system

Abstract

We provide the system. [Solution] A means of receiving inquiries from user terminals, A means for collecting responses from multiple data generation devices, A means for evaluating each response based on its reliability, neutrality, and user preference, A method for presenting the reliability of information regarding payment methods in a ranking format, A means of visually sending the selected response to the user terminal, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern information society, it is difficult for users to obtain reliable and neutral answers from a vast amount of information. In particular, existing information generation devices often contain biased information or biases based on specific interests, and users have to judge the authenticity of the information by themselves. In addition, it is difficult to provide information that meets individual user preferences, so it is an issue to provide a means to obtain efficient, accurate and useful answers.

Means for Solving the Problems

[0005] This invention provides a system that collects responses from multiple information generating devices in response to inquiries received from a user terminal, and evaluates each response based on reliability, neutrality, and user preferences. By selecting the most appropriate response and transmitting it to the user terminal, the system can efficiently provide the neutral and reliable information that the user desires. In this evaluation process, the accuracy and neutrality of the information are ensured by using means to analyze reliability based on the information source and past performance, as well as to detect inconsistencies between the information.

[0006] A "user terminal" is an electronic device used by a user to input and receive information.

[0007] A "server" is a device that receives inquiries from users, collects and evaluates responses from information generation devices, and sends replies to user terminals.

[0008] An "information generation device" is a system or program that generates and provides answers to specific inquiries.

[0009] "Reliability" is an indicator that shows whether information is accurate and trustworthy.

[0010] "Neutrality" is a characteristic that indicates whether information is unbiased and fair.

[0011] "User preferences" refer to the desirable characteristics and conditions for a user, based on their past behavior and preferences.

[0012] "Evaluation" is the process of judging and analyzing the value and appropriateness of information or responses.

[0013] "Selection" is the act of deciding on the most appropriate answer from among several options.

[0014] "Transmission" is the act of moving information from one point to another. [Brief explanation of the drawing]

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

Embodiments for Carrying Out 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 numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the 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 processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0036] This invention relates to an information provision system that allows users to easily obtain information that prioritizes reliability and neutrality. This system is implemented as follows.

[0037] First, the user inputs the information they need using a terminal. Through this input screen, the user specifically describes topics of interest and questions. After receiving this input, the terminal sends it to the server.

[0038] The server sends questions to multiple information generators based on the information it receives. These information generators use different datasets and algorithms, generating answers from their respective perspectives. The server then waits for responses from each information generator and collects all the answers.

[0039] Next, the server evaluates the answers based on reliability, neutrality, and user preferences. For example, if a user asks "How to choose an environmentally friendly car," the server will determine that the answer from an information generator known as an environmental advisor is highly reliable. It also considers what kind of information the user has preferred in the past and selects the most suitable answer from among them. Regarding neutrality, it checks for conflicting information and maintains consistency.

[0040] The selected best answer is sent from the server to the user's terminal. The terminal receives this answer and displays it to the user in an appropriate format. For example, it can be presented visually using graphs or tables.

[0041] In this way, users can easily receive vetted, neutral, and reliable information. This system improves the efficiency and accuracy of users' information gathering.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user enters a question using the terminal's interface. The question is entered in natural language, for example, "Please tell me about environmentally friendly cars." The terminal confirms the user's input.

[0045] Step 2:

[0046] The terminal sends the entered question to the server. At this time, necessary metadata, such as the user ID and timestamp, is also sent along with the question.

[0047] Step 3:

[0048] The server analyzes the received question and distributes it to multiple information generators. Each information generator then begins generating an answer using its own database and algorithm.

[0049] Step 4:

[0050] The server collects the responses generated from each information generation device. Here, multiple responses from different perspectives are gathered and stored in the server's database.

[0051] Step 5:

[0052] The server scores the collected responses based on reliability, neutrality, and user preference. The reliability score is calculated based on the reliability of the information source, and the neutrality score is calculated based on the degree of agreement between responses. User preference is derived from past search history and preference data.

[0053] Step 6:

[0054] The server selects the answer with the highest score as the best answer. In this process, the optimal answer is identified through a weighted average of scores and filtering based on specific criteria.

[0055] Step 7:

[0056] The server sends the selected best answer to the user's terminal. If necessary, the answer content is formatted and converted into a user-friendly format.

[0057] Step 8:

[0058] The device displays the best answer to the user. This allows the user to efficiently obtain highly reliable and neutral information.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] When acquiring information, users often find it difficult to easily obtain information that is highly reliable, neutral, and tailored to their preferences. Existing systems struggle to judge the reliability and neutrality of information collected from various sources, and furthermore, they have the challenge of not being able to provide information that is optimally suited to the user's preferences.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for receiving inquiries from user devices, means for analyzing inquiries and extracting relevant topics and phrases, means for relaying questions to multiple information generation devices, means for collecting answers from multiple information generation devices and evaluating each answer based on reliability, neutrality, and user preference, means for auditing conflicting information and inconsistencies as consistency, and means for selecting the most appropriate answer and transmitting the selected answer to the user device in visual format. This enables users to efficiently obtain information that is highly reliable, neutral, and matches their preferences.

[0064] A "user device" is a terminal that is used by users to input information and has the function of receiving input and transmitting it to a server.

[0065] "Analyzing a query" refers to the process of examining a question received from a user and identifying related topics and phrases.

[0066] An "information generation device" is a device or program that generates answers based on a specified question using different datasets and algorithms.

[0067] "Collecting responses" refers to the act of compiling response data generated from multiple information generation devices.

[0068] "Methods of evaluation" refer to the process of analyzing and making judgments based on the reliability, neutrality, and user preferences of the collected responses.

[0069] "Auditing consistency" refers to the process of detecting inconsistencies and contradictions in information and checking whether the information is consistent.

[0070] "Sending in a visual format" means providing selected information to the user in a format such as diagrams or tables, in order to display it in an easy-to-understand manner.

[0071] This invention is an information provision system that allows users to easily obtain information that prioritizes reliability and neutrality. Users use a user device to input information. For example, if a user wants to know "how to choose an environmentally friendly car," they input their question into the interface of the user device.

[0072] The terminal formats the input information and sends it to the server. The server uses natural language processing software to analyze this information and extract the subject of the question and related keywords. Based on the analyzed information, the server creates appropriate prompt sentences for multiple information generation devices equipped with generative AI models, and uses these prompts to send the question to the information generation devices.

[0073] Information generation devices generate answers from their own unique perspectives using different datasets and algorithms. After the answers from each information generation device are collected, the server evaluates them based on reliability, neutrality, and the user's past preferences. To enhance the reliability of the information, the server performs analysis based on past performance data and information sources, and also detects inconsistencies to ensure neutrality.

[0074] After the evaluation is complete, the server selects the most appropriate answer and sends the results to the user's device in a visual format such as graphs or tables. This allows the user to visually grasp carefully reviewed, neutral, and reliable information.

[0075] As a concrete example, a prompt such as, "Please tell me the key points for choosing an environmentally friendly car," can be input into the AI ​​model. In this way, users can efficiently and accurately obtain the information they need.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user enters their question through the user device. Specifically, the user enters a topic of interest in text format and presses the submit button. The entered question is sent to the terminal as string data.

[0079] Step 2:

[0080] The terminal formats the entered question and forwards it to the server. Formatting includes text normalization and keyword extraction. The formatted data is sent to the server in a structured data format.

[0081] Step 3:

[0082] The server analyzes the received data. Specifically, it uses natural language processing software to extract relevant topics and keywords from the question. This analysis involves topic modeling and morphological analysis, and based on the obtained information, it generates prompt sentences suitable for the next processing step.

[0083] Step 4:

[0084] The server generates prompt sentences based on the analysis results and sends them to multiple information creation devices equipped with generation AI models. The transmitted prompt sentences are processed by the information creation devices, and answers are generated from multiple perspectives.

[0085] Step 5:

[0086] When responses are returned from multiple information generation devices, the server collects them. The collected responses are temporarily stored in a database and used to evaluate their reliability and neutrality.

[0087] Step 6:

[0088] The server evaluates each collected response based on reliability, neutrality, and user preference. Reliability is determined by scoring based on the historical data and accuracy of the information source. Neutrality is evaluated by running an algorithm to detect inconsistencies between responses.

[0089] Step 7:

[0090] The server selects the most appropriate answer based on the evaluation results. The selected answer is supplemented with additional information and visualizations (e.g., graphs and tables) to aid the user's understanding.

[0091] Step 8:

[0092] The selected response is sent from the server to the terminal. The terminal receives this response and displays it in an easy-to-understand format for the user. The user can then review the displayed information and take the necessary actions.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] With the increasing diversity of information regarding electronic payment methods, it is difficult for users to make optimal choices based on reliable and neutral information. This problem arises because there are insufficient means to evaluate the reliability of information and provide it to users in a visible format.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes means for receiving inquiries from a user terminal, means for collecting responses from multiple data generation devices, means for evaluating each response based on its reliability, neutrality, and user preference, means for presenting the reliability of information regarding payment methods in a ranking format, and means for visually transmitting the selected responses to the user terminal. This enables the user to make a selection regarding electronic payment methods based on highly reliable information.

[0098] A "user terminal" is a device used by a user to input or receive information.

[0099] "Means for receiving inquiries" refers to a method or function for receiving information requests from users.

[0100] A "data generation device" is a device or system that generates information or responses using different datasets or algorithms.

[0101] "Means for collecting responses" refers to a method or function for collecting and integrating responses generated from multiple sources.

[0102] "Reliability" refers to the degree to which information is accurate and credible.

[0103] "Neutrality" is a characteristic that indicates information is unbiased and fair.

[0104] "Preferences" are criteria for evaluating information based on the user's past preferences and interests.

[0105] "Means of evaluation" refers to a method or function for determining the value of information or responses based on multiple criteria.

[0106] "Means of presenting information in a ranking format" refers to a method or function for ranking information and displaying it in an easy-to-understand manner for the user.

[0107] "Means of transmission" refers to a method or function for sending selected information or responses to the user's terminal.

[0108] The system for implementing this invention mainly consists of a user terminal, a server, and multiple data generation devices. The user's terminal is a smartphone or smart glasses, which allows the user to input information and receive the results visually. The server uses Amazon Web Services (AWS®) or Google® Cloud Platform as cloud computing services and MongoDB or PostgreSQL as the database. The server is built using Python and the Django framework.

[0109] The server first receives an information request from the user's terminal. When the user requests information about a specific payment method, the server sends this request to multiple data generation devices. These devices generate the information using different datasets and algorithms.

[0110] Next, the server collects responses from each data generator and evaluates them based on their reliability, neutrality, and past user preferences. For information evaluation, machine learning libraries such as TENSORFLOW® and PyTorch are used to analyze data reliability.

[0111] Once the evaluation is complete, the server presents information about payment methods to the user's terminal in a reliability ranking format. For example, if a user asks "What is the safest payment method for online shopping?", the information visually displays options such as credit cards and e-money in order of reliability.

[0112] A concrete example of a prompt is, "In electronic payment services, collect and evaluate highly reliable and user-preferred payment methods from the information generation device." This prompt forms the basis for efficient information evaluation using a generation AI model.

[0113] Through this approach, it becomes possible to create a system that allows users to easily obtain reliable information about electronic payment methods.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The user enters information requests using their terminal. These are specific questions, such as "Which payment method is the most secure?" The entered information is sent from the user's terminal to the server. The output is an information request sent to the server.

[0117] Step 2:

[0118] The server sends the received information request to multiple data generation devices. In doing so, the server uses prompts generated by a generation AI model to request information generation from each data generation device. The input is the user's question, and the output is multiple requests to the data generation devices.

[0119] Step 3:

[0120] Each data generator, upon receiving a request, produces a response using its respective dataset and algorithm. At this stage, the input is the request from the server, and the output is the generated response. The responses are provided from different perspectives, taking reliability and neutrality into consideration.

[0121] Step 4:

[0122] The server collects responses received from each data generator. The input consists of responses from multiple data generators, and the output is integrated response data within the server. This process involves the collection and integration of all data.

[0123] Step 5:

[0124] The server evaluates the collected responses based on reliability, neutrality, and user preference. Machine learning libraries (e.g., TensorFlow and PyTorch) are used to perform data reliability analysis and neutrality checks. The input is the integrated response data, and the output is the evaluation result.

