System

Abstract

To provide a system.SOLUTION: A system includes: means for learning past emails, schedules, dialogue records, prepared materials, and chat contents of all employees using a generative AI model; a feeling engine for recognizing a feeling of a new person in charge; means for inputting a specific prompt sentence into the generative AI model to generate an answer to a question from the new person in charge; and adjustment means for adjusting the contents of the answer according to the feeling. The answer to the question includes information on prepared materials, conference records, and dialogue inside and outside the company of a past person in charge.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] The time wasted and lack of efficiency in task handovers and information searches within an organization are issues that significantly impede business productivity. In particular, when task handover occurs due to organizational changes, transfers, or retirement, it is difficult for new personnel to quickly grasp past information. [Means for solving the problem]

[0005] As a means for solving this problem, the system of the present invention includes a means for using a generative AI model to learn the contents of all employees' past emails, schedules, dialogue records, prepared documents, and chats; an emotion engine for recognizing the emotions of the new representative; a means for generating answers to the questions of the new representative by inputting specific prompt sentences into the generative AI model; and an adjustment means for adjusting the content of the answers according to the emotions, and the answers to the questions include information about the prepared documents, meeting records, and internal and external interactions of previous representatives. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One embodiment of the present invention uses a generation AI that learns from all employees' past emails, schedules, communication tools, chats, created documents, etc. This generation AI uses natural language processing technology to analyze and learn from various types of information. Specifically, it analyzes the content of past emails, schedule details, communication tool and chat logs, the content of created documents, etc., and correlates each piece of information to learn.

[0029] "Example 2"

[0030] Natural language processing technology is used as a means for new staff to ask questions to the generation AI. Specifically, the new staff inputs a question in natural language, and the generation AI analyzes the question and generates an appropriate answer. For example, in response to a question such as "I would like to see the materials created by my predecessor," the generation AI provides a list of materials created by the predecessor.

[0031] "Example 3"

[0032] As a means of reducing the time required for handover work and searching and retrieving work history, the generation AI provides a function to efficiently search and extract the information it has learned. Specifically, the generation AI quickly searches and extracts the information needed by the new employee based on the information it has learned. For example, in response to a request to "see the emails exchanged between the predecessor and clients," the generation AI quickly provides a list of emails exchanged between the predecessor and clients.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The generation AI obtains all employees' past emails, schedules, communication tools, chats, created documents, etc.

[0036] Step 2: Analyze the various pieces of information acquired using natural language processing technology, and learn by associating each piece of information.

[0037] Step 3: Based on the learned information, the information required by the new staff member is quickly searched and extracted.

[0038] "Example 2"

[0039] Step 1: New agent types question in natural language.

[0040] Step 2: The generative AI analyzes the question.

[0041] Step 3: Based on the analysis results, generate an appropriate answer and provide it to the new contact.

[0042] "Example 3"

[0043] Step 1: The new contact person enters a search and retrieval request for the information they need.

[0044] Step 2: Based on the information learned by the generation AI, it quickly searches for and extracts information that corresponds to the request.

[0045] Step 3: Provide the searched and extracted information to the new contact.

[0046] Example 1

[0047] Next, a description will be given of Example 1 of Form 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."

[0048] With conventional business handover systems, it was difficult for new employees to quickly and accurately obtain information about previous employees when organizational changes, transfers, or retirements occurred. Furthermore, there was a lack of a way to efficiently analyze, correlate, and learn from vast amounts of data, such as past emails, schedules, communication tools, chats, and created documents, which meant that handover work and searching and retrieving work history took a great deal of time and effort.

[0049] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a generation AI means for learning the past emails, schedules, communication tools, dialogue records, created documents, etc. of all employees; a means for a new person in charge to ask the generation AI questions to obtain documents created by previous people, meeting records, internal and external communications, etc.; a means for reducing the time required for handover work and search and retrieval of work history; a means for preprocessing collected data and training a generation AI model; a means for evaluating the performance of the generation AI model; and a means for providing information according to user requests using the generation AI model. This enables a new person in charge to quickly and accurately obtain information about previous people, significantly reducing the time required for handover work and search and retrieval of work history.

[0050] The "generative AI means" is a means of studying all employees' past emails, schedules, communication tools, conversation records, documents created, etc., and analyzing and learning using natural language processing technology.

[0051] "New staff" refers to staff who have taken over new duties due to organizational changes, transfers, retirement, etc.

[0052] "Previous person in charge" refers to the person in charge before taking over the duties due to organizational changes, transfer, retirement, etc.

[0053] "Created documents" refers to documents created by employees in the course of their work, and specifically includes reports, presentation materials, plans, etc.

[0054] "Meeting records" refers to documents that record the contents of a meeting, including minutes and meeting notes.

[0055] "Internal and external interactions" refers to communications that employees have with internal and external parties in the course of their work, including email, chat, conference calls, etc.

[0056] "Handover work" refers to a series of tasks required to hand over work to a new employee, including providing past work details and related materials.

[0057] "Search and retrieval time for business history" refers to the time required to search and retrieve information related to past business.

[0058] "Means for preprocessing collected data" refers to means for converting collected data into a format that is easy to analyze, and specifically includes text normalization, tokenization, data filtering, etc.

[0059] A "means for training a generative AI model" is a means for training a generative AI model using preprocessed data.

[0060] "Means for evaluating the performance of a generative AI model" refers to means for evaluating the performance of a generative AI model using evaluation indicators such as precision, recall, and F1 score.

[0061] "Means for providing information in response to a user's request using a generative AI model" refers to means for generating and providing appropriate information using a generative AI model based on a prompt sentence entered by a user.

[0062] This invention is a system that uses a generative AI model that learns from all employees' past emails, schedules, communication tools, conversation records, documents created, etc., allowing new employees to quickly and accurately obtain information about previous employees, significantly reducing the time required for handover work and searching and retrieving work history.

[0063] 1. Program Generation

[0064] The server collects all employees' past emails, schedules, communication tools, conversation records, documents, etc., and generates a generative AI model that learns from this data. This generative AI model analyzes and learns from various types of information using natural language processing technology.

[0065] 2. Data analysis and learning

[0066] The server analyzes and learns data using the following hardware and software:

[0067] Hardware: A server equipped with a powerful GPU (e.g., a powerful graphics processing unit)

[0068] Software: Natural language processing libraries (e.g., natural language processing libraries), database management systems (e.g., database management systems)

[0069] The server analyzes the following data:

[0070] Email: Analyze the content of past emails to extract important keywords and topics.

[0071] Calendar: Analyzes calendar details to learn meeting and event patterns.

[0072] Communication tools and conversation logs: Analyze logs to identify frequently used phrases and topics.

[0073] Created documents: Analyze the content of created documents and extract relevant information.

[0074] The server correlates this information, learns from it, and updates the generative AI model.

[0075] 3. Examples of concrete examples and prompts

[0076] Below are some specific examples of how users can use generative AI models.

[0077] Examples:

[0078] When a user asks a generative AI model to "create an agenda for the next project meeting," the model analyzes past project meeting agendas, related emails, and conversation records to generate an appropriate agenda.

[0079] Example prompt sentence:

[0080] "I'd like you to create an agenda for our next project meeting, using past meeting agendas, relevant emails, and transcripts."

[0081] In this way, the generative AI model leverages past data to provide information that meets the user's needs.

[0082] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0083] Step 1: Data collection

[0084] The server collects data on all employees' past emails, schedules, communication tools, conversation records, documents created, etc. Specifically, it obtains information from the following data sources:

[0085] Input: Internal email server, calendar management system, communication tool logs, file server

[0086] Output: raw data collected

[0087] Specific operation: The server retrieves "all employee emails from January 1, 2022 to December 31, 2022" from the email server, retrieves employee schedule information from the calendar management system, and retrieves conversation records and created documents from communication tool logs and file servers.

[0088] Step 2: Data Preprocessing

[0089] The server preprocesses the collected data. Specifically, it performs the following processes:

[0090] Input: Raw data collected

[0091] Output: Preprocessed data

[0092] Specific operation: The server extracts the subject, body, sender, and recipient from the acquired email data, removes unnecessary HTML tags, and tokenizes the text data into words and phrases, eliminating unnecessary and duplicate data.

[0093] Step 3: Model training

[0094] The server uses the preprocessed data to train the generative AI model. Specifically, training is performed using the following steps:

[0095] Input: Preprocessed data

[0096] Output: Trained generative AI model

[0097] What it does: The server splits the data into training data and test data, trains a generative AI model using the training data, builds the model using a natural language processing library, and tunes hyperparameters to optimize the model's performance.

[0098] Step 4: Model evaluation

[0099] The server evaluates the performance of the generative AI model using test data. Specifically, it uses the following evaluation indicators:

[0100] Input: Test data, trained generative AI model

[0101] Output: Model evaluation results (precision, recall, F1 score)

[0102] Specific operation: The server inputs test data into the generative AI model, compares the model's prediction results with the actual data, and calculates evaluation metrics such as precision, recall, and F1 score to evaluate the model's performance.

[0103] Step 5: Use the model

[0104] The user uses the generative AI model to perform a specific task by entering a prompt like the following:

[0105] Input: User prompt (e.g., "Please create an agenda for our next project meeting.")

[0106] Output: The information generated (e.g., the agenda)

[0107] How it works: When a user types, "I want an agenda for my next project meeting," the server uses the generative AI model to analyze past project meeting agendas, related emails, and conversation records to generate an appropriate agenda. The generated agenda is then displayed on the user's device.

[0108] In this way, specific processing is performed at each step, and information is provided according to the user's request using a generative AI model.

[0109] (Application example 1)

[0110] Next, a description will be given of Application Example 1 of Embodiment 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."

[0111] With conventional work handover systems, it was difficult for new staff to efficiently obtain information from previous staff, resulting in the time-consuming process of handover work and searching and retrieving work history. Furthermore, there was a lack of appropriate procedure suggestions to improve work efficiency within the factory, making it difficult to optimize work.

[0112] 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.

[0113] In this invention, the server includes a generation AI means for learning all employees' past emails, schedules, communication tools, chats, and created documents; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes, internal and external communications, etc.; a means for reducing the time required to search and retrieve handover work and work history; a means for learning past work logs, schedules, communication tool logs, created work instructions, etc., and proposing optimal work procedures to improve work efficiency within the factory; and a means for issuing work instructions in real time. This allows new employees to quickly obtain information about previous employees and significantly reduces the time required to search and retrieve handover work and work history. Furthermore, optimal procedures are proposed to improve work efficiency within the factory, thereby achieving work optimization.

[0114] "Generative AI" is an artificial intelligence that studies all employees' past emails, schedules, communication tools, chats, created documents, etc., and uses natural language processing technology to analyze the information, associate it, and learn.

[0115] "New staff member" refers to an employee who has taken over new duties due to organizational changes, transfer, retirement, etc.

[0116] "Former employee" refers to an employee who was previously in charge of a particular task.

[0117] A "work log" is data that records the history of work performed in a factory.

[0118] "Schedule" is information that shows employees' plans and plans.

[0119] "Communication tools" are tools used for communicating and sharing information between employees, and include chat and email.

[0120] "Prepared materials" refers to documents, reports, and other materials created by employees in the course of their work.

[0121] A "work instruction" is a document that describes work procedures and instructions within a factory.

