system

Abstract

We provide the system. [Solution] A means of obtaining the questions entered by the user in text format, A generating AI means for analyzing the acquired text and extracting related keywords, A method for searching and listing web services based on extracted keywords, A means of proposing the aforementioned listed services to the user, A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 the modern era of information overload, it is difficult for users to find the optimal service or tool for a specific problem they face. In searches using conventional search engines, a lot of time is spent and it is often impossible to reach the optimal solution. Also, even when using review sites etc., there is a problem that it is impossible to easily find highly relevant services due to the vast amount of information.

Means for Solving the Problems

[0005] This invention provides a system that searches for and lists web-based services by acquiring a problem entered by the user in text format, analyzing it with a generative AI, and extracting relevant keywords. This allows for the rapid and accurate suggestion of the most suitable service to the user. Furthermore, by acquiring user feedback on the suggested services and incorporating it into the generative AI's learning process, the accuracy of the suggestions can be continuously improved. In addition, by providing the user with detailed information and evaluations, the system supports more accurate selection.

[0006] A "user" is an individual or legal entity that seeks services or tools to solve problems using the system.

[0007] "Text format" refers to a data format of text or strings of characters that a user uses to describe a problem.

[0008] "Generative AI" refers to artificial intelligence technology that analyzes user input text and extracts relevant keywords from it.

[0009] "Related keywords" are words or phrases that the AI ​​generates based on the user's input of the problem statement and deems important.

[0010] "Web-based services" refer to platforms for software and applications provided via the internet.

[0011] "Listing" is the act of extracting and listing related items based on specific criteria.

[0012] A "proposal" is the act of presenting a user with a service or tool that is considered optimal for solving their problem.

[0013] "Feedback" refers to the act of users providing information such as their impressions, evaluations, and suggestions for improvement after using a service.

[0014] "Accuracy" is an indicator that shows how accurately an AI or system can understand a user's problem and propose an optimal service.

[0015] "Detailed information" refers to information indicating specific contents and features regarding the proposed service.

[0016] "Evaluation" refers to data representing satisfaction levels and evaluation criteria for services provided by other users or users.

Brief Explanation of Drawings

[0017] [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 Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the 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.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is a system that proposes the most suitable web-based services and tools to solve problems that users face in their daily lives and work. The following describes an effective implementation of this system.

[0039] The system consists of a user terminal, a server, and network communication infrastructure. In a specific implementation, the user first inputs their problem in text format through the terminal's interface. The terminal has the functionality to send this input text to the server.

[0040] The server receives this text data and analyzes it using a generative AI equipped with natural language processing technology. The generative AI extracts relevant keywords from the text and identifies key elements related to the problem. Then, based on the extracted keywords, the server uses web-based databases and APIs to search for and list appropriate services and tools.

[0041] Next, the terminal suggests highly relevant services received from the server to the user. These suggestions include detailed information about each service, user ratings, and reviews. This allows the user to select the service best suited to their needs.

[0042] After using the service, users can provide feedback. The device sends this feedback to the server, which uses the received data to improve the AI's algorithm and enhance the accuracy of future suggestions.

[0043] For example, if a user inputs a problem such as "I want to manage my time efficiently," the generating AI will extract keywords such as "time management," "efficiency," and "tools." Based on this, the server will list services such as "online calendars" and "task management apps" and send them to the user's device. The user can then review the suggested services, select the most suitable tool, and use it. In this way, the present invention provides quick and accurate solutions to specific problems faced by users, supporting improvements in work efficiency and daily life.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user enters the problem they want to solve in text format through the terminal's input interface. The terminal temporarily stores this user input as data.

[0047] Step 2:

[0048] The terminal initiates communication to send the entered text data to the server. The server receives the data sent from the terminal and prepares it for analysis.

[0049] Step 3:

[0050] The server inputs the received text into the generating AI, which then analyzes the document using natural language processing technology. The generating AI then uses a "keyword extraction algorithm" to identify relevant keywords from the text.

[0051] Step 4:

[0052] Based on the extracted keywords, the server searches for related services and tools using web-based databases and external APIs. The server then filters and lists the most relevant results.

[0053] Step 5:

[0054] The server sends information about the listed services and tools to the terminal. This information includes service details, ratings, and reviews.

[0055] Step 6:

[0056] The terminal displays a list of services received from the server to the user. The user can review the list and select the service that best suits them.

[0057] Step 7:

[0058] After using the service the user selected, they input their experience using the feedback interface. The device then prepares to send this feedback to the server.

[0059] Step 8:

[0060] The device sends feedback data to the server, which uses this data to improve the algorithm of the generating AI. This improves the accuracy of future suggestions.

[0061] (Example 1)

[0062] 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."

[0063] Traditionally, there was no efficient system to quickly find the optimal solutions to the problems users faced. Users had to search for information themselves and individually check the details of each service, which consumed time and effort. Furthermore, the process of users verifying the effectiveness of the solutions they obtained was also done individually, and there were few opportunities to utilize the results in the future. As a result, the accuracy of service selection and use did not improve, and overall efficiency did not improve.

[0064] 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.

[0065] In this invention, the server includes means for acquiring user-inputted problems in character data format, an automatic generation function for analyzing the acquired character data and extracting relevant information, and means for searching for and listing functions on the computer network based on the extracted information. This makes it possible to quickly and efficiently propose the most suitable service for solving the user's problem and to improve the accuracy of the service proposal based on subsequent feedback.

[0066] A "user" refers to an individual or organization that uses a system to solve a problem.

[0067] "Character data format" refers to a format that represents digital information as text or symbols.

[0068] "Means of acquisition" refers to methods and devices for receiving and storing information provided by users.

[0069] The "automatic generation function" is a mechanism that uses machine learning and artificial intelligence technologies to analyze input data and generate relevant information.

[0070] "Extracting relevant information" refers to the process of identifying and extracting important elements and keywords from the input information.

[0071] "Functions on a computer network" refers to various programs and services provided on digital networks such as the internet.

[0072] "Means of searching and listing" refers to methods of finding and organizing information within a target digital network and presenting it in a usable format.

[0073] "Detailed information" refers to information that includes specific descriptions and attributes of a particular service or feature.

[0074] "Evaluation" refers to a judgment made about a particular service or product based on feedback gathered from users and the market.

[0075] The system of this invention aims to propose the optimal service to solve problems faced by users. This system consists of the user's terminal, a server, and network communication infrastructure.

[0076] The user inputs their problem in text format using a terminal. The terminal has the function to send this input data to the server. In this process, it is possible to use a keyboard or voice input device for text input, taking user usability into consideration.

[0077] The server uses a generative AI model to analyze the received text data. Specifically, the software used is an AI model incorporating natural language processing technology; for example, open-source machine learning libraries (e.g., TENSORFLOW®, PyTorch) can be utilized. The server uses this model to extract important information from the text and identify keywords related to the problem. A possible prompt might be something like, "Tell me about tools to improve my time management efficiency."

[0078] Next, the server uses internet databases and APIs based on the extracted keywords to find and list highly relevant services. This search can utilize publicly known search engine technologies, allowing for rapid information retrieval.

[0079] The listed services are sent back from the server to the terminal and suggested to the user. The user can then select and use the most suitable service by referring to the detailed information and ratings provided on the terminal.

[0080] After using the service, users can provide feedback. The terminal sends this feedback to the server, which uses this data to improve the algorithms of the generated AI model. This improves the accuracy of future suggestions and increases the overall value of the system.

[0081] Thus, the present invention offers accurate and appropriate solutions to specific problems faced by users, significantly improving user convenience and operational efficiency.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The user enters their problem in text format using a terminal. A specific example of this problem is the prompt "I want to manage projects efficiently." The entered text data is checked on the terminal and then sent to the server.

[0085] Step 2:

[0086] The terminal sends text data received from the user to the server. A secure communication protocol (e.g., HTTPS) is used to safely transmit the input data to the server. The input here is text data, and the output is a confirmation of receipt by the server.

[0087] Step 3:

[0088] The server analyzes the received text data. A generative AI model is used for the analysis, analyzing the strings using natural language processing techniques. In this step, the server receives the text data as input and extracts relevant keywords such as project, efficiency, and management as output.

[0089] Step 4:

[0090] The server searches for services on the computer network based on the extracted keywords. Specifically, it searches web databases and APIs to retrieve relevant online tools and applications. Through this process, it uses the keywords obtained as input to create a list of candidate services as output.

[0091] Step 5:

[0092] The server sends a list of services to the terminal. The terminal receives this information and generates the necessary interface to present it to the user. The input is a list of services, and the output is visual suggestions for the user.

[0093] Step 6:

[0094] Users review the details of the services suggested through their device and select and use the most suitable service. A feature is also provided for users to provide feedback, allowing them to input information about their experience and the effectiveness of the services they accessed.

[0095] Step 7:

[0096] The terminal sends user feedback to the server. The server uses the received feedback to improve the algorithm of the generating AI, helping to improve the accuracy of future service suggestions. The input in this step is feedback data, and the output is improvement data for improving the accuracy of future suggestions.

[0097] (Application Example 1)

[0098] 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."

[0099] In modern urban life, residents face a wide range of problems and challenges, and there is a need to quickly provide appropriate services and tools to efficiently resolve them. In particular, there is a lack of mechanisms that enable residents to respond quickly and appropriately to issues such as traffic congestion, the use of public services, and waste disposal. This leads to a decline in residents' quality of life and a decrease in the overall efficiency of the city.

[0100] 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.

[0101] In this invention, the server includes means for acquiring user-inputted problems in data format, generative AI means for analyzing the acquired data and extracting related concepts, and means for searching and organizing internet services based on the extracted concepts. This makes it possible to quickly and accurately propose relevant services and means to residents facing a variety of problems.

[0102] "Means of acquiring user-inputted problems in data format" refers to a system that collects the challenges and questions that users are facing as information and converts them into digital data.

[0103] "Generative AI means for analyzing the acquired data and extracting related concepts" refers to a function that includes a process of processing collected digital data using artificial intelligence technology to identify important concepts related to the problem.

[0104] "A means of searching and organizing internet services based on extracted concepts" refers to a system that uses AI to identify concepts, explore various online services, and present them in a format suitable for the user.

[0105] "Means of proposing the aforementioned organized services to users" refers to display methods introduced to efficiently present useful information and services to users and to support problem-solving.

[0106] "Means for collecting user selection information regarding the aforementioned proposal and optimizing the system for urban management purposes" refers to a method of utilizing data to improve urban management systems by accumulating user-selected services and feedback.

[0107] The system for realizing this invention utilizes a user terminal, a server, and a network to efficiently solve user problems. The user first uses the terminal to input the problem in text format. This data is then transmitted to the server via the network.

[0108] The server analyzes the received data using an advanced generative AI model and extracts relevant concepts. The generative AI leverages natural language processing techniques to recognize key keywords from the data and understand the essence of the problem. Subsequently, based on the extracted concepts, it automatically searches for and organizes relevant services on the internet. This process includes utilizing a database via Web APIs.

[0109] Next, the server sends the organized information to the user terminal. The user terminal has an interface that displays the proposed services in an easy-to-understand format. This interface allows the user to view detailed information about individual services and reviews from other users.

[0110] Furthermore, user behavior data and feedback are sent back to the server and used for data analysis there. This information is used to improve the generative AI algorithm and enhance the accuracy of future suggestions.

[0111] For example, if a user wants to shorten their commute time, the AI ​​can extract concepts such as "commute," "efficiency," and "transportation," and suggest transportation information apps or route suggestion services. Examples of prompts include questions like, "What keywords should be extracted to efficiently solve the user's problem?" and "How should related services be organized and presented to the user?"

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user uses a terminal to input their problem in text format. The entered text data contains information about the nature and details of the problem and is sent directly to the server over the network.

[0115] Step 2:

[0116] The server passes the received text data to a generating AI model, which analyzes the data using natural language processing. In this process, the AI ​​extracts relevant concepts and keywords. Based on the input text data, the AI ​​understands the context and identifies key elements for problem solving.

