system

Abstract

Provide a system. A means for inputting business information from a user terminal; A means of analyzing input business information and searching for optimal solutions and similar cases using a generative AI model; means for generating search results and presenting them to a user terminal; a means for the user to implement the proposed solution and report the results; A means to evaluate the reported results and determine the effectiveness of the efficiencies achieved; A means to register the effects of efficiency improvements in a database and consider whether they can be expanded to other departments. A means for notifying the user of a reward if the effect is recognized; A system including:

Description

[Technical Field]

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

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

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

[0004] In many traditional companies, improving work efficiency is recognized as an important issue, but there is a problem that employees who are busy with their daily work find it difficult to find the time necessary to improve work efficiency. Another issue is that there is a lack of information sharing with other departments, and past success stories are not effectively utilized. Furthermore, there is also the problem that it is difficult to gain approval from superiors because the effectiveness of proposals cannot be demonstrated numerically. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means: a means for inputting business information from a user terminal, a means for analyzing the input business information and using a generative AI model to search for optimal solutions or similar cases, and a means for generating search results and presenting them on the user terminal. The system also includes a means for the user to implement the presented solutions and report the results, a means for evaluating the reported results and determining the effectiveness of the efficiency gains, a means for registering the results in a database and considering whether they can be expanded to other departments, and a means for notifying the user of a reward if the effectiveness is recognized.

[0006] Furthermore, the system includes a means for automatically sending suggestions to other departments based on the evaluated efficiency effects, and a means for awarding a specific title to a user when the efficiency results reported by the user meet certain standards. This allows for rapid and effective efficiency improvements within the company and reduces the burden on employees.

[0007] A "user terminal" is an electronic device that a user uses to input business information.

[0008] "Business information" is detailed data relating to a specific business that a user wishes to improve the efficiency of.

[0009] A "generative AI model" is an artificial intelligence model that analyzes input data and proposes optimal solutions or similar cases.

[0010] A "solution" is a specific means or method that a user implements to improve business efficiency.

[0011] "Similar cases" refer to efficiency improvement efforts undertaken by other departments or users in the past that can be applied to the current case.

[0012] A "database" is a collection of information that accumulates data on success stories and efficiency initiatives within the company.

[0013] "Evaluation" is the process of analyzing the results of the solutions implemented by the user and objectively judging their effectiveness.

[0014] "Rewards" are rewards or incentives given when a user's efficiency efforts are successful.

[0015] A "title" is an honorable title given to a user when their efficiency improvement efforts meet certain standards.

[0016] "Automatically sending" refers to the system automatically sending a proposal to another department when certain conditions are met.

[0017] "Registration" refers to storing data on proven effective initiatives in a database, making them available for future reference and application. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0026] [First embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0039] The present invention is a matching system based on a generative AI model to support the efficiency of internal business operations. This system comprehensively supports everything from inputting business information to presenting efficiency proposals, evaluating and reporting after implementation, and even expanding to other departments. Detailed embodiments of this system are described below.

[0040] Basic system configuration

[0041] The system consists of the following main components:

[0042] 1. User Device

[0043] 2. Server

[0044] 3. Database

[0045] User terminal

[0046] The user terminal provides an interface for users to input business information, check proposed solutions, and submit implementation reports. Users input information about their own business and transmit their requests for efficiency improvement to the system through the terminal.

[0047] server

[0048] The server is the core of the system and is responsible for the following main functions:

[0049] Input analysis: Analyzes business information received from the user's device and extracts important keywords and context.

[0050] Search and suggestion: Using a generative AI model, the system searches a database for similar past cases and optimal solutions, and generates information to suggest to the user.

[0051] Evaluation and registration: Analyze the execution results reported by users, evaluate their effectiveness, and register them in a database.

[0052] Notification: If the effect is recognized, a reward notification will be sent to the user.

[0053] Database

[0054] The database is a collection of information that stores past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[0055] Explanation of program processing

[0056] User input of business information

[0057] Users input detailed information about the tasks they wish to streamline into the terminal, which then organizes this information and sends it to the server in the required format.

[0058] Analysis and search of input information

[0059] The server analyzes the business information received from the device, using natural language processing (NLP) technology to extract specific keywords and phrases. Based on this, a generative AI model searches for optimal solutions and similar cases.

[0060] Generating and presenting search results

[0061] The server organizes the solutions and similar cases extracted from the database and generates a proposal document to be presented to the user's device. The proposal document includes an overview of the initiative, its effects, implementation procedures, etc. The user's device receives this and displays it to the user.

[0062] Implementing proposed solutions and reporting results

[0063] The user implements the proposed solution and reports the results to the server from their device. The report includes specific implementation steps and numerical data showing the effectiveness.

[0064] Evaluating the results and expanding to other departments

[0065] The server evaluates the reported results and determines their effectiveness. If they are proven effective, they are registered in the database and can also automatically send suggestions to other departments.

[0066] Notification and receipt of rewards

[0067] If an initiative is deemed effective, the server will notify the user of a reward. The user can then check the notification on their device and follow the procedure to receive the reward.

[0068] Specific examples

[0069] As an example, let's say a user (Mr. A) wants to "improve the efficiency of regular meetings." Mr. A inputs the problem of "it takes a long time to prepare for meetings and create minutes" from his terminal. The server analyzes this information, searches and extracts similar past cases and suggests "using an automatic minutes generation tool." Mr. A introduces the proposed tool and reports that the time it takes to create minutes has been reduced by 60%, and the server evaluates its effectiveness. If similar effects are confirmed in other departments, the server will notify Mr. A of a reward.

[0070] The above system will efficiently and effectively promote the streamlining of internal operations and contribute to improving productivity throughout the company.

[0071] The processing flow will be explained below.

[0072] Step 1:

[0073] The user inputs detailed information about their work, such as "I would like to make regular meetings more efficient" or "It takes a lot of time to prepare for meetings and create minutes."

[0074] Step 2:

[0075] The terminal organizes and formats the business information entered by the user and sends it to the server, where data consistency and format checks are performed.

[0076] Step 3:

[0077] The server analyzes the received business information and uses natural language processing (NLP) technology to extract important keywords and phrases and understand the business content.

[0078] Step 4:

[0079] Based on the analyzed data, the server uses a generative AI model to search the database for optimal solutions and similar cases, such as an "automatic meeting minutes generation tool."

[0080] Step 5:

[0081] The server organizes the search results and generates a proposal document that includes an overview of the initiative, specific steps, and expected results.

[0082] Step 6:

[0083] The server sends the generated proposal document to the terminal, encrypting the data and checking the transfer protocol.

[0084] Step 7:

[0085] The terminal displays the received proposal document to the user, who then checks the contents of the proposal and decides whether or not to implement it.

[0086] Step 8:

[0087] The user implements the proposed solution. For example, they introduce an "automatic minutes generation tool" and start using it.

[0088] Step 9:

[0089] The user reports the results after implementing the solution, sending detailed data about the specific effects and implementation steps from the device to the server.

[0090] Step 10:

[0091] The server analyzes the reported results and evaluates their effectiveness, including data reconciliation and statistical analysis to measure effectiveness.

[0092] Step 11:

[0093] The server registers the evaluation results in a database, and effective initiatives are recorded as candidates for expansion to other departments.

[0094] Step 12:

[0095] The server sends a reward notification to the user for the efforts that are recognized as effective. This notification includes the content of the reward and instructions on how to receive it.

[0096] Step 13:

[0097] The terminal displays a reward notification to the user, and the user follows the instructions to complete the procedure for receiving the reward.

[0098] This concludes the explanation of the specific processing steps. This flow will effectively and quickly improve business efficiency.

[0099] Example 1

[0100] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0101] Conventional business efficiency systems were unable to accurately analyze users' business information and were unable to quickly and effectively provide optimal solutions or similar cases. It was also difficult to accurately evaluate the results of the proposed solutions, deploy them to other departments, or efficiently reward users. As a result, business efficiency was not fully achieved, and productivity did not improve across the entire company.

[0102] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0103] In this invention, the server includes a means for inputting business information from a user terminal, a means for analyzing the input business information and searching for optimal solutions or similar cases using a generative AI model, and a means for generating search results and presenting them to the user terminal. Thus, when a user inputs business information, the information is analyzed using natural language processing technology, and specific keywords and phrases are extracted. Furthermore, the generative AI model is used to search for optimal solutions or similar cases, and a proposal document is generated, enabling the user to quickly and effectively receive proposals for improving business efficiency.

[0104] In this invention, the server further includes means for the user to implement the proposed solution and report the results, means for evaluating the reported results and determining the effectiveness of the efficiency improvement, means for analyzing input information using natural language processing technology and extracting specific keywords and phrases, means for evaluating the results of the efficiency improvement and automatically sending a proposal to other departments, and means for receiving a report of the quantitative effects and implementation procedures from the user terminal. This makes it possible to accurately evaluate the results of the efficiency improvement measures implemented by the user, effectively deploy them to other departments, and properly reward the user.

[0105] A "user terminal" is a device that processes business information input by a user and displays generated proposal results.

[0106] "Business information" refers to data in which a user inputs detailed information about a business, and includes important information that is useful for improving business efficiency.

[0107] A "generative AI model" is an artificial intelligence model that generates optimal solutions and similar cases based on past data and cases.

[0108] "Natural language processing technology" is a technology that analyzes sentences and document data and extracts specific keywords and phrases.

[0109] A "database" is a collection of information that stores past efficiency improvement cases and evaluation data, and serves as the basis for search and evaluation.

[0110] A "proposal document" is a document containing a solution and execution procedures generated by the server, and is displayed on the user's terminal.

[0111] "Efficiency improvement effect" refers to the results and degree of improvement in operations obtained by implementing the proposed solution.

[0112] A "reward notification" is a notification of a reward sent to a user for an efficiency improvement initiative that has been recognized as effective.

[0113] "Deploying to other departments" means sharing information and making proposals to apply efficiency initiatives that have been proven in one department to other departments.

[0114] "Evaluation" refers to the process of analyzing the execution results reported by users and determining their effectiveness.

[0115] "Reporting" is an action taken by a user to transmit the results of a proposed solution to the server after the user has implemented the solution.

[0116] MODE FOR CARRYING OUT THE INVENTION

[0117] This invention is a system that supports the efficiency of internal business operations based on a generative AI model. The system allows users to input business information, search for and present optimal solutions and similar cases, and comprehensively supports post-implementation evaluation and reporting, as well as deployment to other departments.

[0118] User terminal

[0119] The user terminal provides an interface for users to input business information, review proposed solutions, and report implementation results. Users use this terminal to input detailed information about their business. For example, they can input specific efficiency requests, such as "improving the efficiency of regular meetings," using a browser or dedicated application. This information is organized by the terminal and sent to the server.

[0120] server

[0121] The server is the core of this system and is configured to achieve the following main functions:

[0122] Input analysis: The server analyzes the business information received from the user's device. This analysis uses natural language processing (NLP) technology. For example, specific keywords and phrases such as "meeting preparation" and "minutes creation" are extracted.

[0123] Search and suggest: Using a generative AI model, the system searches a database for optimal solutions or similar cases based on past data and examples. For example, it may search for solutions such as "using an automatic meeting minutes generation tool."

[0124] Proposal generation and presentation: The server generates a proposal document based on the search results. This proposal document includes an overview of the initiative, expected benefits, implementation procedures, etc. This document is sent to the user's terminal and displayed to the user.

[0125] Evaluation and registration: The execution results reported by users are analyzed and their effectiveness is evaluated. The evaluated effectiveness data is registered in a database.

[0126] Automatic deployment: Effective efficiency initiatives are automatically proposed to other departments. These proposals are sent to relevant user devices and departments.

[0127] Reward notification: If the effectiveness is proven, the server will send a reward notification to the user. The user will check this notification on their device and proceed with the reward claim procedure.

[0128] Database

[0129] The database is a collection of information that stores past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[0130] Specific examples

[0131] Here's a specific example: A user wants to "improve the efficiency of regular meetings" and inputs the problem of "it takes a long time to prepare for meetings and create minutes" from their device. The server analyzes this information and uses a generative AI model to search for and extract "use of an automatic minutes generation tool" and suggests it to the user. When the user introduces this tool and reports that the time it takes to create minutes has been reduced by 60%, the server evaluates its effectiveness. If similar results are confirmed in other departments, the server registers this information in its database and automatically sends suggestions to those departments. The user is also notified of a reward and is responsible for the procedures to receive it.

[0132] Prompt Sentence Examples

[0133] An example of a prompt is as follows:

[0134] "Please provide suggestions for streamlining meeting preparation and minutes creation, including examples of effective tools."

[0135] As described above, this system supports efficient and effective internal business operations and contributes to improved productivity.

[0136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0137] Step 1:

[0138] User input of business information

[0139] A user enters details about their business into a terminal.

[0140] Input: User's business information

[0141] Specific operation: The user opens a browser or a dedicated application and enters an item such as "improving the efficiency of regular meetings" or a problem such as "it takes a long time to prepare for meetings and create minutes."

[0142] Output: Business information data organized by the terminal

[0143] Step 2:

[0144] Analysis of entered information

[0145] The server analyzes the business information received from the terminal.

[0146] Input: Business information data received from the terminal

[0147] What it does: The server launches a natural language processing (NLP) engine to analyze the received text data, specifically extracting specific keywords and phrases (e.g., "meeting preparation" or "minutes creation") contained in the business information.

[0148] Output: Extracted keyword and phrase data

[0149] Step 3:

[0150] Search and suggestion generation using generative AI models

[0151] Using a generative AI model, optimal solutions and similar cases are searched for, and proposal documents are generated.

[0152] Input: Extracted keyword and phrase data

[0153] Specific operation: The server uses the generative AI model to search a past database. For example, it searches for similar cases such as "using an automatic meeting minutes generation tool" and selects the optimal solution from among them. Based on the selected solution, it generates a proposal document (outline of the initiative, expected effects, implementation procedures, etc.).

[0154] Output: Proposal document data

[0155] Step 4:

[0156] Submitting and presenting proposal documents

[0157] The server sends the generated proposal document to the user's terminal and presents it.

[0158] Input: Proposal document data

[0159] Specific operation: The server sends the generated proposal document to the user terminal. The user terminal receives this document and displays it to the user. The user confirms the proposed solution.

[0160] Output: Proposal document displayed on user's terminal

[0161] Step 5:

[0162] Implementing proposed solutions and reporting results

[0163] The user implements the proposed solution and reports the results.

[0164] Input: Proposal document, execution result data

[0165] Specific operation: A user generates meeting minutes using the "automatic minutes generation tool" and inputs the results (e.g., the time required to create minutes has been reduced by 60%) from their device. The device organizes this information and sends it to the server.

[0166] Output: Execution result data reported from the terminal

[0167] Step 6:

[0168] Evaluation of execution results

[0169] The server evaluates the reported execution results.

[0170] Input: Execution result data reported from the device

[0171] Specific operation: The server analyzes the execution results and judges the effectiveness of the efficiency improvement. For example, it evaluates a specific numerical value such as "60% reduction in the time required to create minutes."

[0172] Output: Evaluation result data

[0173] Step 7:

[0174] Proposal deployment to other departments

[0175] Based on the evaluation results, suggestions are automatically sent to other departments.

[0176] Input: Evaluation result data

[0177] Specific operation: The server automatically sends proposal information to other departments based on the evaluation data. For example, it automatically forwards a proposal to other departments that "an automatic minutes generation tool would be effective."

[0178] Output: Proposal data sent to other departments

[0179] Step 8:

[0180] Reward notification

[0181] The server sends a reward notice to the user for the efforts that have been recognized as effective.

[0182] Input: Evaluation result data

[0183] Specific operation: The server sends a reward notification via the user's contact information (email address or internal messaging system). The user receives the notification and completes the reward claim procedure.

[0184] Output: Reward notification data sent to user

[0185] The above are the specific steps of the processing of this system.

[0186] (Application example 1)

[0187] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0188] Conventional business efficiency systems are generally based on desktops or laptops, and have the problem of being unable to respond to real-time business efficiency proposals or immediate improvements to on-site work. Even when users receive efficiency proposals, delays in reporting the implementation and feedback of the results often prevent a rapid improvement cycle, hindering productivity improvement. In particular, in workplaces such as factories, where immediate improvements in work efficiency and safety are often required, conventional systems are unable to adequately address these issues.

[0189] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0190] In this invention, the server includes a means for inputting task information from a user terminal, a means for analyzing the input task information and searching for optimal solutions or similar cases using a generative AI model, and a means for acquiring the user's work content through voice input and generating efficiency suggestions. This allows the user to receive efficiency suggestions in real time through the smart glasses. In addition, the proposed solutions are quickly implemented on-site and the results are reported immediately, thereby promoting the efficiency of on-site work in real time and simultaneously achieving improvements in overall productivity and safety.

[0191] A "user terminal" is a device that provides an interface for a user to input business information, confirm efficiency improvement proposals, and report on their implementation.

[0192] "Business information" refers to detailed data and issues related to the business that the user wishes to improve the efficiency of.

[0193] A "generative AI model" is an artificial intelligence algorithm that generates efficiency suggestions and solutions based on input data.

[0194] "Searching means" is a function that uses a generative AI model to search and extract optimal solutions and similar cases from a database based on the business information entered.

[0195] "Search results" are the optimal solution or a set of similar cases extracted by the generative AI model.

[0196] "Means for the user to implement the proposed solution" refers to the means and process for the user to actually implement the proposed efficiency proposal.

[0197] The "means for reporting results" is a means for reporting the effects and results of a suggestion made by a user to the system via a device.

[0198] The "means for evaluating the reported results" is a function that allows the system to analyze the execution results reported by the user and determine the effectiveness of the efficiency improvement.

[0199] A "database" is a collection of information that stores past efficiency improvement cases and evaluation data.

[0200] "Means for deployment to other departments" refers to the function of automatically sending and sharing the evaluated efficiency gains to other departments as proposals.

[0201] "Smart glasses" are devices that users can wear to display information in real time and visually receive suggestions for improving work efficiency.

[0202] The "voice input means" is a function that allows the user to input work content and business information into the system through voice.

[0203] The "means for notifying a reward" is a system function for notifying a user of a reward when the execution result of an efficiency improvement proposal is effective.

[0204] The present invention provides a system for supporting factory workers in improving their work efficiency by using smart glasses. Specific embodiments for carrying out the present invention will be described in detail below.

[0205] Basic system configuration

[0206] The system consists of the following main components:

[0207] 1. User Device:

[0208] A device that provides an interface for users to input business information, confirm efficiency proposals, and report on their implementation. Smart glasses are an example of this.

[0209] 2. Server:

[0210] It acts as the core of the system and is responsible for the following main functions:

[0211] Input analysis: Analyzes business information received from the user's device and extracts important keywords and context.

[0212] Search and suggest: Generative AI models are used to search a database for similar past cases and optimal solutions to generate suggestions.

[0213] Evaluation and registration: The execution results reported by users are analyzed, their effectiveness is evaluated, and they are registered in a database.

[0214] Notification: If the effect is confirmed, the user will be notified of the reward.

[0215] 3. Database:

[0216] It is a collection of information that stores past efficiency case studies and evaluation data. The server uses this database to search and evaluate.

[0217] Program processing

[0218] When a user puts on the smart glasses and works in a factory, the system operates according to the following steps.

[0219] 1. Real-time video capture:

[0220] The smart glasses' camera is used to capture real-time video of the user's work, allowing the user to understand their environment and work situation in real time.

[0221] 2. Voice input of your work:

[0222] The user inputs the details of the work into the smart glasses through voice input, for example, telling the system a specific task such as "I want to improve the speed at which bolts are installed."

[0223] 3. Input analysis and proposal generation:

[0224] The server analyzes the work content input by voice and uses a generative AI model to generate optimal efficiency suggestions, using natural language processing technologies such as OpenAI (registered trademark) APIs.

[0225] 4. Presenting the proposal:

[0226] The generated efficiency suggestions are displayed on the smart glasses' display, and the user can confirm the suggestions and put them into action.

[0227] Specific examples

[0228] For example, a user might say, "I want to improve the speed of bolt installation on the assembly line." The server analyzes this information, and the generative AI model suggests "introducing an automated tool for bolt installation." This suggestion is displayed on the smart glasses, and the user can use the suggested tool and report its effectiveness.

[0229] Prompt Sentence Examples

[0230] For example, use the following prompt:

[0231] 'Please tell me some concrete measures to improve bolt installation speed'

[0232] This allows the system to provide users with quick and effective suggestions for improving work efficiency, improving the quality and safety of work within the factory.

[0233] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0234] Step 1:

[0235] The user puts on the smart glasses and starts working in the factory. The camera in the smart glasses captures the user's work in real time. This operation allows the user's environment and work situation to be understood.

[0236] Input: Factory work footage

[0237] Output: Real-time video data

[0238] Step 2:

[0239] The user speaks to the smart glasses to describe their goal for improving work efficiency. For example, they might say, "I want to improve the speed at which bolts are attached on the assembly line." This voice data is captured by the smart glasses' microphone and converted into text data.

[0240] Input: Voice input data (for efficiency purposes)

[0241] Output: Converted text data

[0242] Step 3:

[0243] The server analyzes the text data received through the smart glasses and uses natural language processing (NLP) technology to analyze the input business information and extract important keywords and context.

[0244] Input: Converted text data

[0245] Output: Parsed keywords and context

[0246] Step 4:

[0247] The server searches a database for optimal solutions and similar cases based on keywords and context analyzed using the generative AI model, using past examples of efficiency improvements and optimal solutions.

[0248] Input: Parsed keywords and context

[0249] Output: Optimal solution and similar cases

[0250] Step 5:

[0251] The server then presents the optimal solution obtained from the search results to the user's device (smart glasses), where specific efficiency measures are visually displayed so that the user can confirm the proposal.

