System

Abstract

A system is provided.SOLUTION: A system comprising: means for uploading material; means for analyzing the uploaded material; means for inputting the analyzed material into a generative AI model for review; and means for providing generated feedback to a user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In many workplaces, it is common for subordinates to ask their superiors to review documents, but if the superior is busy, the review can take a long time, which can make subordinates feel sorry. Furthermore, if there is a lot of feedback to improve the accuracy of the documents, it can take up a lot of time for the superior to work. This reduces the time available to focus on the original work, resulting in a decrease in overall work efficiency. [Means for solving the problem]

[0005] To address this issue, the present invention provides a system that includes a means for uploading documents, a means for analyzing the uploaded documents, a means for inputting the analyzed documents into a generative AI model for review, and a means for providing the generated feedback to a user. The system also features a generative AI model trained to mimic the feedback style of a specific supervisor, and a document analysis means configured to select an appropriate parser depending on the document format and extract text information. This system allows users to have their documents reviewed by a copy robot before directly requesting a review from their supervisor, thereby improving the accuracy of the documents in advance, reducing the burden on the supervisor and improving work efficiency.

[0006] "Means for uploading materials" refers to a function that allows users to send materials to the system from their own terminals.

[0007] "Means for analyzing documents" refers to the function of converting the content into text information according to the format of the uploaded document and formatting it into a format that is easy for the generative AI model to process.

[0008] "Means for inputting data into a generative AI model for review" refers to the function of supplying analyzed data to a generative AI model, evaluating and reviewing the data content, and generating feedback.

[0009] "Means for providing generated feedback to a user" refers to the function of formatting the feedback generated by the generative AI model into an appropriate format and sending it to the user's device for presentation to the user.

[0010] A "generative AI model" refers to an artificial intelligence model that is trained to mimic the feedback style of a particular manager.

[0011] "Document analysis means" refers to a module that selects an appropriate parser depending on the document format and extracts and converts the text information of the document. [Brief explanation of the drawings]

[0012] [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

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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."

[0020] [First embodiment]

[0021] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] The embodiment of the present invention is described below: The system includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, and a means for providing generated feedback to a user.

[0034] Program processing explanation

[0035] Step 1: Upload your materials

[0036] The user accesses the system's dedicated web page from the terminal's web browser.

[0037] The user clicks the "Upload material" button and selects the material file to be reviewed from the local file system.

[0038] When the user presses the "Upload" button, the corresponding material file is sent to the server.

[0039] Step 2: Analyze the material

[0040] The server receives the material file sent by the user.

[0041] The server passes the received document file to the text analysis module, which selects an appropriate parser depending on the document format and extracts the content as text information.

[0042] The server further breaks down the extracted text information and formats it into a format that is easy for the generative AI model to process (for example, plain text or vector format).

[0043] Step 3: Perform the review

[0044] The server inputs the formatted content into a generative AI model.

[0045] The generative AI model analyzes the input materials and generates feedback suggestions and improvement suggestions by imitating the feedback style of a specific manager.

[0046] The generated feedback is output to the server in text format.

[0047] Step 4: Provide feedback

[0048] The server formats the generated feedback for delivery to the user, specifically by organizing the feedback into sections that are easy to understand.

[0049] The feedback content is sent to the user's device and stored in association with the user's account.

[0050] The user can revisit the web page and view the generated feedback in the "Review Feedback" section.

[0051] Specific examples

[0052] For example, consider the case where a user uploads a project plan (e.g., "Project Plan.docx") to the system.

[0053] 1. Uploading materials

[0054] Users access a dedicated web page, click the "Upload Documents" button, select "Project Plan.docx," and upload it.

[0055] 2. Analysis of the data

[0056] The server passes the received "Project Plan.docx" to the text analysis module, which extracts and analyzes text information for each paragraph and section within the document.

[0057] 3. Conducting the review

[0058] The formatted text content is fed into a generative AI model.

[0059] The generative AI model analyzes the content of the document and generates feedback such as, "The goal setting is vague, so add specific target KPIs."

[0060] 4. Providing Feedback

[0061] The server formats the feedback from the generative AI model and provides it to the user, who can then review the generated feedback and make corrections to the material.

[0062] In this way, this system allows users to have their documents reviewed efficiently, reducing the burden on their superiors and improving work efficiency.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] The user accesses the system's dedicated web page from a web browser on their terminal. The user clicks the "Upload Documents" button on the homepage, selects the document file to be reviewed from the local file system (e.g., "Project Plan.docx"), and presses the "Upload" button. This sends the selected file to the server.

[0066] Step 2:

[0067] The server receives the document file sent by the user. The server temporarily stores the file and passes it to the text analysis module. The text analysis module identifies the file format and selects the appropriate parser. For example, if the file is in .docx format, it will use the corresponding parser program to extract the content.

[0068] Step 3:

[0069] The server's text analysis module breaks down the content of the document into paragraphs and sections, extracts the meaning of each part, and converts the extracted text information into a format that is easy for the generative AI model to process (e.g., plain text or feature vectors).

[0070] Step 4:

[0071] The server inputs the preprocessed content into a generative AI model. The generative AI model is trained to mimic the feedback style of a specific manager and analyzes the content of the document to generate feedback. For example, it generates specific suggestions such as, "The goal setting for this section is vague, so please add specific KPIs."

[0072] Step 5:

[0073] The feedback generated by the generative AI model is output in text format to the server, which receives it and formats it appropriately for delivery to the user. The feedback is categorized into sections and summarized for easy user understanding.

[0074] Step 6:

[0075] The formatted feedback content is sent from the server to the user's device, and the user can access the web page again and view the generated feedback in the "Check Feedback" section.

[0076] Step 7:

[0077] The user can revise the document based on the feedback and, if necessary, upload the revised document back to the system and request a re-review in the same way.

[0078] This series of processes allows the user to have the materials reviewed efficiently, reducing the burden on the superior and improving work efficiency.

[0079] Example 1

[0080] 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."

[0081] Conventional document review systems require manual document analysis and review, which often hinders efficient business operations. Furthermore, because reviews are not based on a specific feedback style, it is difficult to obtain consistent feedback. This creates problems for users, who are unable to obtain high-quality reviews quickly, and increases the burden on supervisors.

[0082] 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.

[0083] In this invention, the server includes a means for a user to upload materials to a dedicated web page using a browser on a terminal, a means for the server to receive the uploaded materials and pass them to an analysis module to extract text information, a means for the server to format the extracted text information and input it into a generative AI model, and a means for the generative AI model to generate feedback based on the input materials, and for the server to provide the feedback to the user, thereby enabling the user to receive prompt and consistent feedback.

[0084] "User" refers to a person who uses the system to perform operations such as uploading materials and checking feedback.

[0085] "Terminal" refers to computer equipment such as a PC or smartphone used by a user.

[0086] "Browser" refers to the web browsing software used by a User to access a dedicated web page.

[0087] "Materials" refers to document files and data uploaded by users as the subject of review.

[0088] "Specialized Web Page" means a page on a particular Website that a User accesses to upload materials.

[0089] "Server" refers to the computing device or system that receives materials, analyzes them, runs generative AI models, and provides feedback.

[0090] "Analysis Module" means a software component for extracting textual information from Uploaded Materials.

[0091] "Text information" refers to character string data extracted from the analyzed material.

[0092] "Generative AI model" refers to an artificial intelligence model used to analyze material and generate feedback.

[0093] "Feedback" refers to the comments and suggestions for improvement that the generative AI model generates as a result of analyzing the materials.

[0094] The present invention is a system in which a user uploads materials using a terminal, the server analyzes the materials, inputs them into a generative AI model, and provides feedback. The system is configured as follows:

[0095] Users access a dedicated web page using a web browser (such as Google Chrome or Mozilla Firefox) on a device such as a PC or smartphone. This web page has an "Upload Documents" button, which users click to select and upload the target document file (such as "Project Plan.docx") from their local file system.

[0096] The server receives the document file sent by the user. After receiving the file, the server passes it to a text analysis module (e.g., Apache Tika or spaCy) to analyze and extract the content of the document as text information. This text analysis module selects an appropriate parser depending on the document format (e.g., PDF, DOCX) to extract the text information.

[0097] The server further breaks down the parsed text information and formats it into a format (plain text or vector format) that is easy for a generative AI model (e.g., OpenAI GPT-4 or BERT) to process. When the formatted content is input into the generative AI model, the AI ​​model analyzes the material and generates feedback by imitating a specific feedback style (e.g., a boss's feedback style).

[0098] The generated feedback is retrieved by the server and formatted to be provided to the user. Specifically, the feedback is organized into sections to make it easier to understand. The formatted feedback is sent to the user's device and stored in association with the user's account. The user can then access the web page again and view the generated feedback in the "Review Feedback" section.

[0099] As a concrete example, consider the case where a user uploads a project plan (e.g., "Project Plan.docx") to the system. The user accesses a dedicated web page, clicks the "Upload Document" button, selects "Project Plan.docx," and uploads it. The server passes the received "Project Plan.docx" to a text analysis module, which extracts and analyzes text information for each paragraph and section within the document. The formatted text content of the document is then supplied to a generative AI model (such as OpenAI GPT-4). The generative AI model analyzes the content of the document and generates feedback such as, "The goal setting is vague, so add specific target KPIs."

[0100] An example prompt might be, "Please review the goal setting in the project plan and identify areas for improvement."

[0101] In this way, this system allows users to have their documents reviewed efficiently, reducing the burden on their superiors and improving work efficiency.

[0102] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0103] Step 1:

[0104] The user accesses a dedicated web page

[0105] Input: Access request from the user's device

[0106] How it works: A user accesses the system's dedicated web page using a web browser (such as Google Chrome or Mozilla Firefox) on a PC or smartphone.

[0107] Output: A custom web page is displayed in the user's browser.

[0108] Step 2:

[0109] The user selects and uploads materials

[0110] Input: The document file selected by the user (e.g. "Project Plan.docx")

[0111] Operation: The user clicks the "Upload Document" button, selects the document file to upload from the local file system, and then clicks the "Upload" button to send the document file to the server.

[0112] Output: The document file is sent to the server.

[0113] Step 3:

[0114] The server passes the received material to the text analysis module

[0115] Input: Data file received by the server

[0116] How it works: The server receives a file uploaded by a user, then passes the file to a text analysis module (e.g., Apache Tika, spaCy).

[0117] Output: The data file is sent to the analysis module.

[0118] Step 4:

[0119] The text analysis module analyzes the material.

[0120] Input: Received material file

[0121] Operation: The text analysis module selects a parser according to the format of the document, and analyzes and extracts the content of the document as text information.

[0122] Output: Extracted text information

[0123] Step 5:

[0124] The server formats the extracted text information

[0125] Input: Extracted text information

[0126] How it works: The server formats the parsed text information into a format that is easy for the generative AI model to process (e.g., plain text or vector format).

[0127] Output: Formatted text information

[0128] Step 6:

[0129] The server inputs the formatted content into the generative AI model

[0130] Input: Formatted text information

[0131] How it works: The server inputs this formatted content into a generative AI model (e.g., OpenAI GPT-4).

[0132] Output: The content is fed into the generative AI model

[0133] Step 7:

[0134] A generative AI model analyzes the material and generates feedback

[0135] Input: Content fed into the generative AI model

[0136] How it works: The generative AI model analyzes the input material and generates a review based on a specific feedback style.

[0137] Output: The generated feedback

[0138] Step 8:

[0139] Server generated feedback is retrieved

[0140] Input: Feedback generated by the generative AI model

[0141] How it works: The server receives feedback output from the generative AI model.

[0142] Output: Feedback obtained by the server

[0143] Step 9:

[0144] The server formats the feedback and provides it to the user.

[0145] Input: Generated feedback

[0146] How it works: The server formats the feedback and organizes it into sections for delivery to the user, then sends it to the user's device and stores it in association with the user's account.

[0147] Output: Feedback sent to the user's device

[0148] Step 10:

[0149] User checks feedback

[0150] Input: Feedback sent by the server

[0151] How it works: The user revisits the dedicated web page and views the generated feedback in the "Review Feedback" section.

[0152] Output: The feedback the user saw

[0153] (Application example 1)

[0154] 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."

[0155] It is important to quickly and accurately acquire information about items in factories and on production lines and provide real-time feedback to workers based on that information. However, conventional systems have had difficulty in efficiently accomplishing this. In particular, automating analysis according to different information formats and types of items, and providing immediate feedback to workers have been challenging.

[0156] 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.

[0157] In this invention, the server includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to the worker, and a means for collecting product information using a smart device and displaying the feedback in real time, thereby enabling the product information to be obtained quickly and accurately and specific feedback based on the analyzed information to be provided to the worker in real time.

[0158] "Means for uploading materials" means any device or software that allows a User to submit materials to the System.

[0159] "Means for analyzing uploaded materials" refers to devices or software for analyzing received materials and extracting their contents as text information.

[0160] "Means for inputting analyzed materials into a generative AI model for review" refers to devices or software that provide extracted text information to a generative AI model to generate evaluations and feedback on the materials.

[0161] "Means for providing generated feedback to workers" refers to devices or software that deliver feedback obtained from the generative AI model to workers.

[0162] "Means for using smart devices to collect product information and display the feedback in real time" refers to software and hardware for using smart glasses or other mobile devices to collect product information and display the results in real time.

[0163] The system includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to a worker, and a means for collecting item information using a smart device and displaying the feedback in real time.

[0164] The server provides a dedicated web page that users access from their device's web browser. On this page, users click the "Upload Document" button and select and upload a document file from their local file system. Once the document is uploaded, the server receives it and passes it to a text analysis module for analysis. This text analysis module is equipped with an appropriate parser for each document format, and is able to extract the content as text information. The extracted text information is input into a generative AI model, which generates various feedback.

[0165] The generative AI model analyzes the input materials and generates feedback. For example, based on the content of the materials, it provides feedback such as, "The goal setting is not clear, so specific KPIs should be added." This feedback is formatted by the server and organized by section. The generated feedback is then sent to the user's device and provided to the worker.

[0166] In a factory implementation, smart glasses are used. When a worker wears the smart glasses and scans the barcode of an item using voice commands or gestures, the barcode data is sent to a cloud server. The cloud server searches a database using the received barcode data to obtain related item information. The obtained item information is analyzed by a text analysis module and provided to a generative AI model. The generative AI model analyzes the item information and generates feedback related to quality control and the manufacturing process. For example, feedback such as "The torque value of the screw may not meet the specifications, so please check it again" can be generated and displayed in real time on the smart glasses' display.

[0167] Hardware used:

[0168] Smart Glasses

[0169] Cloud Server

[0170] User's device (PC or smartphone)

[0171] Software used:

[0172] Flask (Python framework)

[0173] Virtual Barcode Scan Module

[0174] Virtual Text Analysis Module

[0175] Virtual generative AI model

[0176] As a concrete example of this system, imagine a scenario in which a worker inspects hardware parts in a factory, scans a barcode with smart glasses, and receives real-time feedback such as, "Please check the torque value of this part." In this case, an example of a prompt sentence to be input to the generative AI model would be, "Please provide feedback on quality control based on the following item information: product name, specifications, torque value, and inspection results."

[0177] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0178] Step 1:

[0179] The user accesses the system's dedicated web page from the web browser on their device. The user clicks the "Upload Documents" button and selects the document files to be reviewed from the local file system. When the user presses the "Upload" button, the corresponding document files are sent to the server. The input here is the document file selected by the user, and the output is the document data sent to the server.

[0180] Step 2:

[0181] The server receives the document file sent by the user. The received document file is passed to the text analysis module. The text analysis module selects an appropriate parser depending on the format of the document and extracts the content as text information. The input here is the uploaded document file, and the output is the extracted text information.

[0182] Step 3:

[0183] The server further breaks down the extracted text information and formats it into a format that is easy for the generative AI model to process (plain text or vector format). The input here is the extracted text information, and the output is the formatted material content.

[0184] Step 4:

[0185] The server inputs the formatted content into a generative AI model, which analyzes the input and generates feedback suggestions based on a specific feedback style. The input is the formatted content, and the output is the generated feedback.

[0186] Step 5:

[0187] The server formats the generated feedback for delivery to the user. Specifically, the feedback is organized into sections for easy understanding. The formatted feedback is sent to the user's device and stored in association with the user's account. The input here is the generated feedback, and the output is the formatted feedback and the stored data.

[0188] Step 6:

[0189] The user revisits the web page and views the generated feedback in the "Review Feedback" section, where the input is the formatted feedback content and the output is the user's feedback viewing results.

[0190] Step 7:

[0191] A worker wears smart glasses and scans the barcode of an item using voice commands or gestures. The smart glasses then send the barcode data to a cloud server and upload the item information. The input is the scanned barcode data, and the output is the item information sent to the cloud server.

[0192] Step 8:

[0193] The cloud server retrieves related product information from the database based on the received barcode data. The retrieved product information is analyzed by a text analysis module and fed to a generative AI model. The input here is the barcode data, and the output is formatted product information.

[0194] Step 9:

[0195] The generative AI model generates feedback on quality control and manufacturing processes based on input product information. For example, "The torque value of the screw may not meet the standard, so please check it again." The input here is the formatted product information, and the output is the generated feedback.

