system

Abstract

We provide the system. [Solution] A means for the client to interact with artificial intelligence and create the details of the request, A means of saving the aforementioned request details in a database and making them public as a list of requests, A means for workers to view the aforementioned list of requests and apply for those requests, A means for processing the aforementioned application information and matching clients with workers, A means for the client to evaluate the worker after the work is completed and to save the evaluation results in an unalterable format, A means for managing compensation information and enabling workers to create documents based on their annual income, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 modern society, there is a problem that personal communication is thinning out, and it is becoming difficult to ask others about daily troubles. Also, the creation of the request content is complicated, and smooth matching between both parties is difficult. Furthermore, the preparation of documents required for the remuneration management and final tax return of workers is time-consuming and inefficient.

Means for Solving the Problems

[0005] This invention provides a system that allows clients to easily create requests by interacting with artificial intelligence. The requests are stored in a database and made public to encourage applications from suitable workers. Furthermore, the system utilizes artificial intelligence and terminals to automate the matching of clients and workers, increasing efficiency. In addition, worker evaluation results are stored in an tamper-proof format, enhancing reliability. The system also reduces the workload on workers through compensation management and assistance with tax return preparation.

[0006] A "client" is an individual or group that requests specific work or assistance from another person in a service transaction between individuals.

[0007] "Artificial intelligence" refers to technologies that mimic human intellectual work and use natural language processing and machine learning to perform dialogue and data processing.

[0008] A "database" is an information management system that efficiently and structurally stores and manages information, allowing data to be searched and utilized when needed.

[0009] A "worker" is an individual or group that provides work or assistance based on a request in a service transaction between individuals.

[0010] "Matching" refers to the process of appropriately connecting clients and workers based on their respective conditions and requirements.

[0011] "Evaluation results" refer to information that shows the results of the client's evaluation of the quality and outcomes of the services provided by the worker.

[0012] "Tamper-proof" refers to a state where data, once saved, cannot be illegally altered or deleted.

[0013] "Compensation information" refers to information that includes details and records regarding the monetary compensation that an employee is entitled to receive.

[0014] "Tax return documents" refer to reports that individuals or organizations submit to tax authorities based on their annual income. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Carrying Out the Invention

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

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

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0036] This invention is a system that allows clients to efficiently create requests using artificial intelligence and then publish those requests to be matched with suitable workers. This system is implemented by combining the functions of a server, terminal, and user.

[0037] The user (the client) initiates a conversation with the artificial intelligence using a messaging app on their device. Specifically, the client inputs a summary of the task and their desired conditions in natural language, which the AI ​​then analyzes and asks for any necessary additional information. For example, in response to the input "I need help assembling furniture this weekend," the AI ​​might respond with instructions such as "Please set specific dates, times, and compensation." Through this conversation, the client can finalize the details of their request.

[0038] The terminal sends the completed request details to the server, which then saves them to a database. The requests are then made publicly available in a list format that workers can view. Users (workers) can check this list from their terminal and apply for requests that match their criteria. The server aggregates the application information and automatically matches the most suitable client with the most suitable worker.

[0039] Once the work is completed, the user (client) uses the terminal to evaluate the worker. This ensures that reliable evaluations are saved on the server, serving as reference information for other users. Furthermore, when organizing annual compensation information, the user (worker) uses the terminal to create appropriate documents with the support of artificial intelligence. This support automatically generates the necessary reports for tax filing.

[0040] At the heart of this system is an interactive dialogue function powered by artificial intelligence that communicates with users. This allows for centralized management of everything from request creation and matching to evaluation and reward management, promoting mutual assistance among individuals. Specific examples include requests for local shopping assistance or everyday help, and the improved reliability of evaluation data contributes to building trust within the community.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user (the requester) launches a messaging app on their device and inputs the details of their request into the artificial intelligence in natural language. The AI ​​analyzes this input and asks the user questions to supplement the necessary information.

[0044] Step 2:

[0045] The user answers questions from the artificial intelligence, clarifying the details of the request. Once the date, time, compensation, and work details are finalized through this interaction, the device sends this information to the server.

[0046] Step 3:

[0047] The server stores the received request information in a database and publishes it as a list of requests. The published list can be accessed from the terminals used by the workers.

[0048] Step 4:

[0049] Users (workers) view a list of available requests through their terminals and select a request that matches their desired conditions. When they apply for a selected request, that information is sent to the server.

[0050] Step 5:

[0051] The server performs appropriate matching based on the received application information. It considers the schedules and compensation conditions of both the client and the worker to determine the optimal combination.

[0052] Step 6:

[0053] Once the task is completed, the user (worker) sends a completion notification to the server using their terminal. The server then notifies the client that the task is complete.

[0054] Step 7:

[0055] The user (client) evaluates the worker based on the results of the work. This evaluation is sent from the terminal to the server, which records it in a database in an immutable state.

[0056] Step 8:

[0057] Users (workers) interact with artificial intelligence to organize their annual compensation information and create the necessary tax return documents. The generated documents can be downloaded via their device.

[0058] (Example 1)

[0059] 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."

[0060] The goal is to solve the challenges of manual detailed settings and cumbersome procedures that arise in the process of clients efficiently creating job requests and quickly matching them with suitable workers. Furthermore, it is necessary to manage reliable information effortlessly in post-request evaluations and compensation management. Additionally, it is required to reduce the effort workers put into quickly organizing their annual income and generating accurate reports.

[0061] 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.

[0062] In this invention, the server includes means for the client to interact with a generated AI model and automatically generate necessary information based on natural language input, means for transmitting the request content to an information management device for storage and public display in a list, and means for aggregating application information and performing optimal matching. This streamlines the process from request content creation to evaluation and reward management, enabling rapid and accurate communication and procedures.

[0063] A "generative AI model" is a machine learning algorithm or program that analyzes natural language input and generates appropriate answers or suggestions for the user.

[0064] An "information management device" is a computer system that receives, stores, and manages data such as requests and application details, and processes or provides that information as needed.

[0065] A "client" refers to an individual or organization that requests the execution of a specific task or operation, and is the initiator of the interaction in this system.

[0066] A "worker" refers to an individual or organization that wishes to accept and carry out a request from a client.

[0067] "Natural language input" refers to text or voice input performed in a language that the user uses on a daily basis (for example, Japanese or English).

[0068] "Optimal matching" is a process that automatically selects the worker who best matches the client's requirements and provides the most suitable combination for carrying out the work.

[0069] "Evaluation" refers to the feedback provided by the client after the completion of a task, regarding the worker's performance and responsiveness.

[0070] "Compensation information" refers to data relating to monetary compensation for workers, including information for calculating and paying such compensation.

[0071] The system in this invention is primarily implemented through the cooperation of the user, terminal, and server. First, the user (requester) interacts with the generative AI model using a messaging application on the terminal. The user inputs the request in natural language as text or voice. For example, they might use a prompt such as, "I need help assembling furniture this weekend."

[0072] The terminal sends the client's input to a generative AI model and begins analysis. The generative AI model analyzes the input based on natural language processing techniques and prompts the user for necessary information. This includes dialogue to identify the user's intentions and conditions. This process continues until the user provides additional information such as a specific date and time or reward.

[0073] The terminal sends the completed request details to the server. The server stores the request details using an information management system (e.g., a database management system) and then publishes the information in a list format. Workers can view the published list through their terminals. From here, workers can apply for requests that meet their criteria.

[0074] The server aggregates multiple application details and uses an automated matching algorithm to determine the optimal combination of client and worker. Once the work is completed, the user (client) evaluates the worker using their terminal, and this evaluation is stored on the server in an immutable format. Other users can also view this evaluation.

[0075] Furthermore, workers can manage their compensation information, and at the end of the fiscal year, they can streamline their tax filing process by automatically generating necessary documents with the support of a generation AI model. This entire process, from request to compensation management, is seamless, providing a highly convenient system for both clients and workers.

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

[0077] Step 1:

[0078] The user (requester) launches the messaging app on their device and begins interacting with the generative AI model. As input, they enter a summary of their request in natural language. Specifically, they enter and send a prompt message such as, "I need help assembling furniture this weekend." Based on this input, the device sends data to the generative AI model and prepares it for analysis.

[0079] Step 2:

[0080] The terminal analyzes the user input sent to the generative AI model. Here, it processes the input natural language and performs data analysis to identify any additional information needed. As output, the generative AI model generates natural language questions that ask the user for specific conditions required for the request (e.g., "specific date and time and reward"). The terminal displays these questions and asks the user for confirmation.

[0081] Step 3:

[0082] The user answers questions from the generative AI model via the terminal and inputs additional necessary information. This finalizes the detailed request. Based on the user's responses, the terminal performs another analysis using the generative AI model and formats the information. Based on the input information, it prepares to send the finalized request data as output to the server.

[0083] Step 4:

[0084] The terminal sends the finalized request data to the server. During this process, the data is properly encrypted and processed to ensure secure transmission. The server stores the received data in a database and generates output to store the request information. Furthermore, it prepares the data for publication as a list accessible to workers.

[0085] Step 5:

[0086] The server formats the request information stored in the database into a list and makes it public. Workers can then use their terminals to view this list. They select candidates from the list as input and apply for requests that meet the criteria. Each application is sent to the server, which aggregates the information and generates output that optimizes the match between clients and workers using a matching algorithm.

[0087] Step 6:

[0088] After the work is completed, the user (client) uses a terminal to evaluate the worker and sends the information to the server. The server generates output that saves the evaluation information to the database in an immutable format. This allows other users to view the evaluation.

[0089] Step 7:

[0090] Users (workers) can view their compensation information on a terminal and create documents for annual income management with the assistance of an AI model. The terminal automatically generates appropriate reports based on the entered compensation data and outputs them in a format that the user can download. This process streamlines the tax filing process for workers.

[0091] (Application Example 1)

[0092] 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."

[0093] In recent years, matching requests from individuals and brick-and-mortar stores with available workers has often been done manually, resulting in time-consuming and labor-intensive processes. Furthermore, the ambiguity of requests and the lack of mutual suitability frequently reduce the efficiency of the matching process. Additionally, brick-and-mortar stores require efficient operational support utilizing product distribution information, but current systems struggle to address this.

[0094] 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.

[0095] In this invention, the server includes means for the client to interact with artificial intelligence and create a request; means for saving the request content in a database and publishing it as a list of requests; and means for extracting information necessary for supporting the operations of physical stores from product distribution information and automatically generating a schedule tailored to customer needs using artificial intelligence. This enables efficient matching of requests with workers and streamlines in-store operations.

[0096] A "client" refers to an individual or organization that wishes to use artificial intelligence to efficiently create work descriptions and match them with workers.

[0097] "Artificial intelligence" refers to a computer system that uses natural language processing to analyze requests, extract information, and automatically generate schedules.

[0098] "Request details" refers to information including the details and conditions of the work entered by the client via artificial intelligence.

[0099] A "database" is an information system that stores and manages data such as request details, evaluation results, and compensation information.

[0100] A "worker" refers to an individual or group that wishes to select and perform a task that matches their requirements from a list of requests.

[0101] "Matching" is the process of comparing the request details created by the client with the qualifications of the workers to find the optimal combination.

[0102] "Tamper-proof format" refers to a state where evaluation results and data, once saved, are managed in a way that prevents unauthorized external modifications.

[0103] "Product distribution information" refers to data related to the inventory and sales of products in physical stores, and is used to support business operations.

[0104] "Automatic schedule generation" refers to the process by which artificial intelligence creates an actionable work plan based on product distribution information and client requests.

[0105] To realize this invention, a system needs to be built with the cooperation of a server, terminals, and users. Specifically, the server will be the central hub, managing everything from request creation and matching to evaluation and reward management.

[0106] The server stores request details, evaluation information, and reward information using a database management system (e.g., Firebase). It also utilizes artificial intelligence models (e.g., Google Cloud Natural Language API for natural language processing) to analyze the request details entered by the user through their device and extract any necessary additional information.

[0107] The terminal functions as a smartphone or similar device, providing an interface that allows users to directly interact with artificial intelligence. By entering request details through this interface, the information is sent to the server. Furthermore, in application examples such as assisting operations in physical stores, product distribution information can also be viewed on the terminal, allowing the AI ​​to generate a work schedule tailored to customer needs.

[0108] As a concrete example, consider its use in a physical store. When a customer requests to reserve an item in the store, the store staff member uses a terminal to input, "I'd like to reserve item XX for the weekend." Based on this input, artificial intelligence analyzes the inventory status and the customer's request, and the server generates an appropriate schedule and notifies the staff member.

[0109] An example of a prompt message would be, "Check the stock of jacket size M and notify the customer immediately of the result." This type of system streamlines operations in physical stores and enables smooth communication and matching between workers and clients.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user enters their request on their device and begins interacting with the artificial intelligence.

[0113] Input: The user enters a request in natural language via a smartphone app, such as "I would like to reserve product XX for the weekend."

[0114] Processing: The terminal sends the entered text to the Google Cloud Natural Language API to parse the details of the request.

[0115] Output: Analyzed items (product name, quantity, date and time, etc.) are generated, and the user is prompted for additional information as needed.

[0116] Step 2:

[0117] The terminal sends the request details to the server and saves them.

[0118] Input: Analyzed request information obtained in Step 1.

[0119] Processing: The terminal converts the parsed request information into JSON format and sends it to the database. Firebase stores this information.

[0120] Output: The request details stored on the server become available for viewing.

[0121] Step 3:

[0122] The server generates a list of requests and makes it publicly available for workers to view.

[0123] Input: Saved request information.

[0124] Processing: The server extracts the latest requests from the database and updates the list in a format that workers can view on the web interface or application.

[0125] Output: The list of requests is displayed on the worker's terminal.

[0126] Step 4:

[0127] Workers check the list of requests on their terminals and apply for them.

[0128] Input: The details of the request viewed by the worker.

[0129] Processing: Workers select a suitable assignment and enter their application information. The terminal sends this information to the server.

