System

Abstract

A system is provided.SOLUTION: The system includes a means for providing a user interface, an input means for allowing a user to input a topic, a selection means for allowing the user to select a generation model to be used for analysis, an analysis means for executing analysis based on the topic input by the user and the selected generation model, and a display means for displaying an analysis result.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 today's digital society, where personalized information and efficiency are essential, users face the challenge of finding the best generative model for their needs from the many available. Existing platforms lack sufficient recommendation and comparison services for such models, leading users to spend a great deal of time selecting the model that best suits them. Furthermore, there is no easy way to compare the characteristics and analytical results of each generative model, leaving users with limited perspectives. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for providing a user interface, an input means for allowing a user to input a topic, a selection means for allowing a user to select a generative model to use in analysis, an analysis means for performing analysis based on the topic input by the user and the selected generative model, and a display means for displaying the analysis results. This allows a user to compare the differences in views and interpretations of multiple generative models on a specified topic. Furthermore, the system provides a means for collecting the analysis results of each generative model for a specific topic and displaying them in table, graph, or tabular format. This allows a user to refer to the answers of different generative models for a single problem, gaining a broad perspective and making it easier to understand the characteristics and reliability of each generative model. This enables users to determine the accuracy of information and access it efficiently.

[0006] A "user interface" is a component that provides a screen and input means for a user to interact with a system.

[0007] "Input means" is a component that allows a user to input the topic or information they wish to analyze.

[0008] The "selection means" is a component that allows the user to select a generative model to use in the analysis.

[0009] "Generative models" refer to algorithms or artificial intelligence that understand and generate human language, including language models and topic models.

[0010] The "analysis means" is a component for automatically analyzing using the topic entered by the user and the selected generative model.

[0011] The "display means" is a component for visually presenting the results obtained by the analysis means to the user.

[0012] "Cloud computing environment" refers to computing resources and services delivered over the Internet, offering a high degree of scalability and flexibility.

[0013] "Multiple generative models" refers to multiple generative models with different characteristics and algorithms, and their use enables multifaceted analysis.

[0014] A "topic" is a specific theme or subject that a user requests to be analyzed. [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] This invention provides a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user. Specific embodiments of this system are described below.

[0037] System Overview

[0038] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative model they want to use. Once the user has input and selected, the server performs analysis based on this information and provides the results to the device. Finally, the device displays the analysis results to the user.

[0039] User Interface

[0040] The server provides a web page for users to access, which includes a free text box for users to enter the topic they want to analyze and a drop-down list for users to select multiple generative models to use.

[0041] Topic and Model Input

[0042] Users input a topic on the webpage and select the generative model to use for analysis (e.g., GPT-3, BERT, T5, etc.). This allows users to specify any topic as the analysis target and simultaneously select multiple generative models for comparative analysis.

[0043] Sending and Receiving Form Data

[0044] After the user enters a topic and selects a model, they click the "Analyze" button on the device to send the form data to the server. The server receives this request and extracts the necessary data to start the analysis process.

[0045] Executing the analysis process

[0046] The server performs analysis using each model based on the user's input topic and the selected generative model. Specifically, it requests each generative model to analyze the topic and collects the results. For example, the GPT-3 model generates a detailed explanation and prediction for the topic, while the BERT model provides a context-based interpretation.

[0047] Collecting and displaying results

[0048] The server organizes the analysis results collected from each generative model and sends them back to the device. The device then displays the received analysis results in tabular and graphical formats, making it easy for users to compare them. This allows users to see the analysis results of multiple generative models at a glance and understand the characteristics and strengths of each model.

[0049] Specific examples

[0050] For example, if a user selects "GPT-3" and "BERT" for the topic "climate change," the server will use each model to generate the following analysis results:

[0051] GPT-3 analysis results: "A detailed explanation of the impacts of climate change"

[0052] BERT analysis results: "Summary of recent news articles related to climate change"

[0053] The device displays these analysis results in a table format, allowing users to compare the views of different models, thereby providing information from multiple perspectives and supporting appropriate decision-making.

[0054] In this way, the present invention is a system that enables users to compare the differences in views and interpretations of multiple generative models on a topic specified by the user, thereby enabling them to judge the accuracy of information and access it efficiently.

[0055] The processing flow will be explained below.

[0056] Step 1:

[0057] The server provides the user interface: it renders a web page that the user accesses, displaying a free text box for topic input and a drop-down list for selecting the generative model to use.

[0058] Step 2:

[0059] The user enters the topic they want to analyze in a free text box on the web page and selects one or more generative models to use for the analysis from a drop-down list.

[0060] Step 3:

[0061] When the user clicks the "Analyze" button, the terminal sends the topic and the selected generative model to the server as form data, which is sent as a POST request.

[0062] Step 4:

[0063] The server receives the POST request, extracts information about the topic and the selected generative model from the request data, and prepares to start analysis based on the extracted data.

[0064] Step 5:

[0065] The server runs individual analyses for the topic and each selected generative model, for example, using a GPT-3 model to generate sentences about the topic and a BERT model to analyze the context related to the topic.

[0066] Step 6:

[0067] The server collects the analysis results obtained from each generative model, organizes them, and converts them into a format that makes it easy for users to compare analysis results from multiple models.

[0068] Step 7:

[0069] The server generates a response containing the organized analysis results and sends it to the device. The response contains the analysis results divided by the model selected by the user.

[0070] Step 8:

[0071] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, allowing users to easily compare them.

[0072] Step 9:

[0073] Users can view the analysis results provided on the web page and compare the views of multiple generative models, which allows users to gain information on a topic from a broader perspective.

[0074] The above is the processing flow of the present invention, which allows the user to check and compare the analysis results of multiple generative models at a glance.

[0075] Example 1

[0076] 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."

[0077] With conventional systems, it was difficult to centrally compare the analysis results of generative AI models for a user-specified topic, making it difficult to understand the characteristics and strengths of each generative model. Furthermore, there was a lack of a mechanism for effectively collecting the analysis results of multiple generative models and displaying them in an easy-to-understand manner for users. This resulted in reduced judgment of the accuracy of information and reduced efficiency in decision-making.

[0078] 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.

[0079] In this invention, the server includes means for providing a user interface, input means for allowing the user to input topics, selection means for the user to select a generative model to use for analysis, analysis means for performing analysis for each generative model based on the topics input by the user and the selected generative models, collection means for collecting analysis results obtained from multiple generative models, and display means for displaying the analysis results in tabular or graphical format. This allows the user to centrally compare the analysis results of multiple generative models and understand the characteristics and strengths of each.

[0080] "User interface" refers to the display screen and input screen that users use to access and operate the system.

[0081] An "input means" is a means by which a user inputs a topic, typically a keyboard or touch screen.

[0082] The "selection means" is a means by which the user selects the generative model to be used in the analysis, and is usually a drop-down list or radio button.

[0083] The "analysis means" is a means having the function of performing analysis with each generative model based on the topic entered by the user and the selected generative model.

[0084] The "collection means" is a means for integrating and organizing the analysis results obtained from multiple generative models.

[0085] "Display means" refers to a means for displaying the collected analysis results in a form that is easy for the user to view, and includes tabular and graphical formats.

[0086] A "prompt sentence" is an instruction sentence used to request analysis from a generative AI model.

[0087] This invention provides a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user. Specific embodiments of this system are described below.

[0088] Providing a user interface

[0089] The server generates a web page to provide the user interface. This web page includes a free text box for users to enter topics and multiple selection methods (e.g., drop-down lists) for selecting the generative model to use. The web page is built using HTML, CSS, and JavaScript.

[0090] Topic and Model Input

[0091] Users enter the topic they want to analyze in a free text box on the provided webpage and select a model to use from multiple generative models (e.g., GPT-3, BERT, T5, etc.). This allows users to analyze any topic and simultaneously select multiple generative models to perform comparative analysis.

[0092] Sending and Receiving Form Data

[0093] After the user inputs a topic and selects a generative model, they click the "Analyze" button, and the device sends the user's input data to the server in JSON format. The server receives this request and extracts the data necessary to start the analysis process.

[0094] Executing the analysis process

[0095] The server performs analysis using each generative model based on the topic entered by the user and the selected generative model. Specifically, it generates a prompt for each generative model and issues an analysis request based on that. For example, when analyzing the topic "climate change," the following prompt is sent to the generative AI model:

[0096] For GPT-3: "Please explain in detail the impacts of climate change with specific examples."

[0097] For BERT: "Summarize a recent news article related to climate change."

[0098] Each generative model performs analysis based on these prompt sentences and sends the results back to the server.

[0099] Collecting and displaying results

[0100] The server collects and organizes the analysis results returned by each generative model. The organized analysis results are then sent back to the device in JSON format, and after receiving them, the device displays them to the user in tabular or graph format. For example, when displayed in table format, the left column shows the results of GPT-3 and the right column shows the results of BERT.

[0101] Specific examples

[0102] For example, if a user selects "GPT-3" and "BERT" for the topic "climate change," the server will use each generative model to generate the following analysis results:

[0103] GPT-3 analysis results: "A detailed explanation of the impacts of climate change"

[0104] BERT analysis results: "Summary of recent news articles related to climate change"

[0105] The device displays these analysis results in a tabular format, allowing users to compare the views of different models, thereby providing information from multiple perspectives and supporting appropriate decision-making.

[0106] In this way, the present invention is a system that enables users to judge the accuracy of information and access information efficiently by allowing them to compare the differences in views and interpretations of multiple generative models on a topic specified by the user.

[0107] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0108] Step 1: Providing a User Interface

[0109] The server generates a user-accessible web page that contains a free text box for users to enter a topic and a drop-down list for selecting the generative model to use. The input is HTML, CSS, and JavaScript code, and the output is a web page that users can interact with.

[0110] Step 2: Input topics and models

[0111] On the provided webpage, the user enters the topic they want to analyze in a free text box and selects the generative model to use from a drop-down list (e.g., GPT-3, BERT, T5). The input of this step is the user's topic and model selection information, and the output is the topic and model selection data ready to be sent to the server.

[0112] Step 3: Sending data

[0113] When the user clicks the "Analyze" button, the device sends the input topic and selected generative model data to the server in JSON format. The input of this step is the topic entered by the user and the selected generative model information, and the output is data that is sent to the server in JSON format.

[0114] Step 4: Receiving the data

[0115] The server receives data sent in JSON format and extracts the necessary data to start the analysis process. The input of this step is the JSON data sent from the terminal, and the output is the extracted topics and generative model information.

[0116] Step 5: Run the analysis process

[0117] Based on the specified topic and the selected generative model, the server generates a prompt for each generative model and requests it to analyze it. For example, for the topic "climate change," the server sends the prompt "Please explain in detail the impacts of climate change with specific examples" to GPT-3, and the prompt "Please summarize recent news articles related to climate change" to BERT. The input for this step is the topic and the selected generative model information, and the output is the prompt sent to each generative model.

[0118] Step 6: Collecting analysis results

[0119] The generative AI model performs analysis based on the prompt received from the server and sends the results back to the server. The server collects the analysis results returned from each generative model. The input of this step is the analysis results from each generative model, and the output is the integrated analysis result data.

[0120] Step 7: View the results

[0121] The server organizes the collected analysis results and sends them back to the terminal in JSON format. The terminal receives them and displays the analysis results in a format that is easy for the user to view (table or graph format). For example, the left column shows the results of GPT-3 and the right column shows the results of BERT. The input to this step is the organized analysis result data, and the output is the analysis result that is displayed to the user.

[0122] Through these specific processing steps, the system is designed to make it easy to compare multiple generative models for a user-specified topic, allowing users to understand the characteristics of each model and obtain more accurate and multifaceted information.

[0123] (Application example 1)

[0124] 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."

[0125] Conventional information analysis systems have difficulty comparing the views and interpretations of multiple generative AI models on a user-specified topic. This prevents users from obtaining information from multiple perspectives, making it difficult to make appropriate decisions. Furthermore, there is a lack of means to provide feedback on the information obtained, making it difficult to improve the user experience.

[0126] 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.

[0127] In this invention, the server includes means for providing a user interface, input means for allowing the user to input a topic, selection means for allowing the user to select a generative model to use for analysis, analysis means for performing analysis based on the topic input by the user and the selected generative model, display means for displaying the analysis results, and feedback means for allowing the user to provide feedback on the analysis results, which makes it easier for the user to compare the analysis results of multiple generative AI models and provide feedback on each analysis result.

[0128] A "user interface" is the means by which a user interacts with a computer system, providing screens and controls for input and output.

[0129] "Input means" refers to a means by which a user provides specific information or data to a system, and includes a text box, microphone, keyboard, etc.

[0130] A "selection means" is a means by which a user selects an option or function to use within a system, including drop-down lists, radio buttons, check boxes, etc.

[0131] "Analysis means" means the means by which a computer program performs analysis or calculations based on data entered or options selected by a user.

[0132] "Display means" refers to a means for visually showing analysis results and other information to users, such as a monitor, display, or screen.

[0133] "Feedback means" refers to the means by which users can provide feedback and evaluations to the system, and includes text input fields, evaluation buttons, etc.

[0134] A "generative model" is an algorithm or network that uses artificial intelligence or machine learning to perform a specific task, particularly a model used in natural language processing.

[0135] This invention provides a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user. Specific embodiments of this system are described below.

[0136] System configuration

[0137] The system consists of the following main components:

[0138] 1. User Interface (UI)

[0139] 2. Input Method

[0140] 3. Selection Method

[0141] 4. Analysis method

[0142] 5. Display means

[0143] 6. Feedback channels

[0144] User Interface

[0145] The server provides a web page for users to access, which includes a free text box for entering the topic they want to analyze, a drop-down list for selecting multiple generative models to use, and an Analyze button.

[0146] Topic and Model Input

[0147] Users input a topic on a webpage and select a generative model (e.g., GPT-3, BERT, etc.) to use for analysis. This allows users to specify any topic as the analysis target and simultaneously select multiple generative models for comparative analysis.

[0148] Sending and Receiving Form Data

[0149] After the user enters a topic and selects a model, they click the "Analyze" button on the device to send the form data to the server. The server receives this request and extracts the necessary data to start the analysis process.

[0150] Executing the analysis process

[0151] The server performs analysis using each model based on the user's input topic and the selected generative model. Specifically, it requests each generative model to analyze the topic and collects the results. For example, the GPT-3 model generates a detailed explanation and prediction for the topic, while the BERT model provides a context-based interpretation.

[0152] The generative models used include OpenAI's GPT-3 and Google's BERT, for example, and these models are invoked via APIs using programming languages ​​such as Python.

[0153] Collecting and displaying results

[0154] The server organizes the analysis results collected from each generative model and sends them back to the device. The device then displays the received analysis results in tabular and graphical formats, making it easy for users to compare them. This allows users to see the analysis results of multiple generative models at a glance and understand the characteristics and strengths of each model.

[0155] Feedback function

[0156] Users can provide feedback on the displayed analysis results, using a text input field and rating buttons to send user ratings and comments to the server.

[0157] Specific examples

[0158] For example, if a user selects "GPT-3" and "BERT" for the topic "climate change," the server will use each model to generate the following analysis results:

[0159] GPT-3 analysis result: "Tell me about climate change."

[0160] BERT analysis results: "Summarize a recent news article about climate change."

[0161] In this way, users can compare the views of different generative models and view each piece of information from multiple angles.

[0162] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0163] Step 1:

[0164] The user enters a topic into an input form on a web page and selects the generative model to use for analysis.

[0165] Input: A list of topics entered by the user and the selected generative model

[0166] Output: The topics and the selected generative model are prepared as form data.

[0167] Step 2:

[0168] The user clicks the "Analyze" button and the device sends the form data to the server.

[0169] Input: User-entered and selected data

[0170] Output: Parsing request data sent to the server

[0171] Step 3:

[0172] The server receives the request and extracts information about the topic and the selected generative model.

[0173] Input: Parse request data received by the server

[0174] Output: A list of topics needed for analysis and the selected generative model

[0175] Step 4:

[0176] The server sends a topic-based analysis request to each generative model and obtains the analysis results from each generative model.

[0177] Input: a list of topics and a selected generative model

[0178] Output: Analysis results from each generative model

[0179] Specific operation: For example, the server sends a prompt to GPT-3, such as "Tell me about climate change," and to BERT, such as "Summarize recent news articles about climate change." The analysis results are obtained using OpenAI's API and Transformer model.

[0180] Step 5:

[0181] The server organizes the analysis results of each generative model it has acquired and prepares them to be sent to the terminal.

[0182] Input: Analysis results obtained from each generative model

[0183] Output: Organized analysis results

[0184] Specific operation: The server formats the results of each generative model into a form that is easy to convert into a table or graph format.

[0185] Step 6:

[0186] The server transmits the analysis results to the terminal, and the terminal receives the results.

[0187] Input: Organized analysis results

[0188] Output: Analysis result notification received by the device

[0189] Step 7:

[0190] The terminal displays the analysis results to the user.

[0191] Input: Analysis results received by the device

[0192] Output: Displaying the analysis results in a user-readable format

[0193] Specific operation: The terminal displays the analysis results in tabular or graphical format on a web page, for example, allowing the results of different generative models to be compared.

[0194] Step 8:

[0195] The user provides feedback on the displayed analysis results.

[0196] Input: User-entered feedback

[0197] Output: Feedback data sent to the server

[0198] Specific operation: Users provide feedback using text input fields and rating buttons, and the data is sent to the server via the device.