[0122] "Real-time work instructions" refers to the generation AI grasping the situation within the factory in real time and providing instructions on the spot on the optimal work procedures.

[0123] "Handover" is the process of conveying the job content and related information from the previous person in charge to the new person in charge.

[0124] "Search and retrieval time for business history" is the time required to search for information on past business operations and find the required data.

[0125] A system for implementing this invention is configured as follows: The server is equipped with a generation AI that learns from all employees' past emails, schedules, communication tools, chats, created documents, etc. This generation AI uses natural language processing technology to analyze and learn from various types of information. Specifically, it analyzes the content of past emails, schedule details, communication tool and chat logs, the content of created documents, etc., and correlates each piece of information to learn.

[0126] By asking the generated AI questions, new staff can obtain documents prepared by previous staff, minutes of meetings, internal and external communications, etc. This can significantly reduce the time spent on handover work and searching and retrieving work history.

[0127] Furthermore, to improve the efficiency of work within the factory, the generative AI learns from past work logs, schedules, communication tool logs, created work instructions, etc., and proposes optimal work procedures. Providing work instructions in real time can improve work efficiency within the factory.

[0128] As a specific example, the following prompt sentence could be input to the generative AI model:

[0129] Please suggest the best procedure based on the following data:

[0130] 2023-10-01 09:00:00: Email - Part A is out of stock.

[0131] 2023-10-01 10:00:00: Schedule - Assembly work for part B.

[0132] 2023-10-01 11:00:00: Chat - Part C requires quality inspection.

[0133] 2023-10-01 12:00:00: Created materials - Assembly instructions for part D.

[0134] The server runs a Python program and inputs prompts to the generation AI using the OpenAI (registered trademark) API. The generation AI then proposes the optimal work procedure based on the input prompts. This allows new workers to quickly obtain information about previous workers, improving work efficiency within the factory.

[0135] The hardware used is a computer installed on a factory robot, and the software uses Python and the OpenAI API, including Pandas, a Python data analysis library, and NLTK, a natural language processing library.

[0136] This system allows new employees to quickly obtain information about previous employees, significantly reducing the time it takes to take over and to search and find work history. It also suggests optimal procedures to improve work efficiency within the factory, resulting in work optimization.

[0137] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0138] Step 1:

[0139] The server reads all employees' past emails, schedules, communication tools, chats, created documents, etc. from CSV files. This obtains various data as a data frame. The input is a CSV file, and the output is a data frame.

[0140] Step 2:

[0141] The server combines the read data into a single data frame. Specifically, it uses the Pandas library to combine each data frame vertically. The input is multiple data frames, and the output is the combined data frame.

[0142] Step 3:

[0143] The server creates a prompt sentence to be input to the generation AI based on the integrated data frame. Specifically, it converts each row of data into text format, arranges it in chronological order, and generates a prompt sentence. The input is the integrated data frame, and the output is the prompt sentence.

[0144] Step 4:

[0145] The server sends the generated prompt to OpenAI's API and obtains the optimal work procedure from the generation AI. Specifically, it uses OpenAI's Completion endpoint to input the prompt and receive the generated text. The input is the prompt, and the output is the generated work procedure.

[0146] Step 5:

[0147] The server displays the generated work procedure to the new worker. Specifically, it displays the generated text on a user interface so that the worker can check it. The input is the generated work procedure, and the output is the information displayed on the user interface.

[0148] Step 6:

[0149] The user performs the work in the factory based on the displayed work procedure. Specifically, the user performs the necessary work according to the procedure proposed by the generation AI. The input is the displayed work procedure, and the output is the performed work.

[0150] Example 2

[0151] Next, a description will be given of Example 2 of Form 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."

[0152] When taking over work due to organizational changes, transfers, retirement, etc., it is difficult for new staff to quickly and accurately obtain information about previous staff. With conventional methods, the handover process and searching and retrieving work history takes a lot of time and effort, resulting in a decrease in productivity.

[0153] 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.

[0154] In this invention, the server includes a means for a new agent to input a question in natural language, a means for the terminal to send the question to the server, a means for the server to analyze the question using natural language processing technology, a means for the server to send a prompt to the generative AI model based on the analysis result, a means for the generative AI model to generate an answer based on the prompt, a means for the server to send the generated answer to the terminal, and a means for the terminal to display the answer to the user. This allows the new agent to quickly and accurately obtain information about the previous agent.

[0155] "New staff" refers to staff who have taken over new duties due to organizational changes, transfers, retirement, etc.

[0156] A "natural language" is a language used by humans on a daily basis, not a specific programming language or technical term.

[0157] A "terminal" is an electronic device such as a computer or smartphone used by a user.

[0158] A "server" is a computer system that receives and processes requests from terminals via a network.

[0159] "Natural language processing technology" is a technology that enables computers to understand and analyze human natural language.

[0160] A "generative AI model" is a model that uses artificial intelligence technology to create appropriate answers or products based on input data.

[0161] A "prompt sentence" is an instruction sentence input to a generative AI model, and is the sentence that serves as the basis for the model to generate an answer.

[0162] An "answer" is information or data that a generative AI model generates based on a prompt.

[0163] "Communication records" are the history of past communications such as emails and chats.

[0164] A "plan" is a future plan or plan recorded on a schedule or calendar.

[0165] "Communication tools" are software that supports communication between users, such as chat apps and messaging services.

[0166] "Created materials" are documents, reports, and other materials created by past staff members.

[0167] Minutes are documents that record the contents of meetings and discussions.

[0168] "Internal and external interactions" are records of communications with other departments within the company and with external business partners.

[0169] This invention is a system that allows new staff to quickly and accurately obtain information about previous staff members. The system involves a process in which a user inputs a question in natural language, and a generative AI model analyzes the question and generates an appropriate answer.

[0170] Hardware and software used

[0171] Device: An electronic device such as a computer or smartphone that a user uses.

[0172] Server: A computer system that receives and processes requests from terminals via a network.

[0173] Natural Language Processing technologies: NLP libraries such as spaCy and NLTK.

[0174] Generative AI models: Generative AI technologies such as OpenAI GPT-3®.

[0175] HTTP request libraries: such as Axios or Fetch API.

[0176] Front-end frameworks: such as React or Vue.js.

[0177] Program processing

[0178] 1. The user enters a question

[0179] A user uses a terminal to input a question in natural language.

[0180] Example: A user types "I want to see the materials created by my predecessor" into a browser input field.

[0181] 2. The device sends a question to the server

[0182] The terminal sends the question entered by the user to the server.

[0183] What happens: The device sends a question to the server's API endpoint using an HTTP POST request.

[0184] 3. The server parses the question

[0185] The server analyzes the received question using natural language processing technology.

[0186] What it does: The server uses the spaCy library to analyze the intent of the question and extract important keywords.

[0187] 4. The server sends a prompt to the generative AI model

[0188] The server sends a prompt to the generative AI model based on the analysis results.

[0189] Specific operation: The server generates a prompt sentence saying, "The new employee wants to see the materials created by his predecessor. Please provide a list of the materials created by his predecessor." and sends it to the generative AI model.

[0190] 5. Generative AI models generate answers

[0191] A generative AI model generates appropriate answers based on the prompt.

[0192] Specific behavior: The generative AI model generates the answer, "The list of documents created by the predecessor is as follows: 1. Project plan 2. Progress report 3. Customer service manual."

[0193] 6. The server sends the answer to the device

[0194] The server sends the answer received from the generative AI model to the device.

[0195] Specific operation: The server sends the generated answer to the terminal as an HTTP response.

[0196] 7. The device displays the answer to the user

[0197] The terminal displays the received answer to the user.

[0198] Specific operation: The device displays the text "The list of documents prepared by the predecessor is as follows: 1. Project plan 2. Progress report 3. Customer service manual" in the browser display area.

[0199] In this way, a series of processes is realized in which a new employee inputs a question in natural language, and the generation AI analyzes the question and provides an appropriate answer.

[0200] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0201] Step 1:

[0202] The user enters a question.

[0203] Input: The user types a question in natural language.

[0204] Specific operation: The user types "I want to see the materials created by my predecessor" into the input field of the device's browser.

[0205] Output: The question you typed will be displayed on the terminal.

[0206] Step 2:

[0207] The terminal sends a question to the server.

[0208] Input: The question entered by the user.

[0209] What happens: The device sends a question to the server's API endpoint using an HTTP POST request.

[0210] Output: The question is sent to the server.

[0211] Step 3:

[0212] The server parses the question.

[0213] Input: The question sent from the terminal.

[0214] What it does: The server uses the spaCy library to analyze the intent of the question and extract important keywords.

[0215] Output: Analysis results of the question (e.g. keywords such as "predecessor", "created", "document").

[0216] Step 4:

[0217] The server sends a prompt to the generative AI model.

[0218] Input: The parsed result of the question.

[0219] Specific operation: The server generates a prompt sentence saying, "The new employee wants to see the materials created by his predecessor. Please provide a list of the materials created by his predecessor." and sends it to the generative AI model.

[0220] Output: The prompt sent to the generative AI model.

[0221] Step 5:

[0222] A generative AI model generates the answer.

[0223] Input: The prompt text sent by the server.

[0224] Specific operation: Based on the prompt, the generative AI model generates the answer, "The list of documents created by the predecessor is as follows: 1. Project plan 2. Progress report 3. Customer service manual."

[0225] Output: The generated answer.

[0226] Step 6:

[0227] The server sends the response to the terminal.

[0228] Input: The answer received from the generative AI model.

[0229] Specific operation: The server sends the generated answer to the terminal as an HTTP response.

[0230] Output: The answer sent to the terminal.

[0231] Step 7:

[0232] The terminal displays the answer to the user.

[0233] Input: The answer sent by the server.

[0234] Specific operation: The device displays the text "The list of documents prepared by the predecessor is as follows: 1. Project plan 2. Progress report 3. Customer service manual" in the browser display area.

[0235] Output: The answer displayed to the user.

[0236] (Application example 2)

[0237] Next, a description will be given of Application Example 2 of Form 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."

[0238] When new employees take over work, it is difficult to quickly and efficiently obtain information such as documents prepared by previous employees, minutes of meetings, and internal and external communications. This also creates a problem where new employees in the factory are unable to quickly obtain the information they need to understand how to operate machines and work procedures, resulting in reduced work efficiency.

[0239] 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 a generation AI means for learning all employees' past emails, schedules, communication tools, chats, created documents, etc.; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc.; a means for reducing the time required for handover work and search and retrieval of work history; and a means for a new employee to input questions in natural language to a robot in the factory, where the generation AI analyzes the questions and provides appropriate answers. This enables the new employee to quickly and efficiently obtain the necessary information, improving the handover and work efficiency.

[0240] "All employees" refers to all staff members of a company or organization.

[0241] "Email" refers to digital messages sent and received over the Internet.

[0242] A "datebook" is a tool for managing the schedules of individuals and organizations, and records scheduled meetings and tasks.

[0243] "Communication tools" refers to software and applications for exchanging information, such as chat and video conferencing.

[0244] "Chat" refers to a means of communication for exchanging text messages in real time.

[0245] "Created materials" refers to digital files such as documents and presentations created in the course of business.

[0246] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze input questions and generate appropriate answers.

[0247] "New staff member" refers to a staff member who has been newly assigned to a task within the organization.

[0248] "Former staff member" refers to a staff member who was previously in charge of a particular task.