[0130] Output: Application information is stored on the server.

[0131] Step 5:

[0132] The server processes the application information and performs matching.

[0133] Input: Application information and request details.

[0134] Processing: The server compares the applicant's qualifications with the job description and runs an algorithm to select the most suitable worker.

[0135] Output: The optimal client-worker pair is formed.

[0136] Step 6:

[0137] After the work is completed, the user evaluates the worker and saves the evaluation in an tamper-proof format.

[0138] Input: Evaluation information and work results.

[0139] Processing: Users enter their ratings from their terminals, and the server stores the rating data in an tamper-proof format using blockchain technology or similar methods.

[0140] Output: Reliable evaluation data is stored on the server and can be referenced in the future.

[0141] Step 7:

[0142] The terminal handles product distribution information, and AI automatically generates schedules for in-store operations.

[0143] Input: Product distribution information and customer needs.

[0144] Processing: The terminal retrieves inventory data, and the AI ​​generates an optimal work schedule based on customer requests and notifies relevant parties.

[0145] Output: Streamlined work plans are provided to and implemented by staff.

[0146] 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.

[0147] This invention aims to improve human-like interactions and matching accuracy by combining an emotion engine with a system that connects clients and workers. This system is realized by combining the functions of a server, terminal, and user.

[0148] The user (the requester) begins interacting with the artificial intelligence using a messaging app on their device. During this time, an emotion engine analyzes the user's emotional state in the background. For example, if the requester is feeling stressed, the emotion engine recognizes this, and the AI ​​can provide appropriate advice regarding the request. In other words, the AI ​​empathizes with the requester's emotions and supports them in fulfilling their request.

[0149] The terminal sends the confirmed request details to the server, which stores the details in a database. The requests are made public as a list for workers to view. Users (workers) check the publicly available list of requests on their terminal and apply for requests they believe they can perform. The emotion engine also operates during the application process, considering the worker's emotional state to facilitate optimal communication. Based on this information, the server matches requesters with workers. In this process, emotion data acts as a filter, resulting in emotionally compatible pairings.

[0150] Upon completion of the work, the user (client) provides an evaluation of the worker via their terminal. During this process, the emotion engine analyzes the client's emotions and generates feedback that reflects their feelings. The evaluation results are stored in a tamper-proof database on the server, ensuring reliability.

[0151] Furthermore, users (workers) organize their annual earnings and, with the support of the emotional engine when necessary, prepare their tax return documents. This enables more intuitive and efficient earnings management, reducing the workload.

[0152] This system enables smoother and more satisfying service delivery by matching emotionally compatible clients with service providers. For example, it can flexibly respond to human emotions, such as matching a nervous client with a service provider who has a relaxed demeanor.

[0153] The following describes the processing flow.

[0154] Step 1:

[0155] The user (the requester) launches a messaging app on their device and enters a summary of their request into the artificial intelligence. During this process, the emotion engine analyzes the user's emotions based on their voice and input.

[0156] Step 2:

[0157] The device adjusts its AI-powered dialogue based on the user's emotional data, asking questions that are sensitive to the user's feelings. The user then confirms the details of their request while receiving advice tailored to their emotions.

[0158] Step 3:

[0159] The terminal sends the completed request details to the server. The server saves the received data to a database and makes it publicly available in the request list for other users to view.

[0160] Step 4:

[0161] Users (workers) view a list of publicly available requests through their terminals. The emotion engine monitors the worker's emotional state and supports them in taking appropriate approaches to requests that interest them.

[0162] Step 5:

[0163] When a worker applies for a job, their device sends that information to the server. The server considers the conditions and sentiment data of both the client and the worker, and performs a matching process.

[0164] Step 6:

[0165] Once matching is complete, the server sends notifications to both the client and the worker, and the work begins.

[0166] Step 7:

[0167] Upon completion of the work, the user (client) evaluates the worker via their terminal. The emotion engine proposes evaluation comments that reflect the client's emotions and sends them to the server.

[0168] Step 8:

[0169] The server stores the evaluation results in a database in a tamper-proof format.

[0170] Step 9:

[0171] Users (workers) interact with artificial intelligence on a terminal to organize their annual compensation, receiving assistance from an emotion engine as needed, and create tax return documents. The terminal provides these documents in a downloadable format for the user.

[0172] (Example 2)

[0173] 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".

[0174] Modern online request and matching systems often fail to consider the emotional compatibility between clients and workers, resulting in poor communication quality, inefficient task completion, and low satisfaction. Furthermore, improvements in the reliability and efficiency of managing evaluation and compensation information are also needed.

[0175] 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.

[0176] In this invention, the server includes means for storing request information in an information recording device and presenting it as a list of requests, means for service providers to view the list of requests and apply to participate, and means for evaluating the emotional state of the client and service provider using an emotion analysis device and providing appropriate responses and associations. This enables highly accurate matching that takes into account the user's emotions, improving the satisfaction of both clients and workers and streamlining compensation management.

[0177] A "client" is someone who requires a service and generates request information through information exchange with an intelligent processing device.

[0178] An "intelligent processing device" is a technological device that exchanges information with clients and service providers, analyzes emotional states, and generates appropriate responses.

[0179] "Request information" refers to information, including the details of the service, generated by the client through an intelligent processing device.

[0180] "Information recording device" is a general term for databases and storage systems used to store request information and evaluation results.

[0181] A "request list" is a list of request information stored in an information recording device, presented in a format that can be viewed by service providers.

[0182] A "service provider" is someone who views the list of requests and applies to participate in a specific request.

[0183] An "emotion analysis device" is a system that analyzes the emotional state of both the client and the service provider, and uses that information to provide appropriate responses and establish relationships.

[0184] "Evaluation results" refer to the results of the client's evaluation of the service provider after the completion of the work, and are recorded in a way that makes them impossible to tamper with.

[0185] "Compensation information" refers to data that includes information about the compensation received by service providers, and is information that supports the creation of documents based on annual income.

[0186] A "user interface" is an interface used by a user to directly exchange information with an intelligent processing unit.

[0187] Modes for carrying out the invention

[0188] This invention is a system that achieves high-quality communication and matching in an online platform connecting clients and workers by combining an emotion engine and a generative AI model. This system functions using server, terminal, and user components.

[0189] The user (client) initiates interaction with the intelligent processing unit using a messaging app installed on their device. During this interaction, a generative AI model generates an appropriate response based on the prompt. For example, if the user input is "I want to design a new website, but I don't know how to proceed," the emotion engine analyzes this anxiety and generates a reassuring response. In this process, a natural language processing library using Python may be used for emotion analysis.

[0190] The terminal securely transmits the user's confirmed request information to the server. This data transfer is performed via the HTTPS protocol, ensuring security. The transmitted information is stored in an SQL database on the server. Based on this data, the server publishes a list of requests viewable by service providers via a web interface. This interface is built using HTML and CSS, and is designed with ease of use and visibility in mind.

[0191] Users (workers) view this list from their terminals and apply to participate in requests that interest them. The application information is also sent to the server, where the applicant's emotional state is evaluated via an emotion engine. This evaluation data is used to support emotional matching between the applicant and the requester.

[0192] The server matches clients with workers based on this data. The generative AI model suggests the optimal pairing based on past matching history and emotional data. This process achieves emotionally optimized matching, improving user satisfaction.

[0193] After the work is completed, the user (client) evaluates the worker using a terminal, and this evaluation is recorded on the server in an immutable format. This evaluation will also be used as data to improve the accuracy of future matching.

[0194] Users (workers) manage their annual earnings and prepare tax return documents via their devices, with assistance from an emotional engine as needed. This streamlines the earnings management process for service providers.

[0195] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0196] Step 1:

[0197] The user (client) initiates interaction with the intelligent processing unit using a messaging app on their device. The input consists of text data indicating the user's request and their emotional state at the time. This text is analyzed by a generative AI model to identify the user's requests and emotional state. Through this process, the emotion engine analyzes the user's anxieties and expectations and outputs the most appropriate advice accordingly.

[0198] Step 2:

[0199] The terminal sends the generated request information and analysis results to the server. The input data consists of the request details, sentiment analysis results, and user ID. This transmission is performed securely using HTTPS. On the server, this data is stored in an SQL database and becomes output data used for future processing.

[0200] Step 3:

[0201] The server uses the stored request data to generate a list of requests accessible to service providers. The input is a dataset of stored request information. The server uses HTML and CSS to build a web interface and output the list. This list is displayed clearly and easily accessible to service providers.

[0202] Step 4:

[0203] Users (workers) use a terminal to view a list of publicly available requests and apply for requests that interest them. As input, service providers provide their profile information and response content. The terminal sends this information to the server and generates output that records the application information in a database.

[0204] Step 5:

[0205] The server uses the submitted information to initiate the matching process between clients and service providers. Input data includes application information, past matching history, and sentiment data. A generative AI model is used to determine the optimal pairing, taking sentiment data into consideration, and the results are output.

[0206] Step 6:

[0207] After the work is completed, the user (client) uses a terminal to evaluate the service provider. The inputs here are an evaluation score and text feedback. The emotion engine analyzes the feedback content, and the server outputs and stores this information in a database in an immutable format.

[0208] Step 7:

[0209] Users (workers) use a terminal to manage their annual earnings and prepare tax returns with the support of an emotional engine as needed. Input data includes earnings information and emotional state. The emotional engine provides appropriate feedback to reduce procedural stress and outputs a generated earnings report.

[0210] (Application Example 2)

[0211] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0212] Traditional systems connecting clients and workers often fail to consider emotional compatibility during matching, leading to problems with client satisfaction and worker efficiency. Furthermore, in services based on human emotions, such as customer service, the lack of proper emotional analysis makes it difficult to provide optimal service.

[0213] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0214] In this invention, the server includes means for the client to interact with artificial intelligence and create a request, means for saving the request content in a database and publishing it as a list of requests, and means for analyzing the emotional state of the client and worker in real time using an emotion analysis engine and applying it to customer service and matching. This makes it possible to match emotionally compatible clients with workers, thereby improving the quality of service.

[0215] A "client" is an entity that needs a service and interacts with the system to provide it.

[0216] "Artificial intelligence" is a technology that uses computer systems or programs to mimic human intellectual behavior, performing tasks such as creating requests and analyzing emotions.

[0217] "Request details" refers to information created by the client through dialogue with artificial intelligence, outlining the content and conditions of the service.

[0218] A "database" is a collection of information organized electronically, used to store request details and evaluation results.

[0219] A "worker" is an entity that provides services by browsing a list of requests and applying for appropriate requests.

[0220] An "emotion analysis engine" is a technology that analyzes a person's emotional state in real time and adjusts the system's operation based on that information.

[0221] "Matching" is the process of optimally combining clients and service providers based on their emotional states and service requirements.

[0222] "Evaluation results" refer to the conclusion of the evaluation that the client gives to the worker after the work is completed, and it is important to save this evaluation in a way that makes it impossible to tamper with.

[0223] "Compensation information" refers to information about the payment received by a worker for the services they provide.

[0224] "Customer service" refers to the act of providing services through direct interaction between an employee and a client or customer.

[0225] This invention is a novel matching system that takes into account the emotional states of both the client and the worker. The system is designed so that the server, terminal, and user functions work together efficiently.

[0226] The server receives requests from clients and stores them in a database. This storage is done quickly and securely to enable the publication of a list of requests. The stored data can later be viewed by workers, allowing them to apply for suitable requests.

[0227] The terminal is a device that clients and workers access directly, and it features a user interface that enables interaction with artificial intelligence. This terminal analyzes emotional states in real time using an emotion analysis engine and provides responses and advice tailored to the client's needs.

[0228] Users take on the roles of both requesters (creating requests) and workers (applying for jobs). Each user's emotional state is analyzed by an emotion analysis engine to enable optimal matching. This analysis uses technology that collects data using cameras and microphones via smart glasses or other devices, and classifies emotions in real time using software such as OpenCV and TENSORFLOW®.

[0229] One concrete example is a supermarket where staff wear smart glasses to analyze customers' facial expressions and provide optimal customer service. For instance, they can improve customer satisfaction by using kind words for customers with blank expressions and providing relaxing service for nervous customers. An example of a prompt for the generating AI model would be: "During customer service at the supermarket, analyze the customer's emotional state in real time and suggest appropriate customer service methods. Emphasize a gentle smile and a friendly tone."

[0230] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0231] Step 1:

[0232] The user (client) initiates a dialogue with artificial intelligence using a terminal and creates a request. The input consists of the client's requirements and conditions, and the output is text data of the specific request. The terminal collects the input through the user interface, analyzes it using natural language processing, and structures it as the request.

[0233] Step 2:

[0234] The terminal sends the created request details to the server, where they are stored in the database. The input is the text data of the request details generated in step 1, and the output is the information securely stored in the database. The server receives the request details and stores them efficiently using an existing database management system.

[0235] Step 3:

[0236] Workers use a terminal to retrieve and view a list of requests from the database. The input is the request details stored in the database, and the output is the list of requests displayed on the worker's screen. The terminal queries the server and displays the relevant request information in list format.

[0237] Step 4:

[0238] On the terminal, the user analyzes their own emotional state through an emotion analysis engine. The input is the user's biometric data (facial expressions, voice, etc.), and the output is the analysis result regarding that emotional state. A camera and microphone on smart glasses or other devices collect the data, and analysis is performed using OpenCV or TensorFlow.

[0239] Step 5:

[0240] The server matches clients with workers based on their emotional states. The results from steps 3 and 4 are combined to achieve the optimal match. The input consists of the emotion analysis engine's output and the client's request; the output is information on the matched pair. A generative AI model analyzes the emotional data and determines the optimal pair based on the prompt text.

[0241] Step 6:

[0242] After the work is completed, the user (client) evaluates the worker via a terminal and sends the results to the server. The input is the client's evaluation, and the output is the evaluation result stored in a database in an immutable format. The user operates the interface on the terminal to input the evaluation, and the server saves that data.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] [Second Embodiment]

[0247] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0248] 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.

