System

Abstract

A system is provided.SOLUTION: The system includes a means for receiving an arbitrary problem or issue inputted by a user, a means for analyzing the received problem or issue, a means for generating solutions from different cultural backgrounds or specialized fields based on the analysis result, and a means for presenting the generated solutions to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, many issues and problems require different solutions depending on the field of expertise and cultural background. However, existing systems often only provide approaches limited to a single perspective, making it difficult to obtain multifaceted solutions. This makes it difficult for users to find new perspectives and ideas. The problem that this invention aims to solve is to provide a system that allows users to obtain optimal solutions from a variety of perspectives. [Means for solving the problem]

[0005] The present invention provides a system including the following means: a means for receiving any task or problem input by a user, a means for analyzing the received task or problem, a means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results, and a means for presenting the generated solutions to the user. The system may also include a means for generating solutions using multiple AI models based on the analysis results, and a means for integrating the generated solutions and formatting them before presenting them to the user. This allows the user to obtain optimal solutions from diverse perspectives.

[0006] "User" refers to a person or organization that inputs issues or problems into the system.

[0007] "Any problem or issue" refers to any type of question or problem that a user may enter into the system for a solution.

[0008] "Means for receiving" refers to an interface or function that allows the system to receive issues or problems entered by the user.

[0009] "Means for analysis" refers to the function of analyzing received issues and problems using natural language processing or other technologies and extracting relevant information and elements.

[0010] "Different cultural backgrounds" refers to knowledge and experiences in cultural spheres and communities that are geographically, historically, and socially different.

[0011] A "specialty field" refers to specialized knowledge and skills in a particular academic field, technology, or industry.

[0012] "Means for generating solutions" refers to the function of using AI models and other technologies to create answers and proposals for issues and problems based on the analysis results.

[0013] "Generated Solutions" refers to solutions or suggestions that are generated by the system and provided to the user.

[0014] "Presentation means" refers to the interface or method by which the system displays and informs the user of the solutions it generates.

[0015] An "AI model" refers to an algorithm or program that uses artificial intelligence technology to generate solutions to specific challenges or problems.

[0016] "Integration means" refers to the functions and methods used to organize multiple solutions into a single coherent format.

[0017] "Formatting methods" refers to the functions and methods used to format the integrated solution in a form that is easy for users to understand. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention is a system that provides multifaceted solutions to user-entered issues and problems. This system is implemented through a process of receiving issues, analyzing them, generating solutions from different cultural backgrounds and areas of expertise, and presenting them to the user.

[0040] User input of assignments

[0041] Users input the issues or problems they want to solve through a web browser interface, and the information they enter is sent to the server as an issue.

[0042] Receiving and saving assignments

[0043] The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

[0044] Analysis of the problem

[0045] The server analyzes the received assignment using a natural language processing (NLP) engine. This analysis process extracts the category and related keywords of the input assignment. For example, if the input assignment is "I am looking for new methods for sustainable agriculture," the NLP engine extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0046] Solution Generation

[0047] The server utilizes multiple AI models based on the analysis results to generate solutions from multiple perspectives. Specifically, it generates solutions using a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

[0048] Solution synthesis and presentation

[0049] The server then integrates the generated solutions and formats them before presenting them to the user. The integrated solution is displayed to the user through a web browser interface, allowing the user to see the solution from multiple perspectives.

[0050] Specific examples

[0051] For example, a specific example will be given in which the task "I am looking for new methods for sustainable agriculture" is entered.

[0052] The user enters a problem, which is received and saved by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The server generates solutions based on the analysis results, utilizing a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," and the multi-perspective AI model generates "Possibilities for urban agriculture using vertical farming." The server integrates these solutions and presents them to the user in the optimal format. Ultimately, the user is able to obtain solutions such as "Sustainable methods based on African agricultural technology" and "Examples of the application of vertical farming in urban areas."

[0053] The system of the present invention allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, thereby enabling effective problem solving.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] Users enter the issue or problem they want to solve through a web browser interface, enter the issue in the input field, and click the submit button to send the issue to the system.

[0057] Step 2:

[0058] The server receives assignments sent by users and stores them temporarily in a database for later analysis.

[0059] Step 3:

[0060] The server calls a natural language processing (NLP) engine to analyze the received and saved assignments. This analysis process extracts the assignment's category and related keywords. For example, for the assignment "Looking for new methods for sustainable agriculture," the following will be extracted: "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0061] Step 4:

[0062] The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. Specific examples include a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "application examples of agricultural technology in Africa," while the multi-perspective AI model generates "the potential of urban agriculture through vertical farming."

[0063] Step 5:

[0064] The server then consolidates the generated solutions into a consistent format, correcting each solution as necessary and formatting it for easy viewing.

[0065] Step 6:

[0066] The server presents the prepared solutions to the user through a web browser interface, allowing the user to see a list of solutions from different perspectives.

[0067] Step 7:

[0068] Users can refer to the presented solutions and use them to solve their own problems as needed, which can give users new perspectives and ideas.

[0069] The system of the present invention provides the user with a variety of solutions through these steps, and supports effective problem solving.

[0070] Example 1

[0071] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0072] In today's increasingly diverse society, the challenges and problems users face are complex, making it difficult to provide effective solutions from a single perspective or approach. Furthermore, integrating knowledge from different cultural backgrounds and fields of expertise requires collaboration and cooperation among people with expertise in each field. However, the systems and methods that enable this are not well established, making it difficult for users to quickly obtain multifaceted solutions.

[0073] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0074] In this invention, the server includes means for receiving any task or problem input by a user, means for temporarily storing the received task or problem in a database, means for analyzing the stored task or problem using a natural language processing engine and extracting categories and related keywords of the input task, means for utilizing multiple AI models to generate solutions from different cultural backgrounds and fields of expertise based on the analysis results, means for integrating the generated multiple solutions and formatting them before presenting them to the user, and means for displaying the formatted solutions to the user, thereby enabling the user to quickly obtain multifaceted and effective solutions.

[0075] "User" refers to the entity that uses the system to input issues and problems and receive solutions.

[0076] "Server" refers to the computer system that receives and analyzes input from users and generates and presents solutions.

[0077] "Challenges and problems" refer to specific situations or difficult situations that users are seeking solutions to.

[0078] "Means for receiving" refers to the function by which the server acquires and stores information entered by the user.

[0079] A "database" refers to an information storage system for temporarily storing and managing data such as issues and problems.

[0080] A "natural language processing engine" is a software module for analyzing human language and extracting categories and keywords for input issues or problems.

[0081] "Means for analysis" refers to the function of analyzing received issues and problems using a natural language processing engine and extracting relevant information.

[0082] "AI model" refers to a model trained using artificial intelligence algorithms to solve a specific problem.

[0083] A "multicultural AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different cultural backgrounds.

[0084] A "multi-perspective AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different specialized fields.

[0085] "Means for generating solutions" refers to the function of generating specific solutions to problems using multicultural AI models and multi-perspective AI models.

[0086] "Means of integration" refers to the function of combining and shaping multiple generated solutions into one.

[0087] "Formatting facilities" refers to the ability to adjust the format and presentation of a solution before presenting it to the user.

[0088] "Means for displaying" refers to the function of presenting the formatted solution to the user in an easy-to-read format.

[0089] The present invention relates to a system for providing multifaceted solutions to user-entered problems and issues. The system receives user-entered problems, stores them in a database, analyzes them using a natural language processing (NLP) engine, and generates solutions using multiple AI models based on the analysis results. The system also integrates the generated solutions, formats them, and presents them to the user.

[0090] Users input the problem they want to solve through a web browser, for example, a specific prompt such as "I'm looking for new methods for sustainable agriculture."

[0091] Once the input is complete, the server receives it and temporarily stores it in an issue database, preferably using a common relational database such as MySQL or PostgreSQL. The saved issue data is also assigned metadata such as a timestamp and user ID.

[0092] Next, the server retrieves the saved task data and analyzes it using an NLP engine. The NLP engine can use natural language processing libraries such as SpaCy or NLTK. Analysis steps include tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, for the task "We are looking for new methods for sustainable agriculture," the results would be "Category: Agriculture," "Keywords: Sustainability, New Methods."

[0093] Based on the analysis results, the server generates solutions using a multicultural AI model and a multi-perspective AI model. These AI models provide solutions from different cultural backgrounds and areas of expertise. For example, the multicultural AI model generates "Examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "Potential for urban agriculture through vertical farming."

[0094] The generated solutions are then integrated and formatted by the server, which includes merging and formatting the text, and the formatted solutions are then presented to the user through a web browser.

[0095] This allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, enabling more effective problem-solving. For example, users can obtain specific solutions such as "sustainable methods based on African agricultural techniques" or "examples of applying vertical farming in urban areas."

[0096] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0097] Step 1: User enters assignment

[0098] Users input the issues or problems they want to solve through a web browser interface. For example, they might type, "Please provide us with some innovative ideas for managing green spaces in urban areas." Once input is complete, this information is sent to the server via an HTML form.

[0099] Input: The prompt text entered by the user

[0100] Output: User input data is sent to the server

[0101] Step 2: The server receives and stores the assignment

[0102] The server receives HTTP requests and retrieves the assignment data sent by the user. The retrieved data is temporarily stored in a relational database such as MySQL or PostgreSQL. Metadata such as timestamps and user IDs are also added to the data for later analysis.

[0103] Input: Issue data from the user

[0104] Output: Issue data stored in a database

[0105] Step 3: The server analyzes the issue

[0106] The server retrieves the saved assignment data and analyzes it using an NLP engine such as SpaCy or NLTK. This analysis process includes tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, if the assignment "Looking for new methods for sustainable agriculture" is input, the NLP engine extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0107] Input: Issue data retrieved from the database

[0108] Output: Category and keyword information

[0109] Step 4: The server generates a solution

[0110] The server generates solutions using a multicultural AI model and a multi-perspective AI model based on the analysis results. The analysis results are input as prompts into the multicultural AI model, which generates solutions from different cultural backgrounds. For example, it generates "Examples of green space management in European urban planning." The same analysis results are input as prompts into the multi-perspective AI model, which generates solutions based on specialized fields. For example, it generates "New methods for managing green spaces in urban areas using technology."

[0111] Input: Analysis results (category and keyword information)

[0112] Output: Multiple solutions

[0113] Step 5: The server synthesizes and presents the solution

[0114] The server integrates and formats the generated solutions. Specifically, it merges the solution text and formats it into a visually easy-to-read form. The formatted solution is saved back in the database and displayed to the user via a web browser. The user can view the multiple solutions and consider approaches to solving the problem from multiple perspectives.

[0115] Input: Multiple solutions

[0116] Output: A consolidated and formatted solution

[0117] These are the main processing steps of this system, which allows users to quickly obtain solutions from a variety of perspectives, enabling more effective problem solving.

[0118] (Application example 1)

[0119] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0120] Existing solution-providing systems rely on a single cultural background or field of expertise, making it difficult to obtain solutions from diverse perspectives. Furthermore, they lack the functionality to effectively analyze users' problems, extract relevant keywords, and generate and present solutions from multiple cultural and perspectives. Therefore, there is a need for systems that can quickly provide optimal solutions to users' problems.

[0121] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0122] In this invention, the server includes: means for receiving any task or problem input by a user; means for analyzing the received task or problem; means for generating solutions from different cultural backgrounds and fields of expertise based on the analysis results; means for presenting the generated solution to the user; means including a natural language processing engine for analyzing the task input by the user and extracting specific keywords; means for utilizing multiple artificial intelligence models for generating solutions from multicultural and multi-perspectives; means for integrating the generated solutions and optimally formatting them; and means for displaying the integrated solution on a display device. This allows the user to gain new perspectives from diverse viewpoints and quickly obtain multifaceted solutions.

[0123] Word definition sentence

[0124] "User" refers to a person who uses the system to input issues and problems.

[0125] "Issue" refers to a problem or question requiring a solution that a user enters into the system.

[0126] A "natural language processing engine" refers to a software component that analyzes input text and extracts specific keywords.

[0127] "Cultural background" refers to the collection of history, customs, values, etc. that exist in different regions and countries.

[0128] A "specialty" refers to an area that requires concentrated specific knowledge or skills.

[0129] "Solution" refers to a specific measure or proposal provided to address a user's issue or problem.

[0130] An "artificial intelligence model" refers to an algorithm or system designed to perform a specific task based on training data.

[0131] "Format" refers to the standards or rules for arranging information in a particular form or structure.

[0132] "Display device" refers to a device for visually presenting the generated solution to a user.

[0133] A "multicultural perspective" refers to an approach to solving problems from the perspectives of various cultures.

[0134] "Multiple perspectives" refers to an approach that looks at a single issue from multiple different angles or perspectives.

[0135] MODE FOR CARRYING OUT THE INVENTION

[0136] The present invention is a system that provides multifaceted solutions to problems and issues entered by a user. This system includes a process that receives and analyzes problems entered by a user through a device such as a smartphone, generates solutions from multiple cultural and multi-perspective perspectives, and provides them to the user.

[0137] System Program

[0138] The system consists of the following components:

[0139] 1. User device: A device such as a smartphone or tablet that is equipped with an interface for users to input tasks.

[0140] 2. Server: Located in a cloud environment, it performs the primary processing of analyzing received challenges and generating solutions. Specifically, it includes a database, a natural language processing engine, multiple artificial intelligence models, and software components for integrating and formatting solutions.

[0141] 3. Display device: Present the solution visually on the user's terminal or other device.

[0142] Explanation of program processing

[0143] The server receives assignments sent from user devices and temporarily stores them in a database. It then uses a natural language processing engine (e.g., the transformers library) to analyze the received assignments and extract specific keywords. This analysis process clarifies the assignment's category and related keywords.

[0144] Based on the analysis results, the server utilizes multiple artificial intelligence models to generate multicultural and multi-perspective solutions, for example, one model generates a "multicultural" solution and another model generates a "multi-perspective" solution.

[0145] The generated solutions are then integrated and optimally formatted by the server, after which the formatted solution is sent to the display device of the user terminal and presented to the user.

[0146] Specific examples

[0147] For example, if the problem "Sales are sluggish" is input, a solution will be generated using the following steps:

[0148] Step 1:

[0149] The problem "Sales are sluggish" is input from the user terminal.

[0150] Step 2:

[0151] The server receives this assignment and analyzes it using a natural language processing engine. As a result of the analysis, keywords such as "renewal case studies," "promotion," "display improvement," and "online marketing" are extracted.

[0152] Step 3:

[0153] The server generates solutions using multiple artificial intelligence models. For example, it generates "success stories in Japan" and "overseas promotion strategies" from a multicultural perspective, and "ways to improve displays" and "techniques for utilizing online marketing" from multiple perspectives.

[0154] Step 4:

[0155] These solutions are integrated, formatted in an optimal form, and presented on the display device of the user terminal.

[0156] Example prompt sentence:

[0157] A user has entered the issue of "Sales are declining." The following keywords have been extracted: "Renewal case study," "Promotion," "Display improvement," and "Online marketing." Based on this, please propose a solution from a multicultural and multi-perspective perspective.

[0158] In this way, the system of the present invention allows users to gain new perspectives from a variety of viewpoints and quickly obtain multifaceted solutions, thereby providing effective solutions to the problems users face.

[0159] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0160] Program processing steps

[0161] Step 1: User enters assignment

[0162] Input: The user inputs the assignment using a smartphone or tablet.

[0163] Operation: The user terminal inputs the assignment through the interface on the web browser and sends the assignment to the system.

[0164] Output: The submitted assignment arrives at the server.

[0165] Step 2: Receive and save the assignment

[0166] Input: The assignment submitted from the user's device.

[0167] How it works: The server receives assignments submitted by users and stores them temporarily in a database.

[0168] Output: The assignment saved in the database.

[0169] Step 3: Analyze the issue

[0170] Input: Issues stored in the database.

[0171] How it works: The server uses a natural language processing engine (e.g. the transformers library) to parse the incoming issue. The parsing process extracts the issue's category and associated keywords.

[0172] Output: Extracted keywords and categories.

[0173] Step 4: Generating solutions – from a multicultural perspective

[0174] Input: Extracted keywords and categories.

[0175] How it works: The server uses an artificial intelligence model to generate solutions from a multicultural perspective. Based on keywords, it generates solutions from a multicultural perspective.

[0176] Output: Solutions from a multicultural perspective.

[0177] Step 5: Solution Generation - Multiple Perspectives

[0178] Input: Extracted keywords and categories.

[0179] How it works: The server uses an artificial intelligence model to generate solutions from multiple perspectives. Based on keywords, it generates solutions from multiple perspectives.

[0180] Output: Multi-perspective solution.

[0181] Step 6: Integrate and format the solution

[0182] Input: Multicultural and multiperspective solutions.

[0183] How it works: The server combines the generated solutions and formats them into the most suitable format.

[0184] Output: Integrated solution.

[0185] Step 7: Presenting an integrated solution

[0186] Input: Integrated solution.

[0187] Operation: The server sends the integrated solution to the user terminal, which displays it visually on its display device.

[0188] Output: The solution presented to the user.

[0189] Specific examples of operation

[0190] Step 1:

[0191] The user enters the issue of "poor sales" into a smartphone app.

[0192] Step 2:

[0193] The user terminal transmits this assignment data to the server, which receives it and stores it in a database.

[0194] Step 3:

[0195] The server launches a natural language processing engine, analyzes the problem text, and extracts keywords such as "renewal case studies," "promotion," "display improvement," and "online marketing."