[0196] Step 10:

[0197] The generated feedback is displayed on the smart glasses display in real time, and the worker takes the necessary action based on the displayed feedback. The input here is the generated feedback, and the output is the real-time feedback display.

[0198] 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.

[0199] The embodiment of the present invention is described below: The system includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to a user, and an emotion engine for recognizing the user's emotions.

[0200] Program processing explanation

[0201] Step 1: Upload your materials

[0202] The user accesses the system's dedicated web page from the terminal's web browser.

[0203] The user clicks the "Upload Material" button, selects the material file to be reviewed from the local file system, and then clicks the "Upload" button, which sends the selected file to the server.

[0204] Step 2: Analyze the material

[0205] The server receives the material file sent from the user.

[0206] The server temporarily stores this file and passes it to the text analysis module, which identifies the file format and selects the appropriate parser. For example, if the file is in .docx format, it will use the corresponding parser program to extract the content.

[0207] The server's text analysis module breaks down the document into paragraphs and sections, extracts the meaning of each part, and converts the extracted text into a format (plain text or feature vectors) that can be easily processed by the generative AI model.

[0208] Step 3: Perform the review

[0209] The server inputs the preprocessed text material into the generative AI model.

[0210] The generative AI model is trained to mimic the feedback style of a specific manager, and generates feedback by analyzing the content of the document, such as "The goal setting is vague, so please add specific KPIs."

[0211] Step 4: Adjust your feedback

[0212] The server activates an emotion engine, which analyzes at least one of the user's facial expression, voice, and input text data to recognize the user's emotion.

[0213] The emotion engine adjusts the expression of the generated feedback based on the emotions it recognizes (e.g., stressed, anxious, relaxed, etc.). For example, if the user is feeling stressed, the tone of the feedback can be softened.

[0214] Step 5: Provide feedback

[0215] The formatted feedback content is sent from the server to the user's device, and the user can access the web page again and view the generated feedback in the "Check Feedback" section.

[0216] The feedback is categorized by section, allowing users to easily understand and make necessary corrections to the material.

[0217] Specific examples

[0218] For example, consider the case where a user uploads a project plan ("Project Plan.docx") to the system.

[0219] 1. Uploading materials

[0220] Users access a dedicated web page, click the "Upload Documents" button, select "Project Plan.docx", and upload it.

[0221] 2. Analysis of the data

[0222] The server passes the received file to a text analysis module, which extracts and analyzes the contents of the material as text information for each paragraph or section.

[0223] 3. Conducting the review

[0224] The formatted text content is input into a generative AI model, which analyzes the material and generates feedback such as "the goals of a particular section are unclear."

[0225] 4. Adjusting feedback

[0226] The server's emotion engine analyzes the user's facial expressions and voice and detects when the user is feeling stressed. In this case, the generated feedback is adjusted to be softer.

[0227] 5. Providing Feedback

[0228] The server formats the generated feedback and provides it to the user, who can view it in the "Review Feedback" section and make any necessary corrections to the material.

[0229] In this way, this system allows users to have their materials reviewed efficiently and receive feedback that reflects their emotions, thereby reducing stress and improving work efficiency.

[0230] The processing flow will be explained below.

[0231] Step 1:

[0232] The user accesses the system's dedicated web page from the web browser on their device. The user clicks the "Upload Documents" button, selects the document files to be reviewed from the local file system, and then clicks the "Upload" button. This sends the selected files to the server.

[0233] Step 2:

[0234] The server receives the document file sent by the user. The server temporarily stores the file and passes it to the text analysis module. The text analysis module identifies the file format and selects the appropriate parser. For example, if the file is in .docx format, it will use the corresponding parser program to extract the content.

[0235] Step 3:

[0236] The server's text analysis module breaks down the document into paragraphs and sections, extracts the meaning of each part, and converts the extracted text into a format (plain text or feature vectors) that can be easily processed by the generative AI model.

[0237] Step 4:

[0238] The server inputs the preprocessed content into a generative AI model. The generative AI model is trained to mimic the feedback style of a specific manager and analyzes the content of the material to generate feedback. For example, it generates specific suggestions such as, "The goal setting is vague, so please add specific KPIs."

[0239] Step 5:

[0240] The server receives the feedback output from the generative AI model and formats it for providing feedback. The feedback content is categorized into sections for easy understanding by the user.

[0241] Step 6:

[0242] The server activates an emotion engine and analyzes at least one of the user's facial expressions, voice, and input text data to recognize the user's emotion. The feedback expression by the generative AI model is adjusted according to the emotion (e.g., stress, anxiety, relaxation, etc.) recognized by the emotion engine. For example, if the user is feeling stressed, the tone of the feedback is softened.

[0243] Step 7:

[0244] The server sends the formatted feedback to the user's device. The user can then access the web page again and view the generated feedback in the "Review Feedback" section. The user can then review the feedback and make any necessary corrections to the document.

[0245] Step 8:

[0246] Users can upload the revised documents back into the system as needed and request a re-review in the same way.

[0247] In this way, this system allows users to have their materials reviewed efficiently and receive feedback that reflects their emotions, thereby reducing stress and improving work efficiency.

[0248] Example 2

[0249] 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."

[0250] Conventional document review systems have the problem of providing inconsistent feedback quality and difficulty in responding flexibly to the user's emotions. Furthermore, the feedback users receive is uniform, often causing stress and anxiety, which can lead to reduced work efficiency. Given these issues, there is a demand for a system that provides efficient feedback that takes emotions into consideration.

[0251] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0252] In this invention, the server includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to the user, and an emotion engine for recognizing the user's emotions and adjusting the expression of the feedback, thereby enabling the provision of efficient feedback that takes the user's emotions into consideration.

[0253] The "means for uploading materials" is a function that allows a user to select a material file from a local file system and send it to the system.

[0254] The "means for analyzing uploaded materials" refers to a function for identifying the content of materials uploaded on a server, dividing them into paragraphs or sections, and extracting text information.

[0255] The "means of inputting information into a generative AI model for review" refers to a function that provides preprocessed text information to a generative AI model and generates feedback based on the content of the material.

[0256] The "means for providing generated feedback to the user" is a function for formatting the feedback created by the generative AI model and displaying it to the user.

[0257] The "emotion engine" is a function that analyzes the user's facial expressions, voice, or input text data, recognizes the user's emotions, and adjusts the feedback expression.

[0258] The present invention includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to a user, and an emotion engine for recognizing the user's emotions and adjusting the expression of the feedback.

[0259] Uploading materials

[0260] The user accesses the system's dedicated web page using the device's web browser. The web page has an "Upload Materials" button. The user clicks this to select review material files from a local PC or other device, then presses the "Upload" button. This sends the selected files to the server, which receives and temporarily stores the files. This process uses the standard HTTP protocol and the browser's file upload function.

[0261] Analysis of data

[0262] The uploaded material is transferred to the text analysis module on the server. The text analysis module identifies the file format of the submitted material and selects the appropriate analysis program. For example, if the file is in .docx format, the Apache POI library is used to extract the content. The analysis breaks the material content into paragraphs and sections and extracts text information. The extracted text information is then converted into a format (plain text or feature vectors) that is easy for the generative AI model to process. A natural language processing library (such as NLTK) can be used here.

[0263] Conducting a review

[0264] The server inputs the analyzed text information into a generative AI model. The generative AI model is trained to mimic a specific feedback style and generates feedback by analyzing the content of the material. For example, it may generate specific suggestions such as, "The goal setting is vague, so please add specific KPIs." This generative AI model is implemented using machine learning libraries (such as TensorFlow and PyTorch).

[0265] Feedback adjustment

[0266] The generated feedback is further adjusted by an emotion engine. The emotion engine analyzes at least one of the user's facial expressions, voice, and input text data to recognize the user's emotions. Specifically, real-time data is collected using a webcam or microphone and then passed through an emotion recognition algorithm. For example, OpenCV or Google Cloud Vision API is used. Based on the emotion recognized by the emotion engine (e.g., stress, anxiety, relaxation, etc.), the tone of the generated feedback is softened or the content is adjusted.

[0267] Providing Feedback

[0268] The final formatted feedback is sent from the server to the user's device. The user can then access the web page again and view the generated feedback in the "Review Feedback" section. The feedback is categorized by paragraph and section, allowing the user to easily understand and make any necessary corrections to the material.

[0269] Prompt Sentence Examples

[0270] For example, here is a sample prompt to input to a generative AI model:

[0271] "This document is vague about the project goals, so please add specific KPIs."

[0272] "The analysis of the current situation is not detailed enough, so please supplement it with concrete numerical data."

[0273] In this way, this system allows users to have their materials reviewed efficiently and receive feedback that reflects their emotions, thereby reducing stress and improving work efficiency.

[0274] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0275] Processing flow

[0276] Step 1: Upload your materials

[0277] Step 2: Analyze the data

[0278] Step 3: Performing the review

[0279] Step 4: Adjusting feedback

[0280] Step 5: Provide feedback

[0281] Explanation of each processing step

[0282] Step 1: Upload your materials

[0283] Operation:

[0284] The user accesses the system's dedicated web page using the terminal's web browser.

[0285] The user clicks the "Upload Material" button and selects a material file from the local file system.

[0286] The user clicks the "Upload" button to upload the selected document file (e.g., Project Plan.docx) to the server.

[0287] input:

[0288] A resource file selected from the local file system.

[0289] output:

[0290] The file uploaded to the server.

[0291] Step 2: Analyze the data

[0292] Operation:

[0293] The server transfers the received material file to the text analysis module.

[0294] The text analysis module identifies the file format and if it is .docx, it uses the Apache POI library to extract the content.

[0295] The text analysis module divides the content of the document into paragraphs and sections and extracts text information.

[0296] input:

[0297] Uploaded material files.

[0298] output:

[0299] Text information divided into paragraphs or sections.

[0300] Step 3: Performing the review

[0301] Operation:

[0302] The server inputs the preprocessed text information into the generative AI model.

[0303] The generative AI model analyzes the materials and generates feedback based on the feedback style it has been trained in. For example, it generates suggestions such as, "The goal setting is vague, so please add specific KPIs."

[0304] input:

[0305] Text information organized into paragraphs and sections.

[0306] output:

[0307] Feedback sentences generated by a generative AI model.

[0308] Step 4: Adjusting feedback

[0309] Operation:

[0310] The server starts the emotion engine.

[0311] The emotion engine analyzes the user's facial expressions, voice, or input text data to recognize the user's emotions. For example, real-time facial expression data is captured via a webcam to identify emotional states such as stress or relaxation.

[0312] The emotion engine adjusts the tone and content of the generated feedback based on the emotions it recognizes. If the user is feeling stressed, the feedback can be softened to something like, "Some of your goals are unclear, so it would be helpful if you could be more specific."

[0313] input:

[0314] Feedback statements generated by the generative AI model and user sentiment data.

[0315] output:

[0316] Tailored feedback statements.

[0317] Step 5: Provide feedback

[0318] Operation:

[0319] The server then sends the final formatted feedback to the user's device.

[0320] The user visits the web page again and views the generated feedback in the "Review Feedback" section.

[0321] The feedback is categorized by section, allowing users to easily understand and make necessary corrections to the material.

[0322] input:

[0323] Tailored feedback statements.

[0324] output:

[0325] The feedback displayed on the user's device.

[0326] Specific examples

[0327] For example, here is a sample prompt to input to a generative AI model:

[0328] "This document is vague about the project goals, so please add specific KPIs."

[0329] "The analysis of the current situation is not detailed enough, so please supplement it with concrete numerical data."

[0330] In this way, efficient and emotionally relevant feedback is provided to the user at each step.

[0331] (Application example 2)

[0332] 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."

[0333] While conventional document review systems have the ability to analyze document content and provide feedback, they lack the ability to adjust feedback based on user emotions and provide real-time feedback notifications. As a result, users often feel stressed and there is a risk of reduced productivity. Furthermore, work efficiency using smart devices has not progressed, making it difficult to provide specific feedback in a manufacturing environment, leaving issues in terms of work efficiency.

[0334] The specific processing by the specific 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 uploading materials, means for analyzing the uploaded materials, means for inputting the analyzed materials into a generative AI model for review, means for providing the generated feedback to the user, an emotion engine for recognizing the user's emotions, a smart device for notifying information in real time, and means for appropriately classifying the notified feedback by section. This makes it possible to provide flexible feedback that takes the user's emotions into consideration and notify information in real time, thereby improving work efficiency and reducing user stress.

[0335] "Means for uploading materials" refers to the functionality that allows users to provide digital information to the system.

[0336] "Means for analyzing uploaded materials" refers to a function for breaking down and understanding the provided digital information by content.

[0337] The "means of inputting into a generative AI model for review" is a function that provides analyzed digital information to an artificial intelligence model, allowing it to evaluate the content and suggest areas for improvement.

[0338] "Means for providing generated feedback to users" refers to a function for communicating evaluation results and improvement suggestions generated by the AI ​​model to users.

[0339] The "emotion engine that recognizes the user's emotions" is a function that analyzes data such as the user's facial expressions and voice to determine their emotional state at that time.

[0340] "Smart devices that provide information in real time" are wearable terminals and mobile devices that can provide information to users instantly.

[0341] The "means for appropriately classifying notified feedback into sections" is a function for dividing the generated review information into parts and presenting them to the user.

[0342] As a specific embodiment of the present invention, we will explain a review system for work instructions in a factory. This system analyzes uploaded work instructions, provides feedback using AI, and adjusts the feedback according to the user's emotional state.

[0343] The server at the heart of the system includes a means for uploading materials, a means for analyzing uploaded materials, a means for inputting analyzed materials into a generative AI model for review, as well as a means for providing generated feedback to users, an emotion engine for recognizing users' emotions, a smart device for notifying information in real time, and a means for appropriately classifying notified feedback into sections.

[0344] Hardware and software used

[0345] 1. Smart Devices

[0346] Smart glasses and smartphones: devices for displaying real-time feedback

[0347] 2. Emotion Engine

[0348] Webcam and microphone: Hardware for analyzing the user's facial expressions and voice

[0349] EmotionRecognizer: Software for emotion analysis

[0350] 3. AI Model

[0351] Transformers Library: Software for running generative AI models

[0352] NLP toolkits (e.g., TextBlob): software for analyzing and processing text data.

[0353] Data processing and calculation

[0354] The server processes the data and performs calculations through the following steps:

[0355] 1. Uploading materials

[0356] Users upload work instructions using their smart devices.

[0357] The uploaded materials are received by the server and temporarily stored.

[0358] 2. Analysis of the data

[0359] The server analyzes the stored material and converts it into text blobs.

[0360] The analyzed text is divided into sections and semantic information for each section is extracted.

[0361] 3. Conducting the review

[0362] The formatted text data is input into a generative AI model to generate feedback.

[0363] The generated feedback is sent to the user's smart device in real time.

[0364] 4. Emotional awareness and feedback regulation

[0365] The server activates the emotion engine and acquires the user's facial expressions and voice data.

[0366] The emotion engine analyzes stress and fatigue levels and adjusts the tone of feedback as needed.

[0367] 5. Real-time notifications and section classification

[0368] The adjusted feedback will be appropriately categorized into sections and notified to the user.

[0369] Individual notifications are also provided for each work section using RFID tags.

[0370] Specific examples

[0371] A user uploads "Work Instructions.pdf" via smart glasses in Factory Section A. The server analyzes the document and uses a generative AI model to generate feedback such as "A particular procedure is unclear." If the user's stress level is high, this feedback is adjusted to a softer tone, such as "Please take a short break and recheck." The user can view this feedback in real time through the smart glasses and take appropriate measures.

[0372] Prompt Sentence Examples

[0373] File path: "Factory Section A / Work Instructions.pdf"

[0374] User Sentiment: "Fatigue"

[0375] As described above, the system of the present invention improves work efficiency and reduces stress by providing flexible feedback that takes into account the user's emotions and by notifying them in real time.

[0376] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0377] Step 1:

[0378] The user uploads work instructions to the system through a smart device (smart glasses or smartphone). The input contains a file selected by the user. This file is sent to the server. The output is the uploaded material stored on the server.

[0379] Step 2:

[0380] The server receives the uploaded material and temporarily stores it. It then passes this file to the analysis module. The input contains the uploaded material file. The output is the material content converted into a text blob. The analysis module identifies the material format and selects the appropriate parser to extract the content.

[0381] Step 3:

[0382] The server inputs the parsed text information into a generative AI model to perform the review. The input includes formatted text information. The output provides specific feedback from the generative AI model. The generative AI model uses the trained model to evaluate the content of the material.

[0383] Step 4:

[0384] The server temporarily stores the generated feedback and activates the emotion engine. The input includes the user's facial expression and voice data. The user's emotional state is acquired through a webcam or microphone, and emotion analysis is performed using the EmotionRecognizer software. The output is the user's emotional state (e.g., stress, fatigue).

[0385] Step 5:

[0386] The server adjusts the expression of the generated feedback based on the analysis results of the emotion engine. The inputs include the generated feedback and the user's emotional state. The output is feedback adjusted according to the emotion. For example, if the user is feeling tired, the tone of the feedback is softened.

[0387] Step 6:

[0388] The server categorizes the tailored feedback by appropriate section and notifies the user's smart device in real time. The input includes the tailored feedback. The output is the feedback organized by section displayed on the smart device. An RFID reader is used to provide individual feedback for each work section.