[0249] 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).

[0250] 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.

[0251] 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.

[0252] 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).

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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".

[0259] This invention is a system that allows clients to efficiently create requests using artificial intelligence and then publish those requests to be matched with suitable workers. This system is implemented by combining the functions of a server, terminal, and user.

[0260] The user (the client) initiates a conversation with the artificial intelligence using a messaging app on their device. Specifically, the client inputs a summary of the task and their desired conditions in natural language, which the AI ​​then analyzes and asks for any necessary additional information. For example, in response to the input "I need help assembling furniture this weekend," the AI ​​might respond with instructions such as "Please set specific dates, times, and compensation." Through this conversation, the client can finalize the details of their request.

[0261] The terminal sends the completed request details to the server, which then saves them to a database. The requests are then made publicly available in a list format that workers can view. Users (workers) can check this list from their terminal and apply for requests that match their criteria. The server aggregates the application information and automatically matches the most suitable client with the most suitable worker.

[0262] Once the work is completed, the user (client) uses the terminal to evaluate the worker. This ensures that reliable evaluations are saved on the server, serving as reference information for other users. Furthermore, when organizing annual compensation information, the user (worker) uses the terminal to create appropriate documents with the support of artificial intelligence. This support automatically generates the necessary reports for tax filing.

[0263] At the heart of this system is an interactive dialogue function powered by artificial intelligence that communicates with users. This allows for centralized management of everything from request creation and matching to evaluation and reward management, promoting mutual assistance among individuals. Specific examples include requests for local shopping assistance or everyday help, and the improved reliability of evaluation data contributes to building trust within the community.

[0264] The following describes the processing flow.

[0265] Step 1:

[0266] The user (the requester) launches a messaging app on their device and inputs the details of their request into the artificial intelligence in natural language. The AI ​​analyzes this input and asks the user questions to supplement the necessary information.

[0267] Step 2:

[0268] The user answers questions from the artificial intelligence, clarifying the details of the request. Once the date, time, compensation, and work details are finalized through this interaction, the device sends this information to the server.

[0269] Step 3:

[0270] The server stores the received request information in a database and publishes it as a list of requests. The published list can be accessed from the terminals used by the workers.

[0271] Step 4:

[0272] Users (workers) view a list of available requests through their terminals and select a request that matches their desired conditions. When they apply for a selected request, that information is sent to the server.

[0273] Step 5:

[0274] The server performs appropriate matching based on the received application information. It considers the schedules and compensation conditions of both the client and the worker to determine the optimal combination.

[0275] Step 6:

[0276] Once the task is completed, the user (worker) sends a completion notification to the server using their terminal. The server then notifies the client that the task is complete.

[0277] Step 7:

[0278] The user (the requester) evaluates the worker based on the results of the work. This evaluation is sent from the terminal to the server, and the server records it in the database in a tamper-proof state.

[0279] Step 8:

[0280] When the user (worker) organizes the information on the annual remuneration, the user interacts with the artificial intelligence and creates the necessary tax return documents. The created documents can be downloaded through the terminal.

[0281] (Example 1)

[0282] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0283] In the process of the requester efficiently creating the work request content and quickly and appropriately matching with the worker, it is required to solve the problems of manual detailed settings and complicated procedures. Also, in the evaluation and remuneration management after the request, it is required to manage highly reliable information without effort. Furthermore, it is required to reduce the labor in the process of the worker quickly organizing the annual income and generating highly accurate reports.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0285] In this invention, the server includes means for the requester to interact with the generation AI model and automatically generate the necessary information based on the natural language input, means for sending the request content to the information management device for storage and listing and publishing, and means for aggregating the application information and performing optimal matching. As a result, the process from the creation of the request content to the evaluation and remuneration management is streamlined, enabling quick and accurate communication and procedures.

[0286] The "generation AI model" is a machine learning algorithm or program that analyzes the input of natural language and generates appropriate answers and suggestions for the user.

[0287] The "information management device" is a computer system that receives, stores, and manages data such as requests and application contents, and processes or provides such information as needed.

[0288] The "requestor" refers to an individual or organization that requests the execution of a specific task or work, and is the initiator of the interaction in this system.

[0289] The "worker" refers to an individual or group that wishes to receive and execute a request from the requestor.

[0290] "Natural language input" refers to input in text or voice using a language that the user uses in daily life (for example, Japanese or English).

[0291] "Optimal matching" is a process of automatically selecting the worker who best meets the conditions required by the requestor and providing a combination suitable for the execution of the work.

[0292] "Evaluation" refers to the feedback provided by the requestor after the completion of the work regarding the performance and response of the worker.

[0293] "Remuneration information" is data regarding the monetary remuneration for the worker, and includes information for the calculation and payment thereof.

[0294] The system in this invention is mainly implemented through the cooperation of the user, terminal, and server. First, the user (requestor) uses the message application on the terminal to interact with the generative AI model. The user inputs the request content in natural language in text or voice. For example, a prompt sentence such as "I need help assembling furniture on the weekend" is used.

[0295] The terminal sends the client's input to a generative AI model and begins analysis. The generative AI model analyzes the input based on natural language processing techniques and prompts the user for necessary information. This includes dialogue to identify the user's intentions and conditions. This process continues until the user provides additional information such as a specific date and time or reward.

[0296] The terminal sends the completed request details to the server. The server stores the request details using an information management system (e.g., a database management system) and then publishes the information in a list format. Workers can view the published list through their terminals. From here, workers can apply for requests that meet their criteria.

[0297] The server aggregates multiple application details and uses an automated matching algorithm to determine the optimal combination of client and worker. Once the work is completed, the user (client) evaluates the worker using their terminal, and this evaluation is stored on the server in an immutable format. Other users can also view this evaluation.

[0298] Furthermore, workers can manage their compensation information, and at the end of the fiscal year, they can streamline their tax filing process by automatically generating necessary documents with the support of a generation AI model. This entire process, from request to compensation management, is seamless, providing a highly convenient system for both clients and workers.

[0299] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0300] Step 1:

[0301] The user (requester) launches the messaging app on their device and begins interacting with the generative AI model. As input, they enter a summary of their request in natural language. Specifically, they enter and send a prompt message such as, "I need help assembling furniture this weekend." Based on this input, the device sends data to the generative AI model and prepares it for analysis.

[0302] Step 2:

[0303] The terminal analyzes the user input sent to the generative AI model. Here, data analysis is performed to process the input natural language and identify the necessary additional information. As output, the generative AI model generates a natural language question asking the user for the specific conditions required for the request (such as "specific date and time or reward"). The terminal displays this question and asks the user for confirmation.

[0304] Step 3:

[0305] The user answers the question from the generative AI model through the terminal and enters additional necessary information. This finally determines the detailed request content. Upon receiving the user's answer, the terminal performs analysis again by the generative AI model to format the information. Based on the input information, it prepares to send the confirmed request data as output to the server.

[0306] Step 4:

[0307] The terminal sends the confirmed request data to the server. In this process, the information is appropriately encrypted and data processing is performed to ensure secure transmission. The server saves the received data in the database and generates an output for saving the request information. Further, it prepares to publish it as a list accessible to workers.

[0308] Step 5:

[0309] The server formats and publishes the request information saved in the database as a list. Workers can use the terminal again to view this list. Select a candidate from the list as input and apply for requests that meet the conditions. Each application is sent to the server, and the server aggregates the information and generates an output for optimizing the requestor and the worker by the matching algorithm.

[0310] Step 6:

[0311] After the work is completed, the user (client) uses a terminal to evaluate the worker and sends the information to the server. The server generates output that saves the evaluation information to the database in an immutable format. This allows other users to view the evaluation.

[0312] Step 7:

[0313] Users (workers) can view their compensation information on a terminal and create documents for annual income management with the assistance of an AI model. The terminal automatically generates appropriate reports based on the entered compensation data and outputs them in a format that the user can download. This process streamlines the tax filing process for workers.

[0314] (Application Example 1)

[0315] 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."

[0316] In recent years, matching requests from individuals and brick-and-mortar stores with available workers has often been done manually, resulting in time-consuming and labor-intensive processes. Furthermore, the ambiguity of requests and the lack of mutual suitability frequently reduce the efficiency of the matching process. Additionally, brick-and-mortar stores require efficient operational support utilizing product distribution information, but current systems struggle to address this.

[0317] 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.

[0318] In this invention, the server includes means for the client to interact with artificial intelligence and create a request; means for saving the request content in a database and publishing it as a list of requests; and means for extracting information necessary for supporting the operations of physical stores from product distribution information and automatically generating a schedule tailored to customer needs using artificial intelligence. This enables efficient matching of requests with workers and streamlines in-store operations.

[0319] A "client" refers to an individual or organization that wishes to use artificial intelligence to efficiently create work descriptions and match them with workers.

[0320] "Artificial intelligence" refers to a computer system that uses natural language processing to analyze requests, extract information, and automatically generate schedules.

[0321] "Request details" refers to information including the details and conditions of the work entered by the client via artificial intelligence.

[0322] A "database" is an information system that stores and manages data such as request details, evaluation results, and compensation information.

[0323] A "worker" refers to an individual or group that wishes to select and perform a task that matches their requirements from a list of requests.

[0324] "Matching" is the process of comparing the request details created by the client with the qualifications of the workers to find the optimal combination.

[0325] "Tamper-proof format" refers to a state where evaluation results and data, once saved, are managed in a way that prevents unauthorized external modifications.

[0326] "Product distribution information" refers to data related to the inventory and sales of products in physical stores, and is used to support business operations.

[0327] "Automatic schedule generation" refers to the process by which artificial intelligence creates an actionable work plan based on product distribution information and client requests.

[0328] To realize this invention, a system needs to be built with the cooperation of a server, terminals, and users. Specifically, the server will be the central hub, managing everything from request creation and matching to evaluation and reward management.

[0329] The server uses a database management system (e.g., Firebase) to store request details, evaluation information, and reward information. It also utilizes artificial intelligence models (e.g., Google Cloud Natural Language API for natural language processing) to analyze the request details entered by the user through their device and extract any necessary additional information.

[0330] The terminal functions as a smartphone or similar device, providing an interface that allows users to directly interact with artificial intelligence. By entering request details through this interface, the information is sent to the server. Furthermore, in application examples such as assisting operations in physical stores, product distribution information can also be viewed on the terminal, allowing the AI ​​to generate a work schedule tailored to customer needs.

[0331] As a concrete example, consider its use in a physical store. When a customer requests to reserve an item in the store, the store staff member uses a terminal to input, "I'd like to reserve item XX for the weekend." Based on this input, artificial intelligence analyzes the inventory status and the customer's request, and the server generates an appropriate schedule and notifies the staff member.

[0332] An example of a prompt message would be, "Check the stock of jacket size M and notify the customer immediately of the result." This type of system streamlines operations in physical stores and enables smooth communication and matching between workers and clients.

[0333] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0334] Step 1:

[0335] The user enters their request on their device and begins interacting with the artificial intelligence.

[0336] Input: The user enters a request in natural language via a smartphone app, such as "I would like to reserve product XX for the weekend."

[0337] Processing: The terminal sends the entered text to the Google Cloud Natural Language API to parse the details of the request.

[0338] Output: Analyzed items (product name, quantity, date and time, etc.) are generated, and the user is prompted for additional information as needed.

[0339] Step 2:

[0340] The terminal sends the request details to the server and saves them.

[0341] Input: Analyzed request information obtained in Step 1.

[0342] Processing: The terminal converts the parsed request information into JSON format and sends it to the database. Firebase stores this information.

[0343] Output: The request details stored on the server become available for viewing.

[0344] Step 3:

[0345] The server generates a list of requests and makes it publicly available for workers to view.

[0346] Input: Saved request information.

[0347] Processing: The server extracts the latest requests from the database and updates the list in a format that workers can view on the web interface or application.

[0348] Output: The list of requests is displayed on the worker's terminal.

[0349] Step 4:

[0350] Workers check the list of requests on their terminals and apply for them.

[0351] Input: The details of the request viewed by the worker.

[0352] Processing: Workers select a suitable assignment and enter their application information. The terminal sends this information to the server.

[0353] Output: Application information is stored on the server.

[0354] Step 5:

[0355] The server processes the application information and performs matching.

[0356] Input: Application information and request details.

[0357] Processing: The server compares the applicant's qualifications with the job description and runs an algorithm to select the most suitable worker.

[0358] Output: The optimal client-worker pair is formed.

[0359] Step 6:

[0360] After the work is completed, the user evaluates the worker and saves the evaluation in an tamper-proof format.

[0361] Input: Evaluation information and work results.

[0362] Processing: Users enter their ratings from their terminals, and the server stores the rating data in an tamper-proof format using blockchain technology or similar methods.

[0363] Output: Reliable evaluation data is stored on the server and can be referenced in the future.

[0364] Step 7:

[0365] The terminal handles product distribution information, and AI automatically generates schedules for in-store operations.

[0366] Input: Product distribution information and customer needs.

[0367] Processing: The terminal retrieves inventory data, and the AI ​​generates an optimal work schedule based on customer requests and notifies relevant parties.

[0368] Output: Streamlined work plans are provided to and implemented by staff.

[0369] 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.

[0370] This invention aims to improve human-like interactions and matching accuracy by combining an emotion engine with a system that connects clients and workers. This system is realized by combining the functions of a server, terminal, and user.

[0371] The user (the requester) begins interacting with the artificial intelligence using a messaging app on their device. During this time, an emotion engine analyzes the user's emotional state in the background. For example, if the requester is feeling stressed, the emotion engine recognizes this, and the AI ​​can provide appropriate advice regarding the request. In other words, the AI ​​empathizes with the requester's emotions and supports them in fulfilling their request.

