[0159] The evaluation results are compiled in list format and saved as an "evaluations.txt" file. Users can download and check the evaluation results from their smartphone app.

[0160] Specific examples

[0161] For example, if a virtual store operator holds a product packaging design contest, the system works as follows:

[0162] Users upload their submitted designs as "applications.txt" files and corresponding image files via a smartphone app. The server receives these and uses a generative AI model to evaluate them as follows:

[0163] Example prompt sentence:

[0164] Please rate the creative contest submissions: Design-001: Creative packaging design, technically innovative, fits the brand.

[0165] The generated evaluation results are saved in "evaluations.txt," which users can download to check the evaluation of their submitted design and the reasons for it.

[0166] This will enable submitted designs to be evaluated fairly and quickly, and applicants to be provided with highly transparent evaluation results.

[0167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0168] Step 1:

[0169] Users prepare their submission designs using a dedicated smartphone app and upload the "applications.txt" text file and corresponding image files. The user's design data is provided to the app as input, and is sent to the server as output.

[0170] Step 2:

[0171] The server receives the submitted design data. The received data includes text files and image files. It takes the uploaded files as input and converts them into an internal list format as output.

[0172] Step 3:

[0173] The server parses the received design submission data, first reading the details of each submission from a text file, then associating the image files with the corresponding entries. It reads text and image data as input and produces a data list ready for analysis as output.

[0174] Step 4:

[0175] The server evaluates the submitted designs using a generative AI model (e.g., OpenAI GPT-4 and CLIP models). The model is provided with a prompt and makes its evaluation based on that. As input, the generative AI model is provided with the following prompt:

[0176] Please rate the creative contest submissions: Design-001: Creative packaging design, technically innovative, fits the brand.

[0177] As output, you will receive the evaluation results for each design and the reasons for them in text format.

[0178] Step 5:

[0179] The server compiles the evaluation results from the generative AI model into a list and saves the integrated evaluation for all designs in the file “evaluations.txt.” The server collects the evaluation results as input and compiles them into a single text file as output.

[0180] Step 6:

[0181] The user downloads the "evaluations.txt" file from the dedicated app and checks the evaluation results and reasons for each design. The evaluation results stored on the server are obtained as input, and the downloaded file is displayed as output.

[0182] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0183] The system of the present invention utilizes a generative AI model to efficiently analyze application data and perform fair and rapid evaluations. The system also incorporates an emotion engine that recognizes user emotions and provides feedback to improve the quality of the evaluation process.

[0184] Program processing

[0185] User:

[0186] First, the user prepares application data in a text file called "applications.txt" through the terminal's GUI, and then uploads this file to the system.

[0187] server:

[0188] The server receives the uploaded "applications.txt" and reads its contents. When reading, each line of the file is retrieved as application data and processed in text format. At this time, the application data is managed in list format.

[0189] The server then analyzes the application data, using a generative AI model to evaluate each application. The generative AI model is a pre-trained artificial intelligence that provides an appropriate evaluation based on the content of the application data.

[0190] The evaluation results are returned in text format from the generative AI model. The server collects these evaluation results and performs evaluation on all application data. The evaluation results are compiled in list format and finally output to a file called "evaluations.txt."

[0191] The server uses a means to verbalize the evaluation results and clearly describes the reasons for the evaluation results, which improves the transparency of the reasons for the judgment and allows users to understand the evaluation.

[0192] The added emotion engine analyzes user reactions and feedback in real time. Emotional data is sent to the server and used to optimize the presentation of evaluation results and improve the evaluation process. Specifically, if a user expresses negative emotions toward a particular evaluation, the reason for this is analyzed, and problems with the evaluation process are identified and improved.

[0193] Device:

[0194] The terminal provides an option to download the "evaluations.txt" file output from the server, allowing users to check the evaluations and reasons for each application.

[0195] Specific examples

[0196] For example, suppose a user submits 50 entries to a creative contest. First, the user puts all the application data into a file called "applications.txt" and uploads this file to the system.

[0197] The server reads this file and evaluates each work using a generative AI model. For example, the generative AI model evaluates the work based on factors such as originality, quality, and suitability for the theme, and obtains the evaluation results for each.

[0198] The emotion engine analyzes in real time how users feel about their evaluation results. For example, if a user expresses dissatisfaction with a particular evaluation result, that emotion data is sent to the server. The server uses this data to adjust the evaluation result and its expression, providing it to the user in a more convincing format.

[0199] The server saves these evaluation results in a text file called "evaluations.txt" and makes it available for users to download. Users can download this file and check the evaluations and reasons for their submissions. In addition, the emotion engine takes user feedback into account during the evaluation process, allowing users to achieve more satisfying results.

[0200] In this way, the system of the present invention automates the analysis and evaluation of application data, realizing a fair and transparent selection process. Furthermore, by incorporating an emotion engine, user feedback can be reflected in real time, further improving the quality and transparency of the evaluation process.

[0201] The processing flow will be explained below.

[0202] Step 1:

[0203] User: The user selects the "applications.txt" file through the terminal GUI and uploads it to the server.

[0204] Step 2:

[0205] Server: The server receives the uploaded "applications.txt" file, which contains the application data, one line per file.

[0206] Step 3:

[0207] Server: The server reads the received "applications.txt" file and parses each line sequentially. The contents of the file are stored internally in list format.

[0208] Step 4:

[0209] Server: The server passes each application data to the generative AI model for evaluation. The generative AI model is a pre-trained artificial intelligence that makes an appropriate evaluation based on the application data.

[0210] Step 5:

[0211] Server: Obtains the evaluation results from the generative AI model and stores them in a single list. The evaluation results include the score for each application and the reason for the evaluation.

[0212] Step 6:

[0213] Server: The server outputs the evaluation results in a file called "evaluations.txt," which contains evaluation comments and scores for each submission.

[0214] Step 7:

[0215] Server: Using the configured emotion engine, analyzes user reactions and feedback in real time and obtains emotion data.

[0216] Step 8:

[0217] Server: Analyzes the user's emotional data obtained by the emotion engine along with the evaluation results, and adjusts the expression and content of the evaluation. If the user has a negative reaction, the cause is identified and the evaluation is reviewed and improved.

[0218] Step 9:

[0219] Terminal: The terminal provides the user with a link or button to download the "evaluations.txt" file output from the server.

[0220] Step 10:

[0221] User: The user downloads the "evaluations.txt" file via their device. After downloading, they can check the evaluation results and reasons for each application data.

[0222] In this way, incorporating an emotion engine can incorporate user feedback in real time, further improving the quality and transparency of the evaluation process.

[0223] Example 2

[0224] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0225] While current application data analysis and evaluation systems can provide evaluations, they are unable to consider users' feelings about the evaluation results, which can lead to users being dissatisfied with the results. Furthermore, they lack the ability to clearly articulate the reasons for the evaluation, resulting in a lack of transparency in the evaluations. Therefore, there is a need for a system that can analyze user feedback in real time and optimize the evaluation process to enable fairer and more transparent evaluations.

[0226] 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.

[0227] In this invention, the server includes a means for receiving application data, a means for analyzing the application data and performing evaluation using a generative AI model, a means for outputting evaluation results, and a means for analyzing users' emotions regarding the evaluation results in real time and optimizing the evaluation process, thereby enabling fair and transparent evaluation that reflects users' emotional feedback.

[0228] "Application data" refers to data that includes information to be evaluated.

[0229] A "generative AI model" is an artificial intelligence model trained for a specific task and used to analyze and evaluate application data.

[0230] "Evaluation results" refer to the scores and judgments output by the generative AI model based on the application data.

[0231] The "emotion engine" analyzes the user's emotions and reflects that feedback in the system's evaluation process.

[0232] A "text file" is a file format that contains text data and is used to store application data and evaluation results.

[0233] "Real time" means that processing is carried out close to the moment a user operation or reaction occurs.

[0234] "Analysis" is the process of dissecting data and drawing specific conclusions or results.

[0235] "Scoring" is the process of assigning a numerical score based on specific evaluation criteria.

[0236] "Optimization" means making adjustments or improvements to achieve the most effective and efficient results for a particular purpose.

[0237] "Transparency" is a characteristic that indicates that a system or process is easy to understand from the outside and is fair.

[0238] This invention is a system that analyzes application data and performs fair and rapid evaluations using a generative AI model. Furthermore, it analyzes user sentiment in real time and incorporates feedback into the evaluation process, improving the transparency and acceptability of evaluations.

[0239] System configuration:

[0240] This system is mainly composed of three components: a server, a terminal, and a user. The server manages the data processing and evaluation process, and the terminal provides an interface with the user. The user inputs data and checks the evaluation results.

[0241] Program processing:

[0242] User:

[0243] First, the user prepares the application data through the terminal GUI. This data is saved in a text file called "applications.txt." The user then selects this file using the terminal's file selection dialog and uploads it to the system.

[0244] server:

[0245] The server receives the uploaded file and reads its contents. Specifically, the application server retrieves "applications.txt" and manages each line as application data in list format. The server then analyzes and evaluates the application data using a generative AI model. The generative AI model scores the applications based on the input data in terms of originality, quality, and suitability for the theme. The evaluation results are saved in text format, and the reasons for the evaluation are clearly stated.

[0246] The emotion engine analyzes user reactions in real time and sends them to the server. Based on this emotion data, the server adjusts and optimizes the evaluation results and their expressions. For example, if a user expresses dissatisfaction with a particular evaluation, the server analyzes the reason and identifies and improves the problematic aspects of the evaluation process.

[0247] Device:

[0248] The device provides a download option for the "evaluations.txt" file so that users can check the evaluation results. Users can download this file and check the evaluation results and reasons.

[0249] Examples:

[0250] For example, if a user submits 50 entries to a creative contest, the application data is compiled in "applications.txt" as follows:

[0251] 1. Work A - Highly original, high quality, and perfectly suited to the theme

[0252] 2. Work B - Original but mediocre quality, somewhat unsuitable for the theme

[0253] 3. Work C - Unoriginal, low quality, and completely unsuitable for the theme

[0254] ...

[0255] 50. Work XX - ···

[0256] When this file is uploaded from a device, the server reads the contents and uses a generative AI model to rate each work. For example, a rating result such as "Work A- Originality: 10, Quality: 10, Relevance to the theme: 10" is generated. The emotion engine analyzes the user's reactions, and if the user expresses dissatisfaction with a particular rating, it adjusts the result and its expression based on that feedback. This allows the user to receive a rating result that they are satisfied with.

[0257] Example prompt sentence:

[0258] Please evaluate the submission data below and score each submission based on originality, quality, and fit to the theme.

[0259] Application Data:

[0260] 1. Work A - Highly original, of excellent quality, and perfectly in line with the theme.

[0261] 2. Work B - Original but of average quality. Does not quite fit the theme.

[0262] 3. Work C - Lacks originality and is of poor quality. Does not fit the theme at all.

[0263] Expected evaluation results:

[0264] 1. Work A - Score: Originality 10, Quality 10, Thematic Relevance 10

[0265] 2. Work B - Score: Originality 8, Quality 6, Thematic Relevance 5

[0266] 3. Work C - Score: Originality 2, Quality 3, Thematic Relevance 1

[0267] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0268] Step 1:

[0269] User:

[0270] The user prepares application data in a text file called "applications.txt" through the terminal GUI. This file contains the application data to be evaluated line by line. Next, the user uploads this file to the system. Specifically, the user opens the file selection dialog, selects "applications.txt", and clicks the upload button.

[0271] input:

[0272] "applications.txt" file

[0273] output:

[0274] Send upload request

[0275] Step 2:

[0276] server:

[0277] The server receives the uploaded "applications.txt". After receiving it, the server reads the contents of this file in text format and manages each line as application data in list format. Specifically, it parses the file line by line and adds the application data to the list.

[0278] input:

[0279] Uploaded "applications.txt" file

[0280] output:

[0281] A list of loaded application data

[0282] Step 3:

[0283] server:

[0284] The server passes the list of application data to the generative AI model for analysis and evaluation. The server inputs each application data into the generative AI model and scores it based on originality, quality, suitability for the theme, etc. The evaluation results generated by the model are obtained in text format.

[0285] input:

[0286] List of application data, generative AI model

[0287] output:

[0288] List of evaluation results

[0289] Step 4:

[0290] server:

[0291] The server collects the evaluation results returned by the generative AI model and clearly verbalizes the reasons for the evaluation. Specifically, it runs a program that generates sentences to justify the evaluation results, and compiles the evaluation results and the reasons for the evaluation in the "evaluations.txt" file.

[0292] input:

[0293] List of evaluation results

[0294] output:

[0295] "evaluations.txt" file

[0296] Step 5:

[0297] server:

[0298] The server uses an emotion engine to analyze user reactions and feedback in real time. Specifically, if a user expresses negative emotions in their evaluation results, the server acquires the emotion data and adjusts the evaluation results and their expressions.

[0299] input:

[0300] User emotion data

[0301] output:

[0302] Adjusted evaluation results or representations

[0303] Step 6:

[0304] Device:

[0305] The terminal provides a download operation for the "evaluations.txt" file so that the user can check the evaluation results. The user can download the evaluation results file from the server by clicking the "Download evaluation results" button in the GUI.

[0306] input:

[0307] Request to download the "evaluations.txt" file

[0308] output:

[0309] The downloaded "evaluations.txt" file

[0310] This process allows users to see the detailed evaluation results and reasons for their submissions in a transparent manner. In addition, by incorporating emotional feedback, users can increase their satisfaction with the evaluation results.

[0311] (Application example 2)

[0312] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0313] Conventional application data evaluation systems have issues with the fairness and speed of the evaluation process, and because they do not take into account user emotions or feedback, users are less satisfied with the evaluation results. This necessitates improvements in the quality of application data analysis and evaluation. It is also necessary to improve the transparency of evaluation results and provide an evaluation process that users can be satisfied with.

[0314] 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.

[0315] In this invention, the server includes means for receiving application data, means for analyzing the application data and evaluating it using a generative AI model, means for outputting the evaluation results, means including an emotion engine for recognizing the user's emotions, and means for optimizing the evaluation process based on user feedback. This not only enables efficient analysis and evaluation of application data, but also enables optimization of the evaluation process taking into account the user's emotions and feedback.

[0316] The "means for receiving application data" is a function that allows the system to receive application data provided by the user.