[0125] Step 6:

[0126] Based on the evaluation results, the server ranks information such as payment methods by reliability and sends it to the user's terminal. The ranking format makes it easier for users to compare. The input is the evaluated data, and the output is returned to the user's terminal as data organized in a ranking format.

[0127] Step 7:

[0128] The user terminal visually displays the received ranking data. Users can then see the payment method with the highest reliability on the screen. Input is in ranking format, and output is a visual display. Users can use this information to select the most suitable payment method.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention relates to an information delivery system that incorporates an emotion engine that recognizes user emotions and optimizes information delivery. This system is implemented as follows.

[0131] First, the user inputs the information they need using a device. On this input screen, the user writes down topics or questions they are interested in in text format. The device is equipped with a function to collect not only the content of the entered text, but also, in some cases, data to recognize the user's emotions, such as their voice and facial expressions.

[0132] The terminal sends information and emotional data to the server. The server analyzes the received inquiries and distributes them to multiple information generating devices, requesting each device to generate a response using its own algorithm.

[0133] After collecting responses, the server evaluates them based on reliability, neutrality, and user preference. Furthermore, it incorporates analysis from an emotion engine that recognizes the user's emotions. This emotion engine analyzes emotions from the user's input text and voice, and has the means to predict how the information will be received. For example, if the emotion engine determines that the user has an urgent problem, it can prioritize responses that are appropriate to that state of mind.

[0134] For example, if a user asks "How can I alleviate anxiety about a new job?", this system uses an emotion engine to read the user's current state based on the answers obtained from the information generation device, and selects answers in an appropriate order that can reduce anxiety.

[0135] The selected best answer is sent from the server to the user's terminal. The terminal receives this answer and is designed to present it to the user in an emotionally sensitive manner. For example, it can be displayed using friendly language.

[0136] This invention enables users to efficiently receive neutral and reliable information that reflects their current emotional state. Therefore, it is expected to improve user satisfaction as a recipient of this information.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] The user enters questions using the terminal's input interface. Simultaneously, data representing the user's emotions, such as tone of voice and facial expressions, is collected, if possible. This information is used as data for the emotion engine.

[0140] Step 2:

[0141] The terminal sends the entered question and collected sentiment data to the server. This communication also includes metadata such as user ID and time information.

[0142] Step 3:

[0143] The server analyzes the received question and distributes the query to multiple information generators. Each information generator collects relevant information, generates an answer, and returns it to the server.

[0144] Step 4:

[0145] The server collects responses from the information generators, organizes them, and stores them in a database. After collection, the evaluation process is ready.

[0146] Step 5:

[0147] The server evaluates the collected responses based on reliability, neutrality, and user preferences. This evaluation is based on the reliability of the information source, the consistency of the information, and past user preferences.

[0148] Step 6:

[0149] The server uses an emotion engine to analyze the user's emotional state. This allows it to understand the user's current feelings and determine the appropriate response for those feelings.

[0150] Step 7:

[0151] The server integrates the scored evaluation results with the emotion engine's analysis to select the most appropriate response as the best answer. For example, if a user is showing anxiety, information that provides reassurance will be prioritized.

[0152] Step 8:

[0153] The server sends the selected best answer to the user's terminal. The terminal displays the answer in a format that takes into account the user's emotional state and delivers it to the user.

[0154] This allows users to efficiently obtain reliable and neutral information in a way that takes their emotions into consideration.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] Conventional information delivery systems provide information without considering the user's emotional state, resulting in a decline in the quality and satisfaction of the information users need. Furthermore, the inability to provide appropriate information based on the user's emotions limits the improvement of the user experience. This problem is particularly serious in situations where users are experiencing stress or anxiety. The present invention aims to recognize the user's emotions and optimize information delivery based on them.

[0158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0159] In this invention, the server includes means for receiving inquiries and sentiment data from a user terminal, means for generating prompt statements and collecting responses using multiple information generating devices, and means for evaluating each response based on its reliability, neutrality, user preferences, and user sentiment. This makes it possible to provide the most appropriate information in a way that takes the user's sentiment into consideration.

[0160] A "user terminal" is an electronic device used by users to input and receive information, and it is a device that collects and communicates input data and emotional data.

[0161] "Emotional data" refers to information that indicates a user's psychological state and emotions, obtained from their voice, facial expressions, and text.

[0162] An "information generation device" refers to an algorithm or platform that generates an appropriate response based on a received prompt message.

[0163] A "prompt statement" is an instruction statement used to convey specific questions or requests to an information generating device.

[0164] "Reliability" is an indicator that shows the accuracy and credibility of information and its sources.

[0165] "Neutrality" refers to a state in which the information provided is free from bias and prejudice, and is fair.

[0166] "User preferences" refer to the characteristics of information that reflect the individual preferences and interests of users.

[0167] An "emotion engine" is an analytical system that recognizes emotions from user input data and performs appropriate information processing.

[0168] "Optimization" is the process of adjusting systems and processes to provide information in a way that best suits the user's needs and emotions.

[0169] One embodiment of this invention is an information provision system consisting of a user terminal, a server, and multiple information generation devices.

[0170] The user first inputs topics of interest in text format via the device. Simultaneously, emotional data such as facial expressions can be acquired using voice input or the camera. The device has software installed for collecting emotional data, providing the necessary information for the emotion engine.

[0171] The server analyzes text and sentiment data received from the terminal. Natural language processing and speech / image analysis technologies are used for the analysis. This allows for a detailed analysis of the user's questions and emotional state. The server then generates prompt sentences suitable for the AI ​​model based on the analyzed data and sends them to the information generation device. At this stage, prompt sentences such as "Please tell me how to cope when I feel stressed in a new environment" are used.

[0172] The information generation device uses a generation AI model to generate the optimal response based on the received prompt. This AI model processes data using machine learning algorithms and creates responses that take diverse information sources into consideration.

[0173] The server evaluates the responses collected from the information generation device. Evaluation criteria include reliability, neutrality, user preference, and emotional state analysis by an emotion engine. Based on the evaluation, the most appropriate response is selected and provided as feedback to the user.

[0174] Ultimately, the device presents the user with the answers received from the server. This process uses user-friendly language and screen design that considers the user's emotions, making the information accessible and user-friendly. This enables more personalized information delivery and improves user satisfaction.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] Users use a device to input topics or questions they are interested in. Input is primarily in text format, but voice input is also possible. The device also captures the user's facial expressions using its camera. The collected data is processed as information necessary to infer the user's emotions. Input data includes specific questions such as "How can I alleviate anxiety about a new job?" and facial expression images.

[0178] Step 2:

[0179] The device sends collected text and sentiment data to the server. The transmitted data includes text, audio, and image information. To analyze this data, the server uses natural language processing to understand the input text and audio / image analysis to recognize emotions. The analysis clarifies the question content and emotional state.

[0180] Step 3:

[0181] The server generates prompt messages based on the analysis results and sends them to multiple information generation devices. These prompt messages are in a format that is easy for the generating AI model to understand. For example, a generated prompt message might be, "Please tell me how to cope when I feel stressed in a new environment." The generation process takes into account the user's emotional state and selects appropriate words.

[0182] Step 4:

[0183] The information generation device processes the received prompt message and generates an answer using a generative AI model. The generative AI model uses machine learning algorithms to learn from a large amount of data before deriving the optimal answer. The generated answer is based on diverse information sources, ensuring high reliability.

[0184] Step 5:

[0185] The server collects responses from information generators and evaluates each response based on its reliability, neutrality, user preference, and emotional state. The evaluation process includes reliability analysis based on data from credible sources and past performance, as well as neutrality assessments such as checking for inconsistencies between pieces of information.

[0186] Step 6:

[0187] The server selects the most appropriate response and sends it to the user's terminal in an emotionally sensitive manner. The selected response is prioritized and delivered to the terminal because it is appropriate for the user's emotional state.

[0188] Step 7:

[0189] The device presents the received responses to the user. The presentation process employs friendly language and design, making the content relatable and emotionally receptive to the user. This allows the user to receive personalized support.

[0190] (Application Example 2)

[0191] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0192] While there is a growing need to provide information that takes user emotions into consideration, conventional information delivery systems have the challenge of not being able to effectively recognize users' emotional states and customize information appropriately based on them. This challenge may lead to a decrease in the quality and satisfaction of the information users receive.

[0193] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0194] In this invention, the server includes means for receiving inquiries from a user terminal, means for collecting responses from multiple information generating devices, and means for recognizing the user's emotional state and selecting the most appropriate response. This enables the optimization and customization of information according to the user's emotional state.

[0195] A "user terminal" is a device used by a user to input information or to display received information.

[0196] "Means for receiving inquiries" refers to a method or device that has the function of receiving information requests transmitted from a user terminal.

[0197] An "information generation device" is a computer system or program that generates relevant answers or information based on received inquiry information.

[0198] "Means for collecting responses" refers to a method or device that has the function of collecting responses generated from multiple information generating devices.

[0199] "Reliability" is a criterion for evaluating whether information is accurate and based on evidence.

[0200] "Neutrality" is a standard used to evaluate whether information is free from bias and contradictions, and is fair.

[0201] "User preferences" are criteria for evaluating information based on the user's preferences and past selection history.

[0202] "User emotional state" refers to the emotional state analyzed from the user's voice and facial expression data.

[0203] "Voice and facial expression data" refers to digital information collected from the user's words and facial expressions.

[0204] "Means for analyzing emotions" refers to a method or device that has the function of interpreting and recognizing a user's emotions using voice and facial expression data.

[0205] "Means for customizing information" refers to a method or device that has the function of adjusting the information provided and how it is presented based on the user's emotional state.

[0206] This invention realizes a system for recognizing user emotions and optimizing information provision based on those emotions. The user begins by inputting information through a user terminal. This terminal is equipped with a microphone for voice input and a camera for facial expression analysis, and captures the user's voice and facial expression data in real time. This data collected by the terminal is transmitted to a server.

[0207] The server uses Google Cloud's Speech-to-Text API to convert audio data into text format, and also uses Microsoft Azure's Face API to analyze facial expression data. The emotion engine on the server analyzes the user's emotional state from this text and facial expression information.

[0208] Based on the analysis of the user's emotional state, the server sends queries to multiple information generators to collect relevant information and responses. The collected responses are evaluated based on the user's emotional state, reliability, neutrality, and preferences, and the most relevant information is selected. This optimized information is then sent to the user's terminal in user-friendly language and presented to the user.

[0209] For example, if a user asks the device, "Tell me how to relax," the system uses its emotion engine to understand that the user is stressed. Then, using a generative AI model, it suggests the most effective relaxation methods for the user and provides specific advice such as "Take a bath" or "Do some light stretching." An example of a prompt might be, "The user is expressing fatigue through their facial expression and voice. Please suggest some simple ways to relax."

[0210] In this way, users can receive customized information based on their own emotions, enabling a more satisfying information delivery compared to conventional systems.

[0211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0212] Step 1:

[0213] The user inputs questions and requests for information through the terminal. During this process, the terminal simultaneously captures voice and facial expression data. Input includes voice commands and questions, as well as visual data from facial expressions. This data is sent directly to the server.

[0214] Step 2:

[0215] The server converts the received audio data into text using Google Cloud's Speech-to-Text API. In this step, the audio itself is the input, and the output is in text format. This conversion clarifies the user's intent and the content of the question.

[0216] Step 3:

[0217] The server uses Microsoft Azure's Face API to analyze the user's emotional state from the received facial expression data. This process takes visual data as input and emotional characteristics as output. This analysis makes it possible to understand the user's emotional state.

[0218] Step 4:

[0219] The server sends queries to multiple information generators based on text data and sentiment features. The input here is the transformed text and sentiment features, and the responses from the information generators are collected as output. Data distribution and response collection gather data from diverse sources.

[0220] Step 5:

[0221] The server evaluates the collected responses based on the user's emotional state, reliability, neutrality, and preferences. Inputs include responses from information generators and user emotional and preference data. Outputs are the responses deemed most appropriate. This evaluation process selects information optimized for the user.

[0222] Step 6:

[0223] The server formats the selected information in user-friendly language and sends it to the user's terminal. The input in this step is an optimized response, and the output is text displayed to the user. The server presents information in a way that is sensitive to the user's emotions.

[0224] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0225] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0226] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0227] [Second Embodiment]

[0228] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0229] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0230] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0231] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0232] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0233] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0234] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0235] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0236] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0237] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0238] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0239] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0240] This invention relates to an information provision system that allows users to easily obtain information that prioritizes reliability and neutrality. This system is implemented as follows.