[0372] The terminal sends the confirmed request details to the server, which stores the details in a database. The requests are made public as a list for workers to view. Users (workers) check the publicly available list of requests on their terminal and apply for requests they believe they can perform. The emotion engine also operates during the application process, considering the worker's emotional state to facilitate optimal communication. Based on this information, the server matches requesters with workers. In this process, emotion data acts as a filter, resulting in emotionally compatible pairings.

[0373] Upon completion of the work, the user (client) provides an evaluation of the worker via their terminal. During this process, the emotion engine analyzes the client's emotions and generates feedback that reflects their feelings. The evaluation results are stored in a tamper-proof database on the server, ensuring reliability.

[0374] Furthermore, users (workers) organize their annual earnings and, with the support of the emotional engine when necessary, prepare their tax return documents. This enables more intuitive and efficient earnings management, reducing the workload.

[0375] This system enables smoother and more satisfying service delivery by matching emotionally compatible clients with service providers. For example, it can flexibly respond to human emotions, such as matching a nervous client with a service provider who has a relaxed demeanor.

[0376] The following describes the processing flow.

[0377] Step 1:

[0378] The user (the requester) launches a messaging app on their device and enters a summary of their request into the artificial intelligence. During this process, the emotion engine analyzes the user's emotions based on their voice and input.

[0379] Step 2:

[0380] The device adjusts its AI-powered dialogue based on the user's emotional data, asking questions that are sensitive to the user's feelings. The user then confirms the details of their request while receiving advice tailored to their emotions.

[0381] Step 3:

[0382] The terminal sends the completed request details to the server. The server saves the received data to a database and makes it publicly available in the request list for other users to view.

[0383] Step 4:

[0384] Users (workers) view a list of publicly available requests through their terminals. The emotion engine monitors the worker's emotional state and supports them in taking appropriate approaches to requests that interest them.

[0385] Step 5:

[0386] When a worker applies for a job, their device sends that information to the server. The server considers the conditions and sentiment data of both the client and the worker, and performs a matching process.

[0387] Step 6:

[0388] Once matching is complete, the server sends notifications to both the client and the worker, and the work begins.

[0389] Step 7:

[0390] Upon completion of the work, the user (client) evaluates the worker via their terminal. The emotion engine proposes evaluation comments that reflect the client's emotions and sends them to the server.

[0391] Step 8:

[0392] The server stores the evaluation results in a database in a tamper-proof format.

[0393] Step 9:

[0394] Users (workers) interact with artificial intelligence on a terminal to organize their annual compensation, receiving assistance from an emotion engine as needed, and create tax return documents. The terminal provides these documents in a downloadable format for the user.

[0395] (Example 2)

[0396] 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".

[0397] Modern online request and matching systems often fail to consider the emotional compatibility between clients and workers, resulting in poor communication quality, inefficient task completion, and low satisfaction. Furthermore, improvements in the reliability and efficiency of managing evaluation and compensation information are also needed.

[0398] 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.

[0399] In this invention, the server includes means for storing request information in an information recording device and presenting it as a list of requests, means for service providers to view the list of requests and apply to participate, and means for evaluating the emotional state of the client and service provider using an emotion analysis device and providing appropriate responses and associations. This enables highly accurate matching that takes into account the user's emotions, improving the satisfaction of both clients and workers and streamlining compensation management.

[0400] A "client" is someone who requires a service and generates request information through information exchange with an intelligent processing device.

[0401] An "intelligent processing device" is a technological device that exchanges information with clients and service providers, analyzes emotional states, and generates appropriate responses.

[0402] "Request information" refers to information, including the details of the service, generated by the client through an intelligent processing device.

[0403] "Information recording device" is a general term for databases and storage systems used to store request information and evaluation results.

[0404] A "request list" is a list of request information stored in an information recording device, presented in a format that can be viewed by service providers.

[0405] A "service provider" is someone who views the list of requests and applies to participate in a specific request.

[0406] An "emotion analysis device" is a system that analyzes the emotional state of both the client and the service provider, and uses that information to provide appropriate responses and establish relationships.

[0407] "Evaluation results" refer to the results of the client's evaluation of the service provider after the completion of the work, and are recorded in a way that makes them impossible to tamper with.

[0408] "Compensation information" refers to data that includes information about the compensation received by service providers, and is information that supports the creation of documents based on annual income.

[0409] A "user interface" is an interface used by a user to directly exchange information with an intelligent processing unit.

[0410] Modes for carrying out the invention

[0411] This invention is a system that achieves high-quality communication and matching in an online platform connecting clients and workers by combining an emotion engine and a generative AI model. This system functions using server, terminal, and user components.

[0412] The user (client) initiates interaction with the intelligent processing unit using a messaging app installed on their device. During this interaction, a generative AI model generates an appropriate response based on the prompt. For example, if the user input is "I want to design a new website, but I don't know how to proceed," the emotion engine analyzes this anxiety and generates a reassuring response. In this process, a natural language processing library using Python may be used for emotion analysis.

[0413] The terminal securely transmits the user's confirmed request information to the server. This data transfer is performed via the HTTPS protocol, ensuring security. The transmitted information is stored in an SQL database on the server. Based on this data, the server publishes a list of requests viewable by service providers via a web interface. This interface is built using HTML and CSS, and is designed with ease of use and visibility in mind.

[0414] Users (workers) view this list from their terminals and apply to participate in requests that interest them. The application information is also sent to the server, where the applicant's emotional state is evaluated via an emotion engine. This evaluation data is used to support emotional matching between the applicant and the requester.

[0415] The server matches clients with workers based on this data. The generative AI model suggests the optimal pairing based on past matching history and emotional data. This process achieves emotionally optimized matching, improving user satisfaction.

[0416] After the work is completed, the user (client) evaluates the worker using a terminal, and this evaluation is recorded on the server in an immutable format. This evaluation will also be used as data to improve the accuracy of future matching.

[0417] Users (workers) manage their annual earnings and prepare tax return documents via their devices, with assistance from an emotional engine as needed. This streamlines the earnings management process for service providers.

[0418] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0419] Step 1:

[0420] The user (client) initiates interaction with the intelligent processing unit using a messaging app on their device. The input consists of text data indicating the user's request and their emotional state at the time. This text is analyzed by a generative AI model to identify the user's requests and emotional state. Through this process, the emotion engine analyzes the user's anxieties and expectations and outputs the most appropriate advice accordingly.

[0421] Step 2:

[0422] The terminal sends the generated request information and analysis results to the server. The input data consists of the request details, sentiment analysis results, and user ID. This transmission is performed securely using HTTPS. On the server, this data is stored in an SQL database and becomes output data used for future processing.

[0423] Step 3:

[0424] The server uses the stored request data to generate a list of requests accessible to service providers. The input is a dataset of stored request information. The server uses HTML and CSS to build a web interface and output the list. This list is displayed clearly and easily accessible to service providers.

[0425] Step 4:

[0426] Users (workers) use a terminal to view a list of publicly available requests and apply for requests that interest them. As input, service providers provide their profile information and response content. The terminal sends this information to the server and generates output that records the application information in a database.

[0427] Step 5:

[0428] The server uses the submitted information to initiate the matching process between clients and service providers. Input data includes application information, past matching history, and sentiment data. A generative AI model is used to determine the optimal pairing, taking sentiment data into consideration, and the results are output.

[0429] Step 6:

[0430] After the work is completed, the user (client) uses a terminal to evaluate the service provider. The inputs here are an evaluation score and text feedback. The emotion engine analyzes the feedback content, and the server outputs and stores this information in a database in an immutable format.

[0431] Step 7:

[0432] Users (workers) use a terminal to manage their annual earnings and prepare tax returns with the support of an emotional engine as needed. Input data includes earnings information and emotional state. The emotional engine provides appropriate feedback to reduce procedural stress and outputs a generated earnings report.

[0433] (Application Example 2)

[0434] 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."

[0435] Traditional systems connecting clients and workers often fail to consider emotional compatibility during matching, leading to problems with client satisfaction and worker efficiency. Furthermore, in services based on human emotions, such as customer service, the lack of proper emotional analysis makes it difficult to provide optimal service.

[0436] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0437] In this invention, the server includes means for the client to interact with artificial intelligence and create a request, means for saving the request content in a database and publishing it as a list of requests, and means for analyzing the emotional state of the client and worker in real time using an emotion analysis engine and applying it to customer service and matching. This makes it possible to match emotionally compatible clients with workers, thereby improving the quality of service.

[0438] A "client" is an entity that needs a service and interacts with the system to provide it.

[0439] "Artificial intelligence" is a technology that uses computer systems or programs to mimic human intellectual behavior, performing tasks such as creating requests and analyzing emotions.

[0440] "Request details" refers to information created by the client through dialogue with artificial intelligence, outlining the content and conditions of the service.

[0441] A "database" is a collection of information organized electronically, used to store request details and evaluation results.

[0442] A "worker" is an entity that provides services by browsing a list of requests and applying for appropriate requests.

[0443] An "emotion analysis engine" is a technology that analyzes a person's emotional state in real time and adjusts the system's operation based on that information.

[0444] "Matching" is the process of optimally combining clients and service providers based on their emotional states and service requirements.

[0445] "Evaluation results" refer to the conclusion of the evaluation that the client gives to the worker after the work is completed, and it is important to save this evaluation in a way that makes it impossible to tamper with.

[0446] "Compensation information" refers to information about the payment received by a worker for the services they provide.

[0447] "Customer service" refers to the act of providing services through direct interaction between an employee and a client or customer.

[0448] This invention is a novel matching system that takes into account the emotional states of both the client and the worker. The system is designed so that the server, terminal, and user functions work together efficiently.

[0449] The server receives requests from clients and stores them in a database. This storage is done quickly and securely to enable the publication of a list of requests. The stored data can later be viewed by workers, allowing them to apply for suitable requests.

[0450] The terminal is a device that clients and workers access directly, and it features a user interface that enables interaction with artificial intelligence. This terminal analyzes emotional states in real time using an emotion analysis engine and provides responses and advice tailored to the client's needs.

[0451] Users take on the roles of requesters (creating requests) and workers (applying for jobs). Each user's emotional state is analyzed by an emotion analysis engine to enable optimal matching. This analysis uses technology that collects data using cameras and microphones via smart glasses or other devices, and classifies emotions in real time using software such as OpenCV and TensorFlow.

[0452] One concrete example is a supermarket where staff wear smart glasses to analyze customers' facial expressions and provide optimal customer service. For instance, they can improve customer satisfaction by using kind words for customers with blank expressions and providing relaxing service for nervous customers. An example of a prompt for the generating AI model would be: "During customer service at the supermarket, analyze the customer's emotional state in real time and suggest appropriate customer service methods. Emphasize a gentle smile and a friendly tone."

[0453] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0454] Step 1:

[0455] The user (client) initiates a dialogue with artificial intelligence using a terminal and creates a request. The input consists of the client's requirements and conditions, and the output is text data of the specific request. The terminal collects the input through the user interface, analyzes it using natural language processing, and structures it as the request.

[0456] Step 2:

[0457] The terminal sends the created request details to the server, where they are stored in the database. The input is the text data of the request details generated in step 1, and the output is the information securely stored in the database. The server receives the request details and stores them efficiently using an existing database management system.

[0458] Step 3:

[0459] Workers use a terminal to retrieve and view a list of requests from the database. The input is the request details stored in the database, and the output is the list of requests displayed on the worker's screen. The terminal queries the server and displays the relevant request information in list format.

[0460] Step 4:

[0461] On the terminal, the user analyzes their own emotional state through an emotion analysis engine. The input is the user's biometric data (facial expressions, voice, etc.), and the output is the analysis result regarding that emotional state. A camera and microphone on smart glasses or other devices collect the data, and analysis is performed using OpenCV or TensorFlow.

[0462] Step 5:

[0463] The server matches clients with workers based on their emotional states. The results from steps 3 and 4 are combined to achieve the optimal match. The input consists of the emotion analysis engine's output and the client's request; the output is information on the matched pair. A generative AI model analyzes the emotional data and determines the optimal pair based on the prompt text.

[0464] Step 6:

[0465] After the work is completed, the user (client) evaluates the worker via a terminal and sends the results to the server. The input is the client's evaluation, and the output is the evaluation result stored in a database in an immutable format. The user operates the interface on the terminal to input the evaluation, and the server saves that data.

[0466] 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.

[0467] 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.

[0468] 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.

[0469] [Third Embodiment]

[0470] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0471] 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.

[0472] 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).

[0473] 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.

[0474] 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.

[0475] 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).

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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".

[0482] This invention is a system that allows clients to efficiently create requests using artificial intelligence and then publish those requests to be matched with suitable workers. This system is implemented by combining the functions of a server, terminal, and user.

[0483] The user (the client) initiates a conversation with the artificial intelligence using a messaging app on their device. Specifically, the client inputs a summary of the task and their desired conditions in natural language, which the AI ​​then analyzes and asks for any necessary additional information. For example, in response to the input "I need help assembling furniture this weekend," the AI ​​might respond with instructions such as "Please set specific dates, times, and compensation." Through this conversation, the client can finalize the details of their request.

[0484] The terminal sends the completed request details to the server, which then saves them to a database. The requests are then made publicly available in a list format that workers can view. Users (workers) can check this list from their terminal and apply for requests that match their criteria. The server aggregates the application information and automatically matches the most suitable client with the most suitable worker.

[0485] Once the work is completed, the user (client) uses the terminal to evaluate the worker. This ensures that reliable evaluations are saved on the server, serving as reference information for other users. Furthermore, when organizing annual compensation information, the user (worker) uses the terminal to create appropriate documents with the support of artificial intelligence. This support automatically generates the necessary reports for tax filing.

[0486] At the heart of this system is an interactive dialogue function powered by artificial intelligence that communicates with users. This allows for centralized management of everything from request creation and matching to evaluation and reward management, promoting mutual assistance among individuals. Specific examples include requests for local shopping assistance or everyday help, and the improved reliability of evaluation data contributes to building trust within the community.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] The user (the requester) launches a messaging app on their device and inputs the details of their request into the artificial intelligence in natural language. The AI ​​analyzes this input and asks the user questions to supplement the necessary information.