[0199] 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.

[0200] This invention is a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user, and can also recognize the user's emotions and reflect them in the analysis results. Specific embodiments of this system are described below.

[0201] System Overview

[0202] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative model to use. The device receives the user input and selection, analyzes the user's emotions using an emotion engine, then adjusts the generative model based on the analyzed emotion information and provides the analysis results to the user.

[0203] User Interface

[0204] The server provides a web page with an interface where users can input topics and select generative models, including the option to input emotional nuances.

[0205] Topic and Model Input

[0206] Users enter the topic they want to analyze in a free text box on the webpage, select a generative model (e.g., GPT-3, BERT, T5, etc.) to use for analysis from a drop-down list, and enter information that expresses a specific emotional state (e.g., joy, sadness, surprise, etc.).

[0207] Sentiment analysis and data transmission

[0208] The device receives user input data, analyzes the user's emotions using the emotion engine, and sends the results to the server, along with information on the topic and generative model, to prepare for analysis.

[0209] Executing the analysis process

[0210] The server performs individual analysis for each model based on the topic entered by the user and the selected generative model. The analysis procedure includes a step of incorporating the results of the emotion engine to adjust the analysis parameters of the generative model and generate analysis results that match the emotion.

[0211] Collecting and displaying results

[0212] The server collects the analysis results from each generative model and organizes them taking into account the results of the emotion engine. This results in analysis results optimized for the user's emotional state. The organized results are presented in a format that makes it easy for users to compare them.

[0213] Results display

[0214] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, adjusted based on the results of the emotion engine.

[0215] Specific examples

[0216] For example, if a user selects GPT-3 and BERT for the topic "climate change" and enters the emotion "anxiety," the server will analyze this emotion using the emotion engine and generate the following results:

[0217] GPT-3 analysis results: "Detailed explanations and predictions of the adverse effects of climate change"

[0218] BERT analysis results: "The latest crisis news articles related to climate change"

[0219] The results are tailored to correspond to the user's emotion of "anxiety." The device organizes these analysis results and displays them in a format that is easy for the user to understand and relate to their emotional state.

[0220] In this way, by combining emotion engines, the present invention is a system that can not only compare the differences in views and interpretations of multiple generative models on a user-specified topic, but also recognize the user's emotions and reflect them in the analysis results, allowing users to obtain comprehensive information with emotional context.

[0221] The processing flow will be explained below.

[0222] Step 1:

[0223] The server provides the user interface: it renders a web page that the user accesses, displaying a free text box for topic entry, an emotion drop-down list for selecting an emotional state, and a drop-down list for selecting the generative model to use.

[0224] Step 2:

[0225] Users enter the topic they want to analyze in a free text box on the webpage, select their emotional state (e.g., joy, sadness, anxiety, etc.) from an emotion drop-down list, and select the generative model to use (e.g., GPT-3, BERT, T5, etc.).

[0226] Step 3:

[0227] When the user clicks the "Analyze" button, the device sends the topic, emotional state, and selected generative model to the server as form data, which is sent as a POST request.

[0228] Step 4:

[0229] The server receives the POST request, extracts information about the topic, emotional state, and selected generative model from the request data, and prepares to analyze the user's emotional state using the emotion engine.

[0230] Step 5:

[0231] The server runs an emotion engine based on the topic and emotional state to perform a detailed analysis of the user's emotions. For example, if a user selects the emotion "anxiety," the analysis results include the intensity of that emotion and related emotional characteristics.

[0232] Step 6:

[0233] The server adjusts the parameters of each generative model based on the analysis results of the emotion engine. For example, if the emotion engine analyzes the user's emotion as "anxiety," the generative model will be adjusted to generate analysis results that are more focused on the area of ​​concern.

[0234] Step 7:

[0235] The server uses the topic and the adjusted parameters to run individual analyses on each selected generative model, each of which performs its own analysis on the specified topic and produces results.

[0236] Step 8:

[0237] The server collects the analysis results from each generative model, organizes them taking into account the results of the emotion engine, and compiles them into a single dataset, ready to present to the user in the most optimal format.

[0238] Step 9:

[0239] The server generates a response containing the organized analysis results and sends it to the device. The response includes the analysis results of each generative model adjusted based on the results of the emotion engine.

[0240] Step 10:

[0241] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, adjusted based on the results of the emotion engine.

[0242] Step 11:

[0243] Users can view the analysis results provided on the webpage and compare the views of multiple generative models, thereby obtaining comprehensive information based on their emotional state.

[0244] The above are the detailed processing steps of the present invention, which allow users to check and compare analysis results from multiple generative models along with emotional context.

[0245] Example 2

[0246] 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."

[0247] Existing analysis systems provide analysis results without considering the user's emotional state, which can prevent users from obtaining the information they desire based on the emotional context. Furthermore, when comparing analysis results from multiple generative models, they lack the functionality to provide results optimized for the user's emotional state. This makes it difficult to obtain analysis results that are intuitive and easy to understand for users.

[0248] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface, input means for allowing the user to input a topic, selection means for allowing the user to select a generative model to be used for analysis, emotion analysis means for analyzing the user's emotions, adjustment means for adjusting analysis parameters of the generative model based on the emotion analysis results, analysis means for performing analysis based on the topic input by the user and the selected generative model, and display means for displaying the analysis results. This makes it possible to provide analysis results that reflect the user's emotional state and obtain information in an intuitive and easy-to-understand format.

[0249] A "user interface" is a means by which a user interacts with a system, and is an interface that allows input and display.

[0250] "Input means" refers to the means by which a user inputs the topic they wish to analyze into the system, and refers to input devices such as a keyboard or touch screen.

[0251] The "selection means" refers to a means for the user to select a generative model to use in the analysis, and refers to a selection device such as a drop-down list or radio buttons.

[0252] "Emotion analysis means" refers to a means for analyzing emotions based on user input data, and refers to emotion analysis engines and natural language processing technology.

[0253] "Adjustment means" refers to a means for adjusting the analysis parameters of the generative model based on the emotion analysis results, and refers to changes in the algorithm or setting parameters.

[0254] "Analysis means" refers to a means for performing analysis based on the topic entered by the user and the selected generative model, and refers to a computer program or AI model.

[0255] "Display means" refers to a means for visually displaying the analysis results to the user, and refers to a display device such as a monitor or display.

[0256] A "generative model" is a model for generating text or information based on specific data, and refers to an artificial intelligence or machine learning algorithm.

[0257] "Emotion analysis result" is information about the user's emotional state obtained by the emotion analysis means.

[0258] This invention is a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user, and can also recognize the user's emotions and reflect them in the analysis results. Specific embodiments of this system are described below.

[0259] System Overview

[0260] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative model to use. The device receives the user input and selection, analyzes the user's emotions using an emotion engine, then adjusts the generative model based on the analyzed emotion information and provides the analysis results to the user.

[0261] User Interface

[0262] The server provides a web page with an interface where users can input topics and select generative models, including the option to input emotional nuances.

[0263] Topic and Model Input

[0264] Users enter the topic they want to analyze in a free text box on the webpage, select a generative model (e.g., GPT-3, BERT, T5, etc.) to use for analysis from a drop-down list, and enter information that expresses a specific emotional state (e.g., joy, sadness, surprise, etc.).

[0265] Sentiment analysis and data transmission

[0266] The device receives user input data and analyzes the user's emotions using an emotion engine. The results are then sent to the server, along with information on the topic and generative model, to prepare for analysis. For example, the IBM Watson Tone Analyzer is used as the emotion engine.

[0267] Executing the analysis process

[0268] The server performs individual analysis for each model based on the topic entered by the user and the selected generative model. The analysis procedure includes a step of incorporating the results of the emotion engine to adjust the analysis parameters of the generative model and generate analysis results that match the emotion.

[0269] Collecting and displaying results

[0270] The server collects the analysis results from each generative model and organizes them taking into account the results of the emotion engine. This results in analysis results optimized for the user's emotional state. The organized results are presented in a format that makes it easy for users to compare them.

[0271] Results display

[0272] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, adjusted based on the results of the emotion engine.

[0273] Specific examples

[0274] For example, if a user selects GPT-3 and BERT for the topic "climate change" and enters the emotion "anxiety," the server will analyze this emotion using the emotion engine and generate the following results:

[0275] GPT-3 analysis results: "Detailed explanations and predictions of the adverse effects of climate change"

[0276] BERT analysis results: "The latest crisis news articles related to climate change"

[0277] The results are tailored to correspond to the user's emotion of "anxiety." The device organizes these analysis results and displays them in a format that is easy for the user to understand and relate to their emotional state.

[0278] Prompt Sentence Examples

[0279] In this system, an example of a prompt for user input is shown below.

[0280] Topic: "Climate Change"

[0281] Generative model: "GPT-3, BERT"

[0282] Emotional state: "Anxiety"

[0283] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0284] Step 1:

[0285] The user accesses the web interface provided by the server, inputs the topic to be analyzed, selects the generative model to use (e.g., GPT-3, BERT, T5, etc.), and inputs a specific emotional state (e.g., joy, sadness, surprise, anxiety, etc.). The input data includes the topic "climate change," the generative model "GPT-3, BERT," and the emotional state "anxiety." When the user clicks the "Submit" button, the data is sent to the server.

[0286] Step 2:

[0287] The device processes the user's input data received from the server. The input data includes topics, generative models, and emotional states, and is immediately sent to the emotion engine for analysis. The emotion engine uses this information to perform a deep analysis of the user's emotional state and generates the results as output. This output data includes the analyzed emotional information and is sent to the server.

[0288] Step 3:

[0289] The server receives the topic, generative model, and sentiment analysis results sent from the device. Based on the received data, a process is initiated to perform individual analysis for each generative model. At this time, the sentiment analysis results are adjusted to affect the analysis parameters. For example, based on the topic "climate change" and the emotional state "anxiety," the analysis parameters for GPT-3 and BERT are set and the analysis is performed. The generative model performs analysis on the topic and outputs the results.

[0290] Step 4:

[0291] The server collects and organizes the analysis results obtained from generative models (GPT-3, BERT, etc.). The results generated by each generative model are adjusted taking into account the results of sentiment analysis. For example, the analysis results of GPT-3 may yield "detailed descriptions and predictions about the negative effects of climate change," while the analysis results of BERT may yield "the latest critical news articles related to climate change." The server integrates these results and prepares them for display in user-friendly formats (tables and graphs).

[0292] Step 5:

[0293] The device receives the analysis results sent from the server and renders a web page to display them. This page displays information in which the analysis results of each generative model have been adjusted based on the emotional state. For example, if a user investigates "climate change" when in the emotional state of "anxiety," the results are displayed in table and graph format for easy comparison. This allows the user to visually confirm detailed analysis results that take emotional state into account.

[0294] (Application example 2)

[0295] 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."

[0296] Conventional advertising generation systems have had difficulty generating advertising content that takes user emotions into account. As a result, they were unable to generate ads that matched the specific emotions of users, and were unable to maximize the effectiveness of the ads. Furthermore, when using multiple generative models to analyze from different perspectives, it was difficult to properly compare the results.

[0297] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface, input means for allowing the user to input a topic, selection means for allowing the user to select a generative model to use for analysis, emotion analysis means for analyzing the user's emotions and adjusting the generative model based on the analysis results, analysis means for performing analysis based on the topic input by the user and the selected generative model, and display means for displaying the analysis results. This makes it possible to generate advertising content suited to the user's emotions and compare analysis results from multiple generative models.

[0298] "User interface" refers to the screens and components through which a user interacts with a system, inputting data and viewing results.

[0299] "Input Means" refers to the device or method by which a user provides topics or other information to the system.

[0300] "Selection means" refers to a device or method for a user to select a generative model to use.

[0301] "Analysis means" refers to a device or method that performs analysis based on the topic entered by the user and the selected generative model.

[0302] "Emotion analysis means" refers to a device or method that analyzes a user's emotions and adjusts a generative model based on the results.

[0303] "Display means" refers to a device or method for visually presenting the analysis results to the user.

[0304] A "generative model" refers to an algorithm or machine learning model that generates a specific output (e.g., advertising content) based on user-specified information.

[0305] "Topic" refers to a particular theme or subject that a user inputs for analysis or generation.

[0306] "Comparable format" refers to an organized format that allows users to easily understand and compare multiple analysis results.

[0307] "Analysis parameters" refer to settings and adjustments that allow a generative model to operate under specific conditions.

[0308] This invention is a system that can compare the differences in the views and interpretations of multiple generative AI models on a topic specified by a user, and can also recognize the user's emotions and reflect them in the analysis results. Specific embodiments of this system are described below.

[0309] System Overview

[0310] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative AI model to use. The device receives the user's input and selection and analyzes the user's emotions using a sentiment analysis engine. It then adjusts the generative AI model based on the analyzed sentiment information and provides the analysis results to the user. The user can then review the generated analysis results and obtain information that matches their sentiment.

[0311] Program Overview

[0312] Hardware and software used

[0313] In this system, the user uses a smartphone as the terminal. The server is located on a cloud service and uses the following software:

[0314] Python: a programming language

[0315] Transformers: Import and use emotion analysis and generative AI models in the Hugging Face library

[0316] requests: A Python HTTP library used to call external APIs (e.g., GPT-3).

[0317] Data Flow and Processing

[0318] 1. User Interface:

[0319] The server serves a web page, displaying an interface where users can input a topic and select the generative AI model to use (e.g., GPT-3, BERT, etc.). The page also includes an option for users to input emotional nuances.

[0320] 2. User Input and Sentiment Analysis:

[0321] Users enter the topic they want to analyze in a free text box on the webpage and select a generative AI model to use for analysis from a drop-down list. Users also enter information that expresses a specific emotional state (e.g., joy, sadness, surprise, etc.). The device receives the user input data and analyzes the user's emotions using an emotion analysis engine.

[0322] 3. Invoke the generative model and generate content:

[0323] The server adjusts the analysis parameters of the generative AI model based on the sentiment analysis results and inputs them into the generative AI model along with the specified topic. For example, it adjusts the analysis parameters for the GPT-3 and GPT-2 generative models to generate advertising content that matches the sentiment.

[0324] 4. Collecting and displaying analysis results:

[0325] The server collects the analysis results from each generative AI model and organizes them based on the results of the emotion analysis engine. This allows for analysis results optimized for the user's emotional state. The organized results are displayed in a format that makes it easy for users to compare.

[0326] Specific examples

[0327] For example, if a user selects "GPT-3" and "GPT-2" for the topic "new smartphones" and enters the emotion "surprise," the server will analyze this emotion using its emotion analysis engine and generate the following results:

[0328] GPT-3 analysis results:

[0329] Introducing the latest smartphone that will leave you amazed! With cutting-edge technology and features that will blow your mind, this device is set to redefine what you think is possible. Experience the future today!

[0330] GPT-2 analysis results:

[0331] Discover the newest advancement in smartphone technology! This groundbreaking device will astonish you with its high-speed performance and state-of-the-art features. Get ready to be surprised!

[0332] Prompt Sentence Examples

[0333] Generate an advertisement for the topic 'New Smartphone' that matches the emotion: 'Amazing'.

[0334] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0335] Step 1:

[0336] Users enter the topic they want to analyze into a form on a webpage, select the generative AI model to use from a drop-down list, and also enter information about their emotional state.

[0337] Input data: topics, generative AI models, sentiment information

[0338] Output data: None

[0339] Step 2:

[0340] The device receives user input data, which includes topics, generative AI models, and sentiment information.

[0341] Input data: User input data (topics, generative AI models, sentiment information)

[0342] Output data: Internal storage of user input data

[0343] Step 3:

[0344] The device uses an emotion analysis engine to analyze the emotion information entered by the user and generates an emotion label as the analysis result.

[0345] Input data: Emotion information

[0346] Data processing: Analyze emotions using a sentiment analysis engine (such as the BERT model) and generate sentiment labels.

[0347] Output data: emotion labels

[0348] Step 4:

[0349] Based on the emotion labels, the device prepares to adjust the analysis parameters of the generative AI model.

[0350] Input data: emotion labels, generative AI model

[0351] Data processing: Setting the analysis parameters of the generative AI model based on emotion labels

[0352] Output data: Adjusted analysis parameters

[0353] Step 5:

[0354] The server runs the analysis on the selected generative AI model using the adjusted analysis parameters and topics.

[0355] Input data: topics, adjusted analysis parameters, generative AI model

[0356] Data calculation: Input data into the generative AI model and generate analysis results.

[0357] Output data: Analysis results from the generative AI model

[0358] Step 6:

[0359] The server collects the analysis results and organizes them for each generative AI model.

[0360] Input data: Analysis results from the generative AI model

[0361] Data processing: Collecting and organizing analysis results (converting them into a format that is easy to compare)

[0362] Output data: Organized analysis results

[0363] Step 7:

[0364] The server generates a web page to present the organized analysis results to the user.

[0365] Input data: Organized analysis results

[0366] Data processing: rendering web pages

[0367] Output data: Analysis results that are displayed to the user

[0368] Step 8:

[0369] Users can check the analysis results of each generative AI model through the provided webpage, check whether the analysis results match the emotional state, and obtain appropriate information.

[0370] Input data: Displayed analysis results

[0371] Output data: None (information acquisition and understanding)

[0372] 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.

[0373] 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.

[0374] 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.

[0375] [Second embodiment]

[0376] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0377] 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.