[0317] "Means for analyzing application data and evaluating using a generative AI model" refers to a function for analyzing and evaluating application data using a generative AI model.

[0318] The "means for outputting evaluation results" is a function for presenting the results evaluated by the generative AI model to the user.

[0319] The "means including an emotion engine for recognizing the user's emotion" is a function for analyzing feedback and reactions from the user and recognizing the emotion.

[0320] The "means for optimizing the evaluation process based on user feedback" is a function for improving and optimizing the entire evaluation process based on feedback provided by the user.

[0321] The system of the present invention automatically evaluates employees in a store and provides the evaluation results fairly and quickly. Specific embodiments of the present invention will be described below.

[0322] First, the employee evaluation system is installed on a device such as a smartphone or tablet. This device is used by store managers to evaluate employee performance. Employee evaluation data is created and managed in the form of "applications.txt."

[0323] The terminal is equipped with a means for receiving application data and performs operations to upload this data to a server. The server receives the uploaded "applications.txt" and analyzes its contents. A generative AI model (e.g., OpenAI GPT-3) is used for the analysis to evaluate each application data. The generative AI model evaluates the employee's performance and generates an evaluation result along with the reasons for the evaluation.

[0324] The server also incorporates an emotion engine that analyzes feedback from store managers and employees in real time and collects emotional data. The emotion engine uses the TextBlob library, for example. The evaluation process is optimized based on the emotional data, providing evaluation results that are easy for users to accept. Specifically, if negative feedback is received, the reasons for this are analyzed and reflected in the evaluation process.

[0325] The evaluation results are output as "evaluations.txt" and provided to the terminal in a downloadable format. Users can download this file and check the evaluation results and reasons for each employee.

[0326] Specific examples are shown below.

[0327] For example, when a store manager evaluates an employee, he or she might enter the following prompt:

[0328] Example prompt:

[0329] "Please rate your employees:

[0330] They are polite and have high customer satisfaction, but they sometimes make mistakes.

[0331] evaluation:"

[0332] The generated evaluation results are as follows:

[0333] Example of evaluation result:

[0334] "Rating: C+

[0335] Reason: Customer feedback has been generally positive, but there are occasional mistakes, so I've given it an overall rating of C+.

[0336] In this way, the system of the present invention can efficiently evaluate employees within a store, improve the transparency of the evaluation results, and increase the user's satisfaction.

[0337] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0338] Step 1:

[0339] The user prepares employee evaluation data as "applications.txt" and uploads it to the system through the terminal GUI.

[0340] Input: "applications.txt" file containing application data

[0341] Output: The device finishes uploading the "applications.txt" file

[0342] Step 2:

[0343] The server receives the uploaded "applications.txt" file and reads its contents.

[0344] Input: "applications.txt" file

[0345] Output: A list of application data in text format

[0346] Step 3:

[0347] The server analyzes the loaded application data using a generative AI model and evaluates each application data.

[0348] Input: List of application data

[0349] Output: A list of the evaluation results and the text of the evaluation reason.

[0350] Step 4:

[0351] The server compiles the evaluation results obtained by the generative AI model in text format as "evaluations.txt."

[0352] Input: List of evaluation results and reasons for evaluation

[0353] Output: "evaluations.txt" file

[0354] Step 5:

[0355] The server analyzes user feedback and reactions in real time using an emotion engine, which uses the TextBlob library to recognize emotions.

[0356] Input: User feedback text

[0357] Output: Emotion data (negative / positive judgment)

[0358] Step 6:

[0359] Based on the emotion data, the server optimizes the content and method of the evaluation process, and in this process, if there is negative feedback, the reasons for it are analyzed in detail and the evaluation results are adjusted.

[0360] Input: Emotion data, evaluation results

[0361] Output: Adjusted evaluation results

[0362] Step 7:

[0363] The device downloads the "evaluations.txt" file from the server, allowing the user to view the evaluation results and the reasons for them.

[0364] Input: "evaluations.txt" file

[0365] Output: The evaluation results and reasons displayed to the user

[0366] Specifically, when a user uploads "applications.txt," the server reads its contents, rates it, and saves the rating results as a generated file. The emotion engine then analyzes the feedback and adjusts the rating results as necessary. Finally, a download link is provided to the device so that the user can review the rating.

[0367] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0368] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0369] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0370] [Second embodiment]

[0371] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0372] 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.

[0373] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0374] 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.

[0375] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0376] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0377] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0378] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0383] The system of the present invention uses a generative AI model to efficiently analyze application data and conduct fair and rapid evaluations, thereby eliminating issues such as unfair selection and time constraints, and providing applicants with a highly transparent evaluation.

[0384] Program processing

[0385] User:

[0386] First, the user prepares application data in a text file called "applications.txt" through the terminal's GUI, and then uploads this file to the system.

[0387] server:

[0388] The server receives the uploaded "applications.txt" and reads its contents. When reading, each line of the file is retrieved as application data and processed in text format. At this time, the application data is managed in list format.

[0389] The server then analyzes the application data, using a generative AI model to evaluate each application. The generative AI model is a pre-trained artificial intelligence that provides an appropriate evaluation based on the content of the application data.

[0390] The evaluation results are returned in text format from the generative AI model. The server collects these evaluation results and performs evaluation on all application data. The evaluation results are compiled in list format and finally output to a file called "evaluations.txt."

[0391] The server uses a means to verbalize the evaluation results and clearly describes the reasons for the evaluation results, which improves the transparency of the reasons for the judgment and allows users to understand the evaluation.

[0392] Device:

[0393] The terminal provides an option to download the "evaluations.txt" file output from the server, allowing users to check the evaluations and reasons for each application.

[0394] Specific examples

[0395] For example, suppose a user submits 50 entries to a creative contest. First, the user puts all the application data into a file called "applications.txt" and uploads this file to the system.

[0396] The server reads this file and evaluates each work using a generative AI model. For example, the generative AI model evaluates the work based on factors such as originality, quality, and suitability for the theme, and obtains the evaluation results for each.

[0397] The server saves the evaluation results in a text file called "evaluations.txt" and makes it available for users to download. Users can download this file and check the evaluation results and reasons for their submissions.

[0398] This allows for fair and prompt evaluation without being influenced by the judges' subjectivity or order. In addition, the reasons for the evaluation are clearly stated, allowing users to accept the results in a satisfactory manner.

[0399] In this way, the present invention automates the analysis and evaluation of application data, realizing a fair and transparent selection process.

[0400] The processing flow will be explained below.

[0401] Step 1:

[0402] User: The user selects the "applications.txt" file through the terminal GUI and uploads it to the server.

[0403] Step 2:

[0404] Server: The server receives the uploaded "applications.txt" file, which contains the application data, one line per file.

[0405] Step 3:

[0406] Server: The server reads the received "applications.txt" file and parses each line sequentially. The contents of the file are stored internally in list format.

[0407] Step 4:

[0408] Server: The server passes each application data to the generative AI model for evaluation. The generative AI model is a pre-trained artificial intelligence that makes an appropriate evaluation based on the application data.

[0409] Step 5:

[0410] Server: Obtains the evaluation results from the generative AI model and stores them in a single list. The evaluation results include the score for each application and the reason for the evaluation.

[0411] Step 6:

[0412] Server: The server outputs the evaluation results for all application data in a file called "evaluations.txt." This file contains evaluation comments and scores for each application.

[0413] Step 7:

[0414] Terminal: The terminal provides the user with a link or button to download the "evaluations.txt" file output from the server.

[0415] Step 8:

[0416] User: The user downloads the "evaluations.txt" file via their device. After downloading, they can check the evaluation results and reasons for each application data.

[0417] By going through the above steps, the system of the present invention efficiently analyzes application data and achieves fair and prompt evaluation.

[0418] Example 1

[0419] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0420] The conventional application data evaluation process lacked fairness and speed because it relied on the evaluator's subjectivity and order. Furthermore, the evaluation process lacked transparency, making it difficult for applicants to accept the evaluation results. These issues reduced the reliability of the evaluation process and had a significant impact on applicants.

[0421] 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.

[0422] In this invention, the server includes means for a user to prepare application data in text file format and upload it via the terminal's GUI, means for the server to receive the uploaded application data and read it in text format, means for managing the application data read by the server in list format and analyzing and evaluating each data using a generative AI model, means for evaluating the application data using prompt sentences from the generative AI model and obtaining the evaluation results in list format, means for aggregating the evaluation results for all application data and verbalizing the reasons for the evaluation and outputting them as a text file, and means for the terminal to download the evaluation result file. This improves the fairness and speed of the evaluation process and enables highly transparent evaluation.

[0423] A "user" is an individual or organization that uses the system to prepare application data and obtain evaluation results.

[0424] A "terminal" is an electronic device such as a computer device or a smartphone that is operated by a user.

[0425] "GUI" is a graphical user interface that allows users to operate a system through a terminal.

[0426] "Application data" refers to a group of data that a user submits to the system for evaluation.

[0427] A "text file" is a file format that stores only character data.

[0428] "Server" means a central processing unit that receives, processes, and evaluates application data.

[0429] "Loading" means that the server holds the application data in file format and makes the contents available for analysis.

[0430] The "list format" is a format in which data is listed, with each data item arranged in order.

[0431] A "generative AI model" is an artificial intelligence model that has been trained in advance with a large amount of data and evaluates applications based on the application data.

[0432] A "prompt" is an input instruction given to a generative AI model that determines how the model evaluates the data.

[0433] "Analysis" is the process of breaking down, converting, and evaluating application data based on a database.

[0434] "Evaluation" refers to the act of analyzing the content of application data using a generative AI model and determining results based on specific criteria.

[0435] "Evaluation results" are judgment results obtained based on analysis by the generative AI model.

[0436] "Verbalization" is the process of converting and describing the evaluation results in a form that users can understand.

[0437] "Output as text file" means saving the evaluation results as a text file.

[0438] "Making the file available for download" means making the file available for the user to obtain through the terminal.

[0439] The present invention is a system that automates the process of a user preparing application data and a server evaluating it. In this system, the user, terminal, and server each play a specific role, and the application data is evaluated with a high degree of fairness and transparency.

[0440] User:

[0441] The user prepares application data in a text file called "applications.txt" using the terminal's GUI. The prepared text file contains multiple lines of application data submitted by the applicant. The user uploads this file to the system through the GUI. For example, the GUI may provide a file selection dialog, and the user can click the upload button to send the file to the system.

[0442] server:

[0443] The server receives the "applications.txt" file uploaded by the user. The server then reads this file in text format and extracts each line as individual application data. The extracted application data is managed in list format. The server then analyzes and evaluates each data using a generative AI model. The generative AI model is a pre-trained artificial intelligence that performs detailed analysis and evaluation of the application data.

[0444] Analysis and evaluation are performed using prompts from the generative AI model. Specific examples of prompts are shown below.

[0445] Example prompt:

[0446] Application Data: [Enter your application data here]

[0447] Evaluation criteria: Entries will be evaluated based on originality, quality, and relevance to the topic.

[0448] Output evaluation result format:

[0449] Originality: [Evaluation Results]

[0450] Quality: [Evaluation Results]

[0451] Theme suitability: [Evaluation result]

[0452] Reason for evaluation: [Describe the reason]

[0453] Please print out the evaluation results and reasons for the following application data.

[0454] The generative AI model evaluates each application data using prompt sentences and obtains the evaluation results in list format.

[0455] The server compiles these evaluation results and outputs them as a text file called "evaluations.txt." The evaluation results clearly state the reasons for the evaluation of each application. The generated "evaluations.txt" file is provided by the server for easy access by users.

[0456] Device:

[0457] The device provides the user with an operation to download the "evaluations.txt" file. The user can click the dedicated download button to download the evaluation result file to the device. The downloaded file contains all the evaluations made by the generative AI model and the reasons for them. The user can review this file to understand the evaluations made to their submission and the reasons for them.

[0458] As described above, the system of the present invention automates the preparation, uploading, analysis, evaluation, and provision of results of application data, realizing a fair and transparent selection process, allowing users to receive evaluation results in a satisfactory manner.

[0459] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0460] Step 1:

[0461] User

[0462] The user uses the GUI on their device to prepare a text file called "applications.txt." This file contains multiple pieces of application data. The user uploads this file to the system through their device. Specifically, the user selects "applications.txt" from the file selection dialog in the GUI and clicks the upload button. The input data is "applications.txt," and this is sent to the system.

[0463] Step 2:

[0464] server

[0465] The server receives "applications.txt" uploaded by the user. Next, the server reads this file in text format and extracts each line as individual application data. The extracted application data is managed in list format. The input data is the received "applications.txt" and the output is the application data in list format.

[0466] Step 3:

[0467] server

[0468] The server passes the acquired list-format application data to a generative AI model for analysis and evaluation. The generative AI model used here receives a prompt as input and evaluates the application data. The input data is a list of application data and the prompt, and the output is the evaluation result corresponding to each application data.

[0469] Step 4:

[0470] server

[0471] The server collects the evaluation results returned by the generative AI model. Each evaluation result is evaluated based on criteria such as originality, quality, and suitability for the theme, with the reasons for each evaluation clearly stated. The input data is a list of the evaluation results returned by the generative AI model, and the output is a list of the evaluation results and their reasons.

[0472] Step 5:

[0473] server

[0474] The server compiles the evaluation results for all application data and outputs them as a text file called "evaluations.txt." This file contains the evaluation results for each application data and the reasons for each. The input data is a list of evaluation results, and the output is the "evaluations.txt" file.

[0475] Step 6:

[0476] Terminal

[0477] The device provides an operation that allows the user to download the evaluation result file "evaluations.txt." The user clicks a dedicated download button to save the evaluation results to their device. The input data is "evaluations.txt" saved on the server, and the output is the "evaluations.txt" file downloaded to the user's device.

[0478] Step 7:

[0479] User

[0480] Users can review the downloaded "evaluations.txt" and understand the evaluation of their submission and the reasons for it. This ensures fairness and transparency in the evaluation, allowing users to receive evaluation results in a manner that satisfies them. The input data is "evaluations.txt," and the output is the user's understanding and agreement.

[0481] (Application example 1)

[0482] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0483] In conventional design contests, the evaluation of submitted works is often influenced by the judges' subjective opinions and order, resulting in a lack of fairness and transparency. Furthermore, because the evaluation process takes time, there is also the problem of a time lag before the results are notified to the applicants. The present invention aims to solve these problems and achieve fair and prompt evaluation.