[0252] Input: Optimal solution and similar cases

[0253] Output: Efficiency suggestions displayed on smart glasses

[0254] Step 6:

[0255] The user implements the efficiency suggestions displayed on the smart glasses, for example, by using a new tool based on the proposed solution, and the results are reported to the server via the smart glasses.

[0256] Input: Efficiency proposal

[0257] Output: Execution result data

[0258] Step 7:

[0259] The server evaluates the reported execution results, analyzes the effectiveness of the execution results, and registers them in a database. The evaluation includes the effectiveness of the results and the rate of improvement.

[0260] Input: Execution result data

[0261] Output: Evaluated effects and database updates

[0262] Step 8:

[0263] If the effectiveness is recognized, the server will notify the user of a reward. In addition, based on the evaluated efficiency effect, suggestions will be automatically sent to other departments, so that similar efficiency measures can be used in other departments as well.

[0264] Input: Evaluated effect

[0265] Output: Reward notice and proposal notice to other departments

[0266] These steps will enable real-time operational efficiency improvements using smart glasses, enabling users to work more efficiently on-site, and are expected to improve overall productivity and safety.

[0267] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0268] The present invention is a matching system that combines a generative AI model and an emotion engine to help users improve their work efficiency. This system comprehensively supports everything from inputting work information to proposing solutions, evaluating and reporting after implementation, and even deploying solutions to other departments. It also takes into account the user's emotional state to make more personalized proposals. Detailed embodiments of this system are described below.

[0269] Basic system configuration

[0270] The system consists of the following main components:

[0271] 1. User Device

[0272] 2. Server

[0273] 3. Database

[0274] 4. Emotion Recognition Engine

[0275] User terminal

[0276] The user terminal provides an interface for users to input work information and emotional data, confirm proposed solutions, and submit implementation reports. Users input information about their work and their current emotional state, and send their requests for efficiency improvement to the system through the terminal.

[0277] server

[0278] The server is the core of the system and is responsible for the following main functions:

[0279] Input analysis: Analyzes business information and emotional data received from the user's device to extract important keywords, context, and emotional state.

[0280] Search and suggest: Using a generative AI model, the system searches a database for similar past cases and optimal solutions, and suggests information that takes into account the user's emotional state.

[0281] Evaluation and registration: The execution results reported by the user are analyzed, their effectiveness is evaluated, and the results are registered in a database. User emotion data is also taken into account in the evaluation.

[0282] Notification: If the effect is recognized, a reward notification will be sent to the user, and a personalized message will be generated based on the user's emotional state.

[0283] Database

[0284] The database is a collection of information that includes past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[0285] Emotion Recognition Engine

[0286] The emotion recognition engine is a component that recognizes and analyzes the user's emotional state from their input data and behavioral data, allowing the system to generate suggestions based on the user's emotional state.

[0287] Explanation of program processing

[0288] User input of business information and emotion data

[0289] The user inputs detailed information about the task they wish to improve efficiency and their current emotional state into the terminal, which then organizes this information and sends it to the server in a format that includes emotional data.

[0290] Analysis and search of input information

[0291] The server analyzes the business information and emotional data received from the device. Natural language processing (NLP) technology and an emotion recognition engine are used to extract specific keywords, phrases, and emotional states. Based on this, a generative AI model searches for optimal solutions and similar cases.

[0292] Generating and presenting search results

[0293] The server organizes solutions and similar cases extracted from the database and generates a proposal document that also takes the user's emotional state into consideration. The proposal document includes an overview of the initiative, specific steps, and expected results, as well as a communication style and feedback appropriate to the user's emotional state. The user's device receives this and displays it to the user.

[0294] Implementing proposed solutions and reporting results

[0295] The user implements the proposed solution and reports the results to the server from their device. The report includes specific steps taken, numerical data showing the effects, and the user's emotional state after implementation.

[0296] Evaluating the results and expanding to other departments

[0297] The server analyzes the reported results and evaluates their effectiveness. The analysis includes statistical analysis to confirm data consistency and measure effectiveness, as well as evaluating emotional data. If the effectiveness is proven, it is registered in the database and a proposal is automatically sent to other departments.

[0298] Notification and receipt of rewards

[0299] If an effort is found to be effective, the server sends the user a reward notification with a personalized message according to their emotional state. The user can then check the notification on their device and follow the procedure to receive the reward.

[0300] Specific examples

[0301] As an example, suppose a user (person A) wishes to "improve the efficiency of regular meetings." Person A inputs their problems and emotional state, such as "it takes a long time to prepare for meetings and write minutes" and "I feel stressed," on their device. The server analyzes this information, searches and extracts similar past cases such as "using an automatic minutes generation tool," and presents them with "effective communication methods." When Person A reports that they have introduced the proposed tool and that the time it takes to write minutes has been reduced by 60%, the server evaluates its effectiveness, taking their emotional state into consideration. If similar effects are confirmed in other departments, the server will provide a personalized message according to their emotional state and notify Person A of their gratitude.

[0302] The above system makes suggestions for work efficiency improvements that take the user's emotional state into account, enabling more personalized and effective support, which not only contributes to improving productivity across the company but also increases employee satisfaction.

[0303] The processing flow will be explained below.

[0304] Step 1:

[0305] The user inputs detailed information about their work and their current emotional state into the terminal. For example, they might input "I would like to improve the efficiency of regular meetings," "It takes a lot of time to prepare for meetings and write minutes," and "I feel stressed."

[0306] Step 2:

[0307] The terminal organizes the business information and emotion data input by the user, formats it, and transmits it to the server.

[0308] Step 3:

[0309] The server analyzes the received business information and emotional data using natural language processing (NLP) technology and an emotion recognition engine to extract important keywords, phrases, and emotional states.

[0310] Step 4:

[0311] Based on the analyzed data, the server uses a generative AI model to search the database for optimal solutions and similar cases, such as an "automatic meeting minutes generation tool."

[0312] Step 5:

[0313] The server organizes the search results and generates a proposal document that takes the user's emotional state into consideration. The proposal document includes an overview of the initiative, specific steps, and expected results, as well as a communication style and feedback appropriate for the user's emotional state.

[0314] Step 6:

[0315] The server sends the generated proposal document to the terminal, encrypting the data and checking the transfer protocol.

[0316] Step 7:

[0317] The terminal displays the received proposal document to the user, who then checks the contents of the proposal and decides whether or not to implement it.

[0318] Step 8:

[0319] The user implements the proposed solution. For example, they introduce an "automatic minutes generation tool" and start using it.

[0320] Step 9:

[0321] The user reports the results after implementing the solution, sending detailed data on the specific effects and steps taken, as well as their emotional state after implementation, from their device to the server.

[0322] Step 10:

[0323] The server analyzes the reported results and evaluates their effectiveness, including statistical analysis to verify data consistency and measure effectiveness, as well as evaluating emotional data.

[0324] Step 11:

[0325] The server registers the evaluation results in a database, and effective initiatives are recorded as candidates for expansion to other departments.

[0326] Step 12:

[0327] The server sends a reward notice to the user for efforts that are recognized as effective, the reward notice including a personalized message according to the user's emotional state.

[0328] Step 13:

[0329] The terminal displays a reward notification to the user, and the user follows the instructions to complete the procedure for receiving the reward.

[0330] This concludes the explanation of the specific processing steps. This flow effectively and quickly improves business efficiency and also provides services that respond to the emotional state of the user.

[0331] Example 2

[0332] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0333] Conventional work efficiency improvement systems only make suggestions based on work information and do not consider the user's emotional state, which can result in ineffective work suggestions. Furthermore, even after the user implements the proposed solution, a comprehensive evaluation that includes emotional data is not performed, which creates challenges in improving user satisfaction.

[0334] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting business information and emotional data from a user terminal, a means for analyzing the input business information and emotional data and searching for optimal solutions or similar cases using a generative AI model, and a means for generating search results and presenting a proposal document that takes the user's emotional state into consideration to the user's terminal. This realizes personalized proposals that take the user's emotional state into consideration, enabling efficient and satisfying business improvement.

[0335] A "user terminal" is a device through which a user inputs business information and emotional data and receives suggestions and notifications from the system.

[0336] "Business information" refers to specific data and content that a user inputs regarding a business, and is information that includes data regarding the current status and issues of the business.

[0337] "Emotion data" refers to data about the current emotional state input by the user, and specifically includes subjective evaluations such as stress and satisfaction.

[0338] The "server" is a central computer system that receives business information and emotion data sent from user terminals, and performs analysis, proposals, and notifications.

[0339] A "generative AI model" is a model that applies artificial intelligence technology to generate optimal solutions and similar cases based on past data and input information.

[0340] A "proposal document" is a document created based on optimal solutions and similar cases generated by the server, taking into account the user's emotional state, and describing the proposal content, effects, implementation procedures, etc.

[0341] A "database" is a collection of information that stores data such as past business information, emotional data, solutions, and evaluation results.

[0342] An "emotion recognition engine" is a component that recognizes and analyzes a user's emotional state from input data and behavioral data.

[0343] "Evaluation" is the process of analyzing the results after the user implements the proposed solution and determining its effectiveness.

[0344] A "personalized message" is an individually customized message created by the server according to the user's emotional state.

[0345] This invention is a system that combines a generative AI model and an emotion recognition engine to help users improve their work efficiency. This system comprehensively supports everything from inputting work information to proposing solutions, evaluating and reporting after implementation, and even deploying solutions to other departments. It also takes the user's emotional state into consideration to make more personalized suggestions.

[0346] Basic system configuration

[0347] The system consists of the following main components:

[0348] 1. User Device

[0349] 2. Server

[0350] 3. Database

[0351] 4. Emotion Recognition Engine

[0352] User terminal

[0353] The user terminal provides an interface for users to input work information and emotional data, confirm proposed solutions, and submit implementation reports. Users input information about their work and their current emotional state, and send their requests for efficiency improvement to the system through the terminal.

[0354] server

[0355] The server is the core of the system and is responsible for the following main functions:

[0356] Input analysis: Analyzes business information and emotional data received from the user's device to extract important keywords, context, and emotional state. Analysis is performed using natural language processing (NLP) technology and an emotion recognition engine.

[0357] Search and suggest: Using a generative AI model, the system searches a database for similar cases and optimal solutions, and suggests information that takes into account the user's emotional state.

[0358] Evaluation and registration: Analyze the execution results reported by users, evaluate their effectiveness, and register them in a database. Emotional data is also taken into account in the evaluation.

[0359] Notification: If successful, users will receive a reward notification with a personalized message based on their emotional state.

[0360] Database

[0361] The database is a collection of information that includes past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[0362] Emotion Recognition Engine

[0363] The emotion recognition engine is a component that recognizes and analyzes the user's emotional state from their input data and behavioral data, allowing the system to generate suggestions based on the user's emotional state.

[0364] Specific examples

[0365] As an example, consider the case where a user (Person A) wishes to "improve the efficiency of regular meetings." Person A inputs his / her problems and emotional state, such as "it takes a long time to prepare for meetings and create minutes" and "I feel stressed," on his / her device. The server analyzes this information, searches and extracts similar past cases such as "using an automatic minutes generation tool," and presents a proposal along with "effective communication methods." When Person A reports that he / she introduced the proposed tool and saw that the time it took to create minutes was reduced by 60%, the server evaluates its effectiveness, taking his / her emotional state into consideration. If similar effects are confirmed in other departments, the server will provide a personalized message according to his / her emotional state and notify Person A of a reward.

[0366] Prompt Sentence Examples

[0367] "I spend a lot of time preparing for and taking minutes of our weekly meetings, and I'd like to improve the efficiency. I'm also finding this work very stressful. I'd like some suggestions on how to solve this problem."

[0368] This system makes suggestions for work efficiency improvements that take the user's emotional state into account, enabling more personalized and effective support, which not only contributes to improving productivity across the company but also increases employee satisfaction.

[0369] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0370] Step 1: User inputs task information and emotion data

[0371] The user inputs work information and emotional data into the user terminal. Specific examples of input include the current state of work and emotional state, such as "it takes a long time to prepare for a meeting" or "I'm feeling stressed." This data is formatted and initial processing is performed by the user terminal. The input data is then sent to the server.

[0372] Input: Business information and emotion data

[0373] Output: Formatted business information and sentiment data

[0374] Specific example of operation: A user enters "meeting preparation," "time-consuming," and "very stressful" into the input form on the device and clicks the send button.

[0375] Step 2: Initial processing of input data and sending to server

[0376] The terminal receives data entered by the user, checks the data format, and performs preprocessing of the data as necessary. For example, it converts data in different formats into a unified format, which makes it easier for the server to analyze it. After preprocessing is complete, the data is sent to the server.

[0377] Input: Formatted business information and emotion data

[0378] Output: Preprocessed data

[0379] Specific example of operation: The device extracts keywords such as "meeting preparation," "time-consuming," and "very stressful," standardizes the data format, and sends it to the server.

[0380] Step 3: Data analysis and search by server

[0381] The server analyzes the received data using natural language processing (NLP) technology and an emotion recognition engine. NLP technology is used to extract specific keywords and phrases from business information, and the emotion recognition engine is used to analyze the emotional data. Based on the results of these analyses, a generative AI model is used to search a database for optimal solutions and similar cases.

[0382] Input: Preprocessed business information and emotion data

[0383] Output: extracted keywords, emotional states, and searched solutions

[0384] Specific operation example: The server receives the data, extracts keywords such as "meeting," "preparation," "time," and "stress," and recognizes the emotional state as "very stressful." Next, based on this information, it searches the database for the optimal solution, such as "use an automatic meeting minutes generation tool."

[0385] Step 4: Server generates and sends proposal document

[0386] The server generates a proposal document based on the search results. The proposal document includes an outline of the initiative, specific steps, expected results, and a communication style and feedback appropriate to the user's emotional state. The document is then sent to the user's device.

[0387] Input: searched solutions, extracted keywords, emotional state

[0388] Output: Proposal document

[0389] Specific example of operation: The server creates a proposal document based on the "automatic minutes generation tool" and sends it to the user's terminal, including content such as "By introducing this tool, meeting preparation time will be reduced by 60% and stress is expected to be reduced."

[0390] Step 5: User implements solution and reports results

[0391] The user checks the proposal document presented by the server and implements the solution. For example, they introduce an automatic meeting minutes generation tool. After implementation, the user reports the results and emotional state from their device to the server. The report includes specific implementation steps, numerical data showing the effects, and the user's emotional state.

[0392] Input: User's performance and emotional state

[0393] Output: Reported performance results and emotional state

[0394] Specific example of operation: A user introduces an automatic minutes generation tool and reports from their device that the time it takes to create minutes has been reduced by 60%, resulting in "meeting preparation time reduced by 60%" and "stress reduced."

[0395] Step 6: Server evaluation and database registration

[0396] The server analyzes the results reported by users and evaluates their effectiveness. The evaluation includes statistical analysis and evaluation of emotional data. The evaluated data is then registered in a database. If the effectiveness is proven, it is automatically deployed to other departments.

[0397] Input: Reported performance results and emotional state

[0398] Output: Evaluated data, updated database

[0399] Specific operation example: The server analyzes reports such as "meeting preparation time reduced by 60%" and "stress reduced," evaluates the effectiveness, and registers it in a database. It also automatically sends a proposal to other departments to "introduce an automatic minutes generation tool."

[0400] Step 7: Server Notification and Personalized Message Delivery

[0401] The server sends a reward notification to the user for the efforts that have been recognized as effective, including a personalized message according to the user's emotional state. The user can check this notification on their device and receive the reward by taking the appropriate steps.

[0402] Input: Evaluated data, user's emotional state

[0403] Output: Thank you notice with personalized message

[0404] Specific operation example: Depending on the user's emotional state, the server sends the user a message such as, "Congratulations on reducing stress! Your efforts have significantly improved the efficiency of your work. We have a reward for you, so please go through the process of receiving it." The user then goes through the process of receiving the reward from their device.

[0405] (Application example 2)

[0406] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0407] Conventional efficiency support systems have the problem of being unable to take into account the user's emotional state and therefore unable to make effective optimization suggestions. Furthermore, in on-site work such as in factories, real-time feedback and work management are difficult, making it difficult to improve work efficiency. Furthermore, there was a need for a method to simultaneously reduce employee stress and improve work efficiency.

[0408] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting business information and emotional data from a user terminal, means for analyzing the input business information and emotional data and searching for optimal solutions or similar cases using a generative AI model, and means for generating search results and presenting them to the user terminal. This makes it possible to propose and provide feedback on optimal business efficiency improvements in real time, taking into account the emotional state of employees.

[0409] "User terminal" refers to a device that allows a user to input, transmit, and check business information and emotional data.

[0410] "Task information" is data that indicates the details and progress of the tasks that a user is performing.

[0411] "Emotion data" is data that represents the user's current mental state and emotions.

[0412] A "generative AI model" is a model that uses machine learning and deep learning technologies to generate optimal solutions and similar cases based on input data.

[0413] "Emotional state" refers to a user's emotional condition or psychological state.

[0414] "Searching means" refers to a method of finding optimal solutions and similar cases based on business information and emotional data analyzed using a generative AI model.

[0415] "Real-time" refers to the ability to respond immediately to the moment business operations progress.

[0416] "Smart glasses" are glasses-type devices that use augmented reality (AR) technology and have the ability to display information in the user's field of vision.

[0417] A "database" is a collection of information for storing searched solutions, similar cases, and evaluated effects.

[0418] "Means of evaluation" refers to a method for determining the effectiveness of efficiency improvements based on the results and emotional state reported by users.

[0419] "Deployment measures" are methods for applying the evaluated effects of business efficiency improvements to other departments.

[0420] "Reward" refers to a reward or compensation given to a user when the proposed solution is implemented and its effectiveness is recognized.

[0421] "Effect" is an index showing how successful the proposed means for improving work efficiency was, or how much the user's work efficiency was improved.

[0422] The present invention provides a matching system that supports users in improving their work efficiency by combining a generative AI model and an emotion engine. A specific embodiment of this system is described below.

[0423] Basic system configuration

[0424] The system consists of the following main components:

[0425] 1. User Device

[0426] 2. Server

[0427] 3. Database

[0428] 4. Emotion Recognition Engine

[0429] 5. Smart Glasses

[0430] User terminal

[0431] The user terminal provides an interface for users to input work information and emotional data, confirm proposed solutions, and submit implementation reports. It also collects and displays real-time work progress information and emotional data through smart glasses. Specifically, users input work details and emotional state and send their requests for efficiency improvements to the system.

[0432] server

[0433] The server is the central part of the system and is responsible for the following main functions:

[0434] Input data analysis: Analyzes business information and emotion data received from user devices to extract important keywords, context, and emotional states. Technologies used here include natural language processing (NLP) and emotion recognition engines.

[0435] Search for optimization suggestions: Using a generative AI model, the system searches a database for similar past cases and optimal solutions, and suggests information that takes into account the user's emotional state.

[0436] Evaluation and registration: The execution results reported by the user are analyzed, their effectiveness is evaluated, and they are registered in a database. Here, the evaluation also takes into account the user's emotional data.

[0437] Notification: If the effect is recognized, a reward notification will be sent to the user, and a personalized message will be generated based on the user's emotional state.

[0438] Database

[0439] The database is a collection of information that includes past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[0440] Emotion Recognition Engine

[0441] The emotion recognition engine is a component that recognizes and analyzes the user's emotional state from their input data and behavioral data, allowing the system to generate suggestions based on the user's emotional state.

[0442] Smart Glasses

[0443] The smart glasses are worn by users while they work and collect and display information on work progress and emotional data in real time. Users can check and implement suggestions for improving efficiency through the smart glasses.

[0444] Specific examples

[0445] As an example, suppose a user (Employee A) is performing "production line quality inspections," but is experiencing delays, frequent errors, and feeling stressed. Employee A inputs this work information and emotional state through smart glasses. The server analyzes this information, searches and extracts "use of inspection automation tools" from similar past cases, and suggests them to Employee A through the smart glasses. Employee A implements the proposed tool and reports that the error rate has been significantly reduced. As a result, the server evaluates its effectiveness, taking the user's emotional state into consideration. If similar effects are confirmed in other departments, the server will provide a personalized message according to Employee A's emotional state and notify Employee A of a reward.

[0446] Examples of prompts:

[0447] "We are currently conducting quality inspections on the line, but the work is delayed. Errors occur frequently, which is frustrating."

[0448] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0449] Step 1:

[0450] A user puts on the smart glasses and inputs business information and emotion data.

[0451] Input: Detailed work information (e.g., progress of quality inspection on the line), current emotional state (e.g., stress).

[0452] Specific operation: The user operates the interface of the smart glasses and records business information and emotional state through text input.

[0453] Step 2:

[0454] The smart glasses transmit the input business information and emotion data to the server.

[0455] Input: Business information and sentiment data entered by the user.

[0456] Output: The dataset that is sent to the server for analysis.

[0457] Specific operation: Using the network function of the smart glasses, the input data is sent to the server in real time.

[0458] Step 3:

[0459] The server analyzes the received business information and emotion data.

[0460] Input: Dataset sent from smart glasses.

[0461] Output: Keywords, context, and emotional state as analysis results.

[0462] Specific operation: The server uses natural language processing (NLP) technology to analyze business information, extract important keywords, and classify emotional data using an emotion recognition engine.

[0463] Step 4:

[0464] Based on the analysis results, the server uses a generative AI model to search a database for optimal solutions and similar cases.

[0465] Input: Parsed task information and emotional state.

[0466] Output: Best solution or list of similar cases.

[0467] Specific operation: The server inputs a prompt sentence into the generative AI model, which generates and extracts optimal solutions and similar cases.

[0468] Step 5:

[0469] The server organizes the search results, generates a suggested document that takes into account the user's emotional state, and sends it to the smart glasses.

[0470] Input: Retrieved solutions, similar cases, and the user's emotional state.

[0471] Output: Proposal document.

[0472] Specific operation: The server creates a proposal document that reflects the emotional data on the generated solution and sends it to the smart glasses.

