system

Abstract

We provide the system. [Solution] A means for receiving a request entered by the user, A means of searching for resources within a group based on requests using generative artificial intelligence, A method for filtering the resource list in search results and limiting it to groups, A means of sending a filtered resource list to the user terminal, A means of displaying detailed information about the resource selected by the user, A means of communicating user requests to resource providers, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional resource and service matching system within a group, users have to manually search for the information they need, which is not only inefficient but also has a risk of leakage of information outside the group. Also, since users can only utilize synergy within the known range, it is difficult to maximize the utilization of group resources. The purpose of this invention is to solve such problems and prevent unnecessary information leakage by enabling users to easily search for and efficiently utilize group resources.

Means for Solving the Problems

[0005] The system provides a means for receiving requests entered by the user, a means for searching for resources within a group based on the request using generative artificial intelligence, a means for filtering the resource list of the search results and limiting it to resources within the group, a means for sending the filtered resource list to the user's terminal, a means for displaying detailed information about the resource selected by the user, and a means for contacting resource providers with the user's request. This system allows users to quickly find the resources they need and efficiently leverage synergies while minimizing information leakage.

[0006] A "user" refers to an individual or legal entity that uses this system to enter requests, search for resources, and communicate with providers.

[0007] "Generative artificial intelligence" refers to artificial intelligence technology that analyzes requests input by users, extracts relevant keywords, searches a database, and finds appropriate resources.

[0008] "Resources within the group" refers to products, services, and the individuals or companies that manage them, provided within the same corporate group or related organizations.

[0009] The "resource list in search results" refers to a list of resources within a group related to the user's request, obtained as a result of a search by a generative artificial intelligence.

[0010] "Filtering" refers to the process of removing information outside a group from the resource list of search results, limiting it to resources within that group.

[0011] "User terminal" refers to a device such as a computer, smartphone, or tablet that a user uses to access and operate this system.

[0012] A "resource provider" refers to a company or individual within a group that provides resources, and is an entity that provides necessary information and services based on user requests. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The system for implementing this invention matches user requests with resources within the group, efficiently provides information, and prevents unnecessary information leaks. The entire system consists of a user terminal, a server, a generative artificial intelligence, and a database.

[0035] 1. User input

[0036] The user enters their request for the necessary resources (for example, "I want to purchase 20 new PCs") into the terminal.

[0037] The terminal receives the request and prepares to send it to the server.

[0038] 2. Receipt of Request

[0039] The server receives requests from the user's terminal.

[0040] The system analyzes the received request and sends it to a generative artificial intelligence.

[0041] 3. AI-powered resource search

[0042] Generative artificial intelligence extracts relevant keywords (for example, "PC", "purchase", "20 units") based on the received request.

[0043] Based on keywords, the system searches the database within the group and generates a list of relevant resources (e.g., "Company A", "Company B", etc.).

[0044] 4. Filtering the results

[0045] The server filters the resource list generated by the generative artificial intelligence.

[0046] The filtering process extracts only the list of information limited to that within the group.

[0047] 5. Sending the list to the user's terminal

[0048] The server converts the filtered list into JSON format and sends it to the user's terminal.

[0049] The user's device parses the received JSON data and displays it to the user as a visual list.

[0050] 6. Displaying user selections and details

[0051] The user selects the desired resource (for example, "Company A") from the displayed list.

[0052] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[0053] The user terminal displays detailed information to the user (e.g., PC price, availability, etc.).

[0054] 7. Implementing the Bridging Process

[0055] The user selects their preferred method of contact (e.g., "email").

[0056] The server contacts resource providers based on the user's request and contact method.

[0057] The resource provider confirms the user's request and proceeds with providing the necessary information and procedures.

[0058] Specific example

[0059] Example: If you want to purchase 20 new PCs

[0060] 1. User input

[0061] The user (sales representative) enters a request into the terminal saying, "I would like to purchase 20 new PCs," and clicks the submit button.

[0062] 2. Receipt of Request

[0063] The server receives this request and sends it to the generative artificial intelligence.

[0064] 3. AI-powered resource search

[0065] Generative artificial intelligence analyzes the request and extracts keywords such as "PC," "purchase," and "20 units."

[0066] Based on the extracted keywords, the system searches the database within the group and generates a list of related resources such as "Company A" and "Company B".

[0067] 4. Filtering the results

[0068] The server filters the search results and extracts a list containing only companies within the group.

[0069] 5. Sending the list to the user's terminal

[0070] The server converts the filtering results into JSON format and sends them to the user's terminal.

[0071] The user's terminal parses the received JSON data and displays a list of "Company A" and "Company B".

[0072] 6. Displaying user selections and details

[0073] The user selects "Company A" and checks the details (PC price, stock availability, etc.).

[0074] 7. Implementing the Bridging Process

[0075] If the user selects "Contact by email," the server sends an email to "Company A" requesting confirmation from the user.

[0076] This allows users to efficiently utilize resources within the group and prevents unnecessary information leaks.

[0077] The following describes the processing flow.

[0078] Step 1:

[0079] Users log in to the system using their devices and enter requests such as "I want to buy XX," "I want to outsource XX," or "I want to sell XX."

[0080] Specific action: Enter the request into the input form displayed in the user interface and click "Submit".

[0081] Step 2:

[0082] The terminal receives the user's request in string format and sends it to the server.

[0083] Specific operation: Send the request details to the server via an HTTP request or API call.

[0084] Step 3:

[0085] The server receives requests from users.

[0086] Specific operation: Converts the received string data into a data format suitable for passing to a generative artificial intelligence.

[0087] Step 4:

[0088] The server sends the converted request data to the generative artificial intelligence.

[0089] Specific operation: Call the API of a generative artificial intelligence and pass the requested data as parameters for analysis.

[0090] Step 5:

[0091] Generative artificial intelligence extracts relevant keywords based on the received request.

[0092] Specific operation: The request content is analyzed using natural language processing technology to identify the keywords necessary for the search.

[0093] Step 6:

[0094] The generative artificial intelligence searches the database within the group based on the extracted keywords and generates a list of relevant resources.

[0095] Specific operation: Generate a database query and retrieve a list of candidates as search results.

[0096] Step 7:

[0097] The server filters the resource list received from the generative artificial intelligence, limiting it to resources within a specific group.

[0098] Specific operation: The system performs a filter on the received list data to extract only the information within each group.

[0099] Step 8:

[0100] The server converts the filtered list into JSON format and sends it to the user's terminal.

[0101] Specific operation: The filtering results are generated as data in JSON format and sent to the user's terminal via API calls or HTTP responses.

[0102] Step 9:

[0103] The user's device parses the received JSON data and displays it to the user as a visual list.

[0104] Specific operation: Parses JSON data and visually displays the list using HTML or the application UI.

[0105] Step 10:

[0106] The user selects the desired resource from the displayed list.

[0107] Specific operation: Clicking an item in the list sends the ID and detailed information of the selected item to the server.

[0108] Step 11:

[0109] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[0110] Specific operation: Retrieve detailed information from the database and send it to the user's terminal via the API response.

[0111] Step 12:

[0112] The user terminal displays detailed information to the user.

[0113] Specific action: Display the received details on the screen so that the user can review them.

[0114] Step 13:

[0115] Users select their preferred method of contact and then contact the resource provider.

[0116] Specific action: Select a contact method option (email, phone, etc.) and send that information to the server.

[0117] Step 14:

[0118] The server will contact the resource provider using the specified contact method to conduct further verification.

[0119] Specific actions: The system will send emails or process communications via internal systems according to the selected contact method.

[0120] (Example 1)

[0121] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0122] Traditional systems were inefficient in resource retrieval and information provision in response to user requests, and also had the potential for unnecessary information leaks. In particular, the process of quickly extracting and filtering appropriate resources from large amounts of data and providing them to users was considered complex and time-consuming. Furthermore, a lack of systems capable of appropriately responding to user requests was also a contributing factor.

[0123] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0124] In this invention, the server includes means for receiving a request entered by a user, means for searching for resources within the organization based on the request using generative artificial intelligence, means for filtering the resource list of the search results and limiting it to resources within the organization, means for sending the filtered resource list to the user terminal, means for displaying detailed information about the resource selected by the user, means for contacting the resource provider with the user's request, means for the user terminal to convert the request into JSON format and send it to the server, means for the server to analyze the received request and send it to the generative artificial intelligence, means for the generative artificial intelligence to extract keywords, means for searching the database based on the keywords and generating a list of relevant resources, means for the server to convert the filtered resource list into JSON format and send it to the user terminal, means for the server to obtain detailed information about the selected resource from the database and send it to the user terminal, means for the server to contact the resource provider based on the contact method selected by the user, and means for the resource provider to respond to the user's request and provide the necessary information. This enables users to efficiently search for appropriate resources and use them quickly, while preventing unnecessary information leaks.

[0125] A "user" refers to an individual or organization that uses this system to enter requests and receive resource searches or information.

[0126] A "request" refers to the content that a user inputs into the system, asking for the provision of specific resources or information.

[0127] "Generative artificial intelligence" refers to machine learning or AI models that analyze user requests, extract relevant keywords, search databases, and list appropriate resources.

[0128] "Resources" refer to information, services, or items that a system provides to meet user requirements.

[0129] "Filtering" refers to the process of selecting and extracting only those resources that meet specific criteria from a list compiled by a generative artificial intelligence.

[0130] A "resource list" refers to a list of relevant resources that remain after a generative artificial intelligence has searched and filtered based on the user's request.

[0131] A "user terminal" refers to a device used by a user to input requests and receive and display resource lists and detailed information.

[0132] "JSON format" is an abbreviation for JavaScript (registered trademark) Object Notation, and refers to a lightweight data exchange format for structuring, storing, and transmitting data.

[0133] A "server" refers to a computer system that provides relay and management functions, such as receiving requests from user terminals, forwarding them to generative artificial intelligence systems, and sending filtering results to user terminals.

[0134] "Keyword extraction" refers to the process by which generative artificial intelligence extracts important words and phrases from a user's request and uses them to search a database.

[0135] A "database query" refers to a question or operation used to extract specific information from a database.

[0136] "Detailed information" refers to additional information about each resource included in the resource list (such as price and availability).

[0137] A "resource provider" refers to a company or individual that provides resources in response to a user's request.

[0138] "Contact method" refers to the means (e.g., email, phone) that a user chooses to use to contact a resource provider.

[0139] The system for implementing this invention efficiently matches user requests with resources within the group and prevents unnecessary information leaks. The entire system consists of a user terminal, a server, a generative artificial intelligence system, and a database.

[0140] Here is a specific example of a case where a user wants to purchase 20 new PCs.

[0141] The user enters a request, such as "I want to purchase 20 new PCs," into a dedicated interface and clicks the submit button. The user's terminal receives this request, converts it to JSON format, and sends it to the server.

[0142] The server receives requests from user terminals and analyzes their content. The analyzed content is sent to a generative artificial intelligence (AI). Based on the received requests, the AI ​​extracts keywords such as "PC," "purchase," and "20 units," and uses these to execute database queries. It searches the database for related resources (e.g., "Company A," "Company B," etc.) and generates a resource list.

[0143] Next, the server filters the generated resource list to extract a list limited to only the resources within the group. This filtered list is then converted to JSON format and sent back to the user's terminal.

[0144] The user terminal receives and parses JSON data and displays it to the user as a visual list. When the user selects "Company A" from the list, the server retrieves detailed information about the selected resource (e.g., PC price and availability) from the database and sends it to the user terminal. The user terminal then displays the detailed information to the user.

[0145] Finally, once the user selects their preferred method of contact (e.g., "email"), the server sends an email to "Company A" based on that method, conveying the user's request. "Company A," as the resource provider, then reviews the user's request and proceeds with providing the necessary information and procedures.

[0146] This series of processes allows users to efficiently utilize resources within the group and prevent unnecessary information leaks.

[0147] Example of a prompt:

[0148] User request: "I want to purchase 20 new PCs."

[0149] Keywords: "New PC", "20 units", "Purchase"

[0150] System role: Receives user requests, searches for relevant resources in the group's database, and generates a list of optimal resources.

[0151] This invention utilizes a generative AI model to respond quickly and accurately to user requests, and enables efficient resource searching and information provision. This makes it possible to provide users with a high level of convenience and peace of mind.

[0152] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0153] Step 1:

[0154] User input

[0155] The user sits down at their PC and opens a dedicated interface. The user enters a specific request, such as "I want to purchase 20 new PCs," into the input field and clicks the submit button.

[0156] Input: The user's specific request (e.g., "I want to purchase 20 new PCs").

[0157] Output: Request data converted to JSON format.

[0158] Specific operation: The terminal receives user input, verifies its contents, and converts the request into JSON format.

[0159] Step 2:

[0160] Receipt of request

[0161] The server receives the request data sent from the terminal.

[0162] Input: Request data in JSON format.

[0163] Output: Analyzed request data.

[0164] Specific operation: The server receives requests from terminals, analyzes their contents, and prepares them for transmission to the generative artificial intelligence.

[0165] Step 3:

[0166] AI-powered resource search

[0167] The generative artificial intelligence extracts keywords based on requests received from the server. Based on the extracted keywords (e.g., "PC", "purchase", "20 units"), it searches the database and generates a list of related resources (e.g., "Company A", "Company B").

[0168] Input: Analyzed request data.

[0169] Output: Resource list.

[0170] Specific operation: The generative artificial intelligence extracts important keywords from the received request, queries the database based on those keywords, and generates a list of relevant resources.

[0171] Step 4:

[0172] Filtering results

[0173] The server receives a resource list sent from a generative artificial intelligence and applies a filtering algorithm to extract a list limited to specific groups.

[0174] Input: Resource list.

[0175] Output: Filtered resource list.

[0176] Specific operation: The server filters the information in the resource list according to specific conditions and removes unnecessary information.

[0177] Step 5:

[0178] Sending the list to the user's terminal

[0179] The server converts the filtered resource list into JSON format and sends it to the user's terminal.

[0180] Input: A filtered list of resources.

[0181] Output: A filtered resource list in JSON format.

[0182] Specific operation: The server converts the filtering results into JSON format and sends them to the user's terminal.

[0183] Step 6:

[0184] Display of user selection and detailed information.

[0185] The user's terminal receives and visually displays a list of resources. The user selects the desired resource from the list (e.g., "Company A").

[0186] Input: A filtered resource list in JSON format.

[0187] Output: User-selected resource information.

[0188] Specific operation: The user's terminal parses the JSON data, displays a list, and communicates the resource selected by the user to the server.

[0189] Step 7:

[0190] Get and display detailed information

[0191] The server retrieves detailed information about the selected resource and sends it to the user's terminal. The user's terminal then displays the detailed information to the user (e.g., PC price, availability).

[0192] Input: User-selected resource information.

[0193] Output: Resource details.

[0194] Specific operation: The server retrieves detailed information about the selected resource from the database and sends it to the user terminal. The user terminal then displays that information.

[0195] Step 8:

[0196] Execution of the bridging

[0197] The user selects their preferred method of contact (e.g., "email"). The server then contacts the resource provider based on that method and relays the user's request.

[0198] Input: User's selected contact method and resource information.

[0199] Output: Responses and actions by resource providers.

[0200] Specific operation: The server contacts the resource provider, taking into account the user's request and contact method. The resource provider confirms the user's request and proceeds with the necessary information and procedures.

[0201] This series of processes allows users to efficiently utilize resources within the group and prevent unnecessary information leaks.

[0202] (Application Example 1)

[0203] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0204] In modern factories, the supply process for parts and materials demands efficiency and reliability, but currently, it often requires a great deal of time and effort. Furthermore, efficiently matching necessary resources is a challenge, leading to decreased productivity and the risk of information leaks. Therefore, there is a need for a system that can quickly and accurately meet the demands for parts and materials, and achieve an efficient supply process.

[0205] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0206] In this invention, the server includes means for receiving requests entered by a user, means for searching for resources within a group based on the request using generative artificial intelligence, and means for filtering the resource list of the search results and limiting it to resources within the group. This enables a factory robot that receives requests for parts and materials, matches them with resources within the group, and supplies them efficiently.

[0207] A "request" is an instruction that a user enters because they need a specific resource or service.

[0208] "Generative artificial intelligence" is an artificial intelligence system that extracts keywords based on user requests and identifies related resources.

[0209] "Filtering" is the process by which a generative artificial intelligence removes unnecessary information from a searched resource list and creates a limited list.

[0210] A "resource list" is a collection of information that lists the resources relevant to a user's request.

[0211] A "user terminal" is a device used by a user to input requests, receive results, and confirm them.

[0212] A "resource provider" is an entity whose role is to provide the necessary resources based on the user's requests.

[0213] A "factory robot" is an automated machine that receives requests for parts and materials, matches them with resources within the group, and supplies them efficiently.

[0214] The system for implementing this invention can efficiently process user requests and quickly meet the demand for parts and materials within the factory by utilizing resources within the group. The following describes each process of this system in detail.

[0215] Hardware and software

[0216] This system includes the following main components:

[0217] 1. User terminal: A device on which a user enters a request and receives the result. Examples include tablets and smartphones.

[0218] 2. Server: Its role is to receive user requests and transmit them to the generative artificial intelligence. It also sends the processed results to the user's terminal.

[0219] 3. Generative Artificial Intelligence (AI): Analyzes user requests, extracts relevant keywords, and searches for resources. Specifically, AI models such as GPT-3 (registered trademark) are used.

[0220] 4. Database: Stores resource information within the group and is used for AI-powered searches.

[0221] 5. Factory robots: Their role is to receive requests for parts and materials and supply them automatically.

[0222] Data processing and calculation

[0223] 1. Receiving and parsing user input:

[0224] Requests from the user's terminal are received by the server and sent to the generative artificial intelligence (AI). For example, if the user inputs "I need 50 of part X," that information is sent to the server.

[0225] 2. Keyword extraction and resource search:

[0226] Generative artificial intelligence analyzes the received request and extracts relevant keywords. Keywords such as "part X" and "50" are extracted, and the database is searched based on these keywords.

[0227] 3. Filtering search results:

[0228] The server filters the resource list generated by the generative artificial intelligence, limiting it to the appropriate resources within the group. The filtering results are converted to JSON format and sent to the user's terminal.

[0229] 4. Displaying and selecting results:

[0230] The user's device parses the received JSON data and displays a visual list of resources. When the user selects a desired resource, detailed information is displayed, such as "stock availability" or "price."

[0231] 5. Contact the resource provider of the request:

[0232] Once the user reviews the resource details and indicates their intention to purchase or acquire it, the server contacts the resource provider directly.

[0233] 6. Supply by factory robots:

[0234] Factory robots receive parts and materials supplied by resource providers and efficiently deliver them to designated locations.

[0235] Specific examples and prompt statements

[0236] The following is an example of an application where a factory robot supplies parts and materials.

[0237] Specific example:

[0238] A factory operator uses a tablet to input a request, such as "We need 50 units of part X." The server sends this request to a generative artificial intelligence (AI), which extracts relevant keywords. After searching the database and filtering the resulting resource list, the AI ​​displays the appropriate supplier to the operator. Once the operator selects a resource, detailed information is displayed. The factory robot then automatically supplies the parts.

[0239] Example of a prompt:

[0240] Input from user terminal: "I need 50 units of part X."

[0241] Robot response: "Searching for suppliers of part X... 4 optimal suppliers found. View details?"

[0242] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0243] Step 1:

[0244] Receiving user input

[0245] User terminal: Users input requests using devices such as tablets or smartphones. Specifically, a user might input "I need 50 units of part X." The terminal then sends this request to the server.

[0246] Input: User request (e.g., "I need 50 units of part X")

[0247] Output: Request data sent to the server

[0248] Step 2:

[0249] Receiving and sending requests

[0250] Server: The server receives request data from the user terminal and prepares it for transmission to the generative artificial intelligence (AI). The server analyzes the request data and converts it into the appropriate format.

[0251] Input: Request data from the user terminal

[0252] Output: Analyzed data sent to the generative artificial intelligence.

[0253] Step 3:

[0254] Keyword extraction and resource search

[0255] Generative artificial intelligence: The AI ​​extracts keywords based on the received, analyzed data. For example, keywords such as "part X" and "50 pieces" might be extracted. Then, it searches a database based on these keywords and generates a list of relevant resources.

[0256] Input: Analyzed data from the server

[0257] Output: Resource list of search results

[0258] Step 4:

[0259] Filtering search results

[0260] Server: The server receives the resource list generated by the AI ​​and performs filtering. This filtering process creates a list limited to resources available within the group.

[0261] Input: Resource list from generative artificial intelligence

[0262] Output: Filtered resource list

[0263] Step 5:

[0264] Converting and sending resource lists

[0265] Server: Converts the filtered resource list into JSON format and sends it to the user's terminal. This makes the list visually easy to understand.

[0266] Input: Filtered resource list

[0267] Output: Resource list in JSON format

[0268] Step 6:

[0269] Displaying the list and providing detailed information

[0270] User Terminal: The user terminal parses the received JSON-formatted resource list and displays it to the user as a visual resource list. The user selects the desired resource and checks detailed information (such as availability and price).

[0271] Input: Resource list in JSON format

[0272] Output: Visually displayed resource list and detailed information

[0273] Step 7:

[0274] Notification of the request to the resource provider

[0275] Server: When a user selects a desired resource, for example, "Contact by email," the server contacts the resource provider. This contact includes the user's request and preferred method of contact.

[0276] Input: User's selection information and contact method

[0277] Output: Notification data sent to resource providers

[0278] Step 8:

[0279] Supply by factory robots

[0280] Factory robots: Factory robots receive parts and materials based on requests from resource providers and deliver them to designated locations. The robots automatically calculate the shortest path and complete the delivery efficiently.

[0281] Input: Supplies from resource providers

[0282] Output: Delivery completion notification to the location specified by the user.

[0283] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0284] The system for implementing this invention not only understands the user's requests and matches them with the resources within the group, but also recognizes the user's emotions and responds accordingly. The entire system is composed of a user terminal, a server, a generative artificial intelligence, an emotion engine, and a database.

[0285] 1. User Input and Emotion Recognition

[0286] When the user inputs a request into the terminal, the terminal is equipped with an emotion engine for recognizing the user's emotion.

[0287] The emotion engine analyzes the user's input content and behavior (such as voice tone, input speed, expression, etc.) and identifies the emotional state.

[0288] The terminal transmits the emotion data recognized by the emotion engine to the server together with the request.