[0196] Step 4:

[0197] The server runs an artificial intelligence model using the extracted keywords to generate solutions from a multicultural perspective, generating "success stories in Japan" and "overseas promotion strategies."

[0198] Step 5:

[0199] The server runs another artificial intelligence model using the extracted keywords to generate multi-perspective solutions, generating "ways to improve display" and "techniques for utilizing online marketing."

[0200] Step 6:

[0201] The server aggregates these solutions and formats them into a format that is easy for the user to understand.

[0202] Step 7:

[0203] The server transmits the integrated solution to the user terminal, which displays it visually on its display device.

[0204] In this way, users can quickly and effectively access solutions from multiple perspectives.

[0205] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0206] The present invention provides a system that provides multifaceted solutions to user-entered issues and problems. This system recognizes the user's emotions and combines them with an emotion engine to provide more optimized solutions.

[0207] User input of assignments

[0208] Users input the issues or problems they want to solve through a web browser interface, and the information they enter is sent to the server as an issue.

[0209] Receiving and saving assignments

[0210] The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

[0211] Analysis of the problem

[0212] The server analyzes the received and saved assignments using a natural language processing (NLP) engine. This analysis process extracts the assignment's category and related keywords. For example, if the assignment is "Looking for new methods for sustainable agriculture," the server extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0213] Emotion recognition

[0214] The device uses an emotion engine to recognize the user's emotions. To do this, it analyzes the user's facial expressions, voice, and keyboard typing patterns when typing. The emotion engine extracts information such as whether the user is tense or relaxed.

[0215] Solution Generation

[0216] The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. Specific examples include a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "Potential for urban agriculture through vertical farming."

[0217] Emotion-Based Adjustment

[0218] The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine. It changes the tone and content of the proposed solutions depending on the user's emotions. For example, if the user is feeling anxious, it provides more specific and detailed solutions, while if the user is relaxed, it suggests new and creative ideas.

[0219] Solution synthesis and presentation

[0220] The server then consolidates the generated solutions into a coherent format, correcting each solution as needed and formatting it for easy viewing. The server then presents the resulting solution to the user through a web browser interface, allowing the user to see a list of solutions from different perspectives.

[0221] Specific examples

[0222] For example, a specific example will be given in which the task "I am looking for new methods for sustainable agriculture" is entered.

[0223] The user inputs the problem, which is received and stored by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The device uses an emotion engine to recognize the user's emotions and determine that the user is feeling anxious. The server generates solutions based on the analysis results using a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "Possibilities for urban agriculture through vertical farming." The server adjusts the solutions to provide detailed and specific information to alleviate the user's anxiety. Finally, the server integrates these solutions, presents them to the user in an easy-to-read format, and provides specific, detailed solutions such as "Sustainable methods based on African agricultural technology" and "Examples of vertical farming applications in urban areas."

[0224] Through these steps, the system of the present invention provides the user with a variety of optimal solutions and makes adjustments according to the user's emotional state, thereby supporting more effective problem-solving.

[0225] The processing flow will be explained below.

[0226] Step 1:

[0227] Users enter the issue or problem they want to solve through a web browser interface by typing the issue into the input field and clicking the submit button.

[0228] Step 2:

[0229] The server receives assignments submitted by users, which are temporarily stored in a database, ready for analysis.

[0230] Step 3:

[0231] The device uses an emotion engine to analyze the user's facial expressions, voice, and keyboard typing patterns to extract emotional information. For example, if the user is feeling anxious, the emotion engine will label it as "anxiety."

[0232] Step 4:

[0233] The server calls a natural language processing (NLP) engine to analyze the received and saved assignments. During this analysis process, the assignment category and related keywords are extracted. For example, if the assignment is "Looking for new methods for sustainable agriculture," the following will be extracted: "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0234] Step 5:

[0235] The server generates solutions from multiple perspectives based on the analysis results and emotional information. Specific examples include a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "application examples of agricultural technology in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

[0236] Step 6:

[0237] The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine: if the user is anxious, it provides detailed and specific solutions, and if the user is relaxed, it adjusts to suggest novel and creative ideas.

[0238] Step 7:

[0239] The server then consolidates the generated solutions into a consistent format, correcting each solution and formatting it in a way that is easy to read.

[0240] Step 8:

[0241] The server presents the prepared solutions to the user through a web browser interface, allowing the user to see a list of solutions from different perspectives.

[0242] Step 9:

[0243] Users can refer to the presented solutions and use them to solve their own problems as needed, which can give users new perspectives and ideas.

[0244] Through these steps, the system of the present invention provides the user with a variety of optimal solutions and makes adjustments according to the user's emotional state, thereby supporting more effective problem-solving.

[0245] Example 2

[0246] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0247] Conventional problem-solving systems do not provide solutions that take into account the user's emotional state, and are therefore unable to provide suggestions that are optimized for the user's emotions. Furthermore, solutions based on analysis results from different cultural backgrounds or specialized fields are also limited. This means that the specific and multifaceted solutions desired by users are not being provided adequately.

[0248] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0249] In this invention, the server includes means for receiving any task or problem input by the user, means for analyzing the received task or problem, means for recognizing the user's emotional state, means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results and the user's emotional state, and means for presenting the generated solutions to the user. This makes it possible to provide optimal solutions tailored to the user's emotions and present multifaceted solutions from different perspectives.

[0250] A "user" is a person or organization that uses the system to input issues and problems and receive solutions.

[0251] "Issues and problems" are matters or problems that users wish to solve through the system.

[0252] "Means for receiving" is a function that allows the system to receive issues and problems sent by users.

[0253] "Means of analysis" refers to the technology and algorithms used to analyze received issues and problems and understand their content.

[0254] "Means for recognizing emotional states" refers to technologies and algorithms for analyzing and determining the user's emotions.

[0255] "Different cultural backgrounds" refer to the cultures, customs, and values ​​of different regions, countries, and societies.

[0256] A "specialty" is knowledge or skills specialized in a particular field or occupation.

[0257] The "means for generating a solution" is a function for generating an appropriate solution based on the analysis results and the user's emotional state.

[0258] "Presentation means" is a function for showing the generated solution to the user.

[0259] A "system" is a comprehensive device or software that performs a series of processes, including receiving and analyzing a user's problem, and generating and presenting a solution.

[0260] The present invention is a system that provides multifaceted and optimal solutions to issues and problems input by a user. The system includes technology that recognizes the user's emotional state and adjusts solutions based on that state. Specific embodiments of the present invention are described below.

[0261] System Configuration

[0262] User assignment input

[0263] Users enter the problem or issue they want to solve through a web browser interface, which consists of a simple web page with text input fields and a submit button, and the information entered by the user is sent to the server as an HTTP request.

[0264] Receiving and saving assignments

[0265] The server receives the tasks submitted by users and temporarily stores them in a database. This reception process is performed using a common web server and database management system. For example, Apache or Nginx is used as the front end, and MySQL or PostgreSQL is used as the database.

[0266] Analysis of the problem

[0267] The server analyzes the received and stored assignments using a natural language processing (NLP) engine, which can use libraries such as OpenNLP or NLTK, to extract assignment categories and related keywords.

[0268] Examples:

[0269] If a user types in "I'm looking for new methods for sustainable agriculture," the NLP engine will extract "Category: Agriculture, Keywords: Sustainability, New Methods."

[0270] Emotion recognition

[0271] The device recognizes the user's emotions using an emotion engine, which can be, for example, Microsoft's Azure Cognitive Services or IBM's Watson. This engine analyzes the user's facial expressions, voice, and keyboard typing patterns when typing to determine the user's emotional state.

[0272] Examples:

[0273] Based on the video and audio data when the user inputs the task, the emotion engine returns "Emotion: Anxiety."

[0274] Solution Generation

[0275] The server generates solutions using multiple AI models based on the analysis results and the user's emotional state, inputting specific prompts into each AI model. For example, a generative AI model such as GPT-3 can be used.

[0276] Examples:

[0277] Prompt for multicultural AI model: "Examples of agricultural technology applications in Africa"

[0278] Prompt for multi-perspective AI model: "The potential of vertical farming in urban agriculture"

[0279] Emotion-Based Adjustment

[0280] The server adjusts the generated solution based on the emotion recognition results, for example, changing the solution to be more specific and detailed if the user feels anxious.

[0281] Examples:

[0282] We will add specific procedures and fee information to the "Examples of Application of Agricultural Technology in Africa" ​​section and adjust the content to reduce user concerns.

[0283] Solution synthesis and presentation

[0284] The server then aggregates the generated solutions, formats them, and presents them to the user, for example by using HTML and CSS to display the solutions in a user-friendly format on a web browser.

[0285] Examples:

[0286] The server consolidates "Examples of agricultural technology applications in Africa" ​​and "Potential for urban agriculture through vertical farming" into a report format, and the solution is displayed when the user reloads the browser.

[0287] In this way, the system of the present invention provides optimal solutions that match the user's emotions and presents multifaceted solutions from different perspectives.

[0288] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0289] Step 1: User enters assignment

[0290] Users enter the issue or problem they want to solve through a web browser interface, which involves describing the issue in a text input field and clicking a "Submit" button.

[0291] Input: Text data about the issue or problem (e.g., "We are looking for new methods for sustainable agriculture.")

[0292] Output: Issue data sent to the server as an HTTP request

[0293] Step 2: Receive and save the assignment

[0294] The server receives the submitted assignment and stores it in a database, which may also validate the format according to the database schema before storing the assignment.

[0295] Input: Issue data sent as an HTTP request

[0296] Output: Issue data stored in a database (e.g., "Issue ID: 12345, Subject: Finding new methods for sustainable agriculture")

[0297] Step 3: Analyze the issue

[0298] The server passes the received assignments to a natural language processing (NLP) engine for analysis, which extracts assignment categories and related keywords.

[0299] Input: Project data stored in the database (e.g., "Project ID: 12345, Content: Finding new methods for sustainable agriculture")

[0300] Data processing and data calculation: NLP engine analyzes the text and extracts important categories and keywords

[0301] Output: Extracted categories and keywords (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods")

[0302] Step 4: Recognize emotions

[0303] The device uses an emotion engine to analyze the user's emotional state. In this step, the user's facial expressions, voice, and keyboard typing patterns are used as input data.

[0304] Input: User's facial expression data, voice data, keyboard keystroke data

[0305] Data processing and calculation: The emotion engine analyzes this data to identify the user's emotional state.

[0306] Output: User's emotional state (e.g., "Emotion: Anxiety")

[0307] Step 5: Generate a solution

[0308] The server generates a solution using multiple AI models based on the analysis results and the emotional state. In this step, each AI model is given an appropriate prompt to generate a solution.

[0309] Input: Analysis results and emotional state (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods, Emotion: Anxiety")

[0310] Data processing and data calculation: Input a prompt statement to each AI model and generate a solution based on it.

[0311] Output: Generated solutions (e.g., "Application of agricultural technology in Africa" ​​and "Possibilities for urban agriculture through vertical farming")

[0312] Step 6: Emotional Adjustment

[0313] The server adjusts the solution based on the user's emotional information recognized by the emotion engine: if the user feels anxious, the solution becomes more specific and detailed.

[0314] Input: User's emotional state and generated solution

[0315] Data processing and data calculation: Modify and adjust parts of the solution based on emotional information

[0316] Output: Tailored solution (e.g., "Example of agricultural technology application in Africa - add specific steps and pricing information")

[0317] Step 7: Synthesis and presentation of the solution

[0318] The server aggregates the solutions, formats them, and presents them to the user in an easy-to-read format on a web browser.

[0319] Input: Tailored solutions (e.g., "Applications of agricultural technology in Africa," "Potential for urban agriculture through vertical farming")

[0320] Data processing and data calculation: Integrating solutions and formatting with HTML and CSS

[0321] Output: The integrated solution displayed in the user's browser

[0322] This completes the entire system process, allowing the user to see a multifaceted and emotionally optimized solution.

[0323] (Application example 2)

[0324] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0325] Conventional systems can provide multifaceted solutions to problems entered by users, but because they do not optimize solutions taking into account the user's emotions, it is difficult to present more appropriate solutions to individual users. Furthermore, there is a lack of methods for tailoring solutions to the user's emotional state and providing personalized information. Therefore, to increase user satisfaction, a system that recognizes the user's emotions and adjusts solutions based on them is needed.

[0326] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0327] In this invention, the server includes means for receiving any task or problem input by a user, means for analyzing the received task or problem, means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results, means for recognizing the user's emotions, means for adjusting the generated solutions based on the user's emotions, and means for presenting the generated solutions to the user, thereby making it possible to provide personalized solutions according to the user's emotional state.

[0328] The "means for receiving any task or problem input by the user" is an interface that receives data such as text, voice, and images input by the user and transmits it to the server.

[0329] "Means for analyzing received issues and problems" refers to analytical functions such as a natural language processing engine that analyzes received data and extracts relevant keywords and categories.

[0330] "Means for generating solutions from different cultural backgrounds and fields of expertise" refers to a function for generating solutions from various cultures and fields of expertise using multicultural AI models and multi-perspective AI models.

[0331] The "means for recognizing the user's emotions" is an emotion engine that analyzes the user's facial expressions, voice, input patterns, etc., and recognizes their emotional state.

[0332] The "means for adjusting the generated solution based on the user's emotions" is a function for appropriately adjusting the tone and content of the solution depending on the user's emotional state recognized by the emotion engine.

[0333] The "means for presenting the generated solution to the user" is an interface for arranging the adjusted solution in an easy-to-view format and displaying it to the user.

[0334] The present invention is a system that provides multifaceted solutions to issues and problems input by users. In particular, it includes a function that recognizes the user's emotions and adjusts the solutions accordingly. The system's components include the following:

[0335] First, a user interface on a smartphone or PC is used to receive any assignments or problems entered by the user. Through this interface, the user inputs the assignments by text or voice. The received assignments or problems are then sent to the server.

[0336] Next, a server is installed as a means of analyzing the received issues and problems. This server uses a natural language processing (NLP) engine to analyze the issues and extract related keywords and categories. For example, if a user types "I want to know about recent environmental issues," the server will extract "Category: Environment" and "Keywords: Recent Environmental Issues."

[0337] Furthermore, the server uses multicultural and multi-perspective AI models to generate solutions from different cultural backgrounds and areas of expertise. These multiple AI models generate solutions from different perspectives and address user issues from multiple angles.

[0338] The device is also equipped with an emotion engine to recognize the user's emotions. This allows it to analyze the user's emotional state from their facial expressions, voice, keystroke patterns, etc. For example, it can recognize whether the user is excited or anxious.

[0339] The server also has the means to tailor the generated solutions based on the user's emotions. It adjusts the content and tone of the solutions depending on the user's perceived emotional state. For example, if the user is anxious, it will provide a more specific and detailed solution, while if the user is relaxed, it will suggest more creative ideas.

[0340] Finally, the server presents the generated solutions to the user through a user interface after arranging them in an easy-to-read format, allowing the user to check the solutions from different perspectives in a list format.

[0341] As a concrete example, consider the case where a user types, "What environmental issues are you interested in these days? Please tell me specifically." In this case, the server analyzes the relevant data, and the emotion engine analyzes the user's emotional state. Next, using the multicultural AI model and multi-perspective AI model, it generates solutions such as "The latest research on global warming" or "The current state and future of plastic pollution." These solutions are then tailored to appeal to the user's interest and proposed to them through the user interface.

[0342] In this way, the present invention can analyze the issues and problems entered by the user from multiple angles, provide optimal solutions from different cultures and fields of expertise, and even adjust them according to the user's emotional state.

[0343] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0344] Step 1:

[0345] Receive any issues or problems entered by the user.

[0346] Specifically, users input tasks via text or voice through a user interface on their smartphone or PC. This input data (tasks and problems) becomes the input data for the system. At this stage, the input data is sent to the server, which then receives it.

[0347] Step 2:

[0348] Analyze received issues and problems.

[0349] The server uses a natural language processing (NLP) engine to analyze the input data received from the user. The data to be analyzed is the text and voice data entered by the user. Specifically, this analysis process extracts related keywords and categories. For example, if the input is "I want to know about recent environmental issues," "Category: Environment" and "Keywords: Recent Environmental Issues" will be extracted.

[0350] Step 3:

[0351] Generate solutions from different cultural backgrounds and disciplines.

[0352] The server generates solutions from different cultural backgrounds and fields of expertise based on the extracted keywords and categories. For this purpose, it uses multicultural and multi-perspective AI models. For example, based on "Category: Environment" and "Keywords: Recent Environmental Issues," it generates solutions such as "Latest Research on Global Warming" and "Current Status and Future of Plastic Pollution."

[0353] Step 4:

[0354] Recognize user emotions.

[0355] The emotion engine installed on the user's device analyzes the user's facial expressions, voice, and input patterns to recognize their emotional state. This data includes facial recognition data, voice data, and typing patterns. For example, it recognizes whether the user is feeling anxious or excited. This emotional information becomes input data for adjusting the generated solution in the next step.

[0356] Step 5:

[0357] The generated solutions are adjusted based on the user's sentiment.

[0358] The server adjusts the content and tone of the generated solution based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if the user is feeling relaxed, it will suggest a more creative idea. In this process, emotional data and solution data are input, and adjusted solution data is output.

[0359] Step 6:

[0360] The generated solution is presented to the user.

[0361] The server then formats the adjusted solutions into an easy-to-read format and presents them to the user through a user interface. In this step, the formatted solution data is input and display data for the user is output. Specifically, the solutions are presented in a list format on the web browser, allowing the user to check them. For example, "Recent Latest Research on Global Warming" and "The Current State and Future of Plastic Pollution" are displayed.