[0473] Step 6:

[0474] The user sees the proposed solution through the smart glasses and implements it.

[0475] Input: Proposal document.

[0476] Output: Execution results (e.g., reduction in error rate).

[0477] Specific action: The user reads the proposal document and implements the proposed solution (e.g., introduction of an inspection automation tool) in the field.

[0478] Step 7:

[0479] The user reports the results of the execution to the server through the smart glasses.

[0480] Input: Execution results and emotional state after execution.

[0481] Output: Result report data sent to the server.

[0482] Specific operation: The user inputs the execution results and emotional state into the smart glasses and sends the data to the server.

[0483] Step 8:

[0484] The server analyzes the reported results and evaluates the effectiveness of the efficiency improvements.

[0485] Input: A dataset of execution results and emotional states.

[0486] Output: Effectiveness evaluation report.

[0487] Specific operation: The server performs statistical analysis and evaluates emotional data to evaluate the effectiveness of efficiency.

[0488] Step 9:

[0489] The server registers the evaluation results in a database and considers whether they can be expanded to other departments.

[0490] Input: Effectiveness evaluation report.

[0491] Output: Registered effectiveness data and decision on whether to expand to other departments.

[0492] Specific operation: The server stores the evaluation results in a database and uses them to decide whether to deploy them to other departments.

[0493] Step 10:

[0494] If the effect is recognized, the server notifies the user of the reward.

[0495] Input: The result of the evaluation as a trigger.

[0496] Output: Gratuity notice.

[0497] Specific operation: The server generates a reward notification for the user, including a personalized message according to the user's emotional state, and sends it to the smart glasses.

[0498] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0499] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0500] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0501] [Second embodiment]

[0502] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0503] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0504] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0505] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0506] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0507] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0508] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0509] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0510] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0511] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0512] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0513] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0514] The present invention is a matching system based on a generative AI model to support the efficiency of internal business operations. This system comprehensively supports everything from inputting business information to presenting efficiency proposals, evaluating and reporting after implementation, and even expanding to other departments. Detailed embodiments of this system are described below.

[0515] Basic system configuration

[0516] The system consists of the following main components:

[0517] 1. User Device

[0518] 2. Server

[0519] 3. Database

[0520] User terminal

[0521] The user terminal provides an interface for users to input business information, check proposed solutions, and submit implementation reports. Users input information about their own business and transmit their requests for efficiency improvement to the system through the terminal.

[0522] server

[0523] The server is the core of the system and is responsible for the following main functions:

[0524] Input analysis: Analyzes business information received from the user's device and extracts important keywords and context.

[0525] Search and suggestion: Using a generative AI model, the system searches a database for similar past cases and optimal solutions, and generates information to suggest to the user.

[0526] Evaluation and registration: Analyze the execution results reported by users, evaluate their effectiveness, and register them in a database.

[0527] Notification: If the effect is recognized, a reward notification will be sent to the user.

[0528] Database

[0529] The database is a collection of information that stores past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[0530] Explanation of program processing

[0531] User input of business information

[0532] Users input detailed information about the tasks they wish to streamline into the terminal, which then organizes this information and sends it to the server in the required format.

[0533] Analysis and search of input information

[0534] The server analyzes the business information received from the device, using natural language processing (NLP) technology to extract specific keywords and phrases. Based on this, a generative AI model searches for optimal solutions and similar cases.

[0535] Generating and presenting search results

[0536] The server organizes the solutions and similar cases extracted from the database and generates a proposal document to be presented to the user's device. The proposal document includes an overview of the initiative, its effects, implementation procedures, etc. The user's device receives this and displays it to the user.

[0537] Implementing proposed solutions and reporting results

[0538] The user implements the proposed solution and reports the results to the server from their device. The report includes specific implementation steps and numerical data showing the effectiveness.

[0539] Evaluating the results and expanding to other departments

[0540] The server evaluates the reported results and determines their effectiveness. If they are proven effective, they are registered in the database and can also automatically send suggestions to other departments.

[0541] Notification and receipt of rewards

[0542] If an initiative is deemed effective, the server will notify the user of a reward. The user can then check the notification on their device and follow the procedure to receive the reward.

[0543] Specific examples

[0544] As an example, let's say a user (Mr. A) wants to "improve the efficiency of regular meetings." Mr. A inputs the problem of "it takes a long time to prepare for meetings and create minutes" from his terminal. The server analyzes this information, searches and extracts similar past cases and suggests "using an automatic minutes generation tool." Mr. A introduces the proposed tool and reports that the time it takes to create minutes has been reduced by 60%, and the server evaluates its effectiveness. If similar effects are confirmed in other departments, the server will notify Mr. A of a reward.

[0545] The above system will efficiently and effectively promote the streamlining of internal operations and contribute to improving productivity throughout the company.

[0546] The processing flow will be explained below.

[0547] Step 1:

[0548] The user inputs detailed information about their work, such as "I would like to make regular meetings more efficient" or "It takes a lot of time to prepare for meetings and create minutes."

[0549] Step 2:

[0550] The terminal organizes and formats the business information entered by the user and sends it to the server, where data consistency and format checks are performed.

[0551] Step 3:

[0552] The server analyzes the received business information and uses natural language processing (NLP) technology to extract important keywords and phrases and understand the business content.

[0553] Step 4:

[0554] Based on the analyzed data, the server uses a generative AI model to search the database for optimal solutions and similar cases, such as an "automatic meeting minutes generation tool."

[0555] Step 5:

[0556] The server organizes the search results and generates a proposal document that includes an overview of the initiative, specific steps, and expected results.

[0557] Step 6:

[0558] The server sends the generated proposal document to the terminal, encrypting the data and checking the transfer protocol.

[0559] Step 7:

[0560] The terminal displays the received proposal document to the user, who then checks the contents of the proposal and decides whether or not to implement it.

[0561] Step 8:

[0562] The user implements the proposed solution. For example, they introduce an "automatic minutes generation tool" and start using it.

[0563] Step 9:

[0564] The user reports the results after implementing the solution, sending detailed data about the specific effects and implementation steps from the device to the server.

[0565] Step 10:

[0566] The server analyzes the reported results and evaluates their effectiveness, including data reconciliation and statistical analysis to measure effectiveness.

[0567] Step 11:

[0568] The server registers the evaluation results in a database, and effective initiatives are recorded as candidates for expansion to other departments.

[0569] Step 12:

[0570] The server sends a reward notification to the user for the efforts that are recognized as effective. This notification includes the content of the reward and instructions on how to receive it.

[0571] Step 13:

[0572] The terminal displays a reward notification to the user, and the user follows the instructions to complete the procedure for receiving the reward.

[0573] This concludes the explanation of the specific processing steps. This flow will effectively and quickly improve business efficiency.

[0574] Example 1

[0575] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0576] Conventional business efficiency systems were unable to accurately analyze users' business information and were unable to quickly and effectively provide optimal solutions or similar cases. It was also difficult to accurately evaluate the results of the proposed solutions, deploy them to other departments, or efficiently reward users. As a result, business efficiency was not fully achieved, and productivity did not improve across the entire company.

[0577] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0578] In this invention, the server includes a means for inputting business information from a user terminal, a means for analyzing the input business information and searching for optimal solutions or similar cases using a generative AI model, and a means for generating search results and presenting them to the user terminal. Thus, when a user inputs business information, the information is analyzed using natural language processing technology, and specific keywords and phrases are extracted. Furthermore, the generative AI model is used to search for optimal solutions or similar cases, and a proposal document is generated, enabling the user to quickly and effectively receive proposals for improving business efficiency.

[0579] In this invention, the server further includes means for the user to implement the proposed solution and report the results, means for evaluating the reported results and determining the effectiveness of the efficiency improvement, means for analyzing input information using natural language processing technology and extracting specific keywords and phrases, means for evaluating the results of the efficiency improvement and automatically sending a proposal to other departments, and means for receiving a report of the quantitative effects and implementation procedures from the user terminal. This makes it possible to accurately evaluate the results of the efficiency improvement measures implemented by the user, effectively deploy them to other departments, and properly reward the user.

[0580] A "user terminal" is a device that processes business information input by a user and displays generated proposal results.

[0581] "Business information" refers to data in which a user inputs detailed information about a business, and includes important information that is useful for improving business efficiency.

[0582] A "generative AI model" is an artificial intelligence model that generates optimal solutions and similar cases based on past data and cases.

[0583] "Natural language processing technology" is a technology that analyzes sentences and document data and extracts specific keywords and phrases.

[0584] A "database" is a collection of information that stores past efficiency improvement cases and evaluation data, and serves as the basis for search and evaluation.

[0585] A "proposal document" is a document containing a solution and execution procedures generated by the server, and is displayed on the user's terminal.

[0586] "Efficiency improvement effect" refers to the results and degree of improvement in operations obtained by implementing the proposed solution.

[0587] A "reward notification" is a notification of a reward sent to a user for an efficiency improvement initiative that has been recognized as effective.

[0588] "Deploying to other departments" means sharing information and making proposals to apply efficiency initiatives that have been proven in one department to other departments.

[0589] "Evaluation" refers to the process of analyzing the execution results reported by users and determining their effectiveness.

[0590] "Reporting" is an action taken by a user to transmit the results of a proposed solution to the server after the user has implemented the solution.

[0591] MODE FOR CARRYING OUT THE INVENTION

[0592] This invention is a system that supports the efficiency of internal business operations based on a generative AI model. The system allows users to input business information, search for and present optimal solutions and similar cases, and comprehensively supports post-implementation evaluation and reporting, as well as deployment to other departments.

[0593] User terminal

[0594] The user terminal provides an interface for users to input business information, review proposed solutions, and report implementation results. Users use this terminal to input detailed information about their business. For example, they can input specific efficiency requests, such as "improving the efficiency of regular meetings," using a browser or dedicated application. This information is organized by the terminal and sent to the server.

[0595] server

[0596] The server is the core of this system and is configured to achieve the following main functions:

[0597] Input analysis: The server analyzes the business information received from the user's device. This analysis uses natural language processing (NLP) technology. For example, specific keywords and phrases such as "meeting preparation" and "minutes creation" are extracted.

[0598] Search and suggest: Using a generative AI model, the system searches a database for optimal solutions or similar cases based on past data and examples. For example, it may search for solutions such as "using an automatic meeting minutes generation tool."

[0599] Proposal generation and presentation: The server generates a proposal document based on the search results. This proposal document includes an overview of the initiative, expected benefits, implementation procedures, etc. This document is sent to the user's terminal and displayed to the user.

[0600] Evaluation and registration: The execution results reported by users are analyzed and their effectiveness is evaluated. The evaluated effectiveness data is registered in a database.

[0601] Automatic deployment: Effective efficiency initiatives are automatically proposed to other departments. These proposals are sent to relevant user devices and departments.

[0602] Reward notification: If the effectiveness is proven, the server will send a reward notification to the user. The user will check this notification on their device and proceed with the reward claim procedure.

[0603] Database

[0604] The database is a collection of information that stores past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[0605] Specific examples

[0606] Here's a specific example: A user wants to "improve the efficiency of regular meetings" and inputs the problem of "it takes a long time to prepare for meetings and create minutes" from their device. The server analyzes this information and uses a generative AI model to search for and extract "use of an automatic minutes generation tool" and suggests it to the user. When the user introduces this tool and reports that the time it takes to create minutes has been reduced by 60%, the server evaluates its effectiveness. If similar results are confirmed in other departments, the server registers this information in its database and automatically sends suggestions to those departments. The user is also notified of a reward and is responsible for the procedures to receive it.

[0607] Prompt Sentence Examples

[0608] An example of a prompt is as follows:

[0609] "Please provide suggestions for streamlining meeting preparation and minutes creation, including examples of effective tools."

[0610] As described above, this system supports efficient and effective internal business operations and contributes to improved productivity.

[0611] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0612] Step 1:

[0613] User input of business information

[0614] A user enters details about their business into a terminal.

[0615] Input: User's business information

[0616] Specific operation: The user opens a browser or a dedicated application and enters an item such as "improving the efficiency of regular meetings" or a problem such as "it takes a long time to prepare for meetings and create minutes."

[0617] Output: Business information data organized by the terminal

[0618] Step 2:

[0619] Analysis of entered information

[0620] The server analyzes the business information received from the terminal.

[0621] Input: Business information data received from the terminal

[0622] What it does: The server launches a natural language processing (NLP) engine to analyze the received text data, specifically extracting specific keywords and phrases (e.g., "meeting preparation" or "minutes creation") contained in the business information.

[0623] Output: Extracted keyword and phrase data

[0624] Step 3:

[0625] Search and suggestion generation using generative AI models

[0626] Using a generative AI model, optimal solutions and similar cases are searched for, and proposal documents are generated.

[0627] Input: Extracted keyword and phrase data

[0628] Specific operation: The server uses the generative AI model to search a past database. For example, it searches for similar cases such as "using an automatic meeting minutes generation tool" and selects the optimal solution from among them. Based on the selected solution, it generates a proposal document (outline of the initiative, expected effects, implementation procedures, etc.).

[0629] Output: Proposal document data

[0630] Step 4:

[0631] Submitting and presenting proposal documents

[0632] The server sends the generated proposal document to the user's terminal and presents it.

[0633] Input: Proposal document data

[0634] Specific operation: The server sends the generated proposal document to the user terminal. The user terminal receives this document and displays it to the user. The user confirms the proposed solution.

[0635] Output: Proposal document displayed on user's terminal

[0636] Step 5:

[0637] Implementing proposed solutions and reporting results

[0638] The user implements the proposed solution and reports the results.

[0639] Input: Proposal document, execution result data

[0640] Specific operation: A user generates meeting minutes using the "automatic minutes generation tool" and inputs the results (e.g., the time required to create minutes has been reduced by 60%) from their device. The device organizes this information and sends it to the server.

[0641] Output: Execution result data reported from the terminal

[0642] Step 6:

[0643] Evaluation of execution results

[0644] The server evaluates the reported execution results.

[0645] Input: Execution result data reported from the device

[0646] Specific operation: The server analyzes the execution results and judges the effectiveness of the efficiency improvement. For example, it evaluates a specific numerical value such as "60% reduction in the time required to create minutes."

[0647] Output: Evaluation result data

[0648] Step 7:

[0649] Proposal deployment to other departments

[0650] Based on the evaluation results, suggestions are automatically sent to other departments.

[0651] Input: Evaluation result data

[0652] Specific operation: The server automatically sends proposal information to other departments based on the evaluation data. For example, it automatically forwards a proposal to other departments that "an automatic minutes generation tool would be effective."

[0653] Output: Proposal data sent to other departments

[0654] Step 8:

[0655] Reward notification

[0656] The server sends a reward notice to the user for the efforts that have been recognized as effective.

[0657] Input: Evaluation result data

[0658] Specific operation: The server sends a reward notification via the user's contact information (email address or internal messaging system). The user receives the notification and completes the reward claim procedure.

[0659] Output: Reward notification data sent to user

[0660] The above are the specific steps of the processing of this system.

[0661] (Application example 1)

[0662] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0663] Conventional business efficiency systems are generally based on desktops or laptops, and have the problem of being unable to respond to real-time business efficiency proposals or immediate improvements to on-site work. Even when users receive efficiency proposals, delays in reporting the implementation and feedback of the results often prevent a rapid improvement cycle, hindering productivity improvement. In particular, in workplaces such as factories, where immediate improvements in work efficiency and safety are often required, conventional systems are unable to adequately address these issues.

[0664] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0665] In this invention, the server includes a means for inputting task information from a user terminal, a means for analyzing the input task information and searching for optimal solutions or similar cases using a generative AI model, and a means for acquiring the user's work content through voice input and generating efficiency suggestions. This allows the user to receive efficiency suggestions in real time through the smart glasses. In addition, the proposed solutions are quickly implemented on-site and the results are reported immediately, thereby promoting the efficiency of on-site work in real time and simultaneously achieving improvements in overall productivity and safety.

[0666] A "user terminal" is a device that provides an interface for a user to input business information, confirm efficiency improvement proposals, and report on their implementation.

[0667] "Business information" refers to detailed data and issues related to the business that the user wishes to improve the efficiency of.

[0668] A "generative AI model" is an artificial intelligence algorithm that generates efficiency suggestions and solutions based on input data.

[0669] "Searching means" is a function that uses a generative AI model to search and extract optimal solutions and similar cases from a database based on the business information entered.

[0670] "Search results" are the optimal solution or a set of similar cases extracted by the generative AI model.

[0671] "Means for the user to implement the proposed solution" refers to the means and process for the user to actually implement the proposed efficiency proposal.

[0672] The "means for reporting results" is a means for reporting the effects and results of a suggestion made by a user to the system via a device.

[0673] The "means for evaluating the reported results" is a function that allows the system to analyze the execution results reported by the user and determine the effectiveness of the efficiency improvement.

[0674] A "database" is a collection of information that stores past efficiency improvement cases and evaluation data.

[0675] "Means for deployment to other departments" refers to the function of automatically sending and sharing the evaluated efficiency gains to other departments as proposals.

[0676] "Smart glasses" are devices that users can wear to display information in real time and visually receive suggestions for improving work efficiency.

[0677] The "voice input means" is a function that allows the user to input work content and business information into the system through voice.

[0678] The "means for notifying a reward" is a system function for notifying a user of a reward when the execution result of an efficiency improvement proposal is effective.

[0679] The present invention provides a system for supporting factory workers in improving their work efficiency by using smart glasses. Specific embodiments for carrying out the present invention will be described in detail below.

[0680] Basic system configuration

[0681] The system consists of the following main components:

[0682] 1. User Device:

[0683] A device that provides an interface for users to input business information, confirm efficiency proposals, and report on their implementation. Smart glasses are an example of this.

[0684] 2. Server:

[0685] It acts as the core of the system and is responsible for the following main functions:

[0686] Input analysis: Analyzes business information received from the user's device and extracts important keywords and context.

[0687] Search and suggest: Generative AI models are used to search a database for similar past cases and optimal solutions to generate suggestions.

[0688] Evaluation and registration: The execution results reported by users are analyzed, their effectiveness is evaluated, and they are registered in a database.

[0689] Notification: If the effect is confirmed, the user will be notified of the reward.

[0690] 3. Database:

[0691] It is a collection of information that stores past efficiency case studies and evaluation data. The server uses this database to search and evaluate.

[0692] Program processing

[0693] When a user puts on the smart glasses and works in a factory, the system operates according to the following steps.

[0694] 1. Real-time video capture:

[0695] The smart glasses' camera is used to capture real-time video of the user's work, allowing the user to understand their environment and work situation in real time.

[0696] 2. Voice input of your work:

[0697] The user inputs the details of the work into the smart glasses through voice input, for example, telling the system a specific task such as "I want to improve the speed at which bolts are installed."

[0698] 3. Input analysis and proposal generation:

[0699] The server analyzes the work content input by voice and uses a generative AI model to generate optimal efficiency suggestions, using natural language processing technologies such as OpenAI's API.

[0700] 4. Presenting the proposal:

[0701] The generated efficiency suggestions are displayed on the smart glasses' display, and the user can confirm the suggestions and put them into action.

[0702] Specific examples

[0703] For example, a user might say, "I want to improve the speed of bolt installation on the assembly line." The server analyzes this information, and the generative AI model suggests "introducing an automated tool for bolt installation." This suggestion is displayed on the smart glasses, and the user can use the suggested tool and report its effectiveness.

[0704] Prompt Sentence Examples

[0705] For example, use the following prompt:

[0706] 'Please tell me some concrete measures to improve bolt installation speed'

[0707] This allows the system to provide users with quick and effective suggestions for improving work efficiency, improving the quality and safety of work within the factory.

[0708] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0709] Step 1:

[0710] The user puts on the smart glasses and starts working in the factory. The camera in the smart glasses captures the user's work in real time. This operation allows the user's environment and work situation to be understood.

[0711] Input: Factory work footage

[0712] Output: Real-time video data

[0713] Step 2:

[0714] The user speaks to the smart glasses to describe their goal for improving work efficiency. For example, they might say, "I want to improve the speed at which bolts are attached on the assembly line." This voice data is captured by the smart glasses' microphone and converted into text data.

[0715] Input: Voice input data (for efficiency purposes)

[0716] Output: Converted text data

[0717] Step 3:

[0718] The server analyzes the text data received through the smart glasses and uses natural language processing (NLP) technology to analyze the input business information and extract important keywords and context.

[0719] Input: Converted text data

[0720] Output: Parsed keywords and context

[0721] Step 4:

[0722] The server searches a database for optimal solutions and similar cases based on keywords and context analyzed using the generative AI model, using past examples of efficiency improvements and optimal solutions.

[0723] Input: Parsed keywords and context

[0724] Output: Optimal solution and similar cases

[0725] Step 5:

[0726] The server then presents the optimal solution obtained from the search results to the user's device (smart glasses), where specific efficiency measures are visually displayed so that the user can confirm the proposal.

[0727] Input: Optimal solution and similar cases

[0728] Output: Efficiency suggestions displayed on smart glasses

[0729] Step 6:

[0730] The user implements the efficiency suggestions displayed on the smart glasses, for example, by using a new tool based on the proposed solution, and the results are reported to the server via the smart glasses.

[0731] Input: Efficiency proposal

[0732] Output: Execution result data

[0733] Step 7:

[0734] The server evaluates the reported execution results, analyzes the effectiveness of the execution results, and registers them in a database. The evaluation includes the effectiveness of the results and the rate of improvement.

[0735] Input: Execution result data

[0736] Output: Evaluated effects and database updates

[0737] Step 8:

[0738] If the effectiveness is recognized, the server will notify the user of a reward. In addition, based on the evaluated efficiency effect, suggestions will be automatically sent to other departments, so that similar efficiency measures can be used in other departments as well.