[0241] First, the user inputs the information they need using a terminal. Through this input screen, the user specifically describes topics of interest and questions. After receiving this input, the terminal sends it to the server.

[0242] The server sends questions to multiple information generators based on the information it receives. These information generators use different datasets and algorithms, generating answers from their respective perspectives. The server then waits for responses from each information generator and collects all the answers.

[0243] Next, the server evaluates the answers based on reliability, neutrality, and user preferences. For example, if a user asks "How to choose an environmentally friendly car," the server will determine that the answer from an information generator known as an environmental advisor is highly reliable. It also considers what kind of information the user has preferred in the past and selects the most suitable answer from among them. Regarding neutrality, it checks for conflicting information and maintains consistency.

[0244] The selected best answer is sent from the server to the user's terminal. The terminal receives this answer and displays it to the user in an appropriate format. For example, it can be presented visually using graphs or tables.

[0245] In this way, users can easily receive vetted, neutral, and reliable information. This system improves the efficiency and accuracy of users' information gathering.

[0246] The following describes the processing flow.

[0247] Step 1:

[0248] The user enters a question using the terminal's interface. The question is entered in natural language, for example, "Please tell me about environmentally friendly cars." The terminal confirms the user's input.

[0249] Step 2:

[0250] The terminal sends the entered question to the server. At this time, in addition to the question content, necessary metadata, such as the user ID and timestamp, is also sent.

[0251] Step 3:

[0252] The server analyzes the received question and distributes it to multiple information generators. Each information generator then begins generating an answer using its own database and algorithm.

[0253] Step 4:

[0254] The server collects the responses generated from each information generation device. Here, multiple responses from different perspectives are gathered and stored in the server's database.

[0255] Step 5:

[0256] The server scores the collected responses based on reliability, neutrality, and user preference. The reliability score is calculated based on the reliability of the information source, and the neutrality score is calculated based on the degree of agreement between responses. User preference is derived from past search history and preference data.

[0257] Step 6:

[0258] The server selects the answer with the highest score as the best answer. In this process, the optimal answer is identified through a weighted average of scores and filtering based on specific criteria.

[0259] Step 7:

[0260] The server sends the selected best answer to the user's terminal. If necessary, the answer content is formatted and converted into a user-friendly format.

[0261] Step 8:

[0262] The device displays the best answer to the user. This allows the user to efficiently obtain highly reliable and neutral information.

[0263] (Example 1)

[0264] Next, we will describe Example 1. 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."

[0265] When acquiring information, users often find it difficult to easily obtain information that is highly reliable, neutral, and tailored to their preferences. Existing systems struggle to judge the reliability and neutrality of information collected from various sources, and furthermore, they have the challenge of not being able to provide information that is optimally suited to the user's preferences.

[0266] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0267] In this invention, the server includes means for receiving inquiries from user devices, means for analyzing inquiries and extracting relevant topics and phrases, means for relaying questions to multiple information generation devices, means for collecting answers from multiple information generation devices and evaluating each answer based on reliability, neutrality, and user preference, means for auditing conflicting information and inconsistencies as consistency, and means for selecting the most appropriate answer and transmitting the selected answer to the user device in visual format. This enables users to efficiently obtain information that is highly reliable, neutral, and matches their preferences.

[0268] A "user device" is a terminal that is used by users to input information and has the function of receiving input and transmitting it to a server.

[0269] "Analyzing a query" refers to the process of examining a question received from a user and identifying related topics and phrases.

[0270] An "information generation device" is a device or program that generates answers based on a specified question using different datasets and algorithms.

[0271] "Collecting responses" refers to the act of compiling response data generated from multiple information generation devices.

[0272] "Methods of evaluation" refer to the process of analyzing and making judgments based on the reliability, neutrality, and user preferences of the collected responses.

[0273] "Auditing consistency" refers to the process of detecting inconsistencies and contradictions in information and checking whether the information is consistent.

[0274] "Sending in a visual format" means providing selected information to the user in a format such as diagrams or tables, in order to display it in an easy-to-understand manner.

[0275] This invention is an information provision system that allows users to easily obtain information that prioritizes reliability and neutrality. Users use a user device to input information. For example, if a user wants to know "how to choose an environmentally friendly car," they input their question into the interface of the user device.

[0276] The terminal formats the input information and sends it to the server. The server uses natural language processing software to analyze this information and extract the subject of the question and related keywords. Based on the analyzed information, the server creates appropriate prompt sentences for multiple information generation devices equipped with generative AI models, and uses these prompts to send the question to the information generation devices.

[0277] Information generation devices generate answers from their own unique perspectives using different datasets and algorithms. After the answers from each information generation device are collected, the server evaluates them based on reliability, neutrality, and the user's past preferences. To enhance the reliability of the information, the server performs analysis based on past performance data and information sources, and also detects inconsistencies to ensure neutrality.

[0278] After the evaluation, the server selects the most suitable answer and sends the results to the user device in a visual format such as graphs or tables. This enables the user to visually grasp the vetted, neutral, and highly reliable information.

[0279] As a specific example, a prompt sentence such as "Please tell me the points for choosing an environmentally friendly car." can be input into the generative AI model. In this way, the user can efficiently and accurately obtain the information they need.

[0280] The flow of the specific process in Example 1 will be described using FIG. 11.

[0281] Step 1:

[0282] The user inputs a question through the user device. Specifically, the user inputs a topic of interest in text format and presses the send button. The input question is sent to the terminal as string data.

[0283] Step 2:

[0284] The terminal formats the input question and transfers it to the server. The formatting includes text normalization and keyword extraction. The formatted data is sent to the server in a structured data format.

[0285] Step 3:

[0286] The server analyzes the received data. Specifically, using natural language processing software, relevant topics and keywords are extracted from the question. In this analysis, topic modeling and morphological analysis are performed, and based on the obtained information, a prompt sentence suitable for the next processing step is generated.

[0287] Step 4:

[0288] The server generates prompt sentences based on the analysis results and sends them to multiple information creation devices equipped with generation AI models. The transmitted prompt sentences are processed by the information creation devices, and answers are generated from multiple perspectives.

[0289] Step 5:

[0290] When responses are returned from multiple information generation devices, the server collects them. The collected responses are temporarily stored in a database and used to evaluate their reliability and neutrality.

[0291] Step 6:

[0292] The server evaluates each collected response based on reliability, neutrality, and user preference. Reliability is determined by scoring based on the historical data and accuracy of the information source. Neutrality is evaluated by running an algorithm to detect inconsistencies between responses.

[0293] Step 7:

[0294] The server selects the most appropriate answer based on the evaluation results. The selected answer is supplemented with additional information and visualizations (e.g., graphs and tables) to aid the user's understanding.

[0295] Step 8:

[0296] The selected response is sent from the server to the terminal. The terminal receives this response and displays it in an easy-to-understand format for the user. The user can then review the displayed information and take the necessary actions.

[0297] (Application Example 1)

[0298] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0299] As information regarding electronic payment methods diversifies, it has become difficult for users to make optimal choices based on highly reliable and neutral information. This problem arises because the means for evaluating the reliability of information and providing it to users in a visible form are insufficient.

[0300] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0301] In this invention, the server includes means for receiving an inquiry from a user terminal, means for collecting responses from a plurality of data generation devices, means for evaluating based on the reliability, neutrality, and user preferences of each response, means for presenting the reliability of information regarding payment methods in a ranking format, and means for visibly transmitting the selected response to the user terminal. As a result, it becomes possible for users to make selections based on highly reliable information regarding electronic payment methods.

[0302] A "user terminal" is a device used by a user to input or receive information.

[0303] "Means for receiving an inquiry" is a method or function for receiving an information request from a user.

[0304] A "data generation device" is a device or system that generates information or responses using different data sets and algorithms.

[0305] "Means for collecting responses" is a method or function for collecting and integrating responses generated from a plurality of information sources.

[0306] "Reliability" is the degree indicating the accuracy and credibility of information.

[0307] "Neutrality" is a characteristic indicating that information is unbiased and fair.

[0308] "Preferences" are criteria for evaluating information based on the user's past preferences and interests.

[0309] "Means of evaluation" refers to a method or function for determining the value of information or responses based on multiple criteria.

[0310] "Means of presenting information in a ranking format" refers to a method or function for ranking information and displaying it in an easy-to-understand manner for the user.

[0311] "Means of transmission" refers to a method or function for sending selected information or responses to the user's terminal.

[0312] The system for implementing this invention mainly consists of a user terminal, a server, and multiple data generation devices. The user's terminal is a smartphone or smart glasses, which allows the user to input information and receive the results visually. The server uses Amazon Web Services (AWS) or Google Cloud Platform as a cloud computing service and MongoDB or PostgreSQL as the database. The server is built using Python and the Django framework.

[0313] The server first receives an information request from the user's terminal. When the user requests information about a specific payment method, the server sends this request to multiple data generation devices. These devices generate the information using different datasets and algorithms.

[0314] Next, the server collects responses from each data generator and evaluates them based on their reliability, neutrality, and past user preferences. TensorFlow and PyTorch are used as machine learning libraries to analyze data reliability and evaluate the information.

[0315] Once the evaluation is complete, the server presents information about payment methods to the user's terminal in a reliability ranking format. For example, if a user asks "What is the safest payment method for online shopping?", the information visually displays options such as credit cards and e-money in order of reliability.

[0316] A concrete example of a prompt is, "In electronic payment services, collect and evaluate highly reliable and user-preferred payment methods from the information generation device." This prompt forms the basis for efficient information evaluation using a generation AI model.

[0317] Through this format, it becomes possible to create a system that allows users to easily obtain reliable information about electronic payment methods.

[0318] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0319] Step 1:

[0320] The user enters information requests using their terminal. These are specific questions, such as "Which payment method is the most secure?" The entered information is sent from the user's terminal to the server. The output is an information request sent to the server.

[0321] Step 2:

[0322] The server sends the received information request to multiple data generation devices. In doing so, the server uses prompts generated by a generation AI model to request information generation from each data generation device. The input is the user's question, and the output is multiple requests to the data generation devices.

[0323] Step 3:

[0324] Each data generator, upon receiving a request, produces a response using its respective dataset and algorithm. At this stage, the input is the request from the server, and the output is the generated response. The responses are provided from different perspectives, taking reliability and neutrality into consideration.

[0325] Step 4:

[0326] The server collects responses received from each data generator. The input consists of responses from multiple data generators, and the output is integrated response data within the server. This process involves the collection and integration of all data.

[0327] Step 5:

[0328] The server evaluates the collected responses based on reliability, neutrality, and user preference. Machine learning libraries (e.g., TensorFlow and PyTorch) are used to perform data reliability analysis and neutrality checks. The input is the integrated response data, and the output is the evaluation result.

[0329] Step 6:

[0330] Based on the evaluation results, the server ranks information such as payment methods by reliability and sends it to the user's terminal. The ranking format makes it easier for users to compare. The input is the evaluated data, and the output is returned to the user's terminal as data organized in a ranking format.

[0331] Step 7:

[0332] The user terminal visually displays the received ranking data. Users can then see the payment method with the highest reliability on the screen. Input is in ranking format, and output is a visual display. Users can use this information to select the most suitable payment method.

[0333] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0334] This invention relates to an information delivery system that incorporates an emotion engine that recognizes user emotions and optimizes information delivery. This system is implemented as follows.

[0335] First, the user inputs the information they need using a device. On this input screen, the user writes down topics or questions they are interested in in text format. The device is equipped with a function to collect not only the content of the entered text, but also, in some cases, data to recognize the user's emotions, such as their voice and facial expressions.

[0336] The terminal sends information and emotional data to the server. The server analyzes the received inquiries and distributes them to multiple information generating devices, requesting each device to generate a response using its own algorithm.

[0337] After collecting responses, the server evaluates them based on reliability, neutrality, and user preference. Furthermore, it incorporates analysis from an emotion engine that recognizes the user's emotions. This emotion engine analyzes emotions from the user's input text and voice, and has the means to predict how the information will be received. For example, if the emotion engine determines that the user has an urgent problem, it can prioritize responses that are appropriate to that state of mind.

[0338] For example, if a user asks "How can I alleviate anxiety about a new job?", this system uses an emotion engine to read the user's current state based on the answers obtained from the information generation device, and selects answers in an appropriate order that can reduce anxiety.

[0339] The selected best answer is sent from the server to the user's terminal. The terminal receives this answer and is designed to present it to the user in an emotionally sensitive manner. For example, it can be displayed using friendly language.