[0249] "Minutes" refers to a document that records the contents of a meeting or conference.

[0250] "Internal and external interactions" refers to communication with parties both within the company and outside the company.

[0251] "Handover work" refers to the procedures and tasks required to hand over work to the next person in charge.

[0252] "Business history" refers to records of past business operations.

[0253] "Search and retrieval time" refers to the time spent finding needed information.

[0254] "In-plant" means the interior of a facility where manufacturing operations take place.

[0255] A "robot" refers to a mechanical device that performs a specific task automatically.

[0256] "Natural language" refers to a language that humans use on a daily basis.

[0257] "Analysis" refers to the process of analyzing input data and understanding its meaning.

[0258] An "appropriate answer" refers to a response that provides accurate and useful information in response to the entered question.

[0259] A system for implementing this invention has the following configuration: First, a server is equipped with a generation AI means that learns from all employees' past emails, schedules, communication tools, chats, created documents, etc. This generation AI means uses natural language processing technology to analyze input questions and generate appropriate answers.

[0260] When a new employee enters a question using a smartphone or tablet, the question is sent to the server. The server then uses a generative AI tool to analyze the question and search for information such as documents prepared by previous employees, minutes of meetings, and internal and external communications. This allows the new employee to quickly obtain the information they need.

[0261] The same is true when a new employee in a factory inputs a question to a robot in natural language. The robot sends the question to a server, and a generative AI means analyzes the question and provides an appropriate answer. For example, in response to a question such as "I would like to see the operating manual for this machine," the generative AI will display a digital version of the operating manual.

[0262] The hardware used includes factory robots (e.g., FANUC, KUKA, etc.), smartphones, and tablets, while the software used includes the OpenAI API (generative AI model) and Python (programming language).

[0263] As a concrete example, if a new employee asks, "I'd like to see the operating manual for this machine," the generating AI will provide an answer such as, "The digital version of the operating manual is here," and display a link or file.

[0264] An example of a prompt is as follows:

[0265] New employee asks: I'd like to see the operating manual for this machine.

[0266] Generate the appropriate answer.

[0267] In this way, the new staff member can quickly and efficiently obtain the necessary information, improving the handover of work and work efficiency.

[0268] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0269] Step 1:

[0270] The user enters a question using a smartphone or tablet, and the entered question is saved in text format on the device.

[0271] Step 2:

[0272] The terminal sends the entered question to the server, which receives the question and stores it in a database.

[0273] Step 3:

[0274] The server passes the received question to the generation AI means, which uses natural language processing technology to analyze the question and extract relevant keywords and context.

[0275] Step 4:

[0276] The generation AI method searches all employees' past emails, calendars, communication tools, chats, created documents, etc. based on the extracted keywords and context, and obtains related information as search results.

[0277] Step 5:

[0278] The server organizes the information obtained from the generation AI means and generates an appropriate answer, which is stored in text format on the server.

[0279] Step 6:

[0280] The server sends the generated answer to the terminal, which then displays the received answer to the user.

[0281] Step 7:

[0282] The user can check the displayed answers and obtain the necessary information. For example, if a link or file for an operation manual is displayed, the user can click it to view it.

[0283] In this way, users can quickly and efficiently obtain the information they need.

[0284] Example 3

[0285] Next, a third embodiment of the third embodiment will be described. 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."

[0286] Previously, handover work and searching and retrieving work history required time and effort, making it difficult for new staff to quickly obtain the necessary information. Furthermore, when work handover occurred due to organizational changes, transfers, or retirement, there was a lack of an efficient way to obtain information about previous staff. This created a risk of disrupting business continuity.

[0287] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0288] In this invention, the server includes a generation AI means for learning all employees' past emails, schedules, communication methods, messages, and documents created, etc., a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc., a means for reducing the time required for handover work and search and retrieval of work history, a means for a user to input an information search request, a means for a terminal to send the user's request to the server, a means for the server to receive the request and generate a prompt for the generation AI model, a means for the server to input the prompt for the generation AI model, a means for the generation AI model to search and extract relevant information from a database, a means for the server to receive results from the generation AI model and send them to the terminal, and a means for the terminal to display the results to the user. This enables a new employee to quickly obtain the information they need and significantly reduces the time required for handover work and search and retrieval of work history.

[0289] "All employees' past emails, schedules, means of communication, messages, documents created, etc." refers to emails sent and received by all employees in the organization in the past, calendars for managing schedules, means of communication used (e.g., telephone, video conference), messages sent and received (e.g., chat apps), and various documents created (e.g., reports, presentation materials).

[0290] "Generative AI means" refers to artificial intelligence technology that uses machine learning algorithms to learn from large amounts of data and generate and provide appropriate information in response to user requests.

[0291] "New employee" refers to an employee who has been newly assigned to a specific task within the organization.

[0292] "Former employee" refers to an employee who previously handled a particular task.

[0293] A "prompt sentence" is an instruction sentence input to a generative AI model that describes a specific request for searching and extracting specific information.

[0294] "Database" refers to a system for systematically storing and managing various data within an organization.

[0295] A "server" refers to a computer system that receives and processes requests from clients over a network.

[0296] "Terminal" refers to a device such as a computer or smartphone that is directly operated by a user.

[0297] "User" refers to an individual or member of an organization who uses the system to search for information.

[0298] "Information retrieval request" refers to a request made by a user to a system to obtain specific information.

[0299] "Search and extraction means" refers to the methods and techniques that a generative AI model uses to find and retrieve relevant information from a database based on a prompt.

[0300] This invention is a system for reducing the time required for handovers and searching and retrieving work history. The system uses a generative AI means that learns from all employees' past emails, schedules, communication methods, messages, and documents. A specific embodiment of this system is described below.

[0301] Hardware and software used

[0302] Hardware: High-performance server (e.g. Dell PowerEdge R740)

[0303] Software: Generative AI models (e.g., OpenAI GPT-4®), database management systems (e.g., MySQL®)

[0304] System configuration

[0305] 1. Server: The server plays a central role in using generative AI models to search and extract information based on user requests.

[0306] 2. Terminal: A terminal is a device through which a user enters information search requests and receives results. Examples include computers and smartphones.

[0307] 3. User: A user is an individual or member of an organization who uses the system to search for information.

[0308] System Operation

[0309] User operations

[0310] Through the terminal interface, the user inputs a request to search for specific information, for example, "I want to see the emails my predecessor exchanged with clients."

[0311] Device behavior

[0312] The terminal receives the user's input and sends the request to the server, where it uses an HTTP request to send data to the server.

[0313] Server Operation

[0314] The server analyzes the request received from the device and generates a prompt to input to the generative AI model, for example, "Show me a list of emails exchanged between my predecessor and the client."

[0315] The server inputs the generated prompt sentence into the generative AI model, which generates a response to the prompt sentence based on pre-trained data.

[0316] The generative AI model analyzes the prompt and searches for and extracts relevant information from a database, such as a list of emails exchanged between a predecessor and a client.

[0317] The server sends the search results received from the generative AI model to the device, again using an HTTP response to send the data.

[0318] Device behavior

[0319] The terminal displays the search results received from the server to the user, who can then check a list of emails exchanged between the predecessor and the client.

[0320] Specific examples

[0321] For example, if a new employee requests to "see the emails exchanged between my predecessor and clients," the device sends this request to the server. The server generates a prompt message saying, "Show me a list of emails exchanged between my predecessor and clients," and inputs this into the generative AI model. The generative AI model searches the database for relevant emails and returns the results to the server. The server then sends the results to the device, which then displays the list of emails to the user.

[0322] In this way, the server, terminal, and user work together to efficiently execute the information search process. This system enables new staff to quickly obtain the necessary information, significantly reducing the time required for handover work and for searching and retrieving work history. The flow of the identification process in the third embodiment will be explained using Figure 15.

[0323] Step 1:

[0324] A user enters a request for information retrieval.

[0325] Through the terminal interface, the user inputs a request to search for specific information, for example, "I want to see the emails my predecessor exchanged with clients."

[0326] Input: User search request

[0327] Output: Search request data sent to the terminal

[0328] Step 2:

[0329] The terminal sends the user's request to the server.

[0330] The terminal receives the user's input and sends the request to the server, where it uses an HTTP request to send data to the server.

[0331] Input: User search request data

[0332] Output: Search request data sent to the server

[0333] Step 3:

[0334] The server receives the request and generates a prompt for the generative AI model.

[0335] The server analyzes the request received from the device and generates a prompt to input to the generative AI model, for example, "Show me a list of emails exchanged between my predecessor and the client."

[0336] Input: Search request data received from the terminal

[0337] Output: Prompt sentence to be input to the generative AI model

[0338] Step 4:

[0339] The server inputs a prompt sentence into the generative AI model.

[0340] The server inputs the generated prompt sentence into the generative AI model, which generates a response to the prompt sentence based on pre-trained data.

[0341] Input: A prompt to be input to the generative AI model

[0342] Output: Search results from a generative AI model

[0343] Step 5:

[0344] A generative AI model searches and extracts relevant information from a database.

[0345] The generative AI model analyzes the prompt and searches for and extracts relevant information from a database, such as a list of emails exchanged between a predecessor and a client.

[0346] Input: prompt statement

[0347] Output: Relevant information extracted from the database

[0348] Step 6:

[0349] The server receives the results from the generative AI model and sends them to the device.

[0350] The server sends the search results received from the generative AI model to the device, again using an HTTP response to send the data.

[0351] Input: Search results from a generative AI model

[0352] Output: Search result data sent to the device

[0353] Step 7:

[0354] The terminal displays the results to the user.

[0355] The terminal displays the search results received from the server to the user, who can then check a list of emails exchanged between the predecessor and the client.

[0356] Input: Search result data received from the server

[0357] Output: The search results that are displayed to the user

[0358] In this way, the server, terminal, and user can work together to efficiently execute the information search process.

[0359] (Application example 3)

[0360] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0361] In logistics centers, it is difficult for new staff to efficiently search and extract past work history and handover information, which results in a significant amount of time spent on handover work and searching and searching work history. In addition, when work handover occurs due to organizational changes, transfers, or retirements, new staff cannot quickly obtain information about previous staff, which can disrupt business continuity.

[0362] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a generation AI means that learns the past emails, schedules, communication tools, chats, created documents, etc. of all employees; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc.; a means for reducing the time required for handover work and search and retrieval of work history; a means for a new employee at a logistics center to efficiently search and extract past work history and handover information; and a means for quickly providing past work history based on a request from the new employee using a generation AI model. This enables the new employee to quickly and efficiently obtain past work history and handover information.

[0363] "Generative AI means" is an artificial intelligence technology that learns from all employees' past emails, schedules, communication tools, chats, created documents, etc., and provides the necessary information.

[0364] "New staff" refers to employees who have taken over new duties due to organizational changes, transfers, retirement, etc.

[0365] "Previous staff member" refers to an employee who took over the duties due to organizational changes, transfer, retirement, etc.

[0366] "Handover" is the process by which a new employee receives work details and related information from the previous employee in order to maintain business continuity.

[0367] "Work history" refers to records of work performed by past personnel and related information.

[0368] A "logistics center" refers to a facility that carries out logistics operations such as storing, sorting, and delivering goods.

[0369] A "generative AI model" is an artificial intelligence model used in generative AI means, which learns from past data and provides information that meets the requirements of new personnel.