[0739] Input: Evaluated effect

[0740] Output: Reward notice and proposal notice to other departments

[0741] These steps will enable real-time operational efficiency improvements using smart glasses, enabling users to work more efficiently on-site, and are expected to improve overall productivity and safety.

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

[0743] The present invention is a matching system that combines a generative AI model and an emotion engine to help users improve their work efficiency. This system comprehensively supports everything from inputting work information to proposing solutions, evaluating and reporting after implementation, and even deploying solutions to other departments. It also takes into account the user's emotional state to make more personalized proposals. Detailed embodiments of this system are described below.

[0744] Basic system configuration

[0745] The system consists of the following main components:

[0746] 1. User Device

[0747] 2. Server

[0748] 3. Database

[0749] 4. Emotion Recognition Engine

[0750] User terminal

[0751] The user terminal provides an interface for users to input work information and emotional data, confirm proposed solutions, and submit implementation reports. Users input information about their work and their current emotional state, and send their requests for efficiency improvement to the system through the terminal.

[0752] server

[0753] The server is the core of the system and is responsible for the following main functions:

[0754] Input analysis: Analyzes business information and emotional data received from the user's device to extract important keywords, context, and emotional state.

[0755] Search and suggest: Using a generative AI model, the system searches a database for similar past cases and optimal solutions, and suggests information that takes into account the user's emotional state.

[0756] Evaluation and registration: The execution results reported by the user are analyzed, their effectiveness is evaluated, and the results are registered in a database. User emotion data is also taken into account in the evaluation.

[0757] Notification: If the effect is recognized, a reward notification will be sent to the user, and a personalized message will be generated based on the user's emotional state.

[0758] Database

[0759] The database is a collection of information that includes past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[0760] Emotion Recognition Engine

[0761] The emotion recognition engine is a component that recognizes and analyzes the user's emotional state from their input data and behavioral data, allowing the system to generate suggestions based on the user's emotional state.

[0762] Explanation of program processing

[0763] User input of business information and emotion data

[0764] The user inputs detailed information about the task they wish to improve efficiency and their current emotional state into the terminal, which then organizes this information and sends it to the server in a format that includes emotional data.

[0765] Analysis and search of input information

[0766] The server analyzes the business information and emotional data received from the device. Natural language processing (NLP) technology and an emotion recognition engine are used to extract specific keywords, phrases, and emotional states. Based on this, a generative AI model searches for optimal solutions and similar cases.

[0767] Generating and presenting search results

[0768] The server organizes solutions and similar cases extracted from the database and generates a proposal document that also takes the user's emotional state into consideration. The proposal document includes an overview of the initiative, specific steps, and expected results, as well as a communication style and feedback appropriate to the user's emotional state. The user's device receives this and displays it to the user.

[0769] Implementing proposed solutions and reporting results

[0770] The user implements the proposed solution and reports the results to the server from their device. The report includes specific steps taken, numerical data showing the effects, and the user's emotional state after implementation.

[0771] Evaluating the results and expanding to other departments

[0772] The server analyzes the reported results and evaluates their effectiveness. The analysis includes statistical analysis to confirm data consistency and measure effectiveness, as well as evaluating emotional data. If the effectiveness is proven, it is registered in the database and a proposal is automatically sent to other departments.

[0773] Notification and receipt of rewards

[0774] If an effort is found to be effective, the server sends the user a reward notification with a personalized message according to their emotional state. The user can then check the notification on their device and follow the procedure to receive the reward.

[0775] Specific examples

[0776] As an example, suppose a user (person A) wishes to "improve the efficiency of regular meetings." Person A inputs their problems and emotional state, such as "it takes a long time to prepare for meetings and write minutes" and "I feel stressed," on their device. The server analyzes this information, searches and extracts similar past cases such as "using an automatic minutes generation tool," and presents them with "effective communication methods." When Person A reports that they have introduced the proposed tool and that the time it takes to write minutes has been reduced by 60%, the server evaluates its effectiveness, taking their emotional state into consideration. If similar effects are confirmed in other departments, the server will provide a personalized message according to their emotional state and notify Person A of their gratitude.

[0777] The above system makes suggestions for work efficiency improvements that take the user's emotional state into account, enabling more personalized and effective support, which not only contributes to improving productivity across the company but also increases employee satisfaction.

[0778] The processing flow will be explained below.

[0779] Step 1:

[0780] The user inputs detailed information about their work and their current emotional state into the terminal. For example, they might input "I would like to improve the efficiency of regular meetings," "It takes a lot of time to prepare for meetings and write minutes," and "I feel stressed."

[0781] Step 2:

[0782] The terminal organizes the business information and emotion data input by the user, formats it, and transmits it to the server.

[0783] Step 3:

[0784] The server analyzes the received business information and emotional data using natural language processing (NLP) technology and an emotion recognition engine to extract important keywords, phrases, and emotional states.

[0785] Step 4:

[0786] Based on the analyzed data, the server uses a generative AI model to search the database for optimal solutions and similar cases, such as an "automatic meeting minutes generation tool."

[0787] Step 5:

[0788] The server organizes the search results and generates a proposal document that takes the user's emotional state into consideration. The proposal document includes an overview of the initiative, specific steps, and expected results, as well as a communication style and feedback appropriate for the user's emotional state.

[0789] Step 6:

[0790] The server sends the generated proposal document to the terminal, encrypting the data and checking the transfer protocol.

[0791] Step 7:

[0792] The terminal displays the received proposal document to the user, who then checks the contents of the proposal and decides whether or not to implement it.

[0793] Step 8:

[0794] The user implements the proposed solution. For example, they introduce an "automatic minutes generation tool" and start using it.

[0795] Step 9:

[0796] The user reports the results after implementing the solution, sending detailed data on the specific effects and steps taken, as well as their emotional state after implementation, from their device to the server.

[0797] Step 10:

[0798] The server analyzes the reported results and evaluates their effectiveness, including statistical analysis to verify data consistency and measure effectiveness, as well as evaluating emotional data.

[0799] Step 11:

[0800] The server registers the evaluation results in a database, and effective initiatives are recorded as candidates for expansion to other departments.

[0801] Step 12:

[0802] The server sends a reward notice to the user for efforts that are recognized as effective, the reward notice including a personalized message according to the user's emotional state.

[0803] Step 13:

[0804] The terminal displays a reward notification to the user, and the user follows the instructions to complete the procedure for receiving the reward.

[0805] This concludes the explanation of the specific processing steps. This flow effectively and quickly improves business efficiency and also provides services that respond to the emotional state of the user.

[0806] Example 2

[0807] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0808] Conventional work efficiency improvement systems only make suggestions based on work information and do not consider the user's emotional state, which can result in ineffective work suggestions. Furthermore, even after the user implements the proposed solution, a comprehensive evaluation that includes emotional data is not performed, which creates challenges in improving user satisfaction.

[0809] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting business information and emotional data from a user terminal, a means for analyzing the input business information and emotional data and searching for optimal solutions or similar cases using a generative AI model, and a means for generating search results and presenting a proposal document that takes the user's emotional state into consideration to the user's terminal. This realizes personalized proposals that take the user's emotional state into consideration, enabling efficient and satisfying business improvement.

[0810] A "user terminal" is a device through which a user inputs business information and emotional data and receives suggestions and notifications from the system.

[0811] "Business information" refers to specific data and content that a user inputs regarding a business, and is information that includes data regarding the current status and issues of the business.

[0812] "Emotion data" refers to data about the current emotional state input by the user, and specifically includes subjective evaluations such as stress and satisfaction.

[0813] The "server" is a central computer system that receives business information and emotion data sent from user terminals, and performs analysis, proposals, and notifications.

[0814] A "generative AI model" is a model that applies artificial intelligence technology to generate optimal solutions and similar cases based on past data and input information.

[0815] A "proposal document" is a document created based on optimal solutions and similar cases generated by the server, taking into account the user's emotional state, and describing the proposal content, effects, implementation procedures, etc.

[0816] A "database" is a collection of information that stores data such as past business information, emotional data, solutions, and evaluation results.

[0817] An "emotion recognition engine" is a component that recognizes and analyzes a user's emotional state from input data and behavioral data.

[0818] "Evaluation" is the process of analyzing the results after the user implements the proposed solution and determining its effectiveness.

[0819] A "personalized message" is an individually customized message created by the server according to the user's emotional state.

[0820] This invention is a system that combines a generative AI model and an emotion recognition engine to help users improve their work efficiency. This system comprehensively supports everything from inputting work information to proposing solutions, evaluating and reporting after implementation, and even deploying solutions to other departments. It also takes the user's emotional state into consideration to make more personalized suggestions.

[0821] Basic system configuration

[0822] The system consists of the following main components:

[0823] 1. User Device

[0824] 2. Server

[0825] 3. Database

[0826] 4. Emotion Recognition Engine

[0827] User terminal

[0828] The user terminal provides an interface for users to input work information and emotional data, confirm proposed solutions, and submit implementation reports. Users input information about their work and their current emotional state, and send their requests for efficiency improvement to the system through the terminal.

[0829] server

[0830] The server is the core of the system and is responsible for the following main functions:

[0831] Input analysis: Analyzes business information and emotional data received from the user's device to extract important keywords, context, and emotional state. Analysis is performed using natural language processing (NLP) technology and an emotion recognition engine.

[0832] Search and suggest: Using a generative AI model, the system searches a database for similar cases and optimal solutions, and suggests information that takes into account the user's emotional state.

[0833] Evaluation and registration: Analyze the execution results reported by users, evaluate their effectiveness, and register them in a database. Emotional data is also taken into account in the evaluation.

[0834] Notification: If successful, users will receive a reward notification with a personalized message based on their emotional state.

[0835] Database

[0836] The database is a collection of information that includes past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[0837] Emotion Recognition Engine

[0838] The emotion recognition engine is a component that recognizes and analyzes the user's emotional state from their input data and behavioral data, allowing the system to generate suggestions based on the user's emotional state.

[0839] Specific examples

[0840] As an example, consider the case where a user (Person A) wishes to "improve the efficiency of regular meetings." Person A inputs his / her problems and emotional state, such as "it takes a long time to prepare for meetings and create minutes" and "I feel stressed," on his / her device. The server analyzes this information, searches and extracts similar past cases such as "using an automatic minutes generation tool," and presents a proposal along with "effective communication methods." When Person A reports that he / she introduced the proposed tool and saw that the time it took to create minutes was reduced by 60%, the server evaluates its effectiveness, taking his / her emotional state into consideration. If similar effects are confirmed in other departments, the server will provide a personalized message according to his / her emotional state and notify Person A of a reward.

[0841] Prompt Sentence Examples

[0842] "I spend a lot of time preparing for and taking minutes of our weekly meetings, and I'd like to improve the efficiency. I'm also finding this work very stressful. I'd like some suggestions on how to solve this problem."

[0843] This system makes suggestions for work efficiency improvements that take the user's emotional state into account, enabling more personalized and effective support, which not only contributes to improving productivity across the company but also increases employee satisfaction.

[0844] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0845] Step 1: User inputs task information and emotion data

[0846] The user inputs work information and emotional data into the user terminal. Specific examples of input include the current state of work and emotional state, such as "it takes a long time to prepare for a meeting" or "I'm feeling stressed." This data is formatted and initial processing is performed by the user terminal. The input data is then sent to the server.

[0847] Input: Business information and emotion data

[0848] Output: Formatted business information and sentiment data

[0849] Specific example of operation: A user enters "meeting preparation," "time-consuming," and "very stressful" into the input form on the device and clicks the send button.

[0850] Step 2: Initial processing of input data and sending to server

[0851] The terminal receives data entered by the user, checks the data format, and performs preprocessing of the data as necessary. For example, it converts data in different formats into a unified format, which makes it easier for the server to analyze it. After preprocessing is complete, the data is sent to the server.

[0852] Input: Formatted business information and emotion data

[0853] Output: Preprocessed data

[0854] Specific example of operation: The device extracts keywords such as "meeting preparation," "time-consuming," and "very stressful," standardizes the data format, and sends it to the server.

[0855] Step 3: Data analysis and search by server

[0856] The server analyzes the received data using natural language processing (NLP) technology and an emotion recognition engine. NLP technology is used to extract specific keywords and phrases from business information, and the emotion recognition engine is used to analyze the emotional data. Based on the results of these analyses, a generative AI model is used to search a database for optimal solutions and similar cases.

[0857] Input: Preprocessed business information and emotion data

[0858] Output: extracted keywords, emotional states, and searched solutions

[0859] Specific operation example: The server receives the data, extracts keywords such as "meeting," "preparation," "time," and "stress," and recognizes the emotional state as "very stressful." Next, based on this information, it searches the database for the optimal solution, such as "use an automatic meeting minutes generation tool."

[0860] Step 4: Server generates and sends proposal document

[0861] The server generates a proposal document based on the search results. The proposal document includes an outline of the initiative, specific steps, expected results, and a communication style and feedback appropriate to the user's emotional state. The document is then sent to the user's device.

[0862] Input: searched solutions, extracted keywords, emotional state

[0863] Output: Proposal document

[0864] Specific example of operation: The server creates a proposal document based on the "automatic minutes generation tool" and sends it to the user's terminal, including content such as "By introducing this tool, meeting preparation time will be reduced by 60% and stress is expected to be reduced."

[0865] Step 5: User implements solution and reports results

[0866] The user checks the proposal document presented by the server and implements the solution. For example, they introduce an automatic meeting minutes generation tool. After implementation, the user reports the results and emotional state from their device to the server. The report includes specific implementation steps, numerical data showing the effects, and the user's emotional state.

[0867] Input: User's performance and emotional state

[0868] Output: Reported performance results and emotional state

[0869] Specific example of operation: A user introduces an automatic minutes generation tool and reports from their device that the time it takes to create minutes has been reduced by 60%, resulting in "meeting preparation time reduced by 60%" and "stress reduced."

[0870] Step 6: Server evaluation and database registration

[0871] The server analyzes the results reported by users and evaluates their effectiveness. The evaluation includes statistical analysis and evaluation of emotional data. The evaluated data is then registered in a database. If the effectiveness is proven, it is automatically deployed to other departments.

[0872] Input: Reported performance results and emotional state

[0873] Output: Evaluated data, updated database

[0874] Specific operation example: The server analyzes reports such as "meeting preparation time reduced by 60%" and "stress reduced," evaluates the effectiveness, and registers it in a database. It also automatically sends a proposal to other departments to "introduce an automatic minutes generation tool."

[0875] Step 7: Server Notification and Personalized Message Delivery

[0876] The server sends a reward notification to the user for the efforts that have been recognized as effective, including a personalized message according to the user's emotional state. The user can check this notification on their device and receive the reward by taking the appropriate steps.

[0877] Input: Evaluated data, user's emotional state

[0878] Output: Thank you notice with personalized message

[0879] Specific operation example: Depending on the user's emotional state, the server sends the user a message such as, "Congratulations on reducing stress! Your efforts have significantly improved the efficiency of your work. We have a reward for you, so please go through the process of receiving it." The user then goes through the process of receiving the reward from their device.

[0880] (Application example 2)

[0881] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0882] Conventional efficiency support systems have the problem of being unable to take into account the user's emotional state and therefore unable to make effective optimization suggestions. Furthermore, in on-site work such as in factories, real-time feedback and work management are difficult, making it difficult to improve work efficiency. Furthermore, there was a need for a method to simultaneously reduce employee stress and improve work efficiency.

[0883] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting business information and emotional data from a user terminal, means for analyzing the input business information and emotional data and searching for optimal solutions or similar cases using a generative AI model, and means for generating search results and presenting them to the user terminal. This makes it possible to propose and provide feedback on optimal business efficiency improvements in real time, taking into account the emotional state of employees.

[0884] "User terminal" refers to a device that allows a user to input, transmit, and check business information and emotional data.

[0885] "Task information" is data that indicates the details and progress of the tasks that a user is performing.

[0886] "Emotion data" is data that represents the user's current mental state and emotions.

[0887] A "generative AI model" is a model that uses machine learning and deep learning technologies to generate optimal solutions and similar cases based on input data.

[0888] "Emotional state" refers to a user's emotional condition or psychological state.

[0889] "Searching means" refers to a method of finding optimal solutions and similar cases based on business information and emotional data analyzed using a generative AI model.

[0890] "Real-time" refers to the ability to respond immediately to the moment business operations progress.

[0891] "Smart glasses" are glasses-type devices that use augmented reality (AR) technology and have the ability to display information in the user's field of vision.

[0892] A "database" is a collection of information for storing searched solutions, similar cases, and evaluated effects.

[0893] "Means of evaluation" refers to a method for determining the effectiveness of efficiency improvements based on the results and emotional state reported by users.

[0894] "Deployment measures" are methods for applying the evaluated effects of business efficiency improvements to other departments.

[0895] "Reward" refers to a reward or compensation given to a user when the proposed solution is implemented and its effectiveness is recognized.

[0896] "Effect" is an index showing how successful the proposed means for improving work efficiency was, or how much the user's work efficiency was improved.

[0897] The present invention provides a matching system that supports users in improving their work efficiency by combining a generative AI model and an emotion engine. A specific embodiment of this system is described below.

[0898] Basic system configuration

[0899] The system consists of the following main components:

[0900] 1. User Device

[0901] 2. Server

[0902] 3. Database

[0903] 4. Emotion Recognition Engine

[0904] 5. Smart Glasses

[0905] User terminal

[0906] The user terminal provides an interface for users to input work information and emotional data, confirm proposed solutions, and submit implementation reports. It also collects and displays real-time work progress information and emotional data through smart glasses. Specifically, users input work details and emotional state and send their requests for efficiency improvements to the system.

[0907] server

[0908] The server is the central part of the system and is responsible for the following main functions:

[0909] Input data analysis: Analyzes business information and emotion data received from user devices to extract important keywords, context, and emotional states. Technologies used here include natural language processing (NLP) and emotion recognition engines.

[0910] Search for optimization suggestions: Using a generative AI model, the system searches a database for similar past cases and optimal solutions, and suggests information that takes into account the user's emotional state.

[0911] Evaluation and registration: The execution results reported by the user are analyzed, their effectiveness is evaluated, and they are registered in a database. Here, the evaluation also takes into account the user's emotional data.

[0912] Notification: If the effect is recognized, a reward notification will be sent to the user, and a personalized message will be generated based on the user's emotional state.

[0913] Database

[0914] The database is a collection of information that includes past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[0915] Emotion Recognition Engine

[0916] The emotion recognition engine is a component that recognizes and analyzes the user's emotional state from their input data and behavioral data, allowing the system to generate suggestions based on the user's emotional state.

[0917] Smart Glasses

[0918] The smart glasses are worn by users while they work and collect and display information on work progress and emotional data in real time. Users can check and implement suggestions for improving efficiency through the smart glasses.

[0919] Specific examples

[0920] As an example, suppose a user (Employee A) is performing "production line quality inspections," but is experiencing delays, frequent errors, and feeling stressed. Employee A inputs this work information and emotional state through smart glasses. The server analyzes this information, searches and extracts "use of inspection automation tools" from similar past cases, and suggests them to Employee A through the smart glasses. Employee A implements the proposed tool and reports that the error rate has been significantly reduced. As a result, the server evaluates its effectiveness, taking the user's emotional state into consideration. If similar effects are confirmed in other departments, the server will provide a personalized message according to Employee A's emotional state and notify Employee A of a reward.

[0921] Examples of prompts:

[0922] "We are currently conducting quality inspections on the line, but the work is delayed. Errors occur frequently, which is frustrating."

[0923] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0924] Step 1:

[0925] A user puts on the smart glasses and inputs business information and emotion data.

[0926] Input: Detailed work information (e.g., progress of quality inspection on the line), current emotional state (e.g., stress).

[0927] Specific operation: The user operates the interface of the smart glasses and records business information and emotional state through text input.

[0928] Step 2:

[0929] The smart glasses transmit the input business information and emotion data to the server.

[0930] Input: Business information and sentiment data entered by the user.

[0931] Output: The dataset that is sent to the server for analysis.

[0932] Specific operation: Using the network function of the smart glasses, the input data is sent to the server in real time.

[0933] Step 3:

[0934] The server analyzes the received business information and emotion data.

[0935] Input: Dataset sent from smart glasses.

[0936] Output: Keywords, context, and emotional state as analysis results.

[0937] Specific operation: The server uses natural language processing (NLP) technology to analyze business information, extract important keywords, and classify emotional data using an emotion recognition engine.

[0938] Step 4:

[0939] Based on the analysis results, the server uses a generative AI model to search a database for optimal solutions and similar cases.

[0940] Input: Parsed task information and emotional state.

[0941] Output: Best solution or list of similar cases.

[0942] Specific operation: The server inputs a prompt sentence into the generative AI model, which generates and extracts optimal solutions and similar cases.

[0943] Step 5:

[0944] The server organizes the search results, generates a suggested document that takes into account the user's emotional state, and sends it to the smart glasses.

[0945] Input: Retrieved solutions, similar cases, and the user's emotional state.

[0946] Output: Proposal document.

[0947] Specific operation: The server creates a proposal document that reflects the emotional data on the generated solution and sends it to the smart glasses.

[0948] Step 6:

[0949] The user sees the proposed solution through the smart glasses and implements it.

[0950] Input: Proposal document.

[0951] Output: Execution results (e.g., reduction in error rate).

[0952] Specific action: The user reads the proposal document and implements the proposed solution (e.g., introduction of an inspection automation tool) in the field.

[0953] Step 7:

[0954] The user reports the results of the execution to the server through the smart glasses.

[0955] Input: Execution results and emotional state after execution.

[0956] Output: Result report data sent to the server.

[0957] Specific operation: The user inputs the execution results and emotional state into the smart glasses and sends the data to the server.

[0958] Step 8:

[0959] The server analyzes the reported results and evaluates the effectiveness of the efficiency improvements.

[0960] Input: A dataset of execution results and emotional states.

[0961] Output: Effectiveness evaluation report.