[0340] This invention enables users to efficiently receive neutral and reliable information that reflects their current emotional state. Therefore, it is expected to improve user satisfaction as a recipient of this information.

[0341] The following describes the processing flow.

[0342] Step 1:

[0343] The user enters questions using the terminal's input interface. Simultaneously, data representing the user's emotions, such as tone of voice and facial expressions, is collected, if possible. This information is used as data for the emotion engine.

[0344] Step 2:

[0345] The terminal sends the entered question and collected sentiment data to the server. This communication also includes metadata such as user ID and time information.

[0346] Step 3:

[0347] The server analyzes the received question and distributes the query to multiple information generators. Each information generator collects relevant information, generates an answer, and returns it to the server.

[0348] Step 4:

[0349] The server collects responses from the information generators, organizes them, and stores them in a database. After collection, the evaluation process is ready.

[0350] Step 5:

[0351] The server evaluates the collected responses based on reliability, neutrality, and user preferences. This evaluation is based on the reliability of the information source, the consistency of the information, and past user preferences.

[0352] Step 6:

[0353] The server uses an emotion engine to analyze the user's emotional state. This allows it to understand the user's current feelings and determine the appropriate response for those feelings.

[0354] Step 7:

[0355] The server integrates the scored evaluation results with the emotion engine's analysis to select the most appropriate response as the best answer. For example, if a user is showing anxiety, information that provides reassurance will be prioritized.

[0356] Step 8:

[0357] The server sends the selected best answer to the user's terminal. The terminal displays the answer in a format that takes into account the user's emotional state and delivers it to the user.

[0358] This allows users to efficiently obtain reliable and neutral information in a way that takes their emotions into consideration.

[0359] (Example 2)

[0360] Next, we will describe Example 2. 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".

[0361] Conventional information delivery systems provide information without considering the user's emotional state, resulting in a decline in the quality and satisfaction of the information users need. Furthermore, the inability to provide appropriate information based on the user's emotions limits the improvement of the user experience. This problem is particularly serious in situations where users are experiencing stress or anxiety. The present invention aims to recognize the user's emotions and optimize information delivery based on them.

[0362] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0363] In this invention, the server includes means for receiving inquiries and sentiment data from a user terminal, means for generating prompt statements and collecting responses using multiple information generating devices, and means for evaluating each response based on its reliability, neutrality, user preferences, and user sentiment. This makes it possible to provide the most appropriate information in a way that takes the user's sentiment into consideration.

[0364] A "user terminal" is an electronic device used by users to input and receive information, and it is a device that collects and communicates input data and emotional data.

[0365] "Emotional data" refers to information that indicates a user's psychological state and emotions, obtained from their voice, facial expressions, and text.

[0366] An "information generation device" refers to an algorithm or platform that generates an appropriate response based on a received prompt message.

[0367] A "prompt statement" is an instruction statement used to convey specific questions or requests to an information generating device.

[0368] "Reliability" is an indicator that shows the accuracy and credibility of information and its sources.

[0369] "Neutrality" refers to a state in which the information provided is free from bias and prejudice, and is fair.

[0370] "User preferences" refer to the characteristics of information that reflect the individual preferences and interests of users.

[0371] An "emotion engine" is an analytical system that recognizes emotions from user input data and performs appropriate information processing.

[0372] "Optimization" is the process of adjusting systems and processes to provide information in a way that best suits the user's needs and emotions.

[0373] One embodiment of this invention is an information provision system consisting of a user terminal, a server, and multiple information generation devices.

[0374] The user first inputs topics of interest in text format via the device. Simultaneously, emotional data such as facial expressions can be acquired using voice input or the camera. The device has software installed for collecting emotional data, providing the necessary information for the emotion engine.

[0375] The server analyzes text and sentiment data received from the terminal. Natural language processing and speech / image analysis technologies are used for the analysis. This allows for a detailed analysis of the user's questions and emotional state. The server then generates prompt sentences suitable for the AI ​​model based on the analyzed data and sends them to the information generation device. At this stage, prompt sentences such as "Please tell me how to cope when I feel stressed in a new environment" are used.

[0376] The information generation device uses a generation AI model to generate the optimal response based on the received prompt. This AI model processes data using machine learning algorithms and creates responses that take diverse information sources into consideration.

[0377] The server evaluates the responses collected from the information generation device. Evaluation criteria include reliability, neutrality, user preference, and emotional state analysis by an emotion engine. Based on the evaluation, the most appropriate response is selected and provided as feedback to the user.

[0378] Ultimately, the device presents the user with the answers received from the server. This process uses user-friendly language and screen design that considers the user's emotions, making the information accessible and user-friendly. This enables more personalized information delivery and improves user satisfaction.

[0379] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0380] Step 1:

[0381] Users use a device to input topics or questions they are interested in. Input is primarily in text format, but voice input is also possible. The device also captures the user's facial expressions using its camera. The collected data is processed as information necessary to infer the user's emotions. Input data includes specific questions such as "How can I alleviate anxiety about a new job?" and facial expression images.

[0382] Step 2:

[0383] The device sends collected text and sentiment data to the server. The transmitted data includes text, audio, and image information. To analyze this data, the server uses natural language processing to understand the input text and audio / image analysis to recognize emotions. The analysis clarifies the question content and emotional state.

[0384] Step 3:

[0385] The server generates prompt messages based on the analysis results and sends them to multiple information generation devices. These prompt messages are in a format that is easy for the generating AI model to understand. For example, a generated prompt message might be, "Please tell me how to cope when I feel stressed in a new environment." The generation process takes into account the user's emotional state and selects appropriate words.

[0386] Step 4:

[0387] The information generation device processes the received prompt message and generates an answer using a generative AI model. The generative AI model uses machine learning algorithms to learn from a large amount of data before deriving the optimal answer. The generated answer is based on diverse information sources, ensuring high reliability.

[0388] Step 5:

[0389] The server collects responses from information generators and evaluates each response based on its reliability, neutrality, user preference, and emotional state. The evaluation process includes reliability analysis based on data from credible sources and past performance, as well as neutrality assessments such as checking for inconsistencies between pieces of information.

[0390] Step 6:

[0391] The server selects the most appropriate response and sends it to the user's terminal in an emotionally sensitive manner. The selected response is prioritized and delivered to the terminal because it is appropriate for the user's emotional state.

[0392] Step 7:

[0393] The device presents the received responses to the user. The presentation process employs friendly language and design, making the content relatable and emotionally receptive to the user. This allows the user to receive personalized support.

[0394] (Application Example 2)

[0395] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0396] While there is a growing need to provide information that takes user emotions into consideration, conventional information delivery systems have the challenge of not being able to effectively recognize users' emotional states and customize information appropriately based on them. This challenge may lead to a decrease in the quality of information users receive and their satisfaction with it.

[0397] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0398] In this invention, the server includes means for receiving inquiries from a user terminal, means for collecting responses from multiple information generating devices, and means for recognizing the user's emotional state and selecting the most appropriate response. This enables the optimization and customization of information according to the user's emotional state.

[0399] A "user terminal" is a device used by a user to input information or to display received information.

[0400] "Means for receiving inquiries" refers to a method or device that has the function of receiving information requests transmitted from a user terminal.

[0401] An "information generation device" is a computer system or program that generates relevant answers or information based on received inquiry information.

[0402] "Means for collecting responses" refers to a method or device that has the function of collecting responses generated from multiple information generating devices.

[0403] "Reliability" is a criterion for evaluating whether information is accurate and based on evidence.

[0404] "Neutrality" is a standard used to evaluate whether information is free from bias and contradictions, and is fair.

[0405] "User preferences" are criteria for evaluating information based on the user's preferences and past selection history.

[0406] "User emotional state" refers to the emotional state analyzed from the user's voice and facial expression data.

[0407] "Voice and facial expression data" refers to digital information collected from the user's words and facial expressions.

[0408] "Means for analyzing emotions" refers to a method or device that has the function of interpreting and recognizing a user's emotions using voice and facial expression data.

[0409] "Means for customizing information" refers to a method or device that has the function of adjusting the information provided and how it is presented based on the user's emotional state.

[0410] This invention realizes a system for recognizing user emotions and optimizing information provision based on those emotions. The user begins by inputting information through a user terminal. This terminal is equipped with a microphone for voice input and a camera for facial expression analysis, and captures the user's voice and facial expression data in real time. This data collected by the terminal is transmitted to a server.

[0411] The server uses Google Cloud's Speech-to-Text API to convert audio data into text format, and also uses Microsoft Azure's Face API to analyze facial expression data. The emotion engine on the server analyzes the user's emotional state from this text and facial expression information.

[0412] Based on the analysis of the user's emotional state, the server sends queries to multiple information generators to collect relevant information and responses. The collected responses are evaluated based on the user's emotional state, reliability, neutrality, and preferences, and the most relevant information is selected. This optimized information is then sent to the user's terminal in user-friendly language and presented to the user.

[0413] For example, if a user asks the device, "Tell me how to relax," the system uses its emotion engine to understand that the user is stressed. Then, using a generative AI model, it suggests the most effective relaxation methods for the user and provides specific advice such as "Take a bath" or "Do some light stretching." An example of a prompt might be, "The user is expressing fatigue through their facial expression and voice. Please suggest some simple ways to relax."

[0414] In this way, users can receive customized information based on their own emotions, enabling a more satisfying information delivery compared to conventional systems.

[0415] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0416] Step 1:

[0417] The user inputs questions and requests for information through the terminal. During this process, the terminal simultaneously captures both voice and facial expression data. Input includes voice commands and questions, as well as visual data from facial expressions. This data is then sent directly to the server.

[0418] Step 2:

[0419] The server converts the received audio data into text using Google Cloud's Speech-to-Text API. In this step, the audio itself is the input, and the output is in text format. This conversion clarifies the user's intent and the content of the question.

[0420] Step 3:

[0421] The server uses Microsoft Azure's Face API to analyze the user's emotional state from the received facial expression data. This process takes visual data as input and emotional characteristics as output. This analysis makes it possible to understand the user's emotional state.

[0422] Step 4:

[0423] The server sends queries to multiple information generators based on text data and sentiment features. The input here is the transformed text and sentiment features, and the responses from the information generators are collected as output. Data distribution and response collection gather data from diverse sources.

[0424] Step 5:

[0425] The server evaluates the collected responses based on the user's emotional state, reliability, neutrality, and preferences. Inputs include responses from information generators and user emotional and preference data. Outputs are the responses deemed most appropriate. This evaluation process selects information optimized for the user.

[0426] Step 6:

[0427] The server formats the selected information in user-friendly language and sends it to the user's terminal. The input in this step is an optimized response, and the output is text displayed to the user. The server presents information in a way that is sensitive to the user's emotions.

[0428] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0429] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0430] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0431] [Third Embodiment]

[0432] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0433] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0434] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0435] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0436] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0437] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0438] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0439] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0440] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0441] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0442] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0443] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0444] This invention relates to an information provision system that allows users to easily obtain information that prioritizes reliability and neutrality. This system is implemented as follows.

[0445] First, the user inputs the information they need using a terminal. Through this input screen, the user specifically describes topics of interest and questions. After receiving this input, the terminal sends it to the server.

[0446] The server sends questions to multiple information generators based on the information it receives. These information generators use different datasets and algorithms, generating answers from their respective perspectives. The server then waits for responses from each information generator and collects all the answers.

[0447] Next, the server evaluates the answers based on reliability, neutrality, and user preferences. For example, if a user asks "How to choose an environmentally friendly car," the server will determine that the answer from an information generator known as an environmental advisor is highly reliable. It also considers what kind of information the user has preferred in the past and selects the most suitable answer from among them. Regarding neutrality, it checks for conflicting information and maintains consistency.

[0448] The selected best answer is sent from the server to the user's terminal. The terminal receives this answer and displays it to the user in an appropriate format. For example, it can be presented visually using graphs or tables.

[0449] In this way, users can easily receive vetted, neutral, and reliable information. This system improves the efficiency and accuracy of users' information gathering.

[0450] The following describes the processing flow.

[0451] Step 1:

[0452] The user enters a question using the terminal's interface. The question is entered in natural language, for example, "Please tell me about environmentally friendly cars." The terminal confirms the user's input.

[0453] Step 2:

[0454] The terminal sends the entered question to the server. At this time, in addition to the question content, necessary metadata, such as the user ID and timestamp, is also sent.