[0389] Step 7:

[0390] Users can view the feedback provided in real time through their smart devices and take appropriate measures. The input includes feedback organized by section. The output is corrections and improvements based on the feedback. Users can easily view the feedback content and work more efficiently.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] [Second embodiment]

[0395] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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).

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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."

[0407] The embodiment of the present invention is described below: The system includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, and a means for providing generated feedback to a user.

[0408] Program processing explanation

[0409] Step 1: Upload your materials

[0410] The user accesses the system's dedicated web page from the terminal's web browser.

[0411] The user clicks the "Upload material" button and selects the material file to be reviewed from the local file system.

[0412] When the user presses the "Upload" button, the corresponding material file is sent to the server.

[0413] Step 2: Analyze the material

[0414] The server receives the material file sent by the user.

[0415] The server passes the received document file to the text analysis module, which selects an appropriate parser depending on the document format and extracts the content as text information.

[0416] The server further breaks down the extracted text information and formats it into a format that is easy for the generative AI model to process (for example, plain text or vector format).

[0417] Step 3: Perform the review

[0418] The server inputs the formatted content into a generative AI model.

[0419] The generative AI model analyzes the input materials and generates feedback suggestions and improvement suggestions by imitating the feedback style of a specific manager.

[0420] The generated feedback is output to the server in text format.

[0421] Step 4: Provide feedback

[0422] The server formats the generated feedback for delivery to the user, specifically by organizing the feedback into sections that are easy to understand.

[0423] The feedback content is sent to the user's device and stored in association with the user's account.

[0424] The user can revisit the web page and view the generated feedback in the "Review Feedback" section.

[0425] Specific examples

[0426] For example, consider the case where a user uploads a project plan (e.g., "Project Plan.docx") to the system.

[0427] 1. Uploading materials

[0428] Users access a dedicated web page, click the "Upload Documents" button, select "Project Plan.docx," and upload it.

[0429] 2. Analysis of the data

[0430] The server passes the received "Project Plan.docx" to the text analysis module, which extracts and analyzes text information for each paragraph and section within the document.

[0431] 3. Conducting the review

[0432] The formatted text content is fed into a generative AI model.

[0433] The generative AI model analyzes the content of the document and generates feedback such as, "The goal setting is vague, so add specific target KPIs."

[0434] 4. Providing Feedback

[0435] The server formats the feedback from the generative AI model and provides it to the user, who can then review the generated feedback and make corrections to the material.

[0436] In this way, this system allows users to have their documents reviewed efficiently, reducing the burden on their superiors and improving work efficiency.

[0437] The processing flow will be explained below.

[0438] Step 1:

[0439] The user accesses the system's dedicated web page from a web browser on their terminal. The user clicks the "Upload Documents" button on the homepage, selects the document file to be reviewed from the local file system (e.g., "Project Plan.docx"), and presses the "Upload" button. This sends the selected file to the server.

[0440] Step 2:

[0441] The server receives the document file sent by the user. The server temporarily stores the file and passes it to the text analysis module. The text analysis module identifies the file format and selects the appropriate parser. For example, if the file is in .docx format, it will use the corresponding parser program to extract the content.

[0442] Step 3:

[0443] The server's text analysis module breaks down the content of the document into paragraphs and sections, extracts the meaning of each part, and converts the extracted text information into a format that is easy for the generative AI model to process (e.g., plain text or feature vectors).

[0444] Step 4:

[0445] The server inputs the preprocessed content into a generative AI model. The generative AI model is trained to mimic the feedback style of a specific manager and analyzes the content of the document to generate feedback. For example, it generates specific suggestions such as, "The goal setting for this section is vague, so please add specific KPIs."

[0446] Step 5:

[0447] The feedback generated by the generative AI model is output in text format to the server, which receives it and formats it appropriately for delivery to the user. The feedback is categorized into sections and summarized for easy user understanding.

[0448] Step 6:

[0449] The formatted feedback content is sent from the server to the user's device, and the user can access the web page again and view the generated feedback in the "Check Feedback" section.

[0450] Step 7:

[0451] The user can revise the document based on the feedback and, if necessary, upload the revised document back to the system and request a re-review in the same way.

[0452] This series of processes allows the user to have the materials reviewed efficiently, reducing the burden on the superior and improving work efficiency.

[0453] Example 1

[0454] 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."

[0455] Conventional document review systems require manual document analysis and review, which often hinders efficient business operations. Furthermore, because reviews are not based on a specific feedback style, it is difficult to obtain consistent feedback. This creates problems for users, who are unable to obtain high-quality reviews quickly, and increases the burden on supervisors.

[0456] 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.

[0457] In this invention, the server includes a means for a user to upload materials to a dedicated web page using a browser on a terminal, a means for the server to receive the uploaded materials and pass them to an analysis module to extract text information, a means for the server to format the extracted text information and input it into a generative AI model, and a means for the generative AI model to generate feedback based on the input materials, and for the server to provide the feedback to the user, thereby enabling the user to receive prompt and consistent feedback.

[0458] "User" refers to a person who uses the system to perform operations such as uploading materials and checking feedback.

[0459] "Terminal" refers to computer equipment such as a PC or smartphone used by a user.

[0460] "Browser" refers to the web browsing software used by a User to access a dedicated web page.

[0461] "Materials" refers to document files and data uploaded by users as the subject of review.

[0462] "Specialized Web Page" means a page on a particular Website that a User accesses to upload materials.

[0463] "Server" refers to the computing device or system that receives materials, analyzes them, runs generative AI models, and provides feedback.

[0464] "Analysis Module" means a software component for extracting textual information from Uploaded Materials.

[0465] "Text information" refers to character string data extracted from the analyzed material.

[0466] "Generative AI model" refers to an artificial intelligence model used to analyze material and generate feedback.

[0467] "Feedback" refers to the comments and suggestions for improvement that the generative AI model generates as a result of analyzing the materials.

[0468] The present invention is a system in which a user uploads materials using a terminal, the server analyzes the materials, inputs them into a generative AI model, and provides feedback. The system is configured as follows:

[0469] Users access a dedicated web page using a web browser (such as Google Chrome or Mozilla Firefox) on a device such as a PC or smartphone. This web page has an "Upload Documents" button, which users click to select and upload the target document file (such as "Project Plan.docx") from their local file system.

[0470] The server receives the document file sent by the user. After receiving the file, the server passes it to a text analysis module (e.g., Apache Tika or spaCy) to analyze and extract the content of the document as text information. This text analysis module selects an appropriate parser depending on the document format (e.g., PDF, DOCX) to extract the text information.

[0471] The server further breaks down the parsed text information and formats it into a format (plain text or vector format) that is easy for a generative AI model (e.g., OpenAI GPT-4 or BERT) to process. When the formatted content is input into the generative AI model, the AI ​​model analyzes the material and generates feedback by imitating a specific feedback style (e.g., a boss's feedback style).

[0472] The generated feedback is retrieved by the server and formatted to be provided to the user. Specifically, the feedback is organized into sections to make it easier to understand. The formatted feedback is sent to the user's device and stored in association with the user's account. The user can then access the web page again and view the generated feedback in the "Review Feedback" section.

[0473] As a concrete example, consider the case where a user uploads a project plan (e.g., "Project Plan.docx") to the system. The user accesses a dedicated web page, clicks the "Upload Document" button, selects "Project Plan.docx," and uploads it. The server passes the received "Project Plan.docx" to a text analysis module, which extracts and analyzes text information for each paragraph and section within the document. The formatted text content of the document is then supplied to a generative AI model (such as OpenAI GPT-4). The generative AI model analyzes the content of the document and generates feedback such as, "The goal setting is vague, so add specific target KPIs."

[0474] An example prompt might be, "Please review the goal setting in the project plan and identify areas for improvement."

[0475] In this way, this system allows users to have their documents reviewed efficiently, reducing the burden on their superiors and improving work efficiency.

[0476] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0477] Step 1:

[0478] The user accesses a dedicated web page

[0479] Input: Access request from the user's device

[0480] How it works: A user accesses the system's dedicated web page using a web browser (such as Google Chrome or Mozilla Firefox) on a PC or smartphone.

[0481] Output: A custom web page is displayed in the user's browser.

[0482] Step 2:

[0483] The user selects and uploads materials

[0484] Input: The document file selected by the user (e.g. "Project Plan.docx")

[0485] Operation: The user clicks the "Upload Document" button, selects the document file to upload from the local file system, and then clicks the "Upload" button to send the document file to the server.

[0486] Output: The document file is sent to the server.

[0487] Step 3:

[0488] The server passes the received material to the text analysis module

[0489] Input: Data file received by the server

[0490] How it works: The server receives a file uploaded by a user, then passes the file to a text analysis module (e.g., Apache Tika, spaCy).

[0491] Output: The data file is sent to the analysis module.

[0492] Step 4:

[0493] The text analysis module analyzes the material.

[0494] Input: Received material file

[0495] Operation: The text analysis module selects a parser according to the format of the document, and analyzes and extracts the content of the document as text information.

[0496] Output: Extracted text information

[0497] Step 5:

[0498] The server formats the extracted text information

[0499] Input: Extracted text information

[0500] How it works: The server formats the parsed text information into a format that is easy for the generative AI model to process (e.g., plain text or vector format).

[0501] Output: Formatted text information

[0502] Step 6:

[0503] The server inputs the formatted content into the generative AI model

[0504] Input: Formatted text information

[0505] How it works: The server inputs this formatted content into a generative AI model (e.g., OpenAI GPT-4).

[0506] Output: The content is fed into the generative AI model

[0507] Step 7:

[0508] A generative AI model analyzes the material and generates feedback

[0509] Input: Content fed into the generative AI model

[0510] How it works: The generative AI model analyzes the input material and generates a review based on a specific feedback style.

[0511] Output: The generated feedback

[0512] Step 8:

[0513] Server generated feedback is retrieved

[0514] Input: Feedback generated by the generative AI model

[0515] How it works: The server receives feedback output from the generative AI model.

[0516] Output: Feedback obtained by the server

[0517] Step 9:

[0518] The server formats the feedback and provides it to the user.

[0519] Input: Generated feedback

[0520] How it works: The server formats the feedback and organizes it into sections for delivery to the user, then sends it to the user's device and stores it in association with the user's account.

[0521] Output: Feedback sent to the user's device

[0522] Step 10:

[0523] User checks feedback

[0524] Input: Feedback sent by the server

[0525] How it works: The user revisits the dedicated web page and views the generated feedback in the "Review Feedback" section.

[0526] Output: The feedback the user saw

[0527] (Application example 1)

[0528] 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."

[0529] It is important to quickly and accurately acquire information about items in factories and on production lines and provide real-time feedback to workers based on that information. However, conventional systems have had difficulty in efficiently accomplishing this. In particular, automating analysis according to different information formats and types of items, and providing immediate feedback to workers have been challenging.

[0530] 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.

[0531] In this invention, the server includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to the worker, and a means for collecting product information using a smart device and displaying the feedback in real time, thereby enabling the product information to be obtained quickly and accurately and specific feedback based on the analyzed information to be provided to the worker in real time.

[0532] "Means for uploading materials" means any device or software that allows a User to submit materials to the System.

[0533] "Means for analyzing uploaded materials" refers to devices or software for analyzing received materials and extracting their contents as text information.

[0534] "Means for inputting analyzed materials into a generative AI model for review" refers to devices or software that provide extracted text information to a generative AI model to generate evaluations and feedback on the materials.

[0535] "Means for providing generated feedback to workers" refers to devices or software that deliver feedback obtained from the generative AI model to workers.

[0536] "Means for using smart devices to collect product information and display the feedback in real time" refers to software and hardware for using smart glasses or other mobile devices to collect product information and display the results in real time.

[0537] The system includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to a worker, and a means for collecting item information using a smart device and displaying the feedback in real time.

[0538] The server provides a dedicated web page that users access from their device's web browser. On this page, users click the "Upload Document" button and select and upload a document file from their local file system. Once the document is uploaded, the server receives it and passes it to a text analysis module for analysis. This text analysis module is equipped with an appropriate parser for each document format, and is able to extract the content as text information. The extracted text information is input into a generative AI model, which generates various feedback.

[0539] The generative AI model analyzes the input materials and generates feedback. For example, based on the content of the materials, it provides feedback such as, "The goal setting is not clear, so specific KPIs should be added." This feedback is formatted by the server and organized by section. The generated feedback is then sent to the user's device and provided to the worker.

[0540] In a factory implementation, smart glasses are used. When a worker wears the smart glasses and scans the barcode of an item using voice commands or gestures, the barcode data is sent to a cloud server. The cloud server searches a database using the received barcode data to obtain related item information. The obtained item information is analyzed by a text analysis module and provided to a generative AI model. The generative AI model analyzes the item information and generates feedback related to quality control and the manufacturing process. For example, feedback such as "The torque value of the screw may not meet the specifications, so please check it again" can be generated and displayed in real time on the smart glasses' display.

[0541] Hardware used:

[0542] Smart Glasses

[0543] Cloud Server

[0544] User's device (PC or smartphone)

[0545] Software used:

[0546] Flask (Python framework)

[0547] Virtual Barcode Scan Module

[0548] Virtual Text Analysis Module

[0549] Virtual generative AI model

[0550] As a concrete example of this system, imagine a scenario in which a worker inspects hardware parts in a factory, scans a barcode with smart glasses, and receives real-time feedback such as, "Please check the torque value of this part." In this case, an example of a prompt sentence to be input to the generative AI model would be, "Please provide feedback on quality control based on the following item information: product name, specifications, torque value, and inspection results."

[0551] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0552] Step 1:

[0553] The user accesses the system's dedicated web page from the web browser on their device. The user clicks the "Upload Documents" button and selects the document files to be reviewed from the local file system. When the user presses the "Upload" button, the corresponding document files are sent to the server. The input here is the document file selected by the user, and the output is the document data sent to the server.

[0554] Step 2:

[0555] The server receives the document file sent by the user. The received document file is passed to the text analysis module. The text analysis module selects an appropriate parser depending on the format of the document and extracts the content as text information. The input here is the uploaded document file, and the output is the extracted text information.

[0556] Step 3:

[0557] The server further breaks down the extracted text information and formats it into a format that is easy for the generative AI model to process (plain text or vector format). The input here is the extracted text information, and the output is the formatted material content.

[0558] Step 4:

[0559] The server inputs the formatted content into a generative AI model, which analyzes the input and generates feedback suggestions based on a specific feedback style. The input is the formatted content, and the output is the generated feedback.

[0560] Step 5:

[0561] The server formats the generated feedback for delivery to the user. Specifically, the feedback is organized into sections for easy understanding. The formatted feedback is sent to the user's device and stored in association with the user's account. The input here is the generated feedback, and the output is the formatted feedback and the stored data.

[0562] Step 6:

[0563] The user revisits the web page and views the generated feedback in the "Review Feedback" section, where the input is the formatted feedback content and the output is the user's feedback viewing results.

[0564] Step 7:

[0565] A worker wears smart glasses and scans the barcode of an item using voice commands or gestures. The smart glasses then send the barcode data to a cloud server and upload the item information. The input is the scanned barcode data, and the output is the item information sent to the cloud server.

[0566] Step 8:

[0567] The cloud server retrieves related product information from the database based on the received barcode data. The retrieved product information is analyzed by a text analysis module and fed to a generative AI model. The input here is the barcode data, and the output is formatted product information.

[0568] Step 9:

[0569] The generative AI model generates feedback on quality control and manufacturing processes based on input product information. For example, "The torque value of the screw may not meet the standard, so please check it again." The input here is the formatted product information, and the output is the generated feedback.

[0570] Step 10:

[0571] The generated feedback is displayed on the smart glasses display in real time, and the worker takes the necessary action based on the displayed feedback. The input here is the generated feedback, and the output is the real-time feedback display.

[0572] 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.

[0573] The embodiment of the present invention is described below: The system includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to a user, and an emotion engine for recognizing the user's emotions.

[0574] Program processing explanation

[0575] Step 1: Upload your materials

[0576] The user accesses the system's dedicated web page from the terminal's web browser.

[0577] The user clicks the "Upload Material" button, selects the material file to be reviewed from the local file system, and then clicks the "Upload" button, which sends the selected file to the server.

[0578] Step 2: Analyze the material

[0579] The server receives the material file sent from the user.

[0580] The server temporarily stores this file and passes it to the text analysis module, which identifies the file format and selects the appropriate parser. For example, if the file is in .docx format, it will use the corresponding parser program to extract the content.

[0581] The server's text analysis module breaks down the document into paragraphs and sections, extracts the meaning of each part, and converts the extracted text into a format (plain text or feature vectors) that can be easily processed by the generative AI model.

[0582] Step 3: Perform the review

[0583] The server inputs the preprocessed text material into the generative AI model.

[0584] The generative AI model is trained to mimic the feedback style of a specific manager, and generates feedback by analyzing the content of the document, such as "The goal setting is vague, so please add specific KPIs."

[0585] Step 4: Adjust your feedback

[0586] The server activates an emotion engine, which analyzes at least one of the user's facial expression, voice, and input text data to recognize the user's emotion.

[0587] The emotion engine adjusts the expression of the generated feedback based on the emotions it recognizes (e.g., stressed, anxious, relaxed, etc.). For example, if the user is feeling stressed, the tone of the feedback can be softened.