[0962] Specific operation: The server performs statistical analysis and evaluates emotional data to evaluate the effectiveness of efficiency.

[0963] Step 9:

[0964] The server registers the evaluation results in a database and considers whether they can be expanded to other departments.

[0965] Input: Effectiveness evaluation report.

[0966] Output: Registered effectiveness data and decision on whether to expand to other departments.

[0967] Specific operation: The server stores the evaluation results in a database and uses them to decide whether to deploy them to other departments.

[0968] Step 10:

[0969] If the effect is recognized, the server notifies the user of the reward.

[0970] Input: The result of the evaluation as a trigger.

[0971] Output: Gratuity notice.

[0972] Specific operation: The server generates a reward notification for the user, including a personalized message according to the user's emotional state, and sends it to the smart glasses.

[0973] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0974] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0975] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0976] [Third embodiment]

[0977] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0978] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0979] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0980] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0981] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0982] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0983] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0984] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0985] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0986] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0987] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0988] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0989] The present invention is a matching system based on a generative AI model to support the efficiency of internal business operations. This system comprehensively supports everything from inputting business information to presenting efficiency proposals, evaluating and reporting after implementation, and even expanding to other departments. Detailed embodiments of this system are described below.

[0990] Basic system configuration

[0991] The system consists of the following main components:

[0992] 1. User Device

[0993] 2. Server

[0994] 3. Database

[0995] User terminal

[0996] The user terminal provides an interface for users to input business information, check proposed solutions, and submit implementation reports. Users input information about their own business and transmit their requests for efficiency improvement to the system through the terminal.

[0997] server

[0998] The server is the core of the system and is responsible for the following main functions:

[0999] Input analysis: Analyzes business information received from the user's device and extracts important keywords and context.

[1000] Search and suggestion: Using a generative AI model, the system searches a database for similar past cases and optimal solutions, and generates information to suggest to the user.

[1001] Evaluation and registration: Analyze the execution results reported by users, evaluate their effectiveness, and register them in a database.

[1002] Notification: If the effect is recognized, a reward notification will be sent to the user.

[1003] Database

[1004] The database is a collection of information that stores past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[1005] Explanation of program processing

[1006] User input of business information

[1007] Users input detailed information about the tasks they wish to streamline into the terminal, which then organizes this information and sends it to the server in the required format.

[1008] Analysis and search of input information

[1009] The server analyzes the business information received from the device, using natural language processing (NLP) technology to extract specific keywords and phrases. Based on this, a generative AI model searches for optimal solutions and similar cases.

[1010] Generating and presenting search results

[1011] The server organizes the solutions and similar cases extracted from the database and generates a proposal document to be presented to the user's device. The proposal document includes an overview of the initiative, its effects, implementation procedures, etc. The user's device receives this and displays it to the user.

[1012] Implementing proposed solutions and reporting results

[1013] The user implements the proposed solution and reports the results to the server from their device. The report includes specific implementation steps and numerical data showing the effectiveness.

[1014] Evaluating the results and expanding to other departments

[1015] The server evaluates the reported results and determines their effectiveness. If they are proven effective, they are registered in the database and can also automatically send suggestions to other departments.

[1016] Notification and receipt of rewards

[1017] If an initiative is deemed effective, the server will notify the user of a reward. The user can then check the notification on their device and follow the procedure to receive the reward.

[1018] Specific examples

[1019] As an example, let's say a user (Mr. A) wants to "improve the efficiency of regular meetings." Mr. A inputs the problem of "it takes a long time to prepare for meetings and create minutes" from his terminal. The server analyzes this information, searches and extracts similar past cases and suggests "using an automatic minutes generation tool." Mr. A introduces the proposed tool and reports that the time it takes to create minutes has been reduced by 60%, and the server evaluates its effectiveness. If similar effects are confirmed in other departments, the server will notify Mr. A of a reward.

[1020] The above system will efficiently and effectively promote the streamlining of internal operations and contribute to improving productivity throughout the company.

[1021] The processing flow will be explained below.

[1022] Step 1:

[1023] The user inputs detailed information about their work, such as "I would like to make regular meetings more efficient" or "It takes a lot of time to prepare for meetings and create minutes."

[1024] Step 2:

[1025] The terminal organizes and formats the business information entered by the user and sends it to the server, where data consistency and format checks are performed.

[1026] Step 3:

[1027] The server analyzes the received business information and uses natural language processing (NLP) technology to extract important keywords and phrases and understand the business content.

[1028] Step 4:

[1029] Based on the analyzed data, the server uses a generative AI model to search the database for optimal solutions and similar cases, such as an "automatic meeting minutes generation tool."

[1030] Step 5:

[1031] The server organizes the search results and generates a proposal document that includes an overview of the initiative, specific steps, and expected results.

[1032] Step 6:

[1033] The server sends the generated proposal document to the terminal, encrypting the data and checking the transfer protocol.

[1034] Step 7:

[1035] The terminal displays the received proposal document to the user, who then checks the contents of the proposal and decides whether or not to implement it.

[1036] Step 8:

[1037] The user implements the proposed solution. For example, they introduce an "automatic minutes generation tool" and start using it.

[1038] Step 9:

[1039] The user reports the results after implementing the solution, sending detailed data about the specific effects and implementation steps from the device to the server.

[1040] Step 10:

[1041] The server analyzes the reported results and evaluates their effectiveness, including data reconciliation and statistical analysis to measure effectiveness.

[1042] Step 11:

[1043] The server registers the evaluation results in a database, and effective initiatives are recorded as candidates for expansion to other departments.

[1044] Step 12:

[1045] The server sends a reward notification to the user for the efforts that are recognized as effective. This notification includes the content of the reward and instructions on how to receive it.

[1046] Step 13:

[1047] The terminal displays a reward notification to the user, and the user follows the instructions to complete the procedure for receiving the reward.

[1048] This concludes the explanation of the specific processing steps. This flow will effectively and quickly improve business efficiency.

[1049] Example 1

[1050] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1051] Conventional business efficiency systems were unable to accurately analyze users' business information and were unable to quickly and effectively provide optimal solutions or similar cases. It was also difficult to accurately evaluate the results of the proposed solutions, deploy them to other departments, or efficiently reward users. As a result, business efficiency was not fully achieved, and productivity did not improve across the entire company.

[1052] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1053] In this invention, the server includes a means for inputting business information from a user terminal, a means for analyzing the input business information and searching for optimal solutions or similar cases using a generative AI model, and a means for generating search results and presenting them to the user terminal. Thus, when a user inputs business information, the information is analyzed using natural language processing technology, and specific keywords and phrases are extracted. Furthermore, the generative AI model is used to search for optimal solutions or similar cases, and a proposal document is generated, enabling the user to quickly and effectively receive proposals for improving business efficiency.

[1054] In this invention, the server further includes means for the user to implement the proposed solution and report the results, means for evaluating the reported results and determining the effectiveness of the efficiency improvement, means for analyzing input information using natural language processing technology and extracting specific keywords and phrases, means for evaluating the results of the efficiency improvement and automatically sending a proposal to other departments, and means for receiving a report of the quantitative effects and implementation procedures from the user terminal. This makes it possible to accurately evaluate the results of the efficiency improvement measures implemented by the user, effectively deploy them to other departments, and properly reward the user.

[1055] A "user terminal" is a device that processes business information input by a user and displays generated proposal results.

[1056] "Business information" refers to data in which a user inputs detailed information about a business, and includes important information that is useful for improving business efficiency.

[1057] A "generative AI model" is an artificial intelligence model that generates optimal solutions and similar cases based on past data and cases.

[1058] "Natural language processing technology" is a technology that analyzes sentences and document data and extracts specific keywords and phrases.

[1059] A "database" is a collection of information that stores past efficiency improvement cases and evaluation data, and serves as the basis for search and evaluation.

[1060] A "proposal document" is a document containing a solution and execution procedures generated by the server, and is displayed on the user's terminal.

[1061] "Efficiency improvement effect" refers to the results and degree of improvement in operations obtained by implementing the proposed solution.

[1062] A "reward notification" is a notification of a reward sent to a user for an efficiency improvement initiative that has been recognized as effective.

[1063] "Deploying to other departments" means sharing information and making proposals to apply efficiency initiatives that have been proven in one department to other departments.

[1064] "Evaluation" refers to the process of analyzing the execution results reported by users and determining their effectiveness.

[1065] "Reporting" is an action taken by a user to transmit the results of a proposed solution to the server after the user has implemented the solution.

[1066] MODE FOR CARRYING OUT THE INVENTION

[1067] This invention is a system that supports the efficiency of internal business operations based on a generative AI model. The system allows users to input business information, search for and present optimal solutions and similar cases, and comprehensively supports post-implementation evaluation and reporting, as well as deployment to other departments.

[1068] User terminal

[1069] The user terminal provides an interface for users to input business information, review proposed solutions, and report implementation results. Users use this terminal to input detailed information about their business. For example, they can input specific efficiency requests, such as "improving the efficiency of regular meetings," using a browser or dedicated application. This information is organized by the terminal and sent to the server.

[1070] server

[1071] The server is the core of this system and is configured to achieve the following main functions:

[1072] Input analysis: The server analyzes the business information received from the user's device. This analysis uses natural language processing (NLP) technology. For example, specific keywords and phrases such as "meeting preparation" and "minutes creation" are extracted.

[1073] Search and suggest: Using a generative AI model, the system searches a database for optimal solutions or similar cases based on past data and examples. For example, it may search for solutions such as "using an automatic meeting minutes generation tool."

[1074] Proposal generation and presentation: The server generates a proposal document based on the search results. This proposal document includes an overview of the initiative, expected benefits, implementation procedures, etc. This document is sent to the user's terminal and displayed to the user.

[1075] Evaluation and registration: The execution results reported by users are analyzed and their effectiveness is evaluated. The evaluated effectiveness data is registered in a database.

[1076] Automatic deployment: Effective efficiency initiatives are automatically proposed to other departments. These proposals are sent to relevant user devices and departments.

[1077] Reward notification: If the effectiveness is proven, the server will send a reward notification to the user. The user will check this notification on their device and proceed with the reward claim procedure.

[1078] Database

[1079] The database is a collection of information that stores past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[1080] Specific examples

[1081] Here's a specific example: A user wants to "improve the efficiency of regular meetings" and inputs the problem of "it takes a long time to prepare for meetings and create minutes" from their device. The server analyzes this information and uses a generative AI model to search for and extract "use of an automatic minutes generation tool" and suggests it to the user. When the user introduces this tool and reports that the time it takes to create minutes has been reduced by 60%, the server evaluates its effectiveness. If similar results are confirmed in other departments, the server registers this information in its database and automatically sends suggestions to those departments. The user is also notified of a reward and is responsible for the procedures to receive it.

[1082] Prompt Sentence Examples

[1083] An example of a prompt is as follows:

[1084] "Please provide suggestions for streamlining meeting preparation and minutes creation, including examples of effective tools."

[1085] As described above, this system supports efficient and effective internal business operations and contributes to improved productivity.

[1086] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1087] Step 1:

[1088] User input of business information

[1089] A user enters details about their business into a terminal.

[1090] Input: User's business information

[1091] Specific operation: The user opens a browser or a dedicated application and enters an item such as "improving the efficiency of regular meetings" or a problem such as "it takes a long time to prepare for meetings and create minutes."

[1092] Output: Business information data organized by the terminal

[1093] Step 2:

[1094] Analysis of entered information

[1095] The server analyzes the business information received from the terminal.

[1096] Input: Business information data received from the terminal

[1097] What it does: The server launches a natural language processing (NLP) engine to analyze the received text data, specifically extracting specific keywords and phrases (e.g., "meeting preparation" or "minutes creation") contained in the business information.

[1098] Output: Extracted keyword and phrase data

[1099] Step 3:

[1100] Search and suggestion generation using generative AI models

[1101] Using a generative AI model, optimal solutions and similar cases are searched for, and proposal documents are generated.

[1102] Input: Extracted keyword and phrase data

[1103] Specific operation: The server uses the generative AI model to search a past database. For example, it searches for similar cases such as "using an automatic meeting minutes generation tool" and selects the optimal solution from among them. Based on the selected solution, it generates a proposal document (outline of the initiative, expected effects, implementation procedures, etc.).

[1104] Output: Proposal document data

[1105] Step 4:

[1106] Submitting and presenting proposal documents

[1107] The server sends the generated proposal document to the user's terminal and presents it.

[1108] Input: Proposal document data

[1109] Specific operation: The server sends the generated proposal document to the user terminal. The user terminal receives this document and displays it to the user. The user confirms the proposed solution.

[1110] Output: Proposal document displayed on user's terminal

[1111] Step 5:

[1112] Implementing proposed solutions and reporting results

[1113] The user implements the proposed solution and reports the results.

[1114] Input: Proposal document, execution result data

[1115] Specific operation: A user generates meeting minutes using the "automatic minutes generation tool" and inputs the results (e.g., the time required to create minutes has been reduced by 60%) from their device. The device organizes this information and sends it to the server.

[1116] Output: Execution result data reported from the terminal

[1117] Step 6:

[1118] Evaluation of execution results

[1119] The server evaluates the reported execution results.

[1120] Input: Execution result data reported from the device

[1121] Specific operation: The server analyzes the execution results and judges the effectiveness of the efficiency improvement. For example, it evaluates a specific numerical value such as "60% reduction in the time required to create minutes."

[1122] Output: Evaluation result data

[1123] Step 7:

[1124] Proposal deployment to other departments

[1125] Based on the evaluation results, suggestions are automatically sent to other departments.

[1126] Input: Evaluation result data

[1127] Specific operation: The server automatically sends proposal information to other departments based on the evaluation data. For example, it automatically forwards a proposal to other departments that "an automatic minutes generation tool would be effective."

[1128] Output: Proposal data sent to other departments

[1129] Step 8:

[1130] Reward notification

[1131] The server sends a reward notice to the user for the efforts that have been recognized as effective.

[1132] Input: Evaluation result data

[1133] Specific operation: The server sends a reward notification via the user's contact information (email address or internal messaging system). The user receives the notification and completes the reward claim procedure.

[1134] Output: Reward notification data sent to user

[1135] The above are the specific steps of the processing of this system.

[1136] (Application example 1)

[1137] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1138] Conventional business efficiency systems are generally based on desktops or laptops, and have the problem of being unable to respond to real-time business efficiency proposals or immediate improvements to on-site work. Even when users receive efficiency proposals, delays in reporting the implementation and feedback of the results often prevent a rapid improvement cycle, hindering productivity improvement. In particular, in workplaces such as factories, where immediate improvements in work efficiency and safety are often required, conventional systems are unable to adequately address these issues.

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

[1140] In this invention, the server includes a means for inputting task information from a user terminal, a means for analyzing the input task information and searching for optimal solutions or similar cases using a generative AI model, and a means for acquiring the user's work content through voice input and generating efficiency suggestions. This allows the user to receive efficiency suggestions in real time through the smart glasses. In addition, the proposed solutions are quickly implemented on-site and the results are reported immediately, thereby promoting the efficiency of on-site work in real time and simultaneously achieving improvements in overall productivity and safety.

[1141] A "user terminal" is a device that provides an interface for a user to input business information, confirm efficiency improvement proposals, and report on their implementation.

[1142] "Business information" refers to detailed data and issues related to the business that the user wishes to improve the efficiency of.

[1143] A "generative AI model" is an artificial intelligence algorithm that generates efficiency suggestions and solutions based on input data.

[1144] "Searching means" is a function that uses a generative AI model to search and extract optimal solutions and similar cases from a database based on the business information entered.

[1145] "Search results" are the optimal solution or a set of similar cases extracted by the generative AI model.

[1146] "Means for the user to implement the proposed solution" refers to the means and process for the user to actually implement the proposed efficiency proposal.

[1147] The "means for reporting results" is a means for reporting the effects and results of a suggestion made by a user to the system via a device.

[1148] The "means for evaluating the reported results" is a function that allows the system to analyze the execution results reported by the user and determine the effectiveness of the efficiency improvement.

[1149] A "database" is a collection of information that stores past efficiency improvement cases and evaluation data.

[1150] "Means for deployment to other departments" refers to the function of automatically sending and sharing the evaluated efficiency gains to other departments as proposals.

[1151] "Smart glasses" are devices that users can wear to display information in real time and visually receive suggestions for improving work efficiency.

[1152] The "voice input means" is a function that allows the user to input work content and business information into the system through voice.

[1153] The "means for notifying a reward" is a system function for notifying a user of a reward when the execution result of an efficiency improvement proposal is effective.

[1154] The present invention provides a system for supporting factory workers in improving their work efficiency by using smart glasses. Specific embodiments for carrying out the present invention will be described in detail below.

[1155] Basic system configuration

[1156] The system consists of the following main components:

[1157] 1. User Device:

[1158] A device that provides an interface for users to input business information, confirm efficiency proposals, and report on their implementation. Smart glasses are an example of this.

[1159] 2. Server:

[1160] It acts as the core of the system and is responsible for the following main functions:

[1161] Input analysis: Analyzes business information received from the user's device and extracts important keywords and context.

[1162] Search and suggest: Generative AI models are used to search a database for similar past cases and optimal solutions to generate suggestions.

[1163] Evaluation and registration: The execution results reported by users are analyzed, their effectiveness is evaluated, and they are registered in a database.

[1164] Notification: If the effect is confirmed, the user will be notified of the reward.

[1165] 3. Database:

[1166] It is a collection of information that stores past efficiency case studies and evaluation data. The server uses this database to search and evaluate.

[1167] Program processing

[1168] When a user puts on the smart glasses and works in a factory, the system operates according to the following steps.

[1169] 1. Real-time video capture:

[1170] The smart glasses' camera is used to capture real-time video of the user's work, allowing the user to understand their environment and work situation in real time.

[1171] 2. Voice input of your work:

[1172] The user inputs the details of the work into the smart glasses through voice input, for example, telling the system a specific task such as "I want to improve the speed at which bolts are installed."

[1173] 3. Input analysis and proposal generation:

[1174] The server analyzes the work content input by voice and uses a generative AI model to generate optimal efficiency suggestions, using natural language processing technologies such as OpenAI's API.

[1175] 4. Presenting the proposal:

[1176] The generated efficiency suggestions are displayed on the smart glasses' display, and the user can confirm the suggestions and put them into action.

[1177] Specific examples

[1178] For example, a user might say, "I want to improve the speed of bolt installation on the assembly line." The server analyzes this information, and the generative AI model suggests "introducing an automated tool for bolt installation." This suggestion is displayed on the smart glasses, and the user can use the suggested tool and report its effectiveness.

[1179] Prompt Sentence Examples

[1180] For example, use the following prompt:

[1181] 'Please tell me some concrete measures to improve bolt installation speed'

[1182] This allows the system to provide users with quick and effective suggestions for improving work efficiency, improving the quality and safety of work within the factory.

[1183] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1184] Step 1:

[1185] The user puts on the smart glasses and starts working in the factory. The camera in the smart glasses captures the user's work in real time. This operation allows the user's environment and work situation to be understood.

[1186] Input: Factory work footage

[1187] Output: Real-time video data

[1188] Step 2:

[1189] The user speaks to the smart glasses to describe their goal for improving work efficiency. For example, they might say, "I want to improve the speed at which bolts are attached on the assembly line." This voice data is captured by the smart glasses' microphone and converted into text data.

[1190] Input: Voice input data (for efficiency purposes)

[1191] Output: Converted text data

[1192] Step 3:

[1193] The server analyzes the text data received through the smart glasses and uses natural language processing (NLP) technology to analyze the input business information and extract important keywords and context.

[1194] Input: Converted text data

[1195] Output: Parsed keywords and context

[1196] Step 4:

[1197] The server searches a database for optimal solutions and similar cases based on keywords and context analyzed using the generative AI model, using past examples of efficiency improvements and optimal solutions.

[1198] Input: Parsed keywords and context

[1199] Output: Optimal solution and similar cases

[1200] Step 5:

[1201] The server then presents the optimal solution obtained from the search results to the user's device (smart glasses), where specific efficiency measures are visually displayed so that the user can confirm the proposal.

[1202] Input: Optimal solution and similar cases

[1203] Output: Efficiency suggestions displayed on smart glasses

[1204] Step 6:

[1205] The user implements the efficiency suggestions displayed on the smart glasses, for example, by using a new tool based on the proposed solution, and the results are reported to the server via the smart glasses.

[1206] Input: Efficiency proposal

[1207] Output: Execution result data

[1208] Step 7:

[1209] The server evaluates the reported execution results, analyzes the effectiveness of the execution results, and registers them in a database. The evaluation includes the effectiveness of the results and the rate of improvement.

[1210] Input: Execution result data

[1211] Output: Evaluated effects and database updates

[1212] Step 8:

[1213] If the effectiveness is recognized, the server will notify the user of a reward. In addition, based on the evaluated efficiency effect, suggestions will be automatically sent to other departments, so that similar efficiency measures can be used in other departments as well.

[1214] Input: Evaluated effect

[1215] Output: Reward notice and proposal notice to other departments

[1216] These steps will enable real-time operational efficiency improvements using smart glasses, enabling users to work more efficiently on-site, and are expected to improve overall productivity and safety.

[1217] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1218] The present invention is a matching system that combines a generative AI model and an emotion engine to help users improve their work efficiency. This system comprehensively supports everything from inputting work information to proposing solutions, evaluating and reporting after implementation, and even deploying solutions to other departments. It also takes into account the user's emotional state to make more personalized proposals. Detailed embodiments of this system are described below.

[1219] Basic system configuration

[1220] The system consists of the following main components:

[1221] 1. User Device

[1222] 2. Server

[1223] 3. Database

[1224] 4. Emotion Recognition Engine

[1225] User terminal

[1226] The user terminal provides an interface for users to input work information and emotional data, confirm proposed solutions, and submit implementation reports. Users input information about their work and their current emotional state, and send their requests for efficiency improvement to the system through the terminal.