[0289] 2. Receiving the Request and Analyzing the Emotion Data

[0290] The server receives the request and emotion data from the user terminal and analyzes them.

[0291] Prepare to pass the received data to the generative artificial intelligence.

[0292] 3. Sending the Request and Emotion Data to the Generative Artificial Intelligence

[0293] The server transmits the converted request data and emotion data to the generative artificial intelligence.

[0294] 4. Resource Search and Optimization by AI

[0295] The generative artificial intelligence extracts relevant keywords based on the received requests and emotion data.

[0296] Based on the keywords and emotion data, search the database within the group and generate a relevant resource list (e.g., "Company A", "Company B", etc.).

[0297] Optimize the search results according to the user's emotion. For example, when the user is feeling stressed, preferentially display companies with generous support.

[0298] 5. Filtering of Results

[0299] The server filters the resource list generated by the generative artificial intelligence and limits it to the resources within the group.

[0300] 6. Sending the List to the User Terminal

[0301] The server converts the filtered list into JSON format and sends it to the user terminal.

[0302] The user terminal analyzes the received JSON data and displays it to the user as a visual list.

[0303] 7. User Selection and Display of Detailed Information

[0304] The user selects the desired resource from the displayed list.

[0305] The server obtains the detailed information of the selected resource and sends it to the user terminal.

[0306] The user terminal displays the detailed information to the user (e.g., the price of the PC, the inventory status, etc.).

[0307] 8. Execution of Bridging

[0308] Users select their preferred method of contact and then contact the resource provider.

[0309] The server contacts resource providers based on the user's request and contact method.

[0310] We adjust our communication methods based on the user's emotions. For example, if a user is feeling anxious, we prioritize direct contact by phone.

[0311] Specific example

[0312] Example: If you want to purchase 20 new PCs

[0313] 1. User input and sentiment recognition

[0314] A user (sales representative) enters a request into the terminal saying, "I want to purchase 20 new PCs." If the user is typing in a hurry, the emotion engine recognizes this and determines that the user is in a hurry.

[0315] 2. Receiving requests and analyzing sentiment data

[0316] The server receives this request and emotion data and prepares to send it to the generative artificial intelligence.

[0317] 3. Sending request and emotion data to the generative artificial intelligence system.

[0318] The server sends request data and emotion data to the generative artificial intelligence.

[0319] 4. AI-powered resource search and optimization

[0320] Generative artificial intelligence analyzes the request and extracts keywords such as "PC," "purchase," and "20 units."

[0321] Based on extracted keywords and sentiment data, the system searches the database within the group and generates a list of relevant resources such as "Company A" and "Company B".

[0322] Since the user is in a hurry, resources that are immediately available will be displayed first.

[0323] 5. Filtering the results

[0324] The server filters the search results and extracts a list containing only companies within the group.

[0325] 6. Sending the list to the user's terminal

[0326] The server converts the filtering results into JSON format and sends them to the user's terminal.

[0327] The user's terminal parses the received JSON data and displays a list of "Company A" and "Company B".

[0328] 7. Displaying user selections and details

[0329] The user selects "Company A" and checks the details (PC price, stock availability, etc.).

[0330] 8. Implementing the Bridging Process

[0331] If the user selects "Contact by phone," the server will contact "Company A" by phone with the user's request.

[0332] This allows users to efficiently utilize resources within the group and prevents unnecessary information leaks. Furthermore, using an emotion engine enables flexible responses tailored to the user's emotions.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] Users log in to the system using their devices and enter requests such as "I want to buy XX," "I want to outsource XX," or "I want to sell XX."

[0336] Specific action: Enter the request into the input form displayed in the user interface and click "Submit".

[0337] Step 2:

[0338] The device receives user requests and uses an emotion engine to recognize the user's emotions.

[0339] Specific operation: The system analyzes the user's typing speed and input content, as well as their facial expressions, to determine their emotions.

[0340] Step 3:

[0341] The emotion engine generates recognized emotion data and prepares it to send to the server along with the request.

[0342] Specific operation: Structure emotion data, integrate it with request data, and convert it into a format that can be sent.

[0343] Step 4:

[0344] The device sends request data and sentiment data to the server.

[0345] Specific operation: Sends HTTP requests and API calls containing request and sentiment data to the server.

[0346] Step 5:

[0347] The server receives requests and sentiment data from users.

[0348] Specific operation: Analyzes the received data and converts it into a data format for passing to a generative artificial intelligence.

[0349] Step 6:

[0350] The server sends the converted request data and emotion data to the generative artificial intelligence.

[0351] Specific operation: Call the API of a generative artificial intelligence and pass the request data and sentiment data as parameters for analysis.

[0352] Step 7:

[0353] Generative artificial intelligence extracts relevant keywords based on received requests and sentiment data.

[0354] Specific operation: The request content is analyzed using natural language processing technology to identify the keywords necessary for the search.

[0355] Step 8:

[0356] The generative artificial intelligence searches the database within the group based on extracted keywords and sentiment data, and generates a list of relevant resources.

[0357] Specific operation: Convert keyword and sentiment data into database queries and retrieve a list of candidates as search results.

[0358] Step 9:

[0359] Generative artificial intelligence optimizes search results according to the user's emotions.

[0360] Specific actions: If the user is in a hurry, the system will prioritize displaying resources that are immediately available.

[0361] Step 10:

[0362] The server filters the resource list received from the generative artificial intelligence, limiting it to resources within a specific group.

[0363] Specific operation: The system performs a filter on the received list data to extract only the information within each group.

[0364] Step 11:

[0365] The server converts the filtered list into JSON format and sends it to the user's terminal.

[0366] Specific operation: The filtering results are generated as data in JSON format and sent to the user's terminal via API calls or HTTP responses.

[0367] Step 12:

[0368] The user's device parses the received JSON data and displays it to the user as a visual list.

[0369] Specific operation: Parses JSON data and visually displays the list using HTML or the application UI.

[0370] Step 13:

[0371] The user selects the desired resource from the displayed list.

[0372] Specific operation: Clicking an item in the list sends the ID and detailed information of the selected item to the server.

[0373] Step 14:

[0374] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[0375] Specific operation: Retrieve detailed information from the database and send it to the user's terminal via the API response.

[0376] Step 15:

[0377] The user terminal displays detailed information to the user.

[0378] Specific action: Display the received details on the screen so that the user can review them.

[0379] Step 16:

[0380] Users select their preferred method of contact and then contact the resource provider.

[0381] Specific action: Select a contact method option (email, phone, etc.) and send that information to the server.

[0382] Step 17:

[0383] The server will contact the resource provider using the specified contact method to conduct further verification.

[0384] Specific actions: The system will send emails or process communications via internal systems according to the selected contact method.

[0385] Step 18:

[0386] The emotion engine adjusts the communication method according to the user's emotions.

[0387] Specific actions: For example, if a user is feeling anxious, priority will be given to phone contact, which allows for direct communication.

[0388] (Example 2)

[0389] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0390] Existing resource matching systems often process requests without considering user emotions, which can lead to decreased user satisfaction. This is especially true when users are in a hurry or experiencing stress, making it difficult to provide appropriate resources. Furthermore, traditional systems often fail to adequately filter search results or provide data in the most optimal format, resulting in insufficient information for users. To address these challenges, a system is needed that recognizes user emotions and responds flexibly based on them.

[0391] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0392] In this invention, the server includes means for receiving a request entered by the user, means for acquiring the user's emotional data using an emotional recognition engine, means for searching for resources within a group based on the request using generative artificial intelligence, means for filtering the resource list of the search results and limiting it to resources within the group, means for sending the filtered resource list to the user terminal in JSON format, means for displaying detailed information about the resource selected by the user, and means for contacting resource providers based on the user's request and emotions. This enables flexible and effective resource provision in response to the user's emotions.

[0393] A "user" refers to an individual or legal entity that uses the system to input requests and seek out the most suitable resources.

[0394] A "request" refers to information that a user enters into the system, such as necessary details, desired services, or products, which the system should then respond to.

[0395] An "emotion recognition engine" refers to software or hardware that analyzes user input, behavior, facial expressions, voice tone, etc., to identify the user's emotional state.

[0396] "Emotional data" refers to data indicating the user's emotional state as identified by the emotion recognition engine.

[0397] "Generative artificial intelligence" refers to artificial intelligence that searches for resources based on user requests and sentiment data, and generates optimized search results.

[0398] "Resources" is a general term for services, products, information, etc., that are provided in response to user requests.

[0399] "Filtering" refers to the process of narrowing down a list of resources searched by a generative artificial intelligence based on specific conditions.

[0400] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight text data format widely used as a data exchange format.

[0401] A "user terminal" refers to a device (e.g., smartphone, tablet, PC, etc.) that a user uses to access a system, input requests, and receive and display results.

[0402] A "resource provider" refers to a company or individual that provides resources in response to a user's request.

[0403] A "database" refers to a system in which information is organized and stored, which generative artificial intelligence uses to search for resources.

[0404] Modes for carrying out the invention

[0405] The system for implementing this invention not only understands user requests and matches them with resources within the group, but also recognizes and responds to user emotions. The system consists of the following components:

[0406] User terminal

[0407] server

[0408] Generative artificial intelligence

[0409] Emotion recognition engine

[0410] database

[0411] Hardware and software to be used

[0412] User terminal: This includes smartphones, tablets, PCs, etc. It is used by the user to input requests, receive results, and display them.

[0413] Server: Performs key processing such as receiving requests, analyzing data, sending data to generative artificial intelligence, filtering search results, and contacting resource providers.

[0414] Emotion Recognition Engine: This uses IBM Watson® as an example to recognize emotions from the user's input, voice tone, input speed, and facial expressions.

[0415] Generative Artificial Intelligence: OpenAI® GPT-3 is used as the generative AI. Keyword extraction and search result optimization are performed based on user requests and sentiment data.

[0416] Database: Use a database such as MySQL (registered trademark) to perform searches based on generated keywords and store and manage the results.

[0417] Data processing and data calculation

[0418] Emotion Recognition: The system analyzes user input, voice tone, input speed, and facial expression data to obtain emotional data. This process utilizes an emotion recognition engine.

[0419] Keyword Extraction and Search: Generative artificial intelligence extracts keywords from user requests and sentiment data, searches the database, and creates a list of appropriate resources.

[0420] Filtering: The server narrows down the list of resources it has searched based on specific criteria, limiting it to resources within a group.

[0421] Data transmission: The filtered resource list is converted to JSON format and sent to the user's terminal. Additionally, resource providers are contacted based on user requests.

[0422] Specific example

[0423] Here's a step-by-step guide for a user who wants to purchase 20 new PCs:

[0424] 1. User input and sentiment recognition:

[0425] A user (sales representative) enters "I want to purchase 20 new PCs" into the terminal. If the user is typing in a hurry, the emotion recognition engine will recognize this and determine that the user is in a hurry.

[0426] 2. Receiving requests and analyzing sentiment data:

[0427] The device sends this request and emotional data to the server. The server analyzes the received data and formats it for transmission to a generative artificial intelligence system.

[0428] 3. Sending request and emotion data to the generative artificial intelligence:

[0429] The server sends request data and emotion data to the generative artificial intelligence.

[0430] 4. AI-powered resource search and optimization:

[0431] Generative artificial intelligence analyzes the request and extracts keywords such as "PC," "purchase," and "20 units." Based on the extracted keywords and sentiment data, it searches the database and generates a list of relevant resources such as "Company A" and "Company B." Because the user is in a hurry, resources that are immediately available are displayed first.

[0432] 5. Filtering the results:

[0433] The server filters the search results and extracts a list containing only companies within the group.

[0434] 6. Sending the list to the user's terminal:

[0435] The server converts the filtering results into JSON format and sends them to the user's terminal. The terminal parses the received JSON data and displays lists of "Company A" and "Company B".

[0436] 7. Displaying user selections and details:

[0437] The user selects "Company A" and checks the details (PC price, stock availability, etc.).

[0438] 8. Implementing the bridging:

[0439] If the user selects "Contact by phone," the server will contact "Company A" by phone with the user's request.

[0440] Example of a prompt

[0441] Example prompt message for a user purchasing 20 new PCs:

[0442] text

[0443] User request: "Purchase 20 new PCs."

[0444] User sentiment data: "Users are in a hurry."

[0445] System prompt: "Please show me companies that can immediately deliver 20 PCs. The user is in a hurry."

[0446] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0447] Step 1:

[0448] The user enters the request.

[0449] The user enters a request into the terminal. For example, they might enter "I want to purchase 20 new PCs."

[0450] Function: The user enters text into an input field and submits it by pressing a specific button.

[0451] Input: User's request text: "I want to purchase 20 new PCs."

[0452] Output: The requested data is stored on the terminal.

[0453] Step 2:

[0454] The device recognizes the user's emotions.

[0455] The device uses an emotion recognition engine to analyze the user's input, behavior, voice tone, input speed, and facial expressions.

[0456] Function: Captures the user's facial expressions and voice using the camera and microphone, and analyzes them with an emotion recognition engine (e.g., IBM Watson).

[0457] Input: User's facial expressions, voice, input speed

[0458] Output: User sentiment data (e.g., "I'm in a hurry") is recognized and retrieved.

[0459] Step 3:

[0460] The device sends request and sentiment data to the server.

[0461] The device sends the acquired request data and sentiment data to the server.

[0462] Function: Converts request data and sentiment data into JSON format and sends it to the server via a secure channel.

[0463] Input: Request data "I want to buy 20 new PCs", Sentiment data "The user is in a hurry"

[0464] Output: Request data and sentiment data are sent to and received by the server.

[0465] Step 4:

[0466] The server analyzes and formats the data.

[0467] The server analyzes the received request data and emotion data, and formats it for transmission to the generative artificial intelligence.

[0468] Function: Performs data validation and format conversion, and formats the data to a format compatible with generative AI (e.g., OpenAI GPT-3).

[0469] Input: Request data and sentiment data in JSON format

[0470] Output: Data converted to a format for transmission to a generative AI.

[0471] Step 5:

[0472] The server sends request and emotion data to the generative artificial intelligence.

[0473] The server sends the converted request data and emotion data to the generative artificial intelligence.

[0474] Function: Sends data to generative AI using an API.

[0475] Input: Formatted request data and sentiment data

[0476] Output: The generative AI receives the data and begins processing.

[0477] Step 6:

[0478] Generative artificial intelligence extracts keywords and searches the database.

[0479] Generative AI extracts keywords from received requests and sentiment data, and searches for related resources in a database.

[0480] Function: Uses natural language processing techniques to extract keywords and execute database queries.

[0481] Input: Request data and sentiment data

[0482] Output: Related resource list (e.g., resource list based on keywords such as "PC", "purchase", and "20 units")

[0483] Step 7:

[0484] Optimization of search results using generative AI

[0485] Generative AI optimizes search results based on user sentiment data. For example, if a user is in a hurry, it prioritizes resources that are immediately available.

[0486] Function: Ranks and filters search results based on sentiment data.

[0487] Input: Search results list, sentiment data "hurried"

[0488] Output: Optimized resource list (e.g., prioritizing "Immediately Available Resources")

[0489] Step 8:

[0490] The server filters the search results.

[0491] The server filters the resource list generated by the generative AI, limiting it to resources within the specified group.

[0492] Function: Applies a filtering algorithm to extract only resources within the group.

[0493] Input: Resource list for generative AI

[0494] Output: Limited resource list (e.g., "Company A", "Company B", etc., including only resources within the group)

[0495] Step 9:

[0496] The server sends the filtering results to the terminal in JSON format.

[0497] The server converts the filtering results into JSON format and sends them to the user's terminal.

[0498] Function: Uses a protocol to convert filtering results into JSON format and send them to the terminal.

[0499] Input: Filtered resource list

[0500] Output: A resource list in JSON format is sent to the terminal.

[0501] Step 10:

[0502] The user selects a resource from a list.

[0503] The user selects the desired resource from the list displayed on the device.

[0504] Function: The user taps or clicks a list item to select it.

[0505] Input: List display and user selection operation

[0506] Output: Selected resource data (e.g., "Company A")

[0507] Step 11:

[0508] The server retrieves detailed information about the resource and sends it to the terminal.

[0509] The server retrieves detailed information related to the selected resource and sends it to the terminal.

[0510] Function: Retrieves detailed information about the selected resource from a database or another API and sends it to the device.

[0511] Input: Selected resource data

[0512] Output: Detailed information (e.g., PC price, stock status) is sent to the terminal.

[0513] Step 12:

[0514] The user selects a contact method, and the server contacts the resource provider.

[0515] The user selects their preferred method of contact, and the server then contacts the resource provider.

[0516] Function: Select a contact method (e.g., email, phone), and the server will contact the resource provider using the corresponding API or protocol.

[0517] Input: Selection of contact method, resource data, sentiment data

[0518] Output: Contact is established with the resource provider (e.g., "Company A" is contacted by phone).

[0519] This series of steps enables efficient resource matching that takes into account user needs and emotions.

[0520] (Application Example 2)

[0521] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0522] Traditional systems have mechanisms to search for and provide resources based on user requests, but they lack the ability to consider the user's emotional state, making it difficult to optimize the user experience. Furthermore, there is a problem in providing appropriate resources quickly and effectively when the user is in an emergency or experiencing a specific emotional state.

[0523] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving a request entered by the user, means for searching for resources within a group based on the request using generative artificial intelligence, means for recognizing the user's emotional state using an emotion engine and processing the data, means for sending a filtered resource list to the user terminal in JSON format, means for displaying detailed information about the resource selected by the user, and means for adjusting the contact method based on the user's emotional state and contacting the resource provider with the request. This enables flexible responses according to the user's emotional state, and allows for the rapid and effective provision of resources, especially in emergencies or when the user is in a specific emotional state.

[0524] "Means for receiving user-entered requests" refers to a mechanism for receiving request data entered by a user into a terminal.

[0525] "A means of searching for resources within a group based on requests using generative artificial intelligence" refers to a mechanism that uses generative artificial intelligence to analyze user request data and identify relevant resources within a group.

[0526] "Means for recognizing a user's emotional state using an emotion engine and processing that data" refers to a mechanism for identifying an emotional state by analyzing the user's voice tone, input speed, facial expressions, etc., and for handling that data.

[0527] "Means for filtering the resource list in search results and limiting it to within a group" refers to a mechanism that filters the resource list searched by a generative artificial intelligence to limit it to only those resources within a group.

[0528] "Means for sending a filtered resource list to the user's terminal in JSON format" refers to a mechanism for converting a filtered resource list into JSON, a structured data format, and sending it to the user's terminal.

[0529] "Means for displaying detailed information about a resource selected by the user" refers to a mechanism for displaying detailed information about a resource selected by the user on the user's terminal.

[0530] "A means of adjusting the method of contact based on the user's emotional state and communicating the request to the resource provider" refers to a mechanism that selects the most appropriate method of contact according to the user's emotional state and notifies the resource provider of the user's request.

[0531] The system for implementing this invention not only understands user requests and matches them with resources within the group, but also recognizes and responds to the user's emotions. The entire system consists of a user terminal, a server, a generative artificial intelligence, an emotion engine, and a database.

[0532] The user inputs their requests using their smartphone, and the entire system operates accordingly. First, the user inputs their requests for desired products or services into their smartphone. Simultaneously, the smartphone is equipped with an emotion engine that analyzes the user's voice tone, input speed, facial expressions, etc., to recognize their emotional state.

[0533] The server receives request data and sentiment data sent from the user terminal. The received data is sent to a generative artificial intelligence (AI) for analysis. The generative AI extracts relevant keywords from the request data and searches a database within the group along with the sentiment data. This process includes keyword extraction and database query execution.

[0534] The resource list in search results is optimized according to the user's emotional state. For example, if the user is feeling urgent or stressed, the system will prioritize displaying resources that offer quick response and comprehensive support. The resulting resource list is then filtered by the server to limit resources to those within a specific group.

[0535] The filtered resource list is converted to JSON format and sent to the user's terminal. The user's terminal parses the received JSON data and displays it to the user as a visual list. The user can select the desired resource from the displayed list and view detailed information, such as the product price and availability.

[0536] Once the user selects their preferred method of contact, the server will choose the most appropriate method based on the user's request and emotional state, and contact the resource provider. For example, if the user is feeling anxious, telephone contact will be prioritized.

[0537] The specific hardware and software used include smartphones (iOS or Android®), emotion recognition APIs (e.g., Microsoft® Azure® Emotion API), and generative artificial intelligence APIs (e.g., OpenAI GPT models). These elements work together to enable flexible responses that respond to the user's emotions.

[0538] As a concrete example, consider a scenario where a user searches for products to relieve fatigue. In this case, if the user enters "I want supplements that relieve fatigue," the emotion engine recognizes the user's level of fatigue. Based on this, the generative artificial intelligence extracts relevant keywords and generates a list of products suitable for that emotional state. For example, the list might include aromatherapy candles and fatigue-relieving supplements.

[0539] Example of a prompt:

[0540] User request: I want a supplement to relieve fatigue.

[0541] Emotional state: Fatigue (high)

[0542] Expected output: Display a list of fatigue-relieving supplements, aromatherapy candles, and other relaxing products, along with price and availability information.

[0543] By combining emotion recognition and generative artificial intelligence in this way, it becomes possible to provide services that optimize the user experience.

[0544] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0545] Step 1:

[0546] Users use their smartphones to input requests for desired products or services. Simultaneously, an emotion engine built into the device analyzes the user's voice tone, input speed, facial expressions, etc., to recognize their emotional state. Based on this sensory data, the emotion engine identifies emotions such as "fatigue" and "stress" and generates data accordingly.

[0547] Input: User requests and sentiment data

[0548] Output: Request data and sentiment data

[0549] Step 2:

[0550] The terminal sends user request data and sentiment data to the server. The server prepares the received data for analysis. This preparation stage involves standardizing the data format and performing necessary preprocessing.

[0551] Input: Request data and sentiment data

[0552] Output: Preprocessed request data and sentiment data

[0553] Step 3:

[0554] The server sends pre-processed request data and sentiment data to the generative artificial intelligence. The generative AI extracts relevant keywords based on the transmitted data and analyzes the sentiment data.

[0555] Input: Preprocessed request data and sentiment data

[0556] Output: Extracted keywords and analyzed sentiment data

[0557] Step 4:

[0558] The generative artificial intelligence uses extracted keywords and analyzed sentiment data to search a database within the group. The search results generate a list of relevant resources. For example, if a user requests "supplements to relieve fatigue," the list will include corresponding supplements and relaxation products.