[0362] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0363] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0364] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0365] [Second embodiment]

[0366] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0367] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0368] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0369] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0370] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0371] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0372] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0373] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0374] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0375] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0376] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0377] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0378] The present invention is a system that provides multifaceted solutions to user-entered issues and problems. This system is implemented through a process of receiving issues, analyzing them, generating solutions from different cultural backgrounds and areas of expertise, and presenting them to the user.

[0379] User input of assignments

[0380] Users input the issues or problems they want to solve through a web browser interface, and the information they enter is sent to the server as an issue.

[0381] Receiving and saving assignments

[0382] The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

[0383] Analysis of the problem

[0384] The server analyzes the received assignment using a natural language processing (NLP) engine. This analysis process extracts the category and related keywords of the input assignment. For example, if the input assignment is "I am looking for new methods for sustainable agriculture," the NLP engine extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0385] Solution Generation

[0386] The server utilizes multiple AI models based on the analysis results to generate solutions from multiple perspectives. Specifically, it generates solutions using a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

[0387] Solution synthesis and presentation

[0388] The server then integrates the generated solutions and formats them before presenting them to the user. The integrated solution is displayed to the user through a web browser interface, allowing the user to see the solution from multiple perspectives.

[0389] Specific examples

[0390] For example, a specific example will be given in which the task "I am looking for new methods for sustainable agriculture" is entered.

[0391] The user enters a problem, which is received and saved by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The server generates solutions based on the analysis results, utilizing a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," and the multi-perspective AI model generates "Possibilities for urban agriculture using vertical farming." The server integrates these solutions and presents them to the user in the optimal format. Ultimately, the user is able to obtain solutions such as "Sustainable methods based on African agricultural technology" and "Examples of the application of vertical farming in urban areas."

[0392] The system of the present invention allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, thereby enabling effective problem solving.

[0393] The processing flow will be explained below.

[0394] Step 1:

[0395] Users enter the issue or problem they want to solve through a web browser interface, enter the issue in the input field, and click the submit button to send the issue to the system.

[0396] Step 2:

[0397] The server receives assignments sent by users and stores them temporarily in a database for later analysis.

[0398] Step 3:

[0399] The server calls a natural language processing (NLP) engine to analyze the received and saved assignments. This analysis process extracts the assignment's category and related keywords. For example, for the assignment "Looking for new methods for sustainable agriculture," the following will be extracted: "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0400] Step 4:

[0401] The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. Specific examples include a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "application examples of agricultural technology in Africa," while the multi-perspective AI model generates "the potential of urban agriculture through vertical farming."

[0402] Step 5:

[0403] The server then consolidates the generated solutions into a consistent format, correcting each solution as necessary and formatting it for easy viewing.

[0404] Step 6:

[0405] The server presents the prepared solutions to the user through a web browser interface, allowing the user to see a list of solutions from different perspectives.

[0406] Step 7:

[0407] Users can refer to the presented solutions and use them to solve their own problems as needed, which can give users new perspectives and ideas.

[0408] The system of the present invention provides the user with a variety of solutions through these steps, and supports effective problem solving.

[0409] Example 1

[0410] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0411] In today's increasingly diverse society, the challenges and problems users face are complex, making it difficult to provide effective solutions from a single perspective or approach. Furthermore, integrating knowledge from different cultural backgrounds and fields of expertise requires collaboration and cooperation among people with expertise in each field. However, the systems and methods that enable this are not well established, making it difficult for users to quickly obtain multifaceted solutions.

[0412] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0413] In this invention, the server includes means for receiving any task or problem input by a user, means for temporarily storing the received task or problem in a database, means for analyzing the stored task or problem using a natural language processing engine and extracting categories and related keywords of the input task, means for utilizing multiple AI models to generate solutions from different cultural backgrounds and fields of expertise based on the analysis results, means for integrating the generated multiple solutions and formatting them before presenting them to the user, and means for displaying the formatted solutions to the user, thereby enabling the user to quickly obtain multifaceted and effective solutions.

[0414] "User" refers to the entity that uses the system to input issues and problems and receive solutions.

[0415] "Server" refers to the computer system that receives and analyzes input from users and generates and presents solutions.

[0416] "Challenges and problems" refer to specific situations or difficult situations that users are seeking solutions to.

[0417] "Means for receiving" refers to the function by which the server acquires and stores information entered by the user.

[0418] A "database" refers to an information storage system for temporarily storing and managing data such as issues and problems.

[0419] A "natural language processing engine" is a software module for analyzing human language and extracting categories and keywords for input issues or problems.

[0420] "Means for analysis" refers to the function of analyzing received issues and problems using a natural language processing engine and extracting relevant information.

[0421] "AI model" refers to a model trained using artificial intelligence algorithms to solve a specific problem.

[0422] A "multicultural AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different cultural backgrounds.

[0423] A "multi-perspective AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different specialized fields.

[0424] "Means for generating solutions" refers to the function of generating specific solutions to problems using multicultural AI models and multi-perspective AI models.

[0425] "Means of integration" refers to the function of combining and shaping multiple generated solutions into one.

[0426] "Formatting facilities" refers to the ability to adjust the format and presentation of a solution before presenting it to the user.

[0427] "Means for displaying" refers to the function of presenting the formatted solution to the user in an easy-to-read format.

[0428] The present invention relates to a system for providing multifaceted solutions to user-entered problems and issues. The system receives user-entered problems, stores them in a database, analyzes them using a natural language processing (NLP) engine, and generates solutions using multiple AI models based on the analysis results. The system also integrates the generated solutions, formats them, and presents them to the user.

[0429] Users input the problem they want to solve through a web browser, for example, a specific prompt such as "I'm looking for new methods for sustainable agriculture."

[0430] Once the input is complete, the server receives it and temporarily stores it in an issue database, preferably using a common relational database such as MySQL or PostgreSQL. The saved issue data is also assigned metadata such as a timestamp and user ID.

[0431] Next, the server retrieves the saved task data and analyzes it using an NLP engine. The NLP engine can use natural language processing libraries such as SpaCy or NLTK. Analysis steps include tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, for the task "We are looking for new methods for sustainable agriculture," the results would be "Category: Agriculture," "Keywords: Sustainability, New Methods."

[0432] Based on the analysis results, the server generates solutions using a multicultural AI model and a multi-perspective AI model. These AI models provide solutions from different cultural backgrounds and areas of expertise. For example, the multicultural AI model generates "Examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "Potential for urban agriculture through vertical farming."

[0433] The generated solutions are then integrated and formatted by the server, which includes merging and formatting the text, and the formatted solutions are then presented to the user through a web browser.

[0434] This allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, enabling more effective problem-solving. For example, users can obtain specific solutions such as "sustainable methods based on African agricultural techniques" or "examples of applying vertical farming in urban areas."

[0435] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0436] Step 1: User enters assignment

[0437] Users input the issues or problems they want to solve through a web browser interface. For example, they might type, "Please provide us with some innovative ideas for managing green spaces in urban areas." Once input is complete, this information is sent to the server via an HTML form.

[0438] Input: The prompt text entered by the user

[0439] Output: User input data is sent to the server

[0440] Step 2: The server receives and stores the assignment

[0441] The server receives HTTP requests and retrieves the assignment data sent by the user. The retrieved data is temporarily stored in a relational database such as MySQL or PostgreSQL. Metadata such as timestamps and user IDs are also added to the data for later analysis.

[0442] Input: Issue data from the user

[0443] Output: Issue data stored in a database

[0444] Step 3: The server analyzes the issue

[0445] The server retrieves the saved assignment data and analyzes it using an NLP engine such as SpaCy or NLTK. This analysis process includes tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, if the assignment "Looking for new methods for sustainable agriculture" is input, the NLP engine extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0446] Input: Issue data retrieved from the database

[0447] Output: Category and keyword information

[0448] Step 4: The server generates a solution

[0449] The server generates solutions using a multicultural AI model and a multi-perspective AI model based on the analysis results. The analysis results are input as prompts into the multicultural AI model, which generates solutions from different cultural backgrounds. For example, it generates "Examples of green space management in European urban planning." The same analysis results are input as prompts into the multi-perspective AI model, which generates solutions based on specialized fields. For example, it generates "New methods for managing green spaces in urban areas using technology."

[0450] Input: Analysis results (category and keyword information)

[0451] Output: Multiple solutions

[0452] Step 5: The server synthesizes and presents the solution

[0453] The server integrates and formats the generated solutions. Specifically, it merges the solution text and formats it into a visually easy-to-read form. The formatted solution is saved back in the database and displayed to the user via a web browser. The user can view the multiple solutions and consider approaches to solving the problem from multiple perspectives.

[0454] Input: Multiple solutions

[0455] Output: A consolidated and formatted solution

[0456] These are the main processing steps of this system, which allows users to quickly obtain solutions from a variety of perspectives, enabling more effective problem solving.

[0457] (Application example 1)

[0458] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0459] Existing solution-providing systems rely on a single cultural background or field of expertise, making it difficult to obtain solutions from diverse perspectives. Furthermore, they lack the functionality to effectively analyze users' problems, extract relevant keywords, and generate and present solutions from multiple cultural and perspectives. Therefore, there is a need for systems that can quickly provide optimal solutions to users' problems.

[0460] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0461] In this invention, the server includes: means for receiving any task or problem input by a user; means for analyzing the received task or problem; means for generating solutions from different cultural backgrounds and fields of expertise based on the analysis results; means for presenting the generated solution to the user; means including a natural language processing engine for analyzing the task input by the user and extracting specific keywords; means for utilizing multiple artificial intelligence models for generating solutions from multicultural and multi-perspectives; means for integrating the generated solutions and optimally formatting them; and means for displaying the integrated solution on a display device. This allows the user to gain new perspectives from diverse viewpoints and quickly obtain multifaceted solutions.

[0462] Word definition sentence

[0463] "User" refers to a person who uses the system to input issues and problems.

[0464] "Issue" refers to a problem or question requiring a solution that a user enters into the system.

[0465] A "natural language processing engine" refers to a software component that analyzes input text and extracts specific keywords.

[0466] "Cultural background" refers to the collection of history, customs, values, etc. that exist in different regions and countries.

[0467] A "specialty" refers to an area that requires concentrated specific knowledge or skills.

[0468] "Solution" refers to a specific measure or proposal provided to address a user's issue or problem.

[0469] An "artificial intelligence model" refers to an algorithm or system designed to perform a specific task based on training data.

[0470] "Format" refers to the standards or rules for arranging information in a particular form or structure.

[0471] "Display device" refers to a device for visually presenting the generated solution to a user.

[0472] A "multicultural perspective" refers to an approach to solving problems from the perspectives of various cultures.

[0473] "Multiple perspectives" refers to an approach that looks at a single issue from multiple different angles or perspectives.

[0474] MODE FOR CARRYING OUT THE INVENTION

[0475] The present invention is a system that provides multifaceted solutions to problems and issues entered by a user. This system includes a process that receives and analyzes problems entered by a user through a device such as a smartphone, generates solutions from multiple cultural and multi-perspective perspectives, and provides them to the user.

[0476] System Program

[0477] The system consists of the following components:

[0478] 1. User device: A device such as a smartphone or tablet that is equipped with an interface for users to input tasks.

[0479] 2. Server: Located in a cloud environment, it performs the primary processing of analyzing received challenges and generating solutions. Specifically, it includes a database, a natural language processing engine, multiple artificial intelligence models, and software components for integrating and formatting solutions.

[0480] 3. Display device: Present the solution visually on the user's terminal or other device.

[0481] Explanation of program processing

[0482] The server receives assignments sent from user devices and temporarily stores them in a database. It then uses a natural language processing engine (e.g., the transformers library) to analyze the received assignments and extract specific keywords. This analysis process clarifies the assignment's category and related keywords.

[0483] Based on the analysis results, the server utilizes multiple artificial intelligence models to generate multicultural and multi-perspective solutions, for example, one model generates a "multicultural" solution and another model generates a "multi-perspective" solution.

[0484] The generated solutions are then integrated and optimally formatted by the server, after which the formatted solution is sent to the display device of the user terminal and presented to the user.

[0485] Specific examples

[0486] For example, if the problem "Sales are sluggish" is input, a solution will be generated using the following steps:

[0487] Step 1:

[0488] The problem "Sales are sluggish" is input from the user terminal.

[0489] Step 2:

[0490] The server receives this assignment and analyzes it using a natural language processing engine. As a result of the analysis, keywords such as "renewal case studies," "promotion," "display improvement," and "online marketing" are extracted.

[0491] Step 3:

[0492] The server generates solutions using multiple artificial intelligence models. For example, it generates "success stories in Japan" and "overseas promotion strategies" from a multicultural perspective, and "ways to improve displays" and "techniques for utilizing online marketing" from multiple perspectives.

[0493] Step 4:

[0494] These solutions are integrated, formatted in an optimal form, and presented on the display device of the user terminal.

[0495] Example prompt sentence:

[0496] A user has entered the issue of "Sales are declining." The following keywords have been extracted: "Renewal case study," "Promotion," "Display improvement," and "Online marketing." Based on this, please propose a solution from a multicultural and multi-perspective perspective.

[0497] In this way, the system of the present invention allows users to gain new perspectives from a variety of viewpoints and quickly obtain multifaceted solutions, thereby providing effective solutions to the problems users face.

[0498] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0499] Program processing steps

[0500] Step 1: User enters assignment

[0501] Input: The user inputs the assignment using a smartphone or tablet.

[0502] Operation: The user terminal inputs the assignment through the interface on the web browser and sends the assignment to the system.

[0503] Output: The submitted assignment arrives at the server.

[0504] Step 2: Receive and save the assignment

[0505] Input: The assignment submitted from the user's device.

[0506] How it works: The server receives assignments submitted by users and stores them temporarily in a database.

[0507] Output: The assignment saved in the database.

[0508] Step 3: Analyze the issue

[0509] Input: Issues stored in the database.

[0510] How it works: The server uses a natural language processing engine (e.g. the transformers library) to parse the incoming issue. The parsing process extracts the issue's category and associated keywords.

[0511] Output: Extracted keywords and categories.

[0512] Step 4: Generating solutions – from a multicultural perspective

[0513] Input: Extracted keywords and categories.

[0514] How it works: The server uses an artificial intelligence model to generate solutions from a multicultural perspective. Based on keywords, it generates solutions from a multicultural perspective.

[0515] Output: Solutions from a multicultural perspective.

[0516] Step 5: Solution Generation - Multiple Perspectives

[0517] Input: Extracted keywords and categories.

[0518] How it works: The server uses an artificial intelligence model to generate solutions from multiple perspectives. Based on keywords, it generates solutions from multiple perspectives.

[0519] Output: Multi-perspective solution.

[0520] Step 6: Integrate and format the solution

[0521] Input: Multicultural and multiperspective solutions.

[0522] How it works: The server combines the generated solutions and formats them into the most suitable format.

[0523] Output: Integrated solution.

[0524] Step 7: Presenting an integrated solution

[0525] Input: Integrated solution.

[0526] Operation: The server sends the integrated solution to the user terminal, which displays it visually on its display device.

[0527] Output: The solution presented to the user.

[0528] Specific examples of operation

[0529] Step 1:

[0530] The user enters the issue of "poor sales" into a smartphone app.

[0531] Step 2:

[0532] The user terminal transmits this assignment data to the server, which receives it and stores it in a database.

[0533] Step 3:

[0534] The server launches a natural language processing engine, analyzes the problem text, and extracts keywords such as "renewal case studies," "promotion," "display improvement," and "online marketing."

[0535] Step 4:

[0536] The server runs an artificial intelligence model using the extracted keywords to generate solutions from a multicultural perspective, generating "success stories in Japan" and "overseas promotion strategies."

[0537] Step 5:

[0538] The server runs another artificial intelligence model using the extracted keywords to generate multi-perspective solutions, generating "ways to improve display" and "techniques for utilizing online marketing."

[0539] Step 6:

[0540] The server aggregates these solutions and formats them into a format that is easy for the user to understand.

[0541] Step 7:

[0542] The server transmits the integrated solution to the user terminal, which displays it visually on its display device.

[0543] In this way, users can quickly and effectively access solutions from multiple perspectives.

[0544] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0545] The present invention provides a system that provides multifaceted solutions to user-entered issues and problems. This system recognizes the user's emotions and combines them with an emotion engine to provide more optimized solutions.

[0546] User input of assignments

[0547] Users input the issues or problems they want to solve through a web browser interface, and the information they enter is sent to the server as an issue.

[0548] Receiving and saving assignments

[0549] The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

[0550] Analysis of the problem

[0551] The server analyzes the received and saved assignments using a natural language processing (NLP) engine. This analysis process extracts the assignment's category and related keywords. For example, if the assignment is "Looking for new methods for sustainable agriculture," the server extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0552] Emotion recognition

[0553] The device uses an emotion engine to recognize the user's emotions. To do this, it analyzes the user's facial expressions, voice, and keyboard typing patterns when typing. The emotion engine extracts information such as whether the user is tense or relaxed.

[0554] Solution Generation

[0555] The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. Specific examples include a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "Potential for urban agriculture through vertical farming."

[0556] Emotion-Based Adjustment

[0557] The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine. It changes the tone and content of the proposed solutions depending on the user's emotions. For example, if the user is feeling anxious, it provides more specific and detailed solutions, while if the user is relaxed, it suggests new and creative ideas.

[0558] Solution synthesis and presentation

[0559] The server then consolidates the generated solutions into a coherent format, correcting each solution as needed and formatting it for easy viewing. The server then presents the resulting solution to the user through a web browser interface, allowing the user to see a list of solutions from different perspectives.