[0588] Step 5: Provide feedback

[0589] The formatted feedback content is sent from the server to the user's device, and the user can access the web page again and view the generated feedback in the "Check Feedback" section.

[0590] The feedback is categorized by section, allowing users to easily understand and make necessary corrections to the material.

[0591] Specific examples

[0592] For example, consider the case where a user uploads a project plan ("Project Plan.docx") to the system.

[0593] 1. Uploading materials

[0594] Users access a dedicated web page, click the "Upload Documents" button, select "Project Plan.docx", and upload it.

[0595] 2. Analysis of the data

[0596] The server passes the received file to a text analysis module, which extracts and analyzes the contents of the material as text information for each paragraph or section.

[0597] 3. Conducting the review

[0598] The formatted text content is input into a generative AI model, which analyzes the material and generates feedback such as "the goals of a particular section are unclear."

[0599] 4. Adjusting feedback

[0600] The server's emotion engine analyzes the user's facial expressions and voice and detects when the user is feeling stressed. In this case, the generated feedback is adjusted to be softer.

[0601] 5. Providing Feedback

[0602] The server formats the generated feedback and provides it to the user, who can view it in the "Review Feedback" section and make any necessary corrections to the material.

[0603] In this way, this system allows users to have their materials reviewed efficiently and receive feedback that reflects their emotions, thereby reducing stress and improving work efficiency.

[0604] The processing flow will be explained below.

[0605] Step 1:

[0606] The user accesses the system's dedicated web page from the web browser on their device. The user clicks the "Upload Documents" button, selects the document files to be reviewed from the local file system, and then clicks the "Upload" button. This sends the selected files to the server.

[0607] Step 2:

[0608] The server receives the document file sent by the user. The server temporarily stores the file and passes it to the text analysis module. The text analysis module identifies the file format and selects the appropriate parser. For example, if the file is in .docx format, it will use the corresponding parser program to extract the content.

[0609] Step 3:

[0610] The server's text analysis module breaks down the document into paragraphs and sections, extracts the meaning of each part, and converts the extracted text into a format (plain text or feature vectors) that can be easily processed by the generative AI model.

[0611] Step 4:

[0612] The server inputs the preprocessed content into a generative AI model. The generative AI model is trained to mimic the feedback style of a specific manager and analyzes the content of the material to generate feedback. For example, it generates specific suggestions such as, "The goal setting is vague, so please add specific KPIs."

[0613] Step 5:

[0614] The server receives the feedback output from the generative AI model and formats it for providing feedback. The feedback content is categorized into sections for easy understanding by the user.

[0615] Step 6:

[0616] The server activates an emotion engine and analyzes at least one of the user's facial expressions, voice, and input text data to recognize the user's emotion. The feedback expression by the generative AI model is adjusted according to the emotion (e.g., stress, anxiety, relaxation, etc.) recognized by the emotion engine. For example, if the user is feeling stressed, the tone of the feedback is softened.

[0617] Step 7:

[0618] The server sends the formatted feedback to the user's device. The user can then access the web page again and view the generated feedback in the "Review Feedback" section. The user can then review the feedback and make any necessary corrections to the document.

[0619] Step 8:

[0620] Users can upload the revised documents back into the system as needed and request a re-review in the same way.

[0621] In this way, this system allows users to have their materials reviewed efficiently and receive feedback that reflects their emotions, thereby reducing stress and improving work efficiency.

[0622] Example 2

[0623] 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."

[0624] Conventional document review systems have the problem of providing inconsistent feedback quality and difficulty in responding flexibly to the user's emotions. Furthermore, the feedback users receive is uniform, often causing stress and anxiety, which can lead to reduced work efficiency. Given these issues, there is a demand for a system that provides efficient feedback that takes emotions into consideration.

[0625] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0626] In this invention, the server includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to the user, and an emotion engine for recognizing the user's emotions and adjusting the expression of the feedback, thereby enabling the provision of efficient feedback that takes the user's emotions into consideration.

[0627] The "means for uploading materials" is a function that allows a user to select a material file from a local file system and send it to the system.

[0628] The "means for analyzing uploaded materials" refers to a function for identifying the content of materials uploaded on a server, dividing them into paragraphs or sections, and extracting text information.

[0629] The "means of inputting information into a generative AI model for review" refers to a function that provides preprocessed text information to a generative AI model and generates feedback based on the content of the material.

[0630] The "means for providing generated feedback to the user" is a function for formatting the feedback created by the generative AI model and displaying it to the user.

[0631] The "emotion engine" is a function that analyzes the user's facial expressions, voice, or input text data, recognizes the user's emotions, and adjusts the feedback expression.

[0632] The present invention includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to a user, and an emotion engine for recognizing the user's emotions and adjusting the expression of the feedback.

[0633] Uploading materials

[0634] The user accesses the system's dedicated web page using the device's web browser. The web page has an "Upload Materials" button. The user clicks this to select review material files from a local PC or other device, then presses the "Upload" button. This sends the selected files to the server, which receives and temporarily stores the files. This process uses the standard HTTP protocol and the browser's file upload function.

[0635] Analysis of data

[0636] The uploaded material is transferred to the text analysis module on the server. The text analysis module identifies the file format of the submitted material and selects the appropriate analysis program. For example, if the file is in .docx format, the Apache POI library is used to extract the content. The analysis breaks the material content into paragraphs and sections and extracts text information. The extracted text information is then converted into a format (plain text or feature vectors) that is easy for the generative AI model to process. A natural language processing library (such as NLTK) can be used here.

[0637] Conducting a review

[0638] The server inputs the analyzed text information into a generative AI model. The generative AI model is trained to mimic a specific feedback style and generates feedback by analyzing the content of the material. For example, it may generate specific suggestions such as, "The goal setting is vague, so please add specific KPIs." This generative AI model is implemented using machine learning libraries (such as TensorFlow and PyTorch).

[0639] Feedback adjustment

[0640] The generated feedback is further adjusted by an emotion engine. The emotion engine analyzes at least one of the user's facial expressions, voice, and input text data to recognize the user's emotions. Specifically, real-time data is collected using a webcam or microphone and then passed through an emotion recognition algorithm. For example, OpenCV or Google Cloud Vision API is used. Based on the emotion recognized by the emotion engine (e.g., stress, anxiety, relaxation, etc.), the tone of the generated feedback is softened or the content is adjusted.

[0641] Providing Feedback

[0642] The final formatted feedback is sent from the server to the user's device. The user can then access the web page again and view the generated feedback in the "Review Feedback" section. The feedback is categorized by paragraph and section, allowing the user to easily understand and make any necessary corrections to the material.

[0643] Prompt Sentence Examples

[0644] For example, here is a sample prompt to input to a generative AI model:

[0645] "This document is vague about the project goals, so please add specific KPIs."

[0646] "The analysis of the current situation is not detailed enough, so please supplement it with concrete numerical data."

[0647] In this way, this system allows users to have their materials reviewed efficiently and receive feedback that reflects their emotions, thereby reducing stress and improving work efficiency.

[0648] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0649] Processing flow

[0650] Step 1: Upload your materials

[0651] Step 2: Analyze the data

[0652] Step 3: Performing the review

[0653] Step 4: Adjusting feedback

[0654] Step 5: Provide feedback

[0655] Explanation of each processing step

[0656] Step 1: Upload your materials

[0657] Operation:

[0658] The user accesses the system's dedicated web page using the terminal's web browser.

[0659] The user clicks the "Upload Material" button and selects a material file from the local file system.

[0660] The user clicks the "Upload" button to upload the selected document file (e.g., Project Plan.docx) to the server.

[0661] input:

[0662] A resource file selected from the local file system.

[0663] output:

[0664] The file uploaded to the server.

[0665] Step 2: Analyze the data

[0666] Operation:

[0667] The server transfers the received material file to the text analysis module.

[0668] The text analysis module identifies the file format and if it is .docx, it uses the Apache POI library to extract the content.

[0669] The text analysis module divides the content of the document into paragraphs and sections and extracts text information.

[0670] input:

[0671] Uploaded material files.

[0672] output:

[0673] Text information divided into paragraphs or sections.

[0674] Step 3: Performing the review

[0675] Operation:

[0676] The server inputs the preprocessed text information into the generative AI model.

[0677] The generative AI model analyzes the materials and generates feedback based on the feedback style it has been trained in. For example, it generates suggestions such as, "The goal setting is vague, so please add specific KPIs."

[0678] input:

[0679] Text information organized into paragraphs and sections.

[0680] output:

[0681] Feedback sentences generated by a generative AI model.

[0682] Step 4: Adjusting feedback

[0683] Operation:

[0684] The server starts the emotion engine.

[0685] The emotion engine analyzes the user's facial expressions, voice, or input text data to recognize the user's emotions. For example, real-time facial expression data is captured via a webcam to identify emotional states such as stress or relaxation.

[0686] The emotion engine adjusts the tone and content of the generated feedback based on the emotions it recognizes. If the user is feeling stressed, the feedback can be softened to something like, "Some of your goals are unclear, so it would be helpful if you could be more specific."

[0687] input:

[0688] Feedback statements generated by the generative AI model and user sentiment data.

[0689] output:

[0690] Tailored feedback statements.

[0691] Step 5: Provide feedback

[0692] Operation:

[0693] The server then sends the final formatted feedback to the user's device.

[0694] The user visits the web page again and views the generated feedback in the "Review Feedback" section.

[0695] The feedback is categorized by section, allowing users to easily understand and make necessary corrections to the material.

[0696] input:

[0697] Tailored feedback statements.

[0698] output:

[0699] The feedback displayed on the user's device.

[0700] Specific examples

[0701] For example, here is a sample prompt to input to a generative AI model:

[0702] "This document is vague about the project goals, so please add specific KPIs."

[0703] "The analysis of the current situation is not detailed enough, so please supplement it with concrete numerical data."

[0704] In this way, efficient and emotionally relevant feedback is provided to the user at each step.

[0705] (Application example 2)

[0706] 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."

[0707] While conventional document review systems have the ability to analyze document content and provide feedback, they lack the ability to adjust feedback based on user emotions and provide real-time feedback notifications. As a result, users often feel stressed and there is a risk of reduced productivity. Furthermore, work efficiency using smart devices has not progressed, making it difficult to provide specific feedback in a manufacturing environment, leaving issues in terms of work efficiency.

[0708] The specific processing by the specific 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 uploading materials, means for analyzing the uploaded materials, means for inputting the analyzed materials into a generative AI model for review, means for providing the generated feedback to the user, an emotion engine for recognizing the user's emotions, a smart device for notifying information in real time, and means for appropriately classifying the notified feedback by section. This makes it possible to provide flexible feedback that takes the user's emotions into consideration and notify information in real time, thereby improving work efficiency and reducing user stress.

[0709] "Means for uploading materials" refers to the functionality that allows users to provide digital information to the system.

[0710] "Means for analyzing uploaded materials" refers to a function for breaking down and understanding the provided digital information by content.

[0711] The "means of inputting into a generative AI model for review" refers to a function that provides analyzed digital information to an artificial intelligence model, allowing it to evaluate the content and suggest areas for improvement.

[0712] "Means for providing generated feedback to users" refers to a function for communicating evaluation results and improvement suggestions generated by the AI ​​model to users.

[0713] The "emotion engine that recognizes the user's emotions" is a function that analyzes data such as the user's facial expressions and voice to determine their emotional state at that time.

[0714] "Smart devices that provide information in real time" are wearable terminals and mobile devices that can provide information to users instantly.

[0715] The "means for appropriately classifying notified feedback into sections" is a function for dividing the generated review information into parts and presenting them to the user.

[0716] As a specific embodiment of the present invention, we will explain a review system for work instructions in a factory. This system analyzes uploaded work instructions, provides feedback using AI, and adjusts the feedback according to the user's emotional state.

[0717] The server at the heart of the system includes a means for uploading materials, a means for analyzing uploaded materials, a means for inputting analyzed materials into a generative AI model for review, as well as a means for providing generated feedback to users, an emotion engine for recognizing users' emotions, a smart device for notifying information in real time, and a means for appropriately classifying notified feedback into sections.

[0718] Hardware and software used

[0719] 1. Smart Devices

[0720] Smart glasses and smartphones: devices for displaying real-time feedback

[0721] 2. Emotion Engine

[0722] Webcam and microphone: Hardware for analyzing the user's facial expressions and voice

[0723] EmotionRecognizer: Software for emotion analysis

[0724] 3. AI Model

[0725] Transformers Library: Software for running generative AI models

[0726] NLP toolkits (e.g., TextBlob): software for analyzing and processing text data.

[0727] Data processing and calculation

[0728] The server processes the data and performs calculations through the following steps:

[0729] 1. Uploading materials

[0730] Users upload work instructions using their smart devices.

[0731] The uploaded materials are received by the server and temporarily stored.

[0732] 2. Analysis of the data

[0733] The server analyzes the stored material and converts it into text blobs.

[0734] The analyzed text is divided into sections and semantic information for each section is extracted.

[0735] 3. Conducting the review

[0736] The formatted text data is input into a generative AI model to generate feedback.

[0737] The generated feedback is sent to the user's smart device in real time.

[0738] 4. Emotional awareness and feedback regulation

[0739] The server activates the emotion engine and acquires the user's facial expressions and voice data.

[0740] The emotion engine analyzes stress and fatigue levels and adjusts the tone of feedback as needed.

[0741] 5. Real-time notifications and section classification

[0742] The adjusted feedback will be appropriately categorized into sections and notified to the user.

[0743] Individual notifications are also provided for each work section using RFID tags.

[0744] Specific examples

[0745] A user uploads "Work Instructions.pdf" via smart glasses in Factory Section A. The server analyzes the document and uses a generative AI model to generate feedback such as "A particular procedure is unclear." If the user's stress level is high, this feedback is adjusted to a softer tone, such as "Please take a short break and recheck." The user can view this feedback in real time through the smart glasses and take appropriate measures.

[0746] Prompt Sentence Examples

[0747] File path: "Factory Section A / Work Instructions.pdf"

[0748] User Sentiment: "Fatigue"

[0749] As described above, the system of the present invention improves work efficiency and reduces stress by providing flexible feedback that takes into account the user's emotions and by notifying them in real time.

[0750] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0751] Step 1:

[0752] The user uploads work instructions to the system through a smart device (smart glasses or smartphone). The input contains a file selected by the user. This file is sent to the server. The output is the uploaded material stored on the server.

[0753] Step 2:

[0754] The server receives the uploaded material and temporarily stores it. It then passes this file to the analysis module. The input contains the uploaded material file. The output is the material content converted into a text blob. The analysis module identifies the material format and selects the appropriate parser to extract the content.

[0755] Step 3:

[0756] The server inputs the parsed text information into a generative AI model to perform the review. The input includes formatted text information. The output provides specific feedback from the generative AI model. The generative AI model uses the trained model to evaluate the content of the material.

[0757] Step 4:

[0758] The server temporarily stores the generated feedback and activates the emotion engine. The input includes the user's facial expression and voice data. The user's emotional state is acquired through a webcam or microphone, and emotion analysis is performed using the EmotionRecognizer software. The output is the user's emotional state (e.g., stress, fatigue).

[0759] Step 5:

[0760] The server adjusts the expression of the generated feedback based on the analysis results of the emotion engine. The inputs include the generated feedback and the user's emotional state. The output is feedback adjusted according to the emotion. For example, if the user is feeling tired, the tone of the feedback is softened.

[0761] Step 6:

[0762] The server categorizes the tailored feedback by appropriate section and notifies the user's smart device in real time. The input includes the tailored feedback. The output is the feedback organized by section displayed on the smart device. An RFID reader is used to provide individual feedback for each work section.

[0763] Step 7:

[0764] Users can view the feedback provided in real time through their smart devices and take appropriate measures. The input includes feedback organized by section. The output is corrections and improvements based on the feedback. Users can easily view the feedback content and work more efficiently.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] [Third embodiment]

[0769] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0770] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0771] 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).

[0772] 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.

[0773] 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.

[0774] 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).

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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."

[0781] The embodiment of the present invention is described below: The system includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, and a means for providing generated feedback to a user.

[0782] Program processing explanation

[0783] Step 1: Upload your materials

[0784] The user accesses the system's dedicated web page from the terminal's web browser.

[0785] The user clicks the "Upload material" button and selects the material file to be reviewed from the local file system.

[0786] When the user presses the "Upload" button, the corresponding material file is sent to the server.

[0787] Step 2: Analyze the material

[0788] The server receives the material file sent by the user.

[0789] The server passes the received document file to the text analysis module, which selects an appropriate parser depending on the document format and extracts the content as text information.

[0790] The server further breaks down the extracted text information and formats it into a format that is easy for the generative AI model to process (for example, plain text or vector format).

[0791] Step 3: Perform the review

[0792] The server inputs the formatted content into a generative AI model.

[0793] The generative AI model analyzes the input materials and generates feedback suggestions and improvement suggestions by imitating the feedback style of a specific manager.

[0794] The generated feedback is output to the server in text format.

[0795] Step 4: Provide feedback

[0796] The server formats the generated feedback for delivery to the user, specifically by organizing the feedback into sections that are easy to understand.

[0797] The feedback content is sent to the user's device and stored in association with the user's account.