[0484] 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.

[0485] In this invention, the server includes means for receiving application data, means for analyzing the application data and evaluating it using a generative AI model, means for outputting the evaluation results, means for generating the reasons for the evaluation in text format, and means for outputting the evaluation results in a list format. This makes it possible to fairly and quickly evaluate submitted designs and provide applicants with highly transparent evaluation results.

[0486] "Application Data" refers to information including designs and ideas submitted for design contests and various selection processes.

[0487] A "generative AI model" is a type of artificial intelligence that is trained in advance using a large amount of data and can evaluate and analyze specific tasks with high accuracy.

[0488] "Evaluation results" are the overall evaluation results of the application data obtained after analysis by the generative AI model.

[0489] The "reason for evaluation" is textual information that clearly explains why the evaluation was made in response to the evaluation result output by the generative AI model.

[0490] A "list format" is a data format in which multiple items are arranged in an orderly manner so that the contents can be understood at a glance.

[0491] The present invention is a system that uses a generative AI model to analyze application data and perform fair and rapid evaluation, and is specifically implemented as follows.

[0492] Generating a Program

[0493] System Configuration

[0494] 1. Server: The server plays a central role in analyzing the application data received from users. It mainly uses the following hardware and software:

[0495] Hardware: A server with a powerful processor and large memory capacity.

[0496] Software: Python programming language and OpenAI GPT-4, CLIP model for image recognition, database system for data management.

[0497] 2. Terminal: Provides an interface for users to upload application data and download evaluation results.

[0498] Hardware: Smartphone, tablet, or personal computer.

[0499] Software: Use a dedicated application or web browser.

[0500] Processing flow

[0501] 1. Receipt of application data

[0502] Users upload their design (text and image files) to a dedicated smartphone app, which the server receives and manages in list format.

[0503] 2. Data analysis and evaluation

[0504] The server analyzes the received submission data using generative AI models (e.g., OpenAI GPT-4 and CLIP models). The models evaluate the designs' creativity, practicality, and theme suitability, and generate the results in text format.

[0505] 3. Output of evaluation results

[0506] The evaluation results are compiled in list format and saved as an "evaluations.txt" file. Users can download and check the evaluation results from their smartphone app.

[0507] Specific examples

[0508] For example, if a virtual store operator holds a product packaging design contest, the system works as follows:

[0509] Users upload their submitted designs as "applications.txt" files and corresponding image files via a smartphone app. The server receives these and uses a generative AI model to evaluate them as follows:

[0510] Example prompt sentence:

[0511] Please rate the creative contest submissions: Design-001: Creative packaging design, technically innovative, fits the brand.

[0512] The generated evaluation results are saved in "evaluations.txt," which users can download to check the evaluation of their submitted design and the reasons for it.

[0513] This will enable submitted designs to be evaluated fairly and quickly, and applicants to be provided with highly transparent evaluation results.

[0514] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0515] Step 1:

[0516] Users prepare their submission designs using a dedicated smartphone app and upload the "applications.txt" text file and corresponding image files. The user's design data is provided to the app as input, and is sent to the server as output.

[0517] Step 2:

[0518] The server receives the submitted design data. The received data includes text files and image files. It takes the uploaded files as input and converts them into an internal list format as output.

[0519] Step 3:

[0520] The server parses the received design submission data, first reading the details of each submission from a text file, then associating the image files with the corresponding entries. It reads text and image data as input and produces a data list ready for analysis as output.

[0521] Step 4:

[0522] The server evaluates the submitted designs using a generative AI model (e.g., OpenAI GPT-4 and CLIP models). The model is provided with a prompt and makes its evaluation based on that. As input, the generative AI model is provided with the following prompt:

[0523] Please rate the creative contest submissions: Design-001: Creative packaging design, technically innovative, fits the brand.

[0524] As output, you will receive the evaluation results for each design and the reasons for them in text format.

[0525] Step 5:

[0526] The server compiles the evaluation results from the generative AI model into a list and saves the integrated evaluation for all designs in the file “evaluations.txt.” The server collects the evaluation results as input and compiles them into a single text file as output.

[0527] Step 6:

[0528] The user downloads the "evaluations.txt" file from the dedicated app and checks the evaluation results and reasons for each design. The evaluation results stored on the server are obtained as input, and the downloaded file is displayed as output.

[0529] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0530] The system of the present invention utilizes a generative AI model to efficiently analyze application data and perform fair and rapid evaluations. The system also incorporates an emotion engine that recognizes user emotions and provides feedback to improve the quality of the evaluation process.

[0531] Program processing

[0532] User:

[0533] First, the user prepares application data in a text file called "applications.txt" through the terminal's GUI, and then uploads this file to the system.

[0534] server:

[0535] The server receives the uploaded "applications.txt" and reads its contents. When reading, each line of the file is retrieved as application data and processed in text format. At this time, the application data is managed in list format.

[0536] The server then analyzes the application data, using a generative AI model to evaluate each application. The generative AI model is a pre-trained artificial intelligence that provides an appropriate evaluation based on the content of the application data.

[0537] The evaluation results are returned in text format from the generative AI model. The server collects these evaluation results and performs evaluation on all application data. The evaluation results are compiled in list format and finally output to a file called "evaluations.txt."

[0538] The server uses a means to verbalize the evaluation results and clearly describes the reasons for the evaluation results, which improves the transparency of the reasons for the judgment and allows users to understand the evaluation.

[0539] The added emotion engine analyzes user reactions and feedback in real time. Emotional data is sent to the server and used to optimize the presentation of evaluation results and improve the evaluation process. Specifically, if a user expresses negative emotions toward a particular evaluation, the reason for this is analyzed, and problems with the evaluation process are identified and improved.

[0540] Device:

[0541] The terminal provides an option to download the "evaluations.txt" file output from the server, allowing users to check the evaluations and reasons for each application.

[0542] Specific examples

[0543] For example, suppose a user submits 50 entries to a creative contest. First, the user puts all the application data into a file called "applications.txt" and uploads this file to the system.

[0544] The server reads this file and evaluates each work using a generative AI model. For example, the generative AI model evaluates the work based on factors such as originality, quality, and suitability for the theme, and obtains the evaluation results for each.

[0545] The emotion engine analyzes in real time how users feel about their evaluation results. For example, if a user expresses dissatisfaction with a particular evaluation result, that emotion data is sent to the server. The server uses this data to adjust the evaluation result and its expression, providing it to the user in a more convincing format.

[0546] The server saves these evaluation results in a text file called "evaluations.txt" and makes it available for users to download. Users can download this file and check the evaluations and reasons for their submissions. In addition, the emotion engine takes user feedback into account during the evaluation process, allowing users to achieve more satisfying results.

[0547] In this way, the system of the present invention automates the analysis and evaluation of application data, realizing a fair and transparent selection process. Furthermore, by incorporating an emotion engine, user feedback can be reflected in real time, further improving the quality and transparency of the evaluation process.

[0548] The processing flow will be explained below.

[0549] Step 1:

[0550] User: The user selects the "applications.txt" file through the terminal GUI and uploads it to the server.

[0551] Step 2:

[0552] Server: The server receives the uploaded "applications.txt" file, which contains the application data, one line per file.

[0553] Step 3:

[0554] Server: The server reads the received "applications.txt" file and parses each line sequentially. The contents of the file are stored internally in list format.

[0555] Step 4:

[0556] Server: The server passes each application data to the generative AI model for evaluation. The generative AI model is a pre-trained artificial intelligence that makes an appropriate evaluation based on the application data.

[0557] Step 5:

[0558] Server: Obtains the evaluation results from the generative AI model and stores them in a single list. The evaluation results include the score for each application and the reason for the evaluation.

[0559] Step 6:

[0560] Server: The server outputs the evaluation results in a file called "evaluations.txt," which contains evaluation comments and scores for each submission.

[0561] Step 7:

[0562] Server: Using the configured emotion engine, analyzes user reactions and feedback in real time and obtains emotion data.

[0563] Step 8:

[0564] Server: Analyzes the user's emotional data obtained by the emotion engine along with the evaluation results, and adjusts the expression and content of the evaluation. If the user has a negative reaction, the cause is identified and the evaluation is reviewed and improved.

[0565] Step 9:

[0566] Terminal: The terminal provides the user with a link or button to download the "evaluations.txt" file output from the server.

[0567] Step 10:

[0568] User: The user downloads the "evaluations.txt" file via their device. After downloading, they can check the evaluation results and reasons for each application data.

[0569] In this way, incorporating an emotion engine can incorporate user feedback in real time, further improving the quality and transparency of the evaluation process.

[0570] Example 2

[0571] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0572] While current application data analysis and evaluation systems can provide evaluations, they are unable to consider users' feelings about the evaluation results, which can lead to users being dissatisfied with the results. Furthermore, they lack the ability to clearly articulate the reasons for the evaluation, resulting in a lack of transparency in the evaluations. Therefore, there is a need for a system that can analyze user feedback in real time and optimize the evaluation process to enable fairer and more transparent evaluations.

[0573] 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.

[0574] In this invention, the server includes a means for receiving application data, a means for analyzing the application data and performing evaluation using a generative AI model, a means for outputting evaluation results, and a means for analyzing users' emotions regarding the evaluation results in real time and optimizing the evaluation process, thereby enabling fair and transparent evaluation that reflects users' emotional feedback.

[0575] "Application data" refers to data that includes information to be evaluated.

[0576] A "generative AI model" is an artificial intelligence model trained for a specific task and used to analyze and evaluate application data.

[0577] "Evaluation results" refer to the scores and judgments output by the generative AI model based on the application data.

[0578] The "emotion engine" analyzes the user's emotions and reflects that feedback in the system's evaluation process.

[0579] A "text file" is a file format that contains text data and is used to store application data and evaluation results.

[0580] "Real time" means that processing is carried out close to the moment a user operation or reaction occurs.

[0581] "Analysis" is the process of dissecting data and drawing specific conclusions or results.

[0582] "Scoring" is the process of assigning a numerical score based on specific evaluation criteria.

[0583] "Optimization" means making adjustments or improvements to achieve the most effective and efficient results for a particular purpose.

[0584] "Transparency" is a characteristic that indicates that a system or process is easy to understand from the outside and is fair.

[0585] This invention is a system that analyzes application data and performs fair and rapid evaluations using a generative AI model. Furthermore, it analyzes user sentiment in real time and incorporates feedback into the evaluation process, improving the transparency and acceptability of evaluations.

[0586] System configuration:

[0587] This system is mainly composed of three components: a server, a terminal, and a user. The server manages the data processing and evaluation process, and the terminal provides an interface with the user. The user inputs data and checks the evaluation results.

[0588] Program processing:

[0589] User:

[0590] First, the user prepares the application data through the terminal GUI. This data is saved in a text file called "applications.txt." The user then selects this file using the terminal's file selection dialog and uploads it to the system.

[0591] server:

[0592] The server receives the uploaded file and reads its contents. Specifically, the application server retrieves "applications.txt" and manages each line as application data in list format. The server then analyzes and evaluates the application data using a generative AI model. The generative AI model scores the applications based on the input data in terms of originality, quality, and suitability for the theme. The evaluation results are saved in text format, and the reasons for the evaluation are clearly stated.

[0593] The emotion engine analyzes user reactions in real time and sends them to the server. Based on this emotion data, the server adjusts and optimizes the evaluation results and their expressions. For example, if a user expresses dissatisfaction with a particular evaluation, the server analyzes the reason and identifies and improves the problematic aspects of the evaluation process.

[0594] Device:

[0595] The device provides a download option for the "evaluations.txt" file so that users can check the evaluation results. Users can download this file and check the evaluation results and reasons.

[0596] Examples:

[0597] For example, if a user submits 50 entries to a creative contest, the application data is compiled in "applications.txt" as follows:

[0598] 1. Work A - Highly original, high quality, and perfectly suited to the theme

[0599] 2. Work B - Original but mediocre quality, somewhat unsuitable for the theme

[0600] 3. Work C - Unoriginal, low quality, and completely unsuitable for the theme

[0601] ...

[0602] 50. Work XX - ···

[0603] When this file is uploaded from a device, the server reads the contents and uses a generative AI model to rate each work. For example, a rating result such as "Work A- Originality: 10, Quality: 10, Relevance to the theme: 10" is generated. The emotion engine analyzes the user's reactions, and if the user expresses dissatisfaction with a particular rating, it adjusts the result and its expression based on that feedback. This allows the user to receive a rating result that they are satisfied with.

[0604] Example prompt sentence:

[0605] Please evaluate the submission data below and score each submission based on originality, quality, and fit to the theme.

[0606] Application Data:

[0607] 1. Work A - Highly original, of excellent quality, and perfectly in line with the theme.

[0608] 2. Work B - Original but of average quality. Does not quite fit the theme.

[0609] 3. Work C - Lacks originality and is of poor quality. Does not fit the theme at all.

[0610] Expected evaluation results:

[0611] 1. Work A - Score: Originality 10, Quality 10, Thematic Relevance 10

[0612] 2. Work B - Score: Originality 8, Quality 6, Thematic Relevance 5

[0613] 3. Work C - Score: Originality 2, Quality 3, Thematic Relevance 1

[0614] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0615] Step 1:

[0616] User:

[0617] The user prepares application data in a text file called "applications.txt" through the terminal GUI. This file contains the application data to be evaluated line by line. Next, the user uploads this file to the system. Specifically, the user opens the file selection dialog, selects "applications.txt", and clicks the upload button.

[0618] input:

[0619] "applications.txt" file

[0620] output:

[0621] Send upload request

[0622] Step 2:

[0623] server:

[0624] The server receives the uploaded "applications.txt". After receiving it, the server reads the contents of this file in text format and manages each line as application data in list format. Specifically, it parses the file line by line and adds the application data to the list.

[0625] input:

[0626] Uploaded "applications.txt" file

[0627] output:

[0628] A list of loaded application data

[0629] Step 3:

[0630] server:

[0631] The server passes the list of application data to the generative AI model for analysis and evaluation. The server inputs each application data into the generative AI model and scores it based on originality, quality, suitability for the theme, etc. The evaluation results generated by the model are obtained in text format.