[1227] server

[1228] The server is the core of the system and is responsible for the following main functions:

[1229] Input analysis: Analyzes business information and emotional data received from the user's device to extract important keywords, context, and emotional state.

[1230] Search and suggest: Using a generative AI model, the system searches a database for similar past cases and optimal solutions, and suggests information that takes into account the user's emotional state.

[1231] Evaluation and registration: The execution results reported by the user are analyzed, their effectiveness is evaluated, and the results are registered in a database. User emotion data is also taken into account in the evaluation.

[1232] Notification: If the effect is recognized, a reward notification will be sent to the user, and a personalized message will be generated based on the user's emotional state.

[1233] Database

[1234] The database is a collection of information that includes past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[1235] Emotion Recognition Engine

[1236] The emotion recognition engine is a component that recognizes and analyzes the user's emotional state from their input data and behavioral data, allowing the system to generate suggestions based on the user's emotional state.

[1237] Explanation of program processing

[1238] User input of business information and emotion data

[1239] The user inputs detailed information about the task they wish to improve efficiency and their current emotional state into the terminal, which then organizes this information and sends it to the server in a format that includes emotional data.

[1240] Analysis and search of input information

[1241] The server analyzes the business information and emotional data received from the device. Natural language processing (NLP) technology and an emotion recognition engine are used to extract specific keywords, phrases, and emotional states. Based on this, a generative AI model searches for optimal solutions and similar cases.

[1242] Generating and presenting search results

[1243] The server organizes solutions and similar cases extracted from the database and generates a proposal document that also takes the user's emotional state into consideration. The proposal document includes an overview of the initiative, specific steps, and expected results, as well as a communication style and feedback appropriate to the user's emotional state. The user's device receives this and displays it to the user.

[1244] Implementing proposed solutions and reporting results

[1245] The user implements the proposed solution and reports the results to the server from their device. The report includes specific steps taken, numerical data showing the effects, and the user's emotional state after implementation.

[1246] Evaluating the results and expanding to other departments

[1247] The server analyzes the reported results and evaluates their effectiveness. The analysis includes statistical analysis to confirm data consistency and measure effectiveness, as well as evaluating emotional data. If the effectiveness is proven, it is registered in the database and a proposal is automatically sent to other departments.

[1248] Notification and receipt of rewards

[1249] If an effort is found to be effective, the server sends the user a reward notification with a personalized message according to their emotional state. The user can then check the notification on their device and follow the procedure to receive the reward.

[1250] Specific examples

[1251] As an example, suppose a user (person A) wishes to "improve the efficiency of regular meetings." Person A inputs their problems and emotional state, such as "it takes a long time to prepare for meetings and write minutes" and "I feel stressed," on their device. The server analyzes this information, searches and extracts similar past cases such as "using an automatic minutes generation tool," and presents them with "effective communication methods." When Person A reports that they have introduced the proposed tool and that the time it takes to write minutes has been reduced by 60%, the server evaluates its effectiveness, taking their emotional state into consideration. If similar effects are confirmed in other departments, the server will provide a personalized message according to their emotional state and notify Person A of their gratitude.

[1252] The above system makes suggestions for work efficiency improvements that take the user's emotional state into account, enabling more personalized and effective support, which not only contributes to improving productivity across the company but also increases employee satisfaction.

[1253] The processing flow will be explained below.

[1254] Step 1:

[1255] The user inputs detailed information about their work and their current emotional state into the terminal. For example, they might input "I would like to improve the efficiency of regular meetings," "It takes a lot of time to prepare for meetings and write minutes," and "I feel stressed."

[1256] Step 2:

[1257] The terminal organizes the business information and emotion data input by the user, formats it, and transmits it to the server.

[1258] Step 3:

[1259] The server analyzes the received business information and emotional data using natural language processing (NLP) technology and an emotion recognition engine to extract important keywords, phrases, and emotional states.

[1260] Step 4:

[1261] Based on the analyzed data, the server uses a generative AI model to search the database for optimal solutions and similar cases, such as an "automatic meeting minutes generation tool."

[1262] Step 5:

[1263] The server organizes the search results and generates a proposal document that takes the user's emotional state into consideration. The proposal document includes an overview of the initiative, specific steps, and expected results, as well as a communication style and feedback appropriate for the user's emotional state.

[1264] Step 6:

[1265] The server sends the generated proposal document to the terminal, encrypting the data and checking the transfer protocol.

[1266] Step 7:

[1267] The terminal displays the received proposal document to the user, who then checks the contents of the proposal and decides whether or not to implement it.

[1268] Step 8:

[1269] The user implements the proposed solution. For example, they introduce an "automatic minutes generation tool" and start using it.

[1270] Step 9:

[1271] The user reports the results after implementing the solution, sending detailed data on the specific effects and steps taken, as well as their emotional state after implementation, from their device to the server.

[1272] Step 10:

[1273] The server analyzes the reported results and evaluates their effectiveness, including statistical analysis to verify data consistency and measure effectiveness, as well as evaluating emotional data.

[1274] Step 11:

[1275] The server registers the evaluation results in a database, and effective initiatives are recorded as candidates for expansion to other departments.

[1276] Step 12:

[1277] The server sends a reward notice to the user for efforts that are recognized as effective, the reward notice including a personalized message according to the user's emotional state.

[1278] Step 13:

[1279] The terminal displays a reward notification to the user, and the user follows the instructions to complete the procedure for receiving the reward.

[1280] This concludes the explanation of the specific processing steps. This flow effectively and quickly improves business efficiency and also provides services that respond to the emotional state of the user.

[1281] Example 2

[1282] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1283] Conventional work efficiency improvement systems only make suggestions based on work information and do not consider the user's emotional state, which can result in ineffective work suggestions. Furthermore, even after the user implements the proposed solution, a comprehensive evaluation that includes emotional data is not performed, which creates challenges in improving user satisfaction.

[1284] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting business information and emotional data from a user terminal, a means for analyzing the input business information and emotional data and searching for optimal solutions or similar cases using a generative AI model, and a means for generating search results and presenting a proposal document that takes the user's emotional state into consideration to the user's terminal. This realizes personalized proposals that take the user's emotional state into consideration, enabling efficient and satisfying business improvement.

[1285] A "user terminal" is a device through which a user inputs business information and emotional data and receives suggestions and notifications from the system.

[1286] "Business information" refers to specific data and content that a user inputs regarding a business, and is information that includes data regarding the current status and issues of the business.

[1287] "Emotion data" refers to data about the current emotional state input by the user, and specifically includes subjective evaluations such as stress and satisfaction.

[1288] The "server" is a central computer system that receives business information and emotion data sent from user terminals, and performs analysis, proposals, and notifications.

[1289] A "generative AI model" is a model that applies artificial intelligence technology to generate optimal solutions and similar cases based on past data and input information.

[1290] A "proposal document" is a document created based on optimal solutions and similar cases generated by the server, taking into account the user's emotional state, and describing the proposal content, effects, implementation procedures, etc.

[1291] A "database" is a collection of information that stores data such as past business information, emotional data, solutions, and evaluation results.

[1292] An "emotion recognition engine" is a component that recognizes and analyzes a user's emotional state from input data and behavioral data.

[1293] "Evaluation" is the process of analyzing the results after the user implements the proposed solution and determining its effectiveness.

[1294] A "personalized message" is an individually customized message created by the server according to the user's emotional state.

[1295] This invention is a system that combines a generative AI model and an emotion recognition engine to help users improve their work efficiency. This system comprehensively supports everything from inputting work information to proposing solutions, evaluating and reporting after implementation, and even deploying solutions to other departments. It also takes the user's emotional state into consideration to make more personalized suggestions.

[1296] Basic system configuration

[1297] The system consists of the following main components:

[1298] 1. User Device

[1299] 2. Server

[1300] 3. Database

[1301] 4. Emotion Recognition Engine

[1302] User terminal

[1303] The user terminal provides an interface for users to input work information and emotional data, confirm proposed solutions, and submit implementation reports. Users input information about their work and their current emotional state, and send their requests for efficiency improvement to the system through the terminal.

[1304] server

[1305] The server is the core of the system and is responsible for the following main functions:

[1306] Input analysis: Analyzes business information and emotional data received from the user's device to extract important keywords, context, and emotional state. Analysis is performed using natural language processing (NLP) technology and an emotion recognition engine.

[1307] Search and suggest: Using a generative AI model, the system searches a database for similar cases and optimal solutions, and suggests information that takes into account the user's emotional state.

[1308] Evaluation and registration: Analyze the execution results reported by users, evaluate their effectiveness, and register them in a database. Emotional data is also taken into account in the evaluation.

[1309] Notification: If successful, users will receive a reward notification with a personalized message based on their emotional state.

[1310] Database

[1311] The database is a collection of information that includes past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[1312] Emotion Recognition Engine

[1313] The emotion recognition engine is a component that recognizes and analyzes the user's emotional state from their input data and behavioral data, allowing the system to generate suggestions based on the user's emotional state.

[1314] Specific examples

[1315] As an example, consider the case where a user (Person A) wishes to "improve the efficiency of regular meetings." Person A inputs his / her problems and emotional state, such as "it takes a long time to prepare for meetings and create minutes" and "I feel stressed," on his / her device. The server analyzes this information, searches and extracts similar past cases such as "using an automatic minutes generation tool," and presents a proposal along with "effective communication methods." When Person A reports that he / she introduced the proposed tool and saw that the time it took to create minutes was reduced by 60%, the server evaluates its effectiveness, taking his / her emotional state into consideration. If similar effects are confirmed in other departments, the server will provide a personalized message according to his / her emotional state and notify Person A of a reward.

[1316] Prompt Sentence Examples

[1317] "I spend a lot of time preparing for and taking minutes of our weekly meetings, and I'd like to improve the efficiency. I'm also finding this work very stressful. I'd like some suggestions on how to solve this problem."

[1318] This system makes suggestions for work efficiency improvements that take the user's emotional state into account, enabling more personalized and effective support, which not only contributes to improving productivity across the company but also increases employee satisfaction.

[1319] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1320] Step 1: User inputs task information and emotion data

[1321] The user inputs work information and emotional data into the user terminal. Specific examples of input include the current state of work and emotional state, such as "it takes a long time to prepare for a meeting" or "I'm feeling stressed." This data is formatted and initial processing is performed by the user terminal. The input data is then sent to the server.

[1322] Input: Business information and emotion data

[1323] Output: Formatted business information and sentiment data

[1324] Specific example of operation: A user enters "meeting preparation," "time-consuming," and "very stressful" into the input form on the device and clicks the send button.

[1325] Step 2: Initial processing of input data and sending to server

[1326] The terminal receives data entered by the user, checks the data format, and performs preprocessing of the data as necessary. For example, it converts data in different formats into a unified format, which makes it easier for the server to analyze it. After preprocessing is complete, the data is sent to the server.

[1327] Input: Formatted business information and emotion data

[1328] Output: Preprocessed data

[1329] Specific example of operation: The device extracts keywords such as "meeting preparation," "time-consuming," and "very stressful," standardizes the data format, and sends it to the server.

[1330] Step 3: Data analysis and search by server

[1331] The server analyzes the received data using natural language processing (NLP) technology and an emotion recognition engine. NLP technology is used to extract specific keywords and phrases from business information, and the emotion recognition engine is used to analyze the emotional data. Based on the results of these analyses, a generative AI model is used to search a database for optimal solutions and similar cases.

[1332] Input: Preprocessed business information and emotion data

[1333] Output: extracted keywords, emotional states, and searched solutions

[1334] Specific operation example: The server receives the data, extracts keywords such as "meeting," "preparation," "time," and "stress," and recognizes the emotional state as "very stressful." Next, based on this information, it searches the database for the optimal solution, such as "use an automatic meeting minutes generation tool."

[1335] Step 4: Server generates and sends proposal document

[1336] The server generates a proposal document based on the search results. The proposal document includes an outline of the initiative, specific steps, expected results, and a communication style and feedback appropriate to the user's emotional state. The document is then sent to the user's device.

[1337] Input: searched solutions, extracted keywords, emotional state

[1338] Output: Proposal document

[1339] Specific example of operation: The server creates a proposal document based on the "automatic minutes generation tool" and sends it to the user's terminal, including content such as "By introducing this tool, meeting preparation time will be reduced by 60% and stress is expected to be reduced."

[1340] Step 5: User implements solution and reports results

[1341] The user checks the proposal document presented by the server and implements the solution. For example, they introduce an automatic meeting minutes generation tool. After implementation, the user reports the results and emotional state from their device to the server. The report includes specific implementation steps, numerical data showing the effects, and the user's emotional state.

[1342] Input: User's performance and emotional state

[1343] Output: Reported performance results and emotional state

[1344] Specific example of operation: A user introduces an automatic minutes generation tool and reports from their device that the time it takes to create minutes has been reduced by 60%, resulting in "meeting preparation time reduced by 60%" and "stress reduced."

[1345] Step 6: Server evaluation and database registration

[1346] The server analyzes the results reported by users and evaluates their effectiveness. The evaluation includes statistical analysis and evaluation of emotional data. The evaluated data is then registered in a database. If the effectiveness is proven, it is automatically deployed to other departments.

[1347] Input: Reported performance results and emotional state

[1348] Output: Evaluated data, updated database

[1349] Specific operation example: The server analyzes reports such as "meeting preparation time reduced by 60%" and "stress reduced," evaluates the effectiveness, and registers it in a database. It also automatically sends a proposal to other departments to "introduce an automatic minutes generation tool."

[1350] Step 7: Server Notification and Personalized Message Delivery

[1351] The server sends a reward notification to the user for the efforts that have been recognized as effective, including a personalized message according to the user's emotional state. The user can check this notification on their device and receive the reward by taking the appropriate steps.

[1352] Input: Evaluated data, user's emotional state

[1353] Output: Thank you notice with personalized message

[1354] Specific operation example: Depending on the user's emotional state, the server sends the user a message such as, "Congratulations on reducing stress! Your efforts have significantly improved the efficiency of your work. We have a reward for you, so please go through the process of receiving it." The user then goes through the process of receiving the reward from their device.

[1355] (Application example 2)

[1356] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1357] Conventional efficiency support systems have the problem of being unable to take into account the user's emotional state and therefore unable to make effective optimization suggestions. Furthermore, in on-site work such as in factories, real-time feedback and work management are difficult, making it difficult to improve work efficiency. Furthermore, there was a need for a method to simultaneously reduce employee stress and improve work efficiency.

[1358] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting business information and emotional data from a user terminal, means for analyzing the input business information and emotional data and searching for optimal solutions or similar cases using a generative AI model, and means for generating search results and presenting them to the user terminal. This makes it possible to propose and provide feedback on optimal business efficiency improvements in real time, taking into account the emotional state of employees.

[1359] "User terminal" refers to a device that allows a user to input, transmit, and check business information and emotional data.

[1360] "Task information" is data that indicates the details and progress of the tasks that a user is performing.

[1361] "Emotion data" is data that represents the user's current mental state and emotions.

[1362] A "generative AI model" is a model that uses machine learning and deep learning technologies to generate optimal solutions and similar cases based on input data.

[1363] "Emotional state" refers to a user's emotional condition or psychological state.

[1364] "Searching means" refers to a method of finding optimal solutions and similar cases based on business information and emotional data analyzed using a generative AI model.

[1365] "Real-time" refers to the ability to respond immediately to the moment business operations progress.

[1366] "Smart glasses" are glasses-type devices that use augmented reality (AR) technology and have the ability to display information in the user's field of vision.

[1367] A "database" is a collection of information for storing searched solutions, similar cases, and evaluated effects.

[1368] "Means of evaluation" refers to a method for determining the effectiveness of efficiency improvements based on the results and emotional state reported by users.

[1369] "Deployment measures" are methods for applying the evaluated effects of business efficiency improvements to other departments.

[1370] "Reward" refers to a reward or compensation given to a user when the proposed solution is implemented and its effectiveness is recognized.

[1371] "Effect" is an index showing how successful the proposed means for improving work efficiency was, or how much the user's work efficiency was improved.

[1372] The present invention provides a matching system that supports users in improving their work efficiency by combining a generative AI model and an emotion engine. A specific embodiment of this system is described below.

[1373] Basic system configuration

[1374] The system consists of the following main components:

[1375] 1. User Device

[1376] 2. Server

[1377] 3. Database

[1378] 4. Emotion Recognition Engine

[1379] 5. Smart Glasses

[1380] User terminal

[1381] The user terminal provides an interface for users to input work information and emotional data, confirm proposed solutions, and submit implementation reports. It also collects and displays real-time work progress information and emotional data through smart glasses. Specifically, users input work details and emotional state and send their requests for efficiency improvements to the system.

[1382] server

[1383] The server is the central part of the system and is responsible for the following main functions:

[1384] Input data analysis: Analyzes business information and emotion data received from user devices to extract important keywords, context, and emotional states. Technologies used here include natural language processing (NLP) and emotion recognition engines.

[1385] Search for optimization suggestions: Using a generative AI model, the system searches a database for similar past cases and optimal solutions, and suggests information that takes into account the user's emotional state.

[1386] Evaluation and registration: The execution results reported by the user are analyzed, their effectiveness is evaluated, and they are registered in a database. Here, the evaluation also takes into account the user's emotional data.

[1387] Notification: If the effect is recognized, a reward notification will be sent to the user, and a personalized message will be generated based on the user's emotional state.

[1388] Database

[1389] The database is a collection of information that includes past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[1390] Emotion Recognition Engine

[1391] The emotion recognition engine is a component that recognizes and analyzes the user's emotional state from their input data and behavioral data, allowing the system to generate suggestions based on the user's emotional state.

[1392] Smart Glasses

[1393] The smart glasses are worn by users while they work and collect and display information on work progress and emotional data in real time. Users can check and implement suggestions for improving efficiency through the smart glasses.

[1394] Specific examples

[1395] As an example, suppose a user (Employee A) is performing "production line quality inspections," but is experiencing delays, frequent errors, and feeling stressed. Employee A inputs this work information and emotional state through smart glasses. The server analyzes this information, searches and extracts "use of inspection automation tools" from similar past cases, and suggests them to Employee A through the smart glasses. Employee A implements the proposed tool and reports that the error rate has been significantly reduced. As a result, the server evaluates its effectiveness, taking the user's emotional state into consideration. If similar effects are confirmed in other departments, the server will provide a personalized message according to Employee A's emotional state and notify Employee A of a reward.

[1396] Examples of prompts:

[1397] "We are currently conducting quality inspections on the line, but the work is delayed. Errors occur frequently, which is frustrating."

[1398] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1399] Step 1:

[1400] A user puts on the smart glasses and inputs business information and emotion data.

[1401] Input: Detailed work information (e.g., progress of quality inspection on the line), current emotional state (e.g., stress).

[1402] Specific operation: The user operates the interface of the smart glasses and records business information and emotional state through text input.

[1403] Step 2:

[1404] The smart glasses transmit the input business information and emotion data to the server.

[1405] Input: Business information and sentiment data entered by the user.

[1406] Output: The dataset that is sent to the server for analysis.

[1407] Specific operation: Using the network function of the smart glasses, the input data is sent to the server in real time.

[1408] Step 3:

[1409] The server analyzes the received business information and emotion data.

[1410] Input: Dataset sent from smart glasses.

[1411] Output: Keywords, context, and emotional state as analysis results.

[1412] Specific operation: The server uses natural language processing (NLP) technology to analyze business information, extract important keywords, and classify emotional data using an emotion recognition engine.

[1413] Step 4:

[1414] Based on the analysis results, the server uses a generative AI model to search a database for optimal solutions and similar cases.

[1415] Input: Parsed task information and emotional state.

[1416] Output: Best solution or list of similar cases.

[1417] Specific operation: The server inputs a prompt sentence into the generative AI model, which generates and extracts optimal solutions and similar cases.

[1418] Step 5:

[1419] The server organizes the search results, generates a suggested document that takes into account the user's emotional state, and sends it to the smart glasses.

[1420] Input: Retrieved solutions, similar cases, and the user's emotional state.

[1421] Output: Proposal document.

[1422] Specific operation: The server creates a proposal document that reflects the emotional data on the generated solution and sends it to the smart glasses.

[1423] Step 6:

[1424] The user sees the proposed solution through the smart glasses and implements it.

[1425] Input: Proposal document.

[1426] Output: Execution results (e.g., reduction in error rate).

[1427] Specific action: The user reads the proposal document and implements the proposed solution (e.g., introduction of an inspection automation tool) in the field.

[1428] Step 7:

[1429] The user reports the results of the execution to the server through the smart glasses.

[1430] Input: Execution results and emotional state after execution.

[1431] Output: Result report data sent to the server.

[1432] Specific operation: The user inputs the execution results and emotional state into the smart glasses and sends the data to the server.

[1433] Step 8:

[1434] The server analyzes the reported results and evaluates the effectiveness of the efficiency improvements.

[1435] Input: A dataset of execution results and emotional states.

[1436] Output: Effectiveness evaluation report.

[1437] Specific operation: The server performs statistical analysis and evaluates emotional data to evaluate the effectiveness of efficiency.

[1438] Step 9:

[1439] The server registers the evaluation results in a database and considers whether they can be expanded to other departments.

[1440] Input: Effectiveness evaluation report.

[1441] Output: Registered effectiveness data and decision on whether to expand to other departments.

[1442] Specific operation: The server stores the evaluation results in a database and uses them to decide whether to deploy them to other departments.

[1443] Step 10:

[1444] If the effect is recognized, the server notifies the user of the reward.

[1445] Input: The result of the evaluation as a trigger.

[1446] Output: Gratuity notice.

[1447] Specific operation: The server generates a reward notification for the user, including a personalized message according to the user's emotional state, and sends it to the smart glasses.