[0117] Step 3:

[0118] The server uses the extracted keywords to search for relevant services and tools on the internet. It gathers information from external databases and service providers via Web APIs, organizing the most relevant options. This generates a list of the most suitable online services.

[0119] Step 4:

[0120] The server sends a list of generated services as data to the terminal. The terminal receives this data and displays it as a list on the user interface. The user can view detailed information and user ratings for each displayed service.

[0121] Step 5:

[0122] The user selects the services they wish to use from those presented and sends this selection information and feedback from their device to the server. The server receives this data and uses it as input to update the algorithm of the generating AI. This feedback improves the AI's learning capabilities and enhances the accuracy of future suggestions.

[0123] By following these steps, a system is created that efficiently provides users with the optimal solutions and tools for the problems they face.

[0124] 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.

[0125] This invention aims to provide a more comprehensive problem-solving experience by combining an emotion engine with a system that proposes the most suitable web-based service for a user's problem. A specific embodiment is shown below.

[0126] First, the user enters their problem in text format through the terminal's interface. The user's input is sent from the terminal to a server, where a generative AI and emotion engine analyze the accumulated data.

[0127] On the server, the input text data is analyzed using natural language processing by a generative AI, and relevant keywords are extracted. Simultaneously, an emotion engine identifies the user's emotions from the text, recognizing emotional information such as "stress" or "excitement."

[0128] Based on recognized keywords and emotional information, the server searches the web for and lists relevant services and tools. Emotional information is particularly used to tailor services to the user, ensuring that services are effectively selected according to the user's emotional state.

[0129] Next, the terminal presents the user with suggestions, including detailed information and reviews, based on the service information received from the server. The user can review this information and select a service that suits their feelings and needs.

[0130] After using the service, users can provide feedback through their device. This feedback evaluates the effectiveness of the service in solving problems and indicates how the service provided affected the user's emotions. The device sends this feedback to the server, which is used to further improve the emotion engine and generative AI algorithms.

[0131] For example, if a user inputs "I'm feeling stressed about a recent project," the emotion engine recognizes the emotion "stress." The generation AI extracts keywords such as "project management" and "stress reduction," and the server lists relaxation techniques and project management tools. The service suggestions are focused on supporting the user's stress reduction. By using the suggested services and providing feedback, the overall system performance can be improved.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user uses a device to access an interface where they can input their problems in text format. For example, the user might enter a specific problem such as "How to reduce stress in a project."

[0135] Step 2:

[0136] The terminal sends the entered text data to the server. The server receives this data and prepares it for analysis.

[0137] Step 3:

[0138] The server uses a generation AI to analyze the received text. Natural language processing technology is used to extract keywords such as "stress reduction" and "project management" from the text.

[0139] Step 4:

[0140] Simultaneously, the server uses an emotion engine to recognize emotions within the text. In this case, the emotion "stress" is identified from the user's input.

[0141] Step 5:

[0142] Based on the extracted keywords and recognized emotions, the server searches for and lists relevant services and tools on the web. For example, it might select "mindfulness apps" and "project management tools."

[0143] Step 6:

[0144] The server sends detailed information and ratings about the listed services to the terminal. The terminal then displays this information to the user.

[0145] Step 7:

[0146] Users select the service that best suits them from those presented on their device and access each service through the provided link.

[0147] Step 8:

[0148] After using the service, users provide feedback on the service's effectiveness and changes in their feelings through the feedback interface on their device.

[0149] Step 9:

[0150] The device sends user feedback to the server. The server collects this data and uses it to improve the algorithms of its generative AI and emotion engine. This improves the accuracy of future suggestions and enables the delivery of more user-centric services.

[0151] (Example 2)

[0152] 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".

[0153] In modern society, it is difficult for users to find information and services that meet their needs from the vast amount of information available online. Furthermore, systems capable of providing appropriate services tailored to users' emotional states are limited. Therefore, there is a need for a system that offers effective and emotionally sensitive service suggestions to solve users' problems.

[0154] 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.

[0155] In this invention, the server includes means for acquiring user input as text data, generation AI means for analyzing the acquired text data and extracting relevant words and phrases, means for identifying the user's emotions from the text data, means for searching and listing services on information resources based on the extracted words and phrases and identified emotions, and means for adjusting and presenting the listed services according to the user's emotions. This makes it possible to efficiently solve the user's problems while taking their emotions into consideration.

[0156] A "user" is an individual or organization that uses an information system to solve a problem.

[0157] A "problem" refers to an issue or need that a user requires to be resolved.

[0158] "Text data" refers to information in text format that is entered by the user.

[0159] "Generative AI means" refers to technologies that use natural language processing techniques to analyze text data and extract related words and phrases.

[0160] "Means for identifying emotions" refers to technologies for identifying emotional information from a user's text data.

[0161] "Information resources" refer to a collection of services and tools that are accessible on the web or other digital platforms.

[0162] "Methods for searching and listing services" refers to technologies that have the functionality to find appropriate services and create a list based on extracted related keywords and identified sentiment information.

[0163] "Means of adjustment and presentation" refers to technologies that appropriately customize service suggestions based on the user's emotional information and then present them to the user.

[0164] This invention is a system that proposes appropriate services in response to a problem entered by the user. The user enters the problem as text data using an interface on the terminal. For example, the user can enter text information such as "I've been feeling stressed at work lately."

[0165] The terminal sends this text data to the server. The server first analyzes the text data using a generative AI model and extracts relevant words and phrases using natural language processing. For example, an existing natural language processing library could be used as the generative AI model. The extracted words and phrases might include "work" and "stress reduction."

[0166] Next, the server uses an emotion recognition engine to identify the user's emotions from the text data. This emotion recognition can utilize an emotion analysis model that employs machine learning algorithms. The identified emotion is "stress."

[0167] Subsequently, the server searches the internet for and lists relevant services based on the identified terms and sentiment information. This process can be efficiently carried out using a search engine API. The listed services are further tailored to the user's specific needs based on the sentiment information.

[0168] The terminal receives this service information sent from the server and presents it to the user. Ideally, this information should include detailed descriptions of each service and user ratings.

[0169] After using the service, users can provide feedback through their device. This feedback is sent to the server and used to improve the algorithms of the generative AI model and sentiment recognition engine in order to further enhance user benefits.

[0170] For example, if a user feels depressed due to lack of exercise, the server will suggest appropriate services based on phrases like "lack of exercise" and "improve mood." Examples include fitness programs and mental health apps. An example of a prompt would be: "If a user feels they want to relax by traveling, what suggestions should be made? Use the emotion engine and generative AI to suggest the best travel plan and relaxation methods." In this way, users can receive services tailored to their emotions and circumstances, thereby resolving their problems.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user inputs their problem as text data through the terminal's interface. In this case, the input would be text in the format of "I've been feeling stressed at work lately." The terminal receives this text data and prepares to send it to the server. The output is the text data sent to the server.

[0174] Step 2:

[0175] The terminal sends the character data entered by the user to the server. To ensure data is securely transmitted over the network, encryption protocols are used. The input is the character data processed on the terminal, and the output is this data that reaches the server.

[0176] Step 3:

[0177] The server passes the received text data to a generating AI model, which analyzes it through natural language processing. Specifically, it tokenizes the text data and extracts keywords. The input is the text data sent to the server, and the output is related words such as "work" and "stress reduction."

[0178] Step 4:

[0179] The server uses an emotion recognition engine to identify the user's emotions. This process involves the execution of machine learning algorithms to determine the emotion category. The input is text data, and the output identifies "stress" as emotion information.

[0180] Step 5:

[0181] The server searches for and lists appropriate services from internet information resources based on words extracted by a generative AI model and emotions identified by an emotion recognition engine. Using an API, highly relevant services are efficiently extracted. Input is related words and emotion information, and output is a list of services.

[0182] Step 6:

[0183] The terminal makes suggestions to the user based on service information received from the server. These suggestions include detailed information and evaluations of each service, allowing the user to select the most appropriate service according to their situation. The input is service information provided by the server, and the output is the service suggestions to the user.

[0184] Step 7:

[0185] Users can use the presented service and then provide feedback by entering an evaluation. The evaluation is entered via the terminal and sent back to the server. The input is the user's feedback, and the output is the evaluation data sent to the server.

[0186] Step 8:

[0187] The server receives feedback from users and uses it to improve the algorithms of its generative AI model and sentiment recognition engine. In this process, the collected evaluation data is used as training data for the model, resulting in more accurate service recommendations. The input is user feedback data, and the output is the improved algorithm.

[0188] (Application Example 2)

[0189] 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".

[0190] In modern e-commerce, it is difficult to suggest appropriate services and payment methods that align with the user's psychological state. As a result, users may experience unnecessary stress or miss out on optimal choices. Furthermore, existing systems are insufficient in considering user emotions when making suggestions, making it difficult to provide a highly satisfying purchasing experience.

[0191] 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.

[0192] In this invention, the server includes means for acquiring user-inputted questions in text format, generation AI means for analyzing the acquired text and extracting relevant information units, means for searching and listing functions on the data network based on the extracted information units, and sentiment analysis means for identifying the user's emotions and adjusting the optimal payment option. This enables the selection of optimal payment options and functions that are in line with the user's emotional state.

[0193] A "user" is an individual or group that uses the system to have problems solved or to receive service suggestions.

[0194] "Text format" refers to string data written in natural language, and is the format in which users input problems and requests.

[0195] An "information unit" refers to keywords or important concepts extracted from text data provided by the user.

[0196] "Generative AI means" refers to artificial intelligence technology that analyzes text input from users and extracts relevant information units.

[0197] A "data network" refers to the infrastructure that provides information and services through the internet, internal networks, and other means.

[0198] "Functionality" is a concept that encompasses services, tools, and options provided on a data network.

[0199] "Emotional analysis methods" refer to technologies that identify a user's psychological state based on text data.

[0200] "Payment options" refer to the various payment methods offered to users for purchases and transactions, including, for example, installment payments.

[0201] This system begins with users entering problems and requests in text format through their terminals. The data entered by the user is sent to the server in real time. The server analyzes the text data using a generative AI model and extracts relevant information units. Natural language processing techniques are utilized at this stage, with Python and TensorFlow being used.

[0202] Next, the server identifies the user's emotional state using emotion analysis tools. This process is performed using Microsoft® Azure® Cognitive Services. Emotion analysis generates keywords corresponding to emotions such as "stress" and "reassurance." The extracted information units and emotion information are used to list the most suitable functions and payment options on the data network. The listed results are sent to the user's terminal and presented as suggestions.

[0203] In particular, the optimal payment option is adjusted based on the user's emotions identified by emotion analysis tools. For example, if a user feels anxious about purchasing an expensive item, installment payment options or cashback plans can be suggested. This allows users to choose a payment method that suits their emotional state, resulting in a highly satisfying purchasing experience.

[0204] As a concrete example, a user might be considering a large payment to purchase a car. In this case, the user enters the text "car purchase" and expresses the emotion of "anxiety." Based on this, a prompt message such as "Please adjust your installment payment plan" is generated, and the most suitable payment method is suggested to the user.

[0205] An example of a prompt message is, based on the text input "I bought a new TV, but I'm worried about the expense," a prompt like "Please suggest a payment plan that will give me peace of mind."

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] Users input problems or requests in text format through their devices. The entered text is converted into data format as "user-entered requests or questions." This data is then sent to the server for further processing.

[0209] Step 2:

[0210] The server inputs the received text data into a generating AI model. This model uses a natural language processing module based on Python and TensorFlow. The AI ​​extracts important information units from the text. Specifically, it extracts relevant words and phrases as "keywords representing the user's requests." These information units are then used in the next processing step.

[0211] Step 3:

[0212] The server uses sentiment analysis tools to identify the user's emotions from the extracted text. This process utilizes Microsoft Azure Cognitive Services to determine the user's psychological state. Simultaneously, it generates keywords related to that emotion. The output is information indicating the user's emotions.

[0213] Step 4:

[0214] The server combines extracted information units with sentiment information to search for the most suitable functions and services available on the data network. Specifically, it lists candidates that match the user's requirements and creates a "list of optimal services."