[0490] Step 2:

[0491] The user answers questions from the artificial intelligence, clarifying the details of the request. Once the date, time, compensation, and work details are finalized through this interaction, the device sends this information to the server.

[0492] Step 3:

[0493] The server stores the received request information in a database and publishes it as a list of requests. The published list can be accessed from the terminals used by the workers.

[0494] Step 4:

[0495] Users (workers) view a list of available requests through their terminals and select a request that matches their desired conditions. When they apply for a selected request, that information is sent to the server.

[0496] Step 5:

[0497] The server performs appropriate matching based on the received application information. It considers the schedules and compensation conditions of both the client and the worker to determine the optimal combination.

[0498] Step 6:

[0499] Once the task is completed, the user (worker) sends a completion notification to the server using their terminal. The server then notifies the client that the task is complete.

[0500] Step 7:

[0501] The user (client) evaluates the worker based on the results of the work. This evaluation is sent from the terminal to the server, which records it in a database in an immutable state.

[0502] Step 8:

[0503] Users (workers) interact with artificial intelligence to organize their annual compensation information and create the necessary tax return documents. The generated documents can be downloaded via their device.

[0504] (Example 1)

[0505] 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."

[0506] The goal is to solve the challenges of manual detailed settings and cumbersome procedures that arise in the process of clients efficiently creating job requests and quickly matching them with suitable workers. Furthermore, it is necessary to manage reliable information effortlessly in post-request evaluations and compensation management. Additionally, it is required to reduce the effort workers put into quickly organizing their annual income and generating accurate reports.

[0507] 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.

[0508] In this invention, the server includes means for the client to interact with a generated AI model and automatically generate necessary information based on natural language input, means for transmitting the request content to an information management device for storage and public display in a list, and means for aggregating application information and performing optimal matching. This streamlines the process from request content creation to evaluation and reward management, enabling rapid and accurate communication and procedures.

[0509] A "generative AI model" is a machine learning algorithm or program that analyzes natural language input and generates appropriate answers or suggestions for the user.

[0510] An "information management device" is a computer system that receives, stores, and manages data such as requests and application details, and processes or provides that information as needed.

[0511] A "client" refers to an individual or organization that requests the execution of a specific task or operation, and is the initiator of the interaction in this system.

[0512] A "worker" refers to an individual or organization that wishes to accept and carry out a request from a client.

[0513] "Natural language input" refers to text or voice input performed in a language that the user uses on a daily basis (for example, Japanese or English).

[0514] "Optimal matching" is a process that automatically selects the worker who best matches the client's requirements and provides the most suitable combination for carrying out the work.

[0515] "Evaluation" refers to the feedback provided by the client after the completion of a task, regarding the worker's performance and responsiveness.

[0516] "Compensation information" refers to data relating to monetary compensation for workers, including information for calculating and paying such compensation.

[0517] The system in this invention is primarily implemented through the cooperation of the user, terminal, and server. First, the user (requester) interacts with the generative AI model using a messaging application on the terminal. The user inputs the request in natural language as text or voice. For example, they might use a prompt such as, "I need help assembling furniture this weekend."

[0518] The terminal sends the client's input to a generative AI model and begins analysis. The generative AI model analyzes the input based on natural language processing techniques and prompts the user for necessary information. This includes dialogue to identify the user's intentions and conditions. This process continues until the user provides additional information such as a specific date and time or reward.

[0519] The terminal sends the completed request details to the server. The server stores the request details using an information management system (e.g., a database management system) and then publishes the information in a list format. Workers can view the published list through their terminals. From here, workers can apply for requests that meet their criteria.

[0520] The server aggregates multiple application details and uses an automated matching algorithm to determine the optimal combination of client and worker. Once the work is completed, the user (client) evaluates the worker using their terminal, and this evaluation is stored on the server in an immutable format. Other users can also view this evaluation.

[0521] Furthermore, workers can manage their compensation information, and at the end of the fiscal year, they can streamline their tax filing process by automatically generating necessary documents with the support of a generation AI model. This entire process, from request to compensation management, is seamless, providing a highly convenient system for both clients and workers.

[0522] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0523] Step 1:

[0524] The user (requester) launches the messaging app on their device and begins interacting with the generative AI model. As input, they enter a summary of their request in natural language. Specifically, they enter and send a prompt message such as, "I need help assembling furniture this weekend." Based on this input, the device sends data to the generative AI model and prepares it for analysis.

[0525] Step 2:

[0526] The terminal analyzes the user input sent to the generative AI model. Here, it processes the input natural language and performs data analysis to identify any additional information needed. As output, the generative AI model generates natural language questions that ask the user for specific conditions required for the request (e.g., "specific date and time and reward"). The terminal displays these questions and asks the user for confirmation.

[0527] Step 3:

[0528] The user answers questions from the generative AI model via the terminal and inputs additional necessary information. This finalizes the detailed request. Based on the user's responses, the terminal performs another analysis using the generative AI model and formats the information. Based on the input information, it prepares to send the finalized request data as output to the server.

[0529] Step 4:

[0530] The terminal sends the finalized request data to the server. During this process, the data is properly encrypted and processed to ensure secure transmission. The server stores the received data in a database and generates output to store the request information. Furthermore, it prepares the data for publication as a list accessible to workers.

[0531] Step 5:

[0532] The server formats the request information stored in the database into a list and makes it public. Workers can then use their terminals to view this list. They select candidates from the list as input and apply for requests that meet the criteria. Each application is sent to the server, which aggregates the information and generates output that optimizes the match between clients and workers using a matching algorithm.

[0533] Step 6:

[0534] After the work is completed, the user (client) uses a terminal to evaluate the worker and sends the information to the server. The server generates output that saves the evaluation information to the database in an immutable format. This allows other users to view the evaluation.

[0535] Step 7:

[0536] Users (workers) can view their compensation information on a terminal and create documents for annual income management with the assistance of an AI model. The terminal automatically generates appropriate reports based on the entered compensation data and outputs them in a format that the user can download. This process streamlines the tax filing process for workers.

[0537] (Application Example 1)

[0538] 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."

[0539] In recent years, matching requests from individuals and brick-and-mortar stores with available workers has often been done manually, resulting in time-consuming and labor-intensive processes. Furthermore, the ambiguity of requests and the lack of mutual suitability frequently reduce the efficiency of the matching process. Additionally, brick-and-mortar stores require efficient operational support utilizing product distribution information, but current systems struggle to address this.

[0540] 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.

[0541] In this invention, the server includes means for the client to interact with artificial intelligence and create a request; means for saving the request content in a database and publishing it as a list of requests; and means for extracting information necessary for supporting the operations of physical stores from product distribution information and automatically generating a schedule tailored to customer needs using artificial intelligence. This enables efficient matching of requests with workers and streamlines in-store operations.

[0542] A "client" refers to an individual or organization that wishes to use artificial intelligence to efficiently create work descriptions and match them with workers.

[0543] "Artificial intelligence" refers to a computer system that uses natural language processing to analyze requests, extract information, and automatically generate schedules.

[0544] "Request details" refers to information including the details and conditions of the work entered by the client via artificial intelligence.

[0545] A "database" is an information system that stores and manages data such as request details, evaluation results, and compensation information.

[0546] A "worker" refers to an individual or group that wishes to select and perform a task that matches their requirements from a list of requests.

[0547] "Matching" is the process of comparing the request details created by the client with the qualifications of the workers to find the optimal combination.

[0548] "Tamper-proof format" refers to a state where evaluation results and data, once saved, are managed in a way that prevents unauthorized external modifications.

[0549] "Product distribution information" refers to data related to the inventory and sales of products in physical stores, and is used to support business operations.

[0550] "Automatic schedule generation" refers to the process by which artificial intelligence creates an actionable work plan based on product distribution information and client requests.

[0551] To realize this invention, a system needs to be built with the cooperation of a server, terminals, and users. Specifically, the server will be the central hub, managing everything from request creation and matching to evaluation and reward management.

[0552] The server uses a database management system (e.g., Firebase) to store request details, evaluation information, and reward information. It also utilizes artificial intelligence models (e.g., Google Cloud Natural Language API for natural language processing) to analyze the request details entered by the user through their device and extract any necessary additional information.

[0553] The terminal functions as a smartphone or similar device, providing an interface that allows users to directly interact with artificial intelligence. By entering request details through this interface, the information is sent to the server. Furthermore, in application examples such as assisting operations in physical stores, product distribution information can also be viewed on the terminal, allowing the AI ​​to generate a work schedule tailored to customer needs.

[0554] As a concrete example, consider its use in a physical store. When a customer requests to reserve an item in the store, the store staff member uses a terminal to input, "I'd like to reserve item XX for the weekend." Based on this input, artificial intelligence analyzes the inventory status and the customer's request, and the server generates an appropriate schedule and notifies the staff member.

[0555] An example of a prompt message would be, "Check the stock of jacket size M and notify the customer immediately of the result." This type of system streamlines operations in physical stores and enables smooth communication and matching between workers and clients.

[0556] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0557] Step 1:

[0558] The user enters their request on their device and begins interacting with the artificial intelligence.

[0559] Input: The user enters a request in natural language via a smartphone app, such as "I would like to reserve product XX for the weekend."

[0560] Processing: The terminal sends the entered text to the Google Cloud Natural Language API to parse the details of the request.

[0561] Output: Analyzed items (product name, quantity, date and time, etc.) are generated, and the user is prompted for additional information as needed.

[0562] Step 2:

[0563] The terminal sends the request details to the server and saves them.

[0564] Input: Analyzed request information obtained in Step 1.

[0565] Processing: The terminal converts the parsed request information into JSON format and sends it to the database. Firebase stores this information.

[0566] Output: The request details stored on the server become available for viewing.

[0567] Step 3:

[0568] The server generates a list of requests and makes it publicly available for workers to view.

[0569] Input: Saved request information.

[0570] Processing: The server extracts the latest requests from the database and updates the list in a format that workers can view on the web interface or application.

[0571] Output: The list of requests is displayed on the worker's terminal.

[0572] Step 4:

[0573] Workers check the list of requests on their terminals and apply for them.

[0574] Input: The details of the request viewed by the worker.

[0575] Processing: Workers select a suitable assignment and enter their application information. The terminal sends this information to the server.

[0576] Output: Application information is stored on the server.

[0577] Step 5:

[0578] The server processes the application information and performs matching.

[0579] Input: Application information and request details.

[0580] Processing: The server compares the applicant's qualifications with the job description and runs an algorithm to select the most suitable worker.

[0581] Output: The optimal client-worker pair is formed.

[0582] Step 6:

[0583] After the work is completed, the user evaluates the worker and saves the evaluation in an tamper-proof format.

[0584] Input: Evaluation information and work results.

[0585] Processing: Users enter their ratings from their terminals, and the server stores the rating data in an tamper-proof format using blockchain technology or similar methods.

[0586] Output: Reliable evaluation data is stored on the server and can be referenced in the future.

[0587] Step 7:

[0588] The terminal handles product distribution information, and AI automatically generates schedules for in-store operations.

[0589] Input: Product distribution information and customer needs.

[0590] Processing: The terminal retrieves inventory data, and the AI ​​generates an optimal work schedule based on customer requests and notifies relevant parties.

[0591] Output: Streamlined work plans are provided to and implemented by staff.

[0592] 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.

[0593] This invention aims to improve human-like interactions and matching accuracy by combining an emotion engine with a system that connects clients and workers. This system is realized by combining the functions of a server, terminal, and user.

[0594] The user (the requester) begins interacting with the artificial intelligence using a messaging app on their device. During this time, an emotion engine analyzes the user's emotional state in the background. For example, if the requester is feeling stressed, the emotion engine recognizes this, and the AI ​​can provide appropriate advice regarding the request. In other words, the AI ​​empathizes with the requester's emotions and supports them in fulfilling their request.

[0595] The terminal sends the confirmed request details to the server, which stores the details in a database. The requests are made public as a list for workers to view. Users (workers) check the publicly available list of requests on their terminal and apply for requests they believe they can perform. The emotion engine also operates during the application process, considering the worker's emotional state to facilitate optimal communication. Based on this information, the server matches requesters with workers. In this process, emotion data acts as a filter, resulting in emotionally compatible pairings.

[0596] Upon completion of the work, the user (client) provides an evaluation of the worker via their terminal. During this process, the emotion engine analyzes the client's emotions and generates feedback that reflects their feelings. The evaluation results are stored in a tamper-proof database on the server, ensuring reliability.

[0597] Furthermore, users (workers) organize their annual earnings and, with the support of the emotional engine when necessary, prepare their tax return documents. This enables more intuitive and efficient earnings management, reducing the workload.

[0598] This system enables smoother and more satisfying service delivery by matching emotionally compatible clients with service providers. For example, it can flexibly respond to human emotions, such as matching a nervous client with a service provider who has a relaxed demeanor.

[0599] The following describes the processing flow.

[0600] Step 1:

[0601] The user (the requester) launches a messaging app on their device and enters a summary of their request into the artificial intelligence. During this process, the emotion engine analyzes the user's emotions based on their voice and input.

[0602] Step 2:

[0603] The device adjusts its AI-powered dialogue based on the user's emotional data, asking questions that are sensitive to the user's feelings. The user then confirms the details of their request while receiving advice tailored to their emotions.

[0604] Step 3:

[0605] The terminal sends the completed request details to the server. The server saves the received data to a database and makes it publicly available in the request list for other users to view.

[0606] Step 4:

[0607] Users (workers) view a list of publicly available requests through their terminals. The emotion engine monitors the worker's emotional state and supports them in taking appropriate approaches to requests that interest them.

[0608] Step 5:

[0609] When a worker applies for a job, their device sends that information to the server. The server considers the conditions and sentiment data of both the client and the worker, and performs a matching process.

[0610] Step 6:

[0611] Once matching is complete, the server sends notifications to both the client and the worker, and the work begins.