[0560] Specific examples

[0561] For example, a specific example will be given in which the task "I am looking for new methods for sustainable agriculture" is entered.

[0562] The user inputs the problem, which is received and stored by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The device uses an emotion engine to recognize the user's emotions and determine that the user is feeling anxious. The server generates solutions based on the analysis results using a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "Possibilities for urban agriculture through vertical farming." The server adjusts the solutions to provide detailed and specific information to alleviate the user's anxiety. Finally, the server integrates these solutions, presents them to the user in an easy-to-read format, and provides specific, detailed solutions such as "Sustainable methods based on African agricultural technology" and "Examples of vertical farming applications in urban areas."

[0563] Through these steps, the system of the present invention provides the user with a variety of optimal solutions and makes adjustments according to the user's emotional state, thereby supporting more effective problem-solving.

[0564] The processing flow will be explained below.

[0565] Step 1:

[0566] Users enter the issue or problem they want to solve through a web browser interface by typing the issue into the input field and clicking the submit button.

[0567] Step 2:

[0568] The server receives assignments submitted by users, which are temporarily stored in a database, ready for analysis.

[0569] Step 3:

[0570] The device uses an emotion engine to analyze the user's facial expressions, voice, and keyboard typing patterns to extract emotional information. For example, if the user is feeling anxious, the emotion engine will label it as "anxiety."

[0571] Step 4:

[0572] The server calls a natural language processing (NLP) engine to analyze the received and saved assignments. During this analysis process, the assignment category and related keywords are extracted. For example, if the assignment is "Looking for new methods for sustainable agriculture," the following will be extracted: "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0573] Step 5:

[0574] The server generates solutions from multiple perspectives based on the analysis results and emotional information. Specific examples include a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "application examples of agricultural technology in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

[0575] Step 6:

[0576] The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine: if the user is anxious, it provides detailed and specific solutions, and if the user is relaxed, it adjusts to suggest novel and creative ideas.

[0577] Step 7:

[0578] The server then consolidates the generated solutions into a consistent format, correcting each solution and formatting it in a way that is easy to read.

[0579] Step 8:

[0580] The server presents the prepared solutions to the user through a web browser interface, allowing the user to see a list of solutions from different perspectives.

[0581] Step 9:

[0582] Users can refer to the presented solutions and use them to solve their own problems as needed, which can give users new perspectives and ideas.

[0583] Through these steps, the system of the present invention provides the user with a variety of optimal solutions and makes adjustments according to the user's emotional state, thereby supporting more effective problem-solving.

[0584] Example 2

[0585] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0586] Conventional problem-solving systems do not provide solutions that take into account the user's emotional state, and are therefore unable to provide suggestions that are optimized for the user's emotions. Furthermore, solutions based on analysis results from different cultural backgrounds or specialized fields are also limited. This means that the specific and multifaceted solutions desired by users are not being provided adequately.

[0587] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0588] In this invention, the server includes means for receiving any task or problem input by the user, means for analyzing the received task or problem, means for recognizing the user's emotional state, means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results and the user's emotional state, and means for presenting the generated solutions to the user. This makes it possible to provide optimal solutions tailored to the user's emotions and present multifaceted solutions from different perspectives.

[0589] A "user" is a person or organization that uses the system to input issues and problems and receive solutions.

[0590] "Issues and problems" are matters or problems that users wish to solve through the system.

[0591] "Means for receiving" is a function that allows the system to receive issues and problems sent by users.

[0592] "Means of analysis" refers to the technology and algorithms used to analyze received issues and problems and understand their content.

[0593] "Means for recognizing emotional states" refers to technologies and algorithms for analyzing and determining the user's emotions.

[0594] "Different cultural backgrounds" refer to the cultures, customs, and values ​​of different regions, countries, and societies.

[0595] A "specialty" is knowledge or skills specialized in a particular field or occupation.

[0596] The "means for generating a solution" is a function for generating an appropriate solution based on the analysis results and the user's emotional state.

[0597] "Presentation means" is a function for showing the generated solution to the user.

[0598] A "system" is a comprehensive device or software that performs a series of processes, including receiving and analyzing a user's problem, and generating and presenting a solution.

[0599] The present invention is a system that provides multifaceted and optimal solutions to issues and problems input by a user. The system includes technology that recognizes the user's emotional state and adjusts solutions based on that state. Specific embodiments of the present invention are described below.

[0600] System Configuration

[0601] User assignment input

[0602] Users enter the problem or issue they want to solve through a web browser interface, which consists of a simple web page with text input fields and a submit button, and the information entered by the user is sent to the server as an HTTP request.

[0603] Receiving and saving assignments

[0604] The server receives the tasks submitted by users and temporarily stores them in a database. This reception process is performed using a common web server and database management system. For example, Apache or Nginx is used as the front end, and MySQL or PostgreSQL is used as the database.

[0605] Analysis of the problem

[0606] The server analyzes the received and stored assignments using a natural language processing (NLP) engine, which can use libraries such as OpenNLP or NLTK, to extract assignment categories and related keywords.

[0607] Examples:

[0608] If a user types in "I'm looking for new methods for sustainable agriculture," the NLP engine will extract "Category: Agriculture, Keywords: Sustainability, New Methods."

[0609] Emotion recognition

[0610] The device recognizes the user's emotions using an emotion engine, which can be, for example, Microsoft's Azure Cognitive Services or IBM's Watson. This engine analyzes the user's facial expressions, voice, and keyboard typing patterns when typing to determine the user's emotional state.

[0611] Examples:

[0612] Based on the video and audio data when the user inputs the task, the emotion engine returns "Emotion: Anxiety."

[0613] Solution Generation

[0614] The server generates solutions using multiple AI models based on the analysis results and the user's emotional state, inputting specific prompts into each AI model. For example, a generative AI model such as GPT-3 can be used.

[0615] Examples:

[0616] Prompt for multicultural AI model: "Examples of agricultural technology applications in Africa"

[0617] Prompt for multi-perspective AI model: "The potential of vertical farming in urban agriculture"

[0618] Emotion-Based Adjustment

[0619] The server adjusts the generated solution based on the emotion recognition results, for example, changing the solution to be more specific and detailed if the user feels anxious.

[0620] Examples:

[0621] We will add specific procedures and fee information to the "Examples of Application of Agricultural Technology in Africa" ​​section and adjust the content to reduce user concerns.

[0622] Solution synthesis and presentation

[0623] The server then aggregates the generated solutions, formats them, and presents them to the user, for example by using HTML and CSS to display the solutions in a user-friendly format on a web browser.

[0624] Examples:

[0625] The server consolidates "Examples of agricultural technology applications in Africa" ​​and "Potential for urban agriculture through vertical farming" into a report format, and the solution is displayed when the user reloads the browser.

[0626] In this way, the system of the present invention provides optimal solutions that match the user's emotions and presents multifaceted solutions from different perspectives.

[0627] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0628] Step 1: User enters assignment

[0629] Users enter the issue or problem they want to solve through a web browser interface, which involves describing the issue in a text input field and clicking a "Submit" button.

[0630] Input: Text data about the issue or problem (e.g., "We are looking for new methods for sustainable agriculture.")

[0631] Output: Issue data sent to the server as an HTTP request

[0632] Step 2: Receive and save the assignment

[0633] The server receives the submitted assignment and stores it in a database, which may also validate the format according to the database schema before storing the assignment.

[0634] Input: Issue data sent as an HTTP request

[0635] Output: Issue data stored in a database (e.g., "Issue ID: 12345, Subject: Finding new methods for sustainable agriculture")

[0636] Step 3: Analyze the issue

[0637] The server passes the received assignments to a natural language processing (NLP) engine for analysis, which extracts assignment categories and related keywords.

[0638] Input: Project data stored in the database (e.g., "Project ID: 12345, Content: Finding new methods for sustainable agriculture")

[0639] Data processing and data calculation: NLP engine analyzes the text and extracts important categories and keywords

[0640] Output: Extracted categories and keywords (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods")

[0641] Step 4: Recognize emotions

[0642] The device uses an emotion engine to analyze the user's emotional state. In this step, the user's facial expressions, voice, and keyboard typing patterns are used as input data.

[0643] Input: User's facial expression data, voice data, keyboard keystroke data

[0644] Data processing and calculation: The emotion engine analyzes this data to identify the user's emotional state.

[0645] Output: User's emotional state (e.g., "Emotion: Anxiety")

[0646] Step 5: Generate a solution

[0647] The server generates a solution using multiple AI models based on the analysis results and the emotional state. In this step, each AI model is given an appropriate prompt to generate a solution.

[0648] Input: Analysis results and emotional state (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods, Emotion: Anxiety")

[0649] Data processing and data calculation: Input a prompt statement to each AI model and generate a solution based on it.

[0650] Output: Generated solutions (e.g., "Application of agricultural technology in Africa" ​​and "Possibilities for urban agriculture through vertical farming")

[0651] Step 6: Emotional Adjustment

[0652] The server adjusts the solution based on the user's emotional information recognized by the emotion engine: if the user feels anxious, the solution becomes more specific and detailed.

[0653] Input: User's emotional state and generated solution

[0654] Data processing and data calculation: Modify and adjust parts of the solution based on emotional information

[0655] Output: Tailored solution (e.g., "Example of agricultural technology application in Africa - add specific steps and pricing information")

[0656] Step 7: Synthesis and presentation of the solution

[0657] The server aggregates the solutions, formats them, and presents them to the user in an easy-to-read format on a web browser.

[0658] Input: Tailored solutions (e.g., "Applications of agricultural technology in Africa," "Potential for urban agriculture through vertical farming")

[0659] Data processing and data calculation: Integrating solutions and formatting with HTML and CSS

[0660] Output: The integrated solution displayed in the user's browser

[0661] This completes the entire system process, allowing the user to see a multifaceted and emotionally optimized solution.

[0662] (Application example 2)

[0663] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0664] Conventional systems can provide multifaceted solutions to problems entered by users, but because they do not optimize solutions taking into account the user's emotions, it is difficult to present more appropriate solutions to individual users. Furthermore, there is a lack of methods for tailoring solutions to the user's emotional state and providing personalized information. Therefore, to increase user satisfaction, a system that recognizes the user's emotions and adjusts solutions based on them is needed.

[0665] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0666] In this invention, the server includes means for receiving any task or problem input by a user, means for analyzing the received task or problem, means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results, means for recognizing the user's emotions, means for adjusting the generated solutions based on the user's emotions, and means for presenting the generated solutions to the user, thereby making it possible to provide personalized solutions according to the user's emotional state.

[0667] The "means for receiving any task or problem input by the user" is an interface that receives data such as text, voice, and images input by the user and transmits it to the server.

[0668] "Means for analyzing received issues and problems" refers to analytical functions such as a natural language processing engine that analyzes received data and extracts relevant keywords and categories.

[0669] "Means for generating solutions from different cultural backgrounds and fields of expertise" refers to a function for generating solutions from various cultures and fields of expertise using multicultural AI models and multi-perspective AI models.

[0670] The "means for recognizing the user's emotions" is an emotion engine that analyzes the user's facial expressions, voice, input patterns, etc., and recognizes their emotional state.

[0671] The "means for adjusting the generated solution based on the user's emotions" is a function for appropriately adjusting the tone and content of the solution depending on the user's emotional state recognized by the emotion engine.

[0672] The "means for presenting the generated solution to the user" is an interface for arranging the adjusted solution in an easy-to-view format and displaying it to the user.

[0673] The present invention is a system that provides multifaceted solutions to issues and problems input by users. In particular, it includes a function that recognizes the user's emotions and adjusts the solutions accordingly. The system's components include the following:

[0674] First, a user interface on a smartphone or PC is used to receive any assignments or problems entered by the user. Through this interface, the user inputs the assignments by text or voice. The received assignments or problems are then sent to the server.

[0675] Next, a server is installed as a means of analyzing the received issues and problems. This server uses a natural language processing (NLP) engine to analyze the issues and extract related keywords and categories. For example, if a user types "I want to know about recent environmental issues," the server will extract "Category: Environment" and "Keywords: Recent Environmental Issues."

[0676] Furthermore, the server uses multicultural and multi-perspective AI models to generate solutions from different cultural backgrounds and areas of expertise. These multiple AI models generate solutions from different perspectives and address user issues from multiple angles.

[0677] The device is also equipped with an emotion engine to recognize the user's emotions. This allows it to analyze the user's emotional state from their facial expressions, voice, keystroke patterns, etc. For example, it can recognize whether the user is excited or anxious.

[0678] The server also has the means to tailor the generated solutions based on the user's emotions. It adjusts the content and tone of the solutions depending on the user's perceived emotional state. For example, if the user is anxious, it will provide a more specific and detailed solution, while if the user is relaxed, it will suggest more creative ideas.

[0679] Finally, the server presents the generated solutions to the user through a user interface after arranging them in an easy-to-read format, allowing the user to check the solutions from different perspectives in a list format.

[0680] As a concrete example, consider the case where a user types, "What environmental issues are you interested in these days? Please tell me specifically." In this case, the server analyzes the relevant data, and the emotion engine analyzes the user's emotional state. Next, using the multicultural AI model and multi-perspective AI model, it generates solutions such as "The latest research on global warming" or "The current state and future of plastic pollution." These solutions are then tailored to appeal to the user's interest and proposed to them through the user interface.

[0681] In this way, the present invention can analyze the issues and problems entered by the user from multiple angles, provide optimal solutions from different cultures and fields of expertise, and even adjust them according to the user's emotional state.

[0682] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0683] Step 1:

[0684] Receive any issues or problems entered by the user.

[0685] Specifically, users input tasks via text or voice through a user interface on their smartphone or PC. This input data (tasks and problems) becomes the input data for the system. At this stage, the input data is sent to the server, which then receives it.

[0686] Step 2:

[0687] Analyze received issues and problems.

[0688] The server uses a natural language processing (NLP) engine to analyze the input data received from the user. The data to be analyzed is the text and voice data entered by the user. Specifically, this analysis process extracts related keywords and categories. For example, if the input is "I want to know about recent environmental issues," "Category: Environment" and "Keywords: Recent Environmental Issues" will be extracted.

[0689] Step 3:

[0690] Generate solutions from different cultural backgrounds and disciplines.

[0691] The server generates solutions from different cultural backgrounds and fields of expertise based on the extracted keywords and categories. For this purpose, it uses multicultural and multi-perspective AI models. For example, based on "Category: Environment" and "Keywords: Recent Environmental Issues," it generates solutions such as "Latest Research on Global Warming" and "Current Status and Future of Plastic Pollution."

[0692] Step 4:

[0693] Recognize user emotions.

[0694] The emotion engine installed on the user's device analyzes the user's facial expressions, voice, and input patterns to recognize their emotional state. This data includes facial recognition data, voice data, and typing patterns. For example, it recognizes whether the user is feeling anxious or excited. This emotional information becomes input data for adjusting the generated solution in the next step.

[0695] Step 5:

[0696] The generated solutions are adjusted based on the user's sentiment.

[0697] The server adjusts the content and tone of the generated solution based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if the user is feeling relaxed, it will suggest a more creative idea. In this process, emotional data and solution data are input, and adjusted solution data is output.

[0698] Step 6:

[0699] The generated solution is presented to the user.

[0700] The server then formats the adjusted solutions into an easy-to-read format and presents them to the user through a user interface. In this step, the formatted solution data is input and display data for the user is output. Specifically, the solutions are presented in a list format on the web browser, allowing the user to check them. For example, "Recent Latest Research on Global Warming" and "The Current State and Future of Plastic Pollution" are displayed.

[0701] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0702] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0703] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0704] [Third embodiment]

[0705] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0706] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0707] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0708] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0709] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0710] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0711] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0712] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0713] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0714] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0715] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0716] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0717] The present invention is a system that provides multifaceted solutions to user-entered issues and problems. This system is implemented through a process of receiving issues, analyzing them, generating solutions from different cultural backgrounds and areas of expertise, and presenting them to the user.

[0718] User input of assignments

[0719] Users input the issues or problems they want to solve through a web browser interface, and the information they enter is sent to the server as an issue.

[0720] Receiving and saving assignments

[0721] The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

[0722] Analysis of the problem

[0723] The server analyzes the received assignment using a natural language processing (NLP) engine. This analysis process extracts the category and related keywords of the input assignment. For example, if the input assignment is "I am looking for new methods for sustainable agriculture," the NLP engine extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0724] Solution Generation

[0725] The server utilizes multiple AI models based on the analysis results to generate solutions from multiple perspectives. Specifically, it generates solutions using a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

[0726] Solution synthesis and presentation

[0727] The server then integrates the generated solutions and formats them before presenting them to the user. The integrated solution is displayed to the user through a web browser interface, allowing the user to see the solution from multiple perspectives.

[0728] Specific examples

[0729] For example, a specific example will be given in which the task "I am looking for new methods for sustainable agriculture" is entered.

[0730] The user enters a problem, which is received and saved by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The server generates solutions based on the analysis results, utilizing a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," and the multi-perspective AI model generates "Possibilities for urban agriculture using vertical farming." The server integrates these solutions and presents them to the user in the optimal format. Ultimately, the user is able to obtain solutions such as "Sustainable methods based on African agricultural technology" and "Examples of the application of vertical farming in urban areas."

[0731] The system of the present invention allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, thereby enabling effective problem solving.

[0732] The processing flow will be explained below.

[0733] Step 1:

[0734] Users enter the issue or problem they want to solve through a web browser interface, enter the issue in the input field, and click the submit button to send the issue to the system.