[0215] Step 5:

[0216] A list of these services is sent to the user's device, allowing them to choose the appropriate service and payment option from the provided choices. This includes emotionally-based payment option suggestions, along with a "most suitable payment method" being presented.

[0217] Step 6:

[0218] The system provides feedback on the services and options selected by the user. This feedback is sent to the server via the device and stored as reference data to improve the generation AI and sentiment analysis methods. "User selections and feedback" are input, enabling system improvements.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention is a system that proposes the most suitable web-based services and tools to solve problems that users face in their daily lives and work. The following describes an effective implementation of this system.

[0236] The system consists of a user terminal, a server, and network communication infrastructure. In a specific implementation, the user first inputs their problem in text format through the terminal's interface. The terminal has the functionality to send this input text to the server.

[0237] The server receives this text data and analyzes it using a generative AI equipped with natural language processing technology. The generative AI extracts relevant keywords from the text and identifies key elements related to the problem. Then, based on the extracted keywords, the server uses web-based databases and APIs to search for and list appropriate services and tools.

[0238] Next, the terminal suggests highly relevant services received from the server to the user. These suggestions include detailed information about each service, user ratings, and reviews. This allows the user to select the service best suited to their needs.

[0239] After using the service, users can provide feedback. The device sends this feedback to the server, which uses the received data to improve the AI's algorithm and enhance the accuracy of future suggestions.

[0240] For example, if a user inputs a problem such as "I want to manage my time efficiently," the generating AI will extract keywords such as "time management," "efficiency," and "tools." Based on this, the server will list services such as "online calendars" and "task management apps" and send them to the user's device. The user can then review the suggested services, select the most suitable tool, and use it. In this way, the present invention provides quick and accurate solutions to specific problems faced by users, supporting improvements in work efficiency and daily life.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The user enters the problem they want to solve in text format through the terminal's input interface. The terminal temporarily stores this user input as data.

[0244] Step 2:

[0245] The terminal initiates communication to send the entered text data to the server. The server receives the data sent from the terminal and prepares it for analysis.

[0246] Step 3:

[0247] The server inputs the received text into the generating AI, which then analyzes the document using natural language processing technology. The generating AI then uses a "keyword extraction algorithm" to identify relevant keywords from the text.

[0248] Step 4:

[0249] Based on the extracted keywords, the server searches for related services and tools using web-based databases and external APIs. The server then filters and lists the most relevant results.

[0250] Step 5:

[0251] The server sends information about the listed services and tools to the terminal. This information includes service details, ratings, and reviews.

[0252] Step 6:

[0253] The terminal displays a list of services received from the server to the user. The user can review the list and select the service that best suits them.

[0254] Step 7:

[0255] After using the service the user selected, they input their experience using the feedback interface. The device then prepares to send this feedback to the server.

[0256] Step 8:

[0257] The device sends feedback data to the server, which uses this data to improve the algorithm of the generating AI. This improves the accuracy of future suggestions.

[0258] (Example 1)

[0259] 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."

[0260] Traditionally, there was no efficient system to quickly find the optimal solutions to the problems users faced. Users had to search for information themselves and individually check the details of each service, which consumed time and effort. Furthermore, the process of users verifying the effectiveness of the solutions they obtained was also done individually, and there were few opportunities to utilize the results in the future. As a result, the accuracy of service selection and use did not improve, and overall efficiency did not improve.

[0261] 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.

[0262] In this invention, the server includes means for acquiring user-inputted problems in character data format, an automatic generation function for analyzing the acquired character data and extracting relevant information, and means for searching for and listing functions on the computer network based on the extracted information. This makes it possible to quickly and efficiently propose the most suitable service for solving the user's problem and to improve the accuracy of the service proposal based on subsequent feedback.

[0263] A "user" refers to an individual or organization that uses a system to solve a problem.

[0264] "Character data format" refers to a format that represents digital information as text or symbols.

[0265] "Means of acquisition" refers to methods and devices for receiving and storing information provided by users.

[0266] The "automatic generation function" is a mechanism that uses machine learning and artificial intelligence technologies to analyze input data and generate relevant information.

[0267] "Extracting relevant information" refers to the process of identifying and extracting important elements and keywords from the input information.

[0268] "Functions on a computer network" refers to various programs and services provided on digital networks such as the internet.

[0269] "Means of searching and listing" refers to methods of finding and organizing information within a target digital network and presenting it in a usable format.

[0270] "Detailed information" refers to information that includes specific descriptions and attributes of a particular service or feature.

[0271] "Evaluation" refers to a judgment made about a particular service or product based on feedback gathered from users and the market.

[0272] The system of this invention aims to propose the optimal service to solve problems faced by users. This system consists of the user's terminal, a server, and network communication infrastructure.

[0273] The user inputs their problem in text format using a terminal. The terminal has the function to send this input data to the server. In this process, it is possible to use a keyboard or voice input device for text input, taking user usability into consideration.

[0274] The server uses a generative AI model to analyze the received text data. Specifically, the software used is an AI model incorporating natural language processing technology; for example, open-source machine learning libraries (e.g., TensorFlow, PyTorch) can be utilized. The server uses this model to extract important information from the text and identify keywords related to the problem. A possible prompt might be something like, "Tell me about tools to improve my time management efficiency."

[0275] Next, the server uses internet databases and APIs based on the extracted keywords to find and list highly relevant services. This search can utilize publicly known search engine technologies, allowing for rapid information retrieval.

[0276] The listed services are sent back from the server to the terminal and suggested to the user. The user can then select and use the most suitable service by referring to the detailed information and ratings provided on the terminal.

[0277] After using the service, users can provide feedback. The terminal sends this feedback to the server, which uses this data to improve the algorithms of the generated AI model. This improves the accuracy of future suggestions and increases the overall value of the system.

[0278] Thus, the present invention offers accurate and appropriate solutions to specific problems faced by users, significantly improving user convenience and operational efficiency.

[0279] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0280] Step 1:

[0281] The user uses the terminal to input their problem in the form of text. As a specific example for this problem, there is a prompt sentence "want to manage projects efficiently". The input text data is confirmed on the terminal and then sent to the server.

[0282] Step 2:

[0283] The terminal sends the text data received from the user to the server. Using a secure communication protocol (e.g., HTTPS), the input data is securely sent to the server. Here, the input is text data, and the output is the receipt confirmation on the server.

[0284] Step 3:

[0285] The server analyzes the received text data. For the analysis, a generative AI model is used to analyze the character string using natural language processing technology. In this step, the text data received as input is taken, and related keywords such as project, efficiency, and management are extracted as output.

[0286] Step 4:

[0287] The server searches for services on the computer network based on the extracted keywords. As a specific operation, it searches databases and APIs on the WEB and obtains related online tools and applications. Through this process, using the keywords obtained as input, a list of candidate services is created as output.

[0288] Step 5:

[0289] The server sends the information of the listed services to the terminal. The terminal receives this and generates the interface necessary to present it to the user. The input is the list data of the services, and the output is the visual proposal information to the user.

[0290] Step 6:

[0291] Users review the details of the services suggested through their device and select and use the most suitable service. A feature is also provided for users to provide feedback, allowing them to input information about their experience and the effectiveness of the services they accessed.

[0292] Step 7:

[0293] The terminal sends user feedback to the server. The server uses the received feedback to improve the algorithm of the generating AI, helping to improve the accuracy of future service suggestions. The input in this step is feedback data, and the output is improvement data for improving the accuracy of future suggestions.

[0294] (Application Example 1)

[0295] 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."

[0296] In modern urban life, residents face a wide range of problems and challenges, and there is a need to quickly provide appropriate services and tools to efficiently resolve them. In particular, there is a lack of mechanisms that enable residents to respond quickly and appropriately to issues such as traffic congestion, the use of public services, and waste disposal. This leads to a decline in residents' quality of life and a decrease in the overall efficiency of the city.

[0297] 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.

[0298] In this invention, the server includes means for acquiring user-inputted problems in data format, generative AI means for analyzing the acquired data and extracting related concepts, and means for searching and organizing internet services based on the extracted concepts. This makes it possible to quickly and accurately propose relevant services and means to residents facing a variety of problems.

[0299] "Means of acquiring user-inputted problems in data format" refers to a system that collects the challenges and questions that users are facing as information and converts them into digital data.

[0300] "Generative AI means for analyzing the acquired data and extracting related concepts" refers to a function that includes a process of processing collected digital data using artificial intelligence technology to identify important concepts related to the problem.

[0301] "A means of searching and organizing internet services based on extracted concepts" refers to a system that uses AI to identify concepts, explore various online services, and present them in a format suitable for the user.

[0302] "Means of proposing the aforementioned organized services to users" refers to display methods introduced to efficiently present useful information and services to users and to support problem-solving.

[0303] "Means for collecting user selection information regarding the aforementioned proposal and optimizing the system for urban management purposes" refers to a method of utilizing data to improve urban management systems by accumulating user-selected services and feedback.

[0304] The system for realizing this invention utilizes a user terminal, a server, and a network to efficiently solve user problems. The user first uses the terminal to input the problem in text format. This data is then transmitted to the server via the network.

[0305] The server analyzes the data received using an advanced generative AI model and extracts relevant concepts. The generative AI utilizes natural language processing technology to recognize important keywords from the data and understand the essence of the problem. Subsequently, based on the extracted concepts, it automatically searches for and organizes relevant services on the Internet. This process involves the utilization of a database using a Web API.

[0306] Next, the server sends the organized information to the user terminal. The user terminal has an interface that displays the proposed services in an easy-to-understand manner for the user. Through this interface, the user can view the detailed information of individual services and the evaluations from other users.

[0307] Furthermore, the user's behavior data and feedback are sent back to the server and used for data analysis on the server. This information is utilized to improve the generative AI algorithm and is used to enhance the accuracy of future proposals.

[0308] As a specific example, when a user hopes to shorten their commuting time, the AI can extract concepts such as "commute", "efficiency", "traffic", and propose traffic information apps or route suggestion services. Also, examples of prompt sentences include questions such as "What keywords should be extracted to efficiently solve the user's problem?" and "How should related services be organized and proposed to the user?"

[0309] The flow of the specific process in Application Example 1 will be described using Figure 12.

[0310] Step 1:

[0311] The user uses the terminal to input their problem in text form. The input text data contains information regarding the nature and details of the problem and is directly sent to the server via the network.

[0312] Step 2:

[0313] The server passes the received text data to a generating AI model, which analyzes the data using natural language processing. In this process, the AI ​​extracts relevant concepts and keywords. Based on the input text data, the AI ​​understands the context and identifies key elements for problem solving.

[0314] Step 3:

[0315] The server uses the extracted keywords to search for relevant services and tools on the internet. It gathers information from external databases and service providers via Web APIs, organizing the most relevant options. This generates a list of the most suitable online services.

[0316] Step 4:

[0317] The server sends a list of generated services as data to the terminal. The terminal receives this data and displays it as a list on the user interface. The user can view detailed information and user ratings for each displayed service.

[0318] Step 5:

[0319] The user selects the services they wish to use from those presented and sends this selection information and feedback from their device to the server. The server receives this data and uses it as input to update the algorithm of the generating AI. This feedback improves the AI's learning capabilities and enhances the accuracy of future suggestions.

[0320] By following these steps, a system is created that efficiently provides users with the optimal solutions and tools for the problems they face.

[0321] 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.

[0322] This invention aims to provide a more comprehensive problem-solving experience by combining an emotion engine with a system that proposes the most suitable web-based service for a user's problem. A specific embodiment is shown below.

[0323] First, the user enters their problem in text format through the terminal's interface. The user's input is sent from the terminal to a server, where a generative AI and emotion engine analyze the accumulated data.

[0324] On the server, the input text data is analyzed using natural language processing by a generative AI, and relevant keywords are extracted. Simultaneously, an emotion engine identifies the user's emotions from the text, recognizing emotional information such as "stress" or "excitement."

[0325] Based on recognized keywords and emotional information, the server searches the web for and lists relevant services and tools. Emotional information is particularly used to tailor services to the user, ensuring that services are effectively selected according to the user's emotional state.