[0378] 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).

[0379] 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.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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."

[0388] This invention provides a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user. Specific embodiments of this system are described below.

[0389] System Overview

[0390] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative model they want to use. Once the user has input and selected, the server performs analysis based on this information and provides the results to the device. Finally, the device displays the analysis results to the user.

[0391] User Interface

[0392] The server provides a web page for users to access, which includes a free text box for users to enter the topic they want to analyze and a drop-down list for users to select multiple generative models to use.

[0393] Topic and Model Input

[0394] Users input a topic on the webpage and select the generative model to use for analysis (e.g., GPT-3, BERT, T5, etc.). This allows users to specify any topic as the analysis target and simultaneously select multiple generative models for comparative analysis.

[0395] Sending and Receiving Form Data

[0396] After the user enters a topic and selects a model, they click the "Analyze" button on the device to send the form data to the server. The server receives this request and extracts the necessary data to start the analysis process.

[0397] Executing the analysis process

[0398] The server performs analysis using each model based on the user's input topic and the selected generative model. Specifically, it requests each generative model to analyze the topic and collects the results. For example, the GPT-3 model generates a detailed explanation and prediction for the topic, while the BERT model provides a context-based interpretation.

[0399] Collecting and displaying results

[0400] The server organizes the analysis results collected from each generative model and sends them back to the device. The device then displays the received analysis results in tabular and graphical formats, making it easy for users to compare them. This allows users to see the analysis results of multiple generative models at a glance and understand the characteristics and strengths of each model.

[0401] Specific examples

[0402] For example, if a user selects "GPT-3" and "BERT" for the topic "climate change," the server will use each model to generate the following analysis results:

[0403] GPT-3 analysis results: "A detailed explanation of the impacts of climate change"

[0404] BERT analysis results: "Summary of recent news articles related to climate change"

[0405] The device displays these analysis results in a table format, allowing users to compare the views of different models, thereby providing information from multiple perspectives and supporting appropriate decision-making.

[0406] In this way, the present invention is a system that enables users to compare the differences in views and interpretations of multiple generative models on a topic specified by the user, thereby enabling them to judge the accuracy of information and access it efficiently.

[0407] The processing flow will be explained below.

[0408] Step 1:

[0409] The server provides the user interface: it renders a web page that the user accesses, displaying a free text box for topic input and a drop-down list for selecting the generative model to use.

[0410] Step 2:

[0411] The user enters the topic they want to analyze in a free text box on the web page and selects one or more generative models to use for the analysis from a drop-down list.

[0412] Step 3:

[0413] When the user clicks the "Analyze" button, the terminal sends the topic and the selected generative model to the server as form data, which is sent as a POST request.

[0414] Step 4:

[0415] The server receives the POST request, extracts information about the topic and the selected generative model from the request data, and prepares to start analysis based on the extracted data.

[0416] Step 5:

[0417] The server runs individual analyses for the topic and each selected generative model, for example, using a GPT-3 model to generate sentences about the topic and a BERT model to analyze the context related to the topic.

[0418] Step 6:

[0419] The server collects the analysis results obtained from each generative model, organizes them, and converts them into a format that makes it easy for users to compare analysis results from multiple models.

[0420] Step 7:

[0421] The server generates a response containing the organized analysis results and sends it to the device. The response contains the analysis results divided by the model selected by the user.

[0422] Step 8:

[0423] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, allowing users to easily compare them.

[0424] Step 9:

[0425] Users can view the analysis results provided on the web page and compare the views of multiple generative models, which allows users to gain information on a topic from a broader perspective.

[0426] The above is the processing flow of the present invention, which allows the user to check and compare the analysis results of multiple generative models at a glance.

[0427] Example 1

[0428] 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."

[0429] With conventional systems, it was difficult to centrally compare the analysis results of generative AI models for a user-specified topic, making it difficult to understand the characteristics and strengths of each generative model. Furthermore, there was a lack of a mechanism for effectively collecting the analysis results of multiple generative models and displaying them in an easy-to-understand manner for users. This resulted in reduced judgment of the accuracy of information and reduced efficiency in decision-making.

[0430] 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.

[0431] In this invention, the server includes means for providing a user interface, input means for allowing the user to input topics, selection means for the user to select a generative model to use for analysis, analysis means for performing analysis for each generative model based on the topics input by the user and the selected generative models, collection means for collecting analysis results obtained from multiple generative models, and display means for displaying the analysis results in tabular or graphical format. This allows the user to centrally compare the analysis results of multiple generative models and understand the characteristics and strengths of each.

[0432] "User interface" refers to the display screen and input screen that users use to access and operate the system.

[0433] An "input means" is a means by which a user inputs a topic, typically a keyboard or touch screen.

[0434] The "selection means" is a means by which the user selects the generative model to be used in the analysis, and is usually a drop-down list or radio button.

[0435] The "analysis means" is a means having the function of performing analysis with each generative model based on the topic entered by the user and the selected generative model.

[0436] The "collection means" is a means for integrating and organizing the analysis results obtained from multiple generative models.

[0437] "Display means" refers to a means for displaying the collected analysis results in a form that is easy for the user to view, and includes tabular and graphical formats.

[0438] A "prompt sentence" is an instruction sentence used to request analysis from a generative AI model.

[0439] This invention provides a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user. Specific embodiments of this system are described below.

[0440] Providing a user interface

[0441] The server generates a web page to provide the user interface. This web page includes a free text box for users to enter topics and multiple selection methods (e.g., drop-down lists) for selecting the generative model to use. The web page is built using HTML, CSS, and JavaScript.

[0442] Topic and Model Input

[0443] Users enter the topic they want to analyze in a free text box on the provided webpage and select a model to use from multiple generative models (e.g., GPT-3, BERT, T5, etc.). This allows users to analyze any topic and simultaneously select multiple generative models to perform comparative analysis.

[0444] Sending and Receiving Form Data

[0445] After the user inputs a topic and selects a generative model, they click the "Analyze" button, and the device sends the user's input data to the server in JSON format. The server receives this request and extracts the data necessary to start the analysis process.

[0446] Executing the analysis process

[0447] The server performs analysis using each generative model based on the topic entered by the user and the selected generative model. Specifically, it generates a prompt for each generative model and issues an analysis request based on that. For example, when analyzing the topic "climate change," the following prompt is sent to the generative AI model:

[0448] For GPT-3: "Please explain in detail the impacts of climate change with specific examples."

[0449] For BERT: "Summarize a recent news article related to climate change."

[0450] Each generative model performs analysis based on these prompt sentences and sends the results back to the server.

[0451] Collecting and displaying results

[0452] The server collects and organizes the analysis results returned by each generative model. The organized analysis results are then sent back to the device in JSON format, and after receiving them, the device displays them to the user in tabular or graph format. For example, when displayed in table format, the left column shows the results of GPT-3 and the right column shows the results of BERT.

[0453] Specific examples

[0454] For example, if a user selects "GPT-3" and "BERT" for the topic "climate change," the server will use each generative model to generate the following analysis results:

[0455] GPT-3 analysis results: "A detailed explanation of the impacts of climate change"

[0456] BERT analysis results: "Summary of recent news articles related to climate change"

[0457] The device displays these analysis results in a tabular format, allowing users to compare the views of different models, thereby providing information from multiple perspectives and supporting appropriate decision-making.

[0458] In this way, the present invention is a system that enables users to judge the accuracy of information and access information efficiently by allowing them to compare the differences in views and interpretations of multiple generative models on a topic specified by the user.

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

[0460] Step 1: Providing a User Interface

[0461] The server generates a user-accessible web page that contains a free text box for users to enter a topic and a drop-down list for selecting the generative model to use. The input is HTML, CSS, and JavaScript code, and the output is a web page that users can interact with.

[0462] Step 2: Input topics and models

[0463] On the provided webpage, the user enters the topic they want to analyze in a free text box and selects the generative model to use from a drop-down list (e.g., GPT-3, BERT, T5). The input of this step is the user's topic and model selection information, and the output is the topic and model selection data ready to be sent to the server.

[0464] Step 3: Sending data

[0465] When the user clicks the "Analyze" button, the device sends the input topic and selected generative model data to the server in JSON format. The input of this step is the topic entered by the user and the selected generative model information, and the output is data that is sent to the server in JSON format.

[0466] Step 4: Receiving the data

[0467] The server receives data sent in JSON format and extracts the necessary data to start the analysis process. The input of this step is the JSON data sent from the terminal, and the output is the extracted topics and generative model information.

[0468] Step 5: Run the analysis process

[0469] Based on the specified topic and the selected generative model, the server generates a prompt for each generative model and requests it to analyze it. For example, for the topic "climate change," the server sends the prompt "Please explain in detail the impacts of climate change with specific examples" to GPT-3, and the prompt "Please summarize recent news articles related to climate change" to BERT. The input for this step is the topic and the selected generative model information, and the output is the prompt sent to each generative model.

[0470] Step 6: Collecting analysis results

[0471] The generative AI model performs analysis based on the prompt received from the server and sends the results back to the server. The server collects the analysis results returned from each generative model. The input of this step is the analysis results from each generative model, and the output is the integrated analysis result data.

[0472] Step 7: View the results

[0473] The server organizes the collected analysis results and sends them back to the terminal in JSON format. The terminal receives them and displays the analysis results in a format that is easy for the user to view (table or graph format). For example, the left column shows the results of GPT-3 and the right column shows the results of BERT. The input to this step is the organized analysis result data, and the output is the analysis result that is displayed to the user.

[0474] Through these specific processing steps, the system is designed to make it easy to compare multiple generative models for a user-specified topic, allowing users to understand the characteristics of each model and obtain more accurate and multifaceted information.

[0475] (Application example 1)

[0476] 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."

[0477] Conventional information analysis systems have difficulty comparing the views and interpretations of multiple generative AI models on a user-specified topic. This prevents users from obtaining information from multiple perspectives, making it difficult to make appropriate decisions. Furthermore, there is a lack of means to provide feedback on the information obtained, making it difficult to improve the user experience.

[0478] 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.

[0479] In this invention, the server includes means for providing a user interface, input means for allowing the user to input a topic, selection means for allowing the user to select a generative model to use for analysis, analysis means for performing analysis based on the topic input by the user and the selected generative model, display means for displaying the analysis results, and feedback means for allowing the user to provide feedback on the analysis results, which makes it easier for the user to compare the analysis results of multiple generative AI models and provide feedback on each analysis result.

[0480] A "user interface" is the means by which a user interacts with a computer system, providing screens and controls for input and output.

[0481] "Input means" refers to a means by which a user provides specific information or data to a system, and includes a text box, microphone, keyboard, etc.

[0482] A "selection means" is a means by which a user selects an option or function to use within a system, including drop-down lists, radio buttons, check boxes, etc.

[0483] "Analysis means" means the means by which a computer program performs analysis or calculations based on data entered or options selected by a user.

[0484] "Display means" refers to a means for visually showing analysis results and other information to users, such as a monitor, display, or screen.

[0485] "Feedback means" refers to the means by which users can provide feedback and evaluations to the system, and includes text input fields, evaluation buttons, etc.

[0486] A "generative model" is an algorithm or network that uses artificial intelligence or machine learning to perform a specific task, particularly a model used in natural language processing.

[0487] This invention provides a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user. Specific embodiments of this system are described below.

[0488] System configuration

[0489] The system consists of the following main components:

[0490] 1. User Interface (UI)

[0491] 2. Input Method

[0492] 3. Selection Method

[0493] 4. Analysis method

[0494] 5. Display means

[0495] 6. Feedback channels

[0496] User Interface

[0497] The server provides a web page for users to access, which includes a free text box for entering the topic they want to analyze, a drop-down list for selecting multiple generative models to use, and an Analyze button.

[0498] Topic and Model Input

[0499] Users input a topic on a webpage and select a generative model (e.g., GPT-3, BERT, etc.) to use for analysis. This allows users to specify any topic as the analysis target and simultaneously select multiple generative models for comparative analysis.

[0500] Sending and Receiving Form Data

[0501] After the user enters a topic and selects a model, they click the "Analyze" button on the device to send the form data to the server. The server receives this request and extracts the necessary data to start the analysis process.

[0502] Executing the analysis process

[0503] The server performs analysis using each model based on the user's input topic and the selected generative model. Specifically, it requests each generative model to analyze the topic and collects the results. For example, the GPT-3 model generates a detailed explanation and prediction for the topic, while the BERT model provides a context-based interpretation.

[0504] The generative models used include OpenAI's GPT-3 and Google's BERT, for example, and these models are invoked via APIs using programming languages ​​such as Python.

[0505] Collecting and displaying results

[0506] The server organizes the analysis results collected from each generative model and sends them back to the device. The device then displays the received analysis results in tabular and graphical formats, making it easy for users to compare them. This allows users to see the analysis results of multiple generative models at a glance and understand the characteristics and strengths of each model.

[0507] Feedback function

[0508] Users can provide feedback on the displayed analysis results, using a text input field and rating buttons to send user ratings and comments to the server.

[0509] Specific examples

[0510] For example, if a user selects "GPT-3" and "BERT" for the topic "climate change," the server will use each model to generate the following analysis results:

[0511] GPT-3 analysis result: "Tell me about climate change."

[0512] BERT analysis results: "Summarize a recent news article about climate change."

[0513] In this way, users can compare the views of different generative models and view each piece of information from multiple angles.

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

[0515] Step 1:

[0516] The user enters a topic into an input form on a web page and selects the generative model to use for analysis.

[0517] Input: A list of topics entered by the user and the selected generative model

[0518] Output: The topics and the selected generative model are prepared as form data.

[0519] Step 2:

[0520] The user clicks the "Analyze" button and the device sends the form data to the server.

[0521] Input: User-entered and selected data

[0522] Output: Parsing request data sent to the server

[0523] Step 3:

[0524] The server receives the request and extracts information about the topic and the selected generative model.

[0525] Input: Parse request data received by the server

[0526] Output: A list of topics needed for analysis and the selected generative model

[0527] Step 4:

[0528] The server sends a topic-based analysis request to each generative model and obtains the analysis results from each generative model.

[0529] Input: a list of topics and a selected generative model

[0530] Output: Analysis results from each generative model

[0531] Specific operation: For example, the server sends a prompt to GPT-3, such as "Tell me about climate change," and to BERT, such as "Summarize recent news articles about climate change." The analysis results are obtained using OpenAI's API and Transformer model.

[0532] Step 5:

[0533] The server organizes the analysis results of each generative model it has acquired and prepares them to be sent to the terminal.

[0534] Input: Analysis results obtained from each generative model

[0535] Output: Organized analysis results

[0536] Specific operation: The server formats the results of each generative model into a form that is easy to convert into a table or graph format.

[0537] Step 6:

[0538] The server transmits the analysis results to the terminal, and the terminal receives the results.

[0539] Input: Organized analysis results

[0540] Output: Analysis result notification received by the device

[0541] Step 7:

[0542] The terminal displays the analysis results to the user.

[0543] Input: Analysis results received by the device

[0544] Output: Displaying the analysis results in a user-readable format

[0545] Specific operation: The terminal displays the analysis results in tabular or graphical format on a web page, for example, allowing the results of different generative models to be compared.

[0546] Step 8:

[0547] The user provides feedback on the displayed analysis results.

[0548] Input: User-entered feedback

[0549] Output: Feedback data sent to the server

[0550] Specific operation: Users provide feedback using text input fields and rating buttons, and the data is sent to the server via the device.

[0551] 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.

[0552] This invention is a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user, and can also recognize the user's emotions and reflect them in the analysis results. Specific embodiments of this system are described below.

[0553] System Overview

[0554] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative model to use. The device receives the user input and selection, analyzes the user's emotions using an emotion engine, then adjusts the generative model based on the analyzed emotion information and provides the analysis results to the user.

[0555] User Interface

[0556] The server provides a web page with an interface where users can input topics and select generative models, including the option to input emotional nuances.

[0557] Topic and Model Input

[0558] Users enter the topic they want to analyze in a free text box on the webpage, select a generative model (e.g., GPT-3, BERT, T5, etc.) to use for analysis from a drop-down list, and enter information that expresses a specific emotional state (e.g., joy, sadness, surprise, etc.).

[0559] Sentiment analysis and data transmission

[0560] The device receives user input data, analyzes the user's emotions using the emotion engine, and sends the results to the server, along with information on the topic and generative model, to prepare for analysis.

[0561] Executing the analysis process

[0562] The server performs individual analysis for each model based on the topic entered by the user and the selected generative model. The analysis procedure includes a step of incorporating the results of the emotion engine to adjust the analysis parameters of the generative model and generate analysis results that match the emotion.

[0563] Collecting and displaying results

[0564] The server collects the analysis results from each generative model and organizes them taking into account the results of the emotion engine. This results in analysis results optimized for the user's emotional state. The organized results are presented in a format that makes it easy for users to compare them.

[0565] Results display

[0566] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, adjusted based on the results of the emotion engine.

[0567] Specific examples

[0568] For example, if a user selects GPT-3 and BERT for the topic "climate change" and enters the emotion "anxiety," the server will analyze this emotion using the emotion engine and generate the following results:

[0569] GPT-3 analysis results: "Detailed explanations and predictions of the adverse effects of climate change"

[0570] BERT analysis results: "The latest crisis news articles related to climate change"

[0571] The results are tailored to correspond to the user's emotion of "anxiety." The device organizes these analysis results and displays them in a format that is easy for the user to understand and relate to their emotional state.