[0735] Step 2:

[0736] The server receives assignments sent by users and stores them temporarily in a database for later analysis.

[0737] Step 3:

[0738] The server calls a natural language processing (NLP) engine to analyze the received and saved assignments. This analysis process extracts the assignment's category and related keywords. For example, for the assignment "Looking for new methods for sustainable agriculture," the following will be extracted: "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0739] Step 4:

[0740] The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. Specific examples include a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "application examples of agricultural technology in Africa," while the multi-perspective AI model generates "the potential of urban agriculture through vertical farming."

[0741] Step 5:

[0742] The server then consolidates the generated solutions into a consistent format, correcting each solution as necessary and formatting it for easy viewing.

[0743] Step 6:

[0744] The server presents the prepared solutions to the user through a web browser interface, allowing the user to see a list of solutions from different perspectives.

[0745] Step 7:

[0746] Users can refer to the presented solutions and use them to solve their own problems as needed, which can give users new perspectives and ideas.

[0747] The system of the present invention provides the user with a variety of solutions through these steps, and supports effective problem solving.

[0748] Example 1

[0749] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0750] In today's increasingly diverse society, the challenges and problems users face are complex, making it difficult to provide effective solutions from a single perspective or approach. Furthermore, integrating knowledge from different cultural backgrounds and fields of expertise requires collaboration and cooperation among people with expertise in each field. However, the systems and methods that enable this are not well established, making it difficult for users to quickly obtain multifaceted solutions.

[0751] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0752] In this invention, the server includes means for receiving any task or problem input by a user, means for temporarily storing the received task or problem in a database, means for analyzing the stored task or problem using a natural language processing engine and extracting categories and related keywords of the input task, means for utilizing multiple AI models to generate solutions from different cultural backgrounds and fields of expertise based on the analysis results, means for integrating the generated multiple solutions and formatting them before presenting them to the user, and means for displaying the formatted solutions to the user, thereby enabling the user to quickly obtain multifaceted and effective solutions.

[0753] "User" refers to the entity that uses the system to input issues and problems and receive solutions.

[0754] "Server" refers to the computer system that receives and analyzes input from users and generates and presents solutions.

[0755] "Challenges and problems" refer to specific situations or difficult situations that users are seeking solutions to.

[0756] "Means for receiving" refers to the function by which the server acquires and stores information entered by the user.

[0757] A "database" refers to an information storage system for temporarily storing and managing data such as issues and problems.

[0758] A "natural language processing engine" is a software module for analyzing human language and extracting categories and keywords for input issues or problems.

[0759] "Means for analysis" refers to the function of analyzing received issues and problems using a natural language processing engine and extracting relevant information.

[0760] "AI model" refers to a model trained using artificial intelligence algorithms to solve a specific problem.

[0761] A "multicultural AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different cultural backgrounds.

[0762] A "multi-perspective AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different specialized fields.

[0763] "Means for generating solutions" refers to the function of generating specific solutions to problems using multicultural AI models and multi-perspective AI models.

[0764] "Means of integration" refers to the function of combining and shaping multiple generated solutions into one.

[0765] "Formatting facilities" refers to the ability to adjust the format and presentation of a solution before presenting it to the user.

[0766] "Means for displaying" refers to the function of presenting the formatted solution to the user in an easy-to-read format.

[0767] The present invention relates to a system for providing multifaceted solutions to user-entered problems and issues. The system receives user-entered problems, stores them in a database, analyzes them using a natural language processing (NLP) engine, and generates solutions using multiple AI models based on the analysis results. The system also integrates the generated solutions, formats them, and presents them to the user.

[0768] Users input the problem they want to solve through a web browser, for example, a specific prompt such as "I'm looking for new methods for sustainable agriculture."

[0769] Once the input is complete, the server receives it and temporarily stores it in an issue database, preferably using a common relational database such as MySQL or PostgreSQL. The saved issue data is also assigned metadata such as a timestamp and user ID.

[0770] Next, the server retrieves the saved task data and analyzes it using an NLP engine. The NLP engine can use natural language processing libraries such as SpaCy or NLTK. Analysis steps include tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, for the task "We are looking for new methods for sustainable agriculture," the results would be "Category: Agriculture," "Keywords: Sustainability, New Methods."

[0771] Based on the analysis results, the server generates solutions using a multicultural AI model and a multi-perspective AI model. These AI models provide solutions from different cultural backgrounds and areas of expertise. For example, the multicultural AI model generates "Examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "Potential for urban agriculture through vertical farming."

[0772] The generated solutions are then integrated and formatted by the server, which includes merging and formatting the text, and the formatted solutions are then presented to the user through a web browser.

[0773] This allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, enabling more effective problem-solving. For example, users can obtain specific solutions such as "sustainable methods based on African agricultural techniques" or "examples of applying vertical farming in urban areas."

[0774] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0775] Step 1: User enters assignment

[0776] Users input the issues or problems they want to solve through a web browser interface. For example, they might type, "Please provide us with some innovative ideas for managing green spaces in urban areas." Once input is complete, this information is sent to the server via an HTML form.

[0777] Input: The prompt text entered by the user

[0778] Output: User input data is sent to the server

[0779] Step 2: The server receives and stores the assignment

[0780] The server receives HTTP requests and retrieves the assignment data sent by the user. The retrieved data is temporarily stored in a relational database such as MySQL or PostgreSQL. Metadata such as timestamps and user IDs are also added to the data for later analysis.

[0781] Input: Issue data from the user

[0782] Output: Issue data stored in a database

[0783] Step 3: The server analyzes the issue

[0784] The server retrieves the saved assignment data and analyzes it using an NLP engine such as SpaCy or NLTK. This analysis process includes tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, if the assignment "Looking for new methods for sustainable agriculture" is input, the NLP engine extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0785] Input: Issue data retrieved from the database

[0786] Output: Category and keyword information

[0787] Step 4: The server generates a solution

[0788] The server generates solutions using a multicultural AI model and a multi-perspective AI model based on the analysis results. The analysis results are input as prompts into the multicultural AI model, which generates solutions from different cultural backgrounds. For example, it generates "Examples of green space management in European urban planning." The same analysis results are input as prompts into the multi-perspective AI model, which generates solutions based on specialized fields. For example, it generates "New methods for managing green spaces in urban areas using technology."

[0789] Input: Analysis results (category and keyword information)

[0790] Output: Multiple solutions

[0791] Step 5: The server synthesizes and presents the solution

[0792] The server integrates and formats the generated solutions. Specifically, it merges the solution text and formats it into a visually easy-to-read form. The formatted solution is saved back in the database and displayed to the user via a web browser. The user can view the multiple solutions and consider approaches to solving the problem from multiple perspectives.

[0793] Input: Multiple solutions

[0794] Output: A consolidated and formatted solution

[0795] These are the main processing steps of this system, which allows users to quickly obtain solutions from a variety of perspectives, enabling more effective problem solving.

[0796] (Application example 1)

[0797] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0798] Existing solution-providing systems rely on a single cultural background or field of expertise, making it difficult to obtain solutions from diverse perspectives. Furthermore, they lack the functionality to effectively analyze users' problems, extract relevant keywords, and generate and present solutions from multiple cultural and perspectives. Therefore, there is a need for systems that can quickly provide optimal solutions to users' problems.

[0799] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0800] In this invention, the server includes: means for receiving any task or problem input by a user; means for analyzing the received task or problem; means for generating solutions from different cultural backgrounds and fields of expertise based on the analysis results; means for presenting the generated solution to the user; means including a natural language processing engine for analyzing the task input by the user and extracting specific keywords; means for utilizing multiple artificial intelligence models for generating solutions from multicultural and multi-perspectives; means for integrating the generated solutions and optimally formatting them; and means for displaying the integrated solution on a display device. This allows the user to gain new perspectives from diverse viewpoints and quickly obtain multifaceted solutions.

[0801] Word definition sentence

[0802] "User" refers to a person who uses the system to input issues and problems.

[0803] "Issue" refers to a problem or question requiring a solution that a user enters into the system.

[0804] A "natural language processing engine" refers to a software component that analyzes input text and extracts specific keywords.

[0805] "Cultural background" refers to the collection of history, customs, values, etc. that exist in different regions and countries.

[0806] A "specialty" refers to an area that requires concentrated specific knowledge or skills.

[0807] "Solution" refers to a specific measure or proposal provided to address a user's issue or problem.

[0808] An "artificial intelligence model" refers to an algorithm or system designed to perform a specific task based on training data.

[0809] "Format" refers to the standards or rules for arranging information in a particular form or structure.

[0810] "Display device" refers to a device for visually presenting the generated solution to a user.

[0811] A "multicultural perspective" refers to an approach to solving problems from the perspectives of various cultures.

[0812] "Multiple perspectives" refers to an approach that looks at a single issue from multiple different angles or perspectives.

[0813] MODE FOR CARRYING OUT THE INVENTION

[0814] The present invention is a system that provides multifaceted solutions to problems and issues entered by a user. This system includes a process that receives and analyzes problems entered by a user through a device such as a smartphone, generates solutions from multiple cultural and multi-perspective perspectives, and provides them to the user.

[0815] System Program

[0816] The system consists of the following components:

[0817] 1. User device: A device such as a smartphone or tablet that is equipped with an interface for users to input tasks.

[0818] 2. Server: Located in a cloud environment, it performs the primary processing of analyzing received challenges and generating solutions. Specifically, it includes a database, a natural language processing engine, multiple artificial intelligence models, and software components for integrating and formatting solutions.

[0819] 3. Display device: Present the solution visually on the user's terminal or other device.

[0820] Explanation of program processing

[0821] The server receives assignments sent from user devices and temporarily stores them in a database. It then uses a natural language processing engine (e.g., the transformers library) to analyze the received assignments and extract specific keywords. This analysis process clarifies the assignment's category and related keywords.

[0822] Based on the analysis results, the server utilizes multiple artificial intelligence models to generate multicultural and multi-perspective solutions, for example, one model generates a "multicultural" solution and another model generates a "multi-perspective" solution.

[0823] The generated solutions are then integrated and optimally formatted by the server, after which the formatted solution is sent to the display device of the user terminal and presented to the user.

[0824] Specific examples

[0825] For example, if the problem "Sales are sluggish" is input, a solution will be generated using the following steps:

[0826] Step 1:

[0827] The problem "Sales are sluggish" is input from the user terminal.

[0828] Step 2:

[0829] The server receives this assignment and analyzes it using a natural language processing engine. As a result of the analysis, keywords such as "renewal case studies," "promotion," "display improvement," and "online marketing" are extracted.

[0830] Step 3:

[0831] The server generates solutions using multiple artificial intelligence models. For example, it generates "success stories in Japan" and "overseas promotion strategies" from a multicultural perspective, and "ways to improve displays" and "techniques for utilizing online marketing" from multiple perspectives.

[0832] Step 4:

[0833] These solutions are integrated, formatted in an optimal form, and presented on the display device of the user terminal.

[0834] Example prompt sentence:

[0835] A user has entered the issue of "Sales are declining." The following keywords have been extracted: "Renewal case study," "Promotion," "Display improvement," and "Online marketing." Based on this, please propose a solution from a multicultural and multi-perspective perspective.

[0836] In this way, the system of the present invention allows users to gain new perspectives from a variety of viewpoints and quickly obtain multifaceted solutions, thereby providing effective solutions to the problems users face.

[0837] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0838] Program processing steps

[0839] Step 1: User enters assignment

[0840] Input: The user inputs the assignment using a smartphone or tablet.

[0841] Operation: The user terminal inputs the assignment through the interface on the web browser and sends the assignment to the system.

[0842] Output: The submitted assignment arrives at the server.

[0843] Step 2: Receive and save the assignment

[0844] Input: The assignment submitted from the user's device.

[0845] How it works: The server receives assignments submitted by users and stores them temporarily in a database.

[0846] Output: The assignment saved in the database.

[0847] Step 3: Analyze the issue

[0848] Input: Issues stored in the database.

[0849] How it works: The server uses a natural language processing engine (e.g. the transformers library) to parse the incoming issue. The parsing process extracts the issue's category and associated keywords.

[0850] Output: Extracted keywords and categories.

[0851] Step 4: Generating solutions – from a multicultural perspective

[0852] Input: Extracted keywords and categories.

[0853] How it works: The server uses an artificial intelligence model to generate solutions from a multicultural perspective. Based on keywords, it generates solutions from a multicultural perspective.

[0854] Output: Solutions from a multicultural perspective.

[0855] Step 5: Solution Generation - Multiple Perspectives

[0856] Input: Extracted keywords and categories.

[0857] How it works: The server uses an artificial intelligence model to generate solutions from multiple perspectives. Based on keywords, it generates solutions from multiple perspectives.

[0858] Output: Multi-perspective solution.

[0859] Step 6: Integrate and format the solution

[0860] Input: Multicultural and multiperspective solutions.

[0861] How it works: The server combines the generated solutions and formats them into the most suitable format.

[0862] Output: Integrated solution.

[0863] Step 7: Presenting an integrated solution

[0864] Input: Integrated solution.

[0865] Operation: The server sends the integrated solution to the user terminal, which displays it visually on its display device.

[0866] Output: The solution presented to the user.

[0867] Specific examples of operation

[0868] Step 1:

[0869] The user enters the issue of "poor sales" into a smartphone app.

[0870] Step 2:

[0871] The user terminal transmits this assignment data to the server, which receives it and stores it in a database.

[0872] Step 3:

[0873] The server launches a natural language processing engine, analyzes the problem text, and extracts keywords such as "renewal case studies," "promotion," "display improvement," and "online marketing."

[0874] Step 4:

[0875] The server runs an artificial intelligence model using the extracted keywords to generate solutions from a multicultural perspective, generating "success stories in Japan" and "overseas promotion strategies."

[0876] Step 5:

[0877] The server runs another artificial intelligence model using the extracted keywords to generate multi-perspective solutions, generating "ways to improve display" and "techniques for utilizing online marketing."

[0878] Step 6:

[0879] The server aggregates these solutions and formats them into a format that is easy for the user to understand.

[0880] Step 7:

[0881] The server transmits the integrated solution to the user terminal, which displays it visually on its display device.

[0882] In this way, users can quickly and effectively access solutions from multiple perspectives.

[0883] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0884] The present invention provides a system that provides multifaceted solutions to user-entered issues and problems. This system recognizes the user's emotions and combines them with an emotion engine to provide more optimized solutions.

[0885] User input of assignments

[0886] Users input the issues or problems they want to solve through a web browser interface, and the information they enter is sent to the server as an issue.

[0887] Receiving and saving assignments

[0888] The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

[0889] Analysis of the problem

[0890] The server analyzes the received and saved assignments using a natural language processing (NLP) engine. This analysis process extracts the assignment's category and related keywords. For example, if the assignment is "Looking for new methods for sustainable agriculture," the server extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0891] Emotion recognition

[0892] The device uses an emotion engine to recognize the user's emotions. To do this, it analyzes the user's facial expressions, voice, and keyboard typing patterns when typing. The emotion engine extracts information such as whether the user is tense or relaxed.

[0893] Solution Generation

[0894] The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. Specific examples include a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "Potential for urban agriculture through vertical farming."

[0895] Emotion-Based Adjustment

[0896] The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine. It changes the tone and content of the proposed solutions depending on the user's emotions. For example, if the user is feeling anxious, it provides more specific and detailed solutions, while if the user is relaxed, it suggests new and creative ideas.

[0897] Solution synthesis and presentation

[0898] The server then consolidates the generated solutions into a coherent format, correcting each solution as needed and formatting it for easy viewing. The server then presents the resulting solution to the user through a web browser interface, allowing the user to see a list of solutions from different perspectives.

[0899] Specific examples

[0900] For example, a specific example will be given in which the task "I am looking for new methods for sustainable agriculture" is entered.

[0901] The user inputs the problem, which is received and stored by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The device uses an emotion engine to recognize the user's emotions and determine that the user is feeling anxious. The server generates solutions based on the analysis results using a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "Possibilities for urban agriculture through vertical farming." The server adjusts the solutions to provide detailed and specific information to alleviate the user's anxiety. Finally, the server integrates these solutions, presents them to the user in an easy-to-read format, and provides specific, detailed solutions such as "Sustainable methods based on African agricultural technology" and "Examples of vertical farming applications in urban areas."

[0902] Through these steps, the system of the present invention provides the user with a variety of optimal solutions and makes adjustments according to the user's emotional state, thereby supporting more effective problem-solving.

[0903] The processing flow will be explained below.

[0904] Step 1:

[0905] Users enter the issue or problem they want to solve through a web browser interface by typing the issue into the input field and clicking the submit button.

[0906] Step 2:

[0907] The server receives assignments submitted by users, which are temporarily stored in a database, ready for analysis.

[0908] Step 3:

[0909] The device uses an emotion engine to analyze the user's facial expressions, voice, and keyboard typing patterns to extract emotional information. For example, if the user is feeling anxious, the emotion engine will label it as "anxiety."

[0910] Step 4:

[0911] The server calls a natural language processing (NLP) engine to analyze the received and saved assignments. During this analysis process, the assignment category and related keywords are extracted. For example, if the assignment is "Looking for new methods for sustainable agriculture," the following will be extracted: "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[0912] Step 5:

[0913] The server generates solutions from multiple perspectives based on the analysis results and emotional information. Specific examples include a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "application examples of agricultural technology in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

[0914] Step 6:

[0915] The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine: if the user is anxious, it provides detailed and specific solutions, and if the user is relaxed, it adjusts to suggest novel and creative ideas.