[0612] Step 7:

[0613] Upon completion of the work, the user (client) evaluates the worker via their terminal. The emotion engine proposes evaluation comments that reflect the client's emotions and sends them to the server.

[0614] Step 8:

[0615] The server stores the evaluation results in a database in a tamper-proof format.

[0616] Step 9:

[0617] Users (workers) interact with artificial intelligence on a terminal to organize their annual compensation, receiving assistance from an emotion engine as needed, and create tax return documents. The terminal provides these documents in a downloadable format for the user.

[0618] (Example 2)

[0619] 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."

[0620] Modern online request and matching systems often fail to consider the emotional compatibility between clients and workers, resulting in poor communication quality, inefficient task completion, and low satisfaction. Furthermore, improvements in the reliability and efficiency of managing evaluation and compensation information are also needed.

[0621] 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.

[0622] In this invention, the server includes means for storing request information in an information recording device and presenting it as a list of requests, means for service providers to view the list of requests and apply to participate, and means for evaluating the emotional state of the client and service provider using an emotion analysis device and providing appropriate responses and associations. This enables highly accurate matching that takes into account the user's emotions, improving the satisfaction of both clients and workers and streamlining compensation management.

[0623] A "client" is someone who requires a service and generates request information through information exchange with an intelligent processing device.

[0624] An "intelligent processing device" is a technological device that exchanges information with clients and service providers, analyzes emotional states, and generates appropriate responses.

[0625] "Request information" refers to information, including the details of the service, generated by the client through an intelligent processing device.

[0626] "Information recording device" is a general term for databases and storage systems used to store request information and evaluation results.

[0627] A "request list" is a list of request information stored in an information recording device, presented in a format that can be viewed by service providers.

[0628] A "service provider" is someone who views the list of requests and applies to participate in a specific request.

[0629] An "emotion analysis device" is a system that analyzes the emotional state of both the client and the service provider, and uses that information to provide appropriate responses and establish relationships.

[0630] "Evaluation results" refer to the results of the client's evaluation of the service provider after the completion of the work, and are recorded in a way that makes them impossible to tamper with.

[0631] "Compensation information" refers to data that includes information about the compensation received by service providers, and is information that supports the creation of documents based on annual income.

[0632] A "user interface" is an interface used by a user to directly exchange information with an intelligent processing unit.

[0633] Modes for carrying out the invention

[0634] This invention is a system that achieves high-quality communication and matching in an online platform connecting clients and workers by combining an emotion engine and a generative AI model. This system functions using server, terminal, and user components.

[0635] The user (client) initiates interaction with the intelligent processing unit using a messaging app installed on their device. During this interaction, a generative AI model generates an appropriate response based on the prompt. For example, if the user input is "I want to design a new website, but I don't know how to proceed," the emotion engine analyzes this anxiety and generates a reassuring response. In this process, a natural language processing library using Python may be used for emotion analysis.

[0636] The terminal securely transmits the user's confirmed request information to the server. This data transfer is performed via the HTTPS protocol, ensuring security. The transmitted information is stored in an SQL database on the server. Based on this data, the server publishes a list of requests viewable by service providers via a web interface. This interface is built using HTML and CSS, and is designed with ease of use and visibility in mind.

[0637] Users (workers) view this list from their terminals and apply to participate in requests that interest them. The application information is also sent to the server, where the applicant's emotional state is evaluated via an emotion engine. This evaluation data is used to support emotional matching between the applicant and the requester.

[0638] The server matches clients with workers based on this data. The generative AI model suggests the optimal pairing based on past matching history and emotional data. This process achieves emotionally optimized matching, improving user satisfaction.

[0639] After the work is completed, the user (client) evaluates the worker using a terminal, and this evaluation is recorded on the server in an immutable format. This evaluation will also be used as data to improve the accuracy of future matching.

[0640] Users (workers) manage their annual earnings and prepare tax return documents via their devices, with assistance from an emotional engine as needed. This streamlines the earnings management process for service providers.

[0641] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0642] Step 1:

[0643] The user (client) initiates interaction with the intelligent processing unit using a messaging app on their device. The input consists of text data indicating the user's request and their emotional state at the time. This text is analyzed by a generative AI model to identify the user's requests and emotional state. Through this process, the emotion engine analyzes the user's anxieties and expectations and outputs the most appropriate advice accordingly.

[0644] Step 2:

[0645] The terminal sends the generated request information and analysis results to the server. The input data consists of the request details, sentiment analysis results, and user ID. This transmission is performed securely using HTTPS. On the server, this data is stored in an SQL database and becomes output data used for future processing.

[0646] Step 3:

[0647] The server uses the stored request data to generate a list of requests accessible to service providers. The input is a dataset of stored request information. The server uses HTML and CSS to build a web interface and output the list. This list is displayed clearly and easily accessible to service providers.

[0648] Step 4:

[0649] Users (workers) use a terminal to view a list of publicly available requests and apply for requests that interest them. As input, service providers provide their profile information and response content. The terminal sends this information to the server and generates output that records the application information in a database.

[0650] Step 5:

[0651] The server uses the submitted information to initiate the matching process between clients and service providers. Input data includes application information, past matching history, and sentiment data. A generative AI model is used to determine the optimal pairing, taking sentiment data into consideration, and the results are output.

[0652] Step 6:

[0653] After the work is completed, the user (client) uses a terminal to evaluate the service provider. The inputs here are an evaluation score and text feedback. The emotion engine analyzes the feedback content, and the server outputs and stores this information in a database in an immutable format.

[0654] Step 7:

[0655] Users (workers) use a terminal to manage their annual earnings and prepare tax returns with the support of an emotional engine as needed. Input data includes earnings information and emotional state. The emotional engine provides appropriate feedback to reduce procedural stress and outputs a generated earnings report.

[0656] (Application Example 2)

[0657] 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."

[0658] Traditional systems connecting clients and workers often fail to consider emotional compatibility during matching, leading to problems with client satisfaction and worker efficiency. Furthermore, in services based on human emotions, such as customer service, the lack of proper emotional analysis makes it difficult to provide optimal service.

[0659] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0660] In this invention, the server includes means for the client to interact with artificial intelligence and create a request, means for saving the request content in a database and publishing it as a list of requests, and means for analyzing the emotional state of the client and worker in real time using an emotion analysis engine and applying it to customer service and matching. This makes it possible to match emotionally compatible clients with workers, thereby improving the quality of service.

[0661] A "client" is an entity that needs a service and interacts with the system to provide it.

[0662] "Artificial intelligence" is a technology that uses computer systems or programs to mimic human intellectual behavior, performing tasks such as creating requests and analyzing emotions.

[0663] "Request details" refers to information created by the client through dialogue with artificial intelligence, outlining the content and conditions of the service.

[0664] A "database" is a collection of information organized electronically, used to store request details and evaluation results.

[0665] A "worker" is an entity that provides services by browsing a list of requests and applying for appropriate requests.

[0666] An "emotion analysis engine" is a technology that analyzes a person's emotional state in real time and adjusts the system's operation based on that information.

[0667] "Matching" is the process of optimally combining clients and service providers based on their emotional states and service requirements.

[0668] "Evaluation results" refer to the conclusion of the evaluation that the client gives to the worker after the work is completed, and it is important to save this evaluation in a way that makes it impossible to tamper with.

[0669] "Compensation information" refers to information about the payment received by a worker for the services they provide.

[0670] "Customer service" refers to the act of providing services through direct interaction between an employee and a client or customer.

[0671] This invention is a novel matching system that takes into account the emotional states of both the client and the worker. The system is designed so that the server, terminal, and user functions work together efficiently.

[0672] The server receives requests from clients and stores them in a database. This storage is done quickly and securely to enable the publication of a list of requests. The stored data can later be viewed by workers, allowing them to apply for suitable requests.

[0673] The terminal is a device that clients and workers access directly, and it features a user interface that enables interaction with artificial intelligence. This terminal analyzes emotional states in real time using an emotion analysis engine and provides responses and advice tailored to the client's needs.

[0674] Users take on the roles of requesters (creating requests) and workers (applying for jobs). Each user's emotional state is analyzed by an emotion analysis engine to enable optimal matching. This analysis uses technology that collects data using cameras and microphones via smart glasses or other devices, and classifies emotions in real time using software such as OpenCV and TensorFlow.

[0675] One concrete example is a supermarket where staff wear smart glasses to analyze customers' facial expressions and provide optimal customer service. For instance, they can improve customer satisfaction by using kind words for customers with blank expressions and providing relaxing service for nervous customers. An example of a prompt for the generating AI model would be: "During customer service at the supermarket, analyze the customer's emotional state in real time and suggest appropriate customer service methods. Emphasize a gentle smile and a friendly tone."

[0676] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0677] Step 1:

[0678] The user (client) initiates a dialogue with artificial intelligence using a terminal and creates a request. The input consists of the client's requirements and conditions, and the output is text data of the specific request. The terminal collects the input through the user interface, analyzes it using natural language processing, and structures it as the request.

[0679] Step 2:

[0680] The terminal sends the created request details to the server, where they are stored in the database. The input is the text data of the request details generated in step 1, and the output is the information securely stored in the database. The server receives the request details and stores them efficiently using an existing database management system.

[0681] Step 3:

[0682] Workers use a terminal to retrieve and view a list of requests from the database. The input is the request details stored in the database, and the output is the list of requests displayed on the worker's screen. The terminal queries the server and displays the relevant request information in list format.

[0683] Step 4:

[0684] On the terminal, the user analyzes their own emotional state through an emotion analysis engine. The input is the user's biometric data (facial expressions, voice, etc.), and the output is the analysis result regarding that emotional state. A camera and microphone on smart glasses or other devices collect the data, and analysis is performed using OpenCV or TensorFlow.

[0685] Step 5:

[0686] The server matches clients with workers based on their emotional states. The results from steps 3 and 4 are combined to achieve the optimal match. The input consists of the emotion analysis engine's output and the client's request; the output is information on the matched pair. A generative AI model analyzes the emotional data and determines the optimal pair based on the prompt text.

[0687] Step 6:

[0688] After the work is completed, the user (client) evaluates the worker via a terminal and sends the results to the server. The input is the client's evaluation, and the output is the evaluation result stored in a database in an immutable format. The user operates the interface on the terminal to input the evaluation, and the server saves that data.

[0689] 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.

[0690] 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.

[0691] 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.

[0692] [Fourth Embodiment]

[0693] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0694] 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.

[0695] 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).

[0696] 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.

[0697] 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.

[0698] 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).

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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".

[0706] This invention is a system that allows clients to efficiently create requests using artificial intelligence and then publish those requests to be matched with suitable workers. This system is implemented by combining the functions of a server, terminal, and user.

[0707] The user (the client) initiates a conversation with the artificial intelligence using a messaging app on their device. Specifically, the client inputs a summary of the task and their desired conditions in natural language, which the AI ​​then analyzes and asks for any necessary additional information. For example, in response to the input "I need help assembling furniture this weekend," the AI ​​might respond with instructions such as "Please set specific dates, times, and compensation." Through this conversation, the client can finalize the details of their request.

[0708] The terminal sends the completed request details to the server, which then saves them to a database. The requests are then made publicly available in a list format that workers can view. Users (workers) can check this list from their terminal and apply for requests that match their criteria. The server aggregates the application information and automatically matches the most suitable client with the most suitable worker.

[0709] Once the work is completed, the user (client) uses the terminal to evaluate the worker. This ensures that reliable evaluations are saved on the server, serving as reference information for other users. Furthermore, when organizing annual compensation information, the user (worker) uses the terminal to create appropriate documents with the support of artificial intelligence. This support automatically generates the necessary reports for tax filing.

[0710] At the heart of this system is an interactive dialogue function powered by artificial intelligence that communicates with users. This allows for centralized management of everything from request creation and matching to evaluation and reward management, promoting mutual assistance among individuals. Specific examples include requests for local shopping assistance or everyday help, and the improved reliability of evaluation data contributes to building trust within the community.

[0711] The following describes the processing flow.

[0712] Step 1:

[0713] The user (the requester) launches a messaging app on their device and inputs the details of their request into the artificial intelligence in natural language. The AI ​​analyzes this input and asks the user questions to supplement the necessary information.

[0714] Step 2:

[0715] The user answers questions from the artificial intelligence, clarifying the details of the request. Once the date, time, compensation, and work details are finalized through this interaction, the device sends this information to the server.

[0716] Step 3:

[0717] The server stores the received request information in a database and publishes it as a list of requests. The published list can be accessed from the terminals used by the workers.

[0718] Step 4:

[0719] Users (workers) view a list of available requests through their terminals and select a request that matches their desired conditions. When they apply for a selected request, that information is sent to the server.

[0720] Step 5:

[0721] The server performs appropriate matching based on the received application information. It considers the schedules and compensation conditions of both the client and the worker to determine the optimal combination.

[0722] Step 6:

[0723] Once the task is completed, the user (worker) sends a completion notification to the server using their terminal. The server then notifies the client that the task is complete.

[0724] Step 7:

[0725] The user (client) evaluates the worker based on the results of the work. This evaluation is sent from the terminal to the server, which records it in a database in an immutable state.

[0726] Step 8:

[0727] Users (workers) interact with artificial intelligence to organize their annual compensation information and create the necessary tax return documents. The generated documents can be downloaded via their device.

[0728] (Example 1)

[0729] 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".

[0730] The goal is to solve the challenges of manual detailed settings and cumbersome procedures that arise in the process of clients efficiently creating job requests and quickly matching them with suitable workers. Furthermore, it is necessary to manage reliable information effortlessly in post-request evaluations and compensation management. Additionally, it is required to reduce the effort workers put into quickly organizing their annual income and generating accurate reports.

[0731] 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.

[0732] In this invention, the server includes means for the client to interact with a generated AI model and automatically generate necessary information based on natural language input, means for transmitting the request content to an information management device for storage and public display in a list, and means for aggregating application information and performing optimal matching. This streamlines the process from request content creation to evaluation and reward management, enabling rapid and accurate communication and procedures.