[0559] Input: Keywords and sentiment data

[0560] Output: Resource List

[0561] Step 5:

[0562] The server filters the generated resource list, limiting it to only resources within the group. This filtering excludes resources outside the group, prioritizing highly reliable resources.

[0563] Input: Resource list

[0564] Output: Filtered resource list

[0565] Step 6:

[0566] The server converts the filtered resource list into JSON format and sends it to the user's terminal. Converting to JSON format makes it easier for the user's terminal to analyze and display the data.

[0567] Input: Filtered resource list

[0568] Output: Resource list converted to JSON format

[0569] Step 7:

[0570] The device parses the received JSON data and displays it to the user as a visual list. The user can then select the desired resource from this list. For example, they can check the price and availability of a product.

[0571] Input: Resource list in JSON format

[0572] Output: Visual resource list

[0573] Step 8:

[0574] The user selects their preferred method of contact and sends that information to the server. The server then adjusts the best method of contact based on the user's emotional state and contacts the resource provider. For example, if the user is feeling anxious, phone contact will be prioritized.

[0575] Input: User selection information and sentiment data

[0576] Output: Contacting resource providers

[0577] This enables the provision of flexible and optimal resources tailored to the user's emotional state.

[0578] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0579] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0580] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0581] [Second Embodiment]

[0582] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0583] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0584] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0585] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0586] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0587] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0588] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0589] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0590] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0591] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0592] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0593] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0594] The system for implementing this invention matches user requests with resources within the group, efficiently provides information, and prevents unnecessary information leaks. The entire system consists of a user terminal, a server, a generative artificial intelligence, and a database.

[0595] 1. User input

[0596] The user enters their request for the necessary resources (for example, "I want to purchase 20 new PCs") into the terminal.

[0597] The terminal receives the request and prepares to send it to the server.

[0598] 2. Receipt of Request

[0599] The server receives requests from the user's terminal.

[0600] The system analyzes the received request and sends it to a generative artificial intelligence.

[0601] 3. AI-powered resource search

[0602] Generative artificial intelligence extracts relevant keywords (for example, "PC", "purchase", "20 units") based on the received request.

[0603] Based on keywords, the system searches the database within the group and generates a list of relevant resources (e.g., "Company A", "Company B", etc.).

[0604] 4. Filtering the results

[0605] The server filters the resource list generated by the generative artificial intelligence.

[0606] The filtering process extracts only the list of information limited to that within the group.

[0607] 5. Sending the list to the user's terminal

[0608] The server converts the filtered list into JSON format and sends it to the user's terminal.

[0609] The user's device parses the received JSON data and displays it to the user as a visual list.

[0610] 6. Displaying user selections and details

[0611] The user selects the desired resource (for example, "Company A") from the displayed list.

[0612] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[0613] The user terminal displays detailed information to the user (e.g., PC price, availability, etc.).

[0614] 7. Implementing the Bridging Process

[0615] The user selects their preferred method of contact (e.g., "email").

[0616] The server contacts resource providers based on the user's request and contact method.

[0617] The resource provider confirms the user's request and proceeds with providing the necessary information and procedures.

[0618] Specific example

[0619] Example: If you want to purchase 20 new PCs

[0620] 1. User input

[0621] The user (sales representative) enters a request into the terminal saying, "I would like to purchase 20 new PCs," and clicks the submit button.

[0622] 2. Receipt of Request

[0623] The server receives this request and sends it to the generative artificial intelligence.

[0624] 3. AI-powered resource search

[0625] Generative artificial intelligence analyzes the request and extracts keywords such as "PC," "purchase," and "20 units."

[0626] Based on the extracted keywords, the system searches the database within the group and generates a list of related resources such as "Company A" and "Company B".

[0627] 4. Filtering the results

[0628] The server filters the search results and extracts a list containing only companies within the group.

[0629] 5. Sending the list to the user's terminal

[0630] The server converts the filtering results into JSON format and sends them to the user's terminal.

[0631] The user's terminal parses the received JSON data and displays a list of "Company A" and "Company B".

[0632] 6. Displaying user selections and details

[0633] The user selects "Company A" and checks the details (PC price, stock availability, etc.).

[0634] 7. Implementing the Bridging Process

[0635] If the user selects "Contact by email," the server sends an email to "Company A" requesting confirmation from the user.

[0636] This allows users to efficiently utilize resources within the group and prevents unnecessary information leaks.

[0637] The following describes the processing flow.

[0638] Step 1:

[0639] Users log in to the system using their devices and enter requests such as "I want to buy XX," "I want to outsource XX," or "I want to sell XX."

[0640] Specific action: Enter the request into the input form displayed in the user interface and click "Submit".

[0641] Step 2:

[0642] The terminal receives the user's request in string format and sends it to the server.

[0643] Specific operation: Send the request details to the server via an HTTP request or API call.

[0644] Step 3:

[0645] The server receives requests from users.

[0646] Specific operation: Converts the received string data into a data format suitable for passing to a generative artificial intelligence.

[0647] Step 4:

[0648] The server sends the converted request data to the generative artificial intelligence.

[0649] Specific operation: Call the API of a generative artificial intelligence and pass the requested data as parameters for analysis.

[0650] Step 5:

[0651] Generative artificial intelligence extracts relevant keywords based on the received request.

[0652] Specific operation: The request content is analyzed using natural language processing technology to identify the keywords necessary for the search.

[0653] Step 6:

[0654] The generative artificial intelligence searches the database within the group based on the extracted keywords and generates a list of relevant resources.

[0655] Specific operation: Generate a database query and retrieve a list of candidates as search results.

[0656] Step 7:

[0657] The server filters the resource list received from the generative artificial intelligence, limiting it to resources within a specific group.

[0658] Specific operation: The system performs a filter on the received list data to extract only the information within each group.

[0659] Step 8:

[0660] The server converts the filtered list into JSON format and sends it to the user's terminal.

[0661] Specific operation: The filtering results are generated as data in JSON format and sent to the user's terminal via API calls or HTTP responses.

[0662] Step 9:

[0663] The user's device parses the received JSON data and displays it to the user as a visual list.

[0664] Specific operation: Parses JSON data and visually displays the list using HTML or the application UI.

[0665] Step 10:

[0666] The user selects the desired resource from the displayed list.

[0667] Specific operation: Clicking an item in the list sends the ID and detailed information of the selected item to the server.

[0668] Step 11:

[0669] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[0670] Specific operation: Retrieve detailed information from the database and send it to the user's terminal via the API response.

[0671] Step 12:

[0672] The user terminal displays detailed information to the user.

[0673] Specific action: Display the received details on the screen so that the user can review them.

[0674] Step 13:

[0675] Users select their preferred method of contact and then contact the resource provider.

[0676] Specific action: Select a contact method option (email, phone, etc.) and send that information to the server.

[0677] Step 14:

[0678] The server will contact the resource provider using the specified contact method to conduct further verification.

[0679] Specific actions: The system will send emails or process communications via internal systems according to the selected contact method.

[0680] (Example 1)

[0681] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0682] Traditional systems were inefficient in resource retrieval and information provision in response to user requests, and also had the potential for unnecessary information leaks. In particular, the process of quickly extracting and filtering appropriate resources from large amounts of data and providing them to users was considered complex and time-consuming. Furthermore, a lack of systems capable of appropriately responding to user requests was also a contributing factor.

[0683] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0684] In this invention, the server includes means for receiving a request entered by a user, means for searching for resources within the organization based on the request using generative artificial intelligence, means for filtering the resource list of the search results and limiting it to resources within the organization, means for sending the filtered resource list to the user terminal, means for displaying detailed information about the resource selected by the user, means for contacting the resource provider with the user's request, means for the user terminal to convert the request into JSON format and send it to the server, means for the server to analyze the received request and send it to the generative artificial intelligence, means for the generative artificial intelligence to extract keywords, means for searching the database based on the keywords and generating a list of relevant resources, means for the server to convert the filtered resource list into JSON format and send it to the user terminal, means for the server to obtain detailed information about the selected resource from the database and send it to the user terminal, means for the server to contact the resource provider based on the contact method selected by the user, and means for the resource provider to respond to the user's request and provide the necessary information. This enables users to efficiently search for appropriate resources and use them quickly, while preventing unnecessary information leaks.

[0685] A "user" refers to an individual or organization that uses this system to enter requests and receive resource searches or information.

[0686] A "request" refers to the content that a user inputs into the system, asking for the provision of specific resources or information.

[0687] "Generative artificial intelligence" refers to machine learning or AI models that analyze user requests, extract relevant keywords, search databases, and list appropriate resources.

[0688] "Resources" refer to information, services, or items that a system provides to meet user requirements.

[0689] "Filtering" refers to the process of selecting and extracting only those resources that meet specific criteria from a list compiled by a generative artificial intelligence.

[0690] A "resource list" refers to a list of relevant resources that remain after a generative artificial intelligence has searched and filtered based on the user's request.

[0691] A "user terminal" refers to a device used by a user to input requests and receive and display resource lists and detailed information.

[0692] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight data exchange format for structuring, storing, and transmitting data.

[0693] A "server" refers to a computer system that provides relay and management functions, such as receiving requests from user terminals, forwarding them to generative artificial intelligence systems, and sending filtering results to user terminals.

[0694] "Keyword extraction" refers to the process by which generative artificial intelligence extracts important words and phrases from a user's request and uses them to search a database.

[0695] A "database query" refers to a question or operation used to extract specific information from a database.

[0696] "Detailed information" refers to additional information about each resource included in the resource list (such as price and availability).

[0697] A "resource provider" refers to a company or individual that provides resources in response to a user's request.

[0698] "Contact method" refers to the means (e.g., email, phone) that a user chooses to use to contact a resource provider.

[0699] The system for implementing this invention efficiently matches user requests with resources within the group and prevents unnecessary information leaks. The entire system consists of a user terminal, a server, a generative artificial intelligence system, and a database.

[0700] Here is a specific example of a case where a user wants to purchase 20 new PCs.

[0701] The user enters a request, such as "I want to purchase 20 new PCs," into a dedicated interface and clicks the submit button. The user's terminal receives this request, converts it to JSON format, and sends it to the server.

[0702] The server receives requests from user terminals and analyzes their content. The analyzed content is sent to a generative artificial intelligence (AI). Based on the received requests, the AI ​​extracts keywords such as "PC," "purchase," and "20 units," and uses these to execute database queries. It searches the database for related resources (e.g., "Company A," "Company B," etc.) and generates a resource list.

[0703] Next, the server filters the generated resource list to extract a list limited to only the resources within the group. This filtered list is then converted to JSON format and sent back to the user's terminal.

[0704] The user terminal receives and parses JSON data and displays it to the user as a visual list. When the user selects "Company A" from the list, the server retrieves detailed information about the selected resource (e.g., PC price and availability) from the database and sends it to the user terminal. The user terminal then displays the detailed information to the user.

[0705] Finally, once the user selects their preferred method of contact (e.g., "email"), the server sends an email to "Company A" based on that method, conveying the user's request. "Company A," as the resource provider, then reviews the user's request and proceeds with providing the necessary information and procedures.

[0706] This series of processes allows users to efficiently utilize resources within the group and prevent unnecessary information leaks.

[0707] Example of a prompt:

[0708] User request: "I want to purchase 20 new PCs."

[0709] Keywords: "New PC", "20 units", "Purchase"

[0710] System role: Receives user requests, searches for relevant resources in the group's database, and generates a list of optimal resources.

[0711] This invention utilizes a generative AI model to respond quickly and accurately to user requests, and enables efficient resource searching and information provision. This makes it possible to provide users with a high level of convenience and peace of mind.

[0712] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0713] Step 1:

[0714] User input

[0715] The user sits down at their PC and opens a dedicated interface. The user enters a specific request, such as "I want to purchase 20 new PCs," into the input field and clicks the submit button.

[0716] Input: The user's specific request (e.g., "I want to purchase 20 new PCs").

[0717] Output: Request data converted to JSON format.

[0718] Specific operation: The terminal receives user input, verifies its contents, and converts the request into JSON format.

[0719] Step 2:

[0720] Receipt of request

[0721] The server receives the request data sent from the terminal.

[0722] Input: Request data in JSON format.

[0723] Output: Analyzed request data.

[0724] Specific operation: The server receives requests from terminals, analyzes their contents, and prepares them for transmission to the generative artificial intelligence.

[0725] Step 3:

[0726] AI-powered resource search

[0727] The generative artificial intelligence extracts keywords based on requests received from the server. Based on the extracted keywords (e.g., "PC", "purchase", "20 units"), it searches the database and generates a list of related resources (e.g., "Company A", "Company B").

[0728] Input: Analyzed request data.

[0729] Output: Resource list.

[0730] Specific operation: The generative artificial intelligence extracts important keywords from the received request, queries the database based on those keywords, and generates a list of relevant resources.

[0731] Step 4:

[0732] Filtering results

[0733] The server receives a resource list sent from a generative artificial intelligence and applies a filtering algorithm to extract a list limited to specific groups.

[0734] Input: Resource list.

[0735] Output: Filtered resource list.

[0736] Specific operation: The server filters the information in the resource list according to specific conditions and removes unnecessary information.

[0737] Step 5:

[0738] Sending the list to the user's terminal

[0739] The server converts the filtered resource list into JSON format and sends it to the user's terminal.

[0740] Input: A filtered list of resources.

[0741] Output: A filtered resource list in JSON format.

[0742] Specific operation: The server converts the filtering results into JSON format and sends them to the user's terminal.

[0743] Step 6:

[0744] Display of user selection and detailed information.

[0745] The user's terminal receives and visually displays a list of resources. The user selects the desired resource from the list (e.g., "Company A").

[0746] Input: A filtered resource list in JSON format.

[0747] Output: User-selected resource information.

[0748] Specific operation: The user's terminal parses the JSON data, displays a list, and communicates the resource selected by the user to the server.

[0749] Step 7:

[0750] Get and display detailed information

[0751] The server retrieves detailed information about the selected resource and sends it to the user's terminal. The user's terminal then displays the detailed information to the user (e.g., PC price, availability).

[0752] Input: User-selected resource information.

[0753] Output: Resource details.

[0754] Specific operation: The server retrieves detailed information about the selected resource from the database and sends it to the user terminal. The user terminal then displays that information.

[0755] Step 8:

[0756] Execution of the bridging

[0757] The user selects their preferred method of contact (e.g., "email"). The server then contacts the resource provider based on that method and relays the user's request.

[0758] Input: User's selected contact method and resource information.

[0759] Output: Responses and actions by resource providers.

[0760] Specific operation: The server contacts the resource provider, taking into account the user's request and contact method. The resource provider confirms the user's request and proceeds with the necessary information and procedures.

[0761] This series of processes allows users to efficiently utilize resources within the group and prevent unnecessary information leaks.

[0762] (Application Example 1)

[0763] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0764] In modern factories, the supply process for parts and materials demands efficiency and reliability, but currently, it often requires a great deal of time and effort. Furthermore, efficiently matching necessary resources is a challenge, leading to decreased productivity and the risk of information leaks. Therefore, there is a need for a system that can quickly and accurately meet the demands for parts and materials, and achieve an efficient supply process.

[0765] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0766] In this invention, the server includes means for receiving requests entered by a user, means for searching for resources within a group based on the request using generative artificial intelligence, and means for filtering the resource list of the search results and limiting it to resources within the group. This enables a factory robot that receives requests for parts and materials, matches them with resources within the group, and supplies them efficiently.

[0767] A "request" is an instruction that a user enters because they need a specific resource or service.

[0768] "Generative artificial intelligence" is an artificial intelligence system that extracts keywords based on user requests and identifies related resources.

[0769] "Filtering" is the process by which a generative artificial intelligence removes unnecessary information from a searched resource list and creates a limited list.

[0770] A "resource list" is a collection of information that lists the resources relevant to a user's request.

[0771] A "user terminal" is a device used by a user to input requests, receive results, and confirm them.

[0772] A "resource provider" is an entity whose role is to provide the necessary resources based on the user's requests.

[0773] A "factory robot" is an automated machine that receives requests for parts and materials, matches them with resources within the group, and supplies them efficiently.

[0774] The system for implementing this invention can efficiently process user requests and quickly meet the demand for parts and materials within the factory by utilizing resources within the group. The following describes each process of this system in detail.

[0775] Hardware and software

[0776] This system includes the following main components:

[0777] 1. User terminal: A device on which a user enters a request and receives the result. Examples include tablets and smartphones.

[0778] 2. Server: Its role is to receive user requests and transmit them to the generative artificial intelligence. It also sends the processed results to the user's terminal.

[0779] 3. Generative Artificial Intelligence (AI): Analyzes user requests, extracts relevant keywords, and searches for resources. Specifically, AI models such as GPT-3 are used.

[0780] 4. Database: Stores resource information within the group and is used for AI-powered searches.

[0781] 5. Factory robots: Their role is to receive requests for parts and materials and supply them automatically.

[0782] Data processing and calculation

[0783] 1. Receiving and parsing user input:

[0784] Requests from the user's terminal are received by the server and sent to the generative artificial intelligence (AI). For example, if the user inputs "I need 50 of part X," that information is sent to the server.

[0785] 2. Keyword extraction and resource search:

[0786] Generative artificial intelligence analyzes the received request and extracts relevant keywords. Keywords such as "part X" and "50" are extracted, and the database is searched based on these keywords.

[0787] 3. Filtering search results:

[0788] The server filters the resource list generated by the generative artificial intelligence, limiting it to the appropriate resources within the group. The filtering results are converted to JSON format and sent to the user's terminal.

[0789] 4. Displaying and selecting results:

[0790] The user's device parses the received JSON data and displays a visual list of resources. When the user selects a desired resource, detailed information is displayed, such as "stock availability" or "price."

[0791] 5. Contact the resource provider of the request:

[0792] Once the user reviews the resource details and indicates their intention to purchase or acquire it, the server contacts the resource provider directly.

[0793] 6. Supply by factory robots:

[0794] Factory robots receive parts and materials supplied by resource providers and efficiently deliver them to designated locations.

[0795] Specific examples and prompt statements

[0796] The following is an example of an application where a factory robot supplies parts and materials.

[0797] Specific example:

[0798] A factory operator uses a tablet to input a request, such as "We need 50 units of part X." The server sends this request to a generative artificial intelligence (AI), which extracts relevant keywords. After searching the database and filtering the resulting resource list, the AI ​​displays the appropriate supplier to the operator. Once the operator selects a resource, detailed information is displayed. The factory robot then automatically supplies the parts.

[0799] Example of a prompt:

[0800] Input from user terminal: "I need 50 units of part X."

[0801] Robot response: "Searching for suppliers of part X... 4 optimal suppliers found. View details?"

[0802] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0803] Step 1:

[0804] Receiving user input

[0805] User terminal: Users input requests using devices such as tablets or smartphones. Specifically, a user might input "I need 50 units of part X." The terminal then sends this request to the server.

[0806] Input: User request (e.g., "I need 50 units of part X")

[0807] Output: Request data sent to the server

[0808] Step 2:

[0809] Receiving and sending requests

[0810] Server: The server receives request data from the user terminal and prepares it for transmission to the generative artificial intelligence (AI). The server analyzes the request data and converts it into the appropriate format.

[0811] Input: Request data from the user terminal

[0812] Output: Analyzed data sent to the generative artificial intelligence.

[0813] Step 3:

[0814] Keyword extraction and resource search

[0815] Generative artificial intelligence: The AI ​​extracts keywords based on the received, analyzed data. For example, keywords such as "part X" and "50 pieces" might be extracted. Then, it searches a database based on these keywords and generates a list of relevant resources.

[0816] Input: Analyzed data from the server

[0817] Output: Resource list of search results

[0818] Step 4:

[0819] Filtering search results

[0820] Server: The server receives the resource list generated by the AI ​​and performs filtering. This filtering process creates a list limited to resources available within the group.

[0821] Input: Resource list from generative artificial intelligence

[0822] Output: Filtered resource list

[0823] Step 5:

[0824] Converting and sending resource lists

[0825] Server: Converts the filtered resource list into JSON format and sends it to the user's terminal. This makes the list visually easy to understand.

[0826] Input: Filtered resource list

[0827] Output: Resource list in JSON format

[0828] Step 6:

[0829] Displaying the list and providing detailed information

[0830] User Terminal: The user terminal parses the received JSON-formatted resource list and displays it to the user as a visual resource list. The user selects the desired resource and checks detailed information (such as availability and price).

[0831] Input: Resource list in JSON format

[0832] Output: Visually displayed resource list and detailed information

[0833] Step 7:

[0834] Notification of the request to the resource provider

[0835] Server: When a user selects a desired resource, for example, "Contact by email," the server contacts the resource provider. This contact includes the user's request and preferred method of contact.

[0836] Input: User's selection information and contact method

[0837] Output: Notification data sent to resource providers

[0838] Step 8:

[0839] Supply by factory robots

[0840] Factory robots: Factory robots receive parts and materials based on requests from resource providers and deliver them to designated locations. The robots automatically calculate the shortest path and complete the delivery efficiently.

[0841] Input: Supplies from resource providers

[0842] Output: Delivery completion notification to the location specified by the user.

[0843] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0844] The system for implementing this invention not only understands user requests and matches them with resources within the group, but also recognizes and responds to the user's emotions. The entire system consists of a user terminal, a server, a generative artificial intelligence, an emotion engine, and a database.

[0845] 1. User input and sentiment recognition

[0846] As soon as a user enters a request into the device, the device is equipped with an emotion engine to recognize the user's emotions.

[0847] The emotion engine analyzes the user's input and behavior (voice tone, input speed, facial expressions, etc.) to identify their emotional state.

[0848] Along with the request, the device sends emotion data recognized by the emotion engine to the server.

[0849] 2. Receiving requests and analyzing sentiment data

[0850] The server receives requests and sentiment data from the user's terminal and analyzes them.

[0851] Prepare to pass the received data to the generative artificial intelligence.

[0852] 3. Sending request and emotion data to the generative artificial intelligence system.

[0853] The server sends the converted request data and emotion data to the generative artificial intelligence.

[0854] 4. AI-powered resource search and optimization

[0855] Generative artificial intelligence extracts relevant keywords based on received requests and sentiment data.

[0856] Based on keywords and sentiment data, the system searches the database within the group and generates a list of relevant resources (e.g., "Company A", "Company B", etc.).