[0370] A "prompt sentence" is an instruction sentence entered into a generative AI model to obtain specific information.

[0371] As an embodiment of the present invention, a system is provided that enables a new employee at a logistics center to efficiently search and extract past work history and handover information. Specific embodiments of the system are described below.

[0372] System Configuration

[0373] The system consists of the following main components:

[0374] 1. Generative AI methods: Artificial intelligence technology that learns from all employees' past emails, calendars, communication tools, chats, and created documents.

[0375] 2. New Agent Device: A device (e.g., smartphone, tablet, or PC) used by the new agent to input questions into the generation AI.

[0376] 3. Server: A server for hosting generative AI models and processing data.

[0377] Program processing

[0378] The server does the following:

[0379] 1. Data collection and training: Collect all employees' past emails, calendars, communication tools, chats, and written materials to train the generative AI model. This process uses software such as Python and the OpenAI API.

[0380] 2. Accepting questions: When a new employee enters a question into the terminal, the question is sent to the server.

[0381] 3. Response generation using a generative AI model: The server uses a generative AI model to search past work history based on the new employee's question and quickly provide appropriate information.

[0382] 4. Providing the results: The server sends the generated response to the new agent's terminal and displays it.

[0383] Hardware and software used

[0384] Hardware: Servers, smartphones, tablets, PCs

[0385] Software: Python, OpenAI API

[0386] Specific examples

[0387] When a new employee inputs a request such as "I would like to see the transaction history of my predecessor with a specific client," the generating AI quickly searches for the transaction history with that client and provides it as a list.

[0388] Prompt Sentence Examples

[0389] Search the past business history of a distribution center based on the following request: I want to see the transaction history of my predecessor with a specific client.

[0390] In this way, new staff at the logistics center can quickly and efficiently obtain past work history and handover information.

[0391] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0392] Step 1:

[0393] The server collects all employees' past emails, schedules, communication tools, chats, created documents, etc. This data is used as training data for the generative AI model. The input is various employee data, and the output is a dataset for training the generative AI model. Specifically, it extracts the necessary information from the database and performs preprocessing to create a dataset.

[0394] Step 2:

[0395] The server uses the collected data to train a generative AI model. The input is the dataset created in step 1, and the output is a trained generative AI model. Specifically, it uses Python or the OpenAI API to supply the dataset to the generative AI model and optimizes the model's parameters.

[0396] Step 3:

[0397] The user (new agent) inputs a question to the generation AI from a terminal. The input is the user's question, and the output is a prompt that is sent to the server. Specifically, the user inputs a question through the terminal interface, and the question is formatted as a prompt.

[0398] Step 4:

[0399] The server receives the prompt sent by the user and queries the generative AI model. The input is the user's prompt, and the output is the response from the generative AI model. Specifically, the prompt is input into the generative AI model, and the model searches past work history to generate appropriate information.

[0400] Step 5:

[0401] The server receives the response from the generative AI model and sends it to the user's device. The input is the response from the generative AI model, and the output is the information displayed on the user's device. Specifically, the server formats the response from the generative AI model and sends it to the user's device.

[0402] Step 6:

[0403] The user checks the information displayed on the terminal and obtains the necessary work history and handover information. The input is the information sent from the server, and the output is the work history and handover information obtained by the user. Specifically, the user checks the terminal screen and obtains the necessary information.

[0404] 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.

[0405] "Example 1"

[0406] One embodiment of the present invention combines a system that includes a generation AI that learns from all employees' past emails, schedules, communication tools, chats, and created documents; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, and the like; and a means for reducing the time required for handover work and searching and retrieving work history. This system also includes an emotion engine that recognizes the user's emotions. In this system, the generation AI recognizes the user's emotions and provides information based on their emotional state. Specifically, the system infers the user's emotional state from the text of past emails and chats, and reflects the inference results in the generation AI's learning.

[0407] "Example 2"

[0408] In another embodiment, the emotion engine recognizes the emotional state of the new agent in real time and adjusts the way the generation AI provides information depending on that emotional state. In this embodiment, the generation AI adjusts the way it provides information depending on the new agent's emotional state, such as whether the new agent is feeling stressed or happy. For example, if the new agent is feeling stressed, the generation AI takes that state into account and adjusts the information it provides to be more concise and easy to understand.

[0409] "Example 3"

[0410] In another embodiment, the emotion engine infers the user's emotional state from information such as past emails, schedules, communication tools, chats, and created documents, and reflects the inference results in the learning of the generation AI. In this embodiment, the user's emotional state is inferred from past information and the inference results are reflected in the learning of the generation AI, allowing the new agent to obtain information about past agents and understand the emotional states behind that information. Specifically, the user's emotional state, such as joy, anger, sadness, or surprise, is inferred from the text of past emails and chats, and the inference results are reflected in the learning of the generation AI.

[0411] The processing flow of each embodiment will be described below.

[0412] "Example 1"

[0413] Step 1: The generative AI learns all employees' past emails, schedules, communication tools, chats, created documents, etc.

[0414] Step 2: The new agent asks the generation AI a question.

[0415] Step 3: The generation AI provides documents created by past staff members, minutes of meetings, internal and external communications, etc.

[0416] Step 4: The emotion engine recognizes the user's emotions, and the generation AI provides information based on that emotional state.

[0417] "Example 2"

[0418] Step 1: The emotion engine recognizes the new contact's emotional state in real time.

[0419] Step 2: Adjust how the generative AI presents information depending on that emotional state.

[0420] "Example 3"

[0421] Step 1: The emotion engine estimates the user's emotional state from information such as past emails, schedules, communication tools, chats, and created documents.

[0422] Step 2: The estimation results are reflected in the learning of the generative AI.

[0423] Step 3: The new agent obtains information about the previous agent and also understands the emotional state behind that information.

[0424] Example 1

[0425] Next, a description will be given of Example 1 of Form 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."

[0426] In conventional work handover systems, it is difficult for new staff to quickly and efficiently obtain information about previous staff, and there are problems with the handover process and searching and retrieving work history, which requires a lot of time and effort.In addition, information provided does not take into account the user's emotional state, making it impossible to reduce user stress and dissatisfaction.

[0427] 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.

[0428] In this invention, the server includes a generation AI means for learning all employees' past emails, schedules, communication tools, conversation records, and created documents; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, meeting records, and internal and external communications; a means for reducing the time required for handover work and search and retrieval of work history; a means for incorporating an emotion engine that recognizes user emotions; and a means for reflecting the emotion engine's inference results in the generation AI's learning data. This allows new employees to quickly and efficiently obtain the information they need, significantly reducing the time required for handover work and search and retrieval of work history. Furthermore, information is provided according to the user's emotional state, reducing user stress and frustration.

[0429] "Generative AI" is an artificial intelligence that studies all employees' past emails, schedules, communication tools, conversation records, created documents, etc., and analyzes and learns using natural language processing technology.

[0430] An "emotion engine" is an analytical model for recognizing a user's emotions and estimating their emotional state.

[0431] "Email" means a digital message sent or received over the Internet.

[0432] A "datebook" is a tool for managing the schedules of individuals and organizations.

[0433] "Communication tools" are software and platforms that enable people in remote locations to communicate, such as email, chat, and video conferencing.

[0434] A "dialogue transcript" is a log of a conversation that takes place through a chat or messaging application.

[0435] "Created materials" refers to digital content such as documents, reports, and presentation materials created in connection with business.

[0436] "New staff" refers to employees who have taken over new duties due to organizational changes, transfers, retirement, etc.

[0437] A "former employee" is an employee who was previously in charge of a particular task.

[0438] A "meeting record" is a document that records the contents of a meeting and the main points of discussion.

[0439] "Internal and external interactions" refers to all communications between parties both within and outside the organization.

[0440] "Handover work" refers to the procedures and information sharing required to transfer work to a new person in charge.

[0441] "Business history" refers to records and history information of business operations that have been carried out in the past.

[0442] This invention is a system that uses a generative AI that learns from all employees' past emails, schedules, communication tools, conversation records, created materials, etc. A specific embodiment of this system will be described below.

[0443] 1. Data Collection and Preprocessing

[0444] The server collects data such as all employees' past emails, schedules, communication tools, conversation records, and created materials. Specifically, emails are obtained from a general mail server (e.g., IMAP server), schedules are obtained from a calendar service (e.g., Google (registered trademark) Calendar API), and communication tools and conversation records are obtained from a messaging platform (e.g., Slack API, MICROSOFT (registered trademark) TEAMS (registered trademark) API).

[0445] The collected data is pre-processed by the server, which includes cleaning the text data, tokenizing it, removing stop words, stemming, etc. For example, unnecessary HTML tags are removed from the body of an email, and words are split into parts to make it easier to analyze.

[0446] 2. Data analysis and generative AI training

[0447] The server analyzes the preprocessed data, using natural language processing techniques to extract and correlate various pieces of information. Specifically, it extracts important keywords and topics from email content and analyzes calendar details to find related events.

[0448] The server then uses the parsed data to train a generative AI model, typically a natural language processing model like the Transformer model or BERT, to learn information about a specific project, for example, by combining historical email and calendar data.

[0449] 3. Incorporating an Emotional Engine

[0450] The server incorporates an emotion engine into the generative AI that recognizes the user's emotions. Specifically, it uses an emotion analysis model to estimate the user's emotional state from the text of past emails and conversation records. For example, if the user is feeling stressed, that emotional state can be detected.

[0451] The estimation results of the emotion engine are reflected in the learning data of the generation AI. This enables the generation AI to provide information according to the user's emotional state. For example, a user who is stressed can be given priority in receiving simple and easy-to-understand information. The adjustment means may adjust the content of the response to be simple and easy to understand if the new agent's emotions include stress.

[0452] 4. Information provision

[0453] The user uses a terminal to input a question to the generation AI. Specifically, they input a prompt sentence. For example, they might input, "I'd like to see the progress report for project X last year."

[0454] The server uses generative AI to search for and provide appropriate information in response to the user's question. It selects the most appropriate information based on the learned data and the inferences of the emotion engine. For example, it searches for progress reports related to Project X and provides them to the user.

[0455] Examples of specific examples and prompts

[0456] Specific examples

[0457] A new employee asks the generating AI, "I'd like to see the progress report for project X from last year."

[0458] The generative AI searches past emails, conversation records, and created documents to provide progress reports related to Project X.

[0459] If the emotion engine detects that a new employee is stressed, the generative AI will prioritize providing concise, easy-to-understand information.

[0460] Prompt Sentence Examples

[0461] "I'd like to see last year's progress report on Project X."

[0462] "Please tell me about the interactions your previous representatives had with Client Y."

[0463] "I'd like to check the minutes of a recent meeting."

[0464] This system allows users to quickly and efficiently obtain the information they need, significantly reducing the time required for handovers and searching through work history. It also provides information tailored to the user's emotional state, reducing stress and frustration.

[0465] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0466] Step 1:

[0467] Data collection

[0468] The server collects data such as all employees' past emails, schedules, communication tools, conversation records, and created materials. Specifically, emails are obtained from a general mail server (e.g., IMAP server), schedules are obtained from a calendar service (e.g., Google Calendar API), and communication tools and conversation records are obtained from messaging platforms (e.g., Slack API, Microsoft Teams API).