[0798] The user can revisit the web page and view the generated feedback in the "Review Feedback" section.

[0799] Specific examples

[0800] For example, consider the case where a user uploads a project plan (e.g., "Project Plan.docx") to the system.

[0801] 1. Uploading materials

[0802] Users access a dedicated web page, click the "Upload Documents" button, select "Project Plan.docx," and upload it.

[0803] 2. Analysis of the data

[0804] The server passes the received "Project Plan.docx" to the text analysis module, which extracts and analyzes text information for each paragraph and section within the document.

[0805] 3. Conducting the review

[0806] The formatted text content is fed into a generative AI model.

[0807] The generative AI model analyzes the content of the document and generates feedback such as, "The goal setting is vague, so add specific target KPIs."

[0808] 4. Providing Feedback

[0809] The server formats the feedback from the generative AI model and provides it to the user, who can then review the generated feedback and make corrections to the material.

[0810] In this way, this system allows users to have their documents reviewed efficiently, reducing the burden on their superiors and improving work efficiency.

[0811] The processing flow will be explained below.

[0812] Step 1:

[0813] The user accesses the system's dedicated web page from a web browser on their terminal. The user clicks the "Upload Documents" button on the homepage, selects the document file to be reviewed from the local file system (e.g., "Project Plan.docx"), and presses the "Upload" button. This sends the selected file to the server.

[0814] Step 2:

[0815] The server receives the document file sent by the user. The server temporarily stores the file and passes it to the text analysis module. The text analysis module identifies the file format and selects the appropriate parser. For example, if the file is in .docx format, it will use the corresponding parser program to extract the content.

[0816] Step 3:

[0817] The server's text analysis module breaks down the content of the document into paragraphs and sections, extracts the meaning of each part, and converts the extracted text information into a format that is easy for the generative AI model to process (e.g., plain text or feature vectors).

[0818] Step 4:

[0819] The server inputs the preprocessed content into a generative AI model. The generative AI model is trained to mimic the feedback style of a specific manager and analyzes the content of the document to generate feedback. For example, it generates specific suggestions such as, "The goal setting for this section is vague, so please add specific KPIs."

[0820] Step 5:

[0821] The feedback generated by the generative AI model is output in text format to the server, which receives it and formats it appropriately for delivery to the user. The feedback is categorized into sections and summarized for easy user understanding.

[0822] Step 6:

[0823] The formatted feedback content is sent from the server to the user's device, and the user can access the web page again and view the generated feedback in the "Check Feedback" section.

[0824] Step 7:

[0825] The user can revise the document based on the feedback and, if necessary, upload the revised document back to the system and request a re-review in the same way.

[0826] This series of processes allows the user to have the materials reviewed efficiently, reducing the burden on the superior and improving work efficiency.

[0827] Example 1

[0828] 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."

[0829] Conventional document review systems require manual document analysis and review, which often hinders efficient business operations. Furthermore, because reviews are not based on a specific feedback style, it is difficult to obtain consistent feedback. This creates problems for users, who are unable to obtain high-quality reviews quickly, and increases the burden on supervisors.

[0830] 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.

[0831] In this invention, the server includes a means for a user to upload materials to a dedicated web page using a browser on a terminal, a means for the server to receive the uploaded materials and pass them to an analysis module to extract text information, a means for the server to format the extracted text information and input it into a generative AI model, and a means for the generative AI model to generate feedback based on the input materials, and for the server to provide the feedback to the user, thereby enabling the user to receive prompt and consistent feedback.

[0832] "User" refers to a person who uses the system to perform operations such as uploading materials and checking feedback.

[0833] "Terminal" refers to computer equipment such as a PC or smartphone used by a user.

[0834] "Browser" refers to the web browsing software used by a User to access a dedicated web page.

[0835] "Materials" refers to document files and data uploaded by users as the subject of review.

[0836] "Specialized Web Page" means a page on a particular Website that a User accesses to upload materials.

[0837] "Server" refers to the computing device or system that receives materials, analyzes them, runs generative AI models, and provides feedback.

[0838] "Analysis Module" means a software component for extracting textual information from Uploaded Materials.

[0839] "Text information" refers to character string data extracted from the analyzed material.

[0840] "Generative AI model" refers to an artificial intelligence model used to analyze material and generate feedback.

[0841] "Feedback" refers to the comments and suggestions for improvement that the generative AI model generates as a result of analyzing the materials.

[0842] The present invention is a system in which a user uploads materials using a terminal, the server analyzes the materials, inputs them into a generative AI model, and provides feedback. The system is configured as follows:

[0843] Users access a dedicated web page using a web browser (such as Google Chrome or Mozilla Firefox) on a device such as a PC or smartphone. This web page has an "Upload Documents" button, which users click to select and upload the target document file (such as "Project Plan.docx") from their local file system.

[0844] The server receives the document file sent by the user. After receiving the file, the server passes it to a text analysis module (e.g., Apache Tika or spaCy) to analyze and extract the content of the document as text information. This text analysis module selects an appropriate parser depending on the document format (e.g., PDF, DOCX) to extract the text information.

[0845] The server further breaks down the parsed text information and formats it into a format (plain text or vector format) that is easy for a generative AI model (e.g., OpenAI GPT-4 or BERT) to process. When the formatted content is input into the generative AI model, the AI ​​model analyzes the material and generates feedback by imitating a specific feedback style (e.g., a boss's feedback style).

[0846] The generated feedback is retrieved by the server and formatted to be provided to the user. Specifically, the feedback is organized into sections to make it easier to understand. The formatted feedback is sent to the user's device and stored in association with the user's account. The user can then access the web page again and view the generated feedback in the "Review Feedback" section.

[0847] As a concrete example, consider the case where a user uploads a project plan (e.g., "Project Plan.docx") to the system. The user accesses a dedicated web page, clicks the "Upload Document" button, selects "Project Plan.docx," and uploads it. The server passes the received "Project Plan.docx" to a text analysis module, which extracts and analyzes text information for each paragraph and section within the document. The formatted text content of the document is then supplied to a generative AI model (such as OpenAI GPT-4). The generative AI model analyzes the content of the document and generates feedback such as, "The goal setting is vague, so add specific target KPIs."

[0848] An example prompt might be, "Please review the goal setting in the project plan and identify areas for improvement."

[0849] In this way, this system allows users to have their documents reviewed efficiently, reducing the burden on their superiors and improving work efficiency.

[0850] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0851] Step 1:

[0852] The user accesses a dedicated web page

[0853] Input: Access request from the user's device

[0854] How it works: A user accesses the system's dedicated web page using a web browser (such as Google Chrome or Mozilla Firefox) on a PC or smartphone.

[0855] Output: A custom web page is displayed in the user's browser.

[0856] Step 2:

[0857] The user selects and uploads materials

[0858] Input: The document file selected by the user (e.g. "Project Plan.docx")

[0859] Operation: The user clicks the "Upload Document" button, selects the document file to upload from the local file system, and then clicks the "Upload" button to send the document file to the server.

[0860] Output: The document file is sent to the server.

[0861] Step 3:

[0862] The server passes the received material to the text analysis module

[0863] Input: Data file received by the server

[0864] How it works: The server receives a file uploaded by a user, then passes the file to a text analysis module (e.g., Apache Tika, spaCy).

[0865] Output: The data file is sent to the analysis module.

[0866] Step 4:

[0867] The text analysis module analyzes the material.

[0868] Input: Received material file

[0869] Operation: The text analysis module selects a parser according to the format of the document, and analyzes and extracts the content of the document as text information.

[0870] Output: Extracted text information

[0871] Step 5:

[0872] The server formats the extracted text information

[0873] Input: Extracted text information

[0874] How it works: The server formats the parsed text information into a format that is easy for the generative AI model to process (e.g., plain text or vector format).

[0875] Output: Formatted text information

[0876] Step 6:

[0877] The server inputs the formatted content into the generative AI model

[0878] Input: Formatted text information

[0879] How it works: The server inputs this formatted content into a generative AI model (e.g., OpenAI GPT-4).

[0880] Output: The content is fed into the generative AI model

[0881] Step 7:

[0882] A generative AI model analyzes the material and generates feedback

[0883] Input: Content fed into the generative AI model

[0884] How it works: The generative AI model analyzes the input material and generates a review based on a specific feedback style.

[0885] Output: The generated feedback

[0886] Step 8:

[0887] Server generated feedback is retrieved

[0888] Input: Feedback generated by the generative AI model

[0889] How it works: The server receives feedback output from the generative AI model.

[0890] Output: Feedback obtained by the server

[0891] Step 9:

[0892] The server formats the feedback and provides it to the user.

[0893] Input: Generated feedback

[0894] How it works: The server formats the feedback and organizes it into sections for delivery to the user, then sends it to the user's device and stores it in association with the user's account.

[0895] Output: Feedback sent to the user's device

[0896] Step 10:

[0897] User checks feedback

[0898] Input: Feedback sent by the server

[0899] How it works: The user revisits the dedicated web page and views the generated feedback in the "Review Feedback" section.

[0900] Output: The feedback the user saw

[0901] (Application example 1)

[0902] 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."

[0903] It is important to quickly and accurately acquire information about items in factories and on production lines and provide real-time feedback to workers based on that information. However, conventional systems have had difficulty in efficiently accomplishing this. In particular, automating analysis according to different information formats and types of items, and providing immediate feedback to workers have been challenging.

[0904] 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.

[0905] In this invention, the server includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to the worker, and a means for collecting product information using a smart device and displaying the feedback in real time, thereby enabling the product information to be obtained quickly and accurately and specific feedback based on the analyzed information to be provided to the worker in real time.

[0906] "Means for uploading materials" means any device or software that allows a User to submit materials to the System.

[0907] "Means for analyzing uploaded materials" refers to devices or software for analyzing received materials and extracting their contents as text information.

[0908] "Means for inputting analyzed materials into a generative AI model for review" refers to devices or software that provide extracted text information to a generative AI model to generate evaluations and feedback on the materials.

[0909] "Means for providing generated feedback to workers" refers to devices or software that deliver feedback obtained from the generative AI model to workers.

[0910] "Means for using smart devices to collect product information and display the feedback in real time" refers to software and hardware for using smart glasses or other mobile devices to collect product information and display the results in real time.

[0911] The system includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to a worker, and a means for collecting item information using a smart device and displaying the feedback in real time.

[0912] The server provides a dedicated web page that users access from their device's web browser. On this page, users click the "Upload Document" button and select and upload a document file from their local file system. Once the document is uploaded, the server receives it and passes it to a text analysis module for analysis. This text analysis module is equipped with an appropriate parser for each document format, and is able to extract the content as text information. The extracted text information is input into a generative AI model, which generates various feedback.

[0913] The generative AI model analyzes the input materials and generates feedback. For example, based on the content of the materials, it provides feedback such as, "The goal setting is not clear, so specific KPIs should be added." This feedback is formatted by the server and organized by section. The generated feedback is then sent to the user's device and provided to the worker.

[0914] In a factory implementation, smart glasses are used. When a worker wears the smart glasses and scans the barcode of an item using voice commands or gestures, the barcode data is sent to a cloud server. The cloud server searches a database using the received barcode data to obtain related item information. The obtained item information is analyzed by a text analysis module and provided to a generative AI model. The generative AI model analyzes the item information and generates feedback related to quality control and the manufacturing process. For example, feedback such as "The torque value of the screw may not meet the specifications, so please check it again" can be generated and displayed in real time on the smart glasses' display.

[0915] Hardware used:

[0916] Smart Glasses

[0917] Cloud Server

[0918] User's device (PC or smartphone)

[0919] Software used:

[0920] Flask (Python framework)

[0921] Virtual Barcode Scan Module

[0922] Virtual Text Analysis Module

[0923] Virtual generative AI model

[0924] As a concrete example of this system, imagine a scenario in which a worker inspects hardware parts in a factory, scans a barcode with smart glasses, and receives real-time feedback such as, "Please check the torque value of this part." In this case, an example of a prompt sentence to be input to the generative AI model would be, "Please provide feedback on quality control based on the following item information: product name, specifications, torque value, and inspection results."

[0925] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0926] Step 1:

[0927] The user accesses the system's dedicated web page from the web browser on their device. The user clicks the "Upload Documents" button and selects the document files to be reviewed from the local file system. When the user presses the "Upload" button, the corresponding document files are sent to the server. The input here is the document file selected by the user, and the output is the document data sent to the server.

[0928] Step 2:

[0929] The server receives the document file sent by the user. The received document file is passed to the text analysis module. The text analysis module selects an appropriate parser depending on the format of the document and extracts the content as text information. The input here is the uploaded document file, and the output is the extracted text information.

[0930] Step 3:

[0931] The server further breaks down the extracted text information and formats it into a format that is easy for the generative AI model to process (plain text or vector format). The input here is the extracted text information, and the output is the formatted material content.

[0932] Step 4:

[0933] The server inputs the formatted content into a generative AI model, which analyzes the input and generates feedback suggestions based on a specific feedback style. The input is the formatted content, and the output is the generated feedback.

[0934] Step 5:

[0935] The server formats the generated feedback for delivery to the user. Specifically, the feedback is organized into sections for easy understanding. The formatted feedback is sent to the user's device and stored in association with the user's account. The input here is the generated feedback, and the output is the formatted feedback and the stored data.

[0936] Step 6:

[0937] The user revisits the web page and views the generated feedback in the "Review Feedback" section, where the input is the formatted feedback content and the output is the user's feedback viewing results.

[0938] Step 7:

[0939] A worker wears smart glasses and scans the barcode of an item using voice commands or gestures. The smart glasses then send the barcode data to a cloud server and upload the item information. The input is the scanned barcode data, and the output is the item information sent to the cloud server.

[0940] Step 8:

[0941] The cloud server retrieves related product information from the database based on the received barcode data. The retrieved product information is analyzed by a text analysis module and fed to a generative AI model. The input here is the barcode data, and the output is formatted product information.

[0942] Step 9:

[0943] The generative AI model generates feedback on quality control and manufacturing processes based on input product information. For example, "The torque value of the screw may not meet the standard, so please check it again." The input here is the formatted product information, and the output is the generated feedback.

[0944] Step 10:

[0945] The generated feedback is displayed on the smart glasses display in real time, and the worker takes the necessary action based on the displayed feedback. The input here is the generated feedback, and the output is the real-time feedback display.

[0946] 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.

[0947] The embodiment of the present invention is described below: The system includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to a user, and an emotion engine for recognizing the user's emotions.

[0948] Program processing explanation

[0949] Step 1: Upload your materials

[0950] The user accesses the system's dedicated web page from the terminal's web browser.

[0951] The user clicks the "Upload Material" button, selects the material file to be reviewed from the local file system, and then clicks the "Upload" button, which sends the selected file to the server.

[0952] Step 2: Analyze the material

[0953] The server receives the material file sent from the user.

[0954] The server temporarily stores this file and passes it to the text analysis module, which identifies the file format and selects the appropriate parser. For example, if the file is in .docx format, it will use the corresponding parser program to extract the content.

[0955] The server's text analysis module breaks down the document into paragraphs and sections, extracts the meaning of each part, and converts the extracted text into a format (plain text or feature vectors) that can be easily processed by the generative AI model.

[0956] Step 3: Perform the review

[0957] The server inputs the preprocessed text material into the generative AI model.

[0958] The generative AI model is trained to mimic the feedback style of a specific manager, and generates specific feedback by analyzing the content of the document, such as "The goal setting is vague, so please add specific KPIs."

[0959] Step 4: Adjust your feedback

[0960] The server activates an emotion engine, which analyzes at least one of the user's facial expression, voice, and input text data to recognize the user's emotion.

[0961] The emotion engine adjusts the expression of the generated feedback based on the emotions it recognizes (e.g., stressed, anxious, relaxed, etc.). For example, if the user is feeling stressed, the tone of the feedback can be softened.

[0962] Step 5: Provide feedback

[0963] The formatted feedback content is sent from the server to the user's device, and the user can access the web page again and view the generated feedback in the "Check Feedback" section.

[0964] The feedback is categorized by section, allowing users to easily understand and make necessary corrections to the material.

[0965] Specific examples

[0966] For example, consider the case where a user uploads a project plan ("Project Plan.docx") to the system.

[0967] 1. Uploading materials

[0968] Users access a dedicated web page, click the "Upload Documents" button, select "Project Plan.docx", and upload it.

[0969] 2. Analysis of the data

[0970] The server passes the received file to a text analysis module, which extracts and analyzes the contents of the material as text information for each paragraph or section.

[0971] 3. Conducting the review

[0972] The formatted text content is input into a generative AI model, which analyzes the material and generates feedback such as "the goals of a particular section are unclear."

[0973] 4. Adjusting feedback

[0974] The server's emotion engine analyzes the user's facial expressions and voice and detects when the user is feeling stressed. In this case, the generated feedback is adjusted to be softer.

[0975] 5. Providing Feedback

[0976] The server formats the generated feedback and provides it to the user, who can view it in the "Review Feedback" section and make any necessary corrections to the material.

[0977] In this way, this system allows users to have their materials reviewed efficiently and receive feedback that reflects their emotions, thereby reducing stress and improving work efficiency.

[0978] The processing flow will be explained below.

[0979] Step 1:

[0980] The user accesses the system's dedicated web page from the web browser on their device. The user clicks the "Upload Documents" button, selects the document files to be reviewed from the local file system, and then clicks the "Upload" button. This sends the selected files to the server.