[0455] Step 3:

[0456] The server analyzes the received question and distributes it to multiple information generators. Each information generator then begins generating an answer using its own database and algorithm.

[0457] Step 4:

[0458] The server collects the responses generated from each information generation device. Here, multiple responses from different perspectives are gathered and stored in the server's database.

[0459] Step 5:

[0460] The server scores the collected responses based on reliability, neutrality, and user preference. The reliability score is calculated based on the reliability of the information source, and the neutrality score is calculated based on the degree of agreement between responses. User preference is derived from past search history and preference data.

[0461] Step 6:

[0462] The server selects the answer with the highest score as the best answer. In this process, the optimal answer is identified through a weighted average of scores and filtering based on specific criteria.

[0463] Step 7:

[0464] The server sends the selected best answer to the user's terminal. If necessary, the answer content is formatted and converted into a user-friendly format.

[0465] Step 8:

[0466] The device displays the best answer to the user. This allows the user to efficiently obtain highly reliable and neutral information.

[0467] (Example 1)

[0468] Next, we will describe Example 1. 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."

[0469] When acquiring information, users often find it difficult to easily obtain information that is highly reliable, neutral, and tailored to their preferences. Existing systems struggle to judge the reliability and neutrality of information collected from various sources, and furthermore, they have the challenge of not being able to provide information that is optimally suited to the user's preferences.

[0470] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0471] In this invention, the server includes means for receiving inquiries from user devices, means for analyzing inquiries and extracting relevant topics and phrases, means for relaying questions to multiple information generation devices, means for collecting answers from multiple information generation devices and evaluating each answer based on reliability, neutrality, and user preference, means for auditing conflicting information and inconsistencies as consistency, and means for selecting the most appropriate answer and transmitting the selected answer to the user device in visual format. This enables users to efficiently obtain information that is highly reliable, neutral, and matches their preferences.

[0472] A "user device" is a terminal that is used by users to input information and has the function of receiving input and transmitting it to a server.

[0473] "Analyzing a query" refers to the process of examining a question received from a user and identifying related topics and phrases.

[0474] An "information generation device" is a device or program that generates answers based on a specified question using different datasets and algorithms.

[0475] "Collecting responses" refers to the act of compiling response data generated from multiple information generation devices.

[0476] "Methods of evaluation" refer to the process of analyzing and making judgments based on the reliability, neutrality, and user preferences of the collected responses.

[0477] "Auditing consistency" refers to the process of detecting inconsistencies and contradictions in information and checking whether the information is consistent.

[0478] "Sending in a visual format" means providing selected information to the user in a format such as diagrams or tables, in order to display it in an easy-to-understand manner.

[0479] This invention is an information provision system that allows users to easily obtain information that prioritizes reliability and neutrality. Users use a user device to input information. For example, if a user wants to know "how to choose an environmentally friendly car," they input their question into the interface of the user device.

[0480] The terminal formats the input information and sends it to the server. The server uses natural language processing software to analyze this information and extract the subject of the question and related keywords. Based on the analyzed information, the server creates appropriate prompt sentences for multiple information generation devices equipped with generative AI models, and uses these prompts to send the question to the information generation devices.

[0481] Information generation devices generate answers from their own unique perspectives using different datasets and algorithms. After the answers from each information generation device are collected, the server evaluates them based on reliability, neutrality, and the user's past preferences. To enhance the reliability of the information, the server performs analysis based on past performance data and information sources, and also detects inconsistencies to ensure neutrality.

[0482] After the evaluation is complete, the server selects the most appropriate answer and sends the results to the user's device in a visual format such as graphs or tables. This allows the user to visually grasp carefully reviewed, neutral, and reliable information.

[0483] As a concrete example, a prompt such as, "Please tell me the key points for choosing an environmentally friendly car," can be input into the AI ​​model. In this way, users can efficiently and accurately obtain the information they need.

[0484] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0485] Step 1:

[0486] The user enters their question through the user device. Specifically, the user enters a topic of interest in text format and presses the submit button. The entered question is sent to the terminal as string data.

[0487] Step 2:

[0488] The terminal formats the entered question and forwards it to the server. Formatting includes text normalization and keyword extraction. The formatted data is sent to the server in a structured data format.

[0489] Step 3:

[0490] The server analyzes the received data. Specifically, it uses natural language processing software to extract relevant topics and keywords from the question. This analysis involves topic modeling and morphological analysis, and based on the obtained information, it generates prompt sentences suitable for the next processing step.

[0491] Step 4:

[0492] The server generates prompt sentences based on the analysis results and sends them to multiple information creation devices equipped with generation AI models. The transmitted prompt sentences are processed by the information creation devices, and answers are generated from multiple perspectives.

[0493] Step 5:

[0494] When responses are returned from multiple information generation devices, the server collects them. The collected responses are temporarily stored in a database and used to evaluate their reliability and neutrality.

[0495] Step 6:

[0496] The server evaluates each collected response based on reliability, neutrality, and user preference. Reliability is determined by scoring based on the historical data and accuracy of the information source. Neutrality is evaluated by running an algorithm to detect inconsistencies between responses.

[0497] Step 7:

[0498] The server selects the most appropriate answer based on the evaluation results. The selected answer is supplemented with additional information and visualizations (e.g., graphs and tables) to aid the user's understanding.

[0499] Step 8:

[0500] The selected response is sent from the server to the terminal. The terminal receives this response and displays it in an easy-to-understand format for the user. The user can then review the displayed information and take the necessary actions.

[0501] (Application Example 1)

[0502] Next, we will explain Application Example 1. In the following explanation, 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."

[0503] With the increasing diversity of information regarding electronic payment methods, it is difficult for users to make optimal choices based on reliable and neutral information. This problem arises because there are insufficient means to evaluate the reliability of information and provide it to users in a visible format.

[0504] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0505] In this invention, the server includes means for receiving inquiries from a user terminal, means for collecting responses from multiple data generation devices, means for evaluating each response based on its reliability, neutrality, and user preference, means for presenting the reliability of information regarding payment methods in a ranking format, and means for visually transmitting the selected responses to the user terminal. This enables the user to make a selection regarding electronic payment methods based on highly reliable information.

[0506] A "user terminal" is a device used by a user to input or receive information.

[0507] "Means for receiving inquiries" refers to a method or function for receiving information requests from users.

[0508] A "data generation device" is a device or system that generates information or responses using different datasets or algorithms.

[0509] "Means for collecting responses" refers to a method or function for collecting and integrating responses generated from multiple sources.

[0510] "Reliability" refers to the degree to which information is accurate and credible.

[0511] "Neutrality" is a characteristic that indicates information is unbiased and fair.

[0512] "Preferences" are criteria for evaluating information based on the user's past preferences and interests.

[0513] "Means of evaluation" refers to a method or function for determining the value of information or responses based on multiple criteria.

[0514] "Means of presenting information in a ranking format" refers to a method or function for ranking information and displaying it in an easy-to-understand manner for the user.

[0515] "Means of transmission" refers to a method or function for sending selected information or responses to the user's terminal.

[0516] The system for implementing this invention mainly consists of a user terminal, a server, and multiple data generation devices. The user's terminal is a smartphone or smart glasses, which allows the user to input information and receive the results visually. The server uses Amazon Web Services (AWS) or Google Cloud Platform as a cloud computing service and MongoDB or PostgreSQL as the database. The server is built using Python and the Django framework.

[0517] The server first receives an information request from the user's terminal. When the user requests information about a specific payment method, the server sends this request to multiple data generation devices. These devices generate the information using different datasets and algorithms.

[0518] Next, the server collects responses from each data generator and evaluates them based on their reliability, neutrality, and past user preferences. TensorFlow and PyTorch are used as machine learning libraries to analyze data reliability and evaluate the information.

[0519] Once the evaluation is complete, the server presents information about payment methods to the user's terminal in a reliability ranking format. For example, if a user asks "What is the safest payment method for online shopping?", the information visually displays options such as credit cards and e-money in order of reliability.

[0520] A concrete example of a prompt is, "In electronic payment services, collect and evaluate highly reliable and user-preferred payment methods from the information generation device." This prompt forms the basis for efficient information evaluation using a generation AI model.

[0521] Through this format, it becomes possible to create a system that allows users to easily obtain reliable information about electronic payment methods.

[0522] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0523] Step 1:

[0524] The user enters information requests using their terminal. These are specific questions, such as "Which payment method is the most secure?" The entered information is sent from the user's terminal to the server. The output is an information request sent to the server.

[0525] Step 2:

[0526] The server sends the received information request to multiple data generation devices. In doing so, the server uses prompts generated by a generation AI model to request information generation from each data generation device. The input is the user's question, and the output is multiple requests to the data generation devices.

[0527] Step 3:

[0528] Each data generator, upon receiving a request, produces a response using its respective dataset and algorithm. At this stage, the input is the request from the server, and the output is the generated response. The responses are provided from different perspectives, taking reliability and neutrality into consideration.

[0529] Step 4:

[0530] The server collects responses received from each data generator. The input consists of responses from multiple data generators, and the output is integrated response data within the server. This process involves the collection and integration of all data.

[0531] Step 5:

[0532] The server evaluates the collected responses based on reliability, neutrality, and user preference. Machine learning libraries (e.g., TensorFlow and PyTorch) are used to perform data reliability analysis and neutrality checks. The input is the integrated response data, and the output is the evaluation result.

[0533] Step 6:

[0534] Based on the evaluation results, the server ranks information such as payment methods by reliability and sends it to the user's terminal. The ranking format makes it easier for users to compare. The input is the evaluated data, and the output is returned to the user's terminal as data organized in a ranking format.

[0535] Step 7:

[0536] The user terminal visually displays the received ranking data. Users can then see the payment method with the highest reliability on the screen. Input is in ranking format, and output is a visual display. Users can use this information to select the most suitable payment method.

[0537] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0538] This invention relates to an information delivery system that incorporates an emotion engine that recognizes user emotions and optimizes information delivery. This system is implemented as follows.

[0539] First, the user inputs the information they need using a device. On this input screen, the user writes down topics or questions they are interested in in text format. The device is equipped with a function to collect not only the content of the entered text, but also, in some cases, data to recognize the user's emotions, such as their voice and facial expressions.

[0540] The terminal sends information and emotional data to the server. The server analyzes the received inquiries and distributes them to multiple information generating devices, requesting each device to generate a response using its own algorithm.

[0541] After collecting responses, the server evaluates them based on reliability, neutrality, and user preference. Furthermore, it incorporates analysis from an emotion engine that recognizes the user's emotions. This emotion engine analyzes emotions from the user's input text and voice, and has the means to predict how the information will be received. For example, if the emotion engine determines that the user has an urgent problem, it can prioritize responses that are appropriate to that state of mind.

[0542] For example, if a user asks "How can I alleviate anxiety about a new job?", this system uses an emotion engine to read the user's current state based on the answers obtained from the information generation device, and selects answers in an appropriate order that can reduce anxiety.

[0543] The selected best answer is sent from the server to the user's terminal. The terminal receives this answer and is designed to present it to the user in an emotionally sensitive manner. For example, it can be displayed using friendly language.

[0544] This invention enables users to efficiently receive neutral and reliable information that reflects their current emotional state. Therefore, it is expected to improve user satisfaction as a recipient of this information.

[0545] The following describes the processing flow.

[0546] Step 1:

[0547] The user enters questions using the terminal's input interface. Simultaneously, data representing the user's emotions, such as tone of voice and facial expressions, is collected, if possible. This information is used as data for the emotion engine.

[0548] Step 2:

[0549] The terminal sends the entered question and collected sentiment data to the server. This communication also includes metadata such as user ID and time information.

[0550] Step 3:

[0551] The server analyzes the received question and distributes the query to multiple information generators. Each information generator collects relevant information, generates an answer, and returns it to the server.

[0552] Step 4:

[0553] The server collects responses from the information generators, organizes them, and stores them in a database. After collection, the evaluation process is ready.

[0554] Step 5:

[0555] The server evaluates the collected responses based on reliability, neutrality, and user preferences. This evaluation is based on the reliability of the information source, the consistency of the information, and past user preferences.

[0556] Step 6:

[0557] The server uses an emotion engine to analyze the user's emotional state. This allows it to understand the user's current feelings and determine the appropriate response for those feelings.

[0558] Step 7:

[0559] The server integrates the scored evaluation results with the emotion engine's analysis to select the most appropriate response as the best answer. For example, if a user is showing anxiety, information that provides reassurance will be prioritized.