[0326] Next, the terminal presents the user with suggestions, including detailed information and reviews, based on the service information received from the server. The user can review this information and select a service that suits their feelings and needs.

[0327] After using the service, users can provide feedback through their device. This feedback evaluates the effectiveness of the service in solving problems and indicates how the service provided affected the user's emotions. The device sends this feedback to the server, which is used to further improve the emotion engine and generative AI algorithms.

[0328] For example, if a user inputs "I'm feeling stressed about a recent project," the emotion engine recognizes the emotion "stress." The generation AI extracts keywords such as "project management" and "stress reduction," and the server lists relaxation techniques and project management tools. The service suggestions are focused on supporting the user's stress reduction. By using the suggested services and providing feedback, the overall system performance can be improved.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The user uses a device to access an interface where they can input their problems in text format. For example, the user might enter a specific problem such as "How to reduce stress in a project."

[0332] Step 2:

[0333] The terminal sends the entered text data to the server. The server receives this data and prepares it for analysis.

[0334] Step 3:

[0335] The server uses a generation AI to analyze the received text. Natural language processing technology is used to extract keywords such as "stress reduction" and "project management" from the text.

[0336] Step 4:

[0337] Simultaneously, the server uses an emotion engine to recognize emotions within the text. In this case, the emotion "stress" is identified from the user's input.

[0338] Step 5:

[0339] Based on the extracted keywords and recognized emotions, the server searches for and lists relevant services and tools on the web. For example, it might select "mindfulness apps" and "project management tools."

[0340] Step 6:

[0341] The server sends detailed information and ratings about the listed services to the terminal. The terminal then displays this information to the user.

[0342] Step 7:

[0343] Users select the service that best suits them from those presented on their device and access each service through the provided link.

[0344] Step 8:

[0345] After using the service, users provide feedback on the service's effectiveness and changes in their feelings through the feedback interface on their device.

[0346] Step 9:

[0347] The device sends user feedback to the server. The server collects this data and uses it to improve the algorithms of its generative AI and emotion engine. This improves the accuracy of future suggestions and enables the delivery of more user-centric services.

[0348] (Example 2)

[0349] 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".

[0350] In modern society, it is difficult for users to find information and services that meet their needs from the vast amount of information available online. Furthermore, systems capable of providing appropriate services tailored to users' emotional states are limited. Therefore, there is a need for a system that offers effective and emotionally sensitive service suggestions to solve users' problems.

[0351] 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.

[0352] In this invention, the server includes means for acquiring user input as text data, generation AI means for analyzing the acquired text data and extracting relevant words and phrases, means for identifying the user's emotions from the text data, means for searching and listing services on information resources based on the extracted words and phrases and identified emotions, and means for adjusting and presenting the listed services according to the user's emotions. This makes it possible to efficiently solve the user's problems while taking their emotions into consideration.

[0353] A "user" is an individual or organization that uses an information system to solve a problem.

[0354] A "problem" refers to an issue or need that a user requires to be resolved.

[0355] "Text data" refers to information in text format that is entered by the user.

[0356] "Generative AI means" refers to technologies that use natural language processing techniques to analyze text data and extract related words and phrases.

[0357] "Means for identifying emotions" refers to technologies for identifying emotional information from a user's text data.

[0358] "Information resources" refer to a collection of services and tools that are accessible on the web or other digital platforms.

[0359] "Methods for searching and listing services" refers to technologies that have the functionality to find appropriate services and create a list based on extracted related keywords and identified sentiment information.

[0360] "Means of adjustment and presentation" refers to technologies that appropriately customize service suggestions based on the user's emotional information and then present them to the user.

[0361] This invention is a system that proposes appropriate services in response to a problem entered by the user. The user enters the problem as text data using an interface on the terminal. For example, the user can enter text information such as "I've been feeling stressed at work lately."

[0362] The terminal sends this text data to the server. The server first analyzes the text data using a generative AI model and extracts relevant words and phrases using natural language processing. For example, an existing natural language processing library could be used as the generative AI model. The extracted words and phrases might include "work" and "stress reduction."

[0363] Next, the server uses an emotion recognition engine to identify the user's emotions from the text data. This emotion recognition can utilize an emotion analysis model that employs machine learning algorithms. The identified emotion is "stress."

[0364] Subsequently, the server searches the internet for and lists relevant services based on the identified terms and sentiment information. This process can be efficiently carried out using a search engine API. The listed services are further tailored to the user's specific needs based on the sentiment information.

[0365] The terminal receives this service information sent from the server and presents it to the user. Ideally, this information should include detailed descriptions of each service and user ratings.

[0366] After using the service, users can provide feedback through their device. This feedback is sent to the server and used to improve the algorithms of the generative AI model and sentiment recognition engine in order to further enhance user benefits.

[0367] For example, if a user feels depressed due to lack of exercise, the server will suggest appropriate services based on phrases like "lack of exercise" and "improve mood." Examples include fitness programs and mental health apps. An example of a prompt would be: "If a user feels they want to relax by traveling, what suggestions should be made? Use the emotion engine and generative AI to suggest the best travel plan and relaxation methods." In this way, users can receive services tailored to their emotions and circumstances, thereby resolving their problems.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The user inputs their problem as text data through the terminal's interface. In this case, the input would be text in the format of "I've been feeling stressed at work lately." The terminal receives this text data and prepares to send it to the server. The output is the text data sent to the server.

[0371] Step 2:

[0372] The terminal sends the character data entered by the user to the server. To ensure data is securely transmitted over the network, encryption protocols are used. The input is the character data processed on the terminal, and the output is this data that reaches the server.

[0373] Step 3:

[0374] The server passes the received text data to a generating AI model, which analyzes it through natural language processing. Specifically, it tokenizes the text data and extracts keywords. The input is the text data sent to the server, and the output is related words such as "work" and "stress reduction."

[0375] Step 4:

[0376] The server uses an emotion recognition engine to identify the user's emotions. This process involves the execution of machine learning algorithms to determine the emotion category. The input is text data, and the output identifies "stress" as emotion information.

[0377] Step 5:

[0378] The server searches for and lists appropriate services from internet information resources based on words extracted by a generative AI model and emotions identified by an emotion recognition engine. Using an API, highly relevant services are efficiently extracted. Input is related words and emotion information, and output is a list of services.

[0379] Step 6:

[0380] The terminal makes suggestions to the user based on service information received from the server. These suggestions include detailed information and evaluations of each service, allowing the user to select the most appropriate service according to their situation. The input is service information provided by the server, and the output is the service suggestions to the user.

[0381] Step 7:

[0382] Users can use the presented service and then provide feedback by entering an evaluation. The evaluation is entered via the terminal and sent back to the server. The input is the user's feedback, and the output is the evaluation data sent to the server.

[0383] Step 8:

[0384] The server receives feedback from users and uses it to improve the algorithms of its generative AI model and sentiment recognition engine. In this process, the collected evaluation data is used as training data for the model, resulting in more accurate service recommendations. The input is user feedback data, and the output is the improved algorithm.

[0385] (Application Example 2)

[0386] 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."

[0387] In modern e-commerce, it is difficult to suggest appropriate services and payment methods that align with the user's psychological state. As a result, users may experience unnecessary stress or miss out on optimal choices. Furthermore, existing systems are insufficient in considering user emotions when making suggestions, making it difficult to provide a highly satisfying purchasing experience.

[0388] 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.

[0389] In this invention, the server includes means for acquiring user-inputted questions in text format, generation AI means for analyzing the acquired text and extracting relevant information units, means for searching and listing functions on the data network based on the extracted information units, and sentiment analysis means for identifying the user's emotions and adjusting the optimal payment option. This enables the selection of optimal payment options and functions that are in line with the user's emotional state.

[0390] A "user" is an individual or group that uses the system to have problems solved or to receive service suggestions.

[0391] "Text format" refers to string data written in natural language, and is the format in which users input problems and requests.

[0392] An "information unit" refers to keywords or important concepts extracted from text data provided by the user.

[0393] "Generative AI means" refers to artificial intelligence technology that analyzes text input from users and extracts relevant information units.

[0394] A "data network" refers to the infrastructure that provides information and services through the internet, internal networks, and other means.

[0395] "Functionality" is a concept that encompasses services, tools, and options provided on a data network.

[0396] "Emotional analysis methods" refer to technologies that identify a user's psychological state based on text data.

[0397] "Payment options" refer to the various payment methods offered to users for purchases and transactions, including, for example, installment payments.

[0398] This system begins with users entering problems and requests in text format through their terminals. The data entered by the user is sent to the server in real time. The server analyzes the text data using a generative AI model and extracts relevant information units. Natural language processing techniques are utilized at this stage, with Python and TensorFlow being used.

[0399] Next, the server identifies the user's emotional state using emotion analysis tools. This process is performed using Microsoft Azure Cognitive Services. Emotion analysis generates keywords corresponding to emotions such as "stress" and "reassurance." The extracted information units and emotion information are used to list the most suitable functions and payment options on the data network. The listed results are sent to the user's terminal and presented as suggestions.

[0400] In particular, the optimal payment option is adjusted based on the user's emotions identified by emotion analysis tools. For example, if a user feels anxious about purchasing an expensive item, installment payment options or cashback plans can be suggested. This allows users to choose a payment method that suits their emotional state, resulting in a highly satisfying purchasing experience.

[0401] As a concrete example, a user might be considering a large payment to purchase a car. In this case, the user enters the text "car purchase" and expresses the emotion of "anxiety." Based on this, a prompt message such as "Please adjust your installment payment plan" is generated, and the most suitable payment method is suggested to the user.

[0402] An example of a prompt message is, based on the text input "I bought a new TV, but I'm worried about the expense," a prompt like "Please suggest a payment plan that will give me peace of mind."

[0403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0404] Step 1:

[0405] Users input problems or requests in text format through their devices. The entered text is converted into data format as "user-entered requests or questions." This data is then sent to the server for further processing.

[0406] Step 2:

[0407] The server inputs the received text data into a generating AI model. This model uses a natural language processing module based on Python and TensorFlow. The AI ​​extracts important information units from the text. Specifically, it extracts relevant words and phrases as "keywords representing the user's requests." These information units are then used in the next processing step.

[0408] Step 3:

[0409] The server uses sentiment analysis tools to identify the user's emotions from the extracted text. This process utilizes Microsoft Azure Cognitive Services to determine the user's psychological state. Simultaneously, it generates keywords related to that emotion. The output is information indicating the user's emotions.

[0410] Step 4:

[0411] The server combines extracted information units with sentiment information to search for the most suitable functions and services available on the data network. Specifically, it lists candidates that match the user's requirements and creates a "list of optimal services."

[0412] Step 5:

[0413] A list of these services is sent to the user's device, allowing them to choose the appropriate service and payment option from the provided choices. This includes emotionally-based payment option suggestions, along with a "most suitable payment method" being presented.

[0414] Step 6:

[0415] The system provides feedback on the services and options selected by the user. This feedback is sent to the server via the device and stored as reference data to improve the generation AI and sentiment analysis methods. "User selections and feedback" are input, enabling system improvements.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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".

[0432] This invention is a system that proposes the most suitable web-based services and tools to solve problems that users face in their daily lives and work. The following describes an effective implementation of this system.

[0433] The system consists of a user terminal, a server, and network communication infrastructure. In a specific implementation, the user first inputs their problem in text format through the terminal's interface. The terminal has the functionality to send this input text to the server.

[0434] The server receives this text data and analyzes it using a generative AI equipped with natural language processing technology. The generative AI extracts relevant keywords from the text and identifies key elements related to the problem. Then, based on the extracted keywords, the server uses web-based databases and APIs to search for and list appropriate services and tools.

[0435] Next, the terminal suggests highly relevant services received from the server to the user. These suggestions include detailed information about each service, user ratings, and reviews. This allows the user to select the service best suited to their needs.

[0436] After using the service, users can provide feedback. The device sends this feedback to the server, which uses the received data to improve the AI's algorithm and enhance the accuracy of future suggestions.