[0733] A "generative AI model" is a machine learning algorithm or program that analyzes natural language input and generates appropriate answers or suggestions for the user.

[0734] An "information management device" is a computer system that receives, stores, and manages data such as requests and application details, and processes or provides that information as needed.

[0735] A "client" refers to an individual or organization that requests the execution of a specific task or operation, and is the initiator of the interaction in this system.

[0736] A "worker" refers to an individual or organization that wishes to accept and carry out a request from a client.

[0737] "Natural language input" refers to text or voice input performed in a language that the user uses on a daily basis (for example, Japanese or English).

[0738] "Optimal matching" is a process that automatically selects the worker who best matches the client's requirements and provides the most suitable combination for carrying out the work.

[0739] "Evaluation" refers to the feedback provided by the client after the completion of a task, regarding the worker's performance and responsiveness.

[0740] "Compensation information" refers to data relating to monetary compensation for workers, including information for calculating and paying such compensation.

[0741] The system in this invention is primarily implemented through the cooperation of the user, terminal, and server. First, the user (requester) interacts with the generative AI model using a messaging application on the terminal. The user inputs the request in natural language as text or voice. For example, they might use a prompt such as, "I need help assembling furniture this weekend."

[0742] The terminal sends the client's input to a generative AI model and begins analysis. The generative AI model analyzes the input based on natural language processing techniques and prompts the user for necessary information. This includes dialogue to identify the user's intentions and conditions. This process continues until the user provides additional information such as a specific date and time or reward.

[0743] The terminal sends the completed request details to the server. The server stores the request details using an information management system (e.g., a database management system) and then publishes the information in a list format. Workers can view the published list through their terminals. From here, workers can apply for requests that meet their criteria.

[0744] The server aggregates multiple application details and uses an automated matching algorithm to determine the optimal combination of client and worker. Once the work is completed, the user (client) evaluates the worker using their terminal, and this evaluation is stored on the server in an immutable format. Other users can also view this evaluation.

[0745] Furthermore, workers can manage their compensation information, and at the end of the fiscal year, they can streamline their tax filing process by automatically generating necessary documents with the support of a generation AI model. This entire process, from request to compensation management, is seamless, providing a highly convenient system for both clients and workers.

[0746] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0747] Step 1:

[0748] The user (requester) launches the messaging app on their device and begins interacting with the generative AI model. As input, they enter a summary of their request in natural language. Specifically, they enter and send a prompt message such as, "I need help assembling furniture this weekend." Based on this input, the device sends data to the generative AI model and prepares it for analysis.

[0749] Step 2:

[0750] The terminal analyzes the user input sent to the generative AI model. Here, it processes the input natural language and performs data analysis to identify any additional information needed. As output, the generative AI model generates natural language questions that ask the user for specific conditions required for the request (e.g., "specific date and time and reward"). The terminal displays these questions and asks the user for confirmation.

[0751] Step 3:

[0752] The user answers questions from the generative AI model via the terminal and inputs additional necessary information. This finalizes the detailed request. Based on the user's responses, the terminal performs another analysis using the generative AI model and formats the information. Based on the input information, it prepares to send the finalized request data as output to the server.

[0753] Step 4:

[0754] The terminal sends the finalized request data to the server. During this process, the data is properly encrypted and processed to ensure secure transmission. The server stores the received data in a database and generates output to store the request information. Furthermore, it prepares the data for publication as a list accessible to workers.

[0755] Step 5:

[0756] The server formats the request information stored in the database into a list and makes it public. Workers can then use their terminals to view this list. They select candidates from the list as input and apply for requests that meet the criteria. Each application is sent to the server, which aggregates the information and generates output that optimizes the match between clients and workers using a matching algorithm.

[0757] Step 6:

[0758] After the work is completed, the user (client) uses a terminal to evaluate the worker and sends the information to the server. The server generates output that saves the evaluation information to the database in an immutable format. This allows other users to view the evaluation.

[0759] Step 7:

[0760] Users (workers) can view their compensation information on a terminal and create documents for annual income management with the assistance of an AI model. The terminal automatically generates appropriate reports based on the entered compensation data and outputs them in a format that the user can download. This process streamlines the tax filing process for workers.

[0761] (Application Example 1)

[0762] 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".

[0763] In recent years, matching requests from individuals and brick-and-mortar stores with available workers has often been done manually, resulting in time-consuming and labor-intensive processes. Furthermore, the ambiguity of requests and the lack of mutual suitability frequently reduce the efficiency of the matching process. Additionally, brick-and-mortar stores require efficient operational support utilizing product distribution information, but current systems struggle to address this.

[0764] 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.

[0765] In this invention, the server includes means for the client to interact with artificial intelligence and create a request; means for saving the request content in a database and publishing it as a list of requests; and means for extracting information necessary for supporting the operations of physical stores from product distribution information and automatically generating a schedule tailored to customer needs using artificial intelligence. This enables efficient matching of requests with workers and streamlines in-store operations.

[0766] A "client" refers to an individual or organization that wishes to use artificial intelligence to efficiently create work descriptions and match them with workers.

[0767] "Artificial intelligence" refers to a computer system that uses natural language processing to analyze requests, extract information, and automatically generate schedules.

[0768] "Request details" refers to information including the details and conditions of the work entered by the client via artificial intelligence.

[0769] A "database" is an information system that stores and manages data such as request details, evaluation results, and compensation information.

[0770] A "worker" refers to an individual or group that wishes to select and perform a task that matches their requirements from a list of requests.

[0771] "Matching" is the process of comparing the request details created by the client with the qualifications of the workers to find the optimal combination.

[0772] "Tamper-proof format" refers to a state where evaluation results and data, once saved, are managed in a way that prevents unauthorized external modifications.

[0773] "Product distribution information" refers to data related to the inventory and sales of products in physical stores, and is used to support business operations.

[0774] "Automatic schedule generation" refers to the process by which artificial intelligence creates an actionable work plan based on product distribution information and client requests.

[0775] To realize this invention, a system needs to be built with the cooperation of a server, terminals, and users. Specifically, the server will be the central hub, managing everything from request creation and matching to evaluation and reward management.

[0776] The server uses a database management system (e.g., Firebase) to store request details, evaluation information, and reward information. It also utilizes artificial intelligence models (e.g., Google Cloud Natural Language API for natural language processing) to analyze the request details entered by the user through their device and extract any necessary additional information.

[0777] The terminal functions as a smartphone or similar device, providing an interface that allows users to directly interact with artificial intelligence. By entering request details through this interface, the information is sent to the server. Furthermore, in application examples such as assisting operations in physical stores, product distribution information can also be viewed on the terminal, allowing the AI ​​to generate a work schedule tailored to customer needs.

[0778] As a concrete example, consider its use in a physical store. When a customer requests to reserve an item in the store, the store staff member uses a terminal to input, "I'd like to reserve item XX for the weekend." Based on this input, artificial intelligence analyzes the inventory status and the customer's request, and the server generates an appropriate schedule and notifies the staff member.

[0779] An example of a prompt message would be, "Check the stock of jacket size M and notify the customer immediately of the result." This type of system streamlines operations in physical stores and enables smooth communication and matching between workers and clients.

[0780] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0781] Step 1:

[0782] The user enters their request on their device and begins interacting with the artificial intelligence.

[0783] Input: The user enters a request in natural language via a smartphone app, such as "I would like to reserve product XX for the weekend."

[0784] Processing: The terminal sends the entered text to the Google Cloud Natural Language API to parse the details of the request.

[0785] Output: Analyzed items (product name, quantity, date and time, etc.) are generated, and the user is prompted for additional information as needed.

[0786] Step 2:

[0787] The terminal sends the request details to the server and saves them.

[0788] Input: Analyzed request information obtained in Step 1.

[0789] Processing: The terminal converts the parsed request information into JSON format and sends it to the database. Firebase stores this information.

[0790] Output: The request details stored on the server become available for viewing.

[0791] Step 3:

[0792] The server generates a list of requests and makes it publicly available for workers to view.

[0793] Input: Saved request information.

[0794] Processing: The server extracts the latest requests from the database and updates the list in a format that workers can view on the web interface or application.

[0795] Output: The list of requests is displayed on the worker's terminal.

[0796] Step 4:

[0797] Workers check the list of requests on their terminals and apply for them.

[0798] Input: The details of the request viewed by the worker.

[0799] Processing: Workers select a suitable assignment and enter their application information. The terminal sends this information to the server.

[0800] Output: Application information is stored on the server.

[0801] Step 5:

[0802] The server processes the application information and performs matching.

[0803] Input: Application information and request details.

[0804] Processing: The server compares the applicant's qualifications with the job description and runs an algorithm to select the most suitable worker.

[0805] Output: The optimal client-worker pair is formed.

[0806] Step 6:

[0807] After the work is completed, the user evaluates the worker and saves the evaluation in an tamper-proof format.

[0808] Input: Evaluation information and work results.

[0809] Processing: Users enter their ratings from their terminals, and the server stores the rating data in an tamper-proof format using blockchain technology or similar methods.

[0810] Output: Reliable evaluation data is stored on the server and can be referenced in the future.

[0811] Step 7:

[0812] The terminal handles product distribution information, and AI automatically generates schedules for in-store operations.

[0813] Input: Product distribution information and customer needs.

[0814] Processing: The terminal retrieves inventory data, and the AI ​​generates an optimal work schedule based on customer requests and notifies relevant parties.

[0815] Output: Streamlined work plans are provided to and implemented by staff.

[0816] 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.

[0817] This invention aims to improve human-like interactions and matching accuracy by combining an emotion engine with a system that connects clients and workers. This system is realized by combining the functions of a server, terminal, and user.

[0818] The user (the requester) begins interacting with the artificial intelligence using a messaging app on their device. During this time, an emotion engine analyzes the user's emotional state in the background. For example, if the requester is feeling stressed, the emotion engine recognizes this, and the AI ​​can provide appropriate advice regarding the request. In other words, the AI ​​empathizes with the requester's emotions and supports them in fulfilling their request.

[0819] The terminal sends the confirmed request details to the server, which stores the details in a database. The requests are made public as a list for workers to view. Users (workers) check the publicly available list of requests on their terminal and apply for requests they believe they can perform. The emotion engine also operates during the application process, considering the worker's emotional state to facilitate optimal communication. Based on this information, the server matches requesters with workers. In this process, emotion data acts as a filter, resulting in emotionally compatible pairings.

[0820] Upon completion of the work, the user (client) provides an evaluation of the worker via their terminal. During this process, the emotion engine analyzes the client's emotions and generates feedback that reflects their feelings. The evaluation results are stored in a tamper-proof database on the server, ensuring reliability.

[0821] Furthermore, users (workers) organize their annual earnings and, with the support of the emotional engine when necessary, prepare their tax return documents. This enables more intuitive and efficient earnings management, reducing the workload.

[0822] This system enables smoother and more satisfying service delivery by matching emotionally compatible clients with service providers. For example, it can flexibly respond to human emotions, such as matching a nervous client with a service provider who has a relaxed demeanor.

[0823] The following describes the processing flow.

[0824] Step 1:

[0825] The user (the requester) launches a messaging app on their device and enters a summary of their request into the artificial intelligence. During this process, the emotion engine analyzes the user's emotions based on their voice and input.

[0826] Step 2:

[0827] The device adjusts its AI-powered dialogue based on the user's emotional data, asking questions that are sensitive to the user's feelings. The user then confirms the details of their request while receiving advice tailored to their emotions.

[0828] Step 3:

[0829] The terminal sends the completed request details to the server. The server saves the received data to a database and makes it publicly available in the request list for other users to view.

[0830] Step 4:

[0831] Users (workers) view a list of publicly available requests through their terminals. The emotion engine monitors the worker's emotional state and supports them in taking appropriate approaches to requests that interest them.

[0832] Step 5:

[0833] When a worker applies for a job, their device sends that information to the server. The server considers the conditions and sentiment data of both the client and the worker, and performs a matching process.

[0834] Step 6:

[0835] Once matching is complete, the server sends notifications to both the client and the worker, and the work begins.

[0836] Step 7:

[0837] Upon completion of the work, the user (client) evaluates the worker via their terminal. The emotion engine proposes evaluation comments that reflect the client's emotions and sends them to the server.

[0838] Step 8:

[0839] The server stores the evaluation results in a database in a tamper-proof format.

[0840] Step 9:

[0841] Users (workers) interact with artificial intelligence on a terminal to organize their annual compensation, receiving assistance from an emotion engine as needed, and create tax return documents. The terminal provides these documents in a downloadable format for the user.

[0842] (Example 2)

[0843] 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".

[0844] Modern online request and matching systems often fail to consider the emotional compatibility between clients and workers, resulting in poor communication quality, inefficient task completion, and low satisfaction. Furthermore, improvements in the reliability and efficiency of managing evaluation and compensation information are also needed.

[0845] 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.

[0846] In this invention, the server includes means for storing request information in an information recording device and presenting it as a list of requests, means for service providers to view the list of requests and apply to participate, and means for evaluating the emotional state of the client and service provider using an emotion analysis device and providing appropriate responses and associations. This enables highly accurate matching that takes into account the user's emotions, improving the satisfaction of both clients and workers and streamlining compensation management.

[0847] A "client" is someone who requires a service and generates request information through information exchange with an intelligent processing device.

[0848] An "intelligent processing device" is a technological device that exchanges information with clients and service providers, analyzes emotional states, and generates appropriate responses.

[0849] "Request information" refers to information, including the details of the service, generated by the client through an intelligent processing device.

[0850] "Information recording device" is a general term for databases and storage systems used to store request information and evaluation results.

[0851] A "request list" is a list of request information stored in an information recording device, presented in a format that can be viewed by service providers.

[0852] A "service provider" is someone who views the list of requests and applies to participate in a specific request.