[0572] In this way, by combining emotion engines, the present invention is a system that can not only compare the differences in views and interpretations of multiple generative models on a user-specified topic, but also recognize the user's emotions and reflect them in the analysis results, allowing users to obtain comprehensive information with emotional context.

[0573] The processing flow will be explained below.

[0574] Step 1:

[0575] The server provides the user interface: it renders a web page that the user accesses, displaying a free text box for topic entry, an emotion drop-down list for selecting an emotional state, and a drop-down list for selecting the generative model to use.

[0576] Step 2:

[0577] Users enter the topic they want to analyze in a free text box on the webpage, select their emotional state (e.g., joy, sadness, anxiety, etc.) from an emotion drop-down list, and select the generative model to use (e.g., GPT-3, BERT, T5, etc.).

[0578] Step 3:

[0579] When the user clicks the "Analyze" button, the device sends the topic, emotional state, and selected generative model to the server as form data, which is sent as a POST request.

[0580] Step 4:

[0581] The server receives the POST request, extracts information about the topic, emotional state, and selected generative model from the request data, and prepares to analyze the user's emotional state using the emotion engine.

[0582] Step 5:

[0583] The server runs an emotion engine based on the topic and emotional state to perform a detailed analysis of the user's emotions. For example, if a user selects the emotion "anxiety," the analysis results include the intensity of that emotion and related emotional characteristics.

[0584] Step 6:

[0585] The server adjusts the parameters of each generative model based on the analysis results of the emotion engine. For example, if the emotion engine analyzes the user's emotion as "anxiety," the generative model will be adjusted to generate analysis results that are more focused on the area of ​​concern.

[0586] Step 7:

[0587] The server uses the topic and the adjusted parameters to run individual analyses on each selected generative model, each of which performs its own analysis on the specified topic and produces results.

[0588] Step 8:

[0589] The server collects the analysis results from each generative model, organizes them taking into account the results of the emotion engine, and compiles them into a single dataset, ready to present to the user in the most optimal format.

[0590] Step 9:

[0591] The server generates a response containing the organized analysis results and sends it to the device. The response includes the analysis results of each generative model adjusted based on the results of the emotion engine.

[0592] Step 10:

[0593] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, adjusted based on the results of the emotion engine.

[0594] Step 11:

[0595] Users can view the analysis results provided on the webpage and compare the views of multiple generative models, thereby obtaining comprehensive information based on their emotional state.

[0596] The above are the detailed processing steps of the present invention, which allow users to check and compare analysis results from multiple generative models along with emotional context.

[0597] Example 2

[0598] 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."

[0599] Existing analysis systems provide analysis results without considering the user's emotional state, which can prevent users from obtaining the information they desire based on the emotional context. Furthermore, when comparing analysis results from multiple generative models, they lack the functionality to provide results optimized for the user's emotional state. This makes it difficult to obtain analysis results that are intuitive and easy to understand for users.

[0600] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface, input means for allowing the user to input a topic, selection means for allowing the user to select a generative model to be used for analysis, emotion analysis means for analyzing the user's emotions, adjustment means for adjusting analysis parameters of the generative model based on the emotion analysis results, analysis means for performing analysis based on the topic input by the user and the selected generative model, and display means for displaying the analysis results. This makes it possible to provide analysis results that reflect the user's emotional state and obtain information in an intuitive and easy-to-understand format.

[0601] A "user interface" is a means by which a user interacts with a system, and is an interface that allows input and display.

[0602] "Input means" refers to the means by which a user inputs the topic they wish to analyze into the system, and refers to input devices such as a keyboard or touch screen.

[0603] The "selection means" refers to a means for the user to select a generative model to use in the analysis, and refers to a selection device such as a drop-down list or radio buttons.

[0604] "Emotion analysis means" refers to a means for analyzing emotions based on user input data, and refers to emotion analysis engines and natural language processing technology.

[0605] "Adjustment means" refers to a means for adjusting the analysis parameters of the generative model based on the emotion analysis results, and refers to changes in the algorithm or setting parameters.

[0606] "Analysis means" refers to a means for performing analysis based on the topic entered by the user and the selected generative model, and refers to a computer program or AI model.

[0607] "Display means" refers to a means for visually displaying the analysis results to the user, and refers to a display device such as a monitor or display.

[0608] A "generative model" is a model for generating text or information based on specific data, and refers to an artificial intelligence or machine learning algorithm.

[0609] "Emotion analysis result" is information about the user's emotional state obtained by the emotion analysis means.

[0610] This invention is a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user, and can also recognize the user's emotions and reflect them in the analysis results. Specific embodiments of this system are described below.

[0611] System Overview

[0612] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative model to use. The device receives the user input and selection, analyzes the user's emotions using an emotion engine, then adjusts the generative model based on the analyzed emotion information and provides the analysis results to the user.

[0613] User Interface

[0614] The server provides a web page with an interface where users can input topics and select generative models, including the option to input emotional nuances.

[0615] Topic and Model Input

[0616] Users enter the topic they want to analyze in a free text box on the webpage, select a generative model (e.g., GPT-3, BERT, T5, etc.) to use for analysis from a drop-down list, and enter information that expresses a specific emotional state (e.g., joy, sadness, surprise, etc.).

[0617] Sentiment analysis and data transmission

[0618] The device receives user input data and analyzes the user's emotions using an emotion engine. The results are then sent to the server, along with information on the topic and generative model, to prepare for analysis. For example, the IBM Watson Tone Analyzer is used as the emotion engine.

[0619] Executing the analysis process

[0620] The server performs individual analysis for each model based on the topic entered by the user and the selected generative model. The analysis procedure includes a step of incorporating the results of the emotion engine to adjust the analysis parameters of the generative model and generate analysis results that match the emotion.

[0621] Collecting and displaying results

[0622] The server collects the analysis results from each generative model and organizes them taking into account the results of the emotion engine. This results in analysis results optimized for the user's emotional state. The organized results are presented in a format that makes it easy for users to compare them.

[0623] Results display

[0624] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, adjusted based on the results of the emotion engine.

[0625] Specific examples

[0626] For example, if a user selects GPT-3 and BERT for the topic "climate change" and enters the emotion "anxiety," the server will analyze this emotion using the emotion engine and generate the following results:

[0627] GPT-3 analysis results: "Detailed explanations and predictions of the adverse effects of climate change"

[0628] BERT analysis results: "The latest crisis news articles related to climate change"

[0629] The results are tailored to correspond to the user's emotion of "anxiety." The device organizes these analysis results and displays them in a format that is easy for the user to understand and relate to their emotional state.

[0630] Prompt Sentence Examples

[0631] In this system, an example of a prompt for user input is shown below.

[0632] Topic: "Climate Change"

[0633] Generative model: "GPT-3, BERT"

[0634] Emotional state: "Anxiety"

[0635] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0636] Step 1:

[0637] The user accesses the web interface provided by the server, inputs the topic to be analyzed, selects the generative model to use (e.g., GPT-3, BERT, T5, etc.), and inputs a specific emotional state (e.g., joy, sadness, surprise, anxiety, etc.). The input data includes the topic "climate change," the generative model "GPT-3, BERT," and the emotional state "anxiety." When the user clicks the "Submit" button, the data is sent to the server.

[0638] Step 2:

[0639] The device processes the user's input data received from the server. The input data includes topics, generative models, and emotional states, and is immediately sent to the emotion engine for analysis. The emotion engine uses this information to perform a deep analysis of the user's emotional state and generates the results as output. This output data includes the analyzed emotional information and is sent to the server.

[0640] Step 3:

[0641] The server receives the topic, generative model, and sentiment analysis results sent from the device. Based on the received data, a process is initiated to perform individual analysis for each generative model. At this time, the sentiment analysis results are adjusted to affect the analysis parameters. For example, based on the topic "climate change" and the emotional state "anxiety," the analysis parameters for GPT-3 and BERT are set and the analysis is performed. The generative model performs analysis on the topic and outputs the results.

[0642] Step 4:

[0643] The server collects and organizes the analysis results obtained from generative models (GPT-3, BERT, etc.). The results generated by each generative model are adjusted taking into account the results of sentiment analysis. For example, the analysis results of GPT-3 may yield "detailed descriptions and predictions about the negative effects of climate change," while the analysis results of BERT may yield "the latest critical news articles related to climate change." The server integrates these results and prepares them for display in user-friendly formats (tables and graphs).

[0644] Step 5:

[0645] The device receives the analysis results sent from the server and renders a web page to display them. This page displays information in which the analysis results of each generative model have been adjusted based on the emotional state. For example, if a user investigates "climate change" when in the emotional state of "anxiety," the results are displayed in table and graph format for easy comparison. This allows the user to visually confirm detailed analysis results that take emotional state into account.

[0646] (Application example 2)

[0647] 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."

[0648] Conventional advertising generation systems have had difficulty generating advertising content that takes user emotions into account. As a result, they were unable to generate ads that matched the specific emotions of users, and were unable to maximize the effectiveness of the ads. Furthermore, when using multiple generative models to analyze from different perspectives, it was difficult to properly compare the results.

[0649] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface, input means for allowing the user to input a topic, selection means for allowing the user to select a generative model to use for analysis, emotion analysis means for analyzing the user's emotions and adjusting the generative model based on the analysis results, analysis means for performing analysis based on the topic input by the user and the selected generative model, and display means for displaying the analysis results. This makes it possible to generate advertising content suited to the user's emotions and compare analysis results from multiple generative models.

[0650] "User interface" refers to the screens and components through which a user interacts with a system, inputting data and viewing results.

[0651] "Input Means" refers to the device or method by which a user provides topics or other information to the system.

[0652] "Selection means" refers to a device or method for a user to select a generative model to use.

[0653] "Analysis means" refers to a device or method that performs analysis based on the topic entered by the user and the selected generative model.

[0654] "Emotion analysis means" refers to a device or method that analyzes a user's emotions and adjusts a generative model based on the results.

[0655] "Display means" refers to a device or method for visually presenting the analysis results to the user.

[0656] A "generative model" refers to an algorithm or machine learning model that generates a specific output (e.g., advertising content) based on user-specified information.

[0657] "Topic" refers to a particular theme or subject that a user inputs for analysis or generation.

[0658] "Comparable format" refers to an organized format that allows users to easily understand and compare multiple analysis results.

[0659] "Analysis parameters" refer to settings and adjustments that allow a generative model to operate under specific conditions.

[0660] This invention is a system that can compare the differences in the views and interpretations of multiple generative AI models on a topic specified by a user, and can also recognize the user's emotions and reflect them in the analysis results. Specific embodiments of this system are described below.

[0661] System Overview

[0662] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative AI model to use. The device receives the user's input and selection and analyzes the user's emotions using a sentiment analysis engine. It then adjusts the generative AI model based on the analyzed sentiment information and provides the analysis results to the user. The user can then review the generated analysis results and obtain information that matches their sentiment.

[0663] Program Overview

[0664] Hardware and software used

[0665] In this system, the user uses a smartphone as the terminal. The server is located on a cloud service and uses the following software:

[0666] Python: a programming language

[0667] Transformers: Import and use emotion analysis and generative AI models in the Hugging Face library

[0668] requests: A Python HTTP library used to call external APIs (e.g., GPT-3).

[0669] Data Flow and Processing

[0670] 1. User Interface:

[0671] The server serves a web page, displaying an interface where users can input a topic and select the generative AI model to use (e.g., GPT-3, BERT, etc.). The page also includes an option for users to input emotional nuances.

[0672] 2. User Input and Sentiment Analysis:

[0673] Users enter the topic they want to analyze in a free text box on the webpage and select a generative AI model to use for analysis from a drop-down list. Users also enter information that expresses a specific emotional state (e.g., joy, sadness, surprise, etc.). The device receives the user input data and analyzes the user's emotions using an emotion analysis engine.

[0674] 3. Invoke the generative model and generate content:

[0675] The server adjusts the analysis parameters of the generative AI model based on the sentiment analysis results and inputs them into the generative AI model along with the specified topic. For example, it adjusts the analysis parameters for the GPT-3 and GPT-2 generative models to generate advertising content that matches the sentiment.

[0676] 4. Collecting and displaying analysis results:

[0677] The server collects the analysis results from each generative AI model and organizes them based on the results of the emotion analysis engine. This allows for analysis results optimized for the user's emotional state. The organized results are displayed in a format that makes it easy for users to compare.

[0678] Specific examples

[0679] For example, if a user selects "GPT-3" and "GPT-2" for the topic "new smartphones" and enters the emotion "surprise," the server will analyze this emotion using its emotion analysis engine and generate the following results:

[0680] GPT-3 analysis results:

[0681] Introducing the latest smartphone that will leave you amazed! With cutting-edge technology and features that will blow your mind, this device is set to redefine what you think is possible. Experience the future today!

[0682] GPT-2 analysis results:

[0683] Discover the newest advancement in smartphone technology! This groundbreaking device will astonish you with its high-speed performance and state-of-the-art features. Get ready to be surprised!

[0684] Prompt Sentence Examples

[0685] Generate an advertisement for the topic 'New Smartphone' that matches the emotion: 'Amazing'.

[0686] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0687] Step 1:

[0688] Users enter the topic they want to analyze into a form on a webpage, select the generative AI model to use from a drop-down list, and also enter information about their emotional state.

[0689] Input data: topics, generative AI models, sentiment information

[0690] Output data: None

[0691] Step 2:

[0692] The device receives user input data, which includes topics, generative AI models, and sentiment information.

[0693] Input data: User input data (topics, generative AI models, sentiment information)

[0694] Output data: Internal storage of user input data

[0695] Step 3:

[0696] The device uses an emotion analysis engine to analyze the emotion information entered by the user and generates an emotion label as the analysis result.

[0697] Input data: Emotion information

[0698] Data processing: Analyze emotions using a sentiment analysis engine (such as the BERT model) and generate sentiment labels.

[0699] Output data: emotion labels

[0700] Step 4:

[0701] Based on the emotion labels, the device prepares to adjust the analysis parameters of the generative AI model.

[0702] Input data: emotion labels, generative AI model

[0703] Data processing: Setting the analysis parameters of the generative AI model based on emotion labels

[0704] Output data: Adjusted analysis parameters

[0705] Step 5:

[0706] The server runs the analysis on the selected generative AI model using the adjusted analysis parameters and topics.

[0707] Input data: topics, adjusted analysis parameters, generative AI model

[0708] Data calculation: Input data into the generative AI model and generate analysis results.

[0709] Output data: Analysis results from the generative AI model

[0710] Step 6:

[0711] The server collects the analysis results and organizes them for each generative AI model.

[0712] Input data: Analysis results from the generative AI model

[0713] Data processing: Collecting and organizing analysis results (converting them into a format that is easy to compare)

[0714] Output data: Organized analysis results

[0715] Step 7:

[0716] The server generates a web page to present the organized analysis results to the user.

[0717] Input data: Organized analysis results

[0718] Data processing: rendering web pages

[0719] Output data: Analysis results that are displayed to the user

[0720] Step 8:

[0721] Users can check the analysis results of each generative AI model through the provided webpage, check whether the analysis results match the emotional state, and obtain appropriate information.

[0722] Input data: Displayed analysis results

[0723] Output data: None (information acquisition and understanding)

[0724] 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.

[0725] 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.

[0726] 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.

[0727] [Third embodiment]

[0728] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0729] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0730] 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).

[0731] 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.

[0732] 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.

[0733] 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).

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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."

[0740] This invention provides a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user. Specific embodiments of this system are described below.

[0741] System Overview

[0742] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative model they want to use. Once the user has input and selected, the server performs analysis based on this information and provides the results to the device. Finally, the device displays the analysis results to the user.

[0743] User Interface

[0744] The server provides a web page for users to access, which includes a free text box for users to enter the topic they want to analyze and a drop-down list for users to select multiple generative models to use.

[0745] Topic and Model Input

[0746] Users input a topic on the webpage and select the generative model to use for analysis (e.g., GPT-3, BERT, T5, etc.). This allows users to specify any topic as the analysis target and simultaneously select multiple generative models for comparative analysis.

[0747] Sending and Receiving Form Data

[0748] After the user enters a topic and selects a model, they click the "Analyze" button on the device to send the form data to the server. The server receives this request and extracts the necessary data to start the analysis process.

[0749] Executing the analysis process

[0750] The server performs analysis using each model based on the user's input topic and the selected generative model. Specifically, it requests each generative model to analyze the topic and collects the results. For example, the GPT-3 model generates a detailed explanation and prediction for the topic, while the BERT model provides a context-based interpretation.

[0751] Collecting and displaying results

[0752] The server organizes the analysis results collected from each generative model and sends them back to the device. The device then displays the received analysis results in tabular and graphical formats, making it easy for users to compare them. This allows users to see the analysis results of multiple generative models at a glance and understand the characteristics and strengths of each model.

[0753] Specific examples

[0754] For example, if a user selects "GPT-3" and "BERT" for the topic "climate change," the server will use each model to generate the following analysis results:

[0755] GPT-3 analysis results: "A detailed explanation of the impacts of climate change"

[0756] BERT analysis results: "Summary of recent news articles related to climate change"

[0757] The device displays these analysis results in a table format, allowing users to compare the views of different models, thereby providing information from multiple perspectives and supporting appropriate decision-making.