[0437] For example, if a user inputs a problem such as "I want to manage my time efficiently," the generating AI will extract keywords such as "time management," "efficiency," and "tools." Based on this, the server will list services such as "online calendars" and "task management apps" and send them to the user's device. The user can then review the suggested services, select the most suitable tool, and use it. In this way, the present invention provides quick and accurate solutions to specific problems faced by users, supporting improvements in work efficiency and daily life.

[0438] The following describes the processing flow.

[0439] Step 1:

[0440] The user enters the problem they want to solve in text format through the terminal's input interface. The terminal temporarily stores this user input as data.

[0441] Step 2:

[0442] The terminal initiates communication to send the entered text data to the server. The server receives the data sent from the terminal and prepares it for analysis.

[0443] Step 3:

[0444] The server inputs the received text into the generating AI, which then analyzes the document using natural language processing technology. The generating AI then uses a "keyword extraction algorithm" to identify relevant keywords from the text.

[0445] Step 4:

[0446] Based on the extracted keywords, the server searches for related services and tools using web-based databases and external APIs. The server then filters and lists the most relevant results.

[0447] Step 5:

[0448] The server sends information about the listed services and tools to the terminal. This information includes service details, ratings, and reviews.

[0449] Step 6:

[0450] The terminal displays a list of services received from the server to the user. The user can review the list and select the service that best suits them.

[0451] Step 7:

[0452] After using the service the user selected, they input their experience using the feedback interface. The device then prepares to send this feedback to the server.

[0453] Step 8:

[0454] The device sends feedback data to the server, which uses this data to improve the algorithm of the generating AI. This improves the accuracy of future suggestions.

[0455] (Example 1)

[0456] 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."

[0457] Traditionally, there was no efficient system to quickly find the optimal solutions to the problems users faced. Users had to search for information themselves and individually check the details of each service, which consumed time and effort. Furthermore, the process of users verifying the effectiveness of the solutions they obtained was also done individually, and there were few opportunities to utilize the results in the future. As a result, the accuracy of service selection and use did not improve, and overall efficiency did not improve.

[0458] 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.

[0459] In this invention, the server includes means for acquiring user-inputted problems in character data format, an automatic generation function for analyzing the acquired character data and extracting relevant information, and means for searching for and listing functions on the computer network based on the extracted information. This makes it possible to quickly and efficiently propose the most suitable service for solving the user's problem and to improve the accuracy of the service proposal based on subsequent feedback.

[0460] A "user" refers to an individual or organization that uses a system to solve a problem.

[0461] "Character data format" refers to a format that represents digital information as text or symbols.

[0462] "Means of acquisition" refers to methods and devices for receiving and storing information provided by users.

[0463] The "automatic generation function" is a mechanism that uses machine learning and artificial intelligence technologies to analyze input data and generate relevant information.

[0464] "Extracting relevant information" refers to the process of identifying and extracting important elements and keywords from the input information.

[0465] "Functions on a computer network" refers to various programs and services provided on digital networks such as the internet.

[0466] "Means of searching and listing" refers to methods of finding and organizing information within a target digital network and presenting it in a usable format.

[0467] "Detailed information" refers to information that includes specific descriptions and attributes of a particular service or feature.

[0468] "Evaluation" refers to a judgment made about a particular service or product based on feedback gathered from users and the market.

[0469] The system of this invention aims to propose the optimal service to solve problems faced by users. This system consists of the user's terminal, a server, and network communication infrastructure.

[0470] The user inputs their problem in text format using a terminal. The terminal has the function to send this input data to the server. In this process, it is possible to use a keyboard or voice input device for text input, taking user usability into consideration.

[0471] The server uses a generative AI model to analyze the received text data. Specifically, the software used is an AI model incorporating natural language processing technology; for example, open-source machine learning libraries (e.g., TensorFlow, PyTorch) can be utilized. The server uses this model to extract important information from the text and identify keywords related to the problem. A possible prompt might be something like, "Tell me about tools to improve my time management efficiency."

[0472] Next, the server uses internet databases and APIs based on the extracted keywords to find and list highly relevant services. This search can utilize publicly known search engine technologies, allowing for rapid information retrieval.

[0473] The listed services are sent back from the server to the terminal and suggested to the user. The user can then select and use the most suitable service by referring to the detailed information and ratings provided on the terminal.

[0474] After using the service, users can provide feedback. The terminal sends this feedback to the server, which uses this data to improve the algorithms of the generated AI model. This improves the accuracy of future suggestions and increases the overall value of the system.

[0475] Thus, the present invention offers accurate and appropriate solutions to specific problems faced by users, significantly improving user convenience and operational efficiency.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The user enters their problem in text format using a terminal. A specific example of this problem is the prompt "I want to manage projects efficiently." The entered text data is checked on the terminal and then sent to the server.

[0479] Step 2:

[0480] The terminal sends text data received from the user to the server. A secure communication protocol (e.g., HTTPS) is used to safely transmit the input data to the server. The input here is text data, and the output is a confirmation of receipt by the server.

[0481] Step 3:

[0482] The server analyzes the received text data. A generative AI model is used for the analysis, analyzing the strings using natural language processing techniques. In this step, the server receives the text data as input and extracts relevant keywords such as project, efficiency, and management as output.

[0483] Step 4:

[0484] The server searches for services on the computer network based on the extracted keywords. Specifically, it searches web databases and APIs to retrieve relevant online tools and applications. Through this process, it uses the keywords obtained as input to create a list of candidate services as output.

[0485] Step 5:

[0486] The server sends a list of services to the terminal. The terminal receives this information and generates the necessary interface to present it to the user. The input is a list of services, and the output is visual suggestions for the user.

[0487] Step 6:

[0488] Users review the details of the services suggested through their device and select and use the most suitable service. A feature is also provided for users to provide feedback, allowing them to input information about their experience and the effectiveness of the services they accessed.

[0489] Step 7:

[0490] The terminal sends user feedback to the server. The server uses the received feedback to improve the algorithm of the generating AI, helping to improve the accuracy of future service suggestions. The input in this step is feedback data, and the output is improvement data for improving the accuracy of future suggestions.

[0491] (Application Example 1)

[0492] 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."

[0493] In modern urban life, residents face a wide range of problems and challenges, and there is a need to quickly provide appropriate services and tools to efficiently resolve them. In particular, there is a lack of mechanisms that enable residents to respond quickly and appropriately to issues such as traffic congestion, the use of public services, and waste disposal. This leads to a decline in residents' quality of life and a decrease in the overall efficiency of the city.

[0494] 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.

[0495] In this invention, the server includes means for acquiring user-inputted problems in data format, generative AI means for analyzing the acquired data and extracting related concepts, and means for searching and organizing internet services based on the extracted concepts. This makes it possible to quickly and accurately propose relevant services and means to residents facing a variety of problems.

[0496] "Means of acquiring user-inputted problems in data format" refers to a system that collects the challenges and questions that users are facing as information and converts them into digital data.

[0497] "Generative AI means for analyzing the acquired data and extracting related concepts" refers to a function that includes a process of processing collected digital data using artificial intelligence technology to identify important concepts related to the problem.

[0498] "A means of searching and organizing internet services based on extracted concepts" refers to a system that uses AI to identify concepts, explore various online services, and present them in a format suitable for the user.

[0499] "Means of proposing the aforementioned organized services to users" refers to display methods introduced to efficiently present useful information and services to users and to support problem-solving.

[0500] "Means for collecting user selection information regarding the aforementioned proposal and optimizing the system for urban management purposes" refers to a method of utilizing data to improve urban management systems by accumulating user-selected services and feedback.

[0501] The system for realizing this invention utilizes a user terminal, a server, and a network to efficiently solve user problems. The user first uses the terminal to input the problem in text format. This data is then transmitted to the server via the network.

[0502] The server analyzes the received data using an advanced generative AI model and extracts relevant concepts. The generative AI leverages natural language processing techniques to recognize key keywords from the data and understand the essence of the problem. Subsequently, based on the extracted concepts, it automatically searches for and organizes relevant services on the internet. This process includes utilizing a database via Web APIs.

[0503] Next, the server sends the organized information to the user terminal. The user terminal has an interface that displays the proposed services in an easy-to-understand format. This interface allows the user to view detailed information about individual services and reviews from other users.

[0504] Furthermore, user behavior data and feedback are sent back to the server and used for data analysis there. This information is used to improve the generative AI algorithm and enhance the accuracy of future suggestions.

[0505] For example, if a user wants to shorten their commute time, the AI ​​can extract concepts such as "commute," "efficiency," and "transportation," and suggest transportation information apps or route suggestion services. Examples of prompts include questions like, "What keywords should be extracted to efficiently solve the user's problem?" and "How should related services be organized and presented to the user?"

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] The user uses a terminal to input their problem in text format. The entered text data contains information about the nature and details of the problem and is sent directly to the server over the network.

[0509] Step 2:

[0510] The server passes the received text data to a generating AI model, which analyzes the data using natural language processing. In this process, the AI ​​extracts relevant concepts and keywords. Based on the input text data, the AI ​​understands the context and identifies key elements for problem solving.

[0511] Step 3:

[0512] The server uses the extracted keywords to search for relevant services and tools on the internet. It gathers information from external databases and service providers via Web APIs, organizing the most relevant options. This generates a list of the most suitable online services.

[0513] Step 4:

[0514] The server sends a list of generated services as data to the terminal. The terminal receives this data and displays it as a list on the user interface. The user can view detailed information and user ratings for each displayed service.

[0515] Step 5:

[0516] The user selects the services they wish to use from those presented and sends this selection information and feedback from their device to the server. The server receives this data and uses it as input to update the algorithm of the generating AI. This feedback improves the AI's learning capabilities and enhances the accuracy of future suggestions.

[0517] By following these steps, a system is created that efficiently provides users with the optimal solutions and tools for the problems they face.

[0518] 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.

[0519] This invention aims to provide a more comprehensive problem-solving experience by combining an emotion engine with a system that proposes the most suitable web-based service for a user's problem. A specific embodiment is shown below.

[0520] First, the user enters their problem in text format through the terminal's interface. The user's input is sent from the terminal to a server, where a generative AI and emotion engine analyze the accumulated data.

[0521] On the server, the input text data is analyzed using natural language processing by a generative AI, and relevant keywords are extracted. Simultaneously, an emotion engine identifies the user's emotions from the text, recognizing emotional information such as "stress" or "excitement."

[0522] Based on recognized keywords and emotional information, the server searches the web for and lists relevant services and tools. Emotional information is particularly used to tailor services to the user, ensuring that services are effectively selected according to the user's emotional state.

[0523] Next, the terminal presents the user with suggestions, including detailed information and reviews, based on the service information received from the server. The user can review this information and select a service that suits their feelings and needs.

[0524] After using the service, users can provide feedback through their device. This feedback evaluates the effectiveness of the service in solving problems and indicates how the service provided affected the user's emotions. The device sends this feedback to the server, which is used to further improve the emotion engine and generative AI algorithms.

[0525] For example, if a user inputs "I'm feeling stressed about a recent project," the emotion engine recognizes the emotion "stress." The generation AI extracts keywords such as "project management" and "stress reduction," and the server lists relaxation techniques and project management tools. The service suggestions are focused on supporting the user's stress reduction. By using the suggested services and providing feedback, the overall system performance can be improved.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The user uses a device to access an interface where they can input their problems in text format. For example, the user might enter a specific problem such as "How to reduce stress in a project."

[0529] Step 2:

[0530] The terminal sends the entered text data to the server. The server receives this data and prepares it for analysis.

[0531] Step 3:

[0532] The server uses a generation AI to analyze the received text. Natural language processing technology is used to extract keywords such as "stress reduction" and "project management" from the text.

[0533] Step 4:

[0534] Simultaneously, the server uses an emotion engine to recognize emotions within the text. In this case, the emotion "stress" is identified from the user's input.

[0535] Step 5:

[0536] Based on the extracted keywords and recognized emotions, the server searches for and lists relevant services and tools on the web. For example, it might select "mindfulness apps" and "project management tools."