[0916] Step 7:

[0917] The server then consolidates the generated solutions into a consistent format, correcting each solution and formatting it in a way that is easy to read.

[0918] Step 8:

[0919] The server presents the prepared solutions to the user through a web browser interface, allowing the user to see a list of solutions from different perspectives.

[0920] Step 9:

[0921] Users can refer to the presented solutions and use them to solve their own problems as needed, which can give users new perspectives and ideas.

[0922] Through these steps, the system of the present invention provides the user with a variety of optimal solutions and makes adjustments according to the user's emotional state, thereby supporting more effective problem-solving.

[0923] Example 2

[0924] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0925] Conventional problem-solving systems do not provide solutions that take into account the user's emotional state, and are therefore unable to provide suggestions that are optimized for the user's emotions. Furthermore, solutions based on analysis results from different cultural backgrounds or specialized fields are also limited. This means that the specific and multifaceted solutions desired by users are not being provided adequately.

[0926] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0927] In this invention, the server includes means for receiving any task or problem input by the user, means for analyzing the received task or problem, means for recognizing the user's emotional state, means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results and the user's emotional state, and means for presenting the generated solutions to the user. This makes it possible to provide optimal solutions tailored to the user's emotions and present multifaceted solutions from different perspectives.

[0928] A "user" is a person or organization that uses the system to input issues and problems and receive solutions.

[0929] "Issues and problems" are matters or problems that users wish to solve through the system.

[0930] "Means for receiving" is a function that allows the system to receive issues and problems sent by users.

[0931] "Means of analysis" refers to the technology and algorithms used to analyze received issues and problems and understand their content.

[0932] "Means for recognizing emotional states" refers to technologies and algorithms for analyzing and determining the user's emotions.

[0933] "Different cultural backgrounds" refer to the cultures, customs, and values ​​of different regions, countries, and societies.

[0934] A "specialty" is knowledge or skills specialized in a particular field or occupation.

[0935] The "means for generating a solution" is a function for generating an appropriate solution based on the analysis results and the user's emotional state.

[0936] "Presentation means" is a function for showing the generated solution to the user.

[0937] A "system" is a comprehensive device or software that performs a series of processes, including receiving and analyzing a user's problem, and generating and presenting a solution.

[0938] The present invention is a system that provides multifaceted and optimal solutions to issues and problems input by a user. The system includes technology that recognizes the user's emotional state and adjusts solutions based on that state. Specific embodiments of the present invention are described below.

[0939] System Configuration

[0940] User assignment input

[0941] Users enter the problem or issue they want to solve through a web browser interface, which consists of a simple web page with text input fields and a submit button, and the information entered by the user is sent to the server as an HTTP request.

[0942] Receiving and saving assignments

[0943] The server receives the tasks submitted by users and temporarily stores them in a database. This reception process is performed using a common web server and database management system. For example, Apache or Nginx is used as the front end, and MySQL or PostgreSQL is used as the database.

[0944] Analysis of the problem

[0945] The server analyzes the received and stored assignments using a natural language processing (NLP) engine, which can use libraries such as OpenNLP or NLTK, to extract assignment categories and related keywords.

[0946] Examples:

[0947] If a user types in "I'm looking for new methods for sustainable agriculture," the NLP engine will extract "Category: Agriculture, Keywords: Sustainability, New Methods."

[0948] Emotion recognition

[0949] The device recognizes the user's emotions using an emotion engine, which can be, for example, Microsoft's Azure Cognitive Services or IBM's Watson. This engine analyzes the user's facial expressions, voice, and keyboard typing patterns when typing to determine the user's emotional state.

[0950] Examples:

[0951] Based on the video and audio data when the user inputs the task, the emotion engine returns "Emotion: Anxiety."

[0952] Solution Generation

[0953] The server generates solutions using multiple AI models based on the analysis results and the user's emotional state, inputting specific prompts into each AI model. For example, a generative AI model such as GPT-3 can be used.

[0954] Examples:

[0955] Prompt for multicultural AI model: "Examples of agricultural technology applications in Africa"

[0956] Prompt for multi-perspective AI model: "The potential of vertical farming in urban agriculture"

[0957] Emotion-Based Adjustment

[0958] The server adjusts the generated solution based on the emotion recognition results, for example, changing the solution to be more specific and detailed if the user feels anxious.

[0959] Examples:

[0960] We will add specific procedures and fee information to the "Examples of Application of Agricultural Technology in Africa" ​​section and adjust the content to reduce user concerns.

[0961] Solution synthesis and presentation

[0962] The server then aggregates the generated solutions, formats them, and presents them to the user, for example by using HTML and CSS to display the solutions in a user-friendly format on a web browser.

[0963] Examples:

[0964] The server consolidates "Examples of agricultural technology applications in Africa" ​​and "Potential for urban agriculture through vertical farming" into a report format, and the solution is displayed when the user reloads the browser.

[0965] In this way, the system of the present invention provides optimal solutions that match the user's emotions and presents multifaceted solutions from different perspectives.

[0966] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0967] Step 1: User enters assignment

[0968] Users enter the issue or problem they want to solve through a web browser interface, which involves describing the issue in a text input field and clicking a "Submit" button.

[0969] Input: Text data about the issue or problem (e.g., "We are looking for new methods for sustainable agriculture.")

[0970] Output: Issue data sent to the server as an HTTP request

[0971] Step 2: Receive and save the assignment

[0972] The server receives the submitted assignment and stores it in a database, which may also validate the format according to the database schema before storing the assignment.

[0973] Input: Issue data sent as an HTTP request

[0974] Output: Issue data stored in a database (e.g., "Issue ID: 12345, Subject: Finding new methods for sustainable agriculture")

[0975] Step 3: Analyze the issue

[0976] The server passes the received assignments to a natural language processing (NLP) engine for analysis, which extracts assignment categories and related keywords.

[0977] Input: Project data stored in the database (e.g., "Project ID: 12345, Content: Finding new methods for sustainable agriculture")

[0978] Data processing and data calculation: NLP engine analyzes the text and extracts important categories and keywords

[0979] Output: Extracted categories and keywords (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods")

[0980] Step 4: Recognize emotions

[0981] The device uses an emotion engine to analyze the user's emotional state. In this step, the user's facial expressions, voice, and keyboard typing patterns are used as input data.

[0982] Input: User's facial expression data, voice data, keyboard keystroke data

[0983] Data processing and calculation: The emotion engine analyzes this data to identify the user's emotional state.

[0984] Output: User's emotional state (e.g., "Emotion: Anxiety")

[0985] Step 5: Generate a solution

[0986] The server generates a solution using multiple AI models based on the analysis results and the emotional state. In this step, each AI model is given an appropriate prompt to generate a solution.

[0987] Input: Analysis results and emotional state (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods, Emotion: Anxiety")

[0988] Data processing and data calculation: Input a prompt statement to each AI model and generate a solution based on it.

[0989] Output: Generated solutions (e.g., "Application of agricultural technology in Africa" ​​and "Possibilities for urban agriculture through vertical farming")

[0990] Step 6: Emotional Adjustment

[0991] The server adjusts the solution based on the user's emotional information recognized by the emotion engine: if the user feels anxious, the solution becomes more specific and detailed.

[0992] Input: User's emotional state and generated solution

[0993] Data processing and data calculation: Modify and adjust parts of the solution based on emotional information

[0994] Output: Tailored solution (e.g., "Example of agricultural technology application in Africa - add specific steps and pricing information")

[0995] Step 7: Synthesis and presentation of the solution

[0996] The server aggregates the solutions, formats them, and presents them to the user in an easy-to-read format on a web browser.

[0997] Input: Tailored solutions (e.g., "Applications of agricultural technology in Africa," "Potential for urban agriculture through vertical farming")

[0998] Data processing and data calculation: Integrating solutions and formatting with HTML and CSS

[0999] Output: The integrated solution displayed in the user's browser

[1000] This completes the entire system process, allowing the user to see a multifaceted and emotionally optimized solution.

[1001] (Application example 2)

[1002] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1003] Conventional systems can provide multifaceted solutions to problems entered by users, but because they do not optimize solutions taking into account the user's emotions, it is difficult to present more appropriate solutions to individual users. Furthermore, there is a lack of methods for tailoring solutions to the user's emotional state and providing personalized information. Therefore, to increase user satisfaction, a system that recognizes the user's emotions and adjusts solutions based on them is needed.

[1004] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1005] In this invention, the server includes means for receiving any task or problem input by a user, means for analyzing the received task or problem, means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results, means for recognizing the user's emotions, means for adjusting the generated solutions based on the user's emotions, and means for presenting the generated solutions to the user, thereby making it possible to provide personalized solutions according to the user's emotional state.

[1006] The "means for receiving any task or problem input by the user" is an interface that receives data such as text, voice, and images input by the user and transmits it to the server.

[1007] "Means for analyzing received issues and problems" refers to analytical functions such as a natural language processing engine that analyzes received data and extracts relevant keywords and categories.

[1008] "Means for generating solutions from different cultural backgrounds and fields of expertise" refers to a function for generating solutions from various cultures and fields of expertise using multicultural AI models and multi-perspective AI models.

[1009] The "means for recognizing the user's emotions" is an emotion engine that analyzes the user's facial expressions, voice, input patterns, etc., and recognizes their emotional state.

[1010] The "means for adjusting the generated solution based on the user's emotions" is a function for appropriately adjusting the tone and content of the solution depending on the user's emotional state recognized by the emotion engine.

[1011] The "means for presenting the generated solution to the user" is an interface for arranging the adjusted solution in an easy-to-view format and displaying it to the user.

[1012] The present invention is a system that provides multifaceted solutions to issues and problems input by users. In particular, it includes a function that recognizes the user's emotions and adjusts the solutions accordingly. The system's components include the following:

[1013] First, a user interface on a smartphone or PC is used to receive any assignments or problems entered by the user. Through this interface, the user inputs the assignments by text or voice. The received assignments or problems are then sent to the server.

[1014] Next, a server is installed as a means of analyzing the received issues and problems. This server uses a natural language processing (NLP) engine to analyze the issues and extract related keywords and categories. For example, if a user types "I want to know about recent environmental issues," the server will extract "Category: Environment" and "Keywords: Recent Environmental Issues."

[1015] Furthermore, the server uses multicultural and multi-perspective AI models to generate solutions from different cultural backgrounds and areas of expertise. These multiple AI models generate solutions from different perspectives and address user issues from multiple angles.

[1016] The device is also equipped with an emotion engine to recognize the user's emotions. This allows it to analyze the user's emotional state from their facial expressions, voice, keystroke patterns, etc. For example, it can recognize whether the user is excited or anxious.

[1017] The server also has the means to tailor the generated solutions based on the user's emotions. It adjusts the content and tone of the solutions depending on the user's perceived emotional state. For example, if the user is anxious, it will provide a more specific and detailed solution, while if the user is relaxed, it will suggest more creative ideas.

[1018] Finally, the server presents the generated solutions to the user through a user interface after arranging them in an easy-to-read format, allowing the user to check the solutions from different perspectives in a list format.

[1019] As a concrete example, consider the case where a user types, "What environmental issues are you interested in these days? Please tell me specifically." In this case, the server analyzes the relevant data, and the emotion engine analyzes the user's emotional state. Next, using the multicultural AI model and multi-perspective AI model, it generates solutions such as "The latest research on global warming" or "The current state and future of plastic pollution." These solutions are then tailored to appeal to the user's interest and proposed to them through the user interface.

[1020] In this way, the present invention can analyze the issues and problems entered by the user from multiple angles, provide optimal solutions from different cultures and fields of expertise, and even adjust them according to the user's emotional state.

[1021] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1022] Step 1:

[1023] Receive any issues or problems entered by the user.

[1024] Specifically, users input tasks via text or voice through a user interface on their smartphone or PC. This input data (tasks and problems) becomes the input data for the system. At this stage, the input data is sent to the server, which then receives it.

[1025] Step 2:

[1026] Analyze received issues and problems.

[1027] The server uses a natural language processing (NLP) engine to analyze the input data received from the user. The data to be analyzed is the text and voice data entered by the user. Specifically, this analysis process extracts related keywords and categories. For example, if the input is "I want to know about recent environmental issues," "Category: Environment" and "Keywords: Recent Environmental Issues" will be extracted.

[1028] Step 3:

[1029] Generate solutions from different cultural backgrounds and disciplines.

[1030] The server generates solutions from different cultural backgrounds and fields of expertise based on the extracted keywords and categories. For this purpose, it uses multicultural and multi-perspective AI models. For example, based on "Category: Environment" and "Keywords: Recent Environmental Issues," it generates solutions such as "Latest Research on Global Warming" and "Current Status and Future of Plastic Pollution."

[1031] Step 4:

[1032] Recognize user emotions.

[1033] The emotion engine installed on the user's device analyzes the user's facial expressions, voice, and input patterns to recognize their emotional state. This data includes facial recognition data, voice data, and typing patterns. For example, it recognizes whether the user is feeling anxious or excited. This emotional information becomes input data for adjusting the generated solution in the next step.

[1034] Step 5:

[1035] The generated solutions are adjusted based on the user's sentiment.

[1036] The server adjusts the content and tone of the generated solution based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if the user is feeling relaxed, it will suggest a more creative idea. In this process, emotional data and solution data are input, and adjusted solution data is output.

[1037] Step 6:

[1038] The generated solution is presented to the user.

[1039] The server then formats the adjusted solutions into an easy-to-read format and presents them to the user through a user interface. In this step, the formatted solution data is input and display data for the user is output. Specifically, the solutions are presented in a list format on the web browser, allowing the user to check them. For example, "Recent Latest Research on Global Warming" and "The Current State and Future of Plastic Pollution" are displayed.

[1040] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1041] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1042] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1043] [Fourth embodiment]

[1044] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1045] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1046] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1047] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1048] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1049] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1050] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1051] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1052] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1053] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1054] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1055] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1056] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1057] The present invention is a system that provides multifaceted solutions to user-entered issues and problems. This system is implemented through a process of receiving issues, analyzing them, generating solutions from different cultural backgrounds and areas of expertise, and presenting them to the user.

[1058] User input of assignments

[1059] Users input the issues or problems they want to solve through a web browser interface, and the information they enter is sent to the server as an issue.

[1060] Receiving and saving assignments

[1061] The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

[1062] Analysis of the problem

[1063] The server analyzes the received assignment using a natural language processing (NLP) engine. This analysis process extracts the category and related keywords of the input assignment. For example, if the input assignment is "I am looking for new methods for sustainable agriculture," the NLP engine extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[1064] Solution Generation

[1065] The server utilizes multiple AI models based on the analysis results to generate solutions from multiple perspectives. Specifically, it generates solutions using a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

[1066] Solution synthesis and presentation

[1067] The server then integrates the generated solutions and formats them before presenting them to the user. The integrated solution is displayed to the user through a web browser interface, allowing the user to see the solution from multiple perspectives.

[1068] Specific examples

[1069] For example, a specific example will be given in which the task "I am looking for new methods for sustainable agriculture" is entered.

[1070] The user enters a problem, which is received and saved by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The server generates solutions based on the analysis results, utilizing a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of the application of agricultural technology in Africa," and the multi-perspective AI model generates "Possibilities for urban agriculture using vertical farming." The server integrates these solutions and presents them to the user in the optimal format. Ultimately, the user is able to obtain solutions such as "Sustainable methods based on African agricultural technology" and "Examples of the application of vertical farming in urban areas."

[1071] The system of the present invention allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, thereby enabling effective problem solving.

[1072] The processing flow will be explained below.

[1073] Step 1:

[1074] Users enter the issue or problem they want to solve through a web browser interface, enter the issue in the input field, and click the submit button to send the issue to the system.

[1075] Step 2:

[1076] The server receives assignments sent by users and stores them temporarily in a database for later analysis.

[1077] Step 3:

[1078] The server calls a natural language processing (NLP) engine to analyze the received and saved assignments. This analysis process extracts the assignment's category and related keywords. For example, for the assignment "Looking for new methods for sustainable agriculture," the following will be extracted: "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[1079] Step 4:

[1080] The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. Specific examples include a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "application examples of agricultural technology in Africa," while the multi-perspective AI model generates "the potential of urban agriculture through vertical farming."

[1081] Step 5:

[1082] The server then consolidates the generated solutions into a consistent format, correcting each solution as necessary and formatting it for easy viewing.

[1083] Step 6:

[1084] The server presents the prepared solutions to the user through a web browser interface, allowing the user to see a list of solutions from different perspectives.

[1085] Step 7:

[1086] Users can refer to the presented solutions and use them to solve their own problems as needed, which can give users new perspectives and ideas.

[1087] The system of the present invention provides the user with a variety of solutions through these steps, and supports effective problem solving.

[1088] Example 1

[1089] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1090] In today's increasingly diverse society, the challenges and problems users face are complex, making it difficult to provide effective solutions from a single perspective or approach. Furthermore, integrating knowledge from different cultural backgrounds and fields of expertise requires collaboration and cooperation among people with expertise in each field. However, the systems and methods that enable this are not well established, making it difficult for users to quickly obtain multifaceted solutions.

[1091] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1092] In this invention, the server includes means for receiving any task or problem input by a user, means for temporarily storing the received task or problem in a database, means for analyzing the stored task or problem using a natural language processing engine and extracting categories and related keywords of the input task, means for utilizing multiple AI models to generate solutions from different cultural backgrounds and fields of expertise based on the analysis results, means for integrating the generated multiple solutions and formatting them before presenting them to the user, and means for displaying the formatted solutions to the user, thereby enabling the user to quickly obtain multifaceted and effective solutions.