[0758] In this way, the present invention is a system that enables users to compare the differences in views and interpretations of multiple generative models on a topic specified by the user, thereby enabling them to judge the accuracy of information and access it efficiently.

[0759] The processing flow will be explained below.

[0760] Step 1:

[0761] The server provides the user interface: it renders a web page that the user accesses, displaying a free text box for topic input and a drop-down list for selecting the generative model to use.

[0762] Step 2:

[0763] The user enters the topic they want to analyze in a free text box on the web page and selects one or more generative models to use for the analysis from a drop-down list.

[0764] Step 3:

[0765] When the user clicks the "Analyze" button, the terminal sends the topic and the selected generative model to the server as form data, which is sent as a POST request.

[0766] Step 4:

[0767] The server receives the POST request, extracts information about the topic and the selected generative model from the request data, and prepares to start analysis based on the extracted data.

[0768] Step 5:

[0769] The server runs individual analyses for the topic and each selected generative model, for example, using a GPT-3 model to generate sentences about the topic and a BERT model to analyze the context related to the topic.

[0770] Step 6:

[0771] The server collects the analysis results obtained from each generative model, organizes them, and converts them into a format that makes it easy for users to compare analysis results from multiple models.

[0772] Step 7:

[0773] The server generates a response containing the organized analysis results and sends it to the device. The response contains the analysis results divided by the model selected by the user.

[0774] Step 8:

[0775] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, allowing users to easily compare them.

[0776] Step 9:

[0777] Users can view the analysis results provided on the web page and compare the views of multiple generative models, which allows users to gain information on a topic from a broader perspective.

[0778] The above is the processing flow of the present invention, which allows the user to check and compare the analysis results of multiple generative models at a glance.

[0779] Example 1

[0780] 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."

[0781] With conventional systems, it was difficult to centrally compare the analysis results of generative AI models for a user-specified topic, making it difficult to understand the characteristics and strengths of each generative model. Furthermore, there was a lack of a mechanism for effectively collecting the analysis results of multiple generative models and displaying them in an easy-to-understand manner for users. This resulted in reduced judgment of the accuracy of information and reduced efficiency in decision-making.

[0782] 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.

[0783] In this invention, the server includes means for providing a user interface, input means for allowing the user to input topics, selection means for the user to select a generative model to use for analysis, analysis means for performing analysis for each generative model based on the topics input by the user and the selected generative models, collection means for collecting analysis results obtained from multiple generative models, and display means for displaying the analysis results in tabular or graphical format. This allows the user to centrally compare the analysis results of multiple generative models and understand the characteristics and strengths of each.

[0784] "User interface" refers to the display screen and input screen that users use to access and operate the system.

[0785] An "input means" is a means by which a user inputs a topic, typically a keyboard or touch screen.

[0786] The "selection means" is a means by which the user selects the generative model to be used in the analysis, and is usually a drop-down list or radio button.

[0787] The "analysis means" is a means having the function of performing analysis with each generative model based on the topic entered by the user and the selected generative model.

[0788] The "collection means" is a means for integrating and organizing the analysis results obtained from multiple generative models.

[0789] "Display means" refers to a means for displaying the collected analysis results in a form that is easy for the user to view, and includes tabular and graphical formats.

[0790] A "prompt sentence" is an instruction sentence used to request analysis from a generative AI model.

[0791] This invention provides a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user. Specific embodiments of this system are described below.

[0792] Providing a user interface

[0793] The server generates a web page to provide the user interface. This web page includes a free text box for users to enter topics and multiple selection methods (e.g., drop-down lists) for selecting the generative model to use. The web page is built using HTML, CSS, and JavaScript.

[0794] Topic and Model Input

[0795] Users enter the topic they want to analyze in a free text box on the provided webpage and select a model to use from multiple generative models (e.g., GPT-3, BERT, T5, etc.). This allows users to analyze any topic and simultaneously select multiple generative models to perform comparative analysis.

[0796] Sending and Receiving Form Data

[0797] After the user inputs a topic and selects a generative model, they click the "Analyze" button, and the device sends the user's input data to the server in JSON format. The server receives this request and extracts the data necessary to start the analysis process.

[0798] Executing the analysis process

[0799] The server performs analysis using each generative model based on the topic entered by the user and the selected generative model. Specifically, it generates a prompt for each generative model and issues an analysis request based on that. For example, when analyzing the topic "climate change," the following prompt is sent to the generative AI model:

[0800] For GPT-3: "Please explain in detail the impacts of climate change with specific examples."

[0801] For BERT: "Summarize a recent news article related to climate change."

[0802] Each generative model performs analysis based on these prompt sentences and sends the results back to the server.

[0803] Collecting and displaying results

[0804] The server collects and organizes the analysis results returned by each generative model. The organized analysis results are then sent back to the device in JSON format, and after receiving them, the device displays them to the user in tabular or graph format. For example, when displayed in table format, the left column shows the results of GPT-3 and the right column shows the results of BERT.

[0805] Specific examples

[0806] For example, if a user selects "GPT-3" and "BERT" for the topic "climate change," the server will use each generative model to generate the following analysis results:

[0807] GPT-3 analysis results: "A detailed explanation of the impacts of climate change"

[0808] BERT analysis results: "Summary of recent news articles related to climate change"

[0809] The device displays these analysis results in a tabular format, allowing users to compare the views of different models, thereby providing information from multiple perspectives and supporting appropriate decision-making.

[0810] In this way, the present invention is a system that enables users to judge the accuracy of information and access information efficiently by allowing them to compare the differences in views and interpretations of multiple generative models on a topic specified by the user.

[0811] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0812] Step 1: Providing a User Interface

[0813] The server generates a user-accessible web page that contains a free text box for users to enter a topic and a drop-down list for selecting the generative model to use. The input is HTML, CSS, and JavaScript code, and the output is a web page that users can interact with.

[0814] Step 2: Input topics and models

[0815] On the provided webpage, the user enters the topic they want to analyze in a free text box and selects the generative model to use from a drop-down list (e.g., GPT-3, BERT, T5). The input of this step is the user's topic and model selection information, and the output is the topic and model selection data ready to be sent to the server.

[0816] Step 3: Sending data

[0817] When the user clicks the "Analyze" button, the device sends the input topic and selected generative model data to the server in JSON format. The input of this step is the topic entered by the user and the selected generative model information, and the output is data that is sent to the server in JSON format.

[0818] Step 4: Receiving the data

[0819] The server receives data sent in JSON format and extracts the necessary data to start the analysis process. The input of this step is the JSON data sent from the terminal, and the output is the extracted topics and generative model information.

[0820] Step 5: Run the analysis process

[0821] Based on the specified topic and the selected generative model, the server generates a prompt for each generative model and requests it to analyze it. For example, for the topic "climate change," the server sends the prompt "Please explain in detail the impacts of climate change with specific examples" to GPT-3, and the prompt "Please summarize recent news articles related to climate change" to BERT. The input for this step is the topic and the selected generative model information, and the output is the prompt sent to each generative model.

[0822] Step 6: Collecting analysis results

[0823] The generative AI model performs analysis based on the prompt received from the server and sends the results back to the server. The server collects the analysis results returned from each generative model. The input of this step is the analysis results from each generative model, and the output is the integrated analysis result data.

[0824] Step 7: View the results

[0825] The server organizes the collected analysis results and sends them back to the terminal in JSON format. The terminal receives them and displays the analysis results in a format that is easy for the user to view (table or graph format). For example, the left column shows the results of GPT-3 and the right column shows the results of BERT. The input to this step is the organized analysis result data, and the output is the analysis result that is displayed to the user.

[0826] Through these specific processing steps, the system is designed to make it easy to compare multiple generative models for a user-specified topic, allowing users to understand the characteristics of each model and obtain more accurate and multifaceted information.

[0827] (Application example 1)

[0828] 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."

[0829] Conventional information analysis systems have difficulty comparing the views and interpretations of multiple generative AI models on a user-specified topic. This prevents users from obtaining information from multiple perspectives, making it difficult to make appropriate decisions. Furthermore, there is a lack of means to provide feedback on the information obtained, making it difficult to improve the user experience.

[0830] 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.

[0831] In this invention, the server includes means for providing a user interface, input means for allowing the user to input a topic, selection means for allowing the user to select a generative model to use for analysis, analysis means for performing analysis based on the topic input by the user and the selected generative model, display means for displaying the analysis results, and feedback means for allowing the user to provide feedback on the analysis results, which makes it easier for the user to compare the analysis results of multiple generative AI models and provide feedback on each analysis result.

[0832] A "user interface" is the means by which a user interacts with a computer system, providing screens and controls for input and output.

[0833] "Input means" refers to a means by which a user provides specific information or data to a system, and includes a text box, microphone, keyboard, etc.

[0834] A "selection means" is a means by which a user selects an option or function to use within a system, including drop-down lists, radio buttons, check boxes, etc.

[0835] "Analysis means" means the means by which a computer program performs analysis or calculations based on data entered or options selected by a user.

[0836] "Display means" refers to a means for visually showing analysis results and other information to users, such as a monitor, display, or screen.

[0837] "Feedback means" refers to the means by which users can provide feedback and evaluations to the system, and includes text input fields, evaluation buttons, etc.

[0838] A "generative model" is an algorithm or network that uses artificial intelligence or machine learning to perform a specific task, particularly a model used in natural language processing.

[0839] This invention provides a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user. Specific embodiments of this system are described below.

[0840] System configuration

[0841] The system consists of the following main components:

[0842] 1. User Interface (UI)

[0843] 2. Input Method

[0844] 3. Selection Method

[0845] 4. Analysis method

[0846] 5. Display means

[0847] 6. Feedback channels

[0848] User Interface

[0849] The server provides a web page for users to access, which includes a free text box for entering the topic they want to analyze, a drop-down list for selecting multiple generative models to use, and an Analyze button.

[0850] Topic and Model Input

[0851] Users input a topic on a webpage and select a generative model (e.g., GPT-3, BERT, etc.) to use for analysis. This allows users to specify any topic as the analysis target and simultaneously select multiple generative models for comparative analysis.

[0852] Sending and Receiving Form Data

[0853] After the user enters a topic and selects a model, they click the "Analyze" button on the device to send the form data to the server. The server receives this request and extracts the necessary data to start the analysis process.

[0854] Executing the analysis process

[0855] The server performs analysis using each model based on the user's input topic and the selected generative model. Specifically, it requests each generative model to analyze the topic and collects the results. For example, the GPT-3 model generates a detailed explanation and prediction for the topic, while the BERT model provides a context-based interpretation.

[0856] The generative models used include OpenAI's GPT-3 and Google's BERT, for example, and these models are invoked via APIs using programming languages ​​such as Python.

[0857] Collecting and displaying results

[0858] The server organizes the analysis results collected from each generative model and sends them back to the device. The device then displays the received analysis results in tabular and graphical formats, making it easy for users to compare them. This allows users to see the analysis results of multiple generative models at a glance and understand the characteristics and strengths of each model.

[0859] Feedback function

[0860] Users can provide feedback on the displayed analysis results, using a text input field and rating buttons to send user ratings and comments to the server.

[0861] Specific examples

[0862] For example, if a user selects "GPT-3" and "BERT" for the topic "climate change," the server will use each model to generate the following analysis results:

[0863] GPT-3 analysis result: "Tell me about climate change."

[0864] BERT analysis results: "Summarize a recent news article about climate change."

[0865] In this way, users can compare the views of different generative models and view each piece of information from multiple angles.

[0866] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0867] Step 1:

[0868] The user enters a topic into an input form on a web page and selects the generative model to use for analysis.

[0869] Input: A list of topics entered by the user and the selected generative model

[0870] Output: The topics and the selected generative model are prepared as form data.

[0871] Step 2:

[0872] The user clicks the "Analyze" button and the device sends the form data to the server.

[0873] Input: User-entered and selected data

[0874] Output: Parsing request data sent to the server

[0875] Step 3:

[0876] The server receives the request and extracts information about the topic and the selected generative model.

[0877] Input: Parse request data received by the server

[0878] Output: A list of topics needed for analysis and the selected generative model

[0879] Step 4:

[0880] The server sends a topic-based analysis request to each generative model and obtains the analysis results from each generative model.

[0881] Input: a list of topics and a selected generative model

[0882] Output: Analysis results from each generative model

[0883] Specific operation: For example, the server sends a prompt to GPT-3, such as "Tell me about climate change," and to BERT, such as "Summarize recent news articles about climate change." The analysis results are obtained using OpenAI's API and Transformer model.

[0884] Step 5:

[0885] The server organizes the analysis results of each generative model it has acquired and prepares them to be sent to the terminal.

[0886] Input: Analysis results obtained from each generative model

[0887] Output: Organized analysis results

[0888] Specific operation: The server formats the results of each generative model into a form that is easy to convert into a table or graph format.

[0889] Step 6:

[0890] The server transmits the analysis results to the terminal, and the terminal receives the results.

[0891] Input: Organized analysis results

[0892] Output: Analysis result notification received by the device

[0893] Step 7:

[0894] The terminal displays the analysis results to the user.

[0895] Input: Analysis results received by the device

[0896] Output: Displaying the analysis results in a user-readable format

[0897] Specific operation: The terminal displays the analysis results in tabular or graphical format on a web page, for example, allowing the results of different generative models to be compared.

[0898] Step 8:

[0899] The user provides feedback on the displayed analysis results.

[0900] Input: User-entered feedback

[0901] Output: Feedback data sent to the server

[0902] Specific operation: Users provide feedback using text input fields and rating buttons, and the data is sent to the server via the device.

[0903] 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.

[0904] This invention is a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user, and can also recognize the user's emotions and reflect them in the analysis results. Specific embodiments of this system are described below.

[0905] System Overview

[0906] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative model to use. The device receives the user input and selection, analyzes the user's emotions using an emotion engine, then adjusts the generative model based on the analyzed emotion information and provides the analysis results to the user.

[0907] User Interface

[0908] The server provides a web page with an interface where users can input topics and select generative models, including the option to input emotional nuances.

[0909] Topic and Model Input

[0910] Users enter the topic they want to analyze in a free text box on the webpage, select a generative model (e.g., GPT-3, BERT, T5, etc.) to use for analysis from a drop-down list, and enter information that expresses a specific emotional state (e.g., joy, sadness, surprise, etc.).

[0911] Sentiment analysis and data transmission

[0912] The device receives user input data, analyzes the user's emotions using the emotion engine, and sends the results to the server, along with information on the topic and generative model, to prepare for analysis.

[0913] Executing the analysis process

[0914] The server performs individual analysis for each model based on the topic entered by the user and the selected generative model. The analysis procedure includes a step of incorporating the results of the emotion engine to adjust the analysis parameters of the generative model and generate analysis results that match the emotion.

[0915] Collecting and displaying results

[0916] The server collects the analysis results from each generative model and organizes them taking into account the results of the emotion engine. This results in analysis results optimized for the user's emotional state. The organized results are presented in a format that makes it easy for users to compare them.

[0917] Results display

[0918] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, adjusted based on the results of the emotion engine.

[0919] Specific examples

[0920] For example, if a user selects GPT-3 and BERT for the topic "climate change" and enters the emotion "anxiety," the server will analyze this emotion using the emotion engine and generate the following results:

[0921] GPT-3 analysis results: "Detailed explanations and predictions of the adverse effects of climate change"

[0922] BERT analysis results: "The latest crisis news articles related to climate change"

[0923] The results are tailored to correspond to the user's emotion of "anxiety." The device organizes these analysis results and displays them in a format that is easy for the user to understand and relate to their emotional state.

[0924] In this way, by combining emotion engines, the present invention is a system that can not only compare the differences in views and interpretations of multiple generative models on a user-specified topic, but also recognize the user's emotions and reflect them in the analysis results, allowing users to obtain comprehensive information with emotional context.

[0925] The processing flow will be explained below.

[0926] Step 1:

[0927] The server provides the user interface: it renders a web page that the user accesses, displaying a free text box for topic entry, an emotion drop-down list for selecting an emotional state, and a drop-down list for selecting the generative model to use.

[0928] Step 2:

[0929] Users enter the topic they want to analyze in a free text box on the webpage, select their emotional state (e.g., joy, sadness, anxiety, etc.) from an emotion drop-down list, and select the generative model to use (e.g., GPT-3, BERT, T5, etc.).

[0930] Step 3:

[0931] When the user clicks the "Analyze" button, the device sends the topic, emotional state, and selected generative model to the server as form data, which is sent as a POST request.

[0932] Step 4:

[0933] The server receives the POST request, extracts information about the topic, emotional state, and selected generative model from the request data, and prepares to analyze the user's emotional state using the emotion engine.

[0934] Step 5:

[0935] The server runs an emotion engine based on the topic and emotional state to perform a detailed analysis of the user's emotions. For example, if a user selects the emotion "anxiety," the analysis results include the intensity of that emotion and related emotional characteristics.

[0936] Step 6:

[0937] The server adjusts the parameters of each generative model based on the analysis results of the emotion engine. For example, if the emotion engine analyzes the user's emotion as "anxiety," the generative model will be adjusted to generate analysis results that are more focused on the area of ​​concern.

[0938] Step 7:

[0939] The server uses the topic and the adjusted parameters to run individual analyses on each selected generative model, each of which performs its own analysis on the specified topic and produces results.

[0940] Step 8:

[0941] The server collects the analysis results from each generative model, organizes them taking into account the results of the emotion engine, and compiles them into a single dataset, ready to present to the user in the most optimal format.