[0857] Optimize search results based on the user's emotions. For example, if a user is feeling stressed, prioritize displaying companies with excellent customer support.

[0858] 5. Filtering the results

[0859] The server filters the resource list generated by the generative artificial intelligence, limiting it to resources within the specified group.

[0860] 6. Sending the list to the user's terminal

[0861] The server converts the filtered list into JSON format and sends it to the user's terminal.

[0862] The user's device parses the received JSON data and displays it to the user as a visual list.

[0863] 7. Displaying user selections and details

[0864] The user selects the desired resource from the displayed list.

[0865] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[0866] The user terminal displays detailed information to the user (e.g., PC price, availability, etc.).

[0867] 8. Implementing the Bridging Process

[0868] Users select their preferred method of contact and then contact the resource provider.

[0869] The server contacts resource providers based on the user's request and contact method.

[0870] We adjust our communication methods based on the user's emotions. For example, if a user is feeling anxious, we prioritize direct contact by phone.

[0871] Specific example

[0872] Example: If you want to purchase 20 new PCs

[0873] 1. User input and sentiment recognition

[0874] A user (sales representative) enters a request into the terminal saying, "I want to purchase 20 new PCs." If the user is typing in a hurry, the emotion engine recognizes this and determines that the user is in a hurry.

[0875] 2. Receiving requests and analyzing sentiment data

[0876] The server receives this request and emotion data and prepares to send it to the generative artificial intelligence.

[0877] 3. Sending request and emotion data to the generative artificial intelligence system.

[0878] The server sends request data and emotion data to the generative artificial intelligence.

[0879] 4. AI-powered resource search and optimization

[0880] Generative artificial intelligence analyzes the request and extracts keywords such as "PC," "purchase," and "20 units."

[0881] Based on extracted keywords and sentiment data, the system searches the database within the group and generates a list of relevant resources such as "Company A" and "Company B".

[0882] Since the user is in a hurry, resources that are immediately available will be displayed first.

[0883] 5. Filtering the results

[0884] The server filters the search results and extracts a list containing only companies within the group.

[0885] 6. Sending the list to the user's terminal

[0886] The server converts the filtering results into JSON format and sends them to the user's terminal.

[0887] The user's terminal parses the received JSON data and displays a list of "Company A" and "Company B".

[0888] 7. Displaying user selections and details

[0889] The user selects "Company A" and checks the details (PC price, stock availability, etc.).

[0890] 8. Implementing the Bridging Process

[0891] If the user selects "Contact by phone," the server will contact "Company A" by phone with the user's request.

[0892] This allows users to efficiently utilize resources within the group and prevents unnecessary information leaks. Furthermore, using an emotion engine enables flexible responses tailored to the user's emotions.

[0893] The following describes the processing flow.

[0894] Step 1:

[0895] Users log in to the system using their devices and enter requests such as "I want to buy XX," "I want to outsource XX," or "I want to sell XX."

[0896] Specific action: Enter the request into the input form displayed in the user interface and click "Submit".

[0897] Step 2:

[0898] The device receives user requests and uses an emotion engine to recognize the user's emotions.

[0899] Specific operation: The system analyzes the user's typing speed and input content, as well as their facial expressions, to determine their emotions.

[0900] Step 3:

[0901] The emotion engine generates recognized emotion data and prepares it to send to the server along with the request.

[0902] Specific operation: Structure emotion data, integrate it with request data, and convert it into a format that can be sent.

[0903] Step 4:

[0904] The device sends request data and sentiment data to the server.

[0905] Specific operation: Sends HTTP requests and API calls containing request and sentiment data to the server.

[0906] Step 5:

[0907] The server receives requests and sentiment data from users.

[0908] Specific operation: Analyzes the received data and converts it into a data format for passing to a generative artificial intelligence.

[0909] Step 6:

[0910] The server sends the converted request data and emotion data to the generative artificial intelligence.

[0911] Specific operation: Call the API of a generative artificial intelligence and pass the request data and sentiment data as parameters for analysis.

[0912] Step 7:

[0913] Generative artificial intelligence extracts relevant keywords based on received requests and sentiment data.

[0914] Specific operation: The request content is analyzed using natural language processing technology to identify the keywords necessary for the search.

[0915] Step 8:

[0916] The generative artificial intelligence searches the database within the group based on extracted keywords and sentiment data, and generates a list of relevant resources.

[0917] Specific operation: Convert keyword and sentiment data into database queries and retrieve a list of candidates as search results.

[0918] Step 9:

[0919] Generative artificial intelligence optimizes search results according to the user's emotions.

[0920] Specific actions: If the user is in a hurry, the system will prioritize displaying resources that are immediately available.

[0921] Step 10:

[0922] The server filters the resource list received from the generative artificial intelligence, limiting it to resources within a specific group.

[0923] Specific operation: The system performs a filter on the received list data to extract only the information within each group.

[0924] Step 11:

[0925] The server converts the filtered list into JSON format and sends it to the user's terminal.

[0926] Specific operation: The filtering results are generated as data in JSON format and sent to the user's terminal via API calls or HTTP responses.

[0927] Step 12:

[0928] The user's device parses the received JSON data and displays it to the user as a visual list.

[0929] Specific operation: Parses JSON data and visually displays the list using HTML or the application UI.

[0930] Step 13:

[0931] The user selects the desired resource from the displayed list.

[0932] Specific operation: Clicking an item in the list sends the ID and detailed information of the selected item to the server.

[0933] Step 14:

[0934] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[0935] Specific operation: Retrieve detailed information from the database and send it to the user's terminal via the API response.

[0936] Step 15:

[0937] The user terminal displays detailed information to the user.

[0938] Specific action: Display the received details on the screen so that the user can review them.

[0939] Step 16:

[0940] Users select their preferred method of contact and then contact the resource provider.

[0941] Specific action: Select a contact method option (email, phone, etc.) and send that information to the server.

[0942] Step 17:

[0943] The server will contact the resource provider using the specified contact method to conduct further verification.

[0944] Specific actions: The system will send emails or process communications via internal systems according to the selected contact method.

[0945] Step 18:

[0946] The emotion engine adjusts the communication method according to the user's emotions.

[0947] Specific actions: For example, if a user is feeling anxious, priority will be given to phone contact, which allows for direct communication.

[0948] (Example 2)

[0949] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0950] Existing resource matching systems often process requests without considering user emotions, which can lead to decreased user satisfaction. This is especially true when users are in a hurry or experiencing stress, making it difficult to provide appropriate resources. Furthermore, traditional systems often fail to adequately filter search results or provide data in the most optimal format, resulting in insufficient information for users. To address these challenges, a system is needed that recognizes user emotions and responds flexibly based on them.

[0951] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0952] In this invention, the server includes means for receiving a request entered by the user, means for acquiring the user's emotional data using an emotional recognition engine, means for searching for resources within a group based on the request using generative artificial intelligence, means for filtering the resource list of the search results and limiting it to resources within the group, means for sending the filtered resource list to the user terminal in JSON format, means for displaying detailed information about the resource selected by the user, and means for contacting resource providers based on the user's request and emotions. This enables flexible and effective resource provision in response to the user's emotions.

[0953] A "user" refers to an individual or legal entity that uses the system to input requests and seek out the most suitable resources.

[0954] A "request" refers to information that a user enters into the system, such as necessary details, desired services, or products, which the system should then respond to.

[0955] An "emotion recognition engine" refers to software or hardware that analyzes user input, behavior, facial expressions, voice tone, etc., to identify the user's emotional state.

[0956] "Emotional data" refers to data indicating the user's emotional state as identified by the emotion recognition engine.

[0957] "Generative artificial intelligence" refers to artificial intelligence that searches for resources based on user requests and sentiment data, and generates optimized search results.

[0958] "Resources" is a general term for services, products, information, etc., that are provided in response to user requests.

[0959] "Filtering" refers to the process of narrowing down a list of resources searched by a generative artificial intelligence based on specific conditions.

[0960] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight text data format widely used as a data exchange format.

[0961] A "user terminal" refers to a device (e.g., smartphone, tablet, PC, etc.) that a user uses to access a system, input requests, and receive and display results.

[0962] A "resource provider" refers to a company or individual that provides resources in response to a user's request.

[0963] A "database" refers to a system in which information is organized and stored, which generative artificial intelligence uses to search for resources.

[0964] Modes for carrying out the invention

[0965] The system for implementing this invention not only understands user requests and matches them with resources within the group, but also recognizes and responds to user emotions. The system consists of the following components:

[0966] User terminal

[0967] server

[0968] Generative artificial intelligence

[0969] Emotion recognition engine

[0970] database

[0971] Hardware and software to be used

[0972] User terminal: This includes smartphones, tablets, PCs, etc. It is used by the user to input requests, receive results, and display them.

[0973] Server: Performs key processing such as receiving requests, analyzing data, sending data to generative artificial intelligence, filtering search results, and contacting resource providers.

[0974] Emotion Recognition Engine: This uses IBM Watson as an example to recognize emotions from the user's input, voice tone, input speed, and facial expressions.

[0975] Generative Artificial Intelligence: OpenAI GPT-3 is used as the generative AI. Keyword extraction and search result optimization are performed based on user requests and sentiment data.

[0976] Database: Use a database such as MySQL to perform searches based on generated keywords and store and manage the results.

[0977] Data processing and data calculation

[0978] Emotion Recognition: The system analyzes user input, voice tone, input speed, and facial expression data to obtain emotional data. This process utilizes an emotion recognition engine.

[0979] Keyword Extraction and Search: Generative artificial intelligence extracts keywords from user requests and sentiment data, searches the database, and creates a list of appropriate resources.

[0980] Filtering: The server narrows down the list of resources it has searched based on specific criteria, limiting it to resources within a group.

[0981] Data transmission: The filtered resource list is converted to JSON format and sent to the user's terminal. Additionally, resource providers are contacted based on user requests.

[0982] Specific example

[0983] Here's a step-by-step guide for a user who wants to purchase 20 new PCs:

[0984] 1. User input and sentiment recognition:

[0985] A user (sales representative) enters "I want to purchase 20 new PCs" into the terminal. If the user is typing in a hurry, the emotion recognition engine will recognize this and determine that the user is in a hurry.

[0986] 2. Receiving requests and analyzing sentiment data:

[0987] The device sends this request and emotional data to the server. The server analyzes the received data and formats it for transmission to a generative artificial intelligence system.

[0988] 3. Sending request and emotion data to the generative artificial intelligence:

[0989] The server sends request data and emotion data to the generative artificial intelligence.

[0990] 4. AI-powered resource search and optimization:

[0991] Generative artificial intelligence analyzes the request and extracts keywords such as "PC," "purchase," and "20 units." Based on the extracted keywords and sentiment data, it searches the database and generates a list of relevant resources such as "Company A" and "Company B." Because the user is in a hurry, resources that are immediately available are displayed first.

[0992] 5. Filtering the results:

[0993] The server filters the search results and extracts a list containing only companies within the group.

[0994] 6. Sending the list to the user's terminal:

[0995] The server converts the filtering results into JSON format and sends them to the user's terminal. The terminal parses the received JSON data and displays lists of "Company A" and "Company B".

[0996] 7. Displaying user selections and details:

[0997] The user selects "Company A" and checks the details (PC price, stock availability, etc.).

[0998] 8. Implementing the bridging:

[0999] If the user selects "Contact by phone," the server will contact "Company A" by phone with the user's request.

[1000] Example of a prompt

[1001] Example prompt message for a user purchasing 20 new PCs:

[1002] text

[1003] User request: "Purchase 20 new PCs."

[1004] User sentiment data: "Users are in a hurry."

[1005] System prompt: "Please show me companies that can immediately deliver 20 PCs. The user is in a hurry."

[1006] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1007] Step 1:

[1008] The user enters the request.

[1009] The user enters a request into the terminal. For example, they might enter "I want to purchase 20 new PCs."

[1010] Function: The user enters text into an input field and submits it by pressing a specific button.

[1011] Input: User's request text: "I want to purchase 20 new PCs."

[1012] Output: The requested data is stored on the terminal.

[1013] Step 2:

[1014] The device recognizes the user's emotions.

[1015] The device uses an emotion recognition engine to analyze the user's input, behavior, voice tone, input speed, and facial expressions.

[1016] Function: Captures the user's facial expressions and voice using the camera and microphone, and analyzes them with an emotion recognition engine (e.g., IBM Watson).

[1017] Input: User's facial expressions, voice, input speed

[1018] Output: User sentiment data (e.g., "I'm in a hurry") is recognized and retrieved.

[1019] Step 3:

[1020] The device sends request and sentiment data to the server.

[1021] The device sends the acquired request data and sentiment data to the server.

[1022] Function: Converts request data and sentiment data into JSON format and sends it to the server via a secure channel.

[1023] Input: Request data "I want to buy 20 new PCs", Sentiment data "The user is in a hurry"

[1024] Output: Request data and sentiment data are sent to and received by the server.

[1025] Step 4:

[1026] The server analyzes and formats the data.

[1027] The server analyzes the received request data and emotion data, and formats it for transmission to the generative artificial intelligence.

[1028] Function: Performs data validation and format conversion, and formats the data to a format compatible with generative AI (e.g., OpenAI GPT-3).

[1029] Input: Request data and sentiment data in JSON format

[1030] Output: Data converted to a format for transmission to a generative AI.

[1031] Step 5:

[1032] The server sends request and emotion data to the generative artificial intelligence.

[1033] The server sends the converted request data and emotion data to the generative artificial intelligence.

[1034] Function: Sends data to generative AI using an API.

[1035] Input: Formatted request data and sentiment data

[1036] Output: The generative AI receives the data and begins processing.

[1037] Step 6:

[1038] Generative artificial intelligence extracts keywords and searches the database.

[1039] Generative AI extracts keywords from received requests and sentiment data, and searches for related resources in a database.

[1040] Function: Uses natural language processing techniques to extract keywords and execute database queries.

[1041] Input: Request data and sentiment data

[1042] Output: Related resource list (e.g., resource list based on keywords such as "PC", "purchase", and "20 units")

[1043] Step 7:

[1044] Optimization of search results using generative AI

[1045] Generative AI optimizes search results based on user sentiment data. For example, if a user is in a hurry, it prioritizes resources that are immediately available.

[1046] Function: Ranks and filters search results based on sentiment data.

[1047] Input: Search results list, sentiment data "hurried"

[1048] Output: Optimized resource list (e.g., prioritizing "Immediately Available Resources")

[1049] Step 8:

[1050] The server filters the search results.

[1051] The server filters the resource list generated by the generative AI, limiting it to resources within the specified group.

[1052] Function: Applies a filtering algorithm to extract only resources within the group.

[1053] Input: Resource list for generative AI

[1054] Output: Limited resource list (e.g., "Company A", "Company B", etc., including only resources within the group)

[1055] Step 9:

[1056] The server sends the filtering results to the terminal in JSON format.

[1057] The server converts the filtering results into JSON format and sends them to the user's terminal.

[1058] Function: Uses a protocol to convert filtering results into JSON format and send them to the terminal.

[1059] Input: Filtered resource list

[1060] Output: A resource list in JSON format is sent to the terminal.

[1061] Step 10:

[1062] The user selects a resource from a list.

[1063] The user selects the desired resource from the list displayed on the device.

[1064] Function: The user taps or clicks a list item to select it.

[1065] Input: List display and user selection operation

[1066] Output: Selected resource data (e.g., "Company A")

[1067] Step 11:

[1068] The server retrieves detailed information about the resource and sends it to the terminal.

[1069] The server retrieves detailed information related to the selected resource and sends it to the terminal.

[1070] Function: Retrieves detailed information about the selected resource from a database or another API and sends it to the device.

[1071] Input: Selected resource data

[1072] Output: Detailed information (e.g., PC price, stock status) is sent to the terminal.

[1073] Step 12:

[1074] The user selects a contact method, and the server contacts the resource provider.

[1075] The user selects their preferred method of contact, and the server then contacts the resource provider.

[1076] Function: Select a contact method (e.g., email, phone), and the server will contact the resource provider using the corresponding API or protocol.

[1077] Input: Selection of contact method, resource data, sentiment data

[1078] Output: Contact is established with the resource provider (e.g., "Company A" is contacted by phone).

[1079] This series of steps enables efficient resource matching that takes into account user needs and emotions.

[1080] (Application Example 2)

[1081] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1082] Traditional systems have mechanisms to search for and provide resources based on user requests, but they lack the ability to consider the user's emotional state, making it difficult to optimize the user experience. Furthermore, there is a problem in providing appropriate resources quickly and effectively when the user is in an emergency or experiencing a specific emotional state.

[1083] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving a request entered by the user, means for searching for resources within a group based on the request using generative artificial intelligence, means for recognizing the user's emotional state using an emotion engine and processing the data, means for sending a filtered resource list to the user terminal in JSON format, means for displaying detailed information about the resource selected by the user, and means for adjusting the contact method based on the user's emotional state and contacting the resource provider with the request. This enables flexible responses according to the user's emotional state, and allows for the rapid and effective provision of resources, especially in emergencies or when the user is in a specific emotional state.

[1084] "Means for receiving user-entered requests" refers to a mechanism for receiving request data entered by a user into a terminal.

[1085] "A means of searching for resources within a group based on requests using generative artificial intelligence" refers to a mechanism that uses generative artificial intelligence to analyze user request data and identify relevant resources within a group.

[1086] "Means for recognizing a user's emotional state using an emotion engine and processing that data" refers to a mechanism for identifying an emotional state by analyzing the user's voice tone, input speed, facial expressions, etc., and for handling that data.

[1087] "Means for filtering the resource list in search results and limiting it to within a group" refers to a mechanism that filters the resource list searched by a generative artificial intelligence to limit it to only those resources within a group.

[1088] "Means for sending a filtered resource list to the user's terminal in JSON format" refers to a mechanism for converting a filtered resource list into JSON, a structured data format, and sending it to the user's terminal.

[1089] "Means for displaying detailed information about a resource selected by the user" refers to a mechanism for displaying detailed information about a resource selected by the user on the user's terminal.

[1090] "A means of adjusting the method of contact based on the user's emotional state and communicating the request to the resource provider" refers to a mechanism that selects the most appropriate method of contact according to the user's emotional state and notifies the resource provider of the user's request.

[1091] The system for implementing this invention not only understands user requests and matches them with resources within the group, but also recognizes and responds to the user's emotions. The entire system consists of a user terminal, a server, a generative artificial intelligence, an emotion engine, and a database.

[1092] The user inputs their requests using their smartphone, and the entire system operates accordingly. First, the user inputs their requests for desired products or services into their smartphone. Simultaneously, the smartphone is equipped with an emotion engine that analyzes the user's voice tone, input speed, facial expressions, etc., to recognize their emotional state.

[1093] The server receives request data and sentiment data sent from the user terminal. The received data is sent to a generative artificial intelligence (AI) for analysis. The generative AI extracts relevant keywords from the request data and searches a database within the group along with the sentiment data. This process includes keyword extraction and database query execution.

[1094] The resource list in search results is optimized according to the user's emotional state. For example, if the user is feeling urgent or stressed, the system will prioritize displaying resources that offer quick response and comprehensive support. The resulting resource list is then filtered by the server to limit resources to those within a specific group.

[1095] The filtered resource list is converted to JSON format and sent to the user's terminal. The user's terminal parses the received JSON data and displays it to the user as a visual list. The user can select the desired resource from the displayed list and view detailed information, such as the product price and availability.

[1096] Once the user selects their preferred method of contact, the server will choose the most appropriate method based on the user's request and emotional state, and contact the resource provider. For example, if the user is feeling anxious, telephone contact will be prioritized.

[1097] The specific hardware and software used include smartphones (iOS or Android), emotion recognition APIs (e.g., Microsoft Azure Emotion API), and generative artificial intelligence APIs (e.g., OpenAI GPT models). These elements work together to enable flexible responses that respond to the user's emotions.

[1098] As a concrete example, consider a scenario where a user searches for products to relieve fatigue. In this case, if the user enters "I want supplements that relieve fatigue," the emotion engine recognizes the user's level of fatigue. Based on this, the generative artificial intelligence extracts relevant keywords and generates a list of products suitable for that emotional state. For example, the list might include aromatherapy candles and fatigue-relieving supplements.

[1099] Example of a prompt:

[1100] User request: I want a supplement to relieve fatigue.

[1101] Emotional state: Fatigue (high)

[1102] Expected output: Display a list of fatigue-relieving supplements, aromatherapy candles, and other relaxing products, along with price and availability information.

[1103] By combining emotion recognition and generative artificial intelligence in this way, it becomes possible to provide services that optimize the user experience.

[1104] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1105] Step 1:

[1106] Users use their smartphones to input requests for desired products or services. Simultaneously, an emotion engine built into the device analyzes the user's voice tone, input speed, facial expressions, etc., to recognize their emotional state. Based on this sensory data, the emotion engine identifies emotions such as "fatigue" and "stress" and generates data accordingly.

[1107] Input: User requests and sentiment data

[1108] Output: Request data and sentiment data

[1109] Step 2:

[1110] The terminal sends user request data and sentiment data to the server. The server prepares the received data for analysis. This preparation stage involves standardizing the data format and performing necessary preprocessing.

[1111] Input: Request data and sentiment data

[1112] Output: Preprocessed request data and sentiment data

[1113] Step 3:

[1114] The server sends pre-processed request data and sentiment data to the generative artificial intelligence. The generative AI extracts relevant keywords based on the transmitted data and analyzes the sentiment data.

[1115] Input: Preprocessed request data and sentiment data

[1116] Output: Extracted keywords and analyzed sentiment data

[1117] Step 4:

[1118] The generative artificial intelligence uses extracted keywords and analyzed sentiment data to search a database within the group. The search results generate a list of relevant resources. For example, if a user requests "supplements to relieve fatigue," the list will include corresponding supplements and relaxation products.

[1119] Input: Keywords and sentiment data

[1120] Output: Resource List

[1121] Step 5:

[1122] The server filters the generated resource list, limiting it to only resources within the group. This filtering excludes resources outside the group, prioritizing highly reliable resources.

[1123] Input: Resource list

[1124] Output: Filtered resource list

[1125] Step 6:

[1126] The server converts the filtered resource list into JSON format and sends it to the user's terminal. Converting to JSON format makes it easier for the user's terminal to analyze and display the data.

[1127] Input: Filtered resource list

[1128] Output: Resource list converted to JSON format

[1129] Step 7:

[1130] The device parses the received JSON data and displays it to the user as a visual list. The user can then select the desired resource from this list. For example, they can check the price and availability of a product.