[0560] Step 8:

[0561] The server sends the selected best answer to the user's terminal. The terminal displays the answer in a format that takes into account the user's emotional state and delivers it to the user.

[0562] This allows users to efficiently obtain reliable and neutral information in a way that takes their emotions into consideration.

[0563] (Example 2)

[0564] Next, we will describe Example 2. 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."

[0565] Conventional information delivery systems provide information without considering the user's emotional state, resulting in a decline in the quality and satisfaction of the information users need. Furthermore, the inability to provide appropriate information based on the user's emotions limits the improvement of the user experience. This problem is particularly serious in situations where users are experiencing stress or anxiety. The present invention aims to recognize the user's emotions and optimize information delivery based on them.

[0566] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0567] In this invention, the server includes means for receiving inquiries and sentiment data from a user terminal, means for generating prompt statements and collecting responses using multiple information generating devices, and means for evaluating each response based on its reliability, neutrality, user preferences, and user sentiment. This makes it possible to provide the most appropriate information in a way that takes the user's sentiment into consideration.

[0568] A "user terminal" is an electronic device used by users to input and receive information, and it is a device that collects and communicates input data and emotional data.

[0569] "Emotional data" refers to information that indicates a user's psychological state and emotions, obtained from their voice, facial expressions, and text.

[0570] An "information generation device" refers to an algorithm or platform that generates an appropriate response based on a received prompt message.

[0571] A "prompt statement" is an instruction statement used to convey specific questions or requests to an information generating device.

[0572] "Reliability" is an indicator that shows the accuracy and credibility of information and its sources.

[0573] "Neutrality" refers to a state in which the information provided is free from bias and prejudice, and is fair.

[0574] "User preferences" refer to the characteristics of information that reflect the individual preferences and interests of users.

[0575] An "emotion engine" is an analytical system that recognizes emotions from user input data and performs appropriate information processing.

[0576] "Optimization" is the process of adjusting systems and processes to provide information in a way that best suits the user's needs and emotions.

[0577] One embodiment of this invention is an information provision system consisting of a user terminal, a server, and multiple information generation devices.

[0578] The user first inputs topics of interest in text format via the device. Simultaneously, emotional data such as facial expressions can be acquired using voice input or the camera. The device has software installed for collecting emotional data, providing the necessary information for the emotion engine.

[0579] The server analyzes text and sentiment data received from the terminal. Natural language processing and speech / image analysis technologies are used for the analysis. This allows for a detailed analysis of the user's questions and emotional state. The server then generates prompt sentences suitable for the AI ​​model based on the analyzed data and sends them to the information generation device. At this stage, prompt sentences such as "Please tell me how to cope when I feel stressed in a new environment" are used.

[0580] The information generation device uses a generation AI model to generate the optimal response based on the received prompt. This AI model processes data using machine learning algorithms and creates responses that take diverse information sources into consideration.

[0581] The server evaluates the responses collected from the information generation device. Evaluation criteria include reliability, neutrality, user preference, and emotional state analysis by an emotion engine. Based on the evaluation, the most appropriate response is selected and provided as feedback to the user.

[0582] Ultimately, the device presents the user with the answers received from the server. This process uses user-friendly language and screen design that considers the user's emotions, making the information accessible and user-friendly. This enables more personalized information delivery and improves user satisfaction.

[0583] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0584] Step 1:

[0585] Users use a device to input topics or questions they are interested in. Input is primarily in text format, but voice input is also possible. The device also captures the user's facial expressions using its camera. The collected data is processed as information necessary to infer the user's emotions. Input data includes specific questions such as "How can I alleviate anxiety about a new job?" and facial expression images.

[0586] Step 2:

[0587] The device sends collected text and sentiment data to the server. The transmitted data includes text, audio, and image information. To analyze this data, the server uses natural language processing to understand the input text and audio / image analysis to recognize emotions. The analysis clarifies the question content and emotional state.

[0588] Step 3:

[0589] The server generates prompt messages based on the analysis results and sends them to multiple information generation devices. These prompt messages are in a format that is easy for the generating AI model to understand. For example, a generated prompt message might be, "Please tell me how to cope when I feel stressed in a new environment." The generation process takes into account the user's emotional state and selects appropriate words.

[0590] Step 4:

[0591] The information generation device processes the received prompt message and generates an answer using a generative AI model. The generative AI model uses machine learning algorithms to learn from a large amount of data before deriving the optimal answer. The generated answer is based on diverse information sources, ensuring high reliability.

[0592] Step 5:

[0593] The server collects responses from information generators and evaluates each response based on its reliability, neutrality, user preference, and emotional state. The evaluation process includes reliability analysis based on data from credible sources and past performance, as well as neutrality assessments such as checking for inconsistencies between pieces of information.

[0594] Step 6:

[0595] The server selects the most appropriate response and sends it to the user's terminal in an emotionally sensitive manner. The selected response is prioritized and delivered to the terminal because it is appropriate for the user's emotional state.

[0596] Step 7:

[0597] The device presents the received responses to the user. The presentation process employs friendly language and design, making the content relatable and emotionally receptive to the user. This allows the user to receive personalized support.

[0598] (Application Example 2)

[0599] Next, we will explain application example 2. In the following explanation, 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."

[0600] While there is a growing need to provide information that takes user emotions into consideration, conventional information delivery systems have the challenge of not being able to effectively recognize users' emotional states and customize information appropriately based on them. This challenge may lead to a decrease in the quality of information users receive and their satisfaction with it.

[0601] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0602] In this invention, the server includes means for receiving inquiries from a user terminal, means for collecting responses from multiple information generating devices, and means for recognizing the user's emotional state and selecting the most appropriate response. This enables the optimization and customization of information according to the user's emotional state.

[0603] A "user terminal" is a device used by a user to input information or to display received information.

[0604] "Means for receiving inquiries" refers to a method or device that has the function of receiving information requests transmitted from a user terminal.

[0605] An "information generation device" is a computer system or program that generates relevant answers or information based on received inquiry information.

[0606] "Means for collecting responses" refers to a method or device that has the function of collecting responses generated from multiple information generating devices.

[0607] "Reliability" is a criterion for evaluating whether information is accurate and based on evidence.

[0608] "Neutrality" is a standard used to evaluate whether information is free from bias and contradictions, and is fair.

[0609] "User preferences" are criteria for evaluating information based on the user's preferences and past selection history.

[0610] "User emotional state" refers to the emotional state analyzed from the user's voice and facial expression data.

[0611] "Voice and facial expression data" refers to digital information collected from the user's words and facial expressions.

[0612] "Means for analyzing emotions" refers to a method or device that has the function of interpreting and recognizing a user's emotions using voice and facial expression data.

[0613] "Means for customizing information" refers to a method or device that has the function of adjusting the information provided and how it is presented based on the user's emotional state.

[0614] This invention realizes a system for recognizing user emotions and optimizing information provision based on those emotions. The user begins by inputting information through a user terminal. This terminal is equipped with a microphone for voice input and a camera for facial expression analysis, and captures the user's voice and facial expression data in real time. This data collected by the terminal is transmitted to a server.

[0615] The server uses Google Cloud's Speech-to-Text API to convert audio data into text format, and also uses Microsoft Azure's Face API to analyze facial expression data. The emotion engine on the server analyzes the user's emotional state from this text and facial expression information.

[0616] Based on the analysis of the user's emotional state, the server sends queries to multiple information generators to collect relevant information and responses. The collected responses are evaluated based on the user's emotional state, reliability, neutrality, and preferences, and the most relevant information is selected. This optimized information is then sent to the user's terminal in user-friendly language and presented to the user.

[0617] For example, if a user asks the device, "Tell me how to relax," the system uses its emotion engine to understand that the user is stressed. Then, using a generative AI model, it suggests the most effective relaxation methods for the user and provides specific advice such as "Take a bath" or "Do some light stretching." An example of a prompt might be, "The user is expressing fatigue through their facial expression and voice. Please suggest some simple ways to relax."

[0618] In this way, users can receive customized information based on their own emotions, enabling a more satisfying information delivery compared to conventional systems.

[0619] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0620] Step 1:

[0621] The user inputs questions and requests for information through the terminal. During this process, the terminal simultaneously captures both voice and facial expression data. Input includes voice commands and questions, as well as visual data from facial expressions. This data is then sent directly to the server.

[0622] Step 2:

[0623] The server converts the received audio data into text using Google Cloud's Speech-to-Text API. In this step, the audio itself is the input, and the output is in text format. This conversion clarifies the user's intent and the content of the question.

[0624] Step 3:

[0625] The server uses Microsoft Azure's Face API to analyze the user's emotional state from the received facial expression data. This process takes visual data as input and emotional characteristics as output. This analysis makes it possible to understand the user's emotional state.

[0626] Step 4:

[0627] The server sends queries to multiple information generators based on text data and sentiment features. The input here is the transformed text and sentiment features, and the responses from the information generators are collected as output. Data distribution and response collection gather data from diverse sources.

[0628] Step 5:

[0629] The server evaluates the collected responses based on the user's emotional state, reliability, neutrality, and preferences. Inputs include responses from information generators and user emotional and preference data. Outputs are the responses deemed most appropriate. This evaluation process selects information optimized for the user.

[0630] Step 6:

[0631] The server formats the selected information in user-friendly language and sends it to the user's terminal. The input in this step is an optimized response, and the output is text displayed to the user. The server presents information in a way that is sensitive to the user's emotions.

[0632] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0633] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0634] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0635] [Fourth Embodiment]

[0636] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0637] As shown in Figure 7, the 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.

[0638] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0639] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0640] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0641] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0642] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0643] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0644] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0645] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0646] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0647] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0648] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0649] This invention relates to an information provision system that allows users to easily obtain information that prioritizes reliability and neutrality. This system is implemented as follows.

[0650] First, the user inputs the information they need using a terminal. Through this input screen, the user specifically describes topics of interest and questions. After receiving this input, the terminal sends it to the server.

[0651] The server sends questions to multiple information generators based on the information it receives. These information generators use different datasets and algorithms, generating answers from their respective perspectives. The server then waits for responses from each information generator and collects all the answers.

[0652] Next, the server evaluates the answers based on reliability, neutrality, and user preferences. For example, if a user asks "How to choose an environmentally friendly car," the server will determine that the answer from an information generator known as an environmental advisor is highly reliable. It also considers what kind of information the user has preferred in the past and selects the most suitable answer from among them. Regarding neutrality, it checks for conflicting information and maintains consistency.

[0653] The selected best answer is sent from the server to the user's terminal. The terminal receives this answer and displays it to the user in an appropriate format. For example, it can be presented visually using graphs or tables.

[0654] In this way, users can easily receive vetted, neutral, and reliable information. This system improves the efficiency and accuracy of users' information gathering.

[0655] The following describes the processing flow.

[0656] Step 1:

[0657] The user enters a question using the terminal's interface. The question is entered in natural language, for example, "Please tell me about environmentally friendly cars." The terminal confirms the user's input.

[0658] Step 2:

[0659] The terminal sends the entered question to the server. At this time, in addition to the question content, necessary metadata, such as the user ID and timestamp, is also sent.

[0660] Step 3:

[0661] The server analyzes the received question and distributes it to multiple information generators. Each information generator then begins generating an answer using its own database and algorithm.

[0662] Step 4:

[0663] The server collects the responses generated from each information generation device. Here, multiple responses from different perspectives are gathered and stored in the server's database.

[0664] Step 5:

[0665] The server scores the collected responses based on reliability, neutrality, and user preference. The reliability score is calculated based on the reliability of the information source, and the neutrality score is calculated based on the degree of agreement between responses. User preference is derived from past search history and preference data.

[0666] Step 6:

[0667] The server selects the answer with the highest score as the best answer. In this process, the optimal answer is identified through a weighted average of scores and filtering based on specific criteria.

[0668] Step 7:

[0669] The server sends the selected best answer to the user's terminal. If necessary, the answer content is formatted and converted into a user-friendly format.

[0670] Step 8:

[0671] The device displays the best answer to the user. This allows the user to efficiently obtain highly reliable and neutral information.

[0672] (Example 1)

[0673] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0674] When acquiring information, users often find it difficult to easily obtain information that is highly reliable, neutral, and tailored to their preferences. Existing systems struggle to judge the reliability and neutrality of information collected from various sources, and furthermore, they have the challenge of not being able to provide information that is optimally suited to the user's preferences.