[1448] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1449] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1450] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1451] [Fourth embodiment]

[1452] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1453] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1454] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1455] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1456] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1457] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1458] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1459] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1460] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1461] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1462] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1463] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1464] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1465] The present invention is a matching system based on a generative AI model to support the efficiency of internal business operations. This system comprehensively supports everything from inputting business information to presenting efficiency proposals, evaluating and reporting after implementation, and even expanding to other departments. Detailed embodiments of this system are described below.

[1466] Basic system configuration

[1467] The system consists of the following main components:

[1468] 1. User Device

[1469] 2. Server

[1470] 3. Database

[1471] User terminal

[1472] The user terminal provides an interface for users to input business information, check proposed solutions, and submit implementation reports. Users input information about their own business and transmit their requests for efficiency improvement to the system through the terminal.

[1473] server

[1474] The server is the core of the system and is responsible for the following main functions:

[1475] Input analysis: Analyzes business information received from the user's device and extracts important keywords and context.

[1476] Search and suggestion: Using a generative AI model, the system searches a database for similar past cases and optimal solutions, and generates information to suggest to the user.

[1477] Evaluation and registration: Analyze the execution results reported by users, evaluate their effectiveness, and register them in a database.

[1478] Notification: If the effect is recognized, a reward notification will be sent to the user.

[1479] Database

[1480] The database is a collection of information that stores past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[1481] Explanation of program processing

[1482] User input of business information

[1483] Users input detailed information about the tasks they wish to streamline into the terminal, which then organizes this information and sends it to the server in the required format.

[1484] Analysis and search of input information

[1485] The server analyzes the business information received from the device, using natural language processing (NLP) technology to extract specific keywords and phrases. Based on this, a generative AI model searches for optimal solutions and similar cases.

[1486] Generating and presenting search results

[1487] The server organizes the solutions and similar cases extracted from the database and generates a proposal document to be presented to the user's device. The proposal document includes an overview of the initiative, its effects, implementation procedures, etc. The user's device receives this and displays it to the user.

[1488] Implementing proposed solutions and reporting results

[1489] The user implements the proposed solution and reports the results to the server from their device. The report includes specific implementation steps and numerical data showing the effectiveness.

[1490] Evaluating the results and expanding to other departments

[1491] The server evaluates the reported results and determines their effectiveness. If they are proven effective, they are registered in the database and can also automatically send suggestions to other departments.

[1492] Notification and receipt of rewards

[1493] If an initiative is deemed effective, the server will notify the user of a reward. The user can then check the notification on their device and follow the procedure to receive the reward.

[1494] Specific examples

[1495] As an example, let's say a user (Mr. A) wants to "improve the efficiency of regular meetings." Mr. A inputs the problem of "it takes a long time to prepare for meetings and create minutes" from his terminal. The server analyzes this information, searches and extracts similar past cases and suggests "using an automatic minutes generation tool." Mr. A introduces the proposed tool and reports that the time it takes to create minutes has been reduced by 60%, and the server evaluates its effectiveness. If similar effects are confirmed in other departments, the server will notify Mr. A of a reward.

[1496] The above system will efficiently and effectively promote the streamlining of internal operations and contribute to improving productivity throughout the company.

[1497] The processing flow will be explained below.

[1498] Step 1:

[1499] The user inputs detailed information about their work, such as "I would like to make regular meetings more efficient" or "It takes a lot of time to prepare for meetings and create minutes."

[1500] Step 2:

[1501] The terminal organizes and formats the business information entered by the user and sends it to the server, where data consistency and format checks are performed.

[1502] Step 3:

[1503] The server analyzes the received business information and uses natural language processing (NLP) technology to extract important keywords and phrases and understand the business content.

[1504] Step 4:

[1505] Based on the analyzed data, the server uses a generative AI model to search the database for optimal solutions and similar cases, such as an "automatic meeting minutes generation tool."

[1506] Step 5:

[1507] The server organizes the search results and generates a proposal document that includes an overview of the initiative, specific steps, and expected results.

[1508] Step 6:

[1509] The server sends the generated proposal document to the terminal, encrypting the data and checking the transfer protocol.

[1510] Step 7:

[1511] The terminal displays the received proposal document to the user, who then checks the contents of the proposal and decides whether or not to implement it.

[1512] Step 8:

[1513] The user implements the proposed solution. For example, they introduce an "automatic minutes generation tool" and start using it.

[1514] Step 9:

[1515] The user reports the results after implementing the solution, sending detailed data about the specific effects and implementation steps from the device to the server.

[1516] Step 10:

[1517] The server analyzes the reported results and evaluates their effectiveness, including data reconciliation and statistical analysis to measure effectiveness.

[1518] Step 11:

[1519] The server registers the evaluation results in a database, and effective initiatives are recorded as candidates for expansion to other departments.

[1520] Step 12:

[1521] The server sends a reward notification to the user for the efforts that are recognized as effective. This notification includes the content of the reward and instructions on how to receive it.

[1522] Step 13:

[1523] The terminal displays a reward notification to the user, and the user follows the instructions to complete the procedure for receiving the reward.

[1524] This concludes the explanation of the specific processing steps. This flow will effectively and quickly improve business efficiency.

[1525] Example 1

[1526] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1527] Conventional business efficiency systems were unable to accurately analyze users' business information and were unable to quickly and effectively provide optimal solutions or similar cases. It was also difficult to accurately evaluate the results of the proposed solutions, deploy them to other departments, or efficiently reward users. As a result, business efficiency was not fully achieved, and productivity did not improve across the entire company.

[1528] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1529] In this invention, the server includes a means for inputting business information from a user terminal, a means for analyzing the input business information and searching for optimal solutions or similar cases using a generative AI model, and a means for generating search results and presenting them to the user terminal. Thus, when a user inputs business information, the information is analyzed using natural language processing technology, and specific keywords and phrases are extracted. Furthermore, the generative AI model is used to search for optimal solutions or similar cases, and a proposal document is generated, enabling the user to quickly and effectively receive proposals for improving business efficiency.

[1530] In this invention, the server further includes means for the user to implement the proposed solution and report the results, means for evaluating the reported results and determining the effectiveness of the efficiency improvement, means for analyzing input information using natural language processing technology and extracting specific keywords and phrases, means for evaluating the results of the efficiency improvement and automatically sending a proposal to other departments, and means for receiving a report of the quantitative effects and implementation procedures from the user terminal. This makes it possible to accurately evaluate the results of the efficiency improvement measures implemented by the user, effectively deploy them to other departments, and properly reward the user.

[1531] A "user terminal" is a device that processes business information input by a user and displays generated proposal results.

[1532] "Business information" refers to data in which a user inputs detailed information about a business, and includes important information that is useful for improving business efficiency.

[1533] A "generative AI model" is an artificial intelligence model that generates optimal solutions and similar cases based on past data and cases.

[1534] "Natural language processing technology" is a technology that analyzes sentences and document data and extracts specific keywords and phrases.

[1535] A "database" is a collection of information that stores past efficiency improvement cases and evaluation data, and serves as the basis for search and evaluation.

[1536] A "proposal document" is a document containing a solution and execution procedures generated by the server, and is displayed on the user's terminal.

[1537] "Efficiency improvement effect" refers to the results and degree of improvement in operations obtained by implementing the proposed solution.

[1538] A "reward notification" is a notification of a reward sent to a user for an efficiency improvement initiative that has been recognized as effective.

[1539] "Deploying to other departments" means sharing information and making proposals to apply efficiency initiatives that have been proven in one department to other departments.

[1540] "Evaluation" refers to the process of analyzing the execution results reported by users and determining their effectiveness.

[1541] "Reporting" is an action taken by a user to transmit the results of a proposed solution to the server after the user has implemented the solution.

[1542] MODE FOR CARRYING OUT THE INVENTION

[1543] This invention is a system that supports the efficiency of internal business operations based on a generative AI model. The system allows users to input business information, search for and present optimal solutions and similar cases, and comprehensively supports post-implementation evaluation and reporting, as well as deployment to other departments.

[1544] User terminal

[1545] The user terminal provides an interface for users to input business information, review proposed solutions, and report implementation results. Users use this terminal to input detailed information about their business. For example, they can input specific efficiency requests, such as "improving the efficiency of regular meetings," using a browser or dedicated application. This information is organized by the terminal and sent to the server.

[1546] server

[1547] The server is the core of this system and is configured to achieve the following main functions:

[1548] Input analysis: The server analyzes the business information received from the user's device. This analysis uses natural language processing (NLP) technology. For example, specific keywords and phrases such as "meeting preparation" and "minutes creation" are extracted.

[1549] Search and suggest: Using a generative AI model, the system searches a database for optimal solutions or similar cases based on past data and examples. For example, it may search for solutions such as "using an automatic meeting minutes generation tool."

[1550] Proposal generation and presentation: The server generates a proposal document based on the search results. This proposal document includes an overview of the initiative, expected benefits, implementation procedures, etc. This document is sent to the user's terminal and displayed to the user.

[1551] Evaluation and registration: The execution results reported by users are analyzed and their effectiveness is evaluated. The evaluated effectiveness data is registered in a database.

[1552] Automatic deployment: Effective efficiency initiatives are automatically proposed to other departments. These proposals are sent to relevant user devices and departments.

[1553] Reward notification: If the effectiveness is proven, the server will send a reward notification to the user. The user will check this notification on their device and proceed with the reward claim procedure.

[1554] Database

[1555] The database is a collection of information that stores past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[1556] Specific examples

[1557] Here's a specific example: A user wants to "improve the efficiency of regular meetings" and inputs the problem of "it takes a long time to prepare for meetings and create minutes" from their device. The server analyzes this information and uses a generative AI model to search for and extract "use of an automatic minutes generation tool" and suggests it to the user. When the user introduces this tool and reports that the time it takes to create minutes has been reduced by 60%, the server evaluates its effectiveness. If similar results are confirmed in other departments, the server registers this information in its database and automatically sends suggestions to those departments. The user is also notified of a reward and is responsible for the procedures to receive it.

[1558] Prompt Sentence Examples

[1559] An example of a prompt is as follows:

[1560] "Please provide suggestions for streamlining meeting preparation and minutes creation, including examples of effective tools."

[1561] As described above, this system supports efficient and effective internal business operations and contributes to improved productivity.

[1562] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1563] Step 1:

[1564] User input of business information

[1565] A user enters details about their business into a terminal.

[1566] Input: User's business information

[1567] Specific operation: The user opens a browser or a dedicated application and enters an item such as "improving the efficiency of regular meetings" or a problem such as "it takes a long time to prepare for meetings and create minutes."

[1568] Output: Business information data organized by the terminal

[1569] Step 2:

[1570] Analysis of entered information

[1571] The server analyzes the business information received from the terminal.

[1572] Input: Business information data received from the terminal

[1573] What it does: The server launches a natural language processing (NLP) engine to analyze the received text data, specifically extracting specific keywords and phrases (e.g., "meeting preparation" or "minutes creation") contained in the business information.

[1574] Output: Extracted keyword and phrase data

[1575] Step 3:

[1576] Search and suggestion generation using generative AI models

[1577] Using a generative AI model, optimal solutions and similar cases are searched for, and proposal documents are generated.

[1578] Input: Extracted keyword and phrase data

[1579] Specific operation: The server uses the generative AI model to search a past database. For example, it searches for similar cases such as "using an automatic meeting minutes generation tool" and selects the optimal solution from among them. Based on the selected solution, it generates a proposal document (outline of the initiative, expected effects, implementation procedures, etc.).

[1580] Output: Proposal document data

[1581] Step 4:

[1582] Submitting and presenting proposal documents

[1583] The server sends the generated proposal document to the user's terminal and presents it.

[1584] Input: Proposal document data

[1585] Specific operation: The server sends the generated proposal document to the user terminal. The user terminal receives this document and displays it to the user. The user confirms the proposed solution.

[1586] Output: Proposal document displayed on user's terminal

[1587] Step 5:

[1588] Implementing proposed solutions and reporting results

[1589] The user implements the proposed solution and reports the results.

[1590] Input: Proposal document, execution result data

[1591] Specific operation: A user generates meeting minutes using the "automatic minutes generation tool" and inputs the results (e.g., the time required to create minutes has been reduced by 60%) from their device. The device organizes this information and sends it to the server.

[1592] Output: Execution result data reported from the terminal

[1593] Step 6:

[1594] Evaluation of execution results

[1595] The server evaluates the reported execution results.

[1596] Input: Execution result data reported from the device

[1597] Specific operation: The server analyzes the execution results and judges the effectiveness of the efficiency improvement. For example, it evaluates a specific numerical value such as "60% reduction in the time required to create minutes."

[1598] Output: Evaluation result data

[1599] Step 7:

[1600] Proposal deployment to other departments

[1601] Based on the evaluation results, suggestions are automatically sent to other departments.

[1602] Input: Evaluation result data

[1603] Specific operation: The server automatically sends proposal information to other departments based on the evaluation data. For example, it automatically forwards a proposal to other departments that "an automatic minutes generation tool would be effective."

[1604] Output: Proposal data sent to other departments

[1605] Step 8:

[1606] Reward notification

[1607] The server sends a reward notice to the user for the efforts that have been recognized as effective.

[1608] Input: Evaluation result data

[1609] Specific operation: The server sends a reward notification via the user's contact information (email address or internal messaging system). The user receives the notification and completes the reward claim procedure.

[1610] Output: Reward notification data sent to user

[1611] The above are the specific steps of the processing of this system.

[1612] (Application example 1)

[1613] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1614] Conventional business efficiency systems are generally based on desktops or laptops, and have the problem of being unable to respond to real-time business efficiency proposals or immediate improvements to on-site work. Even when users receive efficiency proposals, delays in reporting the implementation and feedback of the results often prevent a rapid improvement cycle, hindering productivity improvement. In particular, in workplaces such as factories, where immediate improvements in work efficiency and safety are often required, conventional systems are unable to adequately address these issues.

[1615] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1616] In this invention, the server includes a means for inputting task information from a user terminal, a means for analyzing the input task information and searching for optimal solutions or similar cases using a generative AI model, and a means for acquiring the user's work content through voice input and generating efficiency suggestions. This allows the user to receive efficiency suggestions in real time through the smart glasses. In addition, the proposed solutions are quickly implemented on-site and the results are reported immediately, thereby promoting the efficiency of on-site work in real time and simultaneously achieving improvements in overall productivity and safety.

[1617] A "user terminal" is a device that provides an interface for a user to input business information, confirm efficiency improvement proposals, and report on their implementation.

[1618] "Business information" refers to detailed data and issues related to the business that the user wishes to improve the efficiency of.

[1619] A "generative AI model" is an artificial intelligence algorithm that generates efficiency suggestions and solutions based on input data.

[1620] "Searching means" is a function that uses a generative AI model to search and extract optimal solutions and similar cases from a database based on the business information entered.

[1621] "Search results" are the optimal solution or a set of similar cases extracted by the generative AI model.

[1622] "Means for the user to implement the proposed solution" refers to the means and process for the user to actually implement the proposed efficiency proposal.

[1623] The "means for reporting results" is a means for reporting the effects and results of a suggestion made by a user to the system via a device.

[1624] The "means for evaluating the reported results" is a function that allows the system to analyze the execution results reported by the user and determine the effectiveness of the efficiency improvement.

[1625] A "database" is a collection of information that stores past efficiency improvement cases and evaluation data.

[1626] "Means for deployment to other departments" refers to the function of automatically sending and sharing the evaluated efficiency gains to other departments as proposals.

[1627] "Smart glasses" are devices that users can wear to display information in real time and visually receive suggestions for improving work efficiency.

[1628] The "voice input means" is a function that allows the user to input work content and business information into the system through voice.

[1629] The "means for notifying a reward" is a system function for notifying a user of a reward when the execution result of an efficiency improvement proposal is effective.

[1630] The present invention provides a system for supporting factory workers in improving their work efficiency by using smart glasses. Specific embodiments for carrying out the present invention will be described in detail below.

[1631] Basic system configuration

[1632] The system consists of the following main components:

[1633] 1. User Device:

[1634] A device that provides an interface for users to input business information, confirm efficiency proposals, and report on their implementation. Smart glasses are an example of this.

[1635] 2. Server:

[1636] It acts as the core of the system and is responsible for the following main functions:

[1637] Input analysis: Analyzes business information received from the user's device and extracts important keywords and context.

[1638] Search and suggest: Generative AI models are used to search a database for similar past cases and optimal solutions to generate suggestions.

[1639] Evaluation and registration: The execution results reported by users are analyzed, their effectiveness is evaluated, and they are registered in a database.

[1640] Notification: If the effect is confirmed, the user will be notified of the reward.

[1641] 3. Database:

[1642] It is a collection of information that stores past efficiency case studies and evaluation data. The server uses this database to search and evaluate.

[1643] Program processing

[1644] When a user puts on the smart glasses and works in a factory, the system operates according to the following steps.

[1645] 1. Real-time video capture:

[1646] The smart glasses' camera is used to capture real-time video of the user's work, allowing the user to understand their environment and work situation in real time.

[1647] 2. Voice input of your work:

[1648] The user inputs the details of the work into the smart glasses through voice input, for example, telling the system a specific task such as "I want to improve the speed at which bolts are installed."

[1649] 3. Input analysis and proposal generation:

[1650] The server analyzes the work content input by voice and uses a generative AI model to generate optimal efficiency suggestions, using natural language processing technologies such as OpenAI's API.

[1651] 4. Presenting the proposal:

[1652] The generated efficiency suggestions are displayed on the smart glasses' display, and the user can confirm the suggestions and put them into action.

[1653] Specific examples

[1654] For example, a user might say, "I want to improve the speed of bolt installation on the assembly line." The server analyzes this information, and the generative AI model suggests "introducing an automated tool for bolt installation." This suggestion is displayed on the smart glasses, and the user can use the suggested tool and report its effectiveness.

[1655] Prompt Sentence Examples

[1656] For example, use the following prompt:

[1657] 'Please tell me some concrete measures to improve bolt installation speed'

[1658] This allows the system to provide users with quick and effective suggestions for improving work efficiency, improving the quality and safety of work within the factory.

[1659] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1660] Step 1:

[1661] The user puts on the smart glasses and starts working in the factory. The camera in the smart glasses captures the user's work in real time. This operation allows the user's environment and work situation to be understood.

[1662] Input: Factory work footage

[1663] Output: Real-time video data

[1664] Step 2:

[1665] The user speaks to the smart glasses to describe their goal for improving work efficiency. For example, they might say, "I want to improve the speed at which bolts are attached on the assembly line." This voice data is captured by the smart glasses' microphone and converted into text data.

[1666] Input: Voice input data (for efficiency purposes)

[1667] Output: Converted text data

[1668] Step 3:

[1669] The server analyzes the text data received through the smart glasses and uses natural language processing (NLP) technology to analyze the input business information and extract important keywords and context.

[1670] Input: Converted text data

[1671] Output: Parsed keywords and context

[1672] Step 4:

[1673] The server searches a database for optimal solutions and similar cases based on keywords and context analyzed using the generative AI model, using past examples of efficiency improvements and optimal solutions.

[1674] Input: Parsed keywords and context

[1675] Output: Optimal solution and similar cases

[1676] Step 5:

[1677] The server then presents the optimal solution obtained from the search results to the user's device (smart glasses), where specific efficiency measures are visually displayed so that the user can confirm the proposal.

[1678] Input: Optimal solution and similar cases

[1679] Output: Efficiency suggestions displayed on smart glasses

[1680] Step 6:

[1681] The user implements the efficiency suggestions displayed on the smart glasses, for example, by using a new tool based on the proposed solution, and the results are reported to the server via the smart glasses.

[1682] Input: Efficiency proposal

[1683] Output: Execution result data

[1684] Step 7:

[1685] The server evaluates the reported execution results, analyzes the effectiveness of the execution results, and registers them in a database. The evaluation includes the effectiveness of the results and the rate of improvement.

[1686] Input: Execution result data

[1687] Output: Evaluated effects and database updates

[1688] Step 8:

[1689] If the effectiveness is recognized, the server will notify the user of a reward. In addition, based on the evaluated efficiency effect, suggestions will be automatically sent to other departments, so that similar efficiency measures can be used in other departments as well.

[1690] Input: Evaluated effect

[1691] Output: Reward notice and proposal notice to other departments

[1692] These steps will enable real-time operational efficiency improvements using smart glasses, enabling users to work more efficiently on-site, and are expected to improve overall productivity and safety.

[1693] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1694] The present invention is a matching system that combines a generative AI model and an emotion engine to help users improve their work efficiency. This system comprehensively supports everything from inputting work information to proposing solutions, evaluating and reporting after implementation, and even deploying solutions to other departments. It also takes into account the user's emotional state to make more personalized proposals. Detailed embodiments of this system are described below.

[1695] Basic system configuration

[1696] The system consists of the following main components:

[1697] 1. User Device

[1698] 2. Server

[1699] 3. Database

[1700] 4. Emotion Recognition Engine

[1701] User terminal

[1702] The user terminal provides an interface for users to input work information and emotional data, confirm proposed solutions, and submit implementation reports. Users input information about their work and their current emotional state, and send their requests for efficiency improvement to the system through the terminal.

[1703] server

[1704] The server is the core of the system and is responsible for the following main functions:

[1705] Input analysis: Analyzes business information and emotional data received from the user's device to extract important keywords, context, and emotional state.

[1706] Search and suggest: Using a generative AI model, the system searches a database for similar past cases and optimal solutions, and suggests information that takes into account the user's emotional state.

[1707] Evaluation and registration: The execution results reported by the user are analyzed, their effectiveness is evaluated, and the results are registered in a database. User emotion data is also taken into account in the evaluation.

[1708] Notification: If the effect is recognized, a reward notification will be sent to the user, and a personalized message will be generated based on the user's emotional state.

[1709] Database

[1710] The database is a collection of information that includes past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[1711] Emotion Recognition Engine

[1712] The emotion recognition engine is a component that recognizes and analyzes the user's emotional state from their input data and behavioral data, allowing the system to generate suggestions based on the user's emotional state.