[1131] Input: Resource list in JSON format

[1132] Output: Visual resource list

[1133] Step 8:

[1134] The user selects their preferred method of contact and sends that information to the server. The server then adjusts the best method of contact based on the user's emotional state and contacts the resource provider. For example, if the user is feeling anxious, phone contact will be prioritized.

[1135] Input: User selection information and sentiment data

[1136] Output: Contacting resource providers

[1137] This enables the provision of flexible and optimal resources tailored to the user's emotional state.

[1138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1139] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1140] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1141] [Third Embodiment]

[1142] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1146] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1150] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1151] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1152] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1153] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1154] The system for implementing this invention matches user requests with resources within the group, efficiently provides information, and prevents unnecessary information leaks. The entire system consists of a user terminal, a server, a generative artificial intelligence, and a database.

[1155] 1. User input

[1156] The user enters their request for the necessary resources (for example, "I want to purchase 20 new PCs") into the terminal.

[1157] The terminal receives the request and prepares to send it to the server.

[1158] 2. Receipt of Request

[1159] The server receives requests from the user's terminal.

[1160] The system analyzes the received request and sends it to a generative artificial intelligence.

[1161] 3. AI-powered resource search

[1162] Generative artificial intelligence extracts relevant keywords (for example, "PC", "purchase", "20 units") based on the received request.

[1163] Based on keywords, the system searches the database within the group and generates a list of relevant resources (e.g., "Company A", "Company B", etc.).

[1164] 4. Filtering the results

[1165] The server filters the resource list generated by the generative artificial intelligence.

[1166] The filtering process extracts only the list of information limited to that within the group.

[1167] 5. Sending the list to the user's terminal

[1168] The server converts the filtered list into JSON format and sends it to the user's terminal.

[1169] The user's device parses the received JSON data and displays it to the user as a visual list.

[1170] 6. Displaying user selections and details

[1171] The user selects the desired resource (for example, "Company A") from the displayed list.

[1172] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[1173] The user terminal displays detailed information to the user (e.g., PC price, availability, etc.).

[1174] 7. Implementing the Bridging Process

[1175] The user selects their preferred method of contact (e.g., "email").

[1176] The server contacts resource providers based on the user's request and contact method.

[1177] The resource provider confirms the user's request and proceeds with providing the necessary information and procedures.

[1178] Specific example

[1179] Example: If you want to purchase 20 new PCs

[1180] 1. User input

[1181] The user (sales representative) enters a request into the terminal saying, "I would like to purchase 20 new PCs," and clicks the submit button.

[1182] 2. Receipt of Request

[1183] The server receives this request and sends it to the generative artificial intelligence.

[1184] 3. AI-powered resource search

[1185] Generative artificial intelligence analyzes the request and extracts keywords such as "PC," "purchase," and "20 units."

[1186] Based on the extracted keywords, the system searches the database within the group and generates a list of related resources such as "Company A" and "Company B".

[1187] 4. Filtering the results

[1188] The server filters the search results and extracts a list containing only companies within the group.

[1189] 5. Sending the list to the user's terminal

[1190] The server converts the filtering results into JSON format and sends them to the user's terminal.

[1191] The user's terminal parses the received JSON data and displays a list of "Company A" and "Company B".

[1192] 6. Displaying user selections and details

[1193] The user selects "Company A" and checks the details (PC price, stock availability, etc.).

[1194] 7. Implementing the Bridging Process

[1195] If the user selects "Contact by email," the server sends an email to "Company A" requesting confirmation from the user.

[1196] This allows users to efficiently utilize resources within the group and prevents unnecessary information leaks.

[1197] The following describes the processing flow.

[1198] Step 1:

[1199] Users log in to the system using their devices and enter requests such as "I want to buy XX," "I want to outsource XX," or "I want to sell XX."

[1200] Specific action: Enter the request into the input form displayed in the user interface and click "Submit".

[1201] Step 2:

[1202] The terminal receives the user's request in string format and sends it to the server.

[1203] Specific operation: Send the request details to the server via an HTTP request or API call.

[1204] Step 3:

[1205] The server receives requests from users.

[1206] Specific operation: Converts the received string data into a data format suitable for passing to a generative artificial intelligence.

[1207] Step 4:

[1208] The server sends the converted request data to the generative artificial intelligence.

[1209] Specific operation: Call the API of a generative artificial intelligence and pass the requested data as parameters for analysis.

[1210] Step 5:

[1211] Generative artificial intelligence extracts relevant keywords based on the received request.

[1212] Specific operation: The request content is analyzed using natural language processing technology to identify the keywords necessary for the search.

[1213] Step 6:

[1214] The generative artificial intelligence searches the database within the group based on the extracted keywords and generates a list of relevant resources.

[1215] Specific operation: Generate a database query and retrieve a list of candidates as search results.

[1216] Step 7:

[1217] The server filters the resource list received from the generative artificial intelligence, limiting it to resources within a specific group.

[1218] Specific operation: The system performs a filter on the received list data to extract only the information within each group.

[1219] Step 8:

[1220] The server converts the filtered list into JSON format and sends it to the user's terminal.

[1221] Specific operation: The filtering results are generated as data in JSON format and sent to the user's terminal via API calls or HTTP responses.

[1222] Step 9:

[1223] The user's device parses the received JSON data and displays it to the user as a visual list.

[1224] Specific operation: Parses JSON data and visually displays the list using HTML or the application UI.

[1225] Step 10:

[1226] The user selects the desired resource from the displayed list.

[1227] Specific operation: Clicking an item in the list sends the ID and detailed information of the selected item to the server.

[1228] Step 11:

[1229] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[1230] Specific operation: Retrieve detailed information from the database and send it to the user's terminal via the API response.

[1231] Step 12:

[1232] The user terminal displays detailed information to the user.

[1233] Specific action: Display the received details on the screen so that the user can review them.

[1234] Step 13:

[1235] Users select their preferred method of contact and then contact the resource provider.

[1236] Specific action: Select a contact method option (email, phone, etc.) and send that information to the server.

[1237] Step 14:

[1238] The server will contact the resource provider using the specified contact method to conduct further verification.

[1239] Specific actions: The system will send emails or process communications via internal systems according to the selected contact method.

[1240] (Example 1)

[1241] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1242] Traditional systems were inefficient in resource retrieval and information provision in response to user requests, and also had the potential for unnecessary information leaks. In particular, the process of quickly extracting and filtering appropriate resources from large amounts of data and providing them to users was considered complex and time-consuming. Furthermore, a lack of systems capable of appropriately responding to user requests was also a contributing factor.

[1243] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1244] In this invention, the server includes means for receiving a request entered by a user, means for searching for resources within the organization based on the request using generative artificial intelligence, means for filtering the resource list of the search results and limiting it to resources within the organization, means for sending the filtered resource list to the user terminal, means for displaying detailed information about the resource selected by the user, means for contacting the resource provider with the user's request, means for the user terminal to convert the request into JSON format and send it to the server, means for the server to analyze the received request and send it to the generative artificial intelligence, means for the generative artificial intelligence to extract keywords, means for searching the database based on the keywords and generating a list of relevant resources, means for the server to convert the filtered resource list into JSON format and send it to the user terminal, means for the server to obtain detailed information about the selected resource from the database and send it to the user terminal, means for the server to contact the resource provider based on the contact method selected by the user, and means for the resource provider to respond to the user's request and provide the necessary information. This enables users to efficiently search for appropriate resources and use them quickly, while preventing unnecessary information leaks.

[1245] A "user" refers to an individual or organization that uses this system to enter requests and receive resource searches or information.

[1246] A "request" refers to the content that a user inputs into the system, asking for the provision of specific resources or information.

[1247] "Generative artificial intelligence" refers to machine learning or AI models that analyze user requests, extract relevant keywords, search databases, and list appropriate resources.

[1248] "Resources" refer to information, services, or items that a system provides to meet user requirements.

[1249] "Filtering" refers to the process of selecting and extracting only those resources that meet specific criteria from a list compiled by a generative artificial intelligence.

[1250] A "resource list" refers to a list of relevant resources that remain after a generative artificial intelligence has searched and filtered based on the user's request.

[1251] A "user terminal" refers to a device used by a user to input requests and receive and display resource lists and detailed information.

[1252] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight data exchange format for structuring, storing, and transmitting data.

[1253] A "server" refers to a computer system that provides relay and management functions, such as receiving requests from user terminals, forwarding them to generative artificial intelligence systems, and sending filtering results to user terminals.

[1254] "Keyword extraction" refers to the process by which generative artificial intelligence extracts important words and phrases from a user's request and uses them to search a database.

[1255] A "database query" refers to a question or operation used to extract specific information from a database.

[1256] "Detailed information" refers to additional information about each resource included in the resource list (such as price and availability).

[1257] A "resource provider" refers to a company or individual that provides resources in response to a user's request.

[1258] "Contact method" refers to the means (e.g., email, phone) that a user chooses to use to contact a resource provider.

[1259] The system for implementing this invention efficiently matches user requests with resources within the group and prevents unnecessary information leaks. The entire system consists of a user terminal, a server, a generative artificial intelligence system, and a database.

[1260] Here is a specific example of a case where a user wants to purchase 20 new PCs.

[1261] The user enters a request, such as "I want to purchase 20 new PCs," into a dedicated interface and clicks the submit button. The user's terminal receives this request, converts it to JSON format, and sends it to the server.

[1262] The server receives requests from user terminals and analyzes their content. The analyzed content is sent to a generative artificial intelligence (AI). Based on the received requests, the AI ​​extracts keywords such as "PC," "purchase," and "20 units," and uses these to execute database queries. It searches the database for related resources (e.g., "Company A," "Company B," etc.) and generates a resource list.

[1263] Next, the server filters the generated resource list to extract a list limited to only the resources within the group. This filtered list is then converted to JSON format and sent back to the user's terminal.

[1264] The user terminal receives and parses JSON data and displays it to the user as a visual list. When the user selects "Company A" from the list, the server retrieves detailed information about the selected resource (e.g., PC price and availability) from the database and sends it to the user terminal. The user terminal then displays the detailed information to the user.

[1265] Finally, once the user selects their preferred method of contact (e.g., "email"), the server sends an email to "Company A" based on that method, conveying the user's request. "Company A," as the resource provider, then reviews the user's request and proceeds with providing the necessary information and procedures.

[1266] This series of processes allows users to efficiently utilize resources within the group and prevent unnecessary information leaks.

[1267] Example of a prompt:

[1268] User request: "I want to purchase 20 new PCs."

[1269] Keywords: "New PC", "20 units", "Purchase"

[1270] System role: Receives user requests, searches for relevant resources in the group's database, and generates a list of optimal resources.

[1271] This invention utilizes a generative AI model to respond quickly and accurately to user requests, and enables efficient resource searching and information provision. This makes it possible to provide users with a high level of convenience and peace of mind.

[1272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1273] Step 1:

[1274] User input

[1275] The user sits down at their PC and opens a dedicated interface. The user enters a specific request, such as "I want to purchase 20 new PCs," into the input field and clicks the submit button.

[1276] Input: The user's specific request (e.g., "I want to purchase 20 new PCs").

[1277] Output: Request data converted to JSON format.

[1278] Specific operation: The terminal receives user input, verifies its contents, and converts the request into JSON format.

[1279] Step 2:

[1280] Receipt of request

[1281] The server receives the request data sent from the terminal.

[1282] Input: Request data in JSON format.

[1283] Output: Analyzed request data.

[1284] Specific operation: The server receives requests from terminals, analyzes their contents, and prepares them for transmission to the generative artificial intelligence.

[1285] Step 3:

[1286] AI-powered resource search

[1287] The generative artificial intelligence extracts keywords based on requests received from the server. Based on the extracted keywords (e.g., "PC", "purchase", "20 units"), it searches the database and generates a list of related resources (e.g., "Company A", "Company B").

[1288] Input: Analyzed request data.

[1289] Output: Resource list.

[1290] Specific operation: The generative artificial intelligence extracts important keywords from the received request, queries the database based on those keywords, and generates a list of relevant resources.

[1291] Step 4:

[1292] Filtering results

[1293] The server receives a resource list sent from a generative artificial intelligence and applies a filtering algorithm to extract a list limited to specific groups.

[1294] Input: Resource list.

[1295] Output: Filtered resource list.

[1296] Specific operation: The server filters the information in the resource list according to specific conditions and removes unnecessary information.

[1297] Step 5:

[1298] Sending the list to the user's terminal

[1299] The server converts the filtered resource list into JSON format and sends it to the user's terminal.

[1300] Input: A filtered list of resources.

[1301] Output: A filtered resource list in JSON format.

[1302] Specific operation: The server converts the filtering results into JSON format and sends them to the user's terminal.

[1303] Step 6:

[1304] Display of user selection and detailed information.

[1305] The user's terminal receives and visually displays a list of resources. The user selects the desired resource from the list (e.g., "Company A").

[1306] Input: A filtered resource list in JSON format.

[1307] Output: User-selected resource information.

[1308] Specific operation: The user's terminal parses the JSON data, displays a list, and communicates the resource selected by the user to the server.

[1309] Step 7:

[1310] Get and display detailed information

[1311] The server retrieves detailed information about the selected resource and sends it to the user's terminal. The user's terminal then displays the detailed information to the user (e.g., PC price, availability).

[1312] Input: User-selected resource information.

[1313] Output: Resource details.

[1314] Specific operation: The server retrieves detailed information about the selected resource from the database and sends it to the user terminal. The user terminal then displays that information.

[1315] Step 8:

[1316] Execution of the bridging

[1317] The user selects their preferred method of contact (e.g., "email"). The server then contacts the resource provider based on that method and relays the user's request.

[1318] Input: User's selected contact method and resource information.

[1319] Output: Responses and actions by resource providers.

[1320] Specific operation: The server contacts the resource provider, taking into account the user's request and contact method. The resource provider confirms the user's request and proceeds with the necessary information and procedures.

[1321] This series of processes allows users to efficiently utilize resources within the group and prevent unnecessary information leaks.

[1322] (Application Example 1)

[1323] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1324] In modern factories, the supply process for parts and materials demands efficiency and reliability, but currently, it often requires a great deal of time and effort. Furthermore, efficiently matching necessary resources is a challenge, leading to decreased productivity and the risk of information leaks. Therefore, there is a need for a system that can quickly and accurately meet the demands for parts and materials, and achieve an efficient supply process.

[1325] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1326] In this invention, the server includes means for receiving requests entered by a user, means for searching for resources within a group based on the request using generative artificial intelligence, and means for filtering the resource list of the search results and limiting it to resources within the group. This enables a factory robot that receives requests for parts and materials, matches them with resources within the group, and supplies them efficiently.

[1327] A "request" is an instruction that a user enters because they need a specific resource or service.

[1328] "Generative artificial intelligence" is an artificial intelligence system that extracts keywords based on user requests and identifies related resources.

[1329] "Filtering" is the process by which a generative artificial intelligence removes unnecessary information from a searched resource list and creates a limited list.

[1330] A "resource list" is a collection of information that lists the resources relevant to a user's request.

[1331] A "user terminal" is a device used by a user to input requests, receive results, and confirm them.

[1332] A "resource provider" is an entity whose role is to provide the necessary resources based on the user's requests.

[1333] A "factory robot" is an automated machine that receives requests for parts and materials, matches them with resources within the group, and supplies them efficiently.

[1334] The system for implementing this invention can efficiently process user requests and quickly meet the demand for parts and materials within the factory by utilizing resources within the group. The following describes each process of this system in detail.

[1335] Hardware and software

[1336] This system includes the following main components:

[1337] 1. User terminal: A device on which a user enters a request and receives the result. Examples include tablets and smartphones.

[1338] 2. Server: Its role is to receive user requests and transmit them to the generative artificial intelligence. It also sends the processed results to the user's terminal.

[1339] 3. Generative Artificial Intelligence (AI): Analyzes user requests, extracts relevant keywords, and searches for resources. Specifically, AI models such as GPT-3 are used.

[1340] 4. Database: Stores resource information within the group and is used for AI-powered searches.

[1341] 5. Factory robots: Their role is to receive requests for parts and materials and supply them automatically.

[1342] Data processing and calculation

[1343] 1. Receiving and parsing user input:

[1344] Requests from the user's terminal are received by the server and sent to the generative artificial intelligence (AI). For example, if the user inputs "I need 50 of part X," that information is sent to the server.

[1345] 2. Keyword extraction and resource search:

[1346] Generative artificial intelligence analyzes the received request and extracts relevant keywords. Keywords such as "part X" and "50" are extracted, and the database is searched based on these keywords.

[1347] 3. Filtering search results:

[1348] The server filters the resource list generated by the generative artificial intelligence, limiting it to the appropriate resources within the group. The filtering results are converted to JSON format and sent to the user's terminal.

[1349] 4. Displaying and selecting results:

[1350] The user's device parses the received JSON data and displays a visual list of resources. When the user selects a desired resource, detailed information is displayed, such as "stock availability" or "price."

[1351] 5. Contact the resource provider of the request:

[1352] Once the user reviews the resource details and indicates their intention to purchase or acquire it, the server contacts the resource provider directly.

[1353] 6. Supply by factory robots:

[1354] Factory robots receive parts and materials supplied by resource providers and efficiently deliver them to designated locations.

[1355] Specific examples and prompt statements

[1356] The following is an example of an application where a factory robot supplies parts and materials.

[1357] Specific example:

[1358] A factory operator uses a tablet to input a request, such as "We need 50 units of part X." The server sends this request to a generative artificial intelligence (AI), which extracts relevant keywords. After searching the database and filtering the resulting resource list, the AI ​​displays the appropriate supplier to the operator. Once the operator selects a resource, detailed information is displayed. The factory robot then automatically supplies the parts.

[1359] Example of a prompt:

[1360] Input from user terminal: "I need 50 units of part X."

[1361] Robot response: "Searching for suppliers of part X... 4 optimal suppliers found. View details?"

[1362] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1363] Step 1:

[1364] Receiving user input

[1365] User terminal: Users input requests using devices such as tablets or smartphones. Specifically, a user might input "I need 50 units of part X." The terminal then sends this request to the server.

[1366] Input: User request (e.g., "I need 50 units of part X")

[1367] Output: Request data sent to the server

[1368] Step 2:

[1369] Receiving and sending requests

[1370] Server: The server receives request data from the user terminal and prepares it for transmission to the generative artificial intelligence (AI). The server analyzes the request data and converts it into the appropriate format.

[1371] Input: Request data from the user terminal

[1372] Output: Analyzed data sent to the generative artificial intelligence.

[1373] Step 3:

[1374] Keyword extraction and resource search

[1375] Generative artificial intelligence: The AI ​​extracts keywords based on the received, analyzed data. For example, keywords such as "part X" and "50 pieces" might be extracted. Then, it searches a database based on these keywords and generates a list of relevant resources.

[1376] Input: Analyzed data from the server

[1377] Output: Resource list of search results

[1378] Step 4:

[1379] Filtering search results

[1380] Server: The server receives the resource list generated by the AI ​​and performs filtering. This filtering process creates a list limited to resources available within the group.

[1381] Input: Resource list from generative artificial intelligence

[1382] Output: Filtered resource list

[1383] Step 5:

[1384] Converting and sending resource lists

[1385] Server: Converts the filtered resource list into JSON format and sends it to the user's terminal. This makes the list visually easy to understand.

[1386] Input: Filtered resource list

[1387] Output: Resource list in JSON format

[1388] Step 6:

[1389] Displaying the list and providing detailed information

[1390] User Terminal: The user terminal parses the received JSON-formatted resource list and displays it to the user as a visual resource list. The user selects the desired resource and checks detailed information (such as availability and price).

[1391] Input: Resource list in JSON format

[1392] Output: Visually displayed resource list and detailed information

[1393] Step 7:

[1394] Notification of the request to the resource provider

[1395] Server: When a user selects a desired resource, for example, "Contact by email," the server contacts the resource provider. This contact includes the user's request and preferred method of contact.

[1396] Input: User's selection information and contact method

[1397] Output: Notification data sent to resource providers

[1398] Step 8:

[1399] Supply by factory robots

[1400] Factory robots: Factory robots receive parts and materials based on requests from resource providers and deliver them to designated locations. The robots automatically calculate the shortest path and complete the delivery efficiently.

[1401] Input: Supplies from resource providers

[1402] Output: Delivery completion notification to the location specified by the user.

[1403] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1404] The system for implementing this invention not only understands user requests and matches them with resources within the group, but also recognizes and responds to the user's emotions. The entire system consists of a user terminal, a server, a generative artificial intelligence, an emotion engine, and a database.

[1405] 1. User input and sentiment recognition

[1406] As soon as a user enters a request into the device, the device is equipped with an emotion engine to recognize the user's emotions.

[1407] The emotion engine analyzes the user's input and behavior (voice tone, input speed, facial expressions, etc.) to identify their emotional state.

[1408] Along with the request, the device sends emotion data recognized by the emotion engine to the server.

[1409] 2. Receiving requests and analyzing sentiment data

[1410] The server receives requests and sentiment data from the user's terminal and analyzes them.

[1411] Prepare to pass the received data to the generative artificial intelligence.

[1412] 3. Sending request and emotion data to the generative artificial intelligence system.

[1413] The server sends the converted request data and emotion data to the generative artificial intelligence.

[1414] 4. AI-powered resource search and optimization

[1415] Generative artificial intelligence extracts relevant keywords based on received requests and sentiment data.

[1416] Based on keywords and sentiment data, the system searches the database within the group and generates a list of relevant resources (e.g., "Company A", "Company B", etc.).

[1417] Optimize search results based on the user's emotions. For example, if a user is feeling stressed, prioritize displaying companies with excellent customer support.

[1418] 5. Filtering the results

[1419] The server filters the resource list generated by the generative artificial intelligence, limiting it to resources within the specified group.

[1420] 6. Sending the list to the user's terminal

[1421] The server converts the filtered list into JSON format and sends it to the user's terminal.

[1422] The user's device parses the received JSON data and displays it to the user as a visual list.

[1423] 7. Displaying user selections and details

[1424] The user selects the desired resource from the displayed list.

[1425] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[1426] The user terminal displays detailed information to the user (e.g., PC price, availability, etc.).

[1427] 8. Implementing the Bridging Process

[1428] Users select their preferred method of contact and then contact the resource provider.

[1429] The server contacts resource providers based on the user's request and contact method.