[0537] Step 6:

[0538] The server sends detailed information and ratings about the listed services to the terminal. The terminal then displays this information to the user.

[0539] Step 7:

[0540] Users select the service that best suits them from those presented on their device and access each service through the provided link.

[0541] Step 8:

[0542] After using the service, users provide feedback on the service's effectiveness and changes in their feelings through the feedback interface on their device.

[0543] Step 9:

[0544] The device sends user feedback to the server. The server collects this data and uses it to improve the algorithms of its generative AI and emotion engine. This improves the accuracy of future suggestions and enables the delivery of more user-centric services.

[0545] (Example 2)

[0546] 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."

[0547] In modern society, it is difficult for users to find information and services that meet their needs from the vast amount of information available online. Furthermore, systems capable of providing appropriate services tailored to users' emotional states are limited. Therefore, there is a need for a system that offers effective and emotionally sensitive service suggestions to solve users' problems.

[0548] 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.

[0549] In this invention, the server includes means for acquiring user input as text data, generation AI means for analyzing the acquired text data and extracting relevant words and phrases, means for identifying the user's emotions from the text data, means for searching and listing services on information resources based on the extracted words and phrases and identified emotions, and means for adjusting and presenting the listed services according to the user's emotions. This makes it possible to efficiently solve the user's problems while taking their emotions into consideration.

[0550] A "user" is an individual or organization that uses an information system to solve a problem.

[0551] A "problem" refers to an issue or need that a user requires to be resolved.

[0552] "Text data" refers to information in text format that is entered by the user.

[0553] "Generative AI means" refers to technologies that use natural language processing techniques to analyze text data and extract related words and phrases.

[0554] "Means for identifying emotions" refers to technologies for identifying emotional information from a user's text data.

[0555] "Information resources" refer to a collection of services and tools that are accessible on the web or other digital platforms.

[0556] "Methods for searching and listing services" refers to technologies that have the functionality to find appropriate services and create a list based on extracted related keywords and identified sentiment information.

[0557] "Means of adjustment and presentation" refers to technologies that appropriately customize service suggestions based on the user's emotional information and then present them to the user.

[0558] This invention is a system that proposes appropriate services in response to a problem entered by the user. The user enters the problem as text data using an interface on the terminal. For example, the user can enter text information such as "I've been feeling stressed at work lately."

[0559] The terminal sends this text data to the server. The server first analyzes the text data using a generative AI model and extracts relevant words and phrases using natural language processing. For example, an existing natural language processing library could be used as the generative AI model. The extracted words and phrases might include "work" and "stress reduction."

[0560] Next, the server uses an emotion recognition engine to identify the user's emotions from the text data. This emotion recognition can utilize an emotion analysis model that employs machine learning algorithms. The identified emotion is "stress."

[0561] Subsequently, the server searches the internet for and lists relevant services based on the identified terms and sentiment information. This process can be efficiently carried out using a search engine API. The listed services are further tailored to the user's specific needs based on the sentiment information.

[0562] The terminal receives this service information sent from the server and presents it to the user. Ideally, this information should include detailed descriptions of each service and user ratings.

[0563] After using the service, users can provide feedback through their device. This feedback is sent to the server and used to improve the algorithms of the generative AI model and sentiment recognition engine in order to further enhance user benefits.

[0564] For example, if a user feels depressed due to lack of exercise, the server will suggest appropriate services based on phrases like "lack of exercise" and "improve mood." Examples include fitness programs and mental health apps. An example of a prompt would be: "If a user feels they want to relax by traveling, what suggestions should be made? Use the emotion engine and generative AI to suggest the best travel plan and relaxation methods." In this way, users can receive services tailored to their emotions and circumstances, thereby resolving their problems.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The user inputs their problem as text data through the terminal's interface. In this case, the input would be text in the format of "I've been feeling stressed at work lately." The terminal receives this text data and prepares to send it to the server. The output is the text data sent to the server.

[0568] Step 2:

[0569] The terminal sends the character data entered by the user to the server. To ensure data is securely transmitted over the network, encryption protocols are used. The input is the character data processed on the terminal, and the output is this data that reaches the server.

[0570] Step 3:

[0571] The server passes the received text data to a generating AI model, which analyzes it through natural language processing. Specifically, it tokenizes the text data and extracts keywords. The input is the text data sent to the server, and the output is related words such as "work" and "stress reduction."

[0572] Step 4:

[0573] The server uses an emotion recognition engine to identify the user's emotions. This process involves the execution of machine learning algorithms to determine the emotion category. The input is text data, and the output identifies "stress" as emotion information.

[0574] Step 5:

[0575] The server searches for and lists appropriate services from internet information resources based on words extracted by a generative AI model and emotions identified by an emotion recognition engine. Using an API, highly relevant services are efficiently extracted. Input is related words and emotion information, and output is a list of services.

[0576] Step 6:

[0577] The terminal makes suggestions to the user based on service information received from the server. These suggestions include detailed information and evaluations of each service, allowing the user to select the most appropriate service according to their situation. The input is service information provided by the server, and the output is the service suggestions to the user.

[0578] Step 7:

[0579] Users can use the presented service and then provide feedback by entering an evaluation. The evaluation is entered via the terminal and sent back to the server. The input is the user's feedback, and the output is the evaluation data sent to the server.

[0580] Step 8:

[0581] The server receives feedback from users and uses it to improve the algorithms of its generative AI model and sentiment recognition engine. In this process, the collected evaluation data is used as training data for the model, resulting in more accurate service recommendations. The input is user feedback data, and the output is the improved algorithm.

[0582] (Application Example 2)

[0583] 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."

[0584] In modern e-commerce, it is difficult to suggest appropriate services and payment methods that align with the user's psychological state. As a result, users may experience unnecessary stress or miss out on optimal choices. Furthermore, existing systems are insufficient in considering user emotions when making suggestions, making it difficult to provide a highly satisfying purchasing experience.

[0585] 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.

[0586] In this invention, the server includes means for acquiring user-inputted questions in text format, generation AI means for analyzing the acquired text and extracting relevant information units, means for searching and listing functions on the data network based on the extracted information units, and sentiment analysis means for identifying the user's emotions and adjusting the optimal payment option. This enables the selection of optimal payment options and functions that are in line with the user's emotional state.

[0587] A "user" is an individual or group that uses the system to have problems solved or to receive service suggestions.

[0588] "Text format" refers to string data written in natural language, and is the format in which users input problems and requests.

[0589] An "information unit" refers to keywords or important concepts extracted from text data provided by the user.

[0590] "Generative AI means" refers to artificial intelligence technology that analyzes text input from users and extracts relevant information units.

[0591] A "data network" refers to the infrastructure that provides information and services through the internet, internal networks, and other means.

[0592] "Functionality" is a concept that encompasses services, tools, and options provided on a data network.

[0593] "Emotional analysis methods" refer to technologies that identify a user's psychological state based on text data.

[0594] "Payment options" refer to the various payment methods offered to users for purchases and transactions, including, for example, installment payments.

[0595] This system begins with users entering problems and requests in text format through their terminals. The data entered by the user is sent to the server in real time. The server analyzes the text data using a generative AI model and extracts relevant information units. Natural language processing techniques are utilized at this stage, with Python and TensorFlow being used.

[0596] Next, the server identifies the user's emotional state using emotion analysis tools. This process is performed using Microsoft Azure Cognitive Services. Emotion analysis generates keywords corresponding to emotions such as "stress" and "reassurance." The extracted information units and emotion information are used to list the most suitable functions and payment options on the data network. The listed results are sent to the user's terminal and presented as suggestions.

[0597] In particular, the optimal payment option is adjusted based on the user's emotions identified by emotion analysis tools. For example, if a user feels anxious about purchasing an expensive item, installment payment options or cashback plans can be suggested. This allows users to choose a payment method that suits their emotional state, resulting in a highly satisfying purchasing experience.

[0598] As a concrete example, a user might be considering a large payment to purchase a car. In this case, the user enters the text "car purchase" and expresses the emotion of "anxiety." Based on this, a prompt message such as "Please adjust your installment payment plan" is generated, and the most suitable payment method is suggested to the user.

[0599] An example of a prompt message is, based on the text input "I bought a new TV, but I'm worried about the expense," a prompt like "Please suggest a payment plan that will give me peace of mind."

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] Users input problems or requests in text format through their devices. The entered text is converted into data format as "user-entered requests or questions." This data is then sent to the server for further processing.

[0603] Step 2:

[0604] The server inputs the received text data into a generating AI model. This model uses a natural language processing module based on Python and TensorFlow. The AI ​​extracts important information units from the text. Specifically, it extracts relevant words and phrases as "keywords representing the user's requests." These information units are then used in the next processing step.

[0605] Step 3:

[0606] The server uses sentiment analysis tools to identify the user's emotions from the extracted text. This process utilizes Microsoft Azure Cognitive Services to determine the user's psychological state. Simultaneously, it generates keywords related to that emotion. The output is information indicating the user's emotions.

[0607] Step 4:

[0608] The server combines extracted information units with sentiment information to search for the most suitable functions and services available on the data network. Specifically, it lists candidates that match the user's requirements and creates a "list of optimal services."

[0609] Step 5:

[0610] A list of these services is sent to the user's device, allowing them to choose the appropriate service and payment option from the provided choices. This includes emotionally-based payment option suggestions, along with a "most suitable payment method" being presented.

[0611] Step 6:

[0612] The system provides feedback on the services and options selected by the user. This feedback is sent to the server via the device and stored as reference data to improve the generation AI and sentiment analysis methods. "User selections and feedback" are input, enabling system improvements.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] [Fourth Embodiment]

[0617] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0618] 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.

[0619] 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).

[0620] 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.

[0621] 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.

[0622] 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).

[0623] 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.

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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".

[0630] This invention is a system that proposes the most suitable web-based services and tools to solve problems that users face in their daily lives and work. The following describes an effective implementation of this system.

[0631] The system consists of a user terminal, a server, and network communication infrastructure. In a specific implementation, the user first inputs their problem in text format through the terminal's interface. The terminal has the functionality to send this input text to the server.

[0632] The server receives this text data and analyzes it using a generative AI equipped with natural language processing technology. The generative AI extracts relevant keywords from the text and identifies key elements related to the problem. Then, based on the extracted keywords, the server uses web-based databases and APIs to search for and list appropriate services and tools.

[0633] Next, the terminal suggests highly relevant services received from the server to the user. These suggestions include detailed information about each service, user ratings, and reviews. This allows the user to select the service best suited to their needs.

[0634] After using the service, users can provide feedback. The device sends this feedback to the server, which uses the received data to improve the AI's algorithm and enhance the accuracy of future suggestions.

[0635] For example, if a user inputs a problem such as "I want to manage my time efficiently," the generating AI will extract keywords such as "time management," "efficiency," and "tools." Based on this, the server will list services such as "online calendars" and "task management apps" and send them to the user's device. The user can then review the suggested services, select the most suitable tool, and use it. In this way, the present invention provides quick and accurate solutions to specific problems faced by users, supporting improvements in work efficiency and daily life.

[0636] The following describes the processing flow.

[0637] Step 1:

[0638] The user enters the problem they want to solve in text format through the terminal's input interface. The terminal temporarily stores this user input as data.

[0639] Step 2:

[0640] The terminal initiates communication to send the entered text data to the server. The server receives the data sent from the terminal and prepares it for analysis.

[0641] Step 3:

[0642] The server inputs the received text into the generating AI, which then analyzes the document using natural language processing technology. The generating AI then uses a "keyword extraction algorithm" to identify relevant keywords from the text.

[0643] Step 4:

[0644] Based on the extracted keywords, the server searches for related services and tools using web-based databases and external APIs. The server then filters and lists the most relevant results.

[0645] Step 5:

[0646] The server sends information about the listed services and tools to the terminal. This information includes service details, ratings, and reviews.

[0647] Step 6:

[0648] The terminal displays a list of services received from the server to the user. The user can review the list and select the service that best suits them.