[0632] input:

[0633] List of application data, generative AI model

[0634] output:

[0635] List of evaluation results

[0636] Step 4:

[0637] server:

[0638] The server collects the evaluation results returned by the generative AI model and clearly verbalizes the reasons for the evaluation. Specifically, it runs a program that generates sentences to justify the evaluation results, and compiles the evaluation results and the reasons for the evaluation in the "evaluations.txt" file.

[0639] input:

[0640] List of evaluation results

[0641] output:

[0642] "evaluations.txt" file

[0643] Step 5:

[0644] server:

[0645] The server uses an emotion engine to analyze user reactions and feedback in real time. Specifically, if a user expresses negative emotions in their evaluation results, the server acquires the emotion data and adjusts the evaluation results and their expressions.

[0646] input:

[0647] User emotion data

[0648] output:

[0649] Adjusted evaluation results or representations

[0650] Step 6:

[0651] Device:

[0652] The terminal provides a download operation for the "evaluations.txt" file so that the user can check the evaluation results. The user can download the evaluation results file from the server by clicking the "Download evaluation results" button in the GUI.

[0653] input:

[0654] Request to download the "evaluations.txt" file

[0655] output:

[0656] The downloaded "evaluations.txt" file

[0657] This process allows users to see the detailed evaluation results and reasons for their submissions in a transparent manner. In addition, by incorporating emotional feedback, users can increase their satisfaction with the evaluation results.

[0658] (Application example 2)

[0659] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0660] Conventional application data evaluation systems have issues with the fairness and speed of the evaluation process, and because they do not take into account user emotions or feedback, users are less satisfied with the evaluation results. This necessitates improvements in the quality of application data analysis and evaluation. It is also necessary to improve the transparency of evaluation results and provide an evaluation process that users can be satisfied with.

[0661] 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.

[0662] In this invention, the server includes means for receiving application data, means for analyzing the application data and evaluating it using a generative AI model, means for outputting the evaluation results, means including an emotion engine for recognizing the user's emotions, and means for optimizing the evaluation process based on user feedback. This not only enables efficient analysis and evaluation of application data, but also enables optimization of the evaluation process taking into account the user's emotions and feedback.

[0663] The "means for receiving application data" is a function that allows the system to receive application data provided by the user.

[0664] "Means for analyzing application data and evaluating using a generative AI model" refers to a function for analyzing and evaluating application data using a generative AI model.

[0665] The "means for outputting evaluation results" is a function for presenting the results evaluated by the generative AI model to the user.

[0666] The "means including an emotion engine for recognizing the user's emotion" is a function for analyzing feedback and reactions from the user and recognizing the emotion.

[0667] The "means for optimizing the evaluation process based on user feedback" is a function for improving and optimizing the entire evaluation process based on feedback provided by the user.

[0668] The system of the present invention automatically evaluates employees in a store and provides the evaluation results fairly and quickly. Specific embodiments of the present invention will be described below.

[0669] First, the employee evaluation system is installed on a device such as a smartphone or tablet. This device is used by store managers to evaluate employee performance. Employee evaluation data is created and managed in the form of "applications.txt."

[0670] The terminal is equipped with a means for receiving application data and performs operations to upload this data to a server. The server receives the uploaded "applications.txt" and analyzes its contents. A generative AI model (e.g., OpenAI GPT-3) is used for the analysis to evaluate each application data. The generative AI model evaluates the employee's performance and generates an evaluation result along with the reasons for the evaluation.

[0671] The server also incorporates an emotion engine that analyzes feedback from store managers and employees in real time and collects emotional data. The emotion engine uses the TextBlob library, for example. The evaluation process is optimized based on the emotional data, providing evaluation results that are easy for users to accept. Specifically, if negative feedback is received, the reasons for this are analyzed and reflected in the evaluation process.

[0672] The evaluation results are output as "evaluations.txt" and provided to the terminal in a downloadable format. Users can download this file and check the evaluation results and reasons for each employee.

[0673] Specific examples are shown below.

[0674] For example, when a store manager evaluates an employee, he or she might enter the following prompt:

[0675] Example prompt:

[0676] "Please rate your employees:

[0677] They are polite and have high customer satisfaction, but they sometimes make mistakes.

[0678] evaluation:"

[0679] The generated evaluation results are as follows:

[0680] Example of evaluation result:

[0681] "Rating: C+

[0682] Reason: Customer feedback has been generally positive, but there are occasional mistakes, so I've given it an overall rating of C+.

[0683] In this way, the system of the present invention can efficiently evaluate employees within a store, improve the transparency of the evaluation results, and increase the user's satisfaction.

[0684] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0685] Step 1:

[0686] The user prepares employee evaluation data as "applications.txt" and uploads it to the system through the terminal GUI.

[0687] Input: "applications.txt" file containing application data

[0688] Output: The device finishes uploading the "applications.txt" file

[0689] Step 2:

[0690] The server receives the uploaded "applications.txt" file and reads its contents.

[0691] Input: "applications.txt" file

[0692] Output: A list of application data in text format

[0693] Step 3:

[0694] The server analyzes the loaded application data using a generative AI model and evaluates each application data.

[0695] Input: List of application data

[0696] Output: A list of the evaluation results and the text of the evaluation reason.

[0697] Step 4:

[0698] The server compiles the evaluation results obtained by the generative AI model in text format as "evaluations.txt."

[0699] Input: List of evaluation results and reasons for evaluation

[0700] Output: "evaluations.txt" file

[0701] Step 5:

[0702] The server analyzes user feedback and reactions in real time using an emotion engine, which uses the TextBlob library to recognize emotions.

[0703] Input: User feedback text

[0704] Output: Emotion data (negative / positive judgment)

[0705] Step 6:

[0706] Based on the emotion data, the server optimizes the content and method of the evaluation process, and in this process, if there is negative feedback, the reasons for it are analyzed in detail and the evaluation results are adjusted.

[0707] Input: Emotion data, evaluation results

[0708] Output: Adjusted evaluation results

[0709] Step 7:

[0710] The device downloads the "evaluations.txt" file from the server, allowing the user to view the evaluation results and the reasons for them.

[0711] Input: "evaluations.txt" file

[0712] Output: The evaluation results and reasons displayed to the user

[0713] Specifically, when a user uploads "applications.txt," the server reads its contents, rates it, and saves the rating results as a generated file. The emotion engine then analyzes the feedback and adjusts the rating results as necessary. Finally, a download link is provided to the device so that the user can review the rating.

[0714] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0715] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0716] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0717] [Third embodiment]

[0718] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0719] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0720] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0721] 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.

[0722] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0723] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0724] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0725] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0726] 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.

[0727] 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.

[0728] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0729] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0730] The system of the present invention uses a generative AI model to efficiently analyze application data and conduct fair and rapid evaluations, thereby eliminating issues such as unfair selection and time constraints, and providing applicants with a highly transparent evaluation.

[0731] Program processing

[0732] User:

[0733] First, the user prepares application data in a text file called "applications.txt" through the terminal's GUI, and then uploads this file to the system.

[0734] server:

[0735] The server receives the uploaded "applications.txt" and reads its contents. When reading, each line of the file is retrieved as application data and processed in text format. At this time, the application data is managed in list format.

[0736] The server then analyzes the application data, using a generative AI model to evaluate each application. The generative AI model is a pre-trained artificial intelligence that provides an appropriate evaluation based on the content of the application data.

[0737] The evaluation results are returned in text format from the generative AI model. The server collects these evaluation results and performs evaluation on all application data. The evaluation results are compiled in list format and finally output to a file called "evaluations.txt."

[0738] The server uses a means to verbalize the evaluation results and clearly describes the reasons for the evaluation results, which improves the transparency of the reasons for the judgment and allows users to understand the evaluation.

[0739] Device:

[0740] The terminal provides an option to download the "evaluations.txt" file output from the server, allowing users to check the evaluations and reasons for each application.

[0741] Specific examples

[0742] For example, suppose a user submits 50 entries to a creative contest. First, the user puts all the application data into a file called "applications.txt" and uploads this file to the system.

[0743] The server reads this file and evaluates each work using a generative AI model. For example, the generative AI model evaluates the work based on factors such as originality, quality, and suitability for the theme, and obtains the evaluation results for each.

[0744] The server saves the evaluation results in a text file called "evaluations.txt" and makes it available for users to download. Users can download this file and check the evaluation results and reasons for their submissions.

[0745] This allows for fair and prompt evaluation without being influenced by the judges' subjectivity or order. In addition, the reasons for the evaluation are clearly stated, allowing users to accept the results in a satisfactory manner.

[0746] In this way, the present invention automates the analysis and evaluation of application data, realizing a fair and transparent selection process.

[0747] The processing flow will be explained below.

[0748] Step 1:

[0749] User: The user selects the "applications.txt" file through the terminal GUI and uploads it to the server.

[0750] Step 2:

[0751] Server: The server receives the uploaded "applications.txt" file, which contains the application data, one line per file.

[0752] Step 3:

[0753] Server: The server reads the received "applications.txt" file and parses each line sequentially. The contents of the file are stored internally in list format.

[0754] Step 4:

[0755] Server: The server passes each application data to the generative AI model for evaluation. The generative AI model is a pre-trained artificial intelligence that makes an appropriate evaluation based on the application data.

[0756] Step 5:

[0757] Server: Obtains the evaluation results from the generative AI model and stores them in a single list. The evaluation results include the score for each application and the reason for the evaluation.

[0758] Step 6:

[0759] Server: The server outputs the evaluation results for all application data in a file called "evaluations.txt." This file contains evaluation comments and scores for each application.

[0760] Step 7:

[0761] Terminal: The terminal provides the user with a link or button to download the "evaluations.txt" file output from the server.

[0762] Step 8:

[0763] User: The user downloads the "evaluations.txt" file via their device. After downloading, they can check the evaluation results and reasons for each application data.

[0764] By going through the above steps, the system of the present invention efficiently analyzes application data and achieves fair and prompt evaluation.

[0765] Example 1

[0766] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0767] The conventional application data evaluation process lacked fairness and speed because it relied on the evaluator's subjectivity and order. Furthermore, the evaluation process lacked transparency, making it difficult for applicants to accept the evaluation results. These issues reduced the reliability of the evaluation process and had a significant impact on applicants.

[0768] 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.

[0769] In this invention, the server includes means for a user to prepare application data in text file format and upload it via the terminal's GUI, means for the server to receive the uploaded application data and read it in text format, means for managing the application data read by the server in list format and analyzing and evaluating each data using a generative AI model, means for evaluating the application data using prompt sentences from the generative AI model and obtaining the evaluation results in list format, means for aggregating the evaluation results for all application data and verbalizing the reasons for the evaluation and outputting them as a text file, and means for the terminal to download the evaluation result file. This improves the fairness and speed of the evaluation process and enables highly transparent evaluation.

[0770] A "user" is an individual or organization that uses the system to prepare application data and obtain evaluation results.

[0771] A "terminal" is an electronic device such as a computer device or a smartphone that is operated by a user.

[0772] "GUI" is a graphical user interface that allows users to operate a system through a terminal.

[0773] "Application data" refers to a group of data that a user submits to the system for evaluation.

[0774] A "text file" is a file format that stores only character data.

[0775] "Server" means a central processing unit that receives, processes, and evaluates application data.

[0776] "Loading" means that the server holds the application data in file format and makes the contents available for analysis.

[0777] The "list format" is a format in which data is listed, with each data item arranged in order.

[0778] A "generative AI model" is an artificial intelligence model that has been trained in advance with a large amount of data and evaluates applications based on the application data.

[0779] A "prompt" is an input instruction given to a generative AI model that determines how the model evaluates the data.

[0780] "Analysis" is the process of breaking down, converting, and evaluating application data based on a database.

[0781] "Evaluation" refers to the act of analyzing the content of application data using a generative AI model and determining results based on specific criteria.

[0782] "Evaluation results" are judgment results obtained based on analysis by the generative AI model.

[0783] "Verbalization" is the process of converting and describing the evaluation results in a form that users can understand.

[0784] "Output as text file" means saving the evaluation results as a text file.

[0785] "Making the file available for download" means making the file available for the user to obtain through the terminal.

[0786] The present invention is a system that automates the process of a user preparing application data and a server evaluating it. In this system, the user, terminal, and server each play a specific role, and the application data is evaluated with a high degree of fairness and transparency.

[0787] User:

[0788] The user prepares application data in a text file called "applications.txt" using the terminal's GUI. The prepared text file contains multiple lines of application data submitted by the applicant. The user uploads this file to the system through the GUI. For example, the GUI may provide a file selection dialog, and the user can click the upload button to send the file to the system.

[0789] server:

[0790] The server receives the "applications.txt" file uploaded by the user. The server then reads this file in text format and extracts each line as individual application data. The extracted application data is managed in list format. The server then analyzes and evaluates each data using a generative AI model. The generative AI model is a pre-trained artificial intelligence that performs detailed analysis and evaluation of the application data.

[0791] Analysis and evaluation are performed using prompts from the generative AI model. Specific examples of prompts are shown below.

[0792] Example prompt:

[0793] Application Data: [Enter your application data here]

[0794] Evaluation criteria: Entries will be evaluated based on originality, quality, and relevance to the topic.

[0795] Output evaluation result format:

[0796] Originality: [Evaluation Results]

[0797] Quality: [Evaluation Results]

[0798] Theme suitability: [Evaluation result]

[0799] Reason for evaluation: [Describe the reason]

[0800] Please print out the evaluation results and reasons for the following application data.

[0801] The generative AI model evaluates each application data using prompt sentences and obtains the evaluation results in list format.

[0802] The server compiles these evaluation results and outputs them as a text file called "evaluations.txt." The evaluation results clearly state the reasons for the evaluation of each application. The generated "evaluations.txt" file is provided by the server for easy access by users.

[0803] Device:

[0804] The device provides the user with an operation to download the "evaluations.txt" file. The user can click the dedicated download button to download the evaluation result file to the device. The downloaded file contains all the evaluations made by the generative AI model and the reasons for them. The user can review this file to understand the evaluations made to their submission and the reasons for them.

[0805] As described above, the system of the present invention automates the preparation, uploading, analysis, evaluation, and provision of results of application data, realizing a fair and transparent selection process, allowing users to receive evaluation results in a satisfactory manner.

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

[0807] Step 1:

[0808] User

[0809] The user uses the GUI on their device to prepare a text file called "applications.txt." This file contains multiple pieces of application data. The user uploads this file to the system through their device. Specifically, the user selects "applications.txt" from the file selection dialog in the GUI and clicks the upload button. The input data is "applications.txt," and this is sent to the system.