[1430] We adjust our communication methods based on the user's emotions. For example, if a user is feeling anxious, we prioritize direct contact by phone.

[1431] Specific example

[1432] Example: If you want to purchase 20 new PCs

[1433] 1. User input and sentiment recognition

[1434] A user (sales representative) enters a request into the terminal saying, "I want to purchase 20 new PCs." If the user is typing in a hurry, the emotion engine recognizes this and determines that the user is in a hurry.

[1435] 2. Receiving requests and analyzing sentiment data

[1436] The server receives this request and emotion data and prepares to send it to the generative artificial intelligence.

[1437] 3. Sending request and emotion data to the generative artificial intelligence system.

[1438] The server sends request data and emotion data to the generative artificial intelligence.

[1439] 4. AI-powered resource search and optimization

[1440] Generative artificial intelligence analyzes the request and extracts keywords such as "PC," "purchase," and "20 units."

[1441] Based on extracted keywords and sentiment data, the system searches the database within the group and generates a list of relevant resources such as "Company A" and "Company B".

[1442] Since the user is in a hurry, resources that are immediately available will be displayed first.

[1443] 5. Filtering the results

[1444] The server filters the search results and extracts a list containing only companies within the group.

[1445] 6. Sending the list to the user's terminal

[1446] The server converts the filtering results into JSON format and sends them to the user's terminal.

[1447] The user's terminal parses the received JSON data and displays a list of "Company A" and "Company B".

[1448] 7. Displaying user selections and details

[1449] The user selects "Company A" and checks the details (PC price, stock availability, etc.).

[1450] 8. Implementing the Bridging Process

[1451] If the user selects "Contact by phone," the server will contact "Company A" by phone with the user's request.

[1452] This allows users to efficiently utilize resources within the group and prevents unnecessary information leaks. Furthermore, using an emotion engine enables flexible responses tailored to the user's emotions.

[1453] The following describes the processing flow.

[1454] Step 1:

[1455] Users log in to the system using their devices and enter requests such as "I want to buy XX," "I want to outsource XX," or "I want to sell XX."

[1456] Specific action: Enter the request into the input form displayed in the user interface and click "Submit".

[1457] Step 2:

[1458] The device receives user requests and uses an emotion engine to recognize the user's emotions.

[1459] Specific operation: The system analyzes the user's typing speed and input content, as well as their facial expressions, to determine their emotions.

[1460] Step 3:

[1461] The emotion engine generates recognized emotion data and prepares it to send to the server along with the request.

[1462] Specific operation: Structure emotion data, integrate it with request data, and convert it into a format that can be sent.

[1463] Step 4:

[1464] The device sends request data and sentiment data to the server.

[1465] Specific operation: Sends HTTP requests and API calls containing request and sentiment data to the server.

[1466] Step 5:

[1467] The server receives requests and sentiment data from users.

[1468] Specific operation: Analyzes the received data and converts it into a data format for passing to a generative artificial intelligence.

[1469] Step 6:

[1470] The server sends the converted request data and emotion data to the generative artificial intelligence.

[1471] Specific operation: Call the API of a generative artificial intelligence and pass the request data and sentiment data as parameters for analysis.

[1472] Step 7:

[1473] Generative artificial intelligence extracts relevant keywords based on received requests and sentiment data.

[1474] Specific operation: The request content is analyzed using natural language processing technology to identify the keywords necessary for the search.

[1475] Step 8:

[1476] The generative artificial intelligence searches the database within the group based on extracted keywords and sentiment data, and generates a list of relevant resources.

[1477] Specific operation: Convert keyword and sentiment data into database queries and retrieve a list of candidates as search results.

[1478] Step 9:

[1479] Generative artificial intelligence optimizes search results according to the user's emotions.

[1480] Specific actions: If the user is in a hurry, the system will prioritize displaying resources that are immediately available.

[1481] Step 10:

[1482] The server filters the resource list received from the generative artificial intelligence, limiting it to resources within a specific group.

[1483] Specific operation: The system performs a filter on the received list data to extract only the information within each group.

[1484] Step 11:

[1485] The server converts the filtered list into JSON format and sends it to the user's terminal.

[1486] Specific operation: The filtering results are generated as data in JSON format and sent to the user's terminal via API calls or HTTP responses.

[1487] Step 12:

[1488] The user's device parses the received JSON data and displays it to the user as a visual list.

[1489] Specific operation: Parses JSON data and visually displays the list using HTML or the application UI.

[1490] Step 13:

[1491] The user selects the desired resource from the displayed list.

[1492] Specific operation: Clicking an item in the list sends the ID and detailed information of the selected item to the server.

[1493] Step 14:

[1494] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[1495] Specific operation: Retrieve detailed information from the database and send it to the user's terminal via the API response.

[1496] Step 15:

[1497] The user terminal displays detailed information to the user.

[1498] Specific action: Display the received details on the screen so that the user can review them.

[1499] Step 16:

[1500] Users select their preferred method of contact and then contact the resource provider.

[1501] Specific action: Select a contact method option (email, phone, etc.) and send that information to the server.

[1502] Step 17:

[1503] The server will contact the resource provider using the specified contact method to conduct further verification.

[1504] Specific actions: The system will send emails or process communications via internal systems according to the selected contact method.

[1505] Step 18:

[1506] The emotion engine adjusts the communication method according to the user's emotions.

[1507] Specific actions: For example, if a user is feeling anxious, priority will be given to phone contact, which allows for direct communication.

[1508] (Example 2)

[1509] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1510] Existing resource matching systems often process requests without considering user emotions, which can lead to decreased user satisfaction. This is especially true when users are in a hurry or experiencing stress, making it difficult to provide appropriate resources. Furthermore, traditional systems often fail to adequately filter search results or provide data in the most optimal format, resulting in insufficient information for users. To address these challenges, a system is needed that recognizes user emotions and responds flexibly based on them.

[1511] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1512] In this invention, the server includes means for receiving a request entered by the user, means for acquiring the user's emotional data using an emotional recognition engine, means for searching for resources within a group based on the request using generative artificial intelligence, means for filtering the resource list of the search results and limiting it to resources within the group, means for sending the filtered resource list to the user terminal in JSON format, means for displaying detailed information about the resource selected by the user, and means for contacting resource providers based on the user's request and emotions. This enables flexible and effective resource provision in response to the user's emotions.

[1513] A "user" refers to an individual or legal entity that uses the system to input requests and seek out the most suitable resources.

[1514] A "request" refers to information that a user enters into the system, such as necessary details, desired services, or products, which the system should then respond to.

[1515] An "emotion recognition engine" refers to software or hardware that analyzes user input, behavior, facial expressions, voice tone, etc., to identify the user's emotional state.

[1516] "Emotional data" refers to data indicating the user's emotional state as identified by the emotion recognition engine.

[1517] "Generative artificial intelligence" refers to artificial intelligence that searches for resources based on user requests and sentiment data, and generates optimized search results.

[1518] "Resources" is a general term for services, products, information, etc., that are provided in response to user requests.

[1519] "Filtering" refers to the process of narrowing down a list of resources searched by a generative artificial intelligence based on specific conditions.

[1520] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight text data format widely used as a data exchange format.

[1521] A "user terminal" refers to a device (e.g., smartphone, tablet, PC, etc.) that a user uses to access a system, input requests, and receive and display results.

[1522] A "resource provider" refers to a company or individual that provides resources in response to a user's request.

[1523] A "database" refers to a system in which information is organized and stored, which generative artificial intelligence uses to search for resources.

[1524] Modes for carrying out the invention

[1525] The system for implementing this invention not only understands user requests and matches them with resources within the group, but also recognizes and responds to user emotions. The system consists of the following components:

[1526] User terminal

[1527] server

[1528] Generative artificial intelligence

[1529] Emotion recognition engine

[1530] database

[1531] Hardware and software to be used

[1532] User terminal: This includes smartphones, tablets, PCs, etc. It is used by the user to input requests, receive results, and display them.

[1533] Server: Performs key processing such as receiving requests, analyzing data, sending data to generative artificial intelligence, filtering search results, and contacting resource providers.

[1534] Emotion Recognition Engine: This uses IBM Watson as an example to recognize emotions from the user's input, voice tone, input speed, and facial expressions.

[1535] Generative Artificial Intelligence: OpenAI GPT-3 is used as the generative AI. Keyword extraction and search result optimization are performed based on user requests and sentiment data.

[1536] Database: Use a database such as MySQL to perform searches based on generated keywords and store and manage the results.

[1537] Data processing and data calculation

[1538] Emotion Recognition: The system analyzes user input, voice tone, input speed, and facial expression data to obtain emotional data. This process utilizes an emotion recognition engine.

[1539] Keyword Extraction and Search: Generative artificial intelligence extracts keywords from user requests and sentiment data, searches the database, and creates a list of appropriate resources.

[1540] Filtering: The server narrows down the list of resources it has searched based on specific criteria, limiting it to resources within a group.

[1541] Data transmission: The filtered resource list is converted to JSON format and sent to the user's terminal. Additionally, resource providers are contacted based on user requests.

[1542] Specific example

[1543] Here's a step-by-step guide for a user who wants to purchase 20 new PCs:

[1544] 1. User input and sentiment recognition:

[1545] A user (sales representative) enters "I want to purchase 20 new PCs" into the terminal. If the user is typing in a hurry, the emotion recognition engine will recognize this and determine that the user is in a hurry.

[1546] 2. Receiving requests and analyzing sentiment data:

[1547] The device sends this request and emotional data to the server. The server analyzes the received data and formats it for transmission to a generative artificial intelligence system.

[1548] 3. Sending request and emotion data to the generative artificial intelligence:

[1549] The server sends request data and emotion data to the generative artificial intelligence.

[1550] 4. AI-powered resource search and optimization:

[1551] Generative artificial intelligence analyzes the request and extracts keywords such as "PC," "purchase," and "20 units." Based on the extracted keywords and sentiment data, it searches the database and generates a list of relevant resources such as "Company A" and "Company B." Because the user is in a hurry, resources that are immediately available are displayed first.

[1552] 5. Filtering the results:

[1553] The server filters the search results and extracts a list containing only companies within the group.

[1554] 6. Sending the list to the user's terminal:

[1555] The server converts the filtering results into JSON format and sends them to the user's terminal. The terminal parses the received JSON data and displays lists of "Company A" and "Company B".

[1556] 7. Displaying user selections and details:

[1557] The user selects "Company A" and checks the details (PC price, stock availability, etc.).

[1558] 8. Implementing the bridging:

[1559] If the user selects "Contact by phone," the server will contact "Company A" by phone with the user's request.

[1560] Example of a prompt

[1561] Example prompt message for a user purchasing 20 new PCs:

[1562] text

[1563] User request: "Purchase 20 new PCs."

[1564] User sentiment data: "Users are in a hurry."

[1565] System prompt: "Please show me companies that can immediately deliver 20 PCs. The user is in a hurry."

[1566] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1567] Step 1:

[1568] The user enters the request.

[1569] The user enters a request into the terminal. For example, they might enter "I want to purchase 20 new PCs."

[1570] Function: The user enters text into an input field and submits it by pressing a specific button.

[1571] Input: User's request text: "I want to purchase 20 new PCs."

[1572] Output: The requested data is stored on the terminal.

[1573] Step 2:

[1574] The device recognizes the user's emotions.

[1575] The device uses an emotion recognition engine to analyze the user's input, behavior, voice tone, input speed, and facial expressions.

[1576] Function: Captures the user's facial expressions and voice using the camera and microphone, and analyzes them with an emotion recognition engine (e.g., IBM Watson).

[1577] Input: User's facial expressions, voice, input speed

[1578] Output: User sentiment data (e.g., "I'm in a hurry") is recognized and retrieved.

[1579] Step 3:

[1580] The device sends request and sentiment data to the server.

[1581] The device sends the acquired request data and sentiment data to the server.

[1582] Function: Converts request data and sentiment data into JSON format and sends it to the server via a secure channel.

[1583] Input: Request data "I want to buy 20 new PCs", Sentiment data "The user is in a hurry"

[1584] Output: Request data and sentiment data are sent to and received by the server.

[1585] Step 4:

[1586] The server analyzes and formats the data.

[1587] The server analyzes the received request data and emotion data, and formats it for transmission to the generative artificial intelligence.

[1588] Function: Performs data validation and format conversion, and formats the data to a format compatible with generative AI (e.g., OpenAI GPT-3).

[1589] Input: Request data and sentiment data in JSON format

[1590] Output: Data converted to a format for transmission to a generative AI.

[1591] Step 5:

[1592] The server sends request and emotion data to the generative artificial intelligence.

[1593] The server sends the converted request data and emotion data to the generative artificial intelligence.

[1594] Function: Sends data to generative AI using an API.

[1595] Input: Formatted request data and sentiment data

[1596] Output: The generative AI receives the data and begins processing.

[1597] Step 6:

[1598] Generative artificial intelligence extracts keywords and searches the database.

[1599] Generative AI extracts keywords from received requests and sentiment data, and searches for related resources in a database.

[1600] Function: Uses natural language processing techniques to extract keywords and execute database queries.

[1601] Input: Request data and sentiment data

[1602] Output: Related resource list (e.g., resource list based on keywords such as "PC", "purchase", and "20 units")

[1603] Step 7:

[1604] Optimization of search results using generative AI

[1605] Generative AI optimizes search results based on user sentiment data. For example, if a user is in a hurry, it prioritizes resources that are immediately available.

[1606] Function: Ranks and filters search results based on sentiment data.

[1607] Input: Search results list, sentiment data "hurried"

[1608] Output: Optimized resource list (e.g., prioritizing "Immediately Available Resources")

[1609] Step 8:

[1610] The server filters the search results.

[1611] The server filters the resource list generated by the generative AI, limiting it to resources within the specified group.

[1612] Function: Applies a filtering algorithm to extract only resources within the group.

[1613] Input: Resource list for generative AI

[1614] Output: Limited resource list (e.g., "Company A", "Company B", etc., including only resources within the group)

[1615] Step 9:

[1616] The server sends the filtering results to the terminal in JSON format.

[1617] The server converts the filtering results into JSON format and sends them to the user's terminal.

[1618] Function: Uses a protocol to convert filtering results into JSON format and send them to the terminal.

[1619] Input: Filtered resource list

[1620] Output: A resource list in JSON format is sent to the terminal.

[1621] Step 10:

[1622] The user selects a resource from a list.

[1623] The user selects the desired resource from the list displayed on the device.

[1624] Function: The user taps or clicks a list item to select it.

[1625] Input: List display and user selection operation

[1626] Output: Selected resource data (e.g., "Company A")

[1627] Step 11:

[1628] The server retrieves detailed information about the resource and sends it to the terminal.

[1629] The server retrieves detailed information related to the selected resource and sends it to the terminal.

[1630] Function: Retrieves detailed information about the selected resource from a database or another API and sends it to the device.

[1631] Input: Selected resource data

[1632] Output: Detailed information (e.g., PC price, stock status) is sent to the terminal.

[1633] Step 12:

[1634] The user selects a contact method, and the server contacts the resource provider.

[1635] The user selects their preferred method of contact, and the server then contacts the resource provider.

[1636] Function: Select a contact method (e.g., email, phone), and the server will contact the resource provider using the corresponding API or protocol.

[1637] Input: Selection of contact method, resource data, sentiment data

[1638] Output: Contact is established with the resource provider (e.g., "Company A" is contacted by phone).

[1639] This series of steps enables efficient resource matching that takes into account user needs and emotions.

[1640] (Application Example 2)

[1641] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1642] Traditional systems have mechanisms to search for and provide resources based on user requests, but they lack the ability to consider the user's emotional state, making it difficult to optimize the user experience. Furthermore, there is a problem in providing appropriate resources quickly and effectively when the user is in an emergency or experiencing a specific emotional state.

[1643] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving a request entered by the user, means for searching for resources within a group based on the request using generative artificial intelligence, means for recognizing the user's emotional state using an emotion engine and processing the data, means for sending a filtered resource list to the user terminal in JSON format, means for displaying detailed information about the resource selected by the user, and means for adjusting the contact method based on the user's emotional state and contacting the resource provider with the request. This enables flexible responses according to the user's emotional state, and allows for the rapid and effective provision of resources, especially in emergencies or when the user is in a specific emotional state.

[1644] "Means for receiving user-entered requests" refers to a mechanism for receiving request data entered by a user into a terminal.

[1645] "A means of searching for resources within a group based on requests using generative artificial intelligence" refers to a mechanism that uses generative artificial intelligence to analyze user request data and identify relevant resources within a group.

[1646] "Means for recognizing a user's emotional state using an emotion engine and processing that data" refers to a mechanism for identifying an emotional state by analyzing the user's voice tone, input speed, facial expressions, etc., and for handling that data.

[1647] "Means for filtering the resource list in search results and limiting it to within a group" refers to a mechanism that filters the resource list searched by a generative artificial intelligence to limit it to only those resources within a group.

[1648] "Means for sending a filtered resource list to the user's terminal in JSON format" refers to a mechanism for converting a filtered resource list into JSON, a structured data format, and sending it to the user's terminal.

[1649] "Means for displaying detailed information about a resource selected by the user" refers to a mechanism for displaying detailed information about a resource selected by the user on the user's terminal.

[1650] "A means of adjusting the method of contact based on the user's emotional state and communicating the request to the resource provider" refers to a mechanism that selects the most appropriate method of contact according to the user's emotional state and notifies the resource provider of the user's request.

[1651] The system for implementing this invention not only understands user requests and matches them with resources within the group, but also recognizes and responds to the user's emotions. The entire system consists of a user terminal, a server, a generative artificial intelligence, an emotion engine, and a database.

[1652] The user inputs their requests using their smartphone, and the entire system operates accordingly. First, the user inputs their requests for desired products or services into their smartphone. Simultaneously, the smartphone is equipped with an emotion engine that analyzes the user's voice tone, input speed, facial expressions, etc., to recognize their emotional state.

[1653] The server receives request data and sentiment data sent from the user terminal. The received data is sent to a generative artificial intelligence (AI) for analysis. The generative AI extracts relevant keywords from the request data and searches a database within the group along with the sentiment data. This process includes keyword extraction and database query execution.

[1654] The resource list in search results is optimized according to the user's emotional state. For example, if the user is feeling urgent or stressed, the system will prioritize displaying resources that offer quick response and comprehensive support. The resulting resource list is then filtered by the server to limit resources to those within a specific group.

[1655] The filtered resource list is converted to JSON format and sent to the user's terminal. The user's terminal parses the received JSON data and displays it to the user as a visual list. The user can select the desired resource from the displayed list and view detailed information, such as the product price and availability.

[1656] Once the user selects their preferred method of contact, the server will choose the most appropriate method based on the user's request and emotional state, and contact the resource provider. For example, if the user is feeling anxious, telephone contact will be prioritized.

[1657] The specific hardware and software used include smartphones (iOS or Android), emotion recognition APIs (e.g., Microsoft Azure Emotion API), and generative artificial intelligence APIs (e.g., OpenAI GPT models). These elements work together to enable flexible responses that respond to the user's emotions.

[1658] As a concrete example, consider a scenario where a user searches for products to relieve fatigue. In this case, if the user enters "I want supplements that relieve fatigue," the emotion engine recognizes the user's level of fatigue. Based on this, the generative artificial intelligence extracts relevant keywords and generates a list of products suitable for that emotional state. For example, the list might include aromatherapy candles and fatigue-relieving supplements.

[1659] Example of a prompt:

[1660] User request: I want a supplement to relieve fatigue.

[1661] Emotional state: Fatigue (high)

[1662] Expected output: Display a list of fatigue-relieving supplements, aromatherapy candles, and other relaxing products, along with price and availability information.

[1663] By combining emotion recognition and generative artificial intelligence in this way, it becomes possible to provide services that optimize the user experience.

[1664] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1665] Step 1:

[1666] Users use their smartphones to input requests for desired products or services. Simultaneously, an emotion engine built into the device analyzes the user's voice tone, input speed, facial expressions, etc., to recognize their emotional state. Based on this sensory data, the emotion engine identifies emotions such as "fatigue" and "stress" and generates data accordingly.

[1667] Input: User requests and sentiment data

[1668] Output: Request data and sentiment data

[1669] Step 2:

[1670] The terminal sends user request data and sentiment data to the server. The server prepares the received data for analysis. This preparation stage involves standardizing the data format and performing necessary preprocessing.

[1671] Input: Request data and sentiment data

[1672] Output: Preprocessed request data and sentiment data

[1673] Step 3:

[1674] The server sends pre-processed request data and sentiment data to the generative artificial intelligence. The generative AI extracts relevant keywords based on the transmitted data and analyzes the sentiment data.

[1675] Input: Preprocessed request data and sentiment data

[1676] Output: Extracted keywords and analyzed sentiment data

[1677] Step 4:

[1678] The generative artificial intelligence uses extracted keywords and analyzed sentiment data to search a database within the group. The search results generate a list of relevant resources. For example, if a user requests "supplements to relieve fatigue," the list will include corresponding supplements and relaxation products.

[1679] Input: Keywords and sentiment data

[1680] Output: Resource List

[1681] Step 5:

[1682] The server filters the generated resource list, limiting it to only resources within the group. This filtering excludes resources outside the group, prioritizing highly reliable resources.

[1683] Input: Resource list

[1684] Output: Filtered resource list

[1685] Step 6:

[1686] The server converts the filtered resource list into JSON format and sends it to the user's terminal. Converting to JSON format makes it easier for the user's terminal to analyze and display the data.

[1687] Input: Filtered resource list

[1688] Output: Resource list converted to JSON format

[1689] Step 7:

[1690] The device parses the received JSON data and displays it to the user as a visual list. The user can then select the desired resource from this list. For example, they can check the price and availability of a product.

[1691] Input: Resource list in JSON format

[1692] Output: Visual resource list

[1693] Step 8:

[1694] The user selects their preferred method of contact and sends that information to the server. The server then adjusts the best method of contact based on the user's emotional state and contacts the resource provider. For example, if the user is feeling anxious, phone contact will be prioritized.

[1695] Input: User selection information and sentiment data

[1696] Output: Contacting resource providers

[1697] This enables the provision of flexible and optimal resources tailored to the user's emotional state.

[1698] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1699] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1700] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1701] [Fourth Embodiment]

[1702] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1703] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1704] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1705] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1706] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1707] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1708] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1709] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1710] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1711] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1712] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1713] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1714] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1715] The system for implementing this invention matches user requests with resources within the group, efficiently provides information, and prevents unnecessary information leaks. The entire system consists of a user terminal, a server, a generative artificial intelligence, and a database.