[1093] "User" refers to the entity that uses the system to input issues and problems and receive solutions.

[1094] "Server" refers to the computer system that receives and analyzes input from users and generates and presents solutions.

[1095] "Challenges and problems" refer to specific situations or difficult situations that users are seeking solutions to.

[1096] "Means for receiving" refers to the function by which the server acquires and stores information entered by the user.

[1097] A "database" refers to an information storage system for temporarily storing and managing data such as issues and problems.

[1098] A "natural language processing engine" is a software module for analyzing human language and extracting categories and keywords for input issues or problems.

[1099] "Means for analysis" refers to the function of analyzing received issues and problems using a natural language processing engine and extracting relevant information.

[1100] "AI model" refers to a model trained using artificial intelligence algorithms to solve a specific problem.

[1101] A "multicultural AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different cultural backgrounds.

[1102] A "multi-perspective AI model" refers to an AI model that generates solutions by utilizing knowledge and data from different specialized fields.

[1103] "Means for generating solutions" refers to the function of generating specific solutions to problems using multicultural AI models and multi-perspective AI models.

[1104] "Means of integration" refers to the function of combining and shaping multiple generated solutions into one.

[1105] "Formatting facilities" refers to the ability to adjust the format and presentation of a solution before presenting it to the user.

[1106] "Means for displaying" refers to the function of presenting the formatted solution to the user in an easy-to-read format.

[1107] The present invention relates to a system for providing multifaceted solutions to user-entered problems and issues. The system receives user-entered problems, stores them in a database, analyzes them using a natural language processing (NLP) engine, and generates solutions using multiple AI models based on the analysis results. The system also integrates the generated solutions, formats them, and presents them to the user.

[1108] Users input the problem they want to solve through a web browser, for example, a specific prompt such as "I'm looking for new methods for sustainable agriculture."

[1109] Once the input is complete, the server receives it and temporarily stores it in an issue database, preferably using a common relational database such as MySQL or PostgreSQL. The saved issue data is also assigned metadata such as a timestamp and user ID.

[1110] Next, the server retrieves the saved task data and analyzes it using an NLP engine. The NLP engine can use natural language processing libraries such as SpaCy or NLTK. Analysis steps include tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, for the task "We are looking for new methods for sustainable agriculture," the results would be "Category: Agriculture," "Keywords: Sustainability, New Methods."

[1111] Based on the analysis results, the server generates solutions using a multicultural AI model and a multi-perspective AI model. These AI models provide solutions from different cultural backgrounds and areas of expertise. For example, the multicultural AI model generates "Examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "Potential for urban agriculture through vertical farming."

[1112] The generated solutions are then integrated and formatted by the server, which includes merging and formatting the text, and the formatted solutions are then presented to the user through a web browser.

[1113] This allows users to gain new perspectives from diverse cultural backgrounds and fields of expertise, rather than being limited to a single viewpoint, enabling more effective problem-solving. For example, users can obtain specific solutions such as "sustainable methods based on African agricultural techniques" or "examples of applying vertical farming in urban areas."

[1114] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1115] Step 1: User enters assignment

[1116] Users input the issues or problems they want to solve through a web browser interface. For example, they might type, "Please provide us with some innovative ideas for managing green spaces in urban areas." Once input is complete, this information is sent to the server via an HTML form.

[1117] Input: The prompt text entered by the user

[1118] Output: User input data is sent to the server

[1119] Step 2: The server receives and stores the assignment

[1120] The server receives HTTP requests and retrieves the assignment data sent by the user. The retrieved data is temporarily stored in a relational database such as MySQL or PostgreSQL. Metadata such as timestamps and user IDs are also added to the data for later analysis.

[1121] Input: Issue data from the user

[1122] Output: Issue data stored in a database

[1123] Step 3: The server analyzes the issue

[1124] The server retrieves the saved assignment data and analyzes it using an NLP engine such as SpaCy or NLTK. This analysis process includes tokenization, part-of-speech tagging, category extraction, and keyword extraction. For example, if the assignment "Looking for new methods for sustainable agriculture" is input, the NLP engine extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[1125] Input: Issue data retrieved from the database

[1126] Output: Category and keyword information

[1127] Step 4: The server generates a solution

[1128] The server generates solutions using a multicultural AI model and a multi-perspective AI model based on the analysis results. The analysis results are input as prompts into the multicultural AI model, which generates solutions from different cultural backgrounds. For example, it generates "Examples of green space management in European urban planning." The same analysis results are input as prompts into the multi-perspective AI model, which generates solutions based on specialized fields. For example, it generates "New methods for managing green spaces in urban areas using technology."

[1129] Input: Analysis results (category and keyword information)

[1130] Output: Multiple solutions

[1131] Step 5: The server synthesizes and presents the solution

[1132] The server integrates and formats the generated solutions. Specifically, it merges the solution text and formats it into a visually easy-to-read form. The formatted solution is saved back in the database and displayed to the user via a web browser. The user can view the multiple solutions and consider approaches to solving the problem from multiple perspectives.

[1133] Input: Multiple solutions

[1134] Output: A consolidated and formatted solution

[1135] These are the main processing steps of this system, which allows users to quickly obtain solutions from a variety of perspectives, enabling more effective problem solving.

[1136] (Application example 1)

[1137] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1138] Existing solution-providing systems rely on a single cultural background or field of expertise, making it difficult to obtain solutions from diverse perspectives. Furthermore, they lack the functionality to effectively analyze users' problems, extract relevant keywords, and generate and present solutions from multiple cultural and perspectives. Therefore, there is a need for systems that can quickly provide optimal solutions to users' problems.

[1139] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1140] In this invention, the server includes: means for receiving any task or problem input by a user; means for analyzing the received task or problem; means for generating solutions from different cultural backgrounds and fields of expertise based on the analysis results; means for presenting the generated solution to the user; means including a natural language processing engine for analyzing the task input by the user and extracting specific keywords; means for utilizing multiple artificial intelligence models for generating solutions from multicultural and multi-perspectives; means for integrating the generated solutions and optimally formatting them; and means for displaying the integrated solution on a display device. This allows the user to gain new perspectives from diverse viewpoints and quickly obtain multifaceted solutions.

[1141] Word definition sentence

[1142] "User" refers to a person who uses the system to input issues and problems.

[1143] "Issue" refers to a problem or question requiring a solution that a user enters into the system.

[1144] A "natural language processing engine" refers to a software component that analyzes input text and extracts specific keywords.

[1145] "Cultural background" refers to the collection of history, customs, values, etc. that exist in different regions and countries.

[1146] A "specialty" refers to an area that requires concentrated specific knowledge or skills.

[1147] "Solution" refers to a specific measure or proposal provided to address a user's issue or problem.

[1148] An "artificial intelligence model" refers to an algorithm or system designed to perform a specific task based on training data.

[1149] "Format" refers to the standards or rules for arranging information in a particular form or structure.

[1150] "Display device" refers to a device for visually presenting the generated solution to a user.

[1151] A "multicultural perspective" refers to an approach to solving problems from the perspectives of various cultures.

[1152] "Multiple perspectives" refers to an approach that looks at a single issue from multiple different angles or perspectives.

[1153] MODE FOR CARRYING OUT THE INVENTION

[1154] The present invention is a system that provides multifaceted solutions to problems and issues entered by a user. This system includes a process that receives and analyzes problems entered by a user through a device such as a smartphone, generates solutions from multiple cultural and multi-perspective perspectives, and provides them to the user.

[1155] System Program

[1156] The system consists of the following components:

[1157] 1. User device: A device such as a smartphone or tablet that is equipped with an interface for users to input tasks.

[1158] 2. Server: Located in a cloud environment, it performs the primary processing of analyzing received challenges and generating solutions. Specifically, it includes a database, a natural language processing engine, multiple artificial intelligence models, and software components for integrating and formatting solutions.

[1159] 3. Display device: Present the solution visually on the user's terminal or other device.

[1160] Explanation of program processing

[1161] The server receives assignments sent from user devices and temporarily stores them in a database. It then uses a natural language processing engine (e.g., the transformers library) to analyze the received assignments and extract specific keywords. This analysis process clarifies the assignment's category and related keywords.

[1162] Based on the analysis results, the server utilizes multiple artificial intelligence models to generate multicultural and multi-perspective solutions, for example, one model generates a "multicultural" solution and another model generates a "multi-perspective" solution.

[1163] The generated solutions are then integrated and optimally formatted by the server, after which the formatted solution is sent to the display device of the user terminal and presented to the user.

[1164] Specific examples

[1165] For example, if the problem "Sales are sluggish" is input, a solution will be generated using the following steps:

[1166] Step 1:

[1167] The problem "Sales are sluggish" is input from the user terminal.

[1168] Step 2:

[1169] The server receives this assignment and analyzes it using a natural language processing engine. As a result of the analysis, keywords such as "renewal case studies," "promotion," "display improvement," and "online marketing" are extracted.

[1170] Step 3:

[1171] The server generates solutions using multiple artificial intelligence models. For example, it generates "success stories in Japan" and "overseas promotion strategies" from a multicultural perspective, and "ways to improve displays" and "techniques for utilizing online marketing" from multiple perspectives.

[1172] Step 4:

[1173] These solutions are integrated, formatted in an optimal form, and presented on the display device of the user terminal.

[1174] Example prompt sentence:

[1175] A user has entered the issue of "Sales are declining." The following keywords have been extracted: "Renewal case study," "Promotion," "Display improvement," and "Online marketing." Based on this, please propose a solution from a multicultural and multi-perspective perspective.

[1176] In this way, the system of the present invention allows users to gain new perspectives from a variety of viewpoints and quickly obtain multifaceted solutions, thereby providing effective solutions to the problems users face.

[1177] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1178] Program processing steps

[1179] Step 1: User enters assignment

[1180] Input: The user inputs the assignment using a smartphone or tablet.

[1181] Operation: The user terminal inputs the assignment through the interface on the web browser and sends the assignment to the system.

[1182] Output: The submitted assignment arrives at the server.

[1183] Step 2: Receive and save the assignment

[1184] Input: The assignment submitted from the user's device.

[1185] How it works: The server receives assignments submitted by users and stores them temporarily in a database.

[1186] Output: The assignment saved in the database.

[1187] Step 3: Analyze the issue

[1188] Input: Issues stored in the database.

[1189] How it works: The server uses a natural language processing engine (e.g. the transformers library) to parse the incoming issue. The parsing process extracts the issue's category and associated keywords.

[1190] Output: Extracted keywords and categories.

[1191] Step 4: Generating solutions – from a multicultural perspective

[1192] Input: Extracted keywords and categories.

[1193] How it works: The server uses an artificial intelligence model to generate solutions from a multicultural perspective. Based on keywords, it generates solutions from a multicultural perspective.

[1194] Output: Solutions from a multicultural perspective.

[1195] Step 5: Solution Generation - Multiple Perspectives

[1196] Input: Extracted keywords and categories.

[1197] How it works: The server uses an artificial intelligence model to generate solutions from multiple perspectives. Based on keywords, it generates solutions from multiple perspectives.

[1198] Output: Multi-perspective solution.

[1199] Step 6: Integrate and format the solution

[1200] Input: Multicultural and multiperspective solutions.

[1201] How it works: The server combines the generated solutions and formats them into the most suitable format.

[1202] Output: Integrated solution.

[1203] Step 7: Presenting an integrated solution

[1204] Input: Integrated solution.

[1205] Operation: The server sends the integrated solution to the user terminal, which displays it visually on its display device.

[1206] Output: The solution presented to the user.

[1207] Specific examples of operation

[1208] Step 1:

[1209] The user enters the issue of "poor sales" into a smartphone app.

[1210] Step 2:

[1211] The user terminal transmits this assignment data to the server, which receives it and stores it in a database.

[1212] Step 3:

[1213] The server launches a natural language processing engine, analyzes the problem text, and extracts keywords such as "renewal case studies," "promotion," "display improvement," and "online marketing."

[1214] Step 4:

[1215] The server runs an artificial intelligence model using the extracted keywords to generate solutions from a multicultural perspective, generating "success stories in Japan" and "overseas promotion strategies."

[1216] Step 5:

[1217] The server runs another artificial intelligence model using the extracted keywords to generate multi-perspective solutions, generating "ways to improve display" and "techniques for utilizing online marketing."

[1218] Step 6:

[1219] The server aggregates these solutions and formats them into a format that is easy for the user to understand.

[1220] Step 7:

[1221] The server transmits the integrated solution to the user terminal, which displays it visually on its display device.

[1222] In this way, users can quickly and effectively access solutions from multiple perspectives.

[1223] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1224] The present invention provides a system that provides multifaceted solutions to user-entered issues and problems. This system recognizes the user's emotions and combines them with an emotion engine to provide more optimized solutions.

[1225] User input of assignments

[1226] Users input the issues or problems they want to solve through a web browser interface, and the information they enter is sent to the server as an issue.

[1227] Receiving and saving assignments

[1228] The server receives assignments sent by users and temporarily stores them in a database. The received assignments are then analyzed.

[1229] Analysis of the problem

[1230] The server analyzes the received and saved assignments using a natural language processing (NLP) engine. This analysis process extracts the assignment's category and related keywords. For example, if the assignment is "Looking for new methods for sustainable agriculture," the server extracts "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[1231] Emotion recognition

[1232] The device uses an emotion engine to recognize the user's emotions. To do this, it analyzes the user's facial expressions, voice, and keyboard typing patterns when typing. The emotion engine extracts information such as whether the user is tense or relaxed.

[1233] Solution Generation

[1234] The server uses multiple AI models to generate solutions from multiple perspectives based on the analysis results. Specific examples include a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "Potential for urban agriculture through vertical farming."

[1235] Emotion-Based Adjustment

[1236] The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine. It changes the tone and content of the proposed solutions depending on the user's emotions. For example, if the user is feeling anxious, it provides more specific and detailed solutions, while if the user is relaxed, it suggests new and creative ideas.

[1237] Solution synthesis and presentation

[1238] The server then consolidates the generated solutions into a coherent format, correcting each solution as needed and formatting it for easy viewing. The server then presents the resulting solution to the user through a web browser interface, allowing the user to see a list of solutions from different perspectives.

[1239] Specific examples

[1240] For example, a specific example will be given in which the task "I am looking for new methods for sustainable agriculture" is entered.

[1241] The user inputs the problem, which is received and stored by the server. Next, the NLP engine analyzes the problem and identifies "Category: Agriculture" and "Keywords: Sustainability, New Methods." The device uses an emotion engine to recognize the user's emotions and determine that the user is feeling anxious. The server generates solutions based on the analysis results using a multicultural AI model and a multi-perspective AI model. The multicultural AI model generates "Examples of agricultural technology applications in Africa," while the multi-perspective AI model generates "Possibilities for urban agriculture through vertical farming." The server adjusts the solutions to provide detailed and specific information to alleviate the user's anxiety. Finally, the server integrates these solutions, presents them to the user in an easy-to-read format, and provides specific, detailed solutions such as "Sustainable methods based on African agricultural technology" and "Examples of vertical farming applications in urban areas."

[1242] Through these steps, the system of the present invention provides the user with a variety of optimal solutions and makes adjustments according to the user's emotional state, thereby supporting more effective problem-solving.

[1243] The processing flow will be explained below.

[1244] Step 1:

[1245] Users enter the issue or problem they want to solve through a web browser interface by typing the issue into the input field and clicking the submit button.

[1246] Step 2:

[1247] The server receives assignments submitted by users, which are temporarily stored in a database, ready for analysis.

[1248] Step 3:

[1249] The device uses an emotion engine to analyze the user's facial expressions, voice, and keyboard typing patterns to extract emotional information. For example, if the user is feeling anxious, the emotion engine will label it as "anxiety."

[1250] Step 4:

[1251] The server calls a natural language processing (NLP) engine to analyze the received and saved assignments. During this analysis process, the assignment category and related keywords are extracted. For example, if the assignment is "Looking for new methods for sustainable agriculture," the following will be extracted: "Category: Agriculture" and "Keywords: Sustainability, New Methods."

[1252] Step 5:

[1253] The server generates solutions from multiple perspectives based on the analysis results and emotional information. Specific examples include a multicultural AI model and a multi-perspective AI model. For example, the multicultural AI model generates "application examples of agricultural technology in Africa," while the multi-perspective AI model generates "the potential for urban agriculture through vertical farming."

[1254] Step 6:

[1255] The server adjusts the generated solutions based on the user's emotional information recognized by the emotion engine: if the user is anxious, it provides detailed and specific solutions, and if the user is relaxed, it adjusts to suggest novel and creative ideas.

[1256] Step 7:

[1257] The server then consolidates the generated solutions into a consistent format, correcting each solution and formatting it in a way that is easy to read.

[1258] Step 8:

[1259] The server presents the prepared solutions to the user through a web browser interface, allowing the user to see a list of solutions from different perspectives.

[1260] Step 9:

[1261] Users can refer to the presented solutions and use them to solve their own problems as needed, which can give users new perspectives and ideas.

[1262] Through these steps, the system of the present invention provides the user with a variety of optimal solutions and makes adjustments according to the user's emotional state, thereby supporting more effective problem-solving.

[1263] Example 2

[1264] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1265] Conventional problem-solving systems do not provide solutions that take into account the user's emotional state, and are therefore unable to provide suggestions that are optimized for the user's emotions. Furthermore, solutions based on analysis results from different cultural backgrounds or specialized fields are also limited. This means that the specific and multifaceted solutions desired by users are not being provided adequately.