[0469] Input: Email, calendar, communication tools, conversation records, created materials

[0470] Output: Raw data collected

[0471] Step 2:

[0472] Data Preprocessing

[0473] The server preprocesses the collected data, which includes cleaning the text data, tokenizing it, removing stop words, stemming, etc. For example, it removes unnecessary HTML tags from the body of an email and splits words into a format that is easier to analyze.

[0474] Input: Raw data collected

[0475] Output: Preprocessed data

[0476] Step 3:

[0477] Data analysis

[0478] The server analyzes the preprocessed data, using natural language processing techniques to extract and correlate various pieces of information. Specifically, it extracts important keywords and topics from email content and analyzes calendar details to find related events.

[0479] Input: Preprocessed data

[0480] Output: Parsed data (keywords, topics, event associations)

[0481] Step 4:

[0482] Generative AI training

[0483] The server uses the analyzed data to train a generative AI model, typically a natural language processing model like the Transformer model or BERT, to learn information about a specific project, for example, by combining historical email and calendar data.

[0484] Input: Parsed data

[0485] Output: Trained generative AI model

[0486] Step 5:

[0487] emotion recognition

[0488] The server incorporates an emotion engine into the generative AI that recognizes the user's emotions. Specifically, it uses an emotion analysis model to estimate the user's emotional state from the text of past emails and conversation records. For example, if the user is feeling stressed, that emotional state can be detected.

[0489] Input: Past emails, conversation records

[0490] Output: Estimated emotional state

[0491] Step 6:

[0492] Reflecting emotional data

[0493] The server reflects the emotion engine's estimation results in the learning data of the generation AI, enabling the generation AI to provide information according to the user's emotional state.

[0494] Input: Estimated emotional state, trained generative AI model

[0495] Output: Generative AI model that reflects emotional state

[0496] Step 7:

[0497] User question input

[0498] The user uses a terminal to input a question to the generation AI. Specifically, they input a prompt sentence. For example, they might input, "I'd like to see the progress report for project X last year."

[0499] Input: prompt statement

[0500] Output: User question

[0501] Step 8:

[0502] Information search and provision

[0503] The server uses generative AI to search for and provide appropriate information in response to the user's question. It selects the most appropriate information based on the learned data and the inferences of the emotion engine. For example, it searches for progress reports related to Project X and provides them to the user.

[0504] Input: User questions, generative AI model reflecting emotional state

[0505] Output: Search results (progress reports and other relevant information)

[0506] In this way, users can quickly and efficiently obtain the information they need, significantly reducing the time required for handovers and searching through work history. Furthermore, information is provided according to the user's emotional state, reducing stress and frustration for the user.

[0507] (Application example 1)

[0508] Next, a description will be given of Application Example 1 of Embodiment 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."

[0509] In conventional factory work handover systems, it was difficult for new workers to efficiently retrieve past work history and communication logs, and it took a great deal of time and effort to search for handover work and work history. In addition, information provided did not take into account the emotional state of the workers, which led to the problem of stress and fatigue accumulating.

[0510] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes: a generation AI means for learning all employees' past emails, schedules, communication tools, chats, and created documents; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes, internal and external communications, etc.; a means for reducing the time required for handover work and work history search and retrieval; an emotion engine means for recognizing the worker's emotional state and providing information according to that emotional state; and a means for learning the past work history, schedules, communication logs, created documents, etc. of factory workers and supporting the new worker in efficiently taking over the work. This allows the new worker to efficiently obtain past work history and communication logs, significantly reducing the time and effort required for handover work and searching work history. Furthermore, providing information that takes the worker's emotional state into consideration reduces stress and fatigue. In this way, the system may include a means for supporting the new employee in taking over the work of the previous employee.

[0511] "Generative AI" is an artificial intelligence that studies all employees' past emails, schedules, communication tools, chats, created documents, etc., and analyzes and learns using natural language processing technology.

[0512] "New personnel" refers to workers or employees who have taken over new duties due to organizational changes, transfers, retirement, etc.

[0513] An "emotion engine" is a technology that recognizes the user's emotional state and provides information according to that emotional state.

[0514] "Work history" is a detailed record of work that has been done in the past, and includes information such as the work content, date and time, and person in charge.

[0515] A "communication log" is a record of past interactions that have taken place through email, chat, communication tools, etc.

[0516] "Handover work" refers to the process of communicating the information and procedures necessary to hand over work to a new person in charge.

[0517] "Work history" is a record of work that has been done in the past, and includes information such as the work content, date and time, and person in charge.

[0518] "Information provision" is the act of using generative AI and emotion engines to provide users with the information they need appropriately.

[0519] A system for implementing the present invention has the following configuration.

[0520] System configuration

[0521] 1. Data collection methods:

[0522] The server collects past emails, schedules, communication tools, chats, and documents created by factory workers. The hardware used is a server and database, and the software used is Python, SQL, and natural language processing libraries (e.g., NLTK, spaCy).

[0523] 2. Data analysis and learning methods:

[0524] The server analyzes the collected data using natural language processing technology and trains the generative AI model. The hardware used is a GPU-equipped server, and the software used is TENSORFLOW (registered trademark) and PyTorch.

[0525] 3. Emotion recognition means:

[0526] The server uses an emotion engine to recognize the emotional state of the worker. The hardware used is a camera and a microphone, and the software used is OpenCV and a speech recognition library (e.g., Google Speech-to-Text).

[0527] 4. Information provision method:

[0528] When a new worker inputs a question on the server, the generative AI provides appropriate information based on past data. The way the information is presented is adjusted depending on the worker's emotional state. The hardware used is a tablet and a display, and the software used is Flask (a web application framework).

[0529] Specific examples

[0530] For example, if a new worker asks, "What tasks did you perform during yesterday's shift?", the server uses the generative AI model to search past work history and provide detailed tasks. If the worker is feeling stressed, the server will provide advice such as, "I recommend you take a break to relax."

[0531] Prompt Sentence Examples

[0532] Please tell me what work you did during yesterday's shift.

[0533] In this way, the server allows new workers to take over tasks efficiently and quickly obtain work history and communication logs.In addition, by using an emotion engine, it is possible to provide information according to the worker's emotional state, thereby reducing stress and fatigue.

[0534] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0535] Step 1:

[0536] The server collects past emails, schedules, communication tools, chats, and created documents of factory workers. The input is data from various data sources (email server, schedule management system, chat logs, etc.), and the output is the storage of the collected data in a database. Specifically, it uses Python scripts to retrieve data from each data source and uses SQL to store it in the database.

[0537] Step 2:

[0538] The server analyzes the collected data using natural language processing technology and trains the generative AI model. The input is past emails, schedules, communication logs, created documents, etc. stored in the database, and the output is the trained generative AI model. Specifically, the data is analyzed using Python and natural language processing libraries (NLTK, spaCy), and the generative AI model is trained using TensorFlow or PyTorch.

[0539] Step 3:

[0540] The server uses an emotion engine to recognize the emotional state of the worker. The input is the worker's video and audio data acquired from the camera and microphone, and the output is the recognized emotional state. Specifically, it analyzes the video data using OpenCV, converts the audio data to text using Google Speech-to-Text, and applies the emotion recognition algorithm.

[0541] Step 4:

[0542] When a new worker inputs a question on the server, the generative AI provides appropriate information based on past data. The input is the question (prompt) from the new worker, and the output is the answer generated by the generative AI model. In concrete terms, a web application is built using Flask, and the question input by the user is passed to the generative AI model, which then generates and displays the answer.

[0543] Step 5:

[0544] The server adjusts the way it provides information depending on the emotional state. The input is the recognized emotional state and the answer generated by the generative AI model, and the output is the adjusted way of providing information. Specifically, if the emotional state indicates stress, it will perform processing such as adding advice to relax.

[0545] Step 6:

[0546] The terminal displays the information provided by the server to the new worker. The input is the information sent from the server, and the output is the information displayed on the terminal's display. Specific operations include building an interface to display information on a tablet or display.

[0547] Example 2

[0548] Next, a description will be given of Example 2 of Form 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."

[0549] In conventional work handover systems, it was difficult for new staff to quickly and accurately obtain information about previous staff members, and handover work and searching and retrieving work history required a great deal of time and effort. Furthermore, information provided was not tailored to the emotional state of the new staff member, which could cause stress and confusion.

[0550] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a generation AI means for learning the past emails, schedules, communication tools, dialogue records, prepared documents, etc. of all employees; a means for the new staff member to ask the generation AI a question to obtain prepared documents, meeting records, internal and external communications, etc., of previous staff members; a means for the new staff member to input a question in natural language and for the generation AI to analyze the question and generate an appropriate answer; a means for recognizing the emotional state of the new staff member in real time and adjusting the way the generation AI provides information depending on the emotional state; and a means for reducing the time required for handover work and searching and retrieving work history. This enables the new staff member to quickly and accurately obtain information about previous staff members and provide appropriate information according to their emotional state.

[0551] "All employees' past emails, schedules, communication tools, conversation records, documents created, etc." refers to emails sent and received by all employees in the organization in the past, schedules used for schedule management, tools used for business contact and communication, chat and message records, and various documents created in connection with work.

[0552] "Generative AI means" refers to a system that uses artificial intelligence technology to learn the past data of all employees and generate appropriate answers to questions in natural language.

[0553] "New employee" refers to an employee who has taken over a new role within an organization.

[0554] "Former employees" refer to employees who previously held specific roles within an organization.

[0555] "Entering a question in natural language" means that the user enters a question in a language that they use on a daily basis (e.g., Japanese, English).

[0556] "Analyzing questions and generating appropriate answers" refers to the process of analyzing input natural language questions and generating optimal answers based on their content.

[0557] "Real-time emotional state recognition" refers to detecting the user's current emotions (e.g., stress, joy) in real time and adjusting the system's behavior based on that information.

[0558] "Adjusting the way information is presented" refers to changing the content and format of the information provided depending on the user's emotional state.

[0559] "Reducing the time required for handover work and searching and retrieving work history" refers to shortening the time required for handover work and searching and retrieving work history by enabling new staff to quickly and efficiently obtain information about previous staff.

[0560] This invention is a system for enabling a new employee to quickly and efficiently obtain information about previous employees. This system includes a generation AI means for learning the past emails, schedules, communication tools, conversation records, and prepared documents of all employees, a means for the new employee to ask the generation AI questions to obtain prepared documents, meeting records, internal and external communications, etc. of previous employees, a means for the new employee to input questions in natural language and for the generation AI to analyze the questions and generate appropriate answers, a means for recognizing the emotional state of the new employee in real time and adjusting the way the generation AI provides information depending on that emotional state, and a means for reducing the time required for handover work and searching and retrieving work history.

[0561] Hardware and software used

[0562] Hardware:

[0563] Server equipped with high-performance CPU and GPU

[0564] software:

[0565] Natural language processing libraries (e.g., spaCy, NLTK)

[0566] Generative AI models (e.g., GPT-4)

[0567] Program processing

[0568] The server receives natural language questions entered by users on their devices. It then analyzes the questions using a natural language processing library to extract keywords and context. It then inputs the analysis results as prompts to the generative AI model to generate appropriate answers.

[0569] Specific examples

[0570] For example, if a new employee asks, "I'd like to see the materials created by my predecessor," the server analyzes this question and extracts the keyword "materials created by my predecessor." Next, it inputs a prompt to the generative AI model saying, "Please provide a list of materials created by my predecessor," and the generative AI model generates a list of materials created by the predecessor.