[0942] Step 9:

[0943] The server generates a response containing the organized analysis results and sends it to the device. The response includes the analysis results of each generative model adjusted based on the results of the emotion engine.

[0944] Step 10:

[0945] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, adjusted based on the results of the emotion engine.

[0946] Step 11:

[0947] Users can view the analysis results provided on the webpage and compare the views of multiple generative models, thereby obtaining comprehensive information based on their emotional state.

[0948] The above are the detailed processing steps of the present invention, which allow users to check and compare analysis results from multiple generative models along with emotional context.

[0949] Example 2

[0950] 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."

[0951] Existing analysis systems provide analysis results without considering the user's emotional state, which can prevent users from obtaining the information they desire based on the emotional context. Furthermore, when comparing analysis results from multiple generative models, they lack the functionality to provide results optimized for the user's emotional state. This makes it difficult to obtain analysis results that are intuitive and easy to understand for users.

[0952] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface, input means for allowing the user to input a topic, selection means for allowing the user to select a generative model to be used for analysis, emotion analysis means for analyzing the user's emotions, adjustment means for adjusting analysis parameters of the generative model based on the emotion analysis results, analysis means for performing analysis based on the topic input by the user and the selected generative model, and display means for displaying the analysis results. This makes it possible to provide analysis results that reflect the user's emotional state and obtain information in an intuitive and easy-to-understand format.

[0953] A "user interface" is a means by which a user interacts with a system, and is an interface that allows input and display.

[0954] "Input means" refers to the means by which a user inputs the topic they wish to analyze into the system, and refers to input devices such as a keyboard or touch screen.

[0955] The "selection means" refers to a means for the user to select a generative model to use in the analysis, and refers to a selection device such as a drop-down list or radio buttons.

[0956] "Emotion analysis means" refers to a means for analyzing emotions based on user input data, and refers to emotion analysis engines and natural language processing technology.

[0957] "Adjustment means" refers to a means for adjusting the analysis parameters of the generative model based on the emotion analysis results, and refers to changes in the algorithm or setting parameters.

[0958] "Analysis means" refers to a means for performing analysis based on the topic entered by the user and the selected generative model, and refers to a computer program or AI model.

[0959] "Display means" refers to a means for visually displaying the analysis results to the user, and refers to a display device such as a monitor or display.

[0960] A "generative model" is a model for generating text or information based on specific data, and refers to an artificial intelligence or machine learning algorithm.

[0961] "Emotion analysis result" is information about the user's emotional state obtained by the emotion analysis means.

[0962] This invention is a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user, and can also recognize the user's emotions and reflect them in the analysis results. Specific embodiments of this system are described below.

[0963] System Overview

[0964] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative model to use. The device receives the user input and selection, analyzes the user's emotions using an emotion engine, then adjusts the generative model based on the analyzed emotion information and provides the analysis results to the user.

[0965] User Interface

[0966] The server provides a web page with an interface where users can input topics and select generative models, including the option to input emotional nuances.

[0967] Topic and Model Input

[0968] Users enter the topic they want to analyze in a free text box on the webpage, select a generative model (e.g., GPT-3, BERT, T5, etc.) to use for analysis from a drop-down list, and enter information that expresses a specific emotional state (e.g., joy, sadness, surprise, etc.).

[0969] Sentiment analysis and data transmission

[0970] The device receives user input data and analyzes the user's emotions using an emotion engine. The results are then sent to the server, along with information on the topic and generative model, to prepare for analysis. For example, the IBM Watson Tone Analyzer is used as the emotion engine.

[0971] Executing the analysis process

[0972] The server performs individual analysis for each model based on the topic entered by the user and the selected generative model. The analysis procedure includes a step of incorporating the results of the emotion engine to adjust the analysis parameters of the generative model and generate analysis results that match the emotion.

[0973] Collecting and displaying results

[0974] The server collects the analysis results from each generative model and organizes them taking into account the results of the emotion engine. This results in analysis results optimized for the user's emotional state. The organized results are presented in a format that makes it easy for users to compare them.

[0975] Results display

[0976] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, adjusted based on the results of the emotion engine.

[0977] Specific examples

[0978] For example, if a user selects GPT-3 and BERT for the topic "climate change" and enters the emotion "anxiety," the server will analyze this emotion using the emotion engine and generate the following results:

[0979] GPT-3 analysis results: "Detailed explanations and predictions of the adverse effects of climate change"

[0980] BERT analysis results: "The latest crisis news articles related to climate change"

[0981] The results are tailored to correspond to the user's emotion of "anxiety." The device organizes these analysis results and displays them in a format that is easy for the user to understand and relate to their emotional state.

[0982] Prompt Sentence Examples

[0983] In this system, an example of a prompt for user input is shown below.

[0984] Topic: "Climate Change"

[0985] Generative model: "GPT-3, BERT"

[0986] Emotional state: "Anxiety"

[0987] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0988] Step 1:

[0989] The user accesses the web interface provided by the server, inputs the topic to be analyzed, selects the generative model to use (e.g., GPT-3, BERT, T5, etc.), and inputs a specific emotional state (e.g., joy, sadness, surprise, anxiety, etc.). The input data includes the topic "climate change," the generative model "GPT-3, BERT," and the emotional state "anxiety." When the user clicks the "Submit" button, the data is sent to the server.

[0990] Step 2:

[0991] The device processes the user's input data received from the server. The input data includes topics, generative models, and emotional states, and is immediately sent to the emotion engine for analysis. The emotion engine uses this information to perform a deep analysis of the user's emotional state and generates the results as output. This output data includes the analyzed emotional information and is sent to the server.

[0992] Step 3:

[0993] The server receives the topic, generative model, and sentiment analysis results sent from the device. Based on the received data, a process is initiated to perform individual analysis for each generative model. At this time, the sentiment analysis results are adjusted to affect the analysis parameters. For example, based on the topic "climate change" and the emotional state "anxiety," the analysis parameters for GPT-3 and BERT are set and the analysis is performed. The generative model performs analysis on the topic and outputs the results.

[0994] Step 4:

[0995] The server collects and organizes the analysis results obtained from generative models (GPT-3, BERT, etc.). The results generated by each generative model are adjusted taking into account the results of sentiment analysis. For example, the analysis results of GPT-3 may yield "detailed descriptions and predictions about the negative effects of climate change," while the analysis results of BERT may yield "the latest critical news articles related to climate change." The server integrates these results and prepares them for display in user-friendly formats (tables and graphs).

[0996] Step 5:

[0997] The device receives the analysis results sent from the server and renders a web page to display them. This page displays information in which the analysis results of each generative model have been adjusted based on the emotional state. For example, if a user investigates "climate change" when in the emotional state of "anxiety," the results are displayed in table and graph format for easy comparison. This allows the user to visually confirm detailed analysis results that take emotional state into account.

[0998] (Application example 2)

[0999] 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."

[1000] Conventional advertising generation systems have had difficulty generating advertising content that takes user emotions into account. As a result, they were unable to generate ads that matched the specific emotions of users, and were unable to maximize the effectiveness of the ads. Furthermore, when using multiple generative models to analyze from different perspectives, it was difficult to properly compare the results.

[1001] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface, input means for allowing the user to input a topic, selection means for allowing the user to select a generative model to use for analysis, emotion analysis means for analyzing the user's emotions and adjusting the generative model based on the analysis results, analysis means for performing analysis based on the topic input by the user and the selected generative model, and display means for displaying the analysis results. This makes it possible to generate advertising content suited to the user's emotions and compare analysis results from multiple generative models.

[1002] "User interface" refers to the screens and components through which a user interacts with a system, inputting data and viewing results.

[1003] "Input Means" refers to the device or method by which a user provides topics or other information to the system.

[1004] "Selection means" refers to a device or method for a user to select a generative model to use.

[1005] "Analysis means" refers to a device or method that performs analysis based on the topic entered by the user and the selected generative model.

[1006] "Emotion analysis means" refers to a device or method that analyzes a user's emotions and adjusts a generative model based on the results.

[1007] "Display means" refers to a device or method for visually presenting the analysis results to the user.

[1008] A "generative model" refers to an algorithm or machine learning model that generates a specific output (e.g., advertising content) based on user-specified information.

[1009] "Topic" refers to a particular theme or subject that a user inputs for analysis or generation.

[1010] "Comparable format" refers to an organized format that allows users to easily understand and compare multiple analysis results.

[1011] "Analysis parameters" refer to settings and adjustments that allow a generative model to operate under specific conditions.

[1012] This invention is a system that can compare the differences in the views and interpretations of multiple generative AI models on a topic specified by a user, and can also recognize the user's emotions and reflect them in the analysis results. Specific embodiments of this system are described below.

[1013] System Overview

[1014] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative AI model to use. The device receives the user's input and selection and analyzes the user's emotions using a sentiment analysis engine. It then adjusts the generative AI model based on the analyzed sentiment information and provides the analysis results to the user. The user can then review the generated analysis results and obtain information that matches their sentiment.

[1015] Program Overview

[1016] Hardware and software used

[1017] In this system, the user uses a smartphone as the terminal. The server is located on a cloud service and uses the following software:

[1018] Python: a programming language

[1019] Transformers: Import and use emotion analysis and generative AI models in the Hugging Face library

[1020] requests: A Python HTTP library used to call external APIs (e.g., GPT-3).

[1021] Data Flow and Processing

[1022] 1. User Interface:

[1023] The server serves a web page, displaying an interface where users can input a topic and select the generative AI model to use (e.g., GPT-3, BERT, etc.). The page also includes an option for users to input emotional nuances.

[1024] 2. User Input and Sentiment Analysis:

[1025] Users enter the topic they want to analyze in a free text box on the webpage and select a generative AI model to use for analysis from a drop-down list. Users also enter information that expresses a specific emotional state (e.g., joy, sadness, surprise, etc.). The device receives the user input data and analyzes the user's emotions using an emotion analysis engine.

[1026] 3. Invoke the generative model and generate content:

[1027] The server adjusts the analysis parameters of the generative AI model based on the sentiment analysis results and inputs them into the generative AI model along with the specified topic. For example, it adjusts the analysis parameters for the GPT-3 and GPT-2 generative models to generate advertising content that matches the sentiment.

[1028] 4. Collecting and displaying analysis results:

[1029] The server collects the analysis results from each generative AI model and organizes them based on the results of the emotion analysis engine. This allows for analysis results optimized for the user's emotional state. The organized results are displayed in a format that makes it easy for users to compare.

[1030] Specific examples

[1031] For example, if a user selects "GPT-3" and "GPT-2" for the topic "new smartphones" and enters the emotion "surprise," the server will analyze this emotion using its emotion analysis engine and generate the following results:

[1032] GPT-3 analysis results:

[1033] Introducing the latest smartphone that will leave you amazed! With cutting-edge technology and features that will blow your mind, this device is set to redefine what you think is possible. Experience the future today!

[1034] GPT-2 analysis results:

[1035] Discover the newest advancement in smartphone technology! This groundbreaking device will astonish you with its high-speed performance and state-of-the-art features. Get ready to be surprised!

[1036] Prompt Sentence Examples

[1037] Generate an advertisement for the topic 'New Smartphone' that matches the emotion: 'Amazing'.

[1038] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1039] Step 1:

[1040] Users enter the topic they want to analyze into a form on a webpage, select the generative AI model to use from a drop-down list, and also enter information about their emotional state.

[1041] Input data: topics, generative AI models, sentiment information

[1042] Output data: None

[1043] Step 2:

[1044] The device receives user input data, which includes topics, generative AI models, and sentiment information.

[1045] Input data: User input data (topics, generative AI models, sentiment information)

[1046] Output data: Internal storage of user input data

[1047] Step 3:

[1048] The device uses an emotion analysis engine to analyze the emotion information entered by the user and generates an emotion label as the analysis result.

[1049] Input data: Emotion information

[1050] Data processing: Analyze emotions using a sentiment analysis engine (such as the BERT model) and generate sentiment labels.

[1051] Output data: emotion labels

[1052] Step 4:

[1053] Based on the emotion labels, the device prepares to adjust the analysis parameters of the generative AI model.

[1054] Input data: emotion labels, generative AI model

[1055] Data processing: Setting the analysis parameters of the generative AI model based on emotion labels

[1056] Output data: Adjusted analysis parameters

[1057] Step 5:

[1058] The server runs the analysis on the selected generative AI model using the adjusted analysis parameters and topics.

[1059] Input data: topics, adjusted analysis parameters, generative AI model

[1060] Data calculation: Input data into the generative AI model and generate analysis results.

[1061] Output data: Analysis results from the generative AI model

[1062] Step 6:

[1063] The server collects the analysis results and organizes them for each generative AI model.

[1064] Input data: Analysis results from the generative AI model

[1065] Data processing: Collecting and organizing analysis results (converting them into a format that is easy to compare)

[1066] Output data: Organized analysis results

[1067] Step 7:

[1068] The server generates a web page to present the organized analysis results to the user.

[1069] Input data: Organized analysis results

[1070] Data processing: rendering web pages

[1071] Output data: Analysis results that are displayed to the user

[1072] Step 8:

[1073] Users can check the analysis results of each generative AI model through the provided webpage, check whether the analysis results match the emotional state, and obtain appropriate information.

[1074] Input data: Displayed analysis results

[1075] Output data: None (information acquisition and understanding)

[1076] 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.

[1077] 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.

[1078] 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.

[1079] [Fourth embodiment]

[1080] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1081] 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.

[1082] 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).

[1083] 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.

[1084] 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.

[1085] 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).

[1086] 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.

[1087] 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.

[1088] 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.

[1089] 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.

[1090] 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.

[1091] 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.

[1092] 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."

[1093] This invention provides a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user. Specific embodiments of this system are described below.

[1094] System Overview

[1095] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative model they want to use. Once the user has input and selected, the server performs analysis based on this information and provides the results to the device. Finally, the device displays the analysis results to the user.

[1096] User Interface

[1097] The server provides a web page for users to access, which includes a free text box for users to enter the topic they want to analyze and a drop-down list for users to select multiple generative models to use.

[1098] Topic and Model Input

[1099] Users input a topic on the webpage and select the generative model to use for analysis (e.g., GPT-3, BERT, T5, etc.). This allows users to specify any topic as the analysis target and simultaneously select multiple generative models for comparative analysis.

[1100] Sending and Receiving Form Data

[1101] After the user enters a topic and selects a model, they click the "Analyze" button on the device to send the form data to the server. The server receives this request and extracts the necessary data to start the analysis process.

[1102] Executing the analysis process

[1103] The server performs analysis using each model based on the user's input topic and the selected generative model. Specifically, it requests each generative model to analyze the topic and collects the results. For example, the GPT-3 model generates a detailed explanation and prediction for the topic, while the BERT model provides a context-based interpretation.

[1104] Collecting and displaying results

[1105] The server organizes the analysis results collected from each generative model and sends them back to the device. The device then displays the received analysis results in tabular and graphical formats, making it easy for users to compare them. This allows users to see the analysis results of multiple generative models at a glance and understand the characteristics and strengths of each model.

[1106] Specific examples

[1107] For example, if a user selects "GPT-3" and "BERT" for the topic "climate change," the server will use each model to generate the following analysis results:

[1108] GPT-3 analysis results: "A detailed explanation of the impacts of climate change"

[1109] BERT analysis results: "Summary of recent news articles related to climate change"

[1110] The device displays these analysis results in a table format, allowing users to compare the views of different models, thereby providing information from multiple perspectives and supporting appropriate decision-making.

[1111] In this way, the present invention is a system that enables users to compare the differences in views and interpretations of multiple generative models on a topic specified by the user, thereby enabling them to judge the accuracy of information and access it efficiently.

[1112] The processing flow will be explained below.

[1113] Step 1:

[1114] The server provides the user interface: it renders a web page that the user accesses, displaying a free text box for topic input and a drop-down list for selecting the generative model to use.

[1115] Step 2:

[1116] The user enters the topic they want to analyze in a free text box on the web page and selects one or more generative models to use for the analysis from a drop-down list.

[1117] Step 3:

[1118] When the user clicks the "Analyze" button, the terminal sends the topic and the selected generative model to the server as form data, which is sent as a POST request.

[1119] Step 4:

[1120] The server receives the POST request, extracts information about the topic and the selected generative model from the request data, and prepares to start analysis based on the extracted data.

[1121] Step 5:

[1122] The server runs individual analyses for the topic and each selected generative model, for example, using a GPT-3 model to generate sentences about the topic and a BERT model to analyze the context related to the topic.

[1123] Step 6:

[1124] The server collects the analysis results obtained from each generative model, organizes them, and converts them into a format that makes it easy for users to compare analysis results from multiple models.

[1125] Step 7:

[1126] The server generates a response containing the organized analysis results and sends it to the device. The response contains the analysis results divided by the model selected by the user.

[1127] Step 8:

[1128] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, allowing users to easily compare them.

[1129] Step 9:

[1130] Users can view the analysis results provided on the web page and compare the views of multiple generative models, which allows users to gain information on a topic from a broader perspective.

[1131] The above is the processing flow of the present invention, which allows the user to check and compare the analysis results of multiple generative models at a glance.

[1132] Example 1

[1133] 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."

[1134] With conventional systems, it was difficult to centrally compare the analysis results of generative AI models for a user-specified topic, making it difficult to understand the characteristics and strengths of each generative model. Furthermore, there was a lack of a mechanism for effectively collecting the analysis results of multiple generative models and displaying them in an easy-to-understand manner for users. This resulted in reduced judgment of the accuracy of information and reduced efficiency in decision-making.