[0649] Step 7:

[0650] After using the service the user selected, they input their experience using the feedback interface. The device then prepares to send this feedback to the server.

[0651] Step 8:

[0652] The device sends feedback data to the server, which uses this data to improve the algorithm of the generating AI. This improves the accuracy of future suggestions.

[0653] (Example 1)

[0654] 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".

[0655] Traditionally, there was no efficient system to quickly find the optimal solutions to the problems users faced. Users had to search for information themselves and individually check the details of each service, which consumed time and effort. Furthermore, the process of users verifying the effectiveness of the solutions they obtained was also done individually, and there were few opportunities to utilize the results in the future. As a result, the accuracy of service selection and use did not improve, and overall efficiency did not improve.

[0656] 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.

[0657] In this invention, the server includes means for acquiring user-inputted problems in character data format, an automatic generation function for analyzing the acquired character data and extracting relevant information, and means for searching for and listing functions on the computer network based on the extracted information. This makes it possible to quickly and efficiently propose the most suitable service for solving the user's problem and to improve the accuracy of the service proposal based on subsequent feedback.

[0658] A "user" refers to an individual or organization that uses a system to solve a problem.

[0659] "Character data format" refers to a format that represents digital information as text or symbols.

[0660] "Means of acquisition" refers to methods and devices for receiving and storing information provided by users.

[0661] The "automatic generation function" is a mechanism that uses machine learning and artificial intelligence technologies to analyze input data and generate relevant information.

[0662] "Extracting relevant information" refers to the process of identifying and extracting important elements and keywords from the input information.

[0663] "Functions on a computer network" refers to various programs and services provided on digital networks such as the internet.

[0664] "Means of searching and listing" refers to methods of finding and organizing information within a target digital network and presenting it in a usable format.

[0665] "Detailed information" refers to information that includes specific descriptions and attributes of a particular service or feature.

[0666] "Evaluation" refers to a judgment made about a particular service or product based on feedback gathered from users and the market.

[0667] The system of this invention aims to propose the optimal service to solve problems faced by users. This system consists of the user's terminal, a server, and network communication infrastructure.

[0668] The user inputs their problem in text format using a terminal. The terminal has the function to send this input data to the server. In this process, it is possible to use a keyboard or voice input device for text input, taking user usability into consideration.

[0669] The server uses a generative AI model to analyze the received text data. Specifically, the software used is an AI model incorporating natural language processing technology; for example, open-source machine learning libraries (e.g., TensorFlow, PyTorch) can be utilized. The server uses this model to extract important information from the text and identify keywords related to the problem. A possible prompt might be something like, "Tell me about tools to improve my time management efficiency."

[0670] Next, the server uses internet databases and APIs based on the extracted keywords to find and list highly relevant services. This search can utilize publicly known search engine technologies, allowing for rapid information retrieval.

[0671] The listed services are sent back from the server to the terminal and suggested to the user. The user can then select and use the most suitable service by referring to the detailed information and ratings provided on the terminal.

[0672] After using the service, users can provide feedback. The terminal sends this feedback to the server, which uses this data to improve the algorithms of the generated AI model. This improves the accuracy of future suggestions and increases the overall value of the system.

[0673] Thus, the present invention offers accurate and appropriate solutions to specific problems faced by users, significantly improving user convenience and operational efficiency.

[0674] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0675] Step 1:

[0676] The user enters their problem in text format using a terminal. A specific example of this problem is the prompt "I want to manage projects efficiently." The entered text data is checked on the terminal and then sent to the server.

[0677] Step 2:

[0678] The terminal sends text data received from the user to the server. A secure communication protocol (e.g., HTTPS) is used to safely transmit the input data to the server. The input here is text data, and the output is a confirmation of receipt by the server.

[0679] Step 3:

[0680] The server analyzes the received text data. A generative AI model is used for the analysis, analyzing the strings using natural language processing techniques. In this step, the server receives the text data as input and extracts relevant keywords such as project, efficiency, and management as output.

[0681] Step 4:

[0682] The server searches for services on the computer network based on the extracted keywords. Specifically, it searches web databases and APIs to retrieve relevant online tools and applications. Through this process, it uses the keywords obtained as input to create a list of candidate services as output.

[0683] Step 5:

[0684] The server sends a list of services to the terminal. The terminal receives this information and generates the necessary interface to present it to the user. The input is a list of services, and the output is visual suggestions for the user.

[0685] Step 6:

[0686] Users review the details of the services suggested through their device and select and use the most suitable service. A feature is also provided for users to provide feedback, allowing them to input information about their experience and the effectiveness of the services they accessed.

[0687] Step 7:

[0688] The terminal sends user feedback to the server. The server uses the received feedback to improve the algorithm of the generating AI, helping to improve the accuracy of future service suggestions. The input in this step is feedback data, and the output is improvement data for improving the accuracy of future suggestions.

[0689] (Application Example 1)

[0690] 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".

[0691] In modern urban life, residents face a wide range of problems and challenges, and there is a need to quickly provide appropriate services and tools to efficiently resolve them. In particular, there is a lack of mechanisms that enable residents to respond quickly and appropriately to issues such as traffic congestion, the use of public services, and waste disposal. This leads to a decline in residents' quality of life and a decrease in the overall efficiency of the city.

[0692] 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.

[0693] In this invention, the server includes means for acquiring user-inputted problems in data format, generative AI means for analyzing the acquired data and extracting related concepts, and means for searching and organizing internet services based on the extracted concepts. This makes it possible to quickly and accurately propose relevant services and means to residents facing a variety of problems.

[0694] "Means of acquiring user-inputted problems in data format" refers to a system that collects the challenges and questions that users are facing as information and converts them into digital data.

[0695] "Generative AI means for analyzing the acquired data and extracting related concepts" refers to a function that includes a process of processing collected digital data using artificial intelligence technology to identify important concepts related to the problem.

[0696] "A means of searching and organizing internet services based on extracted concepts" refers to a system that uses AI to identify concepts, explore various online services, and present them in a format suitable for the user.

[0697] "Means of proposing the aforementioned organized services to users" refers to display methods introduced to efficiently present useful information and services to users and to support problem-solving.

[0698] "Means for collecting user selection information regarding the aforementioned proposal and optimizing the system for urban management purposes" refers to a method of utilizing data to improve urban management systems by accumulating user-selected services and feedback.

[0699] The system for realizing this invention utilizes a user terminal, a server, and a network to efficiently solve user problems. The user first uses the terminal to input the problem in text format. This data is then transmitted to the server via the network.

[0700] The server analyzes the received data using an advanced generative AI model and extracts relevant concepts. The generative AI leverages natural language processing techniques to recognize key keywords from the data and understand the essence of the problem. Subsequently, based on the extracted concepts, it automatically searches for and organizes relevant services on the internet. This process includes utilizing a database via Web APIs.

[0701] Next, the server sends the organized information to the user terminal. The user terminal has an interface that displays the proposed services in an easy-to-understand format. This interface allows the user to view detailed information about individual services and reviews from other users.

[0702] Furthermore, user behavior data and feedback are sent back to the server and used for data analysis there. This information is used to improve the generative AI algorithm and enhance the accuracy of future suggestions.

[0703] For example, if a user wants to shorten their commute time, the AI ​​can extract concepts such as "commute," "efficiency," and "transportation," and suggest transportation information apps or route suggestion services. Examples of prompts include questions like, "What keywords should be extracted to efficiently solve the user's problem?" and "How should related services be organized and presented to the user?"

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The user uses a terminal to input their problem in text format. The entered text data contains information about the nature and details of the problem and is sent directly to the server over the network.

[0707] Step 2:

[0708] The server passes the received text data to a generating AI model, which analyzes the data using natural language processing. In this process, the AI ​​extracts relevant concepts and keywords. Based on the input text data, the AI ​​understands the context and identifies key elements for problem solving.

[0709] Step 3:

[0710] The server uses the extracted keywords to search for relevant services and tools on the internet. It gathers information from external databases and service providers via Web APIs, organizing the most relevant options. This generates a list of the most suitable online services.

[0711] Step 4:

[0712] The server sends a list of generated services as data to the terminal. The terminal receives this data and displays it as a list on the user interface. The user can view detailed information and user ratings for each displayed service.

[0713] Step 5:

[0714] The user selects the services they wish to use from those presented and sends this selection information and feedback from their device to the server. The server receives this data and uses it as input to update the algorithm of the generating AI. This feedback improves the AI's learning capabilities and enhances the accuracy of future suggestions.

[0715] By following these steps, a system is created that efficiently provides users with the optimal solutions and tools for the problems they face.

[0716] 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.

[0717] This invention aims to provide a more comprehensive problem-solving experience by combining an emotion engine with a system that proposes the most suitable web-based service for a user's problem. A specific embodiment is shown below.

[0718] First, the user enters their problem in text format through the terminal's interface. The user's input is sent from the terminal to a server, where a generative AI and emotion engine analyze the accumulated data.

[0719] On the server, the input text data is analyzed using natural language processing by a generative AI, and relevant keywords are extracted. Simultaneously, an emotion engine identifies the user's emotions from the text, recognizing emotional information such as "stress" or "excitement."

[0720] Based on recognized keywords and emotional information, the server searches the web for and lists relevant services and tools. Emotional information is particularly used to tailor services to the user, ensuring that services are effectively selected according to the user's emotional state.

[0721] Next, the terminal presents the user with suggestions, including detailed information and reviews, based on the service information received from the server. The user can review this information and select a service that suits their feelings and needs.

[0722] After using the service, users can provide feedback through their device. This feedback evaluates the effectiveness of the service in solving problems and indicates how the service provided affected the user's emotions. The device sends this feedback to the server, which is used to further improve the emotion engine and generative AI algorithms.

[0723] For example, if a user inputs "I'm feeling stressed about a recent project," the emotion engine recognizes the emotion "stress." The generation AI extracts keywords such as "project management" and "stress reduction," and the server lists relaxation techniques and project management tools. The service suggestions are focused on supporting the user's stress reduction. By using the suggested services and providing feedback, the overall system performance can be improved.

[0724] The following describes the processing flow.

[0725] Step 1:

[0726] The user uses a device to access an interface where they can input their problems in text format. For example, the user might enter a specific problem such as "How to reduce stress in a project."

[0727] Step 2:

[0728] The terminal sends the entered text data to the server. The server receives this data and prepares it for analysis.

[0729] Step 3:

[0730] The server uses a generation AI to analyze the received text. Natural language processing technology is used to extract keywords such as "stress reduction" and "project management" from the text.

[0731] Step 4:

[0732] Simultaneously, the server uses an emotion engine to recognize emotions within the text. In this case, the emotion "stress" is identified from the user's input.

[0733] Step 5:

[0734] Based on the extracted keywords and recognized emotions, the server searches for and lists relevant services and tools on the web. For example, it might select "mindfulness apps" and "project management tools."

[0735] Step 6:

[0736] The server sends detailed information and ratings about the listed services to the terminal. The terminal then displays this information to the user.

[0737] Step 7:

[0738] Users select the service that best suits them from those presented on their device and access each service through the provided link.

[0739] Step 8:

[0740] After using the service, users provide feedback on the service's effectiveness and changes in their feelings through the feedback interface on their device.

[0741] Step 9:

[0742] The device sends user feedback to the server. The server collects this data and uses it to improve the algorithms of its generative AI and emotion engine. This improves the accuracy of future suggestions and enables the delivery of more user-centric services.

[0743] (Example 2)

[0744] 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".

[0745] In modern society, it is difficult for users to find information and services that meet their needs from the vast amount of information available online. Furthermore, systems capable of providing appropriate services tailored to users' emotional states are limited. Therefore, there is a need for a system that offers effective and emotionally sensitive service suggestions to solve users' problems.

[0746] 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.

[0747] In this invention, the server includes means for acquiring user input as text data, generation AI means for analyzing the acquired text data and extracting relevant words and phrases, means for identifying the user's emotions from the text data, means for searching and listing services on information resources based on the extracted words and phrases and identified emotions, and means for adjusting and presenting the listed services according to the user's emotions. This makes it possible to efficiently solve the user's problems while taking their emotions into consideration.