[0571] Example prompt sentence:

[0572] "Please provide a list of materials prepared by your predecessor."

[0573] In another embodiment, when an emotion engine is used, the server recognizes the emotional state of the new agent in real time. The emotion engine analyzes the emotional state of the new agent using facial recognition technology and voice analysis technology. For example, if the new agent is feeling stressed, the server inputs a prompt to the generative AI model such as, "Please provide concise and easy-to-understand information to the new agent who is feeling stressed," and the generative AI model provides information appropriate to the state.

[0574] Example prompt (when using the emotion engine):

[0575] "Provide concise, easy-to-understand information for stressed new reps."

[0576] In this way, the server can provide appropriate information depending on the new agent's questions and emotional state.

[0577] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0578] Step 1:

[0579] The user inputs a question in natural language using the input interface of the terminal. For example, the user might input, "I would like to see the materials created by my predecessor." The input question is temporarily stored in the terminal's memory.

[0580] Step 2:

[0581] The terminal sends the question entered by the user to the server. At this time, the question is converted into JSON format and sent to the server via an HTTP request. The input is the user's question, and the output is the JSON data sent to the server.

[0582] Step 3:

[0583] The server parses the received JSON data and analyzes the question using a natural language processing library (e.g., spaCy, NLTK). Specifically, it analyzes the grammatical structure of the question and extracts keywords and context. The input is JSON data, and the output is the parsed keywords and context.

[0584] Step 4:

[0585] The server generates a prompt for the generative AI model (e.g., GPT-4) based on the analysis results. For example, it generates a prompt such as, "Please provide a list of materials created by your predecessor." The input is the analyzed keywords and context, and the output is the generated prompt.

[0586] Step 5:

[0587] The server inputs the generated prompt into the generative AI model and generates an appropriate answer. The generative AI model generates a list of materials created by the predecessor based on the prompt and returns it to the server. The input is the prompt, and the output is the generated answer.

[0588] Step 6:

[0589] The server converts the answer received from the generative AI model into JSON format and sends it to the terminal via an HTTP response. The input is the generated answer, and the output is the JSON data sent to the terminal.

[0590] Step 7:

[0591] The terminal analyzes the received JSON data and displays a list of documents created by the predecessor to the user. For example, links such as "Document 1.pdf" and "Document 2.docx" are displayed on the screen. The input is the JSON data received from the server, and the output is the information displayed to the user.

[0592] In this way, the user can easily check the materials created by his / her predecessor.

[0593] (Application example 2)

[0594] Next, a description will be given of Application Example 2 of Form 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."

[0595] When a new employee takes over a task, it is difficult to quickly and accurately obtain documents, minutes, and internal and external communications prepared by the previous employee. Furthermore, if the new employee is under stress, it can become even more difficult to obtain information. This results in a significant amount of time spent on handover work and searching and retrieving work history, which can lead to a decline in productivity.

[0596] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0597] In this invention, the server includes a generation AI means for learning all employees' past emails, schedules, communication tools, chats, created documents, etc., a means for the new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc., a means for reducing the time required for handover work and searching and retrieving work history, an emotion engine means for recognizing the emotional state of the new employee in real time, and a means for the generation AI to adjust the method of providing information according to the emotional state. This not only enables the new employee to quickly and accurately obtain the information they need, but also makes it possible to provide appropriate information according to their emotional state.

[0598] "Generative AI means" is an artificial intelligence technology that learns from all employees' past emails, schedules, communication tools, chats, created documents, etc., and generates appropriate answers when a new employee inputs a question in natural language.

[0599] "New staff member" refers to an employee who has taken over new duties due to organizational changes, transfer, retirement, etc.

[0600] The "emotion engine means" is a technology that recognizes the emotional state of new staff in real time, and the generation AI adjusts the way information is provided based on that emotional state.

[0601] The "means for adjusting the method of providing information" refers to a technology that appropriately changes the format and content of the information provided by the generating AI depending on the emotional state of the new representative.

[0602] "Means to reduce the time required for handover work and searching and retrieving work history" refers to technology that reduces the time required for handover work and searching and retrieving work history by using generative AI to quickly and accurately obtain documents prepared by previous staff members, minutes of meetings, internal and external communications, etc.

[0603] The system for implementing the present invention includes a generating AI means, an emotion engine means, and a means for adjusting an information provision method. Specific embodiments are described below.

[0604] System Configuration

[0605] The server uses OpenAI's API as its generative AI tool, and the Emotion Recognition library as its emotion engine tool. These software packages are installed on factory robots, allowing them to provide appropriate answers to new employees' questions.

[0606] Hardware

[0607] Factory robots are equipped with the following hardware:

[0608] Microphone: To capture the new agent's voice input.

[0609] Camera: To recognize the facial expressions of new recruits in real time.

[0610] software

[0611] The server uses the following software:

[0612] OpenAI API: As a generative AI tool, it generates appropriate answers to questions entered in natural language.

[0613] Emotion Recognition Library: An emotion engine means to recognize the emotional state of new contacts in real time.

[0614] Data processing and calculation

[0615] 1. Acquisition of voice input: The new agent inputs a question into the robot. The voice data is acquired through the microphone and converted into text data.

[0616] 2. Question analysis: Generative AI means analyzes the text data and generates appropriate answers.

[0617] 3. Recognizing emotional state: The facial expression data of the new agent is acquired through the camera, and the emotion engine means analyzes the data to recognize the emotional state.

[0618] 4. Adjustment of information provision method: Adjust the format and content of the information provided by the generative AI means depending on the emotional state.

[0619] Specific examples

[0620] If a new employee asks, "Please tell me how to operate this machine," the following process will be followed:

[0621] 1. Voice input is acquired through the microphone and converted into text data.

[0622] 2. The generative AI means analyzes the question "Please tell me how to operate this machine" and generates an appropriate answer.

[0623] 3. Facial expression data of the new staff member is acquired through the camera, and the emotion engine means analyzes the data and recognizes that the new staff member is feeling stressed.

[0624] 4. Tailor the information provided by the generative AI means to a concise and easy-to-understand format depending on the emotional state.

[0625] Prompt Sentence Examples

[0626] If a new agent asks, "Please tell me how to operate this machine," and the emotion engine recognizes that the agent is stressed, the generative AI will adjust the response as follows:

[0627] Example prompt sentence:

[0628] New employee: Please tell me how to operate this machine.

[0629] Emotion engine: Recognizing stress states.

[0630] Generative AI: Short answer: To operate this machine, first turn it on, then press the start button.

[0631] In this way, adjusting the way information is presented depending on the emotional state of the new employee can help them acquire information more efficiently.

[0632] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0633] Step 1:

[0634] The user inputs a question to the robot. The input is in the form of voice, and the voice data is picked up through the robot's microphone. The voice data is converted into text data by speech recognition software. The input is voice data, and the output is text data.

[0635] Step 2:

[0636] The server sends text data to the generation AI means. The generation AI means analyzes the text data and generates an appropriate answer. The input is text data, and the output is the generated answer text.

[0637] Step 3:

[0638] The terminal (robot) acquires facial expression data of the user through a camera. The facial expression data is acquired in the form of an image and sent to the emotion engine means. The input is image data, and the output is emotional state data.

[0639] Step 4:

[0640] The server receives the emotional state data analyzed by the emotion engine means. The emotion engine means analyzes the image data and recognizes the user's emotional state. The input is the image data and the output is the emotional state data.

[0641] Step 5:

[0642] The server adjusts the format and content of the information provided by the generating AI means based on the emotional state data. For example, if the user is feeling stressed, the server converts the generated answer text into a concise and easy-to-understand format. The input is the emotional state data and the generated answer text, and the output is the adjusted answer text.

[0643] Step 6:

[0644] The terminal (robot) provides the user with the adjusted answer text. The user receives the answer through voice output or a display. The input is the adjusted answer text, and the output is information provided to the user.

[0645] Example 3

[0646] Next, a third embodiment of the third embodiment will be described. 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."

[0647] Traditional handover procedures and searching and searching work history were time-consuming, making it difficult for new staff to quickly obtain information about previous staff. Furthermore, it was not possible to understand the emotional state of previous staff from past work history or communication history, making it difficult to grasp the emotions behind the information. This hindered work efficiency and smooth handover.

[0648] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0649] In this invention, the server includes a generation AI means for learning the past emails, schedules, communication tools, conversation records, created documents, etc. of all employees, a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc., a means for reducing the time required for handover work and searching and retrieving work history, a means for inferring emotional states from the text of past emails and conversation records and reflecting the inference results in the learning of the generation AI, and a means for a new employee to understand the emotional states behind the information of previous employees. This allows the new employee to quickly obtain information about previous employees and also understand the emotional states behind that information.

[0650] "Generative AI" is an artificial intelligence technology that learns from past data and provides appropriate information in response to user questions.

[0651] "Email" means a digital message sent or received over the Internet.

[0652] A "datebook" is a digital tool for managing a user's schedule and appointments.

[0653] A "communication tool" is software or an application that allows users to exchange messages and information with each other.

[0654] "Dialogue records" are data that store the history of chats and messages between users.

[0655] "Created materials" are digital files such as documents and presentations created by users for business purposes.

[0656] "Emotional state" refers to a user's psychological state, such as joy, anger, sadness, or surprise.

[0657] An "emotion engine" is software for estimating a user's emotional state from text data.

[0658] A "new person in charge" is someone who has taken over new duties due to organizational changes, transfers, retirement, etc.

[0659] The "previous person in charge" is the person who was in charge of the work before the work was handed over to the new person in charge.

[0660] This invention is a system for reducing handover and work history search and retrieval time. The system uses a generative AI model to learn from past data and provides the ability to efficiently search and extract the information needed by new staff.

[0661] The server collects all employees' past emails, schedules, communication tools, conversation records, created documents, etc. and stores this data in a database. Specifically, the following hardware and software are used:

[0662] Data collection:

[0663] The server retrieves emails using the IMAP or POP3 protocol. For example, it retrieves emails using the Gmail API, retrieves calendar data using the Google Calendar API, retrieves conversation records using the Slack API or Microsoft Teams API, and retrieves documents using the Google Drive API or OneDrive API.

[0664] Data preprocessing:

[0665] The server preprocesses the collected data, specifically cleaning, tokenizing, and normalizing the text data using natural language processing libraries (e.g., NLTK, spaCy).

[0666] Training generative AI models:

[0667] The server uses the preprocessed data to train a generative AI model (e.g., GPT-3, BERT), which then understands the user's past work and communication history.

[0668] Emotion estimation:

[0669] The server uses an emotion engine to estimate the user's emotional state from the text of past emails and conversation records. Specifically, it performs emotion analysis using natural language processing libraries (e.g., TextBlob, VADER), and reflects the results in the training of the generative AI model.

[0670] When a new employee searches for specific information, the terminal uses the generative AI model to quickly search for and extract the information. Specifically, the user inputs a prompt into the terminal, and the terminal sends the prompt to the generative AI model. The generative AI model searches for the relevant information and returns the results to the terminal. The terminal then displays the search results to the user.

[0671] For example, if a new employee requests to see the emails exchanged between the previous employee and the client, the following prompt might be used:

[0672] Example prompt sentence:

[0673] "Please show me a list of emails my predecessor exchanged with clients."