[0853] An "emotion analysis device" is a system that analyzes the emotional state of both the client and the service provider, and uses that information to provide appropriate responses and establish relationships.

[0854] "Evaluation results" refer to the results of the client's evaluation of the service provider after the completion of the work, and are recorded in a way that makes them impossible to tamper with.

[0855] "Compensation information" refers to data that includes information about the compensation received by service providers, and is information that supports the creation of documents based on annual income.

[0856] A "user interface" is an interface used by a user to directly exchange information with an intelligent processing unit.

[0857] Modes for carrying out the invention

[0858] This invention is a system that achieves high-quality communication and matching in an online platform connecting clients and workers by combining an emotion engine and a generative AI model. This system functions using server, terminal, and user components.

[0859] The user (client) initiates interaction with the intelligent processing unit using a messaging app installed on their device. During this interaction, a generative AI model generates an appropriate response based on the prompt. For example, if the user input is "I want to design a new website, but I don't know how to proceed," the emotion engine analyzes this anxiety and generates a reassuring response. In this process, a natural language processing library using Python may be used for emotion analysis.

[0860] The terminal securely transmits the user's confirmed request information to the server. This data transfer is performed via the HTTPS protocol, ensuring security. The transmitted information is stored in an SQL database on the server. Based on this data, the server publishes a list of requests viewable by service providers via a web interface. This interface is built using HTML and CSS, and is designed with ease of use and visibility in mind.

[0861] Users (workers) view this list from their terminals and apply to participate in requests that interest them. The application information is also sent to the server, where the applicant's emotional state is evaluated via an emotion engine. This evaluation data is used to support emotional matching between the applicant and the requester.

[0862] The server matches clients with workers based on this data. The generative AI model suggests the optimal pairing based on past matching history and emotional data. This process achieves emotionally optimized matching, improving user satisfaction.

[0863] After the work is completed, the user (client) evaluates the worker using a terminal, and this evaluation is recorded on the server in an immutable format. This evaluation will also be used as data to improve the accuracy of future matching.

[0864] Users (workers) manage their annual earnings and prepare tax return documents via their devices, with assistance from an emotional engine as needed. This streamlines the earnings management process for service providers.

[0865] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0866] Step 1:

[0867] The user (client) initiates interaction with the intelligent processing unit using a messaging app on their device. The input consists of text data indicating the user's request and their emotional state at the time. This text is analyzed by a generative AI model to identify the user's requests and emotional state. Through this process, the emotion engine analyzes the user's anxieties and expectations and outputs the most appropriate advice accordingly.

[0868] Step 2:

[0869] The terminal sends the generated request information and analysis results to the server. The input data consists of the request details, sentiment analysis results, and user ID. This transmission is performed securely using HTTPS. On the server, this data is stored in an SQL database and becomes output data used for future processing.

[0870] Step 3:

[0871] The server uses the stored request data to generate a list of requests accessible to service providers. The input is a dataset of stored request information. The server uses HTML and CSS to build a web interface and output the list. This list is displayed clearly and easily accessible to service providers.

[0872] Step 4:

[0873] Users (workers) use a terminal to view a list of publicly available requests and apply for requests that interest them. As input, service providers provide their profile information and response content. The terminal sends this information to the server and generates output that records the application information in a database.

[0874] Step 5:

[0875] The server uses the submitted information to initiate the matching process between clients and service providers. Input data includes application information, past matching history, and sentiment data. A generative AI model is used to determine the optimal pairing, taking sentiment data into consideration, and the results are output.

[0876] Step 6:

[0877] After the work is completed, the user (client) uses a terminal to evaluate the service provider. The inputs here are an evaluation score and text feedback. The emotion engine analyzes the feedback content, and the server outputs and stores this information in a database in an immutable format.

[0878] Step 7:

[0879] Users (workers) use a terminal to manage their annual earnings and prepare tax returns with the support of an emotional engine as needed. Input data includes earnings information and emotional state. The emotional engine provides appropriate feedback to reduce procedural stress and outputs a generated earnings report.

[0880] (Application Example 2)

[0881] 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".

[0882] Traditional systems connecting clients and workers often fail to consider emotional compatibility during matching, leading to problems with client satisfaction and worker efficiency. Furthermore, in services based on human emotions, such as customer service, the lack of proper emotional analysis makes it difficult to provide optimal service.

[0883] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0884] In this invention, the server includes means for the client to interact with artificial intelligence and create a request, means for saving the request content in a database and publishing it as a list of requests, and means for analyzing the emotional state of the client and worker in real time using an emotion analysis engine and applying it to customer service and matching. This makes it possible to match emotionally compatible clients with workers, thereby improving the quality of service.

[0885] A "client" is an entity that needs a service and interacts with the system to provide it.

[0886] "Artificial intelligence" is a technology that uses computer systems or programs to mimic human intellectual behavior, performing tasks such as creating requests and analyzing emotions.

[0887] "Request details" refers to information created by the client through dialogue with artificial intelligence, outlining the content and conditions of the service.

[0888] A "database" is a collection of information organized electronically, used to store request details and evaluation results.

[0889] A "worker" is an entity that provides services by browsing a list of requests and applying for appropriate requests.

[0890] An "emotion analysis engine" is a technology that analyzes a person's emotional state in real time and adjusts the system's operation based on that information.

[0891] "Matching" is the process of optimally combining clients and service providers based on their emotional states and service requirements.

[0892] "Evaluation results" refer to the conclusion of the evaluation that the client gives to the worker after the work is completed, and it is important to save this evaluation in a way that makes it impossible to tamper with.

[0893] "Compensation information" refers to information about the payment received by a worker for the services they provide.

[0894] "Customer service" refers to the act of providing services through direct interaction between an employee and a client or customer.

[0895] This invention is a novel matching system that takes into account the emotional states of both the client and the worker. The system is designed so that the server, terminal, and user functions work together efficiently.

[0896] The server receives requests from clients and stores them in a database. This storage is done quickly and securely to enable the publication of a list of requests. The stored data can later be viewed by workers, allowing them to apply for suitable requests.

[0897] The terminal is a device that clients and workers access directly, and it features a user interface that enables interaction with artificial intelligence. This terminal analyzes emotional states in real time using an emotion analysis engine and provides responses and advice tailored to the client's needs.

[0898] Users take on the roles of requesters (creating requests) and workers (applying for jobs). Each user's emotional state is analyzed by an emotion analysis engine to enable optimal matching. This analysis uses technology that collects data using cameras and microphones via smart glasses or other devices, and classifies emotions in real time using software such as OpenCV and TensorFlow.

[0899] One concrete example is a supermarket where staff wear smart glasses to analyze customers' facial expressions and provide optimal customer service. For instance, they can improve customer satisfaction by using kind words for customers with blank expressions and providing relaxing service for nervous customers. An example of a prompt for the generating AI model would be: "During customer service at the supermarket, analyze the customer's emotional state in real time and suggest appropriate customer service methods. Emphasize a gentle smile and a friendly tone."

[0900] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0901] Step 1:

[0902] The user (client) initiates a dialogue with artificial intelligence using a terminal and creates a request. The input consists of the client's requirements and conditions, and the output is text data of the specific request. The terminal collects the input through the user interface, analyzes it using natural language processing, and structures it as the request.

[0903] Step 2:

[0904] The terminal sends the created request details to the server, where they are stored in the database. The input is the text data of the request details generated in step 1, and the output is the information securely stored in the database. The server receives the request details and stores them efficiently using an existing database management system.

[0905] Step 3:

[0906] Workers use a terminal to retrieve and view a list of requests from the database. The input is the request details stored in the database, and the output is the list of requests displayed on the worker's screen. The terminal queries the server and displays the relevant request information in list format.

[0907] Step 4:

[0908] On the terminal, the user analyzes their own emotional state through an emotion analysis engine. The input is the user's biometric data (facial expressions, voice, etc.), and the output is the analysis result regarding that emotional state. A camera and microphone on smart glasses or other devices collect the data, and analysis is performed using OpenCV or TensorFlow.

[0909] Step 5:

[0910] The server matches clients with workers based on their emotional states. The results from steps 3 and 4 are combined to achieve the optimal match. The input consists of the emotion analysis engine's output and the client's request; the output is information on the matched pair. A generative AI model analyzes the emotional data and determines the optimal pair based on the prompt text.

[0911] Step 6:

[0912] After the work is completed, the user (client) evaluates the worker via a terminal and sends the results to the server. The input is the client's evaluation, and the output is the evaluation result stored in a database in an immutable format. The user operates the interface on the terminal to input the evaluation, and the server saves that data.

[0913] 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.

[0914] 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.

[0915] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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."

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0933] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0934] The following is further disclosed regarding the embodiments described above.

[0935] (Claim 1)

[0936] A means for the client to interact with artificial intelligence and create the details of the request,

[0937] A means of saving the aforementioned request details in a database and making them public as a list of requests,

[0938] A means for workers to view the aforementioned list of requests and apply for those requests,

[0939] A means for processing the aforementioned application information and matching clients with workers,

[0940] A means for the client to evaluate the worker after the work is completed and to save the evaluation results in an unalterable format,

[0941] A means for managing compensation information and enabling workers to create documents based on their annual income,

[0942] A system that includes this.

[0943] (Claim 2)

[0944] The system according to claim 1, which processes reward information and performs rapid online payment processing.

[0945] (Claim 3)

[0946] The system according to claim 1, comprising a user interface that enables direct interaction with artificial intelligence on a terminal used by a worker or client.

[0947] "Example 1"

[0948] (Claim 1)

[0949] A means by which the client interacts with a generative AI model, creates a request based on natural language input, and automatically asks for necessary information.

[0950] A means of transmitting the aforementioned request details to an information management device for storage and making them public as an information list,

[0951] A means by which workers refer to the aforementioned list of information and apply for information that meets their criteria,

[0952] A means of aggregating the aforementioned application information and optimally matching clients with workers,

[0953] A means by which the client evaluates the worker after the work is completed, saves the evaluation results in an immutable format, and makes them accessible to other users,

[0954] A means of managing compensation information and generating documents based on workers' annual income using an AI model,

[0955] A system that includes this.

[0956] (Claim 2)

[0957] The system according to claim 1, which processes reward information and performs rapid online payment processing.

[0958] (Claim 3)

[0959] The system according to claim 1, which provides a user interface in an information input device that allows interaction with a generated AI model, thereby enabling a client or worker to engage in direct dialogue in natural language.

[0960] "Application Example 1"

[0961] (Claim 1)

[0962] A means for the client to interact with artificial intelligence and create the details of the request,

[0963] A means of saving the aforementioned request details in a database and making them public as a list of requests,

[0964] A means for workers to view the aforementioned list of requests and apply for those requests,

[0965] A means for processing the aforementioned application information and matching clients with workers,

[0966] A means for the client to evaluate the worker after the work is completed and to save the evaluation results in an unalterable format,

[0967] A means for managing compensation information and enabling workers to create documents based on their annual income,

[0968] A method for extracting information necessary for supporting operations at physical stores from product distribution data and automatically generating schedules tailored to customer needs using artificial intelligence,

[0969] A system that includes this.

[0970] (Claim 2)

[0971] The system according to claim 1, which processes reward information and performs rapid electronic payment processing.

[0972] (Claim 3)

[0973] The system according to claim 1, comprising a user interface that enables direct interaction with artificial intelligence on a terminal used by a worker or client.

[0974] "Example 2 of combining an emotion engine"

[0975] (Claim 1)

[0976] A means by which the client exchanges information with an intelligent processing unit to generate request information,

[0977] A means for storing the aforementioned request information in an information recording device and presenting it as a list of requests,

[0978] A means for service providers to view the aforementioned list of requests and apply to participate in the requests,

[0979] A means of analyzing the aforementioned participation information and linking the client with the service provider,

[0980] A means for the client to evaluate the service provider after the work is completed and to record the evaluation results in a way that makes them impossible to tamper with,

[0981] A means of organizing compensation information and enabling service providers to create documents based on their annual income,

[0982] A means for evaluating the emotional state of clients and service providers using an emotion analysis device, and providing appropriate responses and associations,

[0983] A system that includes this.

[0984] (Claim 2)

[0985] The system according to claim 1, which processes reward information and performs rapid digital payment processing.

[0986] (Claim 3)

[0987] The system according to claim 1, comprising a user interface that enables direct information exchange with an intelligent processing device on a terminal used by the user.

[0988] "Application example 2 when combining with an emotional engine"

[0989] (Claim 1)

[0990] A means for the client to interact with artificial intelligence and create the details of the request,

[0991] A means of saving the aforementioned request details in a database and making them public as a list of requests,

[0992] A means for workers to view the aforementioned list of requests and apply for those requests,

[0993] A means for processing the aforementioned application information and matching clients with workers,

[0994] A means for the client to evaluate the worker after the work is completed and to save the evaluation results in an unalterable format,

[0995] A means for managing compensation information and enabling workers to create documents based on their annual income,

[0996] A method for analyzing the emotional state of clients and workers in real time using an emotion analysis engine and applying it to customer service and matching,

[0997] A system that includes this.

[0998] (Claim 2)

[0999] The system according to claim 1, which processes reward information and performs rapid online payment processing.

[1000] (Claim 3)

[1001] The system according to claim 1, comprising a user interface that enables direct interaction with artificial intelligence on a terminal used by a worker or client. [Explanation of Symbols]

[1002] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for the client to interact with artificial intelligence and create the details of the request, A means of saving the aforementioned request details in a database and making them public as a list of requests, A means for workers to view the aforementioned list of requests and apply for those requests, A means for processing the aforementioned application information and matching clients with workers, A means for the client to evaluate the worker after the work is completed and to save the evaluation results in an unalterable format, A means for managing compensation information and enabling workers to create documents based on their annual income, A system that includes this.

2. The system according to claim 1, which processes reward information and performs rapid online payment processing.

3. The system according to claim 1, comprising a user interface that enables direct interaction with artificial intelligence on a terminal used by a worker or client.

Images