[0810] Step 2:

[0811] server

[0812] The server receives "applications.txt" uploaded by the user. Next, the server reads this file in text format and extracts each line as individual application data. The extracted application data is managed in list format. The input data is the received "applications.txt" and the output is the application data in list format.

[0813] Step 3:

[0814] server

[0815] The server passes the acquired list-format application data to a generative AI model for analysis and evaluation. The generative AI model used here receives a prompt as input and evaluates the application data. The input data is a list of application data and the prompt, and the output is the evaluation result corresponding to each application data.

[0816] Step 4:

[0817] server

[0818] The server collects the evaluation results returned by the generative AI model. Each evaluation result is evaluated based on criteria such as originality, quality, and suitability for the theme, with the reasons for each evaluation clearly stated. The input data is a list of the evaluation results returned by the generative AI model, and the output is a list of the evaluation results and their reasons.

[0819] Step 5:

[0820] server

[0821] The server compiles the evaluation results for all application data and outputs them as a text file called "evaluations.txt." This file contains the evaluation results for each application data and the reasons for each. The input data is a list of evaluation results, and the output is the "evaluations.txt" file.

[0822] Step 6:

[0823] Terminal

[0824] The device provides an operation that allows the user to download the evaluation result file "evaluations.txt." The user clicks a dedicated download button to save the evaluation results to their device. The input data is "evaluations.txt" saved on the server, and the output is the "evaluations.txt" file downloaded to the user's device.

[0825] Step 7:

[0826] User

[0827] Users can review the downloaded "evaluations.txt" and understand the evaluation of their submission and the reasons for it. This ensures fairness and transparency in the evaluation, allowing users to receive evaluation results in a manner that satisfies them. The input data is "evaluations.txt," and the output is the user's understanding and agreement.

[0828] (Application example 1)

[0829] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0830] In conventional design contests, the evaluation of submitted works is often influenced by the judges' subjective opinions and order, resulting in a lack of fairness and transparency. Furthermore, because the evaluation process takes time, there is also the problem of a time lag before the results are notified to the applicants. The present invention aims to solve these problems and achieve fair and prompt evaluation.

[0831] 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.

[0832] In this invention, the server includes means for receiving application data, means for analyzing the application data and evaluating it using a generative AI model, means for outputting the evaluation results, means for generating the reasons for the evaluation in text format, and means for outputting the evaluation results in a list format. This makes it possible to fairly and quickly evaluate submitted designs and provide applicants with highly transparent evaluation results.

[0833] "Application Data" refers to information including designs and ideas submitted for design contests and various selection processes.

[0834] A "generative AI model" is a type of artificial intelligence that is trained in advance using a large amount of data and can evaluate and analyze specific tasks with high accuracy.

[0835] "Evaluation results" are the overall evaluation results of the application data obtained after analysis by the generative AI model.

[0836] The "reason for evaluation" is textual information that clearly explains why the evaluation was made in response to the evaluation result output by the generative AI model.

[0837] A "list format" is a data format in which multiple items are arranged in an orderly manner so that the contents can be understood at a glance.

[0838] The present invention is a system that uses a generative AI model to analyze application data and perform fair and rapid evaluation, and is specifically implemented as follows.

[0839] Generating a Program

[0840] System Configuration

[0841] 1. Server: The server plays a central role in analyzing the application data received from users. It mainly uses the following hardware and software:

[0842] Hardware: A server with a powerful processor and large memory capacity.

[0843] Software: Python programming language and OpenAI GPT-4, CLIP model for image recognition, database system for data management.

[0844] 2. Terminal: Provides an interface for users to upload application data and download evaluation results.

[0845] Hardware: Smartphone, tablet, or personal computer.

[0846] Software: Use a dedicated application or web browser.

[0847] Processing flow

[0848] 1. Receipt of application data

[0849] Users upload their design (text and image files) to a dedicated smartphone app, which the server receives and manages in list format.

[0850] 2. Data analysis and evaluation

[0851] The server analyzes the received submission data using generative AI models (e.g., OpenAI GPT-4 and CLIP models). The models evaluate the designs' creativity, practicality, and theme suitability, and generate the results in text format.

[0852] 3. Output of evaluation results

[0853] The evaluation results are compiled in list format and saved as an "evaluations.txt" file. Users can download and check the evaluation results from their smartphone app.

[0854] Specific examples

[0855] For example, if a virtual store operator holds a product packaging design contest, the system works as follows:

[0856] Users upload their submitted designs as "applications.txt" files and corresponding image files via a smartphone app. The server receives these and uses a generative AI model to evaluate them as follows:

[0857] Example prompt sentence:

[0858] Please rate the creative contest submissions: Design-001: Creative packaging design, technically innovative, fits the brand.

[0859] The generated evaluation results are saved in "evaluations.txt," which users can download to check the evaluation of their submitted design and the reasons for it.

[0860] This will enable submitted designs to be evaluated fairly and quickly, and applicants to be provided with highly transparent evaluation results.

[0861] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0862] Step 1:

[0863] Users prepare their submission designs using a dedicated smartphone app and upload the "applications.txt" text file and corresponding image files. The user's design data is provided to the app as input, and is sent to the server as output.

[0864] Step 2:

[0865] The server receives the submitted design data. The received data includes text files and image files. It takes the uploaded files as input and converts them into an internal list format as output.

[0866] Step 3:

[0867] The server parses the received design submission data, first reading the details of each submission from a text file, then associating the image files with the corresponding entries. It reads text and image data as input and produces a data list ready for analysis as output.

[0868] Step 4:

[0869] The server evaluates the submitted designs using a generative AI model (e.g., OpenAI GPT-4 and CLIP models). The model is provided with a prompt and makes its evaluation based on that. As input, the generative AI model is provided with the following prompt:

[0870] Please rate the creative contest submissions: Design-001: Creative packaging design, technically innovative, fits the brand.

[0871] As output, you will receive the evaluation results for each design and the reasons for them in text format.

[0872] Step 5:

[0873] The server compiles the evaluation results from the generative AI model into a list and saves the integrated evaluation for all designs in the file “evaluations.txt.” The server collects the evaluation results as input and compiles them into a single text file as output.

[0874] Step 6:

[0875] The user downloads the "evaluations.txt" file from the dedicated app and checks the evaluation results and reasons for each design. The evaluation results stored on the server are obtained as input, and the downloaded file is displayed as output.

[0876] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0877] The system of the present invention utilizes a generative AI model to efficiently analyze application data and perform fair and rapid evaluations. The system also incorporates an emotion engine that recognizes user emotions and provides feedback to improve the quality of the evaluation process.

[0878] Program processing

[0879] User:

[0880] First, the user prepares application data in a text file called "applications.txt" through the terminal's GUI, and then uploads this file to the system.

[0881] server:

[0882] The server receives the uploaded "applications.txt" and reads its contents. When reading, each line of the file is retrieved as application data and processed in text format. At this time, the application data is managed in list format.

[0883] The server then analyzes the application data, using a generative AI model to evaluate each application. The generative AI model is a pre-trained artificial intelligence that provides an appropriate evaluation based on the content of the application data.

[0884] The evaluation results are returned in text format from the generative AI model. The server collects these evaluation results and performs evaluation on all application data. The evaluation results are compiled in list format and finally output to a file called "evaluations.txt."

[0885] The server uses a means to verbalize the evaluation results and clearly describes the reasons for the evaluation results, which improves the transparency of the reasons for the judgment and allows users to understand the evaluation.

[0886] The added emotion engine analyzes user reactions and feedback in real time. Emotional data is sent to the server and used to optimize the presentation of evaluation results and improve the evaluation process. Specifically, if a user expresses negative emotions toward a particular evaluation, the reason for this is analyzed, and problems with the evaluation process are identified and improved.

[0887] Device:

[0888] The terminal provides an option to download the "evaluations.txt" file output from the server, allowing users to check the evaluations and reasons for each application.

[0889] Specific examples

[0890] For example, suppose a user submits 50 entries to a creative contest. First, the user puts all the application data into a file called "applications.txt" and uploads this file to the system.

[0891] The server reads this file and evaluates each work using a generative AI model. For example, the generative AI model evaluates the work based on factors such as originality, quality, and suitability for the theme, and obtains the evaluation results for each.

[0892] The emotion engine analyzes in real time how users feel about their evaluation results. For example, if a user expresses dissatisfaction with a particular evaluation result, that emotion data is sent to the server. The server uses this data to adjust the evaluation result and its expression, providing it to the user in a more convincing format.

[0893] The server saves these evaluation results in a text file called "evaluations.txt" and makes it available for users to download. Users can download this file and check the evaluations and reasons for their submissions. In addition, the emotion engine takes user feedback into account during the evaluation process, allowing users to achieve more satisfying results.

[0894] In this way, the system of the present invention automates the analysis and evaluation of application data, realizing a fair and transparent selection process. Furthermore, by incorporating an emotion engine, user feedback can be reflected in real time, further improving the quality and transparency of the evaluation process.

[0895] The processing flow will be explained below.

[0896] Step 1:

[0897] User: The user selects the "applications.txt" file through the terminal GUI and uploads it to the server.

[0898] Step 2:

[0899] Server: The server receives the uploaded "applications.txt" file, which contains the application data, one line per file.

[0900] Step 3:

[0901] Server: The server reads the received "applications.txt" file and parses each line sequentially. The contents of the file are stored internally in list format.

[0902] Step 4:

[0903] Server: The server passes each application data to the generative AI model for evaluation. The generative AI model is a pre-trained artificial intelligence that makes an appropriate evaluation based on the application data.

[0904] Step 5:

[0905] Server: Obtains the evaluation results from the generative AI model and stores them in a single list. The evaluation results include the score for each application and the reason for the evaluation.

[0906] Step 6:

[0907] Server: The server outputs the evaluation results in a file called "evaluations.txt," which contains evaluation comments and scores for each submission.

[0908] Step 7:

[0909] Server: Using the configured emotion engine, analyzes user reactions and feedback in real time and obtains emotion data.

[0910] Step 8:

[0911] Server: Analyzes the user's emotional data obtained by the emotion engine along with the evaluation results, and adjusts the expression and content of the evaluation. If the user has a negative reaction, the cause is identified and the evaluation is reviewed and improved.

[0912] Step 9:

[0913] Terminal: The terminal provides the user with a link or button to download the "evaluations.txt" file output from the server.

[0914] Step 10:

[0915] User: The user downloads the "evaluations.txt" file via their device. After downloading, they can check the evaluation results and reasons for each application data.

[0916] In this way, incorporating an emotion engine can incorporate user feedback in real time, further improving the quality and transparency of the evaluation process.

[0917] Example 2

[0918] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0919] While current application data analysis and evaluation systems can provide evaluations, they are unable to consider users' feelings about the evaluation results, which can lead to users being dissatisfied with the results. Furthermore, they lack the ability to clearly articulate the reasons for the evaluation, resulting in a lack of transparency in the evaluations. Therefore, there is a need for a system that can analyze user feedback in real time and optimize the evaluation process to enable fairer and more transparent evaluations.

[0920] 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.

[0921] In this invention, the server includes a means for receiving application data, a means for analyzing the application data and performing evaluation using a generative AI model, a means for outputting evaluation results, and a means for analyzing users' emotions regarding the evaluation results in real time and optimizing the evaluation process, thereby enabling fair and transparent evaluation that reflects users' emotional feedback.

[0922] "Application data" refers to data that includes information to be evaluated.

[0923] A "generative AI model" is an artificial intelligence model trained for a specific task and used to analyze and evaluate application data.

[0924] "Evaluation results" refer to the scores and judgments output by the generative AI model based on the application data.

[0925] The "emotion engine" analyzes the user's emotions and reflects that feedback in the system's evaluation process.

[0926] A "text file" is a file format that contains text data and is used to store application data and evaluation results.

[0927] "Real time" means that processing is carried out close to the moment a user operation or reaction occurs.

[0928] "Analysis" is the process of dissecting data and drawing specific conclusions or results.

[0929] "Scoring" is the process of assigning a numerical score based on specific evaluation criteria.

[0930] "Optimization" means making adjustments or improvements to achieve the most effective and efficient results for a particular purpose.

[0931] "Transparency" is a characteristic that indicates that a system or process is easy to understand from the outside and is fair.

[0932] This invention is a system that analyzes application data and performs fair and rapid evaluations using a generative AI model. Furthermore, it analyzes user sentiment in real time and incorporates feedback into the evaluation process, improving the transparency and acceptability of evaluations.

[0933] System configuration:

[0934] This system is mainly composed of three components: a server, a terminal, and a user. The server manages the data processing and evaluation process, and the terminal provides an interface with the user. The user inputs data and checks the evaluation results.

[0935] Program processing:

[0936] User:

[0937] First, the user prepares the application data through the terminal GUI. This data is saved in a text file called "applications.txt." The user then selects this file using the terminal's file selection dialog and uploads it to the system.

[0938] server:

[0939] The server receives the uploaded file and reads its contents. Specifically, the application server retrieves "applications.txt" and manages each line as application data in list format. The server then analyzes and evaluates the application data using a generative AI model. The generative AI model scores the applications based on the input data in terms of originality, quality, and suitability for the theme. The evaluation results are saved in text format, and the reasons for the evaluation are clearly stated.

[0940] The emotion engine analyzes user reactions in real time and sends them to the server. Based on this emotion data, the server adjusts and optimizes the evaluation results and their expressions. For example, if a user expresses dissatisfaction with a particular evaluation, the server analyzes the reason and identifies and improves the problematic aspects of the evaluation process.

[0941] Device:

[0942] The device provides a download option for the "evaluations.txt" file so that users can check the evaluation results. Users can download this file and check the evaluation results and reasons.

[0943] Examples:

[0944] For example, if a user submits 50 entries to a creative contest, the application data is compiled in "applications.txt" as follows:

[0945] 1. Work A - Highly original, high quality, and perfectly suited to the theme

[0946] 2. Work B - Original but mediocre quality, somewhat unsuitable for the theme

[0947] 3. Work C - Unoriginal, low quality, and completely unsuitable for the theme

[0948] ...