[1716] 1. User input

[1717] The user enters their request for the necessary resources (for example, "I want to purchase 20 new PCs") into the terminal.

[1718] The terminal receives the request and prepares to send it to the server.

[1719] 2. Receipt of Request

[1720] The server receives requests from the user's terminal.

[1721] The system analyzes the received request and sends it to a generative artificial intelligence.

[1722] 3. AI-powered resource search

[1723] Generative artificial intelligence extracts relevant keywords (for example, "PC", "purchase", "20 units") based on the received request.

[1724] Based on keywords, the system searches the database within the group and generates a list of relevant resources (e.g., "Company A", "Company B", etc.).

[1725] 4. Filtering the results

[1726] The server filters the resource list generated by the generative artificial intelligence.

[1727] The filtering process extracts only the list of information limited to that within the group.

[1728] 5. Sending the list to the user's terminal

[1729] The server converts the filtered list into JSON format and sends it to the user's terminal.

[1730] The user's device parses the received JSON data and displays it to the user as a visual list.

[1731] 6. Displaying user selections and details

[1732] The user selects the desired resource (for example, "Company A") from the displayed list.

[1733] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[1734] The user terminal displays detailed information to the user (e.g., PC price, availability, etc.).

[1735] 7. Implementing the Bridging Process

[1736] The user selects their preferred method of contact (e.g., "email").

[1737] The server contacts resource providers based on the user's request and contact method.

[1738] The resource provider confirms the user's request and proceeds with providing the necessary information and procedures.

[1739] Specific example

[1740] Example: If you want to purchase 20 new PCs

[1741] 1. User input

[1742] The user (sales representative) enters a request into the terminal saying, "I would like to purchase 20 new PCs," and clicks the submit button.

[1743] 2. Receipt of Request

[1744] The server receives this request and sends it to the generative artificial intelligence.

[1745] 3. AI-powered resource search

[1746] Generative artificial intelligence analyzes the request and extracts keywords such as "PC," "purchase," and "20 units."

[1747] Based on the extracted keywords, the system searches the database within the group and generates a list of related resources such as "Company A" and "Company B".

[1748] 4. Filtering the results

[1749] The server filters the search results and extracts a list containing only companies within the group.

[1750] 5. Sending the list to the user's terminal

[1751] The server converts the filtering results into JSON format and sends them to the user's terminal.

[1752] The user's terminal parses the received JSON data and displays a list of "Company A" and "Company B".

[1753] 6. Displaying user selections and details

[1754] The user selects "Company A" and checks the details (PC price, stock availability, etc.).

[1755] 7. Implementing the Bridging Process

[1756] If the user selects "Contact by email," the server sends an email to "Company A" requesting confirmation from the user.

[1757] This allows users to efficiently utilize resources within the group and prevents unnecessary information leaks.

[1758] The following describes the processing flow.

[1759] Step 1:

[1760] Users log in to the system using their devices and enter requests such as "I want to buy XX," "I want to outsource XX," or "I want to sell XX."

[1761] Specific action: Enter the request into the input form displayed in the user interface and click "Submit".

[1762] Step 2:

[1763] The terminal receives the user's request in string format and sends it to the server.

[1764] Specific operation: Send the request details to the server via an HTTP request or API call.

[1765] Step 3:

[1766] The server receives requests from users.

[1767] Specific operation: Converts the received string data into a data format suitable for passing to a generative artificial intelligence.

[1768] Step 4:

[1769] The server sends the converted request data to the generative artificial intelligence.

[1770] Specific operation: Call the API of a generative artificial intelligence and pass the requested data as parameters for analysis.

[1771] Step 5:

[1772] Generative artificial intelligence extracts relevant keywords based on the received request.

[1773] Specific operation: The request content is analyzed using natural language processing technology to identify the keywords necessary for the search.

[1774] Step 6:

[1775] The generative artificial intelligence searches the database within the group based on the extracted keywords and generates a list of relevant resources.

[1776] Specific operation: Generate a database query and retrieve a list of candidates as search results.

[1777] Step 7:

[1778] The server filters the resource list received from the generative artificial intelligence, limiting it to resources within a specific group.

[1779] Specific operation: The system performs a filter on the received list data to extract only the information within each group.

[1780] Step 8:

[1781] The server converts the filtered list into JSON format and sends it to the user's terminal.

[1782] Specific operation: The filtering results are generated as data in JSON format and sent to the user's terminal via API calls or HTTP responses.

[1783] Step 9:

[1784] The user's device parses the received JSON data and displays it to the user as a visual list.

[1785] Specific operation: Parses JSON data and visually displays the list using HTML or the application UI.

[1786] Step 10:

[1787] The user selects the desired resource from the displayed list.

[1788] Specific operation: Clicking an item in the list sends the ID and detailed information of the selected item to the server.

[1789] Step 11:

[1790] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[1791] Specific operation: Retrieve detailed information from the database and send it to the user's terminal via the API response.

[1792] Step 12:

[1793] The user terminal displays detailed information to the user.

[1794] Specific action: Display the received details on the screen so that the user can review them.

[1795] Step 13:

[1796] Users select their preferred method of contact and then contact the resource provider.

[1797] Specific action: Select a contact method option (email, phone, etc.) and send that information to the server.

[1798] Step 14:

[1799] The server will contact the resource provider using the specified contact method to conduct further verification.

[1800] Specific actions: The system will send emails or process communications via internal systems according to the selected contact method.

[1801] (Example 1)

[1802] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1803] Traditional systems were inefficient in resource retrieval and information provision in response to user requests, and also had the potential for unnecessary information leaks. In particular, the process of quickly extracting and filtering appropriate resources from large amounts of data and providing them to users was considered complex and time-consuming. Furthermore, a lack of systems capable of appropriately responding to user requests was also a contributing factor.

[1804] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1805] In this invention, the server includes means for receiving a request entered by a user, means for searching for resources within the organization based on the request using generative artificial intelligence, means for filtering the resource list of the search results and limiting it to resources within the organization, means for sending the filtered resource list to the user terminal, means for displaying detailed information about the resource selected by the user, means for contacting the resource provider with the user's request, means for the user terminal to convert the request into JSON format and send it to the server, means for the server to analyze the received request and send it to the generative artificial intelligence, means for the generative artificial intelligence to extract keywords, means for searching the database based on the keywords and generating a list of relevant resources, means for the server to convert the filtered resource list into JSON format and send it to the user terminal, means for the server to obtain detailed information about the selected resource from the database and send it to the user terminal, means for the server to contact the resource provider based on the contact method selected by the user, and means for the resource provider to respond to the user's request and provide the necessary information. This enables users to efficiently search for appropriate resources and use them quickly, while preventing unnecessary information leaks.

[1806] A "user" refers to an individual or organization that uses this system to enter requests and receive resource searches or information.

[1807] A "request" refers to the content that a user inputs into the system, asking for the provision of specific resources or information.

[1808] "Generative artificial intelligence" refers to machine learning or AI models that analyze user requests, extract relevant keywords, search databases, and list appropriate resources.

[1809] "Resources" refer to information, services, or items that a system provides to meet user requirements.

[1810] "Filtering" refers to the process of selecting and extracting only those resources that meet specific criteria from a list compiled by a generative artificial intelligence.

[1811] A "resource list" refers to a list of relevant resources that remain after a generative artificial intelligence has searched and filtered based on the user's request.

[1812] A "user terminal" refers to a device used by a user to input requests and receive and display resource lists and detailed information.

[1813] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight data exchange format for structuring, storing, and transmitting data.

[1814] A "server" refers to a computer system that provides relay and management functions, such as receiving requests from user terminals, forwarding them to generative artificial intelligence systems, and sending filtering results to user terminals.

[1815] "Keyword extraction" refers to the process by which generative artificial intelligence extracts important words and phrases from a user's request and uses them to search a database.

[1816] A "database query" refers to a question or operation used to extract specific information from a database.

[1817] "Detailed information" refers to additional information about each resource included in the resource list (such as price and availability).

[1818] A "resource provider" refers to a company or individual that provides resources in response to a user's request.

[1819] "Contact method" refers to the means (e.g., email, phone) that a user chooses to use to contact a resource provider.

[1820] The system for implementing this invention efficiently matches user requests with resources within the group and prevents unnecessary information leaks. The entire system consists of a user terminal, a server, a generative artificial intelligence system, and a database.

[1821] Here is a specific example of a case where a user wants to purchase 20 new PCs.

[1822] The user enters a request, such as "I want to purchase 20 new PCs," into a dedicated interface and clicks the submit button. The user's terminal receives this request, converts it to JSON format, and sends it to the server.

[1823] The server receives requests from user terminals and analyzes their content. The analyzed content is sent to a generative artificial intelligence (AI). Based on the received requests, the AI ​​extracts keywords such as "PC," "purchase," and "20 units," and uses these to execute database queries. It searches the database for related resources (e.g., "Company A," "Company B," etc.) and generates a resource list.

[1824] Next, the server filters the generated resource list to extract a list limited to only the resources within the group. This filtered list is then converted to JSON format and sent back to the user's terminal.

[1825] The user terminal receives and parses JSON data and displays it to the user as a visual list. When the user selects "Company A" from the list, the server retrieves detailed information about the selected resource (e.g., PC price and availability) from the database and sends it to the user terminal. The user terminal then displays the detailed information to the user.

[1826] Finally, once the user selects their preferred method of contact (e.g., "email"), the server sends an email to "Company A" based on that method, conveying the user's request. "Company A," as the resource provider, then reviews the user's request and proceeds with providing the necessary information and procedures.

[1827] This series of processes allows users to efficiently utilize resources within the group and prevent unnecessary information leaks.

[1828] Example of a prompt:

[1829] User request: "I want to purchase 20 new PCs."

[1830] Keywords: "New PC", "20 units", "Purchase"

[1831] System role: Receives user requests, searches for relevant resources in the group's database, and generates a list of optimal resources.

[1832] This invention utilizes a generative AI model to respond quickly and accurately to user requests, and enables efficient resource searching and information provision. This makes it possible to provide users with a high level of convenience and peace of mind.

[1833] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1834] Step 1:

[1835] User input

[1836] The user sits down at their PC and opens a dedicated interface. The user enters a specific request, such as "I want to purchase 20 new PCs," into the input field and clicks the submit button.

[1837] Input: The user's specific request (e.g., "I want to purchase 20 new PCs").

[1838] Output: Request data converted to JSON format.

[1839] Specific operation: The terminal receives user input, verifies its contents, and converts the request into JSON format.

[1840] Step 2:

[1841] Receipt of request

[1842] The server receives the request data sent from the terminal.

[1843] Input: Request data in JSON format.

[1844] Output: Analyzed request data.

[1845] Specific operation: The server receives requests from terminals, analyzes their contents, and prepares them for transmission to the generative artificial intelligence.

[1846] Step 3:

[1847] AI-powered resource search

[1848] The generative artificial intelligence extracts keywords based on requests received from the server. Based on the extracted keywords (e.g., "PC", "purchase", "20 units"), it searches the database and generates a list of related resources (e.g., "Company A", "Company B").

[1849] Input: Analyzed request data.

[1850] Output: Resource list.

[1851] Specific operation: The generative artificial intelligence extracts important keywords from the received request, queries the database based on those keywords, and generates a list of relevant resources.

[1852] Step 4:

[1853] Filtering results

[1854] The server receives a resource list sent from a generative artificial intelligence and applies a filtering algorithm to extract a list limited to specific groups.

[1855] Input: Resource list.

[1856] Output: Filtered resource list.

[1857] Specific operation: The server filters the information in the resource list according to specific conditions and removes unnecessary information.

[1858] Step 5:

[1859] Sending the list to the user's terminal

[1860] The server converts the filtered resource list into JSON format and sends it to the user's terminal.

[1861] Input: A filtered list of resources.

[1862] Output: A filtered resource list in JSON format.

[1863] Specific operation: The server converts the filtering results into JSON format and sends them to the user's terminal.

[1864] Step 6:

[1865] Display of user selection and detailed information.

[1866] The user's terminal receives and visually displays a list of resources. The user selects the desired resource from the list (e.g., "Company A").

[1867] Input: A filtered resource list in JSON format.

[1868] Output: User-selected resource information.

[1869] Specific operation: The user's terminal parses the JSON data, displays a list, and communicates the resource selected by the user to the server.

[1870] Step 7:

[1871] Get and display detailed information

[1872] The server retrieves detailed information about the selected resource and sends it to the user's terminal. The user's terminal then displays the detailed information to the user (e.g., PC price, availability).

[1873] Input: User-selected resource information.

[1874] Output: Resource details.

[1875] Specific operation: The server retrieves detailed information about the selected resource from the database and sends it to the user terminal. The user terminal then displays that information.

[1876] Step 8:

[1877] Execution of the bridging

[1878] The user selects their preferred method of contact (e.g., "email"). The server then contacts the resource provider based on that method and relays the user's request.

[1879] Input: User's selected contact method and resource information.

[1880] Output: Responses and actions by resource providers.

[1881] Specific operation: The server contacts the resource provider, taking into account the user's request and contact method. The resource provider confirms the user's request and proceeds with the necessary information and procedures.

[1882] This series of processes allows users to efficiently utilize resources within the group and prevent unnecessary information leaks.

[1883] (Application Example 1)

[1884] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1885] In modern factories, the supply process for parts and materials demands efficiency and reliability, but currently, it often requires a great deal of time and effort. Furthermore, efficiently matching necessary resources is a challenge, leading to decreased productivity and the risk of information leaks. Therefore, there is a need for a system that can quickly and accurately meet the demands for parts and materials, and achieve an efficient supply process.

[1886] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1887] In this invention, the server includes means for receiving requests entered by a user, means for searching for resources within a group based on the request using generative artificial intelligence, and means for filtering the resource list of the search results and limiting it to resources within the group. This enables a factory robot that receives requests for parts and materials, matches them with resources within the group, and supplies them efficiently.

[1888] A "request" is an instruction that a user enters because they need a specific resource or service.

[1889] "Generative artificial intelligence" is an artificial intelligence system that extracts keywords based on user requests and identifies related resources.

[1890] "Filtering" is the process by which a generative artificial intelligence removes unnecessary information from a searched resource list and creates a limited list.

[1891] A "resource list" is a collection of information that lists the resources relevant to a user's request.

[1892] A "user terminal" is a device used by a user to input requests, receive results, and confirm them.

[1893] A "resource provider" is an entity whose role is to provide the necessary resources based on the user's requests.

[1894] A "factory robot" is an automated machine that receives requests for parts and materials, matches them with resources within the group, and supplies them efficiently.

[1895] The system for implementing this invention can efficiently process user requests and quickly meet the demand for parts and materials within the factory by utilizing resources within the group. The following describes each process of this system in detail.

[1896] Hardware and software

[1897] This system includes the following main components:

[1898] 1. User terminal: A device on which a user enters a request and receives the result. Examples include tablets and smartphones.

[1899] 2. Server: Its role is to receive user requests and transmit them to the generative artificial intelligence. It also sends the processed results to the user's terminal.

[1900] 3. Generative Artificial Intelligence (AI): Analyzes user requests, extracts relevant keywords, and searches for resources. Specifically, AI models such as GPT-3 are used.

[1901] 4. Database: Stores resource information within the group and is used for AI-powered searches.

[1902] 5. Factory robots: Their role is to receive requests for parts and materials and supply them automatically.

[1903] Data processing and calculation

[1904] 1. Receiving and parsing user input:

[1905] Requests from the user's terminal are received by the server and sent to the generative artificial intelligence (AI). For example, if the user inputs "I need 50 of part X," that information is sent to the server.

[1906] 2. Keyword extraction and resource search:

[1907] Generative artificial intelligence analyzes the received request and extracts relevant keywords. Keywords such as "part X" and "50" are extracted, and the database is searched based on these keywords.

[1908] 3. Filtering search results:

[1909] The server filters the resource list generated by the generative artificial intelligence, limiting it to the appropriate resources within the group. The filtering results are converted to JSON format and sent to the user's terminal.

[1910] 4. Displaying and selecting results:

[1911] The user's device parses the received JSON data and displays a visual list of resources. When the user selects a desired resource, detailed information is displayed, such as "stock availability" or "price."

[1912] 5. Contact the resource provider of the request:

[1913] Once the user reviews the resource details and indicates their intention to purchase or acquire it, the server contacts the resource provider directly.

[1914] 6. Supply by factory robots:

[1915] Factory robots receive parts and materials supplied by resource providers and efficiently deliver them to designated locations.

[1916] Specific examples and prompt statements

[1917] The following is an example of an application where a factory robot supplies parts and materials.

[1918] Specific example:

[1919] A factory operator uses a tablet to input a request, such as "We need 50 units of part X." The server sends this request to a generative artificial intelligence (AI), which extracts relevant keywords. After searching the database and filtering the resulting resource list, the AI ​​displays the appropriate supplier to the operator. Once the operator selects a resource, detailed information is displayed. The factory robot then automatically supplies the parts.

[1920] Example of a prompt:

[1921] Input from user terminal: "I need 50 units of part X."

[1922] Robot response: "Searching for suppliers of part X... 4 optimal suppliers found. View details?"

[1923] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1924] Step 1:

[1925] Receiving user input

[1926] User terminal: Users input requests using devices such as tablets or smartphones. Specifically, a user might input "I need 50 units of part X." The terminal then sends this request to the server.

[1927] Input: User request (e.g., "I need 50 units of part X")

[1928] Output: Request data sent to the server

[1929] Step 2:

[1930] Receiving and sending requests

[1931] Server: The server receives request data from the user terminal and prepares it for transmission to the generative artificial intelligence (AI). The server analyzes the request data and converts it into the appropriate format.

[1932] Input: Request data from the user terminal

[1933] Output: Analyzed data sent to the generative artificial intelligence.

[1934] Step 3:

[1935] Keyword extraction and resource search

[1936] Generative artificial intelligence: The AI ​​extracts keywords based on the received, analyzed data. For example, keywords such as "part X" and "50 pieces" might be extracted. Then, it searches a database based on these keywords and generates a list of relevant resources.

[1937] Input: Analyzed data from the server

[1938] Output: Resource list of search results

[1939] Step 4:

[1940] Filtering search results

[1941] Server: The server receives the resource list generated by the AI ​​and performs filtering. This filtering process creates a list limited to resources available within the group.

[1942] Input: Resource list from generative artificial intelligence

[1943] Output: Filtered resource list

[1944] Step 5:

[1945] Converting and sending resource lists

[1946] Server: Converts the filtered resource list into JSON format and sends it to the user's terminal. This makes the list visually easy to understand.

[1947] Input: Filtered resource list

[1948] Output: Resource list in JSON format

[1949] Step 6:

[1950] Displaying the list and providing detailed information

[1951] User Terminal: The user terminal parses the received JSON-formatted resource list and displays it to the user as a visual resource list. The user selects the desired resource and checks detailed information (such as availability and price).

[1952] Input: Resource list in JSON format

[1953] Output: Visually displayed resource list and detailed information

[1954] Step 7:

[1955] Notification of the request to the resource provider

[1956] Server: When a user selects a desired resource, for example, "Contact by email," the server contacts the resource provider. This contact includes the user's request and preferred method of contact.

[1957] Input: User's selection information and contact method

[1958] Output: Notification data sent to resource providers

[1959] Step 8:

[1960] Supply by factory robots

[1961] Factory robots: Factory robots receive parts and materials based on requests from resource providers and deliver them to designated locations. The robots automatically calculate the shortest path and complete the delivery efficiently.

[1962] Input: Supplies from resource providers

[1963] Output: Delivery completion notification to the location specified by the user.

[1964] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1965] The system for implementing this invention not only understands user requests and matches them with resources within the group, but also recognizes and responds to the user's emotions. The entire system consists of a user terminal, a server, a generative artificial intelligence, an emotion engine, and a database.

[1966] 1. User input and sentiment recognition

[1967] As soon as a user enters a request into the device, the device is equipped with an emotion engine to recognize the user's emotions.

[1968] The emotion engine analyzes the user's input and behavior (voice tone, input speed, facial expressions, etc.) to identify their emotional state.

[1969] Along with the request, the device sends emotion data recognized by the emotion engine to the server.

[1970] 2. Receiving requests and analyzing sentiment data

[1971] The server receives requests and sentiment data from the user's terminal and analyzes them.

[1972] Prepare to pass the received data to the generative artificial intelligence.

[1973] 3. Sending request and emotion data to the generative artificial intelligence system.

[1974] The server sends the converted request data and emotion data to the generative artificial intelligence.

[1975] 4. AI-powered resource search and optimization

[1976] Generative artificial intelligence extracts relevant keywords based on received requests and sentiment data.

[1977] Based on keywords and sentiment data, the system searches the database within the group and generates a list of relevant resources (e.g., "Company A", "Company B", etc.).

[1978] Optimize search results based on the user's emotions. For example, if a user is feeling stressed, prioritize displaying companies with excellent customer support.

[1979] 5. Filtering the results

[1980] The server filters the resource list generated by the generative artificial intelligence, limiting it to resources within the specified group.

[1981] 6. Sending the list to the user's terminal

[1982] The server converts the filtered list into JSON format and sends it to the user's terminal.

[1983] The user's device parses the received JSON data and displays it to the user as a visual list.

[1984] 7. Displaying user selections and details

[1985] The user selects the desired resource from the displayed list.

[1986] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[1987] The user terminal displays detailed information to the user (e.g., PC price, availability, etc.).

[1988] 8. Implementing the Bridging Process

[1989] Users select their preferred method of contact and then contact the resource provider.

[1990] The server contacts resource providers based on the user's request and contact method.

[1991] We adjust our communication methods based on the user's emotions. For example, if a user is feeling anxious, we prioritize direct contact by phone.

[1992] Specific example

[1993] Example: If you want to purchase 20 new PCs

[1994] 1. User input and sentiment recognition

[1995] A user (sales representative) enters a request into the terminal saying, "I want to purchase 20 new PCs." If the user is typing in a hurry, the emotion engine recognizes this and determines that the user is in a hurry.

[1996] 2. Receiving requests and analyzing sentiment data

[1997] The server receives this request and emotion data and prepares to send it to the generative artificial intelligence.

[1998] 3. Sending request and emotion data to the generative artificial intelligence system.

[1999] The server sends request data and emotion data to the generative artificial intelligence.