[1135] 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.

[1136] In this invention, the server includes means for providing a user interface, input means for allowing the user to input topics, selection means for the user to select a generative model to use for analysis, analysis means for performing analysis for each generative model based on the topics input by the user and the selected generative models, collection means for collecting analysis results obtained from multiple generative models, and display means for displaying the analysis results in tabular or graphical format. This allows the user to centrally compare the analysis results of multiple generative models and understand the characteristics and strengths of each.

[1137] "User interface" refers to the display screen and input screen that users use to access and operate the system.

[1138] An "input means" is a means by which a user inputs a topic, typically a keyboard or touch screen.

[1139] The "selection means" is a means by which the user selects the generative model to be used in the analysis, and is usually a drop-down list or radio button.

[1140] The "analysis means" is a means having the function of performing analysis with each generative model based on the topic entered by the user and the selected generative model.

[1141] The "collection means" is a means for integrating and organizing the analysis results obtained from multiple generative models.

[1142] "Display means" refers to a means for displaying the collected analysis results in a form that is easy for the user to view, and includes tabular and graphical formats.

[1143] A "prompt sentence" is an instruction sentence used to request analysis from a generative AI model.

[1144] This invention provides a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user. Specific embodiments of this system are described below.

[1145] Providing a user interface

[1146] The server generates a web page to provide the user interface. This web page includes a free text box for users to enter topics and multiple selection methods (e.g., drop-down lists) for selecting the generative model to use. The web page is built using HTML, CSS, and JavaScript.

[1147] Topic and Model Input

[1148] Users enter the topic they want to analyze in a free text box on the provided webpage and select a model to use from multiple generative models (e.g., GPT-3, BERT, T5, etc.). This allows users to analyze any topic and simultaneously select multiple generative models to perform comparative analysis.

[1149] Sending and Receiving Form Data

[1150] After the user inputs a topic and selects a generative model, they click the "Analyze" button, and the device sends the user's input data to the server in JSON format. The server receives this request and extracts the data necessary to start the analysis process.

[1151] Executing the analysis process

[1152] The server performs analysis using each generative model based on the topic entered by the user and the selected generative model. Specifically, it generates a prompt for each generative model and issues an analysis request based on that. For example, when analyzing the topic "climate change," the following prompt is sent to the generative AI model:

[1153] For GPT-3: "Please explain in detail the impacts of climate change with specific examples."

[1154] For BERT: "Summarize a recent news article related to climate change."

[1155] Each generative model performs analysis based on these prompt sentences and sends the results back to the server.

[1156] Collecting and displaying results

[1157] The server collects and organizes the analysis results returned by each generative model. The organized analysis results are then sent back to the device in JSON format, and after receiving them, the device displays them to the user in tabular or graph format. For example, when displayed in table format, the left column shows the results of GPT-3 and the right column shows the results of BERT.

[1158] Specific examples

[1159] For example, if a user selects "GPT-3" and "BERT" for the topic "climate change," the server will use each generative model to generate the following analysis results:

[1160] GPT-3 analysis results: "A detailed explanation of the impacts of climate change"

[1161] BERT analysis results: "Summary of recent news articles related to climate change"

[1162] The device displays these analysis results in a tabular format, allowing users to compare the views of different models, thereby providing information from multiple perspectives and supporting appropriate decision-making.

[1163] In this way, the present invention is a system that enables users to judge the accuracy of information and access information efficiently by allowing them to compare the differences in views and interpretations of multiple generative models on a topic specified by the user.

[1164] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1165] Step 1: Providing a User Interface

[1166] The server generates a user-accessible web page that contains a free text box for users to enter a topic and a drop-down list for selecting the generative model to use. The input is HTML, CSS, and JavaScript code, and the output is a web page that users can interact with.

[1167] Step 2: Input topics and models

[1168] On the provided webpage, the user enters the topic they want to analyze in a free text box and selects the generative model to use from a drop-down list (e.g., GPT-3, BERT, T5). The input of this step is the user's topic and model selection information, and the output is the topic and model selection data ready to be sent to the server.

[1169] Step 3: Sending data

[1170] When the user clicks the "Analyze" button, the device sends the input topic and selected generative model data to the server in JSON format. The input of this step is the topic entered by the user and the selected generative model information, and the output is data that is sent to the server in JSON format.

[1171] Step 4: Receiving the data

[1172] The server receives data sent in JSON format and extracts the necessary data to start the analysis process. The input of this step is the JSON data sent from the terminal, and the output is the extracted topics and generative model information.

[1173] Step 5: Run the analysis process

[1174] Based on the specified topic and the selected generative model, the server generates a prompt for each generative model and requests it to analyze it. For example, for the topic "climate change," the server sends the prompt "Please explain in detail the impacts of climate change with specific examples" to GPT-3, and the prompt "Please summarize recent news articles related to climate change" to BERT. The input for this step is the topic and the selected generative model information, and the output is the prompt sent to each generative model.

[1175] Step 6: Collecting analysis results

[1176] The generative AI model performs analysis based on the prompt received from the server and sends the results back to the server. The server collects the analysis results returned from each generative model. The input of this step is the analysis results from each generative model, and the output is the integrated analysis result data.

[1177] Step 7: View the results

[1178] The server organizes the collected analysis results and sends them back to the terminal in JSON format. The terminal receives them and displays the analysis results in a format that is easy for the user to view (table or graph format). For example, the left column shows the results of GPT-3 and the right column shows the results of BERT. The input to this step is the organized analysis result data, and the output is the analysis result that is displayed to the user.

[1179] Through these specific processing steps, the system is designed to make it easy to compare multiple generative models for a user-specified topic, allowing users to understand the characteristics of each model and obtain more accurate and multifaceted information.

[1180] (Application example 1)

[1181] 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."

[1182] Conventional information analysis systems have difficulty comparing the views and interpretations of multiple generative AI models on a user-specified topic. This prevents users from obtaining information from multiple perspectives, making it difficult to make appropriate decisions. Furthermore, there is a lack of means to provide feedback on the information obtained, making it difficult to improve the user experience.

[1183] 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.

[1184] In this invention, the server includes means for providing a user interface, input means for allowing the user to input a topic, selection means for allowing the user to select a generative model to use for analysis, analysis means for performing analysis based on the topic input by the user and the selected generative model, display means for displaying the analysis results, and feedback means for allowing the user to provide feedback on the analysis results, which makes it easier for the user to compare the analysis results of multiple generative AI models and provide feedback on each analysis result.

[1185] A "user interface" is the means by which a user interacts with a computer system, providing screens and controls for input and output.

[1186] "Input means" refers to a means by which a user provides specific information or data to a system, and includes a text box, microphone, keyboard, etc.

[1187] A "selection means" is a means by which a user selects an option or function to use within a system, including drop-down lists, radio buttons, check boxes, etc.

[1188] "Analysis means" means the means by which a computer program performs analysis or calculations based on data entered or options selected by a user.

[1189] "Display means" refers to a means for visually showing analysis results and other information to users, such as a monitor, display, or screen.

[1190] "Feedback means" refers to the means by which users can provide feedback and evaluations to the system, and includes text input fields, evaluation buttons, etc.

[1191] A "generative model" is an algorithm or network that uses artificial intelligence or machine learning to perform a specific task, particularly a model used in natural language processing.

[1192] This invention provides a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user. Specific embodiments of this system are described below.

[1193] System configuration

[1194] The system consists of the following main components:

[1195] 1. User Interface (UI)

[1196] 2. Input Method

[1197] 3. Selection Method

[1198] 4. Analysis method

[1199] 5. Display means

[1200] 6. Feedback channels

[1201] User Interface

[1202] The server provides a web page for users to access, which includes a free text box for entering the topic they want to analyze, a drop-down list for selecting multiple generative models to use, and an Analyze button.

[1203] Topic and Model Input

[1204] Users input a topic on a webpage and select a generative model (e.g., GPT-3, BERT, etc.) to use for analysis. This allows users to specify any topic as the analysis target and simultaneously select multiple generative models for comparative analysis.

[1205] Sending and Receiving Form Data

[1206] After the user enters a topic and selects a model, they click the "Analyze" button on the device to send the form data to the server. The server receives this request and extracts the necessary data to start the analysis process.

[1207] Executing the analysis process

[1208] The server performs analysis using each model based on the user's input topic and the selected generative model. Specifically, it requests each generative model to analyze the topic and collects the results. For example, the GPT-3 model generates a detailed explanation and prediction for the topic, while the BERT model provides a context-based interpretation.

[1209] The generative models used include OpenAI's GPT-3 and Google's BERT, for example, and these models are invoked via APIs using programming languages ​​such as Python.

[1210] Collecting and displaying results

[1211] The server organizes the analysis results collected from each generative model and sends them back to the device. The device then displays the received analysis results in tabular and graphical formats, making it easy for users to compare them. This allows users to see the analysis results of multiple generative models at a glance and understand the characteristics and strengths of each model.

[1212] Feedback function

[1213] Users can provide feedback on the displayed analysis results, using a text input field and rating buttons to send user ratings and comments to the server.

[1214] Specific examples

[1215] For example, if a user selects "GPT-3" and "BERT" for the topic "climate change," the server will use each model to generate the following analysis results:

[1216] GPT-3 analysis result: "Tell me about climate change."

[1217] BERT analysis results: "Summarize a recent news article about climate change."

[1218] In this way, users can compare the views of different generative models and view each piece of information from multiple angles.

[1219] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1220] Step 1:

[1221] The user enters a topic into an input form on a web page and selects the generative model to use for analysis.

[1222] Input: A list of topics entered by the user and the selected generative model

[1223] Output: The topics and the selected generative model are prepared as form data.

[1224] Step 2:

[1225] The user clicks the "Analyze" button and the device sends the form data to the server.

[1226] Input: User-entered and selected data

[1227] Output: Parsing request data sent to the server

[1228] Step 3:

[1229] The server receives the request and extracts information about the topic and the selected generative model.

[1230] Input: Parse request data received by the server

[1231] Output: A list of topics needed for analysis and the selected generative model

[1232] Step 4:

[1233] The server sends a topic-based analysis request to each generative model and obtains the analysis results from each generative model.

[1234] Input: a list of topics and a selected generative model

[1235] Output: Analysis results from each generative model

[1236] Specific operation: For example, the server sends a prompt to GPT-3, such as "Tell me about climate change," and to BERT, such as "Summarize recent news articles about climate change." The analysis results are obtained using OpenAI's API and Transformer model.

[1237] Step 5:

[1238] The server organizes the analysis results of each generative model it has acquired and prepares them to be sent to the terminal.

[1239] Input: Analysis results obtained from each generative model

[1240] Output: Organized analysis results

[1241] Specific operation: The server formats the results of each generative model into a form that is easy to convert into a table or graph format.

[1242] Step 6:

[1243] The server transmits the analysis results to the terminal, and the terminal receives the results.

[1244] Input: Organized analysis results

[1245] Output: Analysis result notification received by the device

[1246] Step 7:

[1247] The terminal displays the analysis results to the user.

[1248] Input: Analysis results received by the device

[1249] Output: Displaying the analysis results in a user-readable format

[1250] Specific operation: The terminal displays the analysis results in tabular or graphical format on a web page, for example, allowing the results of different generative models to be compared.

[1251] Step 8:

[1252] The user provides feedback on the displayed analysis results.

[1253] Input: User-entered feedback

[1254] Output: Feedback data sent to the server

[1255] Specific operation: Users provide feedback using text input fields and rating buttons, and the data is sent to the server via the device.

[1256] 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.

[1257] This invention is a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user, and can also recognize the user's emotions and reflect them in the analysis results. Specific embodiments of this system are described below.

[1258] System Overview

[1259] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative model to use. The device receives the user input and selection, analyzes the user's emotions using an emotion engine, then adjusts the generative model based on the analyzed emotion information and provides the analysis results to the user.

[1260] User Interface

[1261] The server provides a web page with an interface where users can input topics and select generative models, including the option to input emotional nuances.

[1262] Topic and Model Input

[1263] Users enter the topic they want to analyze in a free text box on the webpage, select a generative model (e.g., GPT-3, BERT, T5, etc.) to use for analysis from a drop-down list, and enter information that expresses a specific emotional state (e.g., joy, sadness, surprise, etc.).

[1264] Sentiment analysis and data transmission

[1265] The device receives user input data, analyzes the user's emotions using the emotion engine, and sends the results to the server, along with information on the topic and generative model, to prepare for analysis.

[1266] Executing the analysis process

[1267] The server performs individual analysis for each model based on the topic entered by the user and the selected generative model. The analysis procedure includes a step of incorporating the results of the emotion engine to adjust the analysis parameters of the generative model and generate analysis results that match the emotion.

[1268] Collecting and displaying results

[1269] The server collects the analysis results from each generative model and organizes them taking into account the results of the emotion engine. This results in analysis results optimized for the user's emotional state. The organized results are presented in a format that makes it easy for users to compare them.

[1270] Results display

[1271] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, adjusted based on the results of the emotion engine.

[1272] Specific examples

[1273] For example, if a user selects GPT-3 and BERT for the topic "climate change" and enters the emotion "anxiety," the server will analyze this emotion using the emotion engine and generate the following results:

[1274] GPT-3 analysis results: "Detailed explanations and predictions of the adverse effects of climate change"

[1275] BERT analysis results: "The latest crisis news articles related to climate change"

[1276] The results are tailored to correspond to the user's emotion of "anxiety." The device organizes these analysis results and displays them in a format that is easy for the user to understand and relate to their emotional state.

[1277] In this way, by combining emotion engines, the present invention is a system that can not only compare the differences in views and interpretations of multiple generative models on a user-specified topic, but also recognize the user's emotions and reflect them in the analysis results, allowing users to obtain comprehensive information with emotional context.

[1278] The processing flow will be explained below.

[1279] Step 1:

[1280] The server provides the user interface: it renders a web page that the user accesses, displaying a free text box for topic entry, an emotion drop-down list for selecting an emotional state, and a drop-down list for selecting the generative model to use.

[1281] Step 2:

[1282] Users enter the topic they want to analyze in a free text box on the webpage, select their emotional state (e.g., joy, sadness, anxiety, etc.) from an emotion drop-down list, and select the generative model to use (e.g., GPT-3, BERT, T5, etc.).

[1283] Step 3:

[1284] When the user clicks the "Analyze" button, the device sends the topic, emotional state, and selected generative model to the server as form data, which is sent as a POST request.

[1285] Step 4:

[1286] The server receives the POST request, extracts information about the topic, emotional state, and selected generative model from the request data, and prepares to analyze the user's emotional state using the emotion engine.

[1287] Step 5:

[1288] The server runs an emotion engine based on the topic and emotional state to perform a detailed analysis of the user's emotions. For example, if a user selects the emotion "anxiety," the analysis results include the intensity of that emotion and related emotional characteristics.

[1289] Step 6:

[1290] The server adjusts the parameters of each generative model based on the analysis results of the emotion engine. For example, if the emotion engine analyzes the user's emotion as "anxiety," the generative model will be adjusted to generate analysis results that are more focused on the area of ​​concern.

[1291] Step 7:

[1292] The server uses the topic and the adjusted parameters to run individual analyses on each selected generative model, each of which performs its own analysis on the specified topic and produces results.

[1293] Step 8:

[1294] The server collects the analysis results from each generative model, organizes them taking into account the results of the emotion engine, and compiles them into a single dataset, ready to present to the user in the most optimal format.

[1295] Step 9:

[1296] The server generates a response containing the organized analysis results and sends it to the device. The response includes the analysis results of each generative model adjusted based on the results of the emotion engine.

[1297] Step 10:

[1298] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, adjusted based on the results of the emotion engine.

[1299] Step 11:

[1300] Users can view the analysis results provided on the webpage and compare the views of multiple generative models, thereby obtaining comprehensive information based on their emotional state.

[1301] The above are the detailed processing steps of the present invention, which allow users to check and compare analysis results from multiple generative models along with emotional context.

[1302] Example 2

[1303] 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."

[1304] Existing analysis systems provide analysis results without considering the user's emotional state, which can prevent users from obtaining the information they desire based on the emotional context. Furthermore, when comparing analysis results from multiple generative models, they lack the functionality to provide results optimized for the user's emotional state. This makes it difficult to obtain analysis results that are intuitive and easy to understand for users.

[1305] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface, input means for allowing the user to input a topic, selection means for allowing the user to select a generative model to be used for analysis, emotion analysis means for analyzing the user's emotions, adjustment means for adjusting analysis parameters of the generative model based on the emotion analysis results, analysis means for performing analysis based on the topic input by the user and the selected generative model, and display means for displaying the analysis results. This makes it possible to provide analysis results that reflect the user's emotional state and obtain information in an intuitive and easy-to-understand format.

[1306] A "user interface" is a means by which a user interacts with a system, and is an interface that allows input and display.

[1307] "Input means" refers to the means by which a user inputs the topic they wish to analyze into the system, and refers to input devices such as a keyboard or touch screen.

[1308] The "selection means" refers to a means for the user to select a generative model to use in the analysis, and refers to a selection device such as a drop-down list or radio buttons.

[1309] "Emotion analysis means" refers to a means for analyzing emotions based on user input data, and refers to emotion analysis engines and natural language processing technology.