[0674] In response to this prompt, the generative AI model quickly searches and extracts a list of relevant emails and provides them to the new person in charge. As a specific example of emotion estimation, when estimating a user's emotional state from the text of past emails and conversation records, the following prompt is used:

[0675] Example prompt sentence:

[0676] "Please infer the user's emotional state from the text of this email."

[0677] In response to this prompt, the emotion engine estimates the user's emotional state and reflects the results in the learning of the generative AI model. This allows the new agent to understand not only the information of previous agents but also the emotional state behind that information. The flow of the identification process in Example 3 is explained using Figure 21.

[0678] Step 1:

[0679] Data collection

[0680] The server collects data such as users' emails, calendars, communication tools, conversation records, and created materials. Specifically, it retrieves emails using the IMAP and POP3 protocols, retrieves calendar data using the Google Calendar API, retrieves conversation records using the Slack API and Microsoft Teams API, and retrieves created materials using the Google Drive API and OneDrive API.

[0681] Input: User account information

[0682] Output: Collected data (emails, schedules, conversation logs, and documents)

[0683] Step 2:

[0684] Data Preprocessing

[0685] The server preprocesses the collected data. Specifically, it uses natural language processing libraries (e.g., NLTK, spaCy) to clean, tokenize, and normalize the text data. For example, it removes unnecessary HTML tags and special characters to make the text data clean.

[0686] Input: Collected data

[0687] Output: Preprocessed data

[0688] Step 3:

[0689] Training generative AI models

[0690] The server uses the preprocessed data to train a generative AI model (e.g., GPT-3, BERT). Specifically, the preprocessed data is divided into training data and test data, and the generative AI model is trained using the training data. During this process, the model understands the user's past work history and communication history.

[0691] Input: Preprocessed data

[0692] Output: A trained generative AI model

[0693] Step 4:

[0694] Emotion estimation

[0695] The server uses an emotion engine to estimate the user's emotional state from the text of past emails and conversation records. Specifically, it performs emotion analysis using natural language processing libraries (e.g., TextBlob, VADER), and reflects the results in the training of the generative AI model.

[0696] Input: Preprocessed text data

[0697] Output: Estimated emotional state

[0698] Step 5:

[0699] Information Search and Extraction

[0700] When a new employee searches for specific information, the terminal uses the generative AI model to quickly search for and extract the information. Specifically, the user inputs a prompt into the terminal, and the terminal sends the prompt to the generative AI model. The generative AI model searches for the relevant information and returns the results to the terminal. The terminal then displays the search results to the user.

[0701] Input: Prompt text (e.g., "Please display a list of emails exchanged between my predecessor and clients.")

[0702] Output: Search results (list of matching emails)

[0703] Step 6:

[0704] Displaying the results

[0705] The device displays the search results returned by the generative AI model to the user, including a list of relevant emails and estimated emotional states.

[0706] Input: Search results, estimated emotional state

[0707] Output: Information displayed to the user (list of emails, estimated emotional state)

[0708] (Application example 3)

[0709] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0710] Traditional handover procedures and searching and searching work history were time-consuming, making it difficult for new staff to quickly obtain information about previous staff. It was also difficult to understand the emotional state of previous staff, making it difficult to grasp the background and intentions of the work. This resulted in reduced work efficiency and a decline in the quality of the handover.

[0711] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0712] In this invention, the server includes a generation AI means for learning the past emails, schedules, communication tools, chats, created documents, etc. of all employees, a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc., a means for reducing the time required for handover work and searching and retrieving work history, a means for quickly searching and extracting past work history and communication history, and a means for an emotion engine to estimate the emotional state of the worker from information such as past emails, schedules, chats, etc. and reflect the results in the learning of the generation AI. This enables a new employee to quickly obtain information about previous employees and also understand the emotional states behind that information.

[0713] "Generative AI" is an artificial intelligence technology that learns from past data and quickly searches for and extracts the information needed by new staff.

[0714] A "new person in charge" is someone who has taken over new duties due to organizational changes, transfers, retirement, etc.

[0715] A "former person in charge" is a person who was previously in charge of a particular task.

[0716] The "emotion engine" is a technology that estimates a worker's emotional state from information such as past emails, schedules, and chats.

[0717] "Work history" is a record of past tasks and work.

[0718] "Communication history" is a record of past emails, chats, and other interactions.

[0719] "Search and retrieval" is the act of finding and retrieving specific information from a database or record.

[0720] "Handover work" refers to a series of procedures and tasks required to hand over work to a new employee.

[0721] "Business history" is a detailed record of past business operations.

[0722] A system for carrying out this invention is configured as follows: The server includes a generation AI means for learning the past emails, schedules, communication tools, chats, created documents, etc. of all employees, a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc., a means for reducing the time required for handover work and searching and retrieving work history, a means for quickly searching and extracting past work history and communication history, and a means for an emotion engine to estimate the emotional state of the worker from information such as past emails, schedules, chats, etc., and reflect the results in the learning of the generation AI.

[0723] Program processing explanation

[0724] The server first collects all employees' past emails, schedules, communication tools, chats, created documents, etc., and trains the generative AI model. This generative AI model is built using OpenAI's API. When a new employee needs specific information, they input a prompt to the generative AI. For example, they input a prompt such as, "Please tell me the specific work steps performed by my predecessor." Based on this prompt, the generative AI quickly searches and extracts past work history and communication history, and provides the required information.

[0725] Furthermore, the emotion engine estimates the emotional state of the worker from information such as past emails, schedules, and chats. This emotion engine is built using, for example, the sentiment_analysis library. The emotional state estimated by the emotion engine is reflected in the learning of the generative AI. This allows new workers to understand the emotional states of previous workers when they were working.

[0726] Specific examples

[0727] For example, if a new employee wants to know the specific work steps performed by their predecessor, they can enter the following prompt into the generation AI:

[0728] Prompt statement:

[0729] What specific work steps did your predecessor take?

[0730] Based on this prompt, the generation AI quickly searches and extracts relevant information from past work history and provides it to the new worker. The emotion engine also estimates the emotional state associated with the work procedure and provides this to the new worker. This allows the new worker to efficiently take over the work while understanding the emotional state of the previous worker.

[0731] The hardware used includes servers and PCs, and the software used includes the OpenAI API and sentiment_analysis library, which effectively utilizes generative AI models and emotion engines to significantly reduce the time required for handovers and searching and retrieving work history.

[0732] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0733] Step 1:

[0734] The server collects all employees' past emails, schedules, communication tools, chats, created documents, etc. This data is input into the generative AI model for learning. The input data includes email content, schedule details, chat logs, created documents, etc., and the output is a trained generative AI model. Specifically, it extracts the necessary information from the database and feeds it into the generative AI model.

[0735] Step 2:

[0736] The user (new employee) inputs a prompt that requires specific information from the generation AI. For example, the user might input a prompt such as, "Please tell me the specific work steps that your predecessor performed." The input is the prompt, and the output is the answer returned by the generation AI. Specifically, the user inputs the prompt using a terminal and sends it to the server.

[0737] Step 3:

[0738] The server uses a generative AI model to quickly search and extract past work history and communication history based on the input prompt. The input is the prompt and a trained generative AI model, and the output is the searched and extracted information. Specifically, the generative AI model queries the database and retrieves the relevant information.

[0739] Step 4:

[0740] The server uses an emotion engine to estimate the worker's emotional state from the retrieved and extracted information. The input is the retrieved and extracted information, and the output is the estimated emotional state. Specifically, the emotion engine analyzes the text data and classifies the emotional state.

[0741] Step 5:

[0742] The server reflects the estimation results of the emotion engine in the generative AI model. The input is the estimated emotional state, and the output is a generative AI model that reflects the emotional state. Specifically, it updates the parameters of the generative AI model and has it learn the emotional state.

[0743] Step 6:

[0744] The server provides the new agent with the output results of the generative AI model and the inference results of the emotion engine. The input is the output results of the generative AI model and the inference results of the emotion engine, and the output is the information provided to the new agent. Specifically, the server sends the information to the terminal, and the user views it.

[0745] Step 7:

[0746] The user (new worker) uses the provided information to understand the work procedures and emotional state of the previous worker and efficiently take over the work. The input is information provided by the server, and the output is the new worker's understanding and execution of the work. Specifically, the user checks the information on their device and reflects it in the actual work.

[0747] 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.

[0748] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0749] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0750] 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.

[0751] [Second embodiment]

[0752] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0753] 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.

[0754] 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).

[0755] 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.

[0756] 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.

[0757] 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).

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0763] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0764] "Example 1"

[0765] One embodiment of the present invention uses a generation AI that learns from all employees' past emails, schedules, communication tools, chats, created documents, etc. This generation AI uses natural language processing technology to analyze and learn from various types of information. Specifically, it analyzes the content of past emails, schedule details, communication tool and chat logs, the content of created documents, etc., and correlates each piece of information to learn.

[0766] "Example 2"

[0767] Natural language processing technology is used as a means for new staff to ask questions to the generation AI. Specifically, the new staff inputs a question in natural language, and the generation AI analyzes the question and generates an appropriate answer. For example, in response to a question such as "I would like to see the materials created by my predecessor," the generation AI provides a list of materials created by the predecessor.

[0768] "Example 3"

[0769] As a means of reducing the time required for handover work and searching and retrieving work history, the generation AI provides a function to efficiently search and extract the information it has learned. Specifically, the generation AI quickly searches and extracts the information needed by the new employee based on the information it has learned. For example, in response to a request to "see the emails exchanged between the predecessor and clients," the generation AI quickly provides a list of emails exchanged between the predecessor and clients.

[0770] The processing flow of each embodiment will be described below.

[0771] "Example 1"

[0772] Step 1: The generation AI obtains all employees' past emails, schedules, communication tools, chats, created documents, etc.

[0773] Step 2: Analyze the various pieces of information acquired using natural language processing technology, and learn by associating each piece of information.

[0774] Step 3: Based on the learned information, the information required by the new staff member is quickly searched and extracted.

[0775] "Example 2"

[0776] Step 1: New agent types question in natural language.

[0777] Step 2: The generative AI analyzes the question.

[0778] Step 3: Based on the analysis results, generate an appropriate answer and provide it to the new contact.

[0779] "Example 3"

[0780] Step 1: The new contact person enters a search and retrieval request for the information they need.

[0781] Step 2: Based on the information learned by the generation AI, it quickly searches for and extracts information that corresponds to the request.

[0782] Step 3: Provide the searched and extracted information to the new contact.

[0783] Example 1

[0784] Next, a description will be given of Example 1 of Form 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."

[0785] With conventional business handover systems, it was difficult for new employees to quickly and accurately obtain information about previous employees when organizational changes, transfers, or retirements occurred. Furthermore, there was a lack of a way to efficiently analyze, correlate, and learn from vast amounts of data, such as past emails, schedules, communication tools, chats, and created documents, which meant that handover work and searching and retrieving work history took a great deal of time and effort.

[0786] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a generation AI means for learning the past emails, schedules, communication tools, dialogue records, created documents, etc. of all employees; a means for a new person in charge to ask the generation AI questions to obtain documents created by previous people, meeting records, internal and external communications, etc.; a means for reducing the time required for handover work and search and retrieval of work history; a means for preprocessing collected data and training a generation AI model; a means for evaluating the performance of the generation AI model; and a means for providing information according to user requests using the generation AI model. This enables a new person in charge to quickly and accurately obtain information about previous people, significantly reducing the time required for handover work and search and retrieval of work history.