[0949] 50. Work XX - ···

[0950] When this file is uploaded from a device, the server reads the contents and uses a generative AI model to rate each work. For example, a rating result such as "Work A- Originality: 10, Quality: 10, Relevance to the theme: 10" is generated. The emotion engine analyzes the user's reactions, and if the user expresses dissatisfaction with a particular rating, it adjusts the result and its expression based on that feedback. This allows the user to receive a rating result that they are satisfied with.

[0951] Example prompt sentence:

[0952] Please evaluate the submission data below and score each submission based on originality, quality, and fit to the theme.

[0953] Application Data:

[0954] 1. Work A - Highly original, of excellent quality, and perfectly in line with the theme.

[0955] 2. Work B - Original but of average quality. Does not quite fit the theme.

[0956] 3. Work C - Lacks originality and is of poor quality. Does not fit the theme at all.

[0957] Expected evaluation results:

[0958] 1. Work A - Score: Originality 10, Quality 10, Thematic Relevance 10

[0959] 2. Work B - Score: Originality 8, Quality 6, Thematic Relevance 5

[0960] 3. Work C - Score: Originality 2, Quality 3, Thematic Relevance 1

[0961] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0962] Step 1:

[0963] User:

[0964] The user prepares application data in a text file called "applications.txt" through the terminal GUI. This file contains the application data to be evaluated line by line. Next, the user uploads this file to the system. Specifically, the user opens the file selection dialog, selects "applications.txt", and clicks the upload button.

[0965] input:

[0966] "applications.txt" file

[0967] output:

[0968] Send upload request

[0969] Step 2:

[0970] server:

[0971] The server receives the uploaded "applications.txt". After receiving it, the server reads the contents of this file in text format and manages each line as application data in list format. Specifically, it parses the file line by line and adds the application data to the list.

[0972] input:

[0973] Uploaded "applications.txt" file

[0974] output:

[0975] A list of loaded application data

[0976] Step 3:

[0977] server:

[0978] The server passes the list of application data to the generative AI model for analysis and evaluation. The server inputs each application data into the generative AI model and scores it based on originality, quality, suitability for the theme, etc. The evaluation results generated by the model are obtained in text format.

[0979] input:

[0980] List of application data, generative AI model

[0981] output:

[0982] List of evaluation results

[0983] Step 4:

[0984] server:

[0985] The server collects the evaluation results returned by the generative AI model and clearly verbalizes the reasons for the evaluation. Specifically, it runs a program that generates sentences to justify the evaluation results, and compiles the evaluation results and the reasons for the evaluation in the "evaluations.txt" file.

[0986] input:

[0987] List of evaluation results

[0988] output:

[0989] "evaluations.txt" file

[0990] Step 5:

[0991] server:

[0992] The server uses an emotion engine to analyze user reactions and feedback in real time. Specifically, if a user expresses negative emotions in their evaluation results, the server acquires the emotion data and adjusts the evaluation results and their expressions.

[0993] input:

[0994] User emotion data

[0995] output:

[0996] Adjusted evaluation results or representations

[0997] Step 6:

[0998] Device:

[0999] The terminal provides a download operation for the "evaluations.txt" file so that the user can check the evaluation results. The user can download the evaluation results file from the server by clicking the "Download evaluation results" button in the GUI.

[1000] input:

[1001] Request to download the "evaluations.txt" file

[1002] output:

[1003] The downloaded "evaluations.txt" file

[1004] This process allows users to see the detailed evaluation results and reasons for their submissions in a transparent manner. In addition, by incorporating emotional feedback, users can increase their satisfaction with the evaluation results.

[1005] (Application example 2)

[1006] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1007] Conventional application data evaluation systems have issues with the fairness and speed of the evaluation process, and because they do not take into account user emotions or feedback, users are less satisfied with the evaluation results. This necessitates improvements in the quality of application data analysis and evaluation. It is also necessary to improve the transparency of evaluation results and provide an evaluation process that users can be satisfied with.

[1008] 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.

[1009] In this invention, the server includes means for receiving application data, means for analyzing the application data and evaluating it using a generative AI model, means for outputting the evaluation results, means including an emotion engine for recognizing the user's emotions, and means for optimizing the evaluation process based on user feedback. This not only enables efficient analysis and evaluation of application data, but also enables optimization of the evaluation process taking into account the user's emotions and feedback.

[1010] The "means for receiving application data" is a function that allows the system to receive application data provided by the user.

[1011] "Means for analyzing application data and evaluating using a generative AI model" refers to a function for analyzing and evaluating application data using a generative AI model.

[1012] The "means for outputting evaluation results" is a function for presenting the results evaluated by the generative AI model to the user.

[1013] The "means including an emotion engine for recognizing the user's emotion" is a function for analyzing feedback and reactions from the user and recognizing the emotion.

[1014] The "means for optimizing the evaluation process based on user feedback" is a function for improving and optimizing the entire evaluation process based on feedback provided by the user.

[1015] The system of the present invention automatically evaluates employees in a store and provides the evaluation results fairly and quickly. Specific embodiments of the present invention will be described below.

[1016] First, the employee evaluation system is installed on a device such as a smartphone or tablet. This device is used by store managers to evaluate employee performance. Employee evaluation data is created and managed in the form of "applications.txt."

[1017] The terminal is equipped with a means for receiving application data and performs operations to upload this data to a server. The server receives the uploaded "applications.txt" and analyzes its contents. A generative AI model (e.g., OpenAI GPT-3) is used for the analysis to evaluate each application data. The generative AI model evaluates the employee's performance and generates an evaluation result along with the reasons for the evaluation.

[1018] The server also incorporates an emotion engine that analyzes feedback from store managers and employees in real time and collects emotional data. The emotion engine uses the TextBlob library, for example. The evaluation process is optimized based on the emotional data, providing evaluation results that are easy for users to accept. Specifically, if negative feedback is received, the reasons for this are analyzed and reflected in the evaluation process.

[1019] The evaluation results are output as "evaluations.txt" and provided to the terminal in a downloadable format. Users can download this file and check the evaluation results and reasons for each employee.

[1020] Specific examples are shown below.

[1021] For example, when a store manager evaluates an employee, he or she might enter the following prompt:

[1022] Example prompt:

[1023] "Please rate your employees:

[1024] They are polite and have high customer satisfaction, but they sometimes make mistakes.

[1025] evaluation:"

[1026] The generated evaluation results are as follows:

[1027] Example of evaluation result:

[1028] "Rating: C+

[1029] Reason: Customer feedback has been generally positive, but there are occasional mistakes, so I've given it an overall rating of C+.

[1030] In this way, the system of the present invention can efficiently evaluate employees within a store, improve the transparency of the evaluation results, and increase the user's satisfaction.

[1031] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1032] Step 1:

[1033] The user prepares employee evaluation data as "applications.txt" and uploads it to the system through the terminal GUI.

[1034] Input: "applications.txt" file containing application data

[1035] Output: The device finishes uploading the "applications.txt" file

[1036] Step 2:

[1037] The server receives the uploaded "applications.txt" file and reads its contents.

[1038] Input: "applications.txt" file

[1039] Output: A list of application data in text format

[1040] Step 3:

[1041] The server analyzes the loaded application data using a generative AI model and evaluates each application data.

[1042] Input: List of application data

[1043] Output: A list of the evaluation results and the text of the evaluation reason.

[1044] Step 4:

[1045] The server compiles the evaluation results obtained by the generative AI model in text format as "evaluations.txt."

[1046] Input: List of evaluation results and reasons for evaluation

[1047] Output: "evaluations.txt" file

[1048] Step 5:

[1049] The server analyzes user feedback and reactions in real time using an emotion engine, which uses the TextBlob library to recognize emotions.

[1050] Input: User feedback text

[1051] Output: Emotion data (negative / positive judgment)

[1052] Step 6:

[1053] Based on the emotion data, the server optimizes the content and method of the evaluation process, and in this process, if there is negative feedback, the reasons for it are analyzed in detail and the evaluation results are adjusted.

[1054] Input: Emotion data, evaluation results

[1055] Output: Adjusted evaluation results

[1056] Step 7:

[1057] The device downloads the "evaluations.txt" file from the server, allowing the user to view the evaluation results and the reasons for them.

[1058] Input: "evaluations.txt" file

[1059] Output: The evaluation results and reasons displayed to the user

[1060] Specifically, when a user uploads "applications.txt," the server reads its contents, rates it, and saves the rating results as a generated file. The emotion engine then analyzes the feedback and adjusts the rating results as necessary. Finally, a download link is provided to the device so that the user can review the rating.

[1061] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1062] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1063] 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.

[1064] [Fourth embodiment]

[1065] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1066] 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.

[1067] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1068] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1069] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1070] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1071] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1072] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1073] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1074] 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.

[1075] 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.

[1076] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1077] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1078] The system of the present invention uses a generative AI model to efficiently analyze application data and conduct fair and rapid evaluations, thereby eliminating issues such as unfair selection and time constraints, and providing applicants with a highly transparent evaluation.

[1079] Program processing

[1080] User:

[1081] First, the user prepares application data in a text file called "applications.txt" through the terminal's GUI, and then uploads this file to the system.

[1082] server:

[1083] The server receives the uploaded "applications.txt" and reads its contents. When reading, each line of the file is retrieved as application data and processed in text format. At this time, the application data is managed in list format.

[1084] The server then analyzes the application data, using a generative AI model to evaluate each application. The generative AI model is a pre-trained artificial intelligence that provides an appropriate evaluation based on the content of the application data.

[1085] The evaluation results are returned in text format from the generative AI model. The server collects these evaluation results and performs evaluation on all application data. The evaluation results are compiled in list format and finally output to a file called "evaluations.txt."

[1086] The server uses a means to verbalize the evaluation results and clearly describes the reasons for the evaluation results, which improves the transparency of the reasons for the judgment and allows users to understand the evaluation.

[1087] Device:

[1088] The terminal provides an option to download the "evaluations.txt" file output from the server, allowing users to check the evaluations and reasons for each application.

[1089] Specific examples

[1090] For example, suppose a user submits 50 entries to a creative contest. First, the user puts all the application data into a file called "applications.txt" and uploads this file to the system.

[1091] The server reads this file and evaluates each work using a generative AI model. For example, the generative AI model evaluates the work based on factors such as originality, quality, and suitability for the theme, and obtains the evaluation results for each.

[1092] The server saves the evaluation results in a text file called "evaluations.txt" and makes it available for users to download. Users can download this file and check the evaluation results and reasons for their submissions.

[1093] This allows for fair and prompt evaluation without being influenced by the judges' subjectivity or order. In addition, the reasons for the evaluation are clearly stated, allowing users to accept the results in a satisfactory manner.

[1094] In this way, the present invention automates the analysis and evaluation of application data, realizing a fair and transparent selection process.

[1095] The processing flow will be explained below.

[1096] Step 1:

[1097] User: The user selects the "applications.txt" file through the terminal GUI and uploads it to the server.

[1098] Step 2:

[1099] Server: The server receives the uploaded "applications.txt" file, which contains the application data, one line per file.

[1100] Step 3:

[1101] Server: The server reads the received "applications.txt" file and parses each line sequentially. The contents of the file are stored internally in list format.

[1102] Step 4:

[1103] Server: The server passes each application data to the generative AI model for evaluation. The generative AI model is a pre-trained artificial intelligence that makes an appropriate evaluation based on the application data.

[1104] Step 5:

[1105] Server: Obtains the evaluation results from the generative AI model and stores them in a single list. The evaluation results include the score for each application and the reason for the evaluation.

[1106] Step 6:

[1107] Server: The server outputs the evaluation results for all application data in a file called "evaluations.txt." This file contains evaluation comments and scores for each application.

[1108] Step 7:

[1109] Terminal: The terminal provides the user with a link or button to download the "evaluations.txt" file output from the server.

[1110] Step 8:

[1111] User: The user downloads the "evaluations.txt" file via their device. After downloading, they can check the evaluation results and reasons for each application data.

[1112] By going through the above steps, the system of the present invention efficiently analyzes application data and achieves fair and prompt evaluation.

[1113] Example 1

[1114] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1115] The conventional application data evaluation process lacked fairness and speed because it relied on the evaluator's subjectivity and order. Furthermore, the evaluation process lacked transparency, making it difficult for applicants to accept the evaluation results. These issues reduced the reliability of the evaluation process and had a significant impact on applicants.

[1116] 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.

[1117] In this invention, the server includes means for a user to prepare application data in text file format and upload it via the terminal's GUI, means for the server to receive the uploaded application data and read it in text format, means for managing the application data read by the server in list format and analyzing and evaluating each data using a generative AI model, means for evaluating the application data using prompt sentences from the generative AI model and obtaining the evaluation results in list format, means for aggregating the evaluation results for all application data and verbalizing the reasons for the evaluation and outputting them as a text file, and means for the terminal to download the evaluation result file. This improves the fairness and speed of the evaluation process and enables highly transparent evaluation.

[1118] A "user" is an individual or organization that uses the system to prepare application data and obtain evaluation results.

[1119] A "terminal" is an electronic device such as a computer device or a smartphone that is operated by a user.

[1120] "GUI" is a graphical user interface that allows users to operate a system through a terminal.

[1121] "Application data" refers to a group of data that a user submits to the system for evaluation.

[1122] A "text file" is a file format that stores only character data.

[1123] "Server" means a central processing unit that receives, processes, and evaluates application data.

[1124] "Loading" means that the server holds the application data in file format and makes the contents available for analysis.

[1125] The "list format" is a format in which data is listed, with each data item arranged in order.

[1126] A "generative AI model" is an artificial intelligence model that has been trained in advance with a large amount of data and evaluates applications based on the application data.

[1127] A "prompt" is an input instruction given to a generative AI model that determines how the model evaluates the data.

[1128] "Analysis" is the process of breaking down, converting, and evaluating application data based on a database.

[1129] "Evaluation" refers to the act of analyzing the content of application data using a generative AI model and determining results based on specific criteria.

[1130] "Evaluation results" are judgment results obtained based on analysis by the generative AI model.

[1131] "Verbalization" is the process of converting and describing the evaluation results in a form that users can understand.

[1132] "Output as text file" means saving the evaluation results as a text file.

[1133] "Making the file available for download" means making the file available for the user to obtain through the terminal.

[1134] The present invention is a system that automates the process of a user preparing application data and a server evaluating it. In this system, the user, terminal, and server each play a specific role, and the application data is evaluated with a high degree of fairness and transparency.