[0675] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0676] In this invention, the server includes means for receiving inquiries from user devices, means for analyzing inquiries and extracting relevant topics and phrases, means for relaying questions to multiple information generation devices, means for collecting answers from multiple information generation devices and evaluating each answer based on reliability, neutrality, and user preference, means for auditing conflicting information and inconsistencies as consistency, and means for selecting the most appropriate answer and transmitting the selected answer to the user device in visual format. This enables users to efficiently obtain information that is highly reliable, neutral, and matches their preferences.

[0677] A "user device" is a terminal that is used by users to input information and has the function of receiving input and transmitting it to a server.

[0678] "Analyzing a query" refers to the process of examining a question received from a user and identifying related topics and phrases.

[0679] An "information generation device" is a device or program that generates answers based on a specified question using different datasets and algorithms.

[0680] "Collecting responses" refers to the act of compiling response data generated from multiple information generation devices.

[0681] "Methods of evaluation" refer to the process of analyzing and making judgments based on the reliability, neutrality, and user preferences of the collected responses.

[0682] "Auditing consistency" refers to the process of detecting inconsistencies and contradictions in information and checking whether the information is consistent.

[0683] "Sending in a visual format" means providing selected information to the user in a format such as diagrams or tables, in order to display it in an easy-to-understand manner.

[0684] This invention is an information provision system that allows users to easily obtain information that prioritizes reliability and neutrality. Users use a user device to input information. For example, if a user wants to know "how to choose an environmentally friendly car," they input their question into the interface of the user device.

[0685] The terminal formats the input information and sends it to the server. The server uses natural language processing software to analyze this information and extract the subject of the question and related keywords. Based on the analyzed information, the server creates appropriate prompt sentences for multiple information generation devices equipped with generative AI models, and uses these prompts to send the question to the information generation devices.

[0686] Information generation devices generate answers from their own unique perspectives using different datasets and algorithms. After the answers from each information generation device are collected, the server evaluates them based on reliability, neutrality, and the user's past preferences. To enhance the reliability of the information, the server performs analysis based on past performance data and information sources, and also detects inconsistencies to ensure neutrality.

[0687] After the evaluation is complete, the server selects the most appropriate answer and sends the results to the user's device in a visual format such as graphs or tables. This allows the user to visually grasp carefully reviewed, neutral, and reliable information.

[0688] As a concrete example, a prompt such as, "Please tell me the key points for choosing an environmentally friendly car," can be input into the AI ​​model. In this way, users can efficiently and accurately obtain the information they need.

[0689] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0690] Step 1:

[0691] The user enters their question through the user device. Specifically, the user enters a topic of interest in text format and presses the submit button. The entered question is sent to the terminal as string data.

[0692] Step 2:

[0693] The terminal formats the entered question and forwards it to the server. Formatting includes text normalization and keyword extraction. The formatted data is sent to the server in a structured data format.

[0694] Step 3:

[0695] The server analyzes the received data. Specifically, it uses natural language processing software to extract relevant topics and keywords from the question. This analysis involves topic modeling and morphological analysis, and based on the obtained information, it generates prompt sentences suitable for the next processing step.

[0696] Step 4:

[0697] The server generates prompt sentences based on the analysis results and sends them to multiple information creation devices equipped with generation AI models. The transmitted prompt sentences are processed by the information creation devices, and answers are generated from multiple perspectives.

[0698] Step 5:

[0699] When responses are returned from multiple information generation devices, the server collects them. The collected responses are temporarily stored in a database and used to evaluate their reliability and neutrality.

[0700] Step 6:

[0701] The server evaluates each collected response based on reliability, neutrality, and user preference. Reliability is determined by scoring based on the historical data and accuracy of the information source. Neutrality is evaluated by running an algorithm to detect inconsistencies between responses.

[0702] Step 7:

[0703] The server selects the most appropriate answer based on the evaluation results. The selected answer is supplemented with additional information and visualizations (e.g., graphs and tables) to aid the user's understanding.

[0704] Step 8:

[0705] The selected response is sent from the server to the terminal. The terminal receives this response and displays it in an easy-to-understand format for the user. The user can then review the displayed information and take the necessary actions.

[0706] (Application Example 1)

[0707] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0708] With the increasing diversity of information regarding electronic payment methods, it is difficult for users to make optimal choices based on reliable and neutral information. This problem arises because there are insufficient means to evaluate the reliability of information and provide it to users in a visible format.

[0709] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0710] In this invention, the server includes means for receiving inquiries from a user terminal, means for collecting responses from multiple data generation devices, means for evaluating each response based on its reliability, neutrality, and user preference, means for presenting the reliability of information regarding payment methods in a ranking format, and means for visually transmitting the selected responses to the user terminal. This enables the user to make a selection regarding electronic payment methods based on highly reliable information.

[0711] A "user terminal" is a device used by a user to input or receive information.

[0712] "Means for receiving inquiries" refers to a method or function for receiving information requests from users.

[0713] A "data generation device" is a device or system that generates information or responses using different datasets or algorithms.

[0714] "Means for collecting responses" refers to a method or function for collecting and integrating responses generated from multiple sources.

[0715] "Reliability" refers to the degree to which information is accurate and credible.

[0716] "Neutrality" is a characteristic that indicates information is unbiased and fair.

[0717] "Preferences" are criteria for evaluating information based on the user's past preferences and interests.

[0718] "Means of evaluation" refers to a method or function for determining the value of information or responses based on multiple criteria.

[0719] "Means of presenting information in a ranking format" refers to a method or function for ranking information and displaying it in an easy-to-understand manner for the user.

[0720] "Means of transmission" refers to a method or function for sending selected information or responses to the user's terminal.

[0721] The system for implementing this invention mainly consists of a user terminal, a server, and multiple data generation devices. The user's terminal is a smartphone or smart glasses, which allows the user to input information and receive the results visually. The server uses Amazon Web Services (AWS) or Google Cloud Platform as a cloud computing service and MongoDB or PostgreSQL as the database. The server is built using Python and the Django framework.

[0722] The server first receives an information request from the user's terminal. When the user requests information about a specific payment method, the server sends this request to multiple data generation devices. These devices generate the information using different datasets and algorithms.

[0723] Next, the server collects responses from each data generator and evaluates them based on their reliability, neutrality, and past user preferences. TensorFlow and PyTorch are used as machine learning libraries to analyze data reliability and evaluate the information.

[0724] Once the evaluation is complete, the server presents information about payment methods to the user's terminal in a reliability ranking format. For example, if a user asks "What is the safest payment method for online shopping?", the information visually displays options such as credit cards and e-money in order of reliability.

[0725] A concrete example of a prompt is, "In electronic payment services, collect and evaluate highly reliable and user-preferred payment methods from the information generation device." This prompt forms the basis for efficient information evaluation using a generation AI model.

[0726] Through this format, it becomes possible to create a system that allows users to easily obtain reliable information about electronic payment methods.

[0727] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0728] Step 1:

[0729] The user enters information requests using their terminal. These are specific questions, such as "Which payment method is the most secure?" The entered information is sent from the user's terminal to the server. The output is an information request sent to the server.

[0730] Step 2:

[0731] The server sends the received information request to multiple data generation devices. In doing so, the server uses prompts generated by a generation AI model to request information generation from each data generation device. The input is the user's question, and the output is multiple requests to the data generation devices.

[0732] Step 3:

[0733] Each data generator, upon receiving a request, produces a response using its respective dataset and algorithm. At this stage, the input is the request from the server, and the output is the generated response. The responses are provided from different perspectives, taking reliability and neutrality into consideration.

[0734] Step 4:

[0735] The server collects responses received from each data generator. The input consists of responses from multiple data generators, and the output is integrated response data within the server. This process involves the collection and integration of all data.

[0736] Step 5:

[0737] The server evaluates the collected responses based on reliability, neutrality, and user preference. Machine learning libraries (e.g., TensorFlow and PyTorch) are used to perform data reliability analysis and neutrality checks. The input is the integrated response data, and the output is the evaluation result.

[0738] Step 6:

[0739] Based on the evaluation results, the server ranks information such as payment methods by reliability and sends it to the user's terminal. The ranking format makes it easier for users to compare. The input is the evaluated data, and the output is returned to the user's terminal as data organized in a ranking format.

[0740] Step 7:

[0741] The user terminal visually displays the received ranking data. Users can then see the payment method with the highest reliability on the screen. Input is in ranking format, and output is a visual display. Users can use this information to select the most suitable payment method.

[0742] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0743] This invention relates to an information delivery system that incorporates an emotion engine that recognizes user emotions and optimizes information delivery. This system is implemented as follows.

[0744] First, the user inputs the information they need using a device. On this input screen, the user writes down topics or questions they are interested in in text format. The device is equipped with a function to collect not only the content of the entered text, but also, in some cases, data to recognize the user's emotions, such as their voice and facial expressions.

[0745] The terminal sends information and emotional data to the server. The server analyzes the received inquiries and distributes them to multiple information generating devices, requesting each device to generate a response using its own algorithm.

[0746] After collecting responses, the server evaluates them based on reliability, neutrality, and user preference. Furthermore, it incorporates analysis from an emotion engine that recognizes the user's emotions. This emotion engine analyzes emotions from the user's input text and voice, and has the means to predict how the information will be received. For example, if the emotion engine determines that the user has an urgent problem, it can prioritize responses that are appropriate to that state of mind.

[0747] For example, if a user asks "How can I alleviate anxiety about a new job?", this system uses an emotion engine to read the user's current state based on the answers obtained from the information generation device, and selects answers in an appropriate order that can reduce anxiety.

[0748] The selected best answer is sent from the server to the user's terminal. The terminal receives this answer and is designed to present it to the user in an emotionally sensitive manner. For example, it can be displayed using friendly language.

[0749] This invention enables users to efficiently receive neutral and reliable information that reflects their current emotional state. Therefore, it is expected to improve user satisfaction as a recipient of this information.

[0750] The following describes the processing flow.

[0751] Step 1:

[0752] The user enters questions using the terminal's input interface. Simultaneously, data representing the user's emotions, such as tone of voice and facial expressions, is collected, if possible. This information is used as data for the emotion engine.

[0753] Step 2:

[0754] The terminal sends the entered question and collected sentiment data to the server. This communication also includes metadata such as user ID and time information.

[0755] Step 3:

[0756] The server analyzes the received question and distributes the query to multiple information generators. Each information generator collects relevant information, generates an answer, and returns it to the server.

[0757] Step 4:

[0758] The server collects responses from the information generators, organizes them, and stores them in a database. After collection, the evaluation process is ready.

[0759] Step 5:

[0760] The server evaluates the collected responses based on reliability, neutrality, and user preferences. This evaluation is based on the reliability of the information source, the consistency of the information, and past user preferences.

[0761] Step 6:

[0762] The server uses an emotion engine to analyze the user's emotional state. This allows it to understand the user's current feelings and determine the appropriate response for those feelings.

[0763] Step 7:

[0764] The server integrates the scored evaluation results with the emotion engine's analysis to select the most appropriate response as the best answer. For example, if a user is showing anxiety, information that provides reassurance will be prioritized.

[0765] Step 8:

[0766] The server sends the selected best answer to the user's terminal. The terminal displays the answer in a format that takes into account the user's emotional state and delivers it to the user.

[0767] This allows users to efficiently obtain reliable and neutral information in a way that takes their emotions into consideration.

[0768] (Example 2)

[0769] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0770] Conventional information delivery systems provide information without considering the user's emotional state, resulting in a decline in the quality and satisfaction of the information users need. Furthermore, the inability to provide appropriate information based on the user's emotions limits the improvement of the user experience. This problem is particularly serious in situations where users are experiencing stress or anxiety. The present invention aims to recognize the user's emotions and optimize information delivery based on them.

[0771] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0772] In this invention, the server includes means for receiving inquiries and sentiment data from a user terminal, means for generating prompt statements and collecting responses using multiple information generating devices, and means for evaluating each response based on its reliability, neutrality, user preferences, and user sentiment. This makes it possible to provide the most appropriate information in a way that takes the user's sentiment into consideration.

[0773] A "user terminal" is an electronic device used by users to input and receive information, and it is a device that collects and communicates input data and emotional data.

[0774] "Emotional data" refers to information that indicates a user's psychological state and emotions, obtained from their voice, facial expressions, and text.

[0775] An "information generation device" refers to an algorithm or platform that generates an appropriate response based on a received prompt message.