[0748] A "user" is an individual or organization that uses an information system to solve a problem.

[0749] A "problem" refers to an issue or need that a user requires to be resolved.

[0750] "Text data" refers to information in text format that is entered by the user.

[0751] "Generative AI means" refers to technologies that use natural language processing techniques to analyze text data and extract related words and phrases.

[0752] "Means for identifying emotions" refers to technologies for identifying emotional information from a user's text data.

[0753] "Information resources" refer to a collection of services and tools that are accessible on the web or other digital platforms.

[0754] "Methods for searching and listing services" refers to technologies that have the functionality to find appropriate services and create a list based on extracted related keywords and identified sentiment information.

[0755] "Means of adjustment and presentation" refers to technologies that appropriately customize service suggestions based on the user's emotional information and then present them to the user.

[0756] This invention is a system that proposes appropriate services in response to a problem entered by the user. The user enters the problem as text data using an interface on the terminal. For example, the user can enter text information such as "I've been feeling stressed at work lately."

[0757] The terminal sends this text data to the server. The server first analyzes the text data using a generative AI model and extracts relevant words and phrases using natural language processing. For example, an existing natural language processing library could be used as the generative AI model. The extracted words and phrases might include "work" and "stress reduction."

[0758] Next, the server uses an emotion recognition engine to identify the user's emotions from the text data. This emotion recognition can utilize an emotion analysis model that employs machine learning algorithms. The identified emotion is "stress."

[0759] Subsequently, the server searches the internet for and lists relevant services based on the identified terms and sentiment information. This process can be efficiently carried out using a search engine API. The listed services are further tailored to the user's specific needs based on the sentiment information.

[0760] The terminal receives this service information sent from the server and presents it to the user. Ideally, this information should include detailed descriptions of each service and user ratings.

[0761] After using the service, users can provide feedback through their device. This feedback is sent to the server and used to improve the algorithms of the generative AI model and sentiment recognition engine in order to further enhance user benefits.

[0762] For example, if a user feels depressed due to lack of exercise, the server will suggest appropriate services based on phrases like "lack of exercise" and "improve mood." Examples include fitness programs and mental health apps. An example of a prompt would be: "If a user feels they want to relax by traveling, what suggestions should be made? Use the emotion engine and generative AI to suggest the best travel plan and relaxation methods." In this way, users can receive services tailored to their emotions and circumstances, thereby resolving their problems.

[0763] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0764] Step 1:

[0765] The user inputs their problem as text data through the terminal's interface. In this case, the input would be text in the format of "I've been feeling stressed at work lately." The terminal receives this text data and prepares to send it to the server. The output is the text data sent to the server.

[0766] Step 2:

[0767] The terminal sends the character data entered by the user to the server. To ensure data is securely transmitted over the network, encryption protocols are used. The input is the character data processed on the terminal, and the output is this data that reaches the server.

[0768] Step 3:

[0769] The server passes the received text data to a generating AI model, which analyzes it through natural language processing. Specifically, it tokenizes the text data and extracts keywords. The input is the text data sent to the server, and the output is related words such as "work" and "stress reduction."

[0770] Step 4:

[0771] The server uses an emotion recognition engine to identify the user's emotions. This process involves the execution of machine learning algorithms to determine the emotion category. The input is text data, and the output identifies "stress" as emotion information.

[0772] Step 5:

[0773] The server searches for and lists appropriate services from internet information resources based on words extracted by a generative AI model and emotions identified by an emotion recognition engine. Using an API, highly relevant services are efficiently extracted. Input is related words and emotion information, and output is a list of services.

[0774] Step 6:

[0775] The terminal makes suggestions to the user based on service information received from the server. These suggestions include detailed information and evaluations of each service, allowing the user to select the most appropriate service according to their situation. The input is service information provided by the server, and the output is the service suggestions to the user.

[0776] Step 7:

[0777] Users can use the presented service and then provide feedback by entering an evaluation. The evaluation is entered via the terminal and sent back to the server. The input is the user's feedback, and the output is the evaluation data sent to the server.

[0778] Step 8:

[0779] The server receives feedback from users and uses it to improve the algorithms of its generative AI model and sentiment recognition engine. In this process, the collected evaluation data is used as training data for the model, resulting in more accurate service recommendations. The input is user feedback data, and the output is the improved algorithm.

[0780] (Application Example 2)

[0781] 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".

[0782] In modern e-commerce, it is difficult to suggest appropriate services and payment methods that align with the user's psychological state. As a result, users may experience unnecessary stress or miss out on optimal choices. Furthermore, existing systems are insufficient in considering user emotions when making suggestions, making it difficult to provide a highly satisfying purchasing experience.

[0783] 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.

[0784] In this invention, the server includes means for acquiring user-inputted questions in text format, generation AI means for analyzing the acquired text and extracting relevant information units, means for searching and listing functions on the data network based on the extracted information units, and sentiment analysis means for identifying the user's emotions and adjusting the optimal payment option. This enables the selection of optimal payment options and functions that are in line with the user's emotional state.

[0785] A "user" is an individual or group that uses the system to have problems solved or to receive service suggestions.

[0786] "Text format" refers to string data written in natural language, and is the format in which users input problems and requests.

[0787] An "information unit" refers to keywords or important concepts extracted from text data provided by the user.

[0788] "Generative AI means" refers to artificial intelligence technology that analyzes text input from users and extracts relevant information units.

[0789] A "data network" refers to the infrastructure that provides information and services through the internet, internal networks, and other means.

[0790] "Functionality" is a concept that encompasses services, tools, and options provided on a data network.

[0791] "Emotional analysis methods" refer to technologies that identify a user's psychological state based on text data.

[0792] "Payment options" refer to the various payment methods offered to users for purchases and transactions, including, for example, installment payments.

[0793] This system begins with users entering problems and requests in text format through their terminals. The data entered by the user is sent to the server in real time. The server analyzes the text data using a generative AI model and extracts relevant information units. Natural language processing techniques are utilized at this stage, with Python and TensorFlow being used.

[0794] Next, the server identifies the user's emotional state using emotion analysis tools. This process is performed using Microsoft Azure Cognitive Services. Emotion analysis generates keywords corresponding to emotions such as "stress" and "reassurance." The extracted information units and emotion information are used to list the most suitable functions and payment options on the data network. The listed results are sent to the user's terminal and presented as suggestions.

[0795] In particular, the optimal payment option is adjusted based on the user's emotions identified by emotion analysis tools. For example, if a user feels anxious about purchasing an expensive item, installment payment options or cashback plans can be suggested. This allows users to choose a payment method that suits their emotional state, resulting in a highly satisfying purchasing experience.

[0796] As a concrete example, a user might be considering a large payment to purchase a car. In this case, the user enters the text "car purchase" and expresses the emotion of "anxiety." Based on this, a prompt message such as "Please adjust your installment payment plan" is generated, and the most suitable payment method is suggested to the user.

[0797] An example of a prompt message is, based on the text input "I bought a new TV, but I'm worried about the expense," a prompt like "Please suggest a payment plan that will give me peace of mind."

[0798] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0799] Step 1:

[0800] Users input problems or requests in text format through their devices. The entered text is converted into data format as "user-entered requests or questions." This data is then sent to the server for further processing.

[0801] Step 2:

[0802] The server inputs the received text data into a generating AI model. This model uses a natural language processing module based on Python and TensorFlow. The AI ​​extracts important information units from the text. Specifically, it extracts relevant words and phrases as "keywords representing the user's requests." These information units are then used in the next processing step.

[0803] Step 3:

[0804] The server uses sentiment analysis tools to identify the user's emotions from the extracted text. This process utilizes Microsoft Azure Cognitive Services to determine the user's psychological state. Simultaneously, it generates keywords related to that emotion. The output is information indicating the user's emotions.

[0805] Step 4:

[0806] The server combines extracted information units with sentiment information to search for the most suitable functions and services available on the data network. Specifically, it lists candidates that match the user's requirements and creates a "list of optimal services."

[0807] Step 5:

[0808] A list of these services is sent to the user's device, allowing them to choose the appropriate service and payment option from the provided choices. This includes emotionally-based payment option suggestions, along with a "most suitable payment method" being presented.

[0809] Step 6:

[0810] The system provides feedback on the services and options selected by the user. This feedback is sent to the server via the device and stored as reference data to improve the generation AI and sentiment analysis methods. "User selections and feedback" are input, enabling system improvements.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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."

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] The following is further disclosed regarding the embodiments described above.

[0833] (Claim 1)

[0834] A means of obtaining the questions entered by the user in text format,

[0835] A generating AI means for analyzing the acquired text and extracting related keywords,

[0836] A method for searching and listing web services based on extracted keywords,

[0837] A means of proposing the aforementioned listed services to the user,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, further comprising means for obtaining user feedback on the proposed service and for improving the accuracy of the generating AI means.

[0841] (Claim 3)

[0842] The system according to claim 1, comprising means for providing detailed information and evaluation of the proposed service.

[0843] "Example 1"

[0844] (Claim 1)

[0845] A means of obtaining the questions entered by the user in text data format,

[0846] An automatic generation function that analyzes the acquired text data and extracts related information,

[0847] A means of searching for and listing functions on the computer network based on the extracted information,

[0848] A means for presenting the aforementioned list of functions to the user,

[0849] Means for displaying detailed information and evaluation of the aforementioned functions,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, further comprising means for obtaining user evaluation information for the presented function and for improving the accuracy of the automatic generation function.

[0853] (Claim 3)

[0854] The system according to claim 1, comprising means for providing market evaluations and user evaluations related to the aforementioned functions.

[0855] "Application Example 1"

[0856] (Claim 1)

[0857] A means of obtaining the questions entered by the user in data format,

[0858] A generative AI means for analyzing the acquired data and extracting related concepts,

[0859] A means of searching and organizing internet services based on extracted concepts,

[0860] A means of proposing the aforementioned services to users,

[0861] A means for collecting user selection information regarding the aforementioned proposal and optimizing the system for urban management purposes,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, further comprising means for obtaining user feedback on the proposed service and improving the effectiveness of the generating AI means.

[0865] (Claim 3)

[0866] The system according to claim 1, comprising means for supplying detailed information and evaluations relating to the proposed service.

[0867] "Example 2 of combining an emotion engine"

[0868] (Claim 1)

[0869] A method for obtaining user input as text data,

[0870] A generation AI means for analyzing the acquired character data and extracting related words and phrases,

[0871] A means for identifying the user's emotions from the aforementioned text data,

[0872] A means for searching and listing services on information resources based on extracted words and identified sentiments,

[0873] A means of adjusting and presenting the aforementioned listed services according to the user's emotions,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, further comprising means for obtaining user evaluations of the proposed service and for improving the accuracy of the generating AI means and the emotion recognition means.

[0877] (Claim 3)

[0878] The system according to claim 1, comprising means for providing detailed information and evaluation of the proposed service.

[0879] "Application example 2 when combining with an emotional engine"

[0880] (Claim 1)

[0881] A means of obtaining the questions entered by the user in text format,

[0882] A generating AI means for analyzing the acquired text and extracting relevant information units,

[0883] A means for searching and listing functions on the data network based on extracted information units,

[0884] A means of proposing the aforementioned listed functions to the user,

[0885] A sentiment analysis tool for identifying user emotions and adjusting the optimal payment option,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, further comprising means for obtaining user evaluations of the proposed function and for improving the accuracy of the generating AI means.

[0889] (Claim 3)

[0890] The system according to claim 1, comprising means for providing detailed information and evaluation regarding the proposed function. [Explanation of Symbols]

[0891] 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 obtaining the questions entered by the user in text format, A generating AI means for analyzing the acquired text and extracting related keywords, A method for searching and listing web services based on extracted keywords, A means of proposing the aforementioned listed services to the user, A system that includes this.

2. The system according to claim 1, further comprising means for obtaining user feedback on the proposed service and for improving the accuracy of the generating AI means.

3. The system according to claim 1, further comprising means for providing detailed information and evaluation of the proposed service.

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