[2000] 4. AI-powered resource search and optimization

[2001] Generative artificial intelligence analyzes the request and extracts keywords such as "PC," "purchase," and "20 units."

[2002] Based on extracted keywords and sentiment data, the system searches the database within the group and generates a list of relevant resources such as "Company A" and "Company B".

[2003] Since the user is in a hurry, resources that are immediately available will be displayed first.

[2004] 5. Filtering the results

[2005] The server filters the search results and extracts a list containing only companies within the group.

[2006] 6. Sending the list to the user's terminal

[2007] The server converts the filtering results into JSON format and sends them to the user's terminal.

[2008] The user's terminal parses the received JSON data and displays a list of "Company A" and "Company B".

[2009] 7. Displaying user selections and details

[2010] The user selects "Company A" and checks the details (PC price, stock availability, etc.).

[2011] 8. Implementing the Bridging Process

[2012] If the user selects "Contact by phone," the server will contact "Company A" by phone with the user's request.

[2013] This allows users to efficiently utilize resources within the group and prevents unnecessary information leaks. Furthermore, using an emotion engine enables flexible responses tailored to the user's emotions.

[2014] The following describes the processing flow.

[2015] Step 1:

[2016] Users log in to the system using their devices and enter requests such as "I want to buy XX," "I want to outsource XX," or "I want to sell XX."

[2017] Specific action: Enter the request into the input form displayed in the user interface and click "Submit".

[2018] Step 2:

[2019] The device receives user requests and uses an emotion engine to recognize the user's emotions.

[2020] Specific operation: The system analyzes the user's typing speed and input content, as well as their facial expressions, to determine their emotions.

[2021] Step 3:

[2022] The emotion engine generates recognized emotion data and prepares it to send to the server along with the request.

[2023] Specific operation: Structure emotion data, integrate it with request data, and convert it into a format that can be sent.

[2024] Step 4:

[2025] The device sends request data and sentiment data to the server.

[2026] Specific operation: Sends HTTP requests and API calls containing request and sentiment data to the server.

[2027] Step 5:

[2028] The server receives requests and sentiment data from users.

[2029] Specific operation: Analyzes the received data and converts it into a data format for passing to a generative artificial intelligence.

[2030] Step 6:

[2031] The server sends the converted request data and emotion data to the generative artificial intelligence.

[2032] Specific operation: Call the API of a generative artificial intelligence and pass the request data and sentiment data as parameters for analysis.

[2033] Step 7:

[2034] Generative artificial intelligence extracts relevant keywords based on received requests and sentiment data.

[2035] Specific operation: The request content is analyzed using natural language processing technology to identify the keywords necessary for the search.

[2036] Step 8:

[2037] The generative artificial intelligence searches the database within the group based on extracted keywords and sentiment data, and generates a list of relevant resources.

[2038] Specific operation: Convert keyword and sentiment data into database queries and retrieve a list of candidates as search results.

[2039] Step 9:

[2040] Generative artificial intelligence optimizes search results according to the user's emotions.

[2041] Specific actions: If the user is in a hurry, the system will prioritize displaying resources that are immediately available.

[2042] Step 10:

[2043] The server filters the resource list received from the generative artificial intelligence, limiting it to resources within a specific group.

[2044] Specific operation: The system performs a filter on the received list data to extract only the information within each group.

[2045] Step 11:

[2046] The server converts the filtered list into JSON format and sends it to the user's terminal.

[2047] Specific operation: The filtering results are generated as data in JSON format and sent to the user's terminal via API calls or HTTP responses.

[2048] Step 12:

[2049] The user's device parses the received JSON data and displays it to the user as a visual list.

[2050] Specific operation: Parses JSON data and visually displays the list using HTML or the application UI.

[2051] Step 13:

[2052] The user selects the desired resource from the displayed list.

[2053] Specific operation: Clicking an item in the list sends the ID and detailed information of the selected item to the server.

[2054] Step 14:

[2055] The server retrieves detailed information about the selected resource and sends it to the user's terminal.

[2056] Specific operation: Retrieve detailed information from the database and send it to the user's terminal via the API response.

[2057] Step 15:

[2058] The user terminal displays detailed information to the user.

[2059] Specific action: Display the received details on the screen so that the user can review them.

[2060] Step 16:

[2061] Users select their preferred method of contact and then contact the resource provider.

[2062] Specific action: Select a contact method option (email, phone, etc.) and send that information to the server.

[2063] Step 17:

[2064] The server will contact the resource provider using the specified contact method to conduct further verification.

[2065] Specific actions: The system will send emails or process communications via internal systems according to the selected contact method.

[2066] Step 18:

[2067] The emotion engine adjusts the communication method according to the user's emotions.

[2068] Specific actions: For example, if a user is feeling anxious, priority will be given to phone contact, which allows for direct communication.

[2069] (Example 2)

[2070] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2071] Existing resource matching systems often process requests without considering user emotions, which can lead to decreased user satisfaction. This is especially true when users are in a hurry or experiencing stress, making it difficult to provide appropriate resources. Furthermore, traditional systems often fail to adequately filter search results or provide data in the most optimal format, resulting in insufficient information for users. To address these challenges, a system is needed that recognizes user emotions and responds flexibly based on them.

[2072] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[2073] In this invention, the server includes means for receiving a request entered by the user, means for acquiring the user's emotional data using an emotional recognition engine, means for searching for resources within a group based on the request using generative artificial intelligence, means for filtering the resource list of the search results and limiting it to resources within the group, means for sending the filtered resource list to the user terminal in JSON format, means for displaying detailed information about the resource selected by the user, and means for contacting resource providers based on the user's request and emotions. This enables flexible and effective resource provision in response to the user's emotions.

[2074] A "user" refers to an individual or legal entity that uses the system to input requests and seek out the most suitable resources.

[2075] A "request" refers to information that a user enters into the system, such as necessary details, desired services, or products, which the system should then respond to.

[2076] An "emotion recognition engine" refers to software or hardware that analyzes user input, behavior, facial expressions, voice tone, etc., to identify the user's emotional state.

[2077] "Emotional data" refers to data indicating the user's emotional state as identified by the emotion recognition engine.

[2078] "Generative artificial intelligence" refers to artificial intelligence that searches for resources based on user requests and sentiment data, and generates optimized search results.

[2079] "Resources" is a general term for services, products, information, etc., that are provided in response to user requests.

[2080] "Filtering" refers to the process of narrowing down a list of resources searched by a generative artificial intelligence based on specific conditions.

[2081] "JSON format" is an abbreviation for JavaScript Object Notation, and refers to a lightweight text data format widely used as a data exchange format.

[2082] A "user terminal" refers to a device (e.g., smartphone, tablet, PC, etc.) that a user uses to access a system, input requests, and receive and display results.

[2083] A "resource provider" refers to a company or individual that provides resources in response to a user's request.

[2084] A "database" refers to a system in which information is organized and stored, which generative artificial intelligence uses to search for resources.

[2085] Modes for carrying out the invention

[2086] The system for implementing this invention not only understands user requests and matches them with resources within the group, but also recognizes and responds to user emotions. The system consists of the following components:

[2087] User terminal

[2088] server

[2089] Generative artificial intelligence

[2090] Emotion recognition engine

[2091] database

[2092] Hardware and software to be used

[2093] User terminal: This includes smartphones, tablets, PCs, etc. It is used by the user to input requests, receive results, and display them.

[2094] Server: Performs key processing such as receiving requests, analyzing data, sending data to generative artificial intelligence, filtering search results, and contacting resource providers.

[2095] Emotion Recognition Engine: This uses IBM Watson as an example to recognize emotions from the user's input, voice tone, input speed, and facial expressions.

[2096] Generative Artificial Intelligence: OpenAI GPT-3 is used as the generative AI. Keyword extraction and search result optimization are performed based on user requests and sentiment data.

[2097] Database: Use a database such as MySQL to perform searches based on generated keywords and store and manage the results.

[2098] Data processing and data calculation

[2099] Emotion Recognition: The system analyzes user input, voice tone, input speed, and facial expression data to obtain emotional data. This process utilizes an emotion recognition engine.

[2100] Keyword Extraction and Search: Generative artificial intelligence extracts keywords from user requests and sentiment data, searches the database, and creates a list of appropriate resources.

[2101] Filtering: The server narrows down the list of resources it has searched based on specific criteria, limiting it to resources within a group.

[2102] Data transmission: The filtered resource list is converted to JSON format and sent to the user's terminal. Additionally, resource providers are contacted based on user requests.

[2103] Specific example

[2104] Here's a step-by-step guide for a user who wants to purchase 20 new PCs:

[2105] 1. User input and sentiment recognition:

[2106] A user (sales representative) enters "I want to purchase 20 new PCs" into the terminal. If the user is typing in a hurry, the emotion recognition engine will recognize this and determine that the user is in a hurry.

[2107] 2. Receiving requests and analyzing sentiment data:

[2108] The device sends this request and emotional data to the server. The server analyzes the received data and formats it for transmission to a generative artificial intelligence system.

[2109] 3. Sending request and emotion data to the generative artificial intelligence:

[2110] The server sends request data and emotion data to the generative artificial intelligence.

[2111] 4. AI-powered resource search and optimization:

[2112] Generative artificial intelligence analyzes the request and extracts keywords such as "PC," "purchase," and "20 units." Based on the extracted keywords and sentiment data, it searches the database and generates a list of relevant resources such as "Company A" and "Company B." Because the user is in a hurry, resources that are immediately available are displayed first.

[2113] 5. Filtering the results:

[2114] The server filters the search results and extracts a list containing only companies within the group.

[2115] 6. Sending the list to the user's terminal:

[2116] The server converts the filtering results into JSON format and sends them to the user's terminal. The terminal parses the received JSON data and displays lists of "Company A" and "Company B".

[2117] 7. Displaying user selections and details:

[2118] The user selects "Company A" and checks the details (PC price, stock availability, etc.).

[2119] 8. Implementing the bridging:

[2120] If the user selects "Contact by phone," the server will contact "Company A" by phone with the user's request.

[2121] Example of a prompt

[2122] Example prompt message for a user purchasing 20 new PCs:

[2123] text

[2124] User request: "Purchase 20 new PCs."

[2125] User sentiment data: "Users are in a hurry."

[2126] System prompt: "Please show me companies that can immediately deliver 20 PCs. The user is in a hurry."

[2127] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2128] Step 1:

[2129] The user enters the request.

[2130] The user enters a request into the terminal. For example, they might enter "I want to purchase 20 new PCs."

[2131] Function: The user enters text into an input field and submits it by pressing a specific button.

[2132] Input: User's request text: "I want to purchase 20 new PCs."

[2133] Output: The requested data is stored on the terminal.

[2134] Step 2:

[2135] The device recognizes the user's emotions.

[2136] The device uses an emotion recognition engine to analyze the user's input, behavior, voice tone, input speed, and facial expressions.

[2137] Function: Captures the user's facial expressions and voice using the camera and microphone, and analyzes them with an emotion recognition engine (e.g., IBM Watson).

[2138] Input: User's facial expressions, voice, input speed

[2139] Output: User sentiment data (e.g., "I'm in a hurry") is recognized and retrieved.

[2140] Step 3:

[2141] The device sends request and sentiment data to the server.

[2142] The device sends the acquired request data and sentiment data to the server.

[2143] Function: Converts request data and sentiment data into JSON format and sends it to the server via a secure channel.

[2144] Input: Request data "I want to buy 20 new PCs", Sentiment data "The user is in a hurry"

[2145] Output: Request data and sentiment data are sent to and received by the server.

[2146] Step 4:

[2147] The server analyzes and formats the data.

[2148] The server analyzes the received request data and emotion data, and formats it for transmission to the generative artificial intelligence.

[2149] Function: Performs data validation and format conversion, and formats the data to a format compatible with generative AI (e.g., OpenAI GPT-3).

[2150] Input: Request data and sentiment data in JSON format

[2151] Output: Data converted to a format for transmission to a generative AI.

[2152] Step 5:

[2153] The server sends request and emotion data to the generative artificial intelligence.

[2154] The server sends the converted request data and emotion data to the generative artificial intelligence.

[2155] Function: Sends data to generative AI using an API.

[2156] Input: Formatted request data and sentiment data

[2157] Output: The generative AI receives the data and begins processing.

[2158] Step 6:

[2159] Generative artificial intelligence extracts keywords and searches the database.

[2160] Generative AI extracts keywords from received requests and sentiment data, and searches for related resources in a database.

[2161] Function: Uses natural language processing techniques to extract keywords and execute database queries.

[2162] Input: Request data and sentiment data

[2163] Output: Related resource list (e.g., resource list based on keywords such as "PC", "purchase", and "20 units")

[2164] Step 7:

[2165] Optimization of search results using generative AI

[2166] Generative AI optimizes search results based on user sentiment data. For example, if a user is in a hurry, it prioritizes resources that are immediately available.

[2167] Function: Ranks and filters search results based on sentiment data.

[2168] Input: Search results list, sentiment data "hurried"

[2169] Output: Optimized resource list (e.g., prioritizing "Immediately Available Resources")

[2170] Step 8:

[2171] The server filters the search results.

[2172] The server filters the resource list generated by the generative AI, limiting it to resources within the specified group.

[2173] Function: Applies a filtering algorithm to extract only resources within the group.

[2174] Input: Resource list for generative AI

[2175] Output: Limited resource list (e.g., "Company A", "Company B", etc., including only resources within the group)

[2176] Step 9:

[2177] The server sends the filtering results to the terminal in JSON format.

[2178] The server converts the filtering results into JSON format and sends them to the user's terminal.

[2179] Function: Uses a protocol to convert filtering results into JSON format and send them to the terminal.

[2180] Input: Filtered resource list

[2181] Output: A resource list in JSON format is sent to the terminal.

[2182] Step 10:

[2183] The user selects a resource from a list.

[2184] The user selects the desired resource from the list displayed on the device.

[2185] Function: The user taps or clicks a list item to select it.

[2186] Input: List display and user selection operation

[2187] Output: Selected resource data (e.g., "Company A")

[2188] Step 11:

[2189] The server retrieves detailed information about the resource and sends it to the terminal.

[2190] The server retrieves detailed information related to the selected resource and sends it to the terminal.

[2191] Function: Retrieves detailed information about the selected resource from a database or another API and sends it to the device.

[2192] Input: Selected resource data

[2193] Output: Detailed information (e.g., PC price, stock status) is sent to the terminal.

[2194] Step 12:

[2195] The user selects a contact method, and the server contacts the resource provider.

[2196] The user selects their preferred method of contact, and the server then contacts the resource provider.

[2197] Function: Select a contact method (e.g., email, phone), and the server will contact the resource provider using the corresponding API or protocol.

[2198] Input: Selection of contact method, resource data, sentiment data

[2199] Output: Contact is established with the resource provider (e.g., "Company A" is contacted by phone).

[2200] This series of steps enables efficient resource matching that takes into account user needs and emotions.

[2201] (Application Example 2)

[2202] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2203] Traditional systems have mechanisms to search for and provide resources based on user requests, but they lack the ability to consider the user's emotional state, making it difficult to optimize the user experience. Furthermore, there is a problem in providing appropriate resources quickly and effectively when the user is in an emergency or experiencing a specific emotional state.

[2204] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for receiving a request entered by the user, means for searching for resources within a group based on the request using generative artificial intelligence, means for recognizing the user's emotional state using an emotion engine and processing the data, means for sending a filtered resource list to the user terminal in JSON format, means for displaying detailed information about the resource selected by the user, and means for adjusting the contact method based on the user's emotional state and contacting the resource provider with the request. This enables flexible responses according to the user's emotional state, and allows for the rapid and effective provision of resources, especially in emergencies or when the user is in a specific emotional state.

[2205] "Means for receiving user-entered requests" refers to a mechanism for receiving request data entered by a user into a terminal.

[2206] "A means of searching for resources within a group based on requests using generative artificial intelligence" refers to a mechanism that uses generative artificial intelligence to analyze user request data and identify relevant resources within a group.

[2207] "Means for recognizing a user's emotional state using an emotion engine and processing that data" refers to a mechanism for identifying an emotional state by analyzing the user's voice tone, input speed, facial expressions, etc., and for handling that data.

[2208] "Means for filtering the resource list in search results and limiting it to within a group" refers to a mechanism that filters the resource list searched by a generative artificial intelligence to limit it to only those resources within a group.

[2209] "Means for sending a filtered resource list to the user's terminal in JSON format" refers to a mechanism for converting a filtered resource list into JSON, a structured data format, and sending it to the user's terminal.

[2210] "Means for displaying detailed information about a resource selected by the user" refers to a mechanism for displaying detailed information about a resource selected by the user on the user's terminal.

[2211] "A means of adjusting the method of contact based on the user's emotional state and communicating the request to the resource provider" refers to a mechanism that selects the most appropriate method of contact according to the user's emotional state and notifies the resource provider of the user's request.

[2212] The system for implementing this invention not only understands user requests and matches them with resources within the group, but also recognizes and responds to the user's emotions. The entire system consists of a user terminal, a server, a generative artificial intelligence, an emotion engine, and a database.

[2213] The user inputs their requests using their smartphone, and the entire system operates accordingly. First, the user inputs their requests for desired products or services into their smartphone. Simultaneously, the smartphone is equipped with an emotion engine that analyzes the user's voice tone, input speed, facial expressions, etc., to recognize their emotional state.

[2214] The server receives request data and sentiment data sent from the user terminal. The received data is sent to a generative artificial intelligence (AI) for analysis. The generative AI extracts relevant keywords from the request data and searches a database within the group along with the sentiment data. This process includes keyword extraction and database query execution.

[2215] The resource list in search results is optimized according to the user's emotional state. For example, if the user is feeling urgent or stressed, the system will prioritize displaying resources that offer quick response and comprehensive support. The resulting resource list is then filtered by the server to limit resources to those within a specific group.

[2216] The filtered resource list is converted to JSON format and sent to the user's terminal. The user's terminal parses the received JSON data and displays it to the user as a visual list. The user can select the desired resource from the displayed list and view detailed information, such as the product price and availability.

[2217] Once the user selects their preferred method of contact, the server will choose the most appropriate method based on the user's request and emotional state, and contact the resource provider. For example, if the user is feeling anxious, telephone contact will be prioritized.

[2218] The specific hardware and software used include smartphones (iOS or Android), emotion recognition APIs (e.g., Microsoft Azure Emotion API), and generative artificial intelligence APIs (e.g., OpenAI GPT models). These elements work together to enable flexible responses that respond to the user's emotions.

[2219] As a concrete example, consider a scenario where a user searches for products to relieve fatigue. In this case, if the user enters "I want supplements that relieve fatigue," the emotion engine recognizes the user's level of fatigue. Based on this, the generative artificial intelligence extracts relevant keywords and generates a list of products suitable for that emotional state. For example, the list might include aromatherapy candles and fatigue-relieving supplements.

[2220] Example of a prompt:

[2221] User request: I want a supplement to relieve fatigue.

[2222] Emotional state: Fatigue (high)

[2223] Expected output: Display a list of fatigue-relieving supplements, aromatherapy candles, and other relaxing products, along with price and availability information.

[2224] By combining emotion recognition and generative artificial intelligence in this way, it becomes possible to provide services that optimize the user experience.

[2225] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2226] Step 1:

[2227] Users use their smartphones to input requests for desired products or services. Simultaneously, an emotion engine built into the device analyzes the user's voice tone, input speed, facial expressions, etc., to recognize their emotional state. Based on this sensory data, the emotion engine identifies emotions such as "fatigue" and "stress" and generates data accordingly.

[2228] Input: User requests and sentiment data

[2229] Output: Request data and sentiment data

[2230] Step 2:

[2231] The terminal sends user request data and sentiment data to the server. The server prepares the received data for analysis. This preparation stage involves standardizing the data format and performing necessary preprocessing.

[2232] Input: Request data and sentiment data

[2233] Output: Preprocessed request data and sentiment data

[2234] Step 3:

[2235] The server sends pre-processed request data and sentiment data to the generative artificial intelligence. The generative AI extracts relevant keywords based on the transmitted data and analyzes the sentiment data.

[2236] Input: Preprocessed request data and sentiment data

[2237] Output: Extracted keywords and analyzed sentiment data

[2238] Step 4:

[2239] The generative artificial intelligence uses extracted keywords and analyzed sentiment data to search a database within the group. The search results generate a list of relevant resources. For example, if a user requests "supplements to relieve fatigue," the list will include corresponding supplements and relaxation products.

[2240] Input: Keywords and sentiment data

[2241] Output: Resource List

[2242] Step 5:

[2243] The server filters the generated resource list, limiting it to only resources within the group. This filtering excludes resources outside the group, prioritizing highly reliable resources.

[2244] Input: Resource list

[2245] Output: Filtered resource list

[2246] Step 6:

[2247] The server converts the filtered resource list into JSON format and sends it to the user's terminal. Converting to JSON format makes it easier for the user's terminal to analyze and display the data.

[2248] Input: Filtered resource list

[2249] Output: Resource list converted to JSON format

[2250] Step 7:

[2251] The device parses the received JSON data and displays it to the user as a visual list. The user can then select the desired resource from this list. For example, they can check the price and availability of a product.

[2252] Input: Resource list in JSON format

[2253] Output: Visual resource list

[2254] Step 8:

[2255] The user selects their preferred method of contact and sends that information to the server. The server then adjusts the best method of contact based on the user's emotional state and contacts the resource provider. For example, if the user is feeling anxious, phone contact will be prioritized.

[2256] Input: User selection information and sentiment data

[2257] Output: Contacting resource providers

[2258] This enables the provision of flexible and optimal resources tailored to the user's emotional state.

[2259] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2260] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2261] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[2262] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2263] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[2264] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[2265] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[2266] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[2267] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[2268] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[2269] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[2270] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[2271] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[2272] 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.

[2273] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[2274] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[2275] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[2276] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[2277] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[2278] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanati...

Claims

1. A means of receiving requests entered by the user, A means of searching for resources within a group based on requests using generative artificial intelligence, A method for filtering the resource list in search results and limiting it to groups, A means of sending a filtered resource list to the user terminal, A means of displaying detailed information about the resource selected by the user, A means of communicating user requests to resource providers, A system that includes this.

2. The system according to claim 1, comprising means for a generative artificial intelligence to extract keywords and execute a database query based on those keywords.

3. The system according to claim 1, comprising means for sending a filtered resource list in JSON format to a user terminal.

Images