[0981] Step 2:

[0982] The server receives the document file sent by the user. The server temporarily stores the file and passes it to the text analysis module. The text analysis module identifies the file format and selects the appropriate parser. For example, if the file is in .docx format, it will use the corresponding parser program to extract the content.

[0983] Step 3:

[0984] The server's text analysis module breaks down the document into paragraphs and sections, extracts the meaning of each part, and converts the extracted text into a format (plain text or feature vectors) that can be easily processed by the generative AI model.

[0985] Step 4:

[0986] The server inputs the preprocessed content into a generative AI model. The generative AI model is trained to mimic the feedback style of a specific manager and analyzes the content of the material to generate feedback. For example, it generates specific suggestions such as, "The goal setting is vague, so please add specific KPIs."

[0987] Step 5:

[0988] The server receives the feedback output from the generative AI model and formats it for providing feedback. The feedback content is categorized into sections for easy understanding by the user.

[0989] Step 6:

[0990] The server activates an emotion engine and analyzes at least one of the user's facial expressions, voice, and input text data to recognize the user's emotion. The feedback expression by the generative AI model is adjusted according to the emotion (e.g., stress, anxiety, relaxation, etc.) recognized by the emotion engine. For example, if the user is feeling stressed, the tone of the feedback is softened.

[0991] Step 7:

[0992] The server sends the formatted feedback to the user's device. The user can then access the web page again and view the generated feedback in the "Review Feedback" section. The user can then review the feedback and make any necessary corrections to the document.

[0993] Step 8:

[0994] Users can upload the revised documents back into the system as needed and request a re-review in the same way.

[0995] In this way, this system allows users to have their materials reviewed efficiently and receive feedback that reflects their emotions, thereby reducing stress and improving work efficiency.

[0996] Example 2

[0997] 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."

[0998] Conventional document review systems have the problem of providing inconsistent feedback quality and difficulty in responding flexibly to the user's emotions. Furthermore, the feedback users receive is uniform, often causing stress and anxiety, which can lead to reduced work efficiency. Given these issues, there is a demand for a system that provides efficient feedback that takes emotions into consideration.

[0999] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1000] In this invention, the server includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to the user, and an emotion engine for recognizing the user's emotions and adjusting the expression of the feedback, thereby enabling the provision of efficient feedback that takes the user's emotions into consideration.

[1001] The "means for uploading materials" is a function that allows a user to select a material file from a local file system and send it to the system.

[1002] The "means for analyzing uploaded materials" refers to a function for identifying the content of materials uploaded on a server, dividing them into paragraphs or sections, and extracting text information.

[1003] The "means of inputting information into a generative AI model for review" refers to a function that provides preprocessed text information to a generative AI model and generates feedback based on the content of the material.

[1004] The "means for providing generated feedback to the user" is a function for formatting the feedback created by the generative AI model and displaying it to the user.

[1005] The "emotion engine" is a function that analyzes the user's facial expressions, voice, or input text data, recognizes the user's emotions, and adjusts the feedback expression.

[1006] The present invention includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to a user, and an emotion engine for recognizing the user's emotions and adjusting the expression of the feedback.

[1007] Uploading materials

[1008] The user accesses the system's dedicated web page using the device's web browser. The web page has an "Upload Materials" button. The user clicks this to select review material files from a local PC or other device, then presses the "Upload" button. This sends the selected files to the server, which receives and temporarily stores the files. This process uses the standard HTTP protocol and the browser's file upload function.

[1009] Analysis of data

[1010] The uploaded material is transferred to the text analysis module on the server. The text analysis module identifies the file format of the submitted material and selects the appropriate analysis program. For example, if the file is in .docx format, the Apache POI library is used to extract the content. The analysis breaks the material content into paragraphs and sections and extracts text information. The extracted text information is then converted into a format (plain text or feature vectors) that is easy for the generative AI model to process. A natural language processing library (such as NLTK) can be used here.

[1011] Conducting a review

[1012] The server inputs the analyzed text information into a generative AI model. The generative AI model is trained to mimic a specific feedback style and generates feedback by analyzing the content of the material. For example, it may generate specific suggestions such as, "The goal setting is vague, so please add specific KPIs." This generative AI model is implemented using machine learning libraries (such as TensorFlow and PyTorch).

[1013] Feedback adjustment

[1014] The generated feedback is further adjusted by an emotion engine. The emotion engine analyzes at least one of the user's facial expressions, voice, and input text data to recognize the user's emotions. Specifically, real-time data is collected using a webcam or microphone and then passed through an emotion recognition algorithm. For example, OpenCV or Google Cloud Vision API is used. Based on the emotion recognized by the emotion engine (e.g., stress, anxiety, relaxation, etc.), the tone of the generated feedback is softened or the content is adjusted.

[1015] Providing Feedback

[1016] The final formatted feedback is sent from the server to the user's device. The user can then access the web page again and view the generated feedback in the "Review Feedback" section. The feedback is categorized by paragraph and section, allowing the user to easily understand and make any necessary corrections to the material.

[1017] Prompt Sentence Examples

[1018] For example, here is a sample prompt to input to a generative AI model:

[1019] "This document is vague about the project goals, so please add specific KPIs."

[1020] "The analysis of the current situation is not detailed enough, so please supplement it with concrete numerical data."

[1021] In this way, this system allows users to have their materials reviewed efficiently and receive feedback that reflects their emotions, thereby reducing stress and improving work efficiency.

[1022] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1023] Processing flow

[1024] Step 1: Upload your materials

[1025] Step 2: Analyze the data

[1026] Step 3: Performing the review

[1027] Step 4: Adjusting feedback

[1028] Step 5: Provide feedback

[1029] Explanation of each processing step

[1030] Step 1: Upload your materials

[1031] Operation:

[1032] The user accesses the system's dedicated web page using the terminal's web browser.

[1033] The user clicks the "Upload Material" button and selects a material file from the local file system.

[1034] The user clicks the "Upload" button to upload the selected document file (e.g., Project Plan.docx) to the server.

[1035] input:

[1036] A resource file selected from the local file system.

[1037] output:

[1038] The file uploaded to the server.

[1039] Step 2: Analyze the data

[1040] Operation:

[1041] The server transfers the received material file to the text analysis module.

[1042] The text analysis module identifies the file format and if it is .docx, it uses the Apache POI library to extract the content.

[1043] The text analysis module divides the content of the document into paragraphs and sections and extracts text information.

[1044] input:

[1045] Uploaded material files.

[1046] output:

[1047] Text information divided into paragraphs or sections.

[1048] Step 3: Performing the review

[1049] Operation:

[1050] The server inputs the preprocessed text information into the generative AI model.

[1051] The generative AI model analyzes the materials and generates feedback based on the feedback style it has been trained in. For example, it generates suggestions such as, "The goal setting is vague, so please add specific KPIs."

[1052] input:

[1053] Text information organized into paragraphs and sections.

[1054] output:

[1055] Feedback sentences generated by a generative AI model.

[1056] Step 4: Adjusting feedback

[1057] Operation:

[1058] The server starts the emotion engine.

[1059] The emotion engine analyzes the user's facial expressions, voice, or input text data to recognize the user's emotions. For example, real-time facial expression data is captured via a webcam to identify emotional states such as stress or relaxation.

[1060] The emotion engine adjusts the tone and content of the generated feedback based on the emotions it recognizes. If the user is feeling stressed, the feedback can be softened to something like, "Some of your goals are unclear, so it would be helpful if you could be more specific."

[1061] input:

[1062] Feedback statements generated by the generative AI model and user sentiment data.

[1063] output:

[1064] Tailored feedback statements.

[1065] Step 5: Provide feedback

[1066] Operation:

[1067] The server then sends the final formatted feedback to the user's device.

[1068] The user visits the web page again and views the generated feedback in the "Review Feedback" section.

[1069] The feedback is categorized by section, allowing users to easily understand and make necessary corrections to the material.

[1070] input:

[1071] Tailored feedback statements.

[1072] output:

[1073] The feedback displayed on the user's device.

[1074] Specific examples

[1075] For example, here is a sample prompt to input to a generative AI model:

[1076] "This document is vague about the project goals, so please add specific KPIs."

[1077] "The analysis of the current situation is not detailed enough, so please supplement it with concrete numerical data."

[1078] In this way, efficient and emotionally relevant feedback is provided to the user at each step.

[1079] (Application example 2)

[1080] 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."

[1081] While conventional document review systems have the ability to analyze document content and provide feedback, they lack the ability to adjust feedback based on user emotions and provide real-time feedback notifications. As a result, users often feel stressed and there is a risk of reduced productivity. Furthermore, work efficiency using smart devices has not progressed, making it difficult to provide specific feedback in a manufacturing environment, leaving issues in terms of work efficiency.

[1082] The specific processing by the specific 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 uploading materials, means for analyzing the uploaded materials, means for inputting the analyzed materials into a generative AI model for review, means for providing the generated feedback to the user, an emotion engine for recognizing the user's emotions, a smart device for notifying information in real time, and means for appropriately classifying the notified feedback by section. This makes it possible to provide flexible feedback that takes the user's emotions into consideration and notify information in real time, thereby improving work efficiency and reducing user stress.

[1083] "Means for uploading materials" refers to the functionality that allows users to provide digital information to the system.

[1084] "Means for analyzing uploaded materials" refers to a function for breaking down and understanding the provided digital information by content.

[1085] The "means of inputting into a generative AI model for review" is a function that provides analyzed digital information to an artificial intelligence model, allowing it to evaluate the content and suggest areas for improvement.

[1086] "Means for providing generated feedback to users" refers to a function for communicating evaluation results and improvement suggestions generated by the AI ​​model to users.

[1087] The "emotion engine that recognizes the user's emotions" is a function that analyzes data such as the user's facial expressions and voice to determine their emotional state at that time.

[1088] "Smart devices that provide information in real time" are wearable terminals and mobile devices that can provide information to users instantly.

[1089] The "means for appropriately classifying notified feedback into sections" is a function for dividing the generated review information into parts and presenting them to the user.

[1090] As a specific embodiment of the present invention, we will explain a review system for work instructions in a factory. This system analyzes uploaded work instructions, provides feedback using AI, and adjusts the feedback according to the user's emotional state.

[1091] The server at the heart of the system includes a means for uploading materials, a means for analyzing uploaded materials, a means for inputting analyzed materials into a generative AI model for review, as well as a means for providing generated feedback to users, an emotion engine for recognizing users' emotions, a smart device for notifying information in real time, and a means for appropriately classifying notified feedback into sections.

[1092] Hardware and software used

[1093] 1. Smart Devices

[1094] Smart glasses and smartphones: devices for displaying real-time feedback

[1095] 2. Emotion Engine

[1096] Webcam and microphone: Hardware for analyzing the user's facial expressions and voice

[1097] EmotionRecognizer: Software for emotion analysis

[1098] 3. AI Model

[1099] Transformers Library: Software for running generative AI models

[1100] NLP toolkits (e.g., TextBlob): software for analyzing and processing text data.

[1101] Data processing and calculation

[1102] The server processes the data and performs calculations through the following steps:

[1103] 1. Uploading materials

[1104] Users upload work instructions using their smart devices.

[1105] The uploaded materials are received by the server and temporarily stored.

[1106] 2. Analysis of the data

[1107] The server analyzes the stored material and converts it into text blobs.

[1108] The analyzed text is divided into sections and semantic information for each section is extracted.

[1109] 3. Conducting the review

[1110] The formatted text data is input into a generative AI model to generate feedback.

[1111] The generated feedback is sent to the user's smart device in real time.

[1112] 4. Emotional awareness and feedback regulation

[1113] The server activates the emotion engine and acquires the user's facial expressions and voice data.

[1114] The emotion engine analyzes stress and fatigue levels and adjusts the tone of feedback as needed.

[1115] 5. Real-time notifications and section classification

[1116] The adjusted feedback will be appropriately categorized into sections and notified to the user.

[1117] Individual notifications are also provided for each work section using RFID tags.

[1118] Specific examples

[1119] A user uploads "Work Instructions.pdf" via smart glasses in Factory Section A. The server analyzes the document and uses a generative AI model to generate feedback such as "A particular procedure is unclear." If the user's stress level is high, this feedback is adjusted to a softer tone, such as "Please take a short break and recheck." The user can view this feedback in real time through the smart glasses and take appropriate measures.

[1120] Prompt Sentence Examples

[1121] File path: "Factory Section A / Work Instructions.pdf"

[1122] User Sentiment: "Fatigue"

[1123] As described above, the system of the present invention improves work efficiency and reduces stress by providing flexible feedback that takes into account the user's emotions and by notifying them in real time.

[1124] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1125] Step 1:

[1126] The user uploads work instructions to the system through a smart device (smart glasses or smartphone). The input contains a file selected by the user. This file is sent to the server. The output is the uploaded material stored on the server.

[1127] Step 2:

[1128] The server receives the uploaded material and temporarily stores it. It then passes this file to the analysis module. The input contains the uploaded material file. The output is the material content converted into a text blob. The analysis module identifies the material format and selects the appropriate parser to extract the content.

[1129] Step 3:

[1130] The server inputs the parsed text information into a generative AI model to perform the review. The input includes formatted text information. The output provides specific feedback from the generative AI model. The generative AI model uses the trained model to evaluate the content of the material.

[1131] Step 4:

[1132] The server temporarily stores the generated feedback and activates the emotion engine. The input includes the user's facial expression and voice data. The user's emotional state is acquired through a webcam or microphone, and emotion analysis is performed using the EmotionRecognizer software. The output is the user's emotional state (e.g., stress, fatigue).

[1133] Step 5:

[1134] The server adjusts the expression of the generated feedback based on the analysis results of the emotion engine. The inputs include the generated feedback and the user's emotional state. The output is feedback adjusted according to the emotion. For example, if the user is feeling tired, the tone of the feedback is softened.

[1135] Step 6:

[1136] The server categorizes the tailored feedback by appropriate section and notifies the user's smart device in real time. The input includes the tailored feedback. The output is the feedback organized by section displayed on the smart device. An RFID reader is used to provide individual feedback for each work section.

[1137] Step 7:

[1138] Users can view the feedback provided in real time through their smart devices and take appropriate measures. The input includes feedback organized by section. The output is corrections and improvements based on the feedback. Users can easily view the feedback content and work more efficiently.

[1139] 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.

[1140] 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.

[1141] 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.

[1142] [Fourth embodiment]

[1143] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1144] 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.

[1145] 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).

[1146] 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.

[1147] 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.

[1148] 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).

[1149] 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.

[1150] 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.

[1151] 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.

[1152] 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.

[1153] 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.

[1154] 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.

[1155] 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."

[1156] The embodiment of the present invention is described below: The system includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, and a means for providing generated feedback to a user.

[1157] Program processing explanation

[1158] Step 1: Upload your materials

[1159] The user accesses the system's dedicated web page from the terminal's web browser.

[1160] The user clicks the "Upload material" button and selects the material file to be reviewed from the local file system.

[1161] When the user presses the "Upload" button, the corresponding material file is sent to the server.

[1162] Step 2: Analyze the material

[1163] The server receives the material file sent by the user.

[1164] The server passes the received document file to the text analysis module, which selects an appropriate parser depending on the document format and extracts the content as text information.

[1165] The server further breaks down the extracted text information and formats it into a format that is easy for the generative AI model to process (for example, plain text or vector format).

[1166] Step 3: Perform the review

[1167] The server inputs the formatted content into a generative AI model.

[1168] The generative AI model analyzes the input materials and generates feedback suggestions and improvement suggestions by imitating the feedback style of a specific manager.

[1169] The generated feedback is output to the server in text format.

[1170] Step 4: Provide feedback

[1171] The server formats the generated feedback for delivery to the user, specifically by organizing the feedback into sections that are easy to understand.

[1172] The feedback content is sent to the user's device and stored in association with the user's account.

[1173] The user can revisit the web page and view the generated feedback in the "Review Feedback" section.

[1174] Specific examples

[1175] For example, consider the case where a user uploads a project plan (e.g., "Project Plan.docx") to the system.

[1176] 1. Uploading materials

[1177] Users access a dedicated web page, click the "Upload Documents" button, select "Project Plan.docx," and upload it.

[1178] 2. Analysis of the data

[1179] The server passes the received "Project Plan.docx" to the text analysis module, which extracts and analyzes text information for each paragraph and section within the document.

[1180] 3. Conducting the review

[1181] The formatted text content is fed into a generative AI model.

[1182] The generative AI model analyzes the content of the document and generates feedback such as, "The goal setting is vague, so add specific target KPIs."

[1183] 4. Providing Feedback

[1184] The server formats the feedback from the generative AI model and provides it to the user, who can then review the generated feedback and make corrections to the material.

[1185] In this way, this system allows users to have their documents reviewed efficiently, reducing the burden on their superiors and improving work efficiency.

[1186] The processing flow will be explained below.

[1187] Step 1:

[1188] The user accesses the system's dedicated web page from a web browser on their terminal. The user clicks the "Upload Documents" button on the homepage, selects the document file to be reviewed from the local file system (e.g., "Project Plan.docx"), and presses the "Upload" button. This sends the selected file to the server.