[1135] User:

[1136] The user prepares application data in a text file called "applications.txt" using the terminal's GUI. The prepared text file contains multiple lines of application data submitted by the applicant. The user uploads this file to the system through the GUI. For example, the GUI may provide a file selection dialog, and the user can click the upload button to send the file to the system.

[1137] server:

[1138] The server receives the "applications.txt" file uploaded by the user. The server then reads this file in text format and extracts each line as individual application data. The extracted application data is managed in list format. The server then analyzes and evaluates each data using a generative AI model. The generative AI model is a pre-trained artificial intelligence that performs detailed analysis and evaluation of the application data.

[1139] Analysis and evaluation are performed using prompts from the generative AI model. Specific examples of prompts are shown below.

[1140] Example prompt:

[1141] Application Data: [Enter your application data here]

[1142] Evaluation criteria: Entries will be evaluated based on originality, quality, and relevance to the topic.

[1143] Output evaluation result format:

[1144] Originality: [Evaluation Results]

[1145] Quality: [Evaluation Results]

[1146] Theme suitability: [Evaluation result]

[1147] Reason for evaluation: [Describe the reason]

[1148] Please print out the evaluation results and reasons for the following application data.

[1149] The generative AI model evaluates each application data using prompt sentences and obtains the evaluation results in list format.

[1150] The server compiles these evaluation results and outputs them as a text file called "evaluations.txt." The evaluation results clearly state the reasons for the evaluation of each application. The generated "evaluations.txt" file is provided by the server for easy access by users.

[1151] Device:

[1152] The device provides the user with an operation to download the "evaluations.txt" file. The user can click the dedicated download button to download the evaluation result file to the device. The downloaded file contains all the evaluations made by the generative AI model and the reasons for them. The user can review this file to understand the evaluations made to their submission and the reasons for them.

[1153] As described above, the system of the present invention automates the preparation, uploading, analysis, evaluation, and provision of results of application data, realizing a fair and transparent selection process, allowing users to receive evaluation results in a satisfactory manner.

[1154] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1155] Step 1:

[1156] User

[1157] The user uses the GUI on their device to prepare a text file called "applications.txt." This file contains multiple pieces of application data. The user uploads this file to the system through their device. Specifically, the user selects "applications.txt" from the file selection dialog in the GUI and clicks the upload button. The input data is "applications.txt," and this is sent to the system.

[1158] Step 2:

[1159] server

[1160] The server receives "applications.txt" uploaded by the user. Next, the server reads this file in text format and extracts each line as individual application data. The extracted application data is managed in list format. The input data is the received "applications.txt" and the output is the application data in list format.

[1161] Step 3:

[1162] server

[1163] The server passes the acquired list-format application data to a generative AI model for analysis and evaluation. The generative AI model used here receives a prompt as input and evaluates the application data. The input data is a list of application data and the prompt, and the output is the evaluation result corresponding to each application data.

[1164] Step 4:

[1165] server

[1166] The server collects the evaluation results returned by the generative AI model. Each evaluation result is evaluated based on criteria such as originality, quality, and suitability for the theme, with the reasons for each evaluation clearly stated. The input data is a list of the evaluation results returned by the generative AI model, and the output is a list of the evaluation results and their reasons.

[1167] Step 5:

[1168] server

[1169] The server compiles the evaluation results for all application data and outputs them as a text file called "evaluations.txt." This file contains the evaluation results for each application data and the reasons for each. The input data is a list of evaluation results, and the output is the "evaluations.txt" file.

[1170] Step 6:

[1171] Terminal

[1172] The device provides an operation that allows the user to download the evaluation result file "evaluations.txt." The user clicks a dedicated download button to save the evaluation results to their device. The input data is "evaluations.txt" saved on the server, and the output is the "evaluations.txt" file downloaded to the user's device.

[1173] Step 7:

[1174] User

[1175] Users can review the downloaded "evaluations.txt" and understand the evaluation of their submission and the reasons for it. This ensures fairness and transparency in the evaluation, allowing users to receive evaluation results in a manner that satisfies them. The input data is "evaluations.txt," and the output is the user's understanding and agreement.

[1176] (Application example 1)

[1177] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1178] In conventional design contests, the evaluation of submitted works is often influenced by the judges' subjective opinions and order, resulting in a lack of fairness and transparency. Furthermore, because the evaluation process takes time, there is also the problem of a time lag before the results are notified to the applicants. The present invention aims to solve these problems and achieve fair and prompt evaluation.

[1179] 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.

[1180] In this invention, the server includes means for receiving application data, means for analyzing the application data and evaluating it using a generative AI model, means for outputting the evaluation results, means for generating the reasons for the evaluation in text format, and means for outputting the evaluation results in a list format. This makes it possible to fairly and quickly evaluate submitted designs and provide applicants with highly transparent evaluation results.

[1181] "Application Data" refers to information including designs and ideas submitted for design contests and various selection processes.

[1182] A "generative AI model" is a type of artificial intelligence that is trained in advance using a large amount of data and can evaluate and analyze specific tasks with high accuracy.

[1183] "Evaluation results" are the overall evaluation results of the application data obtained after analysis by the generative AI model.

[1184] The "reason for evaluation" is textual information that clearly explains why the evaluation was made in response to the evaluation result output by the generative AI model.

[1185] A "list format" is a data format in which multiple items are arranged in an orderly manner so that the contents can be understood at a glance.

[1186] The present invention is a system that uses a generative AI model to analyze application data and perform fair and rapid evaluation, and is specifically implemented as follows.

[1187] Generating a Program

[1188] System Configuration

[1189] 1. Server: The server plays a central role in analyzing the application data received from users. It mainly uses the following hardware and software:

[1190] Hardware: A server with a powerful processor and large memory capacity.

[1191] Software: Python programming language and OpenAI GPT-4, CLIP model for image recognition, database system for data management.

[1192] 2. Terminal: Provides an interface for users to upload application data and download evaluation results.

[1193] Hardware: Smartphone, tablet, or personal computer.

[1194] Software: Use a dedicated application or web browser.

[1195] Processing flow

[1196] 1. Receipt of application data

[1197] Users upload their design (text and image files) to a dedicated smartphone app, which the server receives and manages in list format.

[1198] 2. Data analysis and evaluation

[1199] The server analyzes the received submission data using generative AI models (e.g., OpenAI GPT-4 and CLIP models). The models evaluate the designs' creativity, practicality, and theme suitability, and generate the results in text format.

[1200] 3. Output of evaluation results

[1201] The evaluation results are compiled in list format and saved as an "evaluations.txt" file. Users can download and check the evaluation results from their smartphone app.

[1202] Specific examples

[1203] For example, if a virtual store operator holds a product packaging design contest, the system works as follows:

[1204] Users upload their submitted designs as "applications.txt" files and corresponding image files via a smartphone app. The server receives these and uses a generative AI model to evaluate them as follows:

[1205] Example prompt sentence:

[1206] Please rate the creative contest submissions: Design-001: Creative packaging design, technically innovative, fits the brand.

[1207] The generated evaluation results are saved in "evaluations.txt," which users can download to check the evaluation of their submitted design and the reasons for it.

[1208] This will enable submitted designs to be evaluated fairly and quickly, and applicants to be provided with highly transparent evaluation results.

[1209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1210] Step 1:

[1211] Users prepare their submission designs using a dedicated smartphone app and upload the "applications.txt" text file and corresponding image files. The user's design data is provided to the app as input, and is sent to the server as output.

[1212] Step 2:

[1213] The server receives the submitted design data. The received data includes text files and image files. It takes the uploaded files as input and converts them into an internal list format as output.

[1214] Step 3:

[1215] The server parses the received design submission data, first reading the details of each submission from a text file, then associating the image files with the corresponding entries. It reads text and image data as input and produces a data list ready for analysis as output.

[1216] Step 4:

[1217] The server evaluates the submitted designs using a generative AI model (e.g., OpenAI GPT-4 and CLIP models). The model is provided with a prompt and makes its evaluation based on that. As input, the generative AI model is provided with the following prompt:

[1218] Please rate the creative contest submissions: Design-001: Creative packaging design, technically innovative, fits the brand.

[1219] As output, you will receive the evaluation results for each design and the reasons for them in text format.

[1220] Step 5:

[1221] The server compiles the evaluation results from the generative AI model into a list and saves the integrated evaluation for all designs in the file “evaluations.txt.” The server collects the evaluation results as input and compiles them into a single text file as output.

[1222] Step 6:

[1223] The user downloads the "evaluations.txt" file from the dedicated app and checks the evaluation results and reasons for each design. The evaluation results stored on the server are obtained as input, and the downloaded file is displayed as output.

[1224] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1225] The system of the present invention utilizes a generative AI model to efficiently analyze application data and perform fair and rapid evaluations. The system also incorporates an emotion engine that recognizes user emotions and provides feedback to improve the quality of the evaluation process.

[1226] Program processing

[1227] User:

[1228] First, the user prepares application data in a text file called "applications.txt" through the terminal's GUI, and then uploads this file to the system.

[1229] server:

[1230] The server receives the uploaded "applications.txt" and reads its contents. When reading, each line of the file is retrieved as application data and processed in text format. At this time, the application data is managed in list format.

[1231] The server then analyzes the application data, using a generative AI model to evaluate each application. The generative AI model is a pre-trained artificial intelligence that provides an appropriate evaluation based on the content of the application data.

[1232] The evaluation results are returned in text format from the generative AI model. The server collects these evaluation results and performs evaluation on all application data. The evaluation results are compiled in list format and finally output to a file called "evaluations.txt."

[1233] The server uses a means to verbalize the evaluation results and clearly describes the reasons for the evaluation results, which improves the transparency of the reasons for the judgment and allows users to understand the evaluation.

[1234] The added emotion engine analyzes user reactions and feedback in real time. Emotional data is sent to the server and used to optimize the presentation of evaluation results and improve the evaluation process. Specifically, if a user expresses negative emotions toward a particular evaluation, the reason for this is analyzed, and problems with the evaluation process are identified and improved.

[1235] Device:

[1236] The terminal provides an option to download the "evaluations.txt" file output from the server, allowing users to check the evaluations and reasons for each application.

[1237] Specific examples

[1238] For example, suppose a user submits 50 entries to a creative contest. First, the user puts all the application data into a file called "applications.txt" and uploads this file to the system.

[1239] The server reads this file and evaluates each work using a generative AI model. For example, the generative AI model evaluates the work based on factors such as originality, quality, and suitability for the theme, and obtains the evaluation results for each.

[1240] The emotion engine analyzes in real time how users feel about their evaluation results. For example, if a user expresses dissatisfaction with a particular evaluation result, that emotion data is sent to the server. The server uses this data to adjust the evaluation result and its expression, providing it to the user in a more convincing format.

[1241] The server saves these evaluation results in a text file called "evaluations.txt" and makes it available for users to download. Users can download this file and check the evaluations and reasons for their submissions. In addition, the emotion engine takes user feedback into account during the evaluation process, allowing users to achieve more satisfying results.

[1242] In this way, the system of the present invention automates the analysis and evaluation of application data, realizing a fair and transparent selection process. Furthermore, by incorporating an emotion engine, user feedback can be reflected in real time, further improving the quality and transparency of the evaluation process.

[1243] The processing flow will be explained below.

[1244] Step 1:

[1245] User: The user selects the "applications.txt" file through the terminal GUI and uploads it to the server.

[1246] Step 2:

[1247] Server: The server receives the uploaded "applications.txt" file, which contains the application data, one line per file.

[1248] Step 3:

[1249] Server: The server reads the received "applications.txt" file and parses each line sequentially. The contents of the file are stored internally in list format.

[1250] Step 4:

[1251] Server: The server passes each application data to the generative AI model for evaluation. The generative AI model is a pre-trained artificial intelligence that makes an appropriate evaluation based on the application data.

[1252] Step 5:

[1253] Server: Obtains the evaluation results from the generative AI model and stores them in a single list. The evaluation results include the score for each application and the reason for the evaluation.

[1254] Step 6:

[1255] Server: The server outputs the evaluation results in a file called "evaluations.txt," which contains evaluation comments and scores for each submission.

[1256] Step 7:

[1257] Server: Using the configured emotion engine, analyzes user reactions and feedback in real time and obtains emotion data.

[1258] Step 8:

[1259] Server: Analyzes the user's emotional data obtained by the emotion engine along with the evaluation results, and adjusts the expression and content of the evaluation. If the user has a negative reaction, the cause is identified and the evaluation is reviewed and improved.

[1260] Step 9:

[1261] Terminal: The terminal provides the user with a link or button to download the "evaluations.txt" file output from the server.

[1262] Step 10:

[1263] User: The user downloads the "evaluations.txt" file via their device. After downloading, they can check the evaluation results and reasons for each application data.

[1264] In this way, incorporating an emotion engine can incorporate user feedback in real time, further improving the quality and transparency of the evaluation process.

[1265] Example 2

[1266] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1267] While current application data analysis and evaluation systems can provide evaluations, they are unable to consider users' feelings about the evaluation results, which can lead to users being dissatisfied with the results. Furthermore, they lack the ability to clearly articulate the reasons for the evaluation, resulting in a lack of transparency in the evaluations. Therefore, there is a need for a system that can analyze user feedback in real time and optimize the evaluation process to enable fairer and more transparent evaluations.

[1268] 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.

[1269] In this invention, the server includes a means for receiving application data, a means for analyzing the application data and performing evaluation using a generative AI model, a means for outputting evaluation results, and a means for analyzing users' emotions regarding the evaluation results in real time and optimizing the evaluation process, thereby enabling fair and transparent evaluation that reflects users' emotional feedback.

[1270] "Application data" refers to data that includes information to be evaluated.

[1271] A "generative AI model" is an artificial intelligence model trained for a specific task and used to analyze and evaluate application data.

[1272] "Evaluation results" refer to the scores and judgments output by the generative AI model based on the application data.

[1273] The "emotion engine" analyzes the user's emotions and reflects that feedback in the system's evaluation process.

[1274] A "text file" is a file format that contains text data and is used to store application data and evaluation results.

[1275] "Real time" means that processing is carried out close to the moment a user operation or reaction occurs.

[1276] "Analysis" is the process of dissecting data and drawing specific conclusions or results.

[1277] "Scoring" is the process of assigning a numerical score based on specific evaluation criteria.