[1310] "Adjustment means" refers to a means for adjusting the analysis parameters of the generative model based on the emotion analysis results, and refers to changes in the algorithm or setting parameters.

[1311] "Analysis means" refers to a means for performing analysis based on the topic entered by the user and the selected generative model, and refers to a computer program or AI model.

[1312] "Display means" refers to a means for visually displaying the analysis results to the user, and refers to a display device such as a monitor or display.

[1313] A "generative model" is a model for generating text or information based on specific data, and refers to an artificial intelligence or machine learning algorithm.

[1314] "Emotion analysis result" is information about the user's emotional state obtained by the emotion analysis means.

[1315] This invention is a system that can compare the differences in views and interpretations of multiple generative models on a topic specified by a user, and can also recognize the user's emotions and reflect them in the analysis results. Specific embodiments of this system are described below.

[1316] System Overview

[1317] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative model to use. The device receives the user input and selection, analyzes the user's emotions using an emotion engine, then adjusts the generative model based on the analyzed emotion information and provides the analysis results to the user.

[1318] User Interface

[1319] The server provides a web page with an interface where users can input topics and select generative models, including the option to input emotional nuances.

[1320] Topic and Model Input

[1321] Users enter the topic they want to analyze in a free text box on the webpage, select a generative model (e.g., GPT-3, BERT, T5, etc.) to use for analysis from a drop-down list, and enter information that expresses a specific emotional state (e.g., joy, sadness, surprise, etc.).

[1322] Sentiment analysis and data transmission

[1323] The device receives user input data and analyzes the user's emotions using an emotion engine. The results are then sent to the server, along with information on the topic and generative model, to prepare for analysis. For example, the IBM Watson Tone Analyzer is used as the emotion engine.

[1324] Executing the analysis process

[1325] The server performs individual analysis for each model based on the topic entered by the user and the selected generative model. The analysis procedure includes a step of incorporating the results of the emotion engine to adjust the analysis parameters of the generative model and generate analysis results that match the emotion.

[1326] Collecting and displaying results

[1327] The server collects the analysis results from each generative model and organizes them taking into account the results of the emotion engine. This results in analysis results optimized for the user's emotional state. The organized results are presented in a format that makes it easy for users to compare them.

[1328] Results display

[1329] The device renders a web page to display the analysis results received from the server. The rendered page displays the analysis results of each model in table and graph format, adjusted based on the results of the emotion engine.

[1330] Specific examples

[1331] For example, if a user selects GPT-3 and BERT for the topic "climate change" and enters the emotion "anxiety," the server will analyze this emotion using the emotion engine and generate the following results:

[1332] GPT-3 analysis results: "Detailed explanations and predictions of the adverse effects of climate change"

[1333] BERT analysis results: "The latest crisis news articles related to climate change"

[1334] The results are tailored to correspond to the user's emotion of "anxiety." The device organizes these analysis results and displays them in a format that is easy for the user to understand and relate to their emotional state.

[1335] Prompt Sentence Examples

[1336] In this system, an example of a prompt for user input is shown below.

[1337] Topic: "Climate Change"

[1338] Generative model: "GPT-3, BERT"

[1339] Emotional state: "Anxiety"

[1340] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1341] Step 1:

[1342] The user accesses the web interface provided by the server, inputs the topic to be analyzed, selects the generative model to use (e.g., GPT-3, BERT, T5, etc.), and inputs a specific emotional state (e.g., joy, sadness, surprise, anxiety, etc.). The input data includes the topic "climate change," the generative model "GPT-3, BERT," and the emotional state "anxiety." When the user clicks the "Submit" button, the data is sent to the server.

[1343] Step 2:

[1344] The device processes the user's input data received from the server. The input data includes topics, generative models, and emotional states, and is immediately sent to the emotion engine for analysis. The emotion engine uses this information to perform a deep analysis of the user's emotional state and generates the results as output. This output data includes the analyzed emotional information and is sent to the server.

[1345] Step 3:

[1346] The server receives the topic, generative model, and sentiment analysis results sent from the device. Based on the received data, a process is initiated to perform individual analysis for each generative model. At this time, the sentiment analysis results are adjusted to affect the analysis parameters. For example, based on the topic "climate change" and the emotional state "anxiety," the analysis parameters for GPT-3 and BERT are set and the analysis is performed. The generative model performs analysis on the topic and outputs the results.

[1347] Step 4:

[1348] The server collects and organizes the analysis results obtained from generative models (GPT-3, BERT, etc.). The results generated by each generative model are adjusted taking into account the results of sentiment analysis. For example, the analysis results of GPT-3 may yield "detailed descriptions and predictions about the negative effects of climate change," while the analysis results of BERT may yield "the latest critical news articles related to climate change." The server integrates these results and prepares them for display in user-friendly formats (tables and graphs).

[1349] Step 5:

[1350] The device receives the analysis results sent from the server and renders a web page to display them. This page displays information in which the analysis results of each generative model have been adjusted based on the emotional state. For example, if a user investigates "climate change" when in the emotional state of "anxiety," the results are displayed in table and graph format for easy comparison. This allows the user to visually confirm detailed analysis results that take emotional state into account.

[1351] (Application example 2)

[1352] 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."

[1353] Conventional advertising generation systems have had difficulty generating advertising content that takes user emotions into account. As a result, they were unable to generate ads that matched the specific emotions of users, and were unable to maximize the effectiveness of the ads. Furthermore, when using multiple generative models to analyze from different perspectives, it was difficult to properly compare the results.

[1354] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface, input means for allowing the user to input a topic, selection means for allowing the user to select a generative model to use for analysis, emotion analysis means for analyzing the user's emotions and adjusting the generative model based on the analysis results, analysis means for performing analysis based on the topic input by the user and the selected generative model, and display means for displaying the analysis results. This makes it possible to generate advertising content suited to the user's emotions and compare analysis results from multiple generative models.

[1355] "User interface" refers to the screens and components through which a user interacts with a system, inputting data and viewing results.

[1356] "Input Means" refers to the device or method by which a user provides topics or other information to the system.

[1357] "Selection means" refers to a device or method for a user to select a generative model to use.

[1358] "Analysis means" refers to a device or method that performs analysis based on the topic entered by the user and the selected generative model.

[1359] "Emotion analysis means" refers to a device or method that analyzes a user's emotions and adjusts a generative model based on the results.

[1360] "Display means" refers to a device or method for visually presenting the analysis results to the user.

[1361] A "generative model" refers to an algorithm or machine learning model that generates a specific output (e.g., advertising content) based on user-specified information.

[1362] "Topic" refers to a particular theme or subject that a user inputs for analysis or generation.

[1363] "Comparable format" refers to an organized format that allows users to easily understand and compare multiple analysis results.

[1364] "Analysis parameters" refer to settings and adjustments that allow a generative model to operate under specific conditions.

[1365] This invention is a system that can compare the differences in the views and interpretations of multiple generative AI models on a topic specified by a user, and can also recognize the user's emotions and reflect them in the analysis results. Specific embodiments of this system are described below.

[1366] System Overview

[1367] The server provides a user interface, allowing users to input the topic they want to analyze and select the generative AI model to use. The device receives the user's input and selection and analyzes the user's emotions using a sentiment analysis engine. It then adjusts the generative AI model based on the analyzed sentiment information and provides the analysis results to the user. The user can then review the generated analysis results and obtain information that matches their sentiment.

[1368] Program Overview

[1369] Hardware and software used

[1370] In this system, the user uses a smartphone as the terminal. The server is located on a cloud service and uses the following software:

[1371] Python: a programming language

[1372] Transformers: Import and use emotion analysis and generative AI models in the Hugging Face library

[1373] requests: A Python HTTP library used to call external APIs (e.g., GPT-3).

[1374] Data Flow and Processing

[1375] 1. User Interface:

[1376] The server serves a web page, displaying an interface where users can input a topic and select the generative AI model to use (e.g., GPT-3, BERT, etc.). The page also includes an option for users to input emotional nuances.

[1377] 2. User Input and Sentiment Analysis:

[1378] Users enter the topic they want to analyze in a free text box on the webpage and select a generative AI model to use for analysis from a drop-down list. Users also enter information that expresses a specific emotional state (e.g., joy, sadness, surprise, etc.). The device receives the user input data and analyzes the user's emotions using an emotion analysis engine.

[1379] 3. Invoke the generative model and generate content:

[1380] The server adjusts the analysis parameters of the generative AI model based on the sentiment analysis results and inputs them into the generative AI model along with the specified topic. For example, it adjusts the analysis parameters for the GPT-3 and GPT-2 generative models to generate advertising content that matches the sentiment.

[1381] 4. Collecting and displaying analysis results:

[1382] The server collects the analysis results from each generative AI model and organizes them based on the results of the emotion analysis engine. This allows for analysis results optimized for the user's emotional state. The organized results are displayed in a format that makes it easy for users to compare.

[1383] Specific examples

[1384] For example, if a user selects "GPT-3" and "GPT-2" for the topic "new smartphones" and enters the emotion "surprise," the server will analyze this emotion using its emotion analysis engine and generate the following results:

[1385] GPT-3 analysis results:

[1386] Introducing the latest smartphone that will leave you amazed! With cutting-edge technology and features that will blow your mind, this device is set to redefine what you think is possible. Experience the future today!

[1387] GPT-2 analysis results:

[1388] Discover the newest advancement in smartphone technology! This groundbreaking device will astonish you with its high-speed performance and state-of-the-art features. Get ready to be surprised!

[1389] Prompt Sentence Examples

[1390] Generate an advertisement for the topic 'New Smartphone' that matches the emotion: 'Amazing'.

[1391] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1392] Step 1:

[1393] Users enter the topic they want to analyze into a form on a webpage, select the generative AI model to use from a drop-down list, and also enter information about their emotional state.

[1394] Input data: topics, generative AI models, sentiment information

[1395] Output data: None

[1396] Step 2:

[1397] The device receives user input data, which includes topics, generative AI models, and sentiment information.

[1398] Input data: User input data (topics, generative AI models, sentiment information)

[1399] Output data: Internal storage of user input data

[1400] Step 3:

[1401] The device uses an emotion analysis engine to analyze the emotion information entered by the user and generates an emotion label as the analysis result.

[1402] Input data: Emotion information

[1403] Data processing: Analyze emotions using a sentiment analysis engine (such as the BERT model) and generate sentiment labels.

[1404] Output data: emotion labels

[1405] Step 4:

[1406] Based on the emotion labels, the device prepares to adjust the analysis parameters of the generative AI model.

[1407] Input data: emotion labels, generative AI model

[1408] Data processing: Setting the analysis parameters of the generative AI model based on emotion labels

[1409] Output data: Adjusted analysis parameters

[1410] Step 5:

[1411] The server runs the analysis on the selected generative AI model using the adjusted analysis parameters and topics.

[1412] Input data: topics, adjusted analysis parameters, generative AI model

[1413] Data calculation: Input data into the generative AI model and generate analysis results.

[1414] Output data: Analysis results from the generative AI model

[1415] Step 6:

[1416] The server collects the analysis results and organizes them for each generative AI model.

[1417] Input data: Analysis results from the generative AI model

[1418] Data processing: Collecting and organizing analysis results (converting them into a format that is easy to compare)

[1419] Output data: Organized analysis results

[1420] Step 7:

[1421] The server generates a web page to present the organized analysis results to the user.

[1422] Input data: Organized analysis results

[1423] Data processing: rendering web pages

[1424] Output data: Analysis results that are displayed to the user

[1425] Step 8:

[1426] Users can check the analysis results of each generative AI model through the provided webpage, check whether the analysis results match the emotional state, and obtain appropriate information.

[1427] Input data: Displayed analysis results

[1428] Output data: None (information acquisition and understanding)

[1429] 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.

[1430] 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.

[1431] 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.

[1432] 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.

[1433] 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.

[1434] 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.

[1435] 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).

[1436] 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.

[1437] 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."

[1438] 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.

[1439] 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).

[1440] 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.

[1441] 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.

[1442] 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.

[1443] 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.

[1444] 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.

[1445] 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.

[1446] 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.

[1447] 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.

[1448] 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.

[1449] 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.

[1450] The following is further disclosed regarding the above embodiment.

[1451] (Claim 1)

[1452] a means for providing a user interface;

[1453] an input means for allowing a user to input a topic;

[1454] A selection means for the user to select a generative model to use in the analysis;

[1455] an analysis means for performing analysis based on the topic input by the user and the selected generative model;

[1456] a display means for displaying the analysis results;

[1457] A system including:

[1458] (Claim 2)

[1459] Collecting analysis results from multiple generative models

[1460] Further comprising a display means for displaying the analysis results of each generative model in a comparable format.

[1461] 10. The system of claim 1.

[1462] (Claim 3)

[1463] The analysis means executes an analysis for each generative model on the specified topic and collects the analysis results.

[1464] 3. The system of claim 1 or claim 2.

[1465] (Claim 4)

[1466] The display means displays the analysis results in a table, graph, or tabular format.

[1467] A system according to any one of claims 1 to 3.

[1468] (Claim 5)

[1469] A user interface is provided via the Internet, and the analysis means and display means are executed in a cloud computing environment.

[1470] A system according to any one of claims 1 to 4.

[1471] "Example 1"

[1472] (Claim 1)

[1473] a means for providing a user interface;

[1474] an input means for allowing a user to input a topic;

[1475] A selection means for the user to select a generative model to use in the analysis;

[1476] an analysis means for performing analysis for each generative model based on the topic input by the user and the selected generative model;

[1477] a collection means for collecting analysis results obtained from a plurality of generative models;

[1478] a display means for displaying the analysis results in a table or graph format;

[1479] A system including:

[1480] (Claim 2)

[1481] 10. The system of claim 1, further comprising a display means for collecting analysis results from a plurality of generative models and displaying the analysis results of each generative model in a comparable format.

[1482] (Claim 3)

[1483] 3. The system according to claim 1, wherein the analysis means generates a prompt sentence for a specified topic and issues an analysis request to each generative model.

[1484] "Application Example 1"

[1485] (Claim 1)

[1486] a means for providing a user interface;

[1487] an input means for allowing a user to input a topic;

[1488] A selection means for the user to select a generative model to use in the analysis;

[1489] an analysis means for performing analysis based on the topic input by the user and the selected generative model;

[1490] a display means for displaying the analysis results;

[1491] A feedback mechanism that allows users to provide feedback on the analysis results;

[1492] A system including:

[1493] (Claim 2)

[1494] Collecting analysis results from multiple generative models

[1495] Further comprising a display means for displaying the analysis results of each generative model in a comparable format.

[1496] 10. The system of claim 1.

[1497] (Claim 3)

[1498] The analysis means executes an analysis for each generative model on the specified topic and collects the analysis results.

[1499] 10. The system of claim 1.

[1500] "Example 2: Combining Emotion Engines"

[1501] (Claim 1)

[1502] a means for providing a user interface;

[1503] an input means for allowing a user to input a topic;

[1504] A selection means for the user to select a generative model to use in the analysis;

[1505] An emotion analysis means for analyzing the emotion of a user;

[1506] an adjustment means for adjusting analysis parameters of the generative model based on the emotion analysis result;

[1507] an analysis means for performing analysis based on the topic input by the user and the selected generative model;

[1508] a display means for displaying the analysis results;

[1509] A system including:

[1510] (Claim 2)

[1511] Collecting analysis results from multiple generative models

[1512] A means for displaying the analysis results of each generative model in a comparable format;

[1513] and further comprising means for organizing the analysis results based on the emotional state of the user.

[1514] 10. The system of claim 1.

[1515] (Claim 3)

[1516] The analysis means executes an analysis for each generative model on the specified topic and collects the analysis results.

[1517] 3. The system of claim 1 or claim 2.

[1518] "Application example 2 when combining emotion engines"

[1519] (Claim 1)

[1520] a means for providing a user interface;

[1521] an input means for allowing a user to input a topic;

[1522] A selection means for the user to select a generative model to use in the analysis;

[1523] an analysis means for performing analysis based on the topic input by the user and the selected generative model;

[1524] An emotion analysis means for analyzing a user's emotion and adjusting a generative model based on the analysis result;

[1525] a display means for displaying the analysis results;

[1526] A system including:

[1527] (Claim 2)

[1528] The system of claim 1, further comprising a display means for collecting analysis results from a plurality of generative models, adjusting the analysis results of each generative model based on user emotions, and displaying them in a comparable format.

[1529] (Claim 3)

[1530] The system of claim 1, wherein the analysis means performs analysis for each generative model on a specified topic, adjusts analysis parameters of each generative model based on the user's sentiment analysis results, and collects the analysis results. [Explanation of symbols]

[1531] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for providing a user interface; an input means for allowing a user to input a topic; A selection means for the user to select a generative model to use in the analysis; an analysis means for performing analysis based on the topic input by the user and the selected generative model; a display means for displaying the analysis results; A system including:

2. Collecting analysis results from multiple generative models Further comprising a display means for displaying the analysis results of each generative model in a comparable format. The system of claim 1 .

3. The analysis means executes an analysis for each generative model on the specified topic and collects the analysis results. The system according to claim 1 or claim 2.

4. The display means displays the analysis results in a table, graph, or tabular format. A system according to any one of claims 1 to 3.

5. A user interface is provided via the Internet, and the analysis means and display means are executed in a cloud computing environment.

5. A system according to any one of claims 1 to 4.

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