[1189] Step 2:

[1190] The server receives the document file sent by the user. The server temporarily stores the file and passes it to the text analysis module. The text analysis module identifies the file format and selects the appropriate parser. For example, if the file is in .docx format, it will use the corresponding parser program to extract the content.

[1191] Step 3:

[1192] The server's text analysis module breaks down the content of the document into paragraphs and sections, extracts the meaning of each part, and converts the extracted text information into a format that is easy for the generative AI model to process (e.g., plain text or feature vectors).

[1193] Step 4:

[1194] The server inputs the preprocessed content into a generative AI model. The generative AI model is trained to mimic the feedback style of a specific manager and analyzes the content of the document to generate feedback. For example, it generates specific suggestions such as, "The goal setting for this section is vague, so please add specific KPIs."

[1195] Step 5:

[1196] The feedback generated by the generative AI model is output in text format to the server, which receives it and formats it appropriately for delivery to the user. The feedback is categorized into sections and summarized for easy user understanding.

[1197] Step 6:

[1198] The formatted feedback content is sent from the server to the user's device, and the user can access the web page again and view the generated feedback in the "Check Feedback" section.

[1199] Step 7:

[1200] The user can revise the document based on the feedback and, if necessary, upload the revised document back to the system and request a re-review in the same way.

[1201] This series of processes allows the user to have the materials reviewed efficiently, reducing the burden on the superior and improving work efficiency.

[1202] Example 1

[1203] 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."

[1204] Conventional document review systems require manual document analysis and review, which often hinders efficient business operations. Furthermore, because reviews are not based on a specific feedback style, it is difficult to obtain consistent feedback. This creates problems for users, who are unable to obtain high-quality reviews quickly, and increases the burden on supervisors.

[1205] 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.

[1206] In this invention, the server includes a means for a user to upload materials to a dedicated web page using a browser on a terminal, a means for the server to receive the uploaded materials and pass them to an analysis module to extract text information, a means for the server to format the extracted text information and input it into a generative AI model, and a means for the generative AI model to generate feedback based on the input materials, and for the server to provide the feedback to the user, thereby enabling the user to receive prompt and consistent feedback.

[1207] "User" refers to a person who uses the system to perform operations such as uploading materials and checking feedback.

[1208] "Terminal" refers to computer equipment such as a PC or smartphone used by a user.

[1209] "Browser" refers to the web browsing software used by a User to access a dedicated web page.

[1210] "Materials" refers to document files and data uploaded by users as the subject of review.

[1211] "Specialized Web Page" means a page on a particular Website that a User accesses to upload materials.

[1212] "Server" refers to the computing device or system that receives materials, analyzes them, runs generative AI models, and provides feedback.

[1213] "Analysis Module" means a software component for extracting textual information from Uploaded Materials.

[1214] "Text information" refers to character string data extracted from the analyzed material.

[1215] "Generative AI model" refers to an artificial intelligence model used to analyze material and generate feedback.

[1216] "Feedback" refers to the comments and suggestions for improvement that the generative AI model generates as a result of analyzing the materials.

[1217] The present invention is a system in which a user uploads materials using a terminal, the server analyzes the materials, inputs them into a generative AI model, and provides feedback. The system is configured as follows:

[1218] Users access a dedicated web page using a web browser (such as Google Chrome or Mozilla Firefox) on a device such as a PC or smartphone. This web page has an "Upload Documents" button, which users click to select and upload the target document file (such as "Project Plan.docx") from their local file system.

[1219] The server receives the document file sent by the user. After receiving the file, the server passes it to a text analysis module (e.g., Apache Tika or spaCy) to analyze and extract the content of the document as text information. This text analysis module selects an appropriate parser depending on the document format (e.g., PDF, DOCX) to extract the text information.

[1220] The server further breaks down the parsed text information and formats it into a format (plain text or vector format) that is easy for a generative AI model (e.g., OpenAI GPT-4 or BERT) to process. When the formatted content is input into the generative AI model, the AI ​​model analyzes the material and generates feedback by imitating a specific feedback style (e.g., a boss's feedback style).

[1221] The generated feedback is retrieved by the server and formatted to be provided to the user. Specifically, the feedback is organized into sections to make it easier to understand. The formatted feedback is sent to the user's device and stored in association with the user's account. The user can then access the web page again and view the generated feedback in the "Review Feedback" section.

[1222] As a concrete example, consider the case where a user uploads a project plan (e.g., "Project Plan.docx") to the system. The user accesses a dedicated web page, clicks the "Upload Document" button, selects "Project Plan.docx," and uploads it. The server passes the received "Project Plan.docx" to a text analysis module, which extracts and analyzes text information for each paragraph and section within the document. The formatted text content of the document is then supplied to a generative AI model (such as OpenAI GPT-4). The generative AI model analyzes the content of the document and generates feedback such as, "The goal setting is vague, so add specific target KPIs."

[1223] An example prompt might be, "Please review the goal setting in the project plan and identify areas for improvement."

[1224] In this way, this system allows users to have their documents reviewed efficiently, reducing the burden on their superiors and improving work efficiency.

[1225] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1226] Step 1:

[1227] The user accesses a dedicated web page

[1228] Input: Access request from the user's device

[1229] How it works: A user accesses the system's dedicated web page using a web browser (such as Google Chrome or Mozilla Firefox) on a PC or smartphone.

[1230] Output: A custom web page is displayed in the user's browser.

[1231] Step 2:

[1232] The user selects and uploads materials

[1233] Input: The document file selected by the user (e.g. "Project Plan.docx")

[1234] Operation: The user clicks the "Upload Document" button, selects the document file to upload from the local file system, and then clicks the "Upload" button to send the document file to the server.

[1235] Output: The document file is sent to the server.

[1236] Step 3:

[1237] The server passes the received material to the text analysis module

[1238] Input: Data file received by the server

[1239] How it works: The server receives a file uploaded by a user, then passes the file to a text analysis module (e.g., Apache Tika, spaCy).

[1240] Output: The data file is sent to the analysis module.

[1241] Step 4:

[1242] The text analysis module analyzes the material.

[1243] Input: Received material file

[1244] Operation: The text analysis module selects a parser according to the format of the document, and analyzes and extracts the content of the document as text information.

[1245] Output: Extracted text information

[1246] Step 5:

[1247] The server formats the extracted text information

[1248] Input: Extracted text information

[1249] How it works: The server formats the parsed text information into a format that is easy for the generative AI model to process (e.g., plain text or vector format).

[1250] Output: Formatted text information

[1251] Step 6:

[1252] The server inputs the formatted content into the generative AI model

[1253] Input: Formatted text information

[1254] How it works: The server inputs this formatted content into a generative AI model (e.g., OpenAI GPT-4).

[1255] Output: The content is fed into the generative AI model

[1256] Step 7:

[1257] A generative AI model analyzes the material and generates feedback

[1258] Input: Content fed into the generative AI model

[1259] How it works: The generative AI model analyzes the input material and generates a review based on a specific feedback style.

[1260] Output: The generated feedback

[1261] Step 8:

[1262] Server generated feedback is retrieved

[1263] Input: Feedback generated by the generative AI model

[1264] How it works: The server receives feedback output from the generative AI model.

[1265] Output: Feedback obtained by the server

[1266] Step 9:

[1267] The server formats the feedback and provides it to the user.

[1268] Input: Generated feedback

[1269] How it works: The server formats the feedback and organizes it into sections for delivery to the user, then sends it to the user's device and stores it in association with the user's account.

[1270] Output: Feedback sent to the user's device

[1271] Step 10:

[1272] User checks feedback

[1273] Input: Feedback sent by the server

[1274] How it works: The user revisits the dedicated web page and views the generated feedback in the "Review Feedback" section.

[1275] Output: The feedback the user saw

[1276] (Application example 1)

[1277] 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."

[1278] It is important to quickly and accurately acquire information about items in factories and on production lines and provide real-time feedback to workers based on that information. However, conventional systems have had difficulty in efficiently accomplishing this. In particular, automating analysis according to different information formats and types of items, and providing immediate feedback to workers have been challenging.

[1279] 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.

[1280] In this invention, the server includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to the worker, and a means for collecting product information using a smart device and displaying the feedback in real time, thereby enabling the product information to be obtained quickly and accurately and specific feedback based on the analyzed information to be provided to the worker in real time.

[1281] "Means for uploading materials" means any device or software that allows a User to submit materials to the System.

[1282] "Means for analyzing uploaded materials" refers to devices or software for analyzing received materials and extracting their contents as text information.

[1283] "Means for inputting analyzed materials into a generative AI model for review" refers to devices or software that provide extracted text information to a generative AI model to generate evaluations and feedback on the materials.

[1284] "Means for providing generated feedback to workers" refers to devices or software that deliver feedback obtained from the generative AI model to workers.

[1285] "Means for using smart devices to collect product information and display the feedback in real time" refers to software and hardware for using smart glasses or other mobile devices to collect product information and display the results in real time.

[1286] The system includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to a worker, and a means for collecting item information using a smart device and displaying the feedback in real time.

[1287] The server provides a dedicated web page that users access from their device's web browser. On this page, users click the "Upload Document" button and select and upload a document file from their local file system. Once the document is uploaded, the server receives it and passes it to a text analysis module for analysis. This text analysis module is equipped with an appropriate parser for each document format, and is able to extract the content as text information. The extracted text information is input into a generative AI model, which generates various feedback.

[1288] The generative AI model analyzes the input materials and generates feedback. For example, based on the content of the materials, it provides feedback such as, "The goal setting is not clear, so specific KPIs should be added." This feedback is formatted by the server and organized by section. The generated feedback is then sent to the user's device and provided to the worker.

[1289] In a factory implementation, smart glasses are used. When a worker wears the smart glasses and scans the barcode of an item using voice commands or gestures, the barcode data is sent to a cloud server. The cloud server searches a database using the received barcode data to obtain related item information. The obtained item information is analyzed by a text analysis module and provided to a generative AI model. The generative AI model analyzes the item information and generates feedback related to quality control and the manufacturing process. For example, feedback such as "The torque value of the screw may not meet the specifications, so please check it again" can be generated and displayed in real time on the smart glasses' display.

[1290] Hardware used:

[1291] Smart Glasses

[1292] Cloud Server

[1293] User's device (PC or smartphone)

[1294] Software used:

[1295] Flask (Python framework)

[1296] Virtual Barcode Scan Module

[1297] Virtual Text Analysis Module

[1298] Virtual generative AI model

[1299] As a concrete example of this system, imagine a scenario in which a worker inspects hardware parts in a factory, scans a barcode with smart glasses, and receives real-time feedback such as, "Please check the torque value of this part." In this case, an example of a prompt sentence to be input to the generative AI model would be, "Please provide feedback on quality control based on the following item information: product name, specifications, torque value, and inspection results."

[1300] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1301] Step 1:

[1302] The user accesses the system's dedicated web page from the web browser on their device. The user clicks the "Upload Documents" button and selects the document files to be reviewed from the local file system. When the user presses the "Upload" button, the corresponding document files are sent to the server. The input here is the document file selected by the user, and the output is the document data sent to the server.

[1303] Step 2:

[1304] The server receives the document file sent by the user. The received document file is passed to the text analysis module. The text analysis module selects an appropriate parser depending on the format of the document and extracts the content as text information. The input here is the uploaded document file, and the output is the extracted text information.

[1305] Step 3:

[1306] The server further breaks down the extracted text information and formats it into a format that is easy for the generative AI model to process (plain text or vector format). The input here is the extracted text information, and the output is the formatted material content.

[1307] Step 4:

[1308] The server inputs the formatted content into a generative AI model, which analyzes the input and generates feedback suggestions based on a specific feedback style. The input is the formatted content, and the output is the generated feedback.

[1309] Step 5:

[1310] The server formats the generated feedback for delivery to the user. Specifically, the feedback is organized into sections for easy understanding. The formatted feedback is sent to the user's device and stored in association with the user's account. The input here is the generated feedback, and the output is the formatted feedback and the stored data.

[1311] Step 6:

[1312] The user revisits the web page and views the generated feedback in the "Review Feedback" section, where the input is the formatted feedback content and the output is the user's feedback viewing results.

[1313] Step 7:

[1314] A worker wears smart glasses and scans the barcode of an item using voice commands or gestures. The smart glasses then send the barcode data to a cloud server and upload the item information. The input is the scanned barcode data, and the output is the item information sent to the cloud server.

[1315] Step 8:

[1316] The cloud server retrieves related product information from the database based on the received barcode data. The retrieved product information is analyzed by a text analysis module and fed to a generative AI model. The input here is the barcode data, and the output is formatted product information.

[1317] Step 9:

[1318] The generative AI model generates feedback on quality control and manufacturing processes based on input product information. For example, "The torque value of the screw may not meet the standard, so please check it again." The input here is the formatted product information, and the output is the generated feedback.

[1319] Step 10:

[1320] The generated feedback is displayed on the smart glasses display in real time, and the worker takes the necessary action based on the displayed feedback. The input here is the generated feedback, and the output is the real-time feedback display.

[1321] 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.

[1322] The embodiment of the present invention is described below: The system includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to a user, and an emotion engine for recognizing the user's emotions.

[1323] Program processing explanation

[1324] Step 1: Upload your materials

[1325] The user accesses the system's dedicated web page from the terminal's web browser.

[1326] The user clicks the "Upload Material" button, selects the material file to be reviewed from the local file system, and then clicks the "Upload" button, which sends the selected file to the server.

[1327] Step 2: Analyze the material

[1328] The server receives the material file sent from the user.

[1329] The server temporarily stores this file and passes it to the text analysis module, which identifies the file format and selects the appropriate parser. For example, if the file is in .docx format, it will use the corresponding parser program to extract the content.

[1330] The server's text analysis module breaks down the document into paragraphs and sections, extracts the meaning of each part, and converts the extracted text into a format (plain text or feature vectors) that can be easily processed by the generative AI model.

[1331] Step 3: Perform the review

[1332] The server inputs the preprocessed text material into the generative AI model.

[1333] The generative AI model is trained to mimic the feedback style of a specific manager, and generates feedback by analyzing the content of the document, such as "The goal setting is vague, so please add specific KPIs."

[1334] Step 4: Adjust your feedback

[1335] The server activates an emotion engine, which analyzes at least one of the user's facial expression, voice, and input text data to recognize the user's emotion.

[1336] The emotion engine adjusts the expression of the generated feedback based on the emotions it recognizes (e.g., stressed, anxious, relaxed, etc.). For example, if the user is feeling stressed, the tone of the feedback can be softened.

[1337] Step 5: Provide feedback

[1338] The formatted feedback content is sent from the server to the user's device, and the user can access the web page again and view the generated feedback in the "Check Feedback" section.

[1339] The feedback is categorized by section, allowing users to easily understand and make necessary corrections to the material.

[1340] Specific examples

[1341] For example, consider the case where a user uploads a project plan ("Project Plan.docx") to the system.

[1342] 1. Uploading materials

[1343] Users access a dedicated web page, click the "Upload Documents" button, select "Project Plan.docx", and upload it.

[1344] 2. Analysis of the data

[1345] The server passes the received file to a text analysis module, which extracts and analyzes the contents of the material as text information for each paragraph or section.

[1346] 3. Conducting the review

[1347] The formatted text content is input into a generative AI model, which analyzes the material and generates feedback such as "the goals of a particular section are unclear."

[1348] 4. Adjusting feedback

[1349] The server's emotion engine analyzes the user's facial expressions and voice and detects when the user is feeling stressed. In this case, the generated feedback is adjusted to be softer.

[1350] 5. Providing Feedback

[1351] The server formats the generated feedback and provides it to the user, who can view it in the "Review Feedback" section and make any necessary corrections to the material.

[1352] In this way, this system allows users to have their materials reviewed efficiently and receive feedback that reflects their emotions, thereby reducing stress and improving work efficiency.

[1353] The processing flow will be explained below.

[1354] Step 1:

[1355] The user accesses the system's dedicated web page from the web browser on their device. The user clicks the "Upload Documents" button, selects the document files to be reviewed from the local file system, and then clicks the "Upload" button. This sends the selected files to the server.

[1356] Step 2:

[1357] The server receives the document file sent by the user. The server temporarily stores the file and passes it to the text analysis module. The text analysis module identifies the file format and selects the appropriate parser. For example, if the file is in .docx format, it will use the corresponding parser program to extract the content.

[1358] Step 3:

[1359] The server's text analysis module breaks down the document into paragraphs and sections, extracts the meaning of each part, and converts the extracted text into a format (plain text or feature vectors) that can be easily processed by the generative AI model.

[1360] Step 4:

[1361] The server inputs the preprocessed content into a generative AI model. The generative AI model is trained to mimic the feedback style of a specific manager and analyzes the content of the material to generate feedback. For example, it generates specific suggestions such as, "The goal setting is vague, so please add specific KPIs."

[1362] Step 5:

[1363] The server receives the feedback output from the generative AI model and formats it for providing feedback. The feedback content is categorized into sections for easy understanding by the user.

[1364] Step 6:

[1365] The server activates an emotion engine and analyzes at least one of the user's facial expressions, voice, and input text data to recognize the user's emotion. The feedback expression by the generative AI model is adjusted according to the emotion (e.g., stress, anxiety, relaxation, etc.) recognized by the emotion engine. For example, if the user is feeling stressed, the tone of the feedback is softened.