[1278] "Optimization" means making adjustments or improvements to achieve the most effective and efficient results for a particular purpose.

[1279] "Transparency" is a characteristic that indicates that a system or process is easy to understand from the outside and is fair.

[1280] This invention is a system that analyzes application data and performs fair and rapid evaluations using a generative AI model. Furthermore, it analyzes user sentiment in real time and incorporates feedback into the evaluation process, improving the transparency and acceptability of evaluations.

[1281] System configuration:

[1282] This system is mainly composed of three components: a server, a terminal, and a user. The server manages the data processing and evaluation process, and the terminal provides an interface with the user. The user inputs data and checks the evaluation results.

[1283] Program processing:

[1284] User:

[1285] First, the user prepares the application data through the terminal GUI. This data is saved in a text file called "applications.txt." The user then selects this file using the terminal's file selection dialog and uploads it to the system.

[1286] server:

[1287] The server receives the uploaded file and reads its contents. Specifically, the application server retrieves "applications.txt" and manages each line as application data in list format. The server then analyzes and evaluates the application data using a generative AI model. The generative AI model scores the applications based on the input data in terms of originality, quality, and suitability for the theme. The evaluation results are saved in text format, and the reasons for the evaluation are clearly stated.

[1288] The emotion engine analyzes user reactions in real time and sends them to the server. Based on this emotion data, the server adjusts and optimizes the evaluation results and their expressions. For example, if a user expresses dissatisfaction with a particular evaluation, the server analyzes the reason and identifies and improves the problematic aspects of the evaluation process.

[1289] Device:

[1290] The device provides a download option for the "evaluations.txt" file so that users can check the evaluation results. Users can download this file and check the evaluation results and reasons.

[1291] Examples:

[1292] For example, if a user submits 50 entries to a creative contest, the application data is compiled in "applications.txt" as follows:

[1293] 1. Work A - Highly original, high quality, and perfectly suited to the theme

[1294] 2. Work B - Original but mediocre quality, somewhat unsuitable for the theme

[1295] 3. Work C - Unoriginal, low quality, and completely unsuitable for the theme

[1296] ...

[1297] 50. Work XX - ···

[1298] When this file is uploaded from a device, the server reads the contents and uses a generative AI model to rate each work. For example, a rating result such as "Work A- Originality: 10, Quality: 10, Relevance to the theme: 10" is generated. The emotion engine analyzes the user's reactions, and if the user expresses dissatisfaction with a particular rating, it adjusts the result and its expression based on that feedback. This allows the user to receive a rating result that they are satisfied with.

[1299] Example prompt sentence:

[1300] Please evaluate the submission data below and score each submission based on originality, quality, and fit to the theme.

[1301] Application Data:

[1302] 1. Work A - Highly original, of excellent quality, and perfectly in line with the theme.

[1303] 2. Work B - Original but of average quality. Does not quite fit the theme.

[1304] 3. Work C - Lacks originality and is of poor quality. Does not fit the theme at all.

[1305] Expected evaluation results:

[1306] 1. Work A - Score: Originality 10, Quality 10, Thematic Relevance 10

[1307] 2. Work B - Score: Originality 8, Quality 6, Thematic Relevance 5

[1308] 3. Work C - Score: Originality 2, Quality 3, Thematic Relevance 1

[1309] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1310] Step 1:

[1311] User:

[1312] The user prepares application data in a text file called "applications.txt" through the terminal GUI. This file contains the application data to be evaluated line by line. Next, the user uploads this file to the system. Specifically, the user opens the file selection dialog, selects "applications.txt", and clicks the upload button.

[1313] input:

[1314] "applications.txt" file

[1315] output:

[1316] Send upload request

[1317] Step 2:

[1318] server:

[1319] The server receives the uploaded "applications.txt". After receiving it, the server reads the contents of this file in text format and manages each line as application data in list format. Specifically, it parses the file line by line and adds the application data to the list.

[1320] input:

[1321] Uploaded "applications.txt" file

[1322] output:

[1323] A list of loaded application data

[1324] Step 3:

[1325] server:

[1326] The server passes the list of application data to the generative AI model for analysis and evaluation. The server inputs each application data into the generative AI model and scores it based on originality, quality, suitability for the theme, etc. The evaluation results generated by the model are obtained in text format.

[1327] input:

[1328] List of application data, generative AI model

[1329] output:

[1330] List of evaluation results

[1331] Step 4:

[1332] server:

[1333] The server collects the evaluation results returned by the generative AI model and clearly verbalizes the reasons for the evaluation. Specifically, it runs a program that generates sentences to justify the evaluation results, and compiles the evaluation results and the reasons for the evaluation in the "evaluations.txt" file.

[1334] input:

[1335] List of evaluation results

[1336] output:

[1337] "evaluations.txt" file

[1338] Step 5:

[1339] server:

[1340] The server uses an emotion engine to analyze user reactions and feedback in real time. Specifically, if a user expresses negative emotions in their evaluation results, the server acquires the emotion data and adjusts the evaluation results and their expressions.

[1341] input:

[1342] User emotion data

[1343] output:

[1344] Adjusted evaluation results or representations

[1345] Step 6:

[1346] Device:

[1347] The terminal provides a download operation for the "evaluations.txt" file so that the user can check the evaluation results. The user can download the evaluation results file from the server by clicking the "Download evaluation results" button in the GUI.

[1348] input:

[1349] Request to download the "evaluations.txt" file

[1350] output:

[1351] The downloaded "evaluations.txt" file

[1352] This process allows users to see the detailed evaluation results and reasons for their submissions in a transparent manner. In addition, by incorporating emotional feedback, users can increase their satisfaction with the evaluation results.

[1353] (Application example 2)

[1354] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1355] Conventional application data evaluation systems have issues with the fairness and speed of the evaluation process, and because they do not take into account user emotions or feedback, users are less satisfied with the evaluation results. This necessitates improvements in the quality of application data analysis and evaluation. It is also necessary to improve the transparency of evaluation results and provide an evaluation process that users can be satisfied with.

[1356] 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.

[1357] In this invention, the server includes means for receiving application data, means for analyzing the application data and evaluating it using a generative AI model, means for outputting the evaluation results, means including an emotion engine for recognizing the user's emotions, and means for optimizing the evaluation process based on user feedback. This not only enables efficient analysis and evaluation of application data, but also enables optimization of the evaluation process taking into account the user's emotions and feedback.

[1358] The "means for receiving application data" is a function that allows the system to receive application data provided by the user.

[1359] "Means for analyzing application data and evaluating using a generative AI model" refers to a function for analyzing and evaluating application data using a generative AI model.

[1360] The "means for outputting evaluation results" is a function for presenting the results evaluated by the generative AI model to the user.

[1361] The "means including an emotion engine for recognizing the user's emotion" is a function for analyzing feedback and reactions from the user and recognizing the emotion.

[1362] The "means for optimizing the evaluation process based on user feedback" is a function for improving and optimizing the entire evaluation process based on feedback provided by the user.

[1363] The system of the present invention automatically evaluates employees in a store and provides the evaluation results fairly and quickly. Specific embodiments of the present invention will be described below.

[1364] First, the employee evaluation system is installed on a device such as a smartphone or tablet. This device is used by store managers to evaluate employee performance. Employee evaluation data is created and managed in the form of "applications.txt."

[1365] The terminal is equipped with a means for receiving application data and performs operations to upload this data to a server. The server receives the uploaded "applications.txt" and analyzes its contents. A generative AI model (e.g., OpenAI GPT-3) is used for the analysis to evaluate each application data. The generative AI model evaluates the employee's performance and generates an evaluation result along with the reasons for the evaluation.

[1366] The server also incorporates an emotion engine that analyzes feedback from store managers and employees in real time and collects emotional data. The emotion engine uses the TextBlob library, for example. The evaluation process is optimized based on the emotional data, providing evaluation results that are easy for users to accept. Specifically, if negative feedback is received, the reasons for this are analyzed and reflected in the evaluation process.

[1367] The evaluation results are output as "evaluations.txt" and provided to the terminal in a downloadable format. Users can download this file and check the evaluation results and reasons for each employee.

[1368] Specific examples are shown below.

[1369] For example, when a store manager evaluates an employee, he or she might enter the following prompt:

[1370] Example prompt:

[1371] "Please rate your employees:

[1372] They are polite and have high customer satisfaction, but they sometimes make mistakes.

[1373] evaluation:"

[1374] The generated evaluation results are as follows:

[1375] Example of evaluation result:

[1376] "Rating: C+

[1377] Reason: Customer feedback has been generally positive, but there are occasional mistakes, so I've given it an overall rating of C+.

[1378] In this way, the system of the present invention can efficiently evaluate employees within a store, improve the transparency of the evaluation results, and increase the user's satisfaction.

[1379] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1380] Step 1:

[1381] The user prepares employee evaluation data as "applications.txt" and uploads it to the system through the terminal GUI.

[1382] Input: "applications.txt" file containing application data

[1383] Output: The device finishes uploading the "applications.txt" file

[1384] Step 2:

[1385] The server receives the uploaded "applications.txt" file and reads its contents.

[1386] Input: "applications.txt" file

[1387] Output: A list of application data in text format

[1388] Step 3:

[1389] The server analyzes the loaded application data using a generative AI model and evaluates each application data.

[1390] Input: List of application data

[1391] Output: A list of the evaluation results and the text of the evaluation reason.

[1392] Step 4:

[1393] The server compiles the evaluation results obtained by the generative AI model in text format as "evaluations.txt."

[1394] Input: List of evaluation results and reasons for evaluation

[1395] Output: "evaluations.txt" file

[1396] Step 5:

[1397] The server analyzes user feedback and reactions in real time using an emotion engine, which uses the TextBlob library to recognize emotions.

[1398] Input: User feedback text

[1399] Output: Emotion data (negative / positive judgment)

[1400] Step 6:

[1401] Based on the emotion data, the server optimizes the content and method of the evaluation process, and in this process, if there is negative feedback, the reasons for it are analyzed in detail and the evaluation results are adjusted.

[1402] Input: Emotion data, evaluation results

[1403] Output: Adjusted evaluation results

[1404] Step 7:

[1405] The device downloads the "evaluations.txt" file from the server, allowing the user to view the evaluation results and the reasons for them.

[1406] Input: "evaluations.txt" file

[1407] Output: The evaluation results and reasons displayed to the user

[1408] Specifically, when a user uploads "applications.txt," the server reads its contents, rates it, and saves the rating results as a generated file. The emotion engine then analyzes the feedback and adjusts the rating results as necessary. Finally, a download link is provided to the device so that the user can review the rating.

[1409] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1410] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1411] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1412] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1413] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1414] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1415] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1416] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1417] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1418] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1419] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1420] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1421] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1422] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1423] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1424] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1425] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1426] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1427] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1428] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1429] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1430] The following is further disclosed regarding the above embodiment.

[1431] (Claim 1)

[1432] means for receiving application data;

[1433] A means of analyzing application data and evaluating it using a generative AI model;

[1434] means for outputting the evaluation result;

[1435] A system including:

[1436] (Claim 2)

[1437] 10. The system of claim 1, wherein the application data is read from a text file.

[1438] (Claim 3)

[1439] The system of claim 1, which verbalizes the reasons for evaluation based on the evaluation by the generative AI model.

[1440] "Example 1"

[1441] (Claim 1)

[1442] A method for users to prepare application data in text file format and upload it through the terminal GUI,

[1443] A means for the server to receive the uploaded application data and read it in text format;

[1444] The server manages the application data read in list format, and uses a generative AI model to analyze and evaluate each data.

[1445] A means for evaluating application data using prompt sentences from a generative AI model and obtaining evaluation results in list form;

[1446] A means to compile the evaluation results for all application data, verbalize the reasons for the evaluation, and output them as a text file;

[1447] A means for enabling the terminal to download the evaluation result file;

[1448] A system including:

[1449] (Claim 2)

[1450] 10. The system of claim 1, wherein the application data is read from a text file.

[1451] (Claim 3)

[1452] The system of claim 1, which verbalizes the reasons for evaluation based on the evaluation by the generative AI model.

[1453] "Application Example 1"

[1454] (Claim 1)

[1455] means for receiving application data;

[1456] A means of analyzing application data and evaluating it using a generative AI model;

[1457] means for outputting the evaluation result;

[1458] a means for generating the evaluation reasons in text form;

[1459] A means for outputting the evaluation results in a list format;

[1460] A system including:

[1461] (Claim 2)

[1462] 2. The system of claim 1, wherein submitted designs are read from text files and image files.

[1463] (Claim 3)

[1464] The system of claim 1 evaluates the creativity, practicality, and theme suitability of submitted designs based on evaluation by a generative AI model.

[1465] "Example 2: Combining Emotion Engines"

[1466] (Claim 1)

[1467] means for receiving application data;

[1468] A means of analyzing application data and evaluating it using a generative AI model;

[1469] means for outputting the evaluation result;

[1470] A means for analyzing user sentiment regarding evaluation results in real time and optimizing the evaluation process;

[1471] A system including:

[1472] (Claim 2)

[1473] 10. The system of claim 1, wherein the application data is read from a text file.

[1474] (Claim 3)

[1475] The system according to claim 1, which verbalizes the reasons for the evaluation based on the evaluation by the generative AI model and saves the evaluation results in a text file.

[1476] (Claim 4)

[1477] 2. The system according to claim 1, wherein the system analyzes the emotions expressed by the user in the evaluation results and adjusts the evaluation expressions based on the data.

[1478] "Application example 2 when combining emotion engines"

[1479] (Claim 1)

[1480] means for receiving application data;

[1481] A means of analyzing application data and evaluating it using a generative AI model;

[1482] means for outputting the evaluation result;

[1483] means including an emotion engine for recognizing an emotion of a user;

[1484] a means for optimizing the evaluation process based on user feedback;

[1485] A system including:

[1486] (Claim 2)

[1487] 10. The system of claim 1, wherein the application data is read from a text file.

[1488] (Claim 3)

[1489] The system of claim 1, which verbalizes the reasons for evaluation based on the evaluation by the generative AI model. [Explanation of symbols]

[1490] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving application data; A means of analyzing application data and evaluating it using a generative AI model; means for outputting the evaluation result; A system including:

2. 10. The system of claim 1, wherein the application data is read from a text file.

3. The system according to claim 1, which verbalizes the reasons for evaluation based on the evaluation by the generative AI model.

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