[0776] A "prompt statement" is an instruction statement used to convey specific questions or requests to an information generating device.

[0777] "Reliability" is an indicator that shows the accuracy and credibility of information and its sources.

[0778] "Neutrality" refers to a state in which the information provided is free from bias and prejudice, and is fair.

[0779] "User preferences" refer to the characteristics of information that reflect the individual preferences and interests of users.

[0780] An "emotion engine" is an analytical system that recognizes emotions from user input data and performs appropriate information processing.

[0781] "Optimization" is the process of adjusting systems and processes to provide information in a way that best suits the user's needs and emotions.

[0782] One embodiment of this invention is an information provision system consisting of a user terminal, a server, and multiple information generation devices.

[0783] The user first inputs topics of interest in text format via the device. Simultaneously, emotional data such as facial expressions can be acquired using voice input or the camera. The device has software installed for collecting emotional data, providing the necessary information for the emotion engine.

[0784] The server analyzes text and sentiment data received from the terminal. Natural language processing and speech / image analysis technologies are used for the analysis. This allows for a detailed analysis of the user's questions and emotional state. The server then generates prompt sentences suitable for the AI ​​model based on the analyzed data and sends them to the information generation device. At this stage, prompt sentences such as "Please tell me how to cope when I feel stressed in a new environment" are used.

[0785] The information generation device uses a generation AI model to generate the optimal response based on the received prompt. This AI model processes data using machine learning algorithms and creates responses that take diverse information sources into consideration.

[0786] The server evaluates the responses collected from the information generation device. Evaluation criteria include reliability, neutrality, user preference, and emotional state analysis by an emotion engine. Based on the evaluation, the most appropriate response is selected and provided as feedback to the user.

[0787] Ultimately, the device presents the user with the answers received from the server. This process uses user-friendly language and screen design that considers the user's emotions, making the information accessible and user-friendly. This enables more personalized information delivery and improves user satisfaction.

[0788] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0789] Step 1:

[0790] Users use a device to input topics or questions they are interested in. Input is primarily in text format, but voice input is also possible. The device also captures the user's facial expressions using its camera. The collected data is processed as information necessary to infer the user's emotions. Input data includes specific questions such as "How can I alleviate anxiety about a new job?" and facial expression images.

[0791] Step 2:

[0792] The device sends collected text and sentiment data to the server. The transmitted data includes text, audio, and image information. To analyze this data, the server uses natural language processing to understand the input text and audio / image analysis to recognize emotions. The analysis clarifies the question content and emotional state.

[0793] Step 3:

[0794] The server generates prompt messages based on the analysis results and sends them to multiple information generation devices. These prompt messages are in a format that is easy for the generating AI model to understand. For example, a generated prompt message might be, "Please tell me how to cope when I feel stressed in a new environment." The generation process takes into account the user's emotional state and selects appropriate words.

[0795] Step 4:

[0796] The information generation device processes the received prompt message and generates an answer using a generative AI model. The generative AI model uses machine learning algorithms to learn from a large amount of data before deriving the optimal answer. The generated answer is based on diverse information sources, ensuring high reliability.

[0797] Step 5:

[0798] The server collects responses from information generators and evaluates each response based on its reliability, neutrality, user preference, and emotional state. The evaluation process includes reliability analysis based on data from credible sources and past performance, as well as neutrality assessments such as checking for inconsistencies between pieces of information.

[0799] Step 6:

[0800] The server selects the most appropriate response and sends it to the user's terminal in an emotionally sensitive manner. The selected response is prioritized and delivered to the terminal because it is appropriate for the user's emotional state.

[0801] Step 7:

[0802] The device presents the received responses to the user. The presentation process employs friendly language and design, making the content relatable and emotionally receptive to the user. This allows the user to receive personalized support.

[0803] (Application Example 2)

[0804] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0805] While there is a growing need to provide information that takes user emotions into consideration, conventional information delivery systems have the challenge of not being able to effectively recognize users' emotional states and customize information appropriately based on them. This challenge may lead to a decrease in the quality of information users receive and their satisfaction with it.

[0806] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0807] In this invention, the server includes means for receiving inquiries from a user terminal, means for collecting responses from multiple information generating devices, and means for recognizing the user's emotional state and selecting the most appropriate response. This enables the optimization and customization of information according to the user's emotional state.

[0808] A "user terminal" is a device used by a user to input information or to display received information.

[0809] "Means for receiving inquiries" refers to a method or device that has the function of receiving information requests transmitted from a user terminal.

[0810] An "information generation device" is a computer system or program that generates relevant answers or information based on received inquiry information.

[0811] "Means for collecting responses" refers to a method or device that has the function of collecting responses generated from multiple information generating devices.

[0812] "Reliability" is a criterion for evaluating whether information is accurate and based on evidence.

[0813] "Neutrality" is a standard used to evaluate whether information is free from bias and contradictions, and is fair.

[0814] "User preferences" are criteria for evaluating information based on the user's preferences and past selection history.

[0815] "User emotional state" refers to the emotional state analyzed from the user's voice and facial expression data.

[0816] "Voice and facial expression data" refers to digital information collected from the user's words and facial expressions.

[0817] "Means for analyzing emotions" refers to a method or device that has the function of interpreting and recognizing a user's emotions using voice and facial expression data.

[0818] "Means for customizing information" refers to a method or device that has the function of adjusting the information provided and how it is presented based on the user's emotional state.

[0819] This invention realizes a system for recognizing user emotions and optimizing information provision based on those emotions. The user begins by inputting information through a user terminal. This terminal is equipped with a microphone for voice input and a camera for facial expression analysis, and captures the user's voice and facial expression data in real time. This data collected by the terminal is transmitted to a server.

[0820] The server uses Google Cloud's Speech-to-Text API to convert audio data into text format, and also uses Microsoft Azure's Face API to analyze facial expression data. The emotion engine on the server analyzes the user's emotional state from this text and facial expression information.

[0821] Based on the analysis of the user's emotional state, the server sends queries to multiple information generators to collect relevant information and responses. The collected responses are evaluated based on the user's emotional state, reliability, neutrality, and preferences, and the most relevant information is selected. This optimized information is then sent to the user's terminal in user-friendly language and presented to the user.

[0822] For example, if a user asks the device, "Tell me how to relax," the system uses its emotion engine to understand that the user is stressed. Then, using a generative AI model, it suggests the most effective relaxation methods for the user and provides specific advice such as "Take a bath" or "Do some light stretching." An example of a prompt might be, "The user is expressing fatigue through their facial expression and voice. Please suggest some simple ways to relax."

[0823] In this way, users can receive customized information based on their own emotions, enabling a more satisfying information delivery compared to conventional systems.

[0824] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0825] Step 1:

[0826] The user inputs questions and requests for information through the terminal. During this process, the terminal simultaneously captures both voice and facial expression data. Input includes voice commands and questions, as well as visual data from facial expressions. This data is then sent directly to the server.

[0827] Step 2:

[0828] The server converts the received audio data into text using Google Cloud's Speech-to-Text API. In this step, the audio itself is the input, and the output is in text format. This conversion clarifies the user's intent and the content of the question.

[0829] Step 3:

[0830] The server uses Microsoft Azure's Face API to analyze the user's emotional state from the received facial expression data. This process takes visual data as input and emotional characteristics as output. This analysis makes it possible to understand the user's emotional state.

[0831] Step 4:

[0832] The server sends queries to multiple information generators based on text data and sentiment features. The input here is the transformed text and sentiment features, and the responses from the information generators are collected as output. Data distribution and response collection gather data from diverse sources.

[0833] Step 5:

[0834] The server evaluates the collected responses based on the user's emotional state, reliability, neutrality, and preferences. Inputs include responses from information generators and user emotional and preference data. Outputs are the responses deemed most appropriate. This evaluation process selects information optimized for the user.

[0835] Step 6:

[0836] The server formats the selected information in user-friendly language and sends it to the user's terminal. The input in this step is an optimized response, and the output is text displayed to the user. The server presents information in a way that is sensitive to the user's emotions.

[0837] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0838] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0839] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0840] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0841] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0842] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0843] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0844] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0845] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0846] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0847] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0848] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0849] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0850] 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.

[0851] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0852] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0853] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0854] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0855] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0856] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0857] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0858] The following is further disclosed regarding the embodiments described above.

[0859] (Claim 1)

[0860] A means of receiving inquiries from user terminals,

[0861] A means of collecting responses from multiple information generating devices,

[0862] A means of evaluating each response based on its reliability, neutrality, and user preferences,

[0863] The means of selecting the most appropriate answer,

[0864] A means of sending the selected answer to the user's terminal,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, wherein the evaluation includes an analysis of reliability based on information sources and past performance.

[0868] (Claim 3)

[0869] The system according to claim 1, wherein the evaluation includes means for analyzing neutrality by detecting inconsistencies between pieces of information.

[0870] "Example 1"

[0871] (Claim 1)

[0872] A means for receiving inquiries from user devices,

[0873] A means for analyzing inquiries and extracting relevant topics and phrases,

[0874] A means of relaying questions to multiple information generation devices,

[0875] A means of collecting responses from multiple information generation devices,

[0876] A means of evaluating each response based on its reliability, neutrality, and user preference,

[0877] A means of auditing conflicting information and contradictions as consistency,

[0878] The means of selecting the most appropriate answer,

[0879] A means for transmitting the selected answers to the user's device in a visual format,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, wherein the evaluation includes an analysis of reliability based on information sources and past operations.

[0883] (Claim 3)

[0884] The system according to claim 1, wherein the evaluation includes means for analyzing neutrality by detecting inconsistencies between pieces of information.

[0885] "Application Example 1"

[0886] (Claim 1)

[0887] A means of receiving inquiries from user terminals,

[0888] A means for collecting responses from multiple data generation devices,

[0889] A means for evaluating each response based on its reliability, neutrality, and user preference,

[0890] A method for presenting the reliability of information regarding payment methods in a ranking format,

[0891] A means of visually sending the selected response to the user terminal,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, wherein the evaluation includes an analysis of reliability based on information sources and past performance.

[0895] (Claim 3)

[0896] The system according to claim 1, wherein the evaluation includes means for analyzing neutrality by detecting inconsistencies between pieces of information.

[0897] "Example 2 of combining an emotion engine"

[0898] (Claim 1)

[0899] A means for receiving inquiries and sentiment data from user terminals,

[0900] A means for generating prompt sentences using multiple information generation devices and collecting responses,

[0901] A means of evaluating each response based on its reliability, neutrality, user preferences, and user sentiment,

[0902] A means of selecting the most appropriate answer while taking the user's feelings into consideration,

[0903] A means of presenting the selected answer to the user's terminal in an emotionally sensitive manner,

[0904] A system that includes this.

[0905] (Claim 2)

[0906] The system according to claim 1, wherein the evaluation includes a reliability analysis based on information sources and past performance, and an evaluation based on the user's emotional state.

[0907] (Claim 3)

[0908] The system according to claim 1, wherein the evaluation includes detecting inconsistencies between pieces of information, analyzing neutrality, and further including sentiment analysis by an emotion engine.

[0909] "Application example 2 when combining with an emotional engine"

[0910] (Claim 1)

[0911] A means of receiving inquiries from user terminals,

[0912] A means of collecting responses from multiple information generating devices,

[0913] A means of evaluating each response based on its reliability, neutrality, and user preferences,

[0914] A means of recognizing the user's emotional state and selecting the most appropriate response,

[0915] A means of sending the selected answer to the user's terminal,

[0916] A means of analyzing emotions using voice and facial expression data,

[0917] A means of customizing information based on analyzed sentiment data,

[0918] A system that includes this.

[0919] (Claim 2)

[0920] The system according to claim 1, wherein the evaluation and selection include analysis based on emotional information, including the user's voice and facial expression data.

[0921] (Claim 3)

[0922] The system according to claim 1, comprising means for presenting information in friendly language that corresponds to emotional data. [Explanation of Symbols]

[0923] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving inquiries from user terminals, A means for collecting responses from multiple data generation devices, A means for evaluating each response based on its reliability, neutrality, and user preference, A method for presenting the reliability of information regarding payment methods in a ranking format, A means of visually sending the selected response to the user terminal, A system that includes this.

2. The system according to claim 1, wherein the evaluation includes an analysis of reliability based on information sources and past performance.

3. The system according to claim 1, wherein the evaluation includes means for analyzing neutrality by detecting inconsistencies between pieces of information.

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