[1266] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1267] In this invention, the server includes means for receiving any task or problem input by the user, means for analyzing the received task or problem, means for recognizing the user's emotional state, means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results and the user's emotional state, and means for presenting the generated solutions to the user. This makes it possible to provide optimal solutions tailored to the user's emotions and present multifaceted solutions from different perspectives.

[1268] A "user" is a person or organization that uses the system to input issues and problems and receive solutions.

[1269] "Issues and problems" are matters or problems that users wish to solve through the system.

[1270] "Means for receiving" is a function that allows the system to receive issues and problems sent by users.

[1271] "Means of analysis" refers to the technology and algorithms used to analyze received issues and problems and understand their content.

[1272] "Means for recognizing emotional states" refers to technologies and algorithms for analyzing and determining the user's emotions.

[1273] "Different cultural backgrounds" refer to the cultures, customs, and values ​​of different regions, countries, and societies.

[1274] A "specialty" is knowledge or skills specialized in a particular field or occupation.

[1275] The "means for generating a solution" is a function for generating an appropriate solution based on the analysis results and the user's emotional state.

[1276] "Presentation means" is a function for showing the generated solution to the user.

[1277] A "system" is a comprehensive device or software that performs a series of processes, including receiving and analyzing a user's problem, and generating and presenting a solution.

[1278] The present invention is a system that provides multifaceted and optimal solutions to issues and problems input by a user. The system includes technology that recognizes the user's emotional state and adjusts solutions based on that state. Specific embodiments of the present invention are described below.

[1279] System Configuration

[1280] User assignment input

[1281] Users enter the problem or issue they want to solve through a web browser interface, which consists of a simple web page with text input fields and a submit button, and the information entered by the user is sent to the server as an HTTP request.

[1282] Receiving and saving assignments

[1283] The server receives the tasks submitted by users and temporarily stores them in a database. This reception process is performed using a common web server and database management system. For example, Apache or Nginx is used as the front end, and MySQL or PostgreSQL is used as the database.

[1284] Analysis of the problem

[1285] The server analyzes the received and stored assignments using a natural language processing (NLP) engine, which can use libraries such as OpenNLP or NLTK, to extract assignment categories and related keywords.

[1286] Examples:

[1287] If a user types in "I'm looking for new methods for sustainable agriculture," the NLP engine will extract "Category: Agriculture, Keywords: Sustainability, New Methods."

[1288] Emotion recognition

[1289] The device recognizes the user's emotions using an emotion engine, which can be, for example, Microsoft's Azure Cognitive Services or IBM's Watson. This engine analyzes the user's facial expressions, voice, and keyboard typing patterns when typing to determine the user's emotional state.

[1290] Examples:

[1291] Based on the video and audio data when the user inputs the task, the emotion engine returns "Emotion: Anxiety."

[1292] Solution Generation

[1293] The server generates solutions using multiple AI models based on the analysis results and the user's emotional state, inputting specific prompts into each AI model. For example, a generative AI model such as GPT-3 can be used.

[1294] Examples:

[1295] Prompt for multicultural AI model: "Examples of agricultural technology applications in Africa"

[1296] Prompt for multi-perspective AI model: "The potential of vertical farming in urban agriculture"

[1297] Emotion-Based Adjustment

[1298] The server adjusts the generated solution based on the emotion recognition results, for example, changing the solution to be more specific and detailed if the user feels anxious.

[1299] Examples:

[1300] We will add specific procedures and fee information to the "Examples of Application of Agricultural Technology in Africa" ​​section and adjust the content to reduce user concerns.

[1301] Solution synthesis and presentation

[1302] The server then aggregates the generated solutions, formats them, and presents them to the user, for example by using HTML and CSS to display the solutions in a user-friendly format on a web browser.

[1303] Examples:

[1304] The server consolidates "Examples of agricultural technology applications in Africa" ​​and "Potential for urban agriculture through vertical farming" into a report format, and the solution is displayed when the user reloads the browser.

[1305] In this way, the system of the present invention provides optimal solutions that match the user's emotions and presents multifaceted solutions from different perspectives.

[1306] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1307] Step 1: User enters assignment

[1308] Users enter the issue or problem they want to solve through a web browser interface, which involves describing the issue in a text input field and clicking a "Submit" button.

[1309] Input: Text data about the issue or problem (e.g., "We are looking for new methods for sustainable agriculture.")

[1310] Output: Issue data sent to the server as an HTTP request

[1311] Step 2: Receive and save the assignment

[1312] The server receives the submitted assignment and stores it in a database, which may also validate the format according to the database schema before storing the assignment.

[1313] Input: Issue data sent as an HTTP request

[1314] Output: Issue data stored in a database (e.g., "Issue ID: 12345, Subject: Finding new methods for sustainable agriculture")

[1315] Step 3: Analyze the issue

[1316] The server passes the received assignments to a natural language processing (NLP) engine for analysis, which extracts assignment categories and related keywords.

[1317] Input: Project data stored in the database (e.g., "Project ID: 12345, Content: Finding new methods for sustainable agriculture")

[1318] Data processing and data calculation: NLP engine analyzes the text and extracts important categories and keywords

[1319] Output: Extracted categories and keywords (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods")

[1320] Step 4: Recognize emotions

[1321] The device uses an emotion engine to analyze the user's emotional state. In this step, the user's facial expressions, voice, and keyboard typing patterns are used as input data.

[1322] Input: User's facial expression data, voice data, keyboard keystroke data

[1323] Data processing and calculation: The emotion engine analyzes this data to identify the user's emotional state.

[1324] Output: User's emotional state (e.g., "Emotion: Anxiety")

[1325] Step 5: Generate a solution

[1326] The server generates a solution using multiple AI models based on the analysis results and the emotional state. In this step, each AI model is given an appropriate prompt to generate a solution.

[1327] Input: Analysis results and emotional state (e.g., "Category: Agriculture, Keywords: Sustainability, New Methods, Emotion: Anxiety")

[1328] Data processing and data calculation: Input a prompt statement to each AI model and generate a solution based on it.

[1329] Output: Generated solutions (e.g., "Application of agricultural technology in Africa" ​​and "Possibilities for urban agriculture through vertical farming")

[1330] Step 6: Emotional Adjustment

[1331] The server adjusts the solution based on the user's emotional information recognized by the emotion engine: if the user feels anxious, the solution becomes more specific and detailed.

[1332] Input: User's emotional state and generated solution

[1333] Data processing and data calculation: Modify and adjust parts of the solution based on emotional information

[1334] Output: Tailored solution (e.g., "Example of agricultural technology application in Africa - add specific steps and pricing information")

[1335] Step 7: Synthesis and presentation of the solution

[1336] The server aggregates the solutions, formats them, and presents them to the user in an easy-to-read format on a web browser.

[1337] Input: Tailored solutions (e.g., "Applications of agricultural technology in Africa," "Potential for urban agriculture through vertical farming")

[1338] Data processing and data calculation: Integrating solutions and formatting with HTML and CSS

[1339] Output: The integrated solution displayed in the user's browser

[1340] This completes the entire system process, allowing the user to see a multifaceted and emotionally optimized solution.

[1341] (Application example 2)

[1342] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1343] Conventional systems can provide multifaceted solutions to problems entered by users, but because they do not optimize solutions taking into account the user's emotions, it is difficult to present more appropriate solutions to individual users. Furthermore, there is a lack of methods for tailoring solutions to the user's emotional state and providing personalized information. Therefore, to increase user satisfaction, a system that recognizes the user's emotions and adjusts solutions based on them is needed.

[1344] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1345] In this invention, the server includes means for receiving any task or problem input by a user, means for analyzing the received task or problem, means for generating solutions from different cultural backgrounds or fields of expertise based on the analysis results, means for recognizing the user's emotions, means for adjusting the generated solutions based on the user's emotions, and means for presenting the generated solutions to the user, thereby making it possible to provide personalized solutions according to the user's emotional state.

[1346] The "means for receiving any task or problem input by the user" is an interface that receives data such as text, voice, and images input by the user and transmits it to the server.

[1347] "Means for analyzing received issues and problems" refers to analytical functions such as a natural language processing engine that analyzes received data and extracts relevant keywords and categories.

[1348] "Means for generating solutions from different cultural backgrounds and fields of expertise" refers to a function for generating solutions from various cultures and fields of expertise using multicultural AI models and multi-perspective AI models.

[1349] The "means for recognizing the user's emotions" is an emotion engine that analyzes the user's facial expressions, voice, input patterns, etc., and recognizes their emotional state.

[1350] The "means for adjusting the generated solution based on the user's emotions" is a function for appropriately adjusting the tone and content of the solution depending on the user's emotional state recognized by the emotion engine.

[1351] The "means for presenting the generated solution to the user" is an interface for arranging the adjusted solution in an easy-to-view format and displaying it to the user.

[1352] The present invention is a system that provides multifaceted solutions to issues and problems input by users. In particular, it includes a function that recognizes the user's emotions and adjusts the solutions accordingly. The system's components include the following:

[1353] First, a user interface on a smartphone or PC is used to receive any assignments or problems entered by the user. Through this interface, the user inputs the assignments by text or voice. The received assignments or problems are then sent to the server.

[1354] Next, a server is installed as a means of analyzing the received issues and problems. This server uses a natural language processing (NLP) engine to analyze the issues and extract related keywords and categories. For example, if a user types "I want to know about recent environmental issues," the server will extract "Category: Environment" and "Keywords: Recent Environmental Issues."

[1355] Furthermore, the server uses multicultural and multi-perspective AI models to generate solutions from different cultural backgrounds and areas of expertise. These multiple AI models generate solutions from different perspectives and address user issues from multiple angles.

[1356] The device is also equipped with an emotion engine to recognize the user's emotions. This allows it to analyze the user's emotional state from their facial expressions, voice, keystroke patterns, etc. For example, it can recognize whether the user is excited or anxious.

[1357] The server also has the means to tailor the generated solutions based on the user's emotions. It adjusts the content and tone of the solutions depending on the user's perceived emotional state. For example, if the user is anxious, it will provide a more specific and detailed solution, while if the user is relaxed, it will suggest more creative ideas.

[1358] Finally, the server presents the generated solutions to the user through a user interface after arranging them in an easy-to-read format, allowing the user to check the solutions from different perspectives in a list format.

[1359] As a concrete example, consider the case where a user types, "What environmental issues are you interested in these days? Please tell me specifically." In this case, the server analyzes the relevant data, and the emotion engine analyzes the user's emotional state. Next, using the multicultural AI model and multi-perspective AI model, it generates solutions such as "The latest research on global warming" or "The current state and future of plastic pollution." These solutions are then tailored to appeal to the user's interest and proposed to them through the user interface.

[1360] In this way, the present invention can analyze the issues and problems entered by the user from multiple angles, provide optimal solutions from different cultures and fields of expertise, and even adjust them according to the user's emotional state.

[1361] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1362] Step 1:

[1363] Receive any issues or problems entered by the user.

[1364] Specifically, users input tasks via text or voice through a user interface on their smartphone or PC. This input data (tasks and problems) becomes the input data for the system. At this stage, the input data is sent to the server, which then receives it.

[1365] Step 2:

[1366] Analyze received issues and problems.

[1367] The server uses a natural language processing (NLP) engine to analyze the input data received from the user. The data to be analyzed is the text and voice data entered by the user. Specifically, this analysis process extracts related keywords and categories. For example, if the input is "I want to know about recent environmental issues," "Category: Environment" and "Keywords: Recent Environmental Issues" will be extracted.

[1368] Step 3:

[1369] Generate solutions from different cultural backgrounds and disciplines.

[1370] The server generates solutions from different cultural backgrounds and fields of expertise based on the extracted keywords and categories. For this purpose, it uses multicultural and multi-perspective AI models. For example, based on "Category: Environment" and "Keywords: Recent Environmental Issues," it generates solutions such as "Latest Research on Global Warming" and "Current Status and Future of Plastic Pollution."

[1371] Step 4:

[1372] Recognize user emotions.

[1373] The emotion engine installed on the user's device analyzes the user's facial expressions, voice, and input patterns to recognize their emotional state. This data includes facial recognition data, voice data, and typing patterns. For example, it recognizes whether the user is feeling anxious or excited. This emotional information becomes input data for adjusting the generated solution in the next step.

[1374] Step 5:

[1375] The generated solutions are adjusted based on the user's sentiment.

[1376] The server adjusts the content and tone of the generated solution based on the user's emotional information recognized by the emotion engine. For example, if the user is feeling anxious, it will provide a more specific and detailed solution, and if the user is feeling relaxed, it will suggest a more creative idea. In this process, emotional data and solution data are input, and adjusted solution data is output.

[1377] Step 6:

[1378] The generated solution is presented to the user.

[1379] The server then formats the adjusted solutions into an easy-to-read format and presents them to the user through a user interface. In this step, the formatted solution data is input and display data for the user is output. Specifically, the solutions are presented in a list format on the web browser, allowing the user to check them. For example, "Recent Latest Research on Global Warming" and "The Current State and Future of Plastic Pollution" are displayed.

[1380] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1381] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1382] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1383] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1384] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1385] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1386] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1387] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1388] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1389] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1390] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1391] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1392] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1393] 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.

[1394] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1395] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1396] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1397] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1398] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1399] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1400] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1401] The following is further disclosed regarding the above embodiment.

[1402] (Claim 1)

[1403] means for receiving any issues or problems input by a user;

[1404] A means of analyzing received issues and problems;

[1405] A means of generating solutions from different cultural backgrounds and disciplines based on the analysis results;

[1406] means for presenting the generated solution to a user;

[1407] A system including:

[1408] (Claim 2)

[1409] 10. The system of claim 1, further comprising means for generating solutions using a plurality of AI models based on the analysis results.

[1410] (Claim 3)

[1411] 10. The system of claim 1, further comprising means for synthesizing and formatting the generated solutions before presenting them to the user.

[1412] "Example 1"

[1413] (Claim 1)

[1414] means for receiving any issues or problems input by a user;

[1415] A means of temporarily storing received issues and problems in a database;

[1416] A means of analyzing saved issues and problems using a natural language processing engine and extracting categories and related keywords for the input issue;

[1417] A means of leveraging multiple AI models to generate solutions from different cultural backgrounds and disciplines based on the analysis results;

[1418] a means for integrating and formatting the generated solutions before presenting them to the user;

[1419] a means for displaying the formatted solution to the user;

[1420] A system including:

[1421] (Claim 2)

[1422] 2. The system according to claim 1, further comprising means for analyzing the input task using a natural language processing engine.

[1423] (Claim 3)

[1424] The system according to claim 1, further comprising means for generating a solution using a multicultural AI model and a multi-perspective AI model based on the analysis results.

[1425] (Claim 4)

[1426] 10. The system of claim 1, further comprising means for synthesizing generated solutions and merging and formatting the text before presenting it to the user.

[1427] "Application Example 1"

[1428] New Claims

[1429] (Claim 1)

[1430] means for receiving any issues or problems input by a user;

[1431] A means of analyzing received issues and problems;

[1432] A means of generating solutions from different cultural backgrounds and disciplines based on the analysis results;

[1433] means for presenting the generated solution to a user;

[1434] A means including a natural language processing engine that analyzes the problem input by the user and extracts specific keywords;

[1435] A means of utilizing multiple artificial intelligence models to generate solutions from multicultural and multi-perspectives;

[1436] A means of integrating and optimally formatting the generated solutions;

[1437] means for displaying the integrated solution on a display device;

[1438] A system including:

[1439] (Claim 2)

[1440] 10. The system of claim 1, further comprising means for generating solutions using a plurality of artificial intelligence models based on the analysis results.

[1441] (Claim 3)

[1442] 10. The system of claim 1, further comprising means for synthesizing and formatting the generated solutions before presenting them to the user.

[1443] "Example 2: Combining Emotion Engines"

[1444] (Claim 1)

[1445] means for receiving any issues or problems input by a user;

[1446] A means of analyzing received issues and problems;

[1447] means for recognizing the emotional state of a user;

[1448] A means for generating solutions from different cultural backgrounds and disciplines based on the analysis results and the user's emotional state;

[1449] means for presenting the generated solution to a user;

[1450] A system including:

[1451] (Claim 2)

[1452] 10. The system of claim 1, further comprising means for generating solutions using a plurality of learning models based on the analysis results and the emotional state of the user.

[1453] (Claim 3)

[1454] 10. The system of claim 1, further comprising means for synthesizing and formatting the generated solutions before presenting them to the user.

[1455] "Application example 2 when combining emotion engines"

[1456] (Claim 1)

[1457] means for receiving any issues or problems input by a user;

[1458] A means of analyzing received issues and problems;

[1459] A means of generating solutions from different cultural backgrounds and disciplines based on the analysis results;

[1460] means for recognizing a user's emotion;

[1461] means for adjusting the generated solutions based on the user's sentiment;

[1462] means for presenting the generated solution to a user;

[1463] A system including:

[1464] (Claim 2)

[1465] 10. The system of claim 1, further comprising means for generating solutions using a plurality of AI models based on the analysis results.

[1466] (Claim 3)

[1467] 10. The system of claim 1, further comprising means for synthesizing and formatting the generated solutions before presenting them to the user. [Explanation of symbols]

[1468] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving any issues or problems input by a user; A means of analyzing received issues and problems; A means of generating solutions from different cultural backgrounds and disciplines based on the analysis results; means for presenting the generated solution to a user; A system including:

2. The system of claim 1, further comprising means for generating solutions using a plurality of AI models based on the analysis results.

3. 10. The system of claim 1, further comprising means for synthesizing and formatting the generated solutions before presenting them to the user.

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