[1366] Step 7:

[1367] The server sends the formatted feedback to the user's device. The user can then access the web page again and view the generated feedback in the "Review Feedback" section. The user can then review the feedback and make any necessary corrections to the document.

[1368] Step 8:

[1369] Users can upload the revised documents back into the system as needed and request a re-review in the same way.

[1370] In this way, this system allows users to have their materials reviewed efficiently and receive feedback that reflects their emotions, thereby reducing stress and improving work efficiency.

[1371] Example 2

[1372] 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."

[1373] Conventional document review systems have the problem of providing inconsistent feedback quality and difficulty in responding flexibly to the user's emotions. Furthermore, the feedback users receive is uniform, often causing stress and anxiety, which can lead to reduced work efficiency. Given these issues, there is a demand for a system that provides efficient feedback that takes emotions into consideration.

[1374] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1375] In this invention, the server includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to the user, and an emotion engine for recognizing the user's emotions and adjusting the expression of the feedback, thereby enabling the provision of efficient feedback that takes the user's emotions into consideration.

[1376] The "means for uploading materials" is a function that allows a user to select a material file from a local file system and send it to the system.

[1377] The "means for analyzing uploaded materials" refers to a function for identifying the content of materials uploaded on a server, dividing them into paragraphs or sections, and extracting text information.

[1378] The "means of inputting information into a generative AI model for review" refers to a function that provides preprocessed text information to a generative AI model and generates feedback based on the content of the material.

[1379] The "means for providing generated feedback to the user" is a function for formatting the feedback created by the generative AI model and displaying it to the user.

[1380] The "emotion engine" is a function that analyzes the user's facial expressions, voice, or input text data, recognizes the user's emotions, and adjusts the feedback expression.

[1381] The present invention includes a means for uploading materials, a means for analyzing the uploaded materials, a means for inputting the analyzed materials into a generative AI model for review, a means for providing the generated feedback to a user, and an emotion engine for recognizing the user's emotions and adjusting the expression of the feedback.

[1382] Uploading materials

[1383] The user accesses the system's dedicated web page using the device's web browser. The web page has an "Upload Materials" button. The user clicks this to select review material files from a local PC or other device, then presses the "Upload" button. This sends the selected files to the server, which receives and temporarily stores the files. This process uses the standard HTTP protocol and the browser's file upload function.

[1384] Analysis of data

[1385] The uploaded material is transferred to the text analysis module on the server. The text analysis module identifies the file format of the submitted material and selects the appropriate analysis program. For example, if the file is in .docx format, the Apache POI library is used to extract the content. The analysis breaks the material content into paragraphs and sections and extracts text information. The extracted text information is then converted into a format (plain text or feature vectors) that is easy for the generative AI model to process. A natural language processing library (such as NLTK) can be used here.

[1386] Conducting a review

[1387] The server inputs the analyzed text information into a generative AI model. The generative AI model is trained to mimic a specific feedback style and generates feedback by analyzing the content of the material. For example, it may generate specific suggestions such as, "The goal setting is vague, so please add specific KPIs." This generative AI model is implemented using machine learning libraries (such as TensorFlow and PyTorch).

[1388] Feedback adjustment

[1389] The generated feedback is further adjusted by an emotion engine. The emotion engine analyzes at least one of the user's facial expressions, voice, and input text data to recognize the user's emotions. Specifically, real-time data is collected using a webcam or microphone and then passed through an emotion recognition algorithm. For example, OpenCV or Google Cloud Vision API is used. Based on the emotion recognized by the emotion engine (e.g., stress, anxiety, relaxation, etc.), the tone of the generated feedback is softened or the content is adjusted.

[1390] Providing Feedback

[1391] The final formatted feedback is sent from the server to the user's device. The user can then access the web page again and view the generated feedback in the "Review Feedback" section. The feedback is categorized by paragraph and section, allowing the user to easily understand and make any necessary corrections to the material.

[1392] Prompt Sentence Examples

[1393] For example, here is a sample prompt to input to a generative AI model:

[1394] "This document is vague about the project goals, so please add specific KPIs."

[1395] "The analysis of the current situation is not detailed enough, so please supplement it with concrete numerical data."

[1396] In this way, this system allows users to have their materials reviewed efficiently and receive feedback that reflects their emotions, thereby reducing stress and improving work efficiency.

[1397] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1398] Processing flow

[1399] Step 1: Upload your materials

[1400] Step 2: Analyze the data

[1401] Step 3: Performing the review

[1402] Step 4: Adjusting feedback

[1403] Step 5: Provide feedback

[1404] Explanation of each processing step

[1405] Step 1: Upload your materials

[1406] Operation:

[1407] The user accesses the system's dedicated web page using the terminal's web browser.

[1408] The user clicks the "Upload Material" button and selects a material file from the local file system.

[1409] The user clicks the "Upload" button to upload the selected document file (e.g., Project Plan.docx) to the server.

[1410] input:

[1411] A resource file selected from the local file system.

[1412] output:

[1413] The file uploaded to the server.

[1414] Step 2: Analyze the data

[1415] Operation:

[1416] The server transfers the received material file to the text analysis module.

[1417] The text analysis module identifies the file format and if it is .docx, it uses the Apache POI library to extract the content.

[1418] The text analysis module divides the content of the document into paragraphs and sections and extracts text information.

[1419] input:

[1420] Uploaded material files.

[1421] output:

[1422] Text information divided into paragraphs or sections.

[1423] Step 3: Performing the review

[1424] Operation:

[1425] The server inputs the preprocessed text information into the generative AI model.

[1426] The generative AI model analyzes the materials and generates feedback based on the feedback style it has been trained in. For example, it generates suggestions such as, "The goal setting is vague, so please add specific KPIs."

[1427] input:

[1428] Text information organized into paragraphs and sections.

[1429] output:

[1430] Feedback sentences generated by a generative AI model.

[1431] Step 4: Adjusting feedback

[1432] Operation:

[1433] The server starts the emotion engine.

[1434] The emotion engine analyzes the user's facial expressions, voice, or input text data to recognize the user's emotions. For example, real-time facial expression data is captured via a webcam to identify emotional states such as stress or relaxation.

[1435] The emotion engine adjusts the tone and content of the generated feedback based on the emotions it recognizes. If the user is feeling stressed, the feedback can be softened to something like, "Some of your goals are unclear, so it would be helpful if you could be more specific."

[1436] input:

[1437] Feedback statements generated by the generative AI model and user sentiment data.

[1438] output:

[1439] Tailored feedback statements.

[1440] Step 5: Provide feedback

[1441] Operation:

[1442] The server then sends the final formatted feedback to the user's device.

[1443] The user visits the web page again and views the generated feedback in the "Review Feedback" section.

[1444] The feedback is categorized by section, allowing users to easily understand and make necessary corrections to the material.

[1445] input:

[1446] Tailored feedback statements.

[1447] output:

[1448] The feedback displayed on the user's device.

[1449] Specific examples

[1450] For example, here is a sample prompt to input to a generative AI model:

[1451] "This document is vague about the project goals, so please add specific KPIs."

[1452] "The analysis of the current situation is not detailed enough, so please supplement it with concrete numerical data."

[1453] In this way, efficient and emotionally relevant feedback is provided to the user at each step.

[1454] (Application example 2)

[1455] 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."

[1456] While conventional document review systems have the ability to analyze document content and provide feedback, they lack the ability to adjust feedback based on user emotions and provide real-time feedback notifications. As a result, users often feel stressed and there is a risk of reduced productivity. Furthermore, work efficiency using smart devices has not progressed, making it difficult to provide specific feedback in a manufacturing environment, leaving issues in terms of work efficiency.

[1457] The specific processing by the specific 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 uploading materials, means for analyzing the uploaded materials, means for inputting the analyzed materials into a generative AI model for review, means for providing the generated feedback to the user, an emotion engine for recognizing the user's emotions, a smart device for notifying information in real time, and means for appropriately classifying the notified feedback by section. This makes it possible to provide flexible feedback that takes the user's emotions into consideration and notify information in real time, thereby improving work efficiency and reducing user stress.

[1458] "Means for uploading materials" refers to the functionality that allows users to provide digital information to the system.

[1459] "Means for analyzing uploaded materials" refers to a function for breaking down and understanding the provided digital information by content.

[1460] The "means of inputting into a generative AI model for review" is a function that provides analyzed digital information to an artificial intelligence model, allowing it to evaluate the content and suggest areas for improvement.

[1461] "Means for providing generated feedback to users" refers to a function for communicating evaluation results and improvement suggestions generated by the AI ​​model to users.

[1462] The "emotion engine that recognizes the user's emotions" is a function that analyzes data such as the user's facial expressions and voice to determine their emotional state at that time.

[1463] "Smart devices that provide information in real time" are wearable terminals and mobile devices that can provide information to users instantly.

[1464] The "means for appropriately classifying notified feedback into sections" is a function for dividing the generated review information into parts and presenting them to the user.

[1465] As a specific embodiment of the present invention, we will explain a review system for work instructions in a factory. This system analyzes uploaded work instructions, provides feedback using AI, and adjusts the feedback according to the user's emotional state.

[1466] The server at the heart of the system includes a means for uploading materials, a means for analyzing uploaded materials, a means for inputting analyzed materials into a generative AI model for review, as well as a means for providing generated feedback to users, an emotion engine for recognizing users' emotions, a smart device for notifying information in real time, and a means for appropriately classifying notified feedback into sections.

[1467] Hardware and software used

[1468] 1. Smart Devices

[1469] Smart glasses and smartphones: devices for displaying real-time feedback

[1470] 2. Emotion Engine

[1471] Webcam and microphone: Hardware for analyzing the user's facial expressions and voice

[1472] EmotionRecognizer: Software for emotion analysis

[1473] 3. AI Model

[1474] Transformers Library: Software for running generative AI models

[1475] NLP toolkits (e.g., TextBlob): software for analyzing and processing text data.

[1476] Data processing and calculation

[1477] The server processes the data and performs calculations through the following steps:

[1478] 1. Uploading materials

[1479] Users upload work instructions using their smart devices.

[1480] The uploaded materials are received by the server and temporarily stored.

[1481] 2. Analysis of the data

[1482] The server analyzes the stored material and converts it into text blobs.

[1483] The analyzed text is divided into sections and semantic information for each section is extracted.

[1484] 3. Conducting the review

[1485] The formatted text data is input into a generative AI model to generate feedback.

[1486] The generated feedback is sent to the user's smart device in real time.

[1487] 4. Emotional awareness and feedback regulation

[1488] The server activates the emotion engine and acquires the user's facial expressions and voice data.

[1489] The emotion engine analyzes stress and fatigue levels and adjusts the tone of feedback as needed.

[1490] 5. Real-time notifications and section classification

[1491] The adjusted feedback will be appropriately categorized into sections and notified to the user.

[1492] Individual notifications are also provided for each work section using RFID tags.

[1493] Specific examples

[1494] A user uploads "Work Instructions.pdf" via smart glasses in Factory Section A. The server analyzes the document and uses a generative AI model to generate feedback such as "A particular procedure is unclear." If the user's stress level is high, this feedback is adjusted to a softer tone, such as "Please take a short break and recheck." The user can view this feedback in real time through the smart glasses and take appropriate measures.

[1495] Prompt Sentence Examples

[1496] File path: "Factory Section A / Work Instructions.pdf"

[1497] User Sentiment: "Fatigue"

[1498] As described above, the system of the present invention improves work efficiency and reduces stress by providing flexible feedback that takes into account the user's emotions and by notifying them in real time.

[1499] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1500] Step 1:

[1501] The user uploads work instructions to the system through a smart device (smart glasses or smartphone). The input contains a file selected by the user. This file is sent to the server. The output is the uploaded material stored on the server.

[1502] Step 2:

[1503] The server receives the uploaded material and temporarily stores it. It then passes this file to the analysis module. The input contains the uploaded material file. The output is the material content converted into a text blob. The analysis module identifies the material format and selects the appropriate parser to extract the content.

[1504] Step 3:

[1505] The server inputs the parsed text information into a generative AI model to perform the review. The input includes formatted text information. The output provides specific feedback from the generative AI model. The generative AI model uses the trained model to evaluate the content of the material.

[1506] Step 4:

[1507] The server temporarily stores the generated feedback and activates the emotion engine. The input includes the user's facial expression and voice data. The user's emotional state is acquired through a webcam or microphone, and emotion analysis is performed using the EmotionRecognizer software. The output is the user's emotional state (e.g., stress, fatigue).

[1508] Step 5:

[1509] The server adjusts the expression of the generated feedback based on the analysis results of the emotion engine. The inputs include the generated feedback and the user's emotional state. The output is feedback adjusted according to the emotion. For example, if the user is feeling tired, the tone of the feedback is softened.

[1510] Step 6:

[1511] The server categorizes the tailored feedback by appropriate section and notifies the user's smart device in real time. The input includes the tailored feedback. The output is the feedback organized by section displayed on the smart device. An RFID reader is used to provide individual feedback for each work section.

[1512] Step 7:

[1513] Users can view the feedback provided in real time through their smart devices and take appropriate measures. The input includes feedback organized by section. The output is corrections and improvements based on the feedback. Users can easily view the feedback content and work more efficiently.

[1514] 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.

[1515] 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.

[1516] 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.

[1517] 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.

[1518] 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.

[1519] 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.

[1520] 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).

[1521] 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.

[1522] 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."

[1523] 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.

[1524] 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).

[1525] 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.

[1526] 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.

[1527] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1528] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1529] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1530] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1531] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1532] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1533] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1534] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1535] The following is further disclosed regarding the above embodiment.

[1536] (Claim 1)

[1537] A means of uploading materials;

[1538] means for analyzing the uploaded materials; and

[1539] A means of inputting the analyzed materials into a generative AI model for review;

[1540] means for providing the generated feedback to a user;

[1541] A system including:

[1542] (Claim 2)

[1543] 10. The system of claim 1, wherein the generative AI model is trained to mimic the feedback style of a particular manager.

[1544] (Claim 3)

[1545] 2. The system according to claim 1, wherein the document analysis means is configured to select an appropriate parser depending on the document format and extract text information.

[1546] "Example 1"

[1547] (Claim 1)

[1548] A means for users to upload materials to a dedicated web page using a browser on their device;

[1549] a server receiving the uploaded material and passing it to an analysis module to extract text information;

[1550] A means for the server to format the extracted text information and input it into the generative AI model;

[1551] A means for the generative AI model to generate feedback based on the input materials and for the server to provide the feedback to the user;

[1552] A system including:

[1553] (Claim 2)

[1554] 10. The system of claim 1, wherein the generative AI model is trained to mimic a particular feedback style.

[1555] (Claim 3)

[1556] 2. The system according to claim 1, wherein the document analysis means is configured to select an appropriate parser depending on the document format and extract text information.

[1557] "Application Example 1"

[1558] (Claim 1)

[1559] A means of uploading materials;

[1560] means for analyzing the uploaded materials; and

[1561] A means of inputting the analyzed materials into a generative AI model for review;

[1562] a means for providing the generated feedback to the worker;

[1563] a means for collecting product information using a smart device and displaying the feedback in real time;

[1564] A system including:

[1565] (Claim 2)

[1566] 10. The system of claim 1, wherein the generative AI model is trained to mimic the feedback style of a particular manager.

[1567] (Claim 3)

[1568] The system of claim 1, wherein the document analysis means is configured to select an appropriate parser depending on the document format and extract text information, and further configured to obtain related item information from barcode scan data.

[1569] "Example 2: Combining Emotion Engines"

[1570] (Claim 1)

[1571] A means of uploading materials;

[1572] means for analyzing the uploaded materials; and

[1573] A means of inputting the analyzed materials into a generative AI model for review;

[1574] means for providing the generated feedback to a user;

[1575] A system including an emotion engine that recognizes a user's emotions and adjusts the expression of feedback.

[1576] (Claim 2)

[1577] 10. The system of claim 1, wherein the generative AI model is trained to mimic a particular feedback style.

[1578] (Claim 3)

[1579] 2. The system according to claim 1, wherein the document analysis means is configured to select an appropriate analysis program depending on the document format and extract text information.

[1580] "Application example 2 when combining emotion engines"

[1581] (Claim 1)

[1582] A means of uploading materials;

[1583] means for analyzing the uploaded materials; and

[1584] A means of inputting the analyzed materials into a generative AI model for review;

[1585] means for providing the generated feedback to a user;

[1586] An emotion engine that recognizes the user's emotions;

[1587] Smart devices that notify you of information in real time,

[1588] A means of properly categorizing the feedback you receive into sections;

[1589] A system including:

[1590] (Claim 2)

[1591] 10. The system of claim 1, wherein the generative AI model is trained to mimic the feedback style of a particular manager.

[1592] (Claim 3)

[1593] 2. The system according to claim 1, wherein the document analysis means is configured to select an appropriate parser depending on the document format and extract text information. [Explanation of symbols]

[1594] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of uploading materials; means for analyzing the uploaded materials; and A means of inputting the analyzed materials into a generative AI model for review; means for providing the generated feedback to a user; A system including:

2. The system of claim 1 , wherein the generative AI model is trained to mimic the feedback style of a particular manager.

3. 2. The system according to claim 1, wherein said document analysis means is configured to select an appropriate parser depending on the document format and extract text information.

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