[1713] Explanation of program processing

[1714] User input of business information and emotion data

[1715] The user inputs detailed information about the task they wish to improve efficiency and their current emotional state into the terminal, which then organizes this information and sends it to the server in a format that includes emotional data.

[1716] Analysis and search of input information

[1717] The server analyzes the business information and emotional data received from the device. Natural language processing (NLP) technology and an emotion recognition engine are used to extract specific keywords, phrases, and emotional states. Based on this, a generative AI model searches for optimal solutions and similar cases.

[1718] Generating and presenting search results

[1719] The server organizes solutions and similar cases extracted from the database and generates a proposal document that also takes the user's emotional state into consideration. The proposal document includes an overview of the initiative, specific steps, and expected results, as well as a communication style and feedback appropriate to the user's emotional state. The user's device receives this and displays it to the user.

[1720] Implementing proposed solutions and reporting results

[1721] The user implements the proposed solution and reports the results to the server from their device. The report includes specific steps taken, numerical data showing the effects, and the user's emotional state after implementation.

[1722] Evaluating the results and expanding to other departments

[1723] The server analyzes the reported results and evaluates their effectiveness. The analysis includes statistical analysis to confirm data consistency and measure effectiveness, as well as evaluating emotional data. If the effectiveness is proven, it is registered in the database and a proposal is automatically sent to other departments.

[1724] Notification and receipt of rewards

[1725] If an effort is found to be effective, the server sends the user a reward notification with a personalized message according to their emotional state. The user can then check the notification on their device and follow the procedure to receive the reward.

[1726] Specific examples

[1727] As an example, suppose a user (person A) wishes to "improve the efficiency of regular meetings." Person A inputs their problems and emotional state, such as "it takes a long time to prepare for meetings and write minutes" and "I feel stressed," on their device. The server analyzes this information, searches and extracts similar past cases such as "using an automatic minutes generation tool," and presents them with "effective communication methods." When Person A reports that they have introduced the proposed tool and that the time it takes to write minutes has been reduced by 60%, the server evaluates its effectiveness, taking their emotional state into consideration. If similar effects are confirmed in other departments, the server will provide a personalized message according to their emotional state and notify Person A of their gratitude.

[1728] The above system makes suggestions for work efficiency improvements that take the user's emotional state into account, enabling more personalized and effective support, which not only contributes to improving productivity across the company but also increases employee satisfaction.

[1729] The processing flow will be explained below.

[1730] Step 1:

[1731] The user inputs detailed information about their work and their current emotional state into the terminal. For example, they might input "I would like to improve the efficiency of regular meetings," "It takes a lot of time to prepare for meetings and write minutes," and "I feel stressed."

[1732] Step 2:

[1733] The terminal organizes the business information and emotion data input by the user, formats it, and transmits it to the server.

[1734] Step 3:

[1735] The server analyzes the received business information and emotional data using natural language processing (NLP) technology and an emotion recognition engine to extract important keywords, phrases, and emotional states.

[1736] Step 4:

[1737] Based on the analyzed data, the server uses a generative AI model to search the database for optimal solutions and similar cases, such as an "automatic meeting minutes generation tool."

[1738] Step 5:

[1739] The server organizes the search results and generates a proposal document that takes the user's emotional state into consideration. The proposal document includes an overview of the initiative, specific steps, and expected results, as well as a communication style and feedback appropriate for the user's emotional state.

[1740] Step 6:

[1741] The server sends the generated proposal document to the terminal, encrypting the data and checking the transfer protocol.

[1742] Step 7:

[1743] The terminal displays the received proposal document to the user, who then checks the contents of the proposal and decides whether or not to implement it.

[1744] Step 8:

[1745] The user implements the proposed solution. For example, they introduce an "automatic minutes generation tool" and start using it.

[1746] Step 9:

[1747] The user reports the results after implementing the solution, sending detailed data on the specific effects and steps taken, as well as their emotional state after implementation, from their device to the server.

[1748] Step 10:

[1749] The server analyzes the reported results and evaluates their effectiveness, including statistical analysis to verify data consistency and measure effectiveness, as well as evaluating emotional data.

[1750] Step 11:

[1751] The server registers the evaluation results in a database, and effective initiatives are recorded as candidates for expansion to other departments.

[1752] Step 12:

[1753] The server sends a reward notice to the user for efforts that are recognized as effective, the reward notice including a personalized message according to the user's emotional state.

[1754] Step 13:

[1755] The terminal displays a reward notification to the user, and the user follows the instructions to complete the procedure for receiving the reward.

[1756] This concludes the explanation of the specific processing steps. This flow effectively and quickly improves business efficiency and also provides services that respond to the emotional state of the user.

[1757] Example 2

[1758] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1759] Conventional work efficiency improvement systems only make suggestions based on work information and do not consider the user's emotional state, which can result in ineffective work suggestions. Furthermore, even after the user implements the proposed solution, a comprehensive evaluation that includes emotional data is not performed, which creates challenges in improving user satisfaction.

[1760] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting business information and emotional data from a user terminal, a means for analyzing the input business information and emotional data and searching for optimal solutions or similar cases using a generative AI model, and a means for generating search results and presenting a proposal document that takes the user's emotional state into consideration to the user's terminal. This realizes personalized proposals that take the user's emotional state into consideration, enabling efficient and satisfying business improvement.

[1761] A "user terminal" is a device through which a user inputs business information and emotional data and receives suggestions and notifications from the system.

[1762] "Business information" refers to specific data and content that a user inputs regarding a business, and is information that includes data regarding the current status and issues of the business.

[1763] "Emotion data" refers to data about the current emotional state input by the user, and specifically includes subjective evaluations such as stress and satisfaction.

[1764] The "server" is a central computer system that receives business information and emotion data sent from user terminals, and performs analysis, proposals, and notifications.

[1765] A "generative AI model" is a model that applies artificial intelligence technology to generate optimal solutions and similar cases based on past data and input information.

[1766] A "proposal document" is a document created based on optimal solutions and similar cases generated by the server, taking into account the user's emotional state, and describing the proposal content, effects, implementation procedures, etc.

[1767] A "database" is a collection of information that stores data such as past business information, emotional data, solutions, and evaluation results.

[1768] An "emotion recognition engine" is a component that recognizes and analyzes a user's emotional state from input data and behavioral data.

[1769] "Evaluation" is the process of analyzing the results after the user implements the proposed solution and determining its effectiveness.

[1770] A "personalized message" is an individually customized message created by the server according to the user's emotional state.

[1771] This invention is a system that combines a generative AI model and an emotion recognition engine to help users improve their work efficiency. This system comprehensively supports everything from inputting work information to proposing solutions, evaluating and reporting after implementation, and even deploying solutions to other departments. It also takes the user's emotional state into consideration to make more personalized suggestions.

[1772] Basic system configuration

[1773] The system consists of the following main components:

[1774] 1. User Device

[1775] 2. Server

[1776] 3. Database

[1777] 4. Emotion Recognition Engine

[1778] User terminal

[1779] The user terminal provides an interface for users to input work information and emotional data, confirm proposed solutions, and submit implementation reports. Users input information about their work and their current emotional state, and send their requests for efficiency improvement to the system through the terminal.

[1780] server

[1781] The server is the core of the system and is responsible for the following main functions:

[1782] Input analysis: Analyzes business information and emotional data received from the user's device to extract important keywords, context, and emotional state. Analysis is performed using natural language processing (NLP) technology and an emotion recognition engine.

[1783] Search and suggest: Using a generative AI model, the system searches a database for similar cases and optimal solutions, and suggests information that takes into account the user's emotional state.

[1784] Evaluation and registration: Analyze the execution results reported by users, evaluate their effectiveness, and register them in a database. Emotional data is also taken into account in the evaluation.

[1785] Notification: If successful, users will receive a reward notification with a personalized message based on their emotional state.

[1786] Database

[1787] The database is a collection of information that includes past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[1788] Emotion Recognition Engine

[1789] The emotion recognition engine is a component that recognizes and analyzes the user's emotional state from their input data and behavioral data, allowing the system to generate suggestions based on the user's emotional state.

[1790] Specific examples

[1791] As an example, consider the case where a user (Person A) wishes to "improve the efficiency of regular meetings." Person A inputs his / her problems and emotional state, such as "it takes a long time to prepare for meetings and create minutes" and "I feel stressed," on his / her device. The server analyzes this information, searches and extracts similar past cases such as "using an automatic minutes generation tool," and presents a proposal along with "effective communication methods." When Person A reports that he / she introduced the proposed tool and saw that the time it took to create minutes was reduced by 60%, the server evaluates its effectiveness, taking his / her emotional state into consideration. If similar effects are confirmed in other departments, the server will provide a personalized message according to his / her emotional state and notify Person A of a reward.

[1792] Prompt Sentence Examples

[1793] "I spend a lot of time preparing for and taking minutes of our weekly meetings, and I'd like to improve the efficiency. I'm also finding this work very stressful. I'd like some suggestions on how to solve this problem."

[1794] This system makes suggestions for work efficiency improvements that take the user's emotional state into account, enabling more personalized and effective support, which not only contributes to improving productivity across the company but also increases employee satisfaction.

[1795] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1796] Step 1: User inputs task information and emotion data

[1797] The user inputs work information and emotional data into the user terminal. Specific examples of input include the current state of work and emotional state, such as "it takes a long time to prepare for a meeting" or "I'm feeling stressed." This data is formatted and initial processing is performed by the user terminal. The input data is then sent to the server.

[1798] Input: Business information and emotion data

[1799] Output: Formatted business information and sentiment data

[1800] Specific example of operation: A user enters "meeting preparation," "time-consuming," and "very stressful" into the input form on the device and clicks the send button.

[1801] Step 2: Initial processing of input data and sending to server

[1802] The terminal receives data entered by the user, checks the data format, and performs preprocessing of the data as necessary. For example, it converts data in different formats into a unified format, which makes it easier for the server to analyze it. After preprocessing is complete, the data is sent to the server.

[1803] Input: Formatted business information and emotion data

[1804] Output: Preprocessed data

[1805] Specific example of operation: The device extracts keywords such as "meeting preparation," "time-consuming," and "very stressful," standardizes the data format, and sends it to the server.

[1806] Step 3: Data analysis and search by server

[1807] The server analyzes the received data using natural language processing (NLP) technology and an emotion recognition engine. NLP technology is used to extract specific keywords and phrases from business information, and the emotion recognition engine is used to analyze the emotional data. Based on the results of these analyses, a generative AI model is used to search a database for optimal solutions and similar cases.

[1808] Input: Preprocessed business information and emotion data

[1809] Output: extracted keywords, emotional states, and searched solutions

[1810] Specific operation example: The server receives the data, extracts keywords such as "meeting," "preparation," "time," and "stress," and recognizes the emotional state as "very stressful." Next, based on this information, it searches the database for the optimal solution, such as "use an automatic meeting minutes generation tool."

[1811] Step 4: Server generates and sends proposal document

[1812] The server generates a proposal document based on the search results. The proposal document includes an outline of the initiative, specific steps, expected results, and a communication style and feedback appropriate to the user's emotional state. The document is then sent to the user's device.

[1813] Input: searched solutions, extracted keywords, emotional state

[1814] Output: Proposal document

[1815] Specific example of operation: The server creates a proposal document based on the "automatic minutes generation tool" and sends it to the user's terminal, including content such as "By introducing this tool, meeting preparation time will be reduced by 60% and stress is expected to be reduced."

[1816] Step 5: User implements solution and reports results

[1817] The user checks the proposal document presented by the server and implements the solution. For example, they introduce an automatic meeting minutes generation tool. After implementation, the user reports the results and emotional state from their device to the server. The report includes specific implementation steps, numerical data showing the effects, and the user's emotional state.

[1818] Input: User's performance and emotional state

[1819] Output: Reported performance results and emotional state

[1820] Specific example of operation: A user introduces an automatic minutes generation tool and reports from their device that the time it takes to create minutes has been reduced by 60%, resulting in "meeting preparation time reduced by 60%" and "stress reduced."

[1821] Step 6: Server evaluation and database registration

[1822] The server analyzes the results reported by users and evaluates their effectiveness. The evaluation includes statistical analysis and evaluation of emotional data. The evaluated data is then registered in a database. If the effectiveness is proven, it is automatically deployed to other departments.

[1823] Input: Reported performance results and emotional state

[1824] Output: Evaluated data, updated database

[1825] Specific operation example: The server analyzes reports such as "meeting preparation time reduced by 60%" and "stress reduced," evaluates the effectiveness, and registers it in a database. It also automatically sends a proposal to other departments to "introduce an automatic minutes generation tool."

[1826] Step 7: Server Notification and Personalized Message Delivery

[1827] The server sends a reward notification to the user for the efforts that have been recognized as effective, including a personalized message according to the user's emotional state. The user can check this notification on their device and receive the reward by taking the appropriate steps.

[1828] Input: Evaluated data, user's emotional state

[1829] Output: Thank you notice with personalized message

[1830] Specific operation example: Depending on the user's emotional state, the server sends the user a message such as, "Congratulations on reducing stress! Your efforts have significantly improved the efficiency of your work. We have a reward for you, so please go through the process of receiving it." The user then goes through the process of receiving the reward from their device.

[1831] (Application example 2)

[1832] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1833] Conventional efficiency support systems have the problem of being unable to take into account the user's emotional state and therefore unable to make effective optimization suggestions. Furthermore, in on-site work such as in factories, real-time feedback and work management are difficult, making it difficult to improve work efficiency. Furthermore, there was a need for a method to simultaneously reduce employee stress and improve work efficiency.

[1834] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting business information and emotional data from a user terminal, means for analyzing the input business information and emotional data and searching for optimal solutions or similar cases using a generative AI model, and means for generating search results and presenting them to the user terminal. This makes it possible to propose and provide feedback on optimal business efficiency improvements in real time, taking into account the emotional state of employees.

[1835] "User terminal" refers to a device that allows a user to input, transmit, and check business information and emotional data.

[1836] "Task information" is data that indicates the details and progress of the tasks that a user is performing.

[1837] "Emotion data" is data that represents the user's current mental state and emotions.

[1838] A "generative AI model" is a model that uses machine learning and deep learning technologies to generate optimal solutions and similar cases based on input data.

[1839] "Emotional state" refers to a user's emotional condition or psychological state.

[1840] "Searching means" refers to a method of finding optimal solutions and similar cases based on business information and emotional data analyzed using a generative AI model.

[1841] "Real-time" refers to the ability to respond immediately to the moment business operations progress.

[1842] "Smart glasses" are glasses-type devices that use augmented reality (AR) technology and have the ability to display information in the user's field of vision.

[1843] A "database" is a collection of information for storing searched solutions, similar cases, and evaluated effects.

[1844] "Means of evaluation" refers to a method for determining the effectiveness of efficiency improvements based on the results and emotional state reported by users.

[1845] "Deployment measures" are methods for applying the evaluated effects of business efficiency improvements to other departments.

[1846] "Reward" refers to a reward or compensation given to a user when the proposed solution is implemented and its effectiveness is recognized.

[1847] "Effect" is an index showing how successful the proposed means for improving work efficiency was, or how much the user's work efficiency was improved.

[1848] The present invention provides a matching system that supports users in improving their work efficiency by combining a generative AI model and an emotion engine. A specific embodiment of this system is described below.

[1849] Basic system configuration

[1850] The system consists of the following main components:

[1851] 1. User Device

[1852] 2. Server

[1853] 3. Database

[1854] 4. Emotion Recognition Engine

[1855] 5. Smart Glasses

[1856] User terminal

[1857] The user terminal provides an interface for users to input work information and emotional data, confirm proposed solutions, and submit implementation reports. It also collects and displays real-time work progress information and emotional data through smart glasses. Specifically, users input work details and emotional state and send their requests for efficiency improvements to the system.

[1858] server

[1859] The server is the central part of the system and is responsible for the following main functions:

[1860] Input data analysis: Analyzes business information and emotion data received from user devices to extract important keywords, context, and emotional states. Technologies used here include natural language processing (NLP) and emotion recognition engines.

[1861] Search for optimization suggestions: Using a generative AI model, the system searches a database for similar past cases and optimal solutions, and suggests information that takes into account the user's emotional state.

[1862] Evaluation and registration: The execution results reported by the user are analyzed, their effectiveness is evaluated, and they are registered in a database. Here, the evaluation also takes into account the user's emotional data.

[1863] Notification: If the effect is recognized, a reward notification will be sent to the user, and a personalized message will be generated based on the user's emotional state.

[1864] Database

[1865] The database is a collection of information that includes past efficiency improvement cases and evaluation data. The server uses this database to perform searches and evaluations.

[1866] Emotion Recognition Engine

[1867] The emotion recognition engine is a component that recognizes and analyzes the user's emotional state from their input data and behavioral data, allowing the system to generate suggestions based on the user's emotional state.

[1868] Smart Glasses

[1869] The smart glasses are worn by users while they work and collect and display information on work progress and emotional data in real time. Users can check and implement suggestions for improving efficiency through the smart glasses.

[1870] Specific examples

[1871] As an example, suppose a user (Employee A) is performing "production line quality inspections," but is experiencing delays, frequent errors, and feeling stressed. Employee A inputs this work information and emotional state through smart glasses. The server analyzes this information, searches and extracts "use of inspection automation tools" from similar past cases, and suggests them to Employee A through the smart glasses. Employee A implements the proposed tool and reports that the error rate has been significantly reduced. As a result, the server evaluates its effectiveness, taking the user's emotional state into consideration. If similar effects are confirmed in other departments, the server will provide a personalized message according to Employee A's emotional state and notify Employee A of a reward.

[1872] Examples of prompts:

[1873] "We are currently conducting quality inspections on the line, but the work is delayed. Errors occur frequently, which is frustrating."

[1874] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1875] Step 1:

[1876] A user puts on the smart glasses and inputs business information and emotion data.

[1877] Input: Detailed work information (e.g., progress of quality inspection on the line), current emotional state (e.g., stress).

[1878] Specific operation: The user operates the interface of the smart glasses and records business information and emotional state through text input.

[1879] Step 2:

[1880] The smart glasses transmit the input business information and emotion data to the server.

[1881] Input: Business information and sentiment data entered by the user.

[1882] Output: The dataset that is sent to the server for analysis.

[1883] Specific operation: Using the network function of the smart glasses, the input data is sent to the server in real time.

[1884] Step 3:

[1885] The server analyzes the received business information and emotion data.

[1886] Input: Dataset sent from smart glasses.

[1887] Output: Keywords, context, and emotional state as analysis results.

[1888] Specific operation: The server uses natural language processing (NLP) technology to analyze business information, extract important keywords, and classify emotional data using an emotion recognition engine.

[1889] Step 4:

[1890] Based on the analysis results, the server uses a generative AI model to search a database for optimal solutions and similar cases.

[1891] Input: Parsed task information and emotional state.

[1892] Output: Best solution or list of similar cases.

[1893] Specific operation: The server inputs a prompt sentence into the generative AI model, which generates and extracts optimal solutions and similar cases.

[1894] Step 5:

[1895] The server organizes the search results, generates a suggested document that takes into account the user's emotional state, and sends it to the smart glasses.

[1896] Input: Retrieved solutions, similar cases, and the user's emotional state.

[1897] Output: Proposal document.

[1898] Specific operation: The server creates a proposal document that reflects the emotional data on the generated solution and sends it to the smart glasses.

[1899] Step 6:

[1900] The user sees the proposed solution through the smart glasses and implements it.

[1901] Input: Proposal document.

[1902] Output: Execution results (e.g., reduction in error rate).

[1903] Specific action: The user reads the proposal document and implements the proposed solution (e.g., introduction of an inspection automation tool) in the field.

[1904] Step 7:

[1905] The user reports the results of the execution to the server through the smart glasses.

[1906] Input: Execution results and emotional state after execution.

[1907] Output: Result report data sent to the server.

[1908] Specific operation: The user inputs the execution results and emotional state into the smart glasses and sends the data to the server.

[1909] Step 8:

[1910] The server analyzes the reported results and evaluates the effectiveness of the efficiency improvements.

[1911] Input: A dataset of execution results and emotional states.

[1912] Output: Effectiveness evaluation report.

[1913] Specific operation: The server performs statistical analysis and evaluates emotional data to evaluate the effectiveness of efficiency.

[1914] Step 9:

[1915] The server registers the evaluation results in a database and considers whether they can be expanded to other departments.

[1916] Input: Effectiveness evaluation report.

[1917] Output: Registered effectiveness data and decision on whether to expand to other departments.

[1918] Specific operation: The server stores the evaluation results in a database and uses them to decide whether to deploy them to other departments.

[1919] Step 10:

[1920] If the effect is recognized, the server notifies the user of the reward.

[1921] Input: The result of the evaluation as a trigger.

[1922] Output: Gratuity notice.

[1923] Specific operation: The server generates a reward notification for the user, including a personalized message according to the user's emotional state, and sends it to the smart glasses.

[1924] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1925] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1926] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1927] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1928] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1929] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1930] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1931] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1932] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1933] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1934] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1935] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1936] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1937] Alternatively, the specific processing program 56 may be stored in a storage de...

Claims

1. A means for inputting business information from a user terminal; A means of analyzing input business information and searching for optimal solutions and similar cases using a generative AI model; means for generating search results and presenting them to a user terminal; a means for the user to implement the proposed solution and report the results; A means to evaluate the reported results and determine the effectiveness of the efficiencies achieved; A means to register the effects of efficiency improvements in a database and consider whether they can be expanded to other departments. A means for notifying the user of a reward if the effect is recognized; A system including:

2. The system according to claim 1 , further comprising means for automatically sending suggestions to other departments based on the evaluated efficiency gains.

3. The system according to claim 1 , further comprising means for giving a specific title to a user when the efficiency results reported by the user meet a certain standard.

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