[0787] The "generative AI means" is a means of studying all employees' past emails, schedules, communication tools, conversation records, documents created, etc., and analyzing and learning using natural language processing technology.

[0788] "New staff" refers to staff who have taken over new duties due to organizational changes, transfers, retirement, etc.

[0789] "Previous person in charge" refers to the person in charge before taking over the duties due to organizational changes, transfer, retirement, etc.

[0790] "Created documents" refers to documents created by employees in the course of their work, and specifically includes reports, presentation materials, plans, etc.

[0791] "Meeting records" refers to documents that record the contents of a meeting, including minutes and meeting notes.

[0792] "Internal and external interactions" refers to communications that employees have with internal and external parties in the course of their work, including email, chat, conference calls, etc.

[0793] "Handover work" refers to a series of tasks required to hand over work to a new employee, including providing past work details and related materials.

[0794] "Search and retrieval time for business history" refers to the time required to search and retrieve information related to past business.

[0795] "Means for preprocessing collected data" refers to means for converting collected data into a format that is easy to analyze, and specifically includes text normalization, tokenization, data filtering, etc.

[0796] A "means for training a generative AI model" is a means for training a generative AI model using preprocessed data.

[0797] "Means for evaluating the performance of a generative AI model" refers to means for evaluating the performance of a generative AI model using evaluation indicators such as precision, recall, and F1 score.

[0798] "Means for providing information in response to a user's request using a generative AI model" refers to means for generating and providing appropriate information using a generative AI model based on a prompt sentence entered by a user.

[0799] This invention is a system that uses a generative AI model that learns from all employees' past emails, schedules, communication tools, conversation records, documents created, etc., allowing new employees to quickly and accurately obtain information about previous employees, significantly reducing the time required for handover work and searching and retrieving work history.

[0800] 1. Program Generation

[0801] The server collects all employees' past emails, schedules, communication tools, conversation records, documents, etc., and generates a generative AI model that learns from this data. This generative AI model analyzes and learns from various types of information using natural language processing technology.

[0802] 2. Data analysis and learning

[0803] The server analyzes and learns data using the following hardware and software:

[0804] Hardware: A server equipped with a powerful GPU (e.g., a powerful graphics processing unit)

[0805] Software: Natural language processing libraries (e.g., natural language processing libraries), database management systems (e.g., database management systems)

[0806] The server analyzes the following data:

[0807] Email: Analyze the content of past emails to extract important keywords and topics.

[0808] Calendar: Analyzes calendar details to learn meeting and event patterns.

[0809] Communication tools and conversation logs: Analyze logs to identify frequently used phrases and topics.

[0810] Created documents: Analyze the content of created documents and extract relevant information.

[0811] The server correlates this information, learns from it, and updates the generative AI model.

[0812] 3. Examples of concrete examples and prompts

[0813] Below are some specific examples of how users can use generative AI models.

[0814] Examples:

[0815] When a user asks a generative AI model to "create an agenda for the next project meeting," the model analyzes past project meeting agendas, related emails, and conversation records to generate an appropriate agenda.

[0816] Example prompt sentence:

[0817] "I'd like you to create an agenda for our next project meeting, using past meeting agendas, relevant emails, and transcripts."

[0818] In this way, the generative AI model leverages past data to provide information that meets the user's needs.

[0819] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0820] Step 1: Data collection

[0821] The server collects data on all employees' past emails, schedules, communication tools, conversation records, documents created, etc. Specifically, it obtains information from the following data sources:

[0822] Input: Internal email server, calendar management system, communication tool logs, file server

[0823] Output: raw data collected

[0824] Specific operation: The server retrieves "all employee emails from January 1, 2022 to December 31, 2022" from the email server, retrieves employee schedule information from the calendar management system, and retrieves conversation records and created documents from communication tool logs and file servers.

[0825] Step 2: Data Preprocessing

[0826] The server preprocesses the collected data. Specifically, it performs the following processes:

[0827] Input: Raw data collected

[0828] Output: Preprocessed data

[0829] Specific operation: The server extracts the subject, body, sender, and recipient from the acquired email data, removes unnecessary HTML tags, and tokenizes the text data into words and phrases, eliminating unnecessary and duplicate data.

[0830] Step 3: Model training

[0831] The server uses the preprocessed data to train the generative AI model. Specifically, training is performed using the following steps:

[0832] Input: Preprocessed data

[0833] Output: Trained generative AI model

[0834] What it does: The server splits the data into training data and test data, trains a generative AI model using the training data, builds the model using a natural language processing library, and tunes hyperparameters to optimize the model's performance.

[0835] Step 4: Model evaluation

[0836] The server evaluates the performance of the generative AI model using test data. Specifically, it uses the following evaluation indicators:

[0837] Input: Test data, trained generative AI model

[0838] Output: Model evaluation results (precision, recall, F1 score)

[0839] Specific operation: The server inputs test data into the generative AI model, compares the model's prediction results with the actual data, and calculates evaluation metrics such as precision, recall, and F1 score to evaluate the model's performance.

[0840] Step 5: Use the model

[0841] The user uses the generative AI model to perform a specific task by entering a prompt like the following:

[0842] Input: User prompt (e.g., "Please create an agenda for our next project meeting.")

[0843] Output: The information generated (e.g., the agenda)

[0844] How it works: When a user types, "I want an agenda for my next project meeting," the server uses the generative AI model to analyze past project meeting agendas, related emails, and conversation records to generate an appropriate agenda. The generated agenda is then displayed on the user's device.

[0845] In this way, specific processing is performed at each step, and information is provided according to the user's request using a generative AI model.

[0846] (Application example 1)

[0847] Next, a description will be given of Application Example 1 of Form 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."

[0848] With conventional work handover systems, it was difficult for new staff to efficiently obtain information from previous staff, resulting in the time-consuming process of handover work and searching and retrieving work history. Furthermore, there was a lack of appropriate procedure suggestions to improve work efficiency within the factory, making it difficult to optimize work.

[0849] 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.

[0850] In this invention, the server includes a generation AI means for learning all employees' past emails, schedules, communication tools, chats, and created documents; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes, internal and external communications, etc.; a means for reducing the time required to search and retrieve handover work and work history; a means for learning past work logs, schedules, communication tool logs, created work instructions, etc., and proposing optimal work procedures to improve work efficiency within the factory; and a means for issuing work instructions in real time. This allows new employees to quickly obtain information about previous employees and significantly reduces the time required to search and retrieve handover work and work history. Furthermore, optimal procedures are proposed to improve work efficiency within the factory, thereby achieving work optimization.

[0851] "Generative AI" is an artificial intelligence that studies all employees' past emails, schedules, communication tools, chats, created documents, etc., and uses natural language processing technology to analyze the information, associate it, and learn.

[0852] "New staff member" refers to an employee who has taken over new duties due to organizational changes, transfer, retirement, etc.

[0853] "Former employee" refers to an employee who was previously in charge of a particular task.

[0854] A "work log" is data that records the history of work performed in a factory.

[0855] "Schedule" is information that shows employees' plans and plans.

[0856] "Communication tools" are tools used for communicating and sharing information between employees, and include chat and email.

[0857] "Prepared materials" refers to documents, reports, and other materials created by employees in the course of their work.

[0858] A "work instruction" is a document that describes work procedures and instructions within a factory.

[0859] "Real-time work instructions" refers to the generation AI grasping the situation within the factory in real time and providing instructions on the spot on the optimal work procedures.

[0860] "Handover" is the process of conveying the job content and related information from the previous person in charge to the new person in charge.

[0861] "Search and retrieval time for business history" is the time required to search for information on past business operations and find the required data.

[0862] A system for implementing this invention is configured as follows: The server is equipped with a generation AI that learns from all employees' past emails, schedules, communication tools, chats, created documents, etc. This generation AI uses natural language processing technology to analyze and learn from various types of information. Specifically, it analyzes the content of past emails, schedule details, communication tool and chat logs, the content of created documents, etc., and correlates each piece of information to learn.

[0863] By asking the generated AI questions, new staff can obtain documents prepared by previous staff, minutes of meetings, internal and external communications, etc. This can significantly reduce the time spent on handover work and searching and retrieving work history.

[0864] Furthermore, to improve the efficiency of work within the factory, the generative AI learns from past work logs, schedules, communication tool logs, created work instructions, etc., and proposes optimal work procedures. Providing work instructions in real time can improve work efficiency within the factory.

[0865] As a specific example, the following prompt sentence could be input to the generative AI model:

[0866] Please suggest the best procedure based on the following data:

[0867] 2023-10-01 09:00:00: Email - Part A is out of stock.

[0868] 2023-10-01 10:00:00: Schedule - Assembly work for part B.

[0869] 2023-10-01 11:00:00: Chat - Part C requires quality inspection.

[0870] 2023-10-01 12:00:00: Created materials - Assembly instructions for part D.

[0871] The server runs a Python program and inputs prompts to the generation AI using OpenAI's API. The generation AI then proposes optimal work procedures based on the input prompts. This allows new workers to quickly acquire information about previous workers, improving work efficiency within the factory.

[0872] The hardware used is a computer installed on a factory robot, and the software uses Python and the OpenAI API, including Pandas, a Python data analysis library, and NLTK, a natural language processing library.

[0873] This system allows new employees to quickly obtain information about previous employees, significantly reducing the time it takes to take over and to search and find work history. It also suggests optimal procedures to improve work efficiency within the factory, resulting in work optimization.

[0874] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0875] Step 1:

[0876] The server reads all employees' past emails, schedules, communication tools, chats, created documents, etc. from CSV files. This obtains various data as a data frame. The input is a CSV file, and the output is a data frame.

[0877] Step 2:

[0878] The server combines the read data into a single data frame. Specifically, it uses the Pandas library to combine each data frame vertically. The input is multiple data frames, and the output is the combined data frame.

[0879] Step 3:

[0880] The server creates a prompt sentence to be input to the generation AI based on the integrated data frame. Specifically, it converts each row of data into text format, arranges it in chronological order, and generates a prompt sentence. The input is the integrated data frame, and the output is the prompt sentence.

[0881] Step 4:

[0882] The server sends the generated prompt to OpenAI's API and obtains the optimal work procedure from the generation AI. Specifically, it uses OpenAI's Completion endpoint to input the prompt and receive the generated text. The input is the prompt, and the output is the generated work procedure.

[0883] Step 5:

[0884] The server displays the generated work procedure to the new worker. Specifically, it displays the generated text on a user interface so that the worker can check it. The input is the generated work procedure, and the output is the information displayed on the user interface.

[0885] Step 6:

[0886] The user performs the work in the factory based on the displayed work procedure. Specifically, the user performs the necessary work according to the procedure proposed by the generation AI. The input is the displayed work procedure, and the output is the performed work.

[0887] Example 2

[0888] Next, a description will be given of Example 2 of Form 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."

[0889] When taking over work due to organizational changes, transfers, retirement, etc., it is difficult for new staff to quickly and accurately obtain information about previous staff. With conventional methods, the handover process and searching and retrieving work history takes a lot of time and effort, resulting in a decrease in productivity.

[0890] 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.

[0891] In this invention, the server includes a means for a new agent to input a question in natural language, a means for the terminal to send the question to the server, a means for the server to analyze the question using natural language processing technology, a means for the server to send a prompt to the generative AI model based on the analysis result, a means for the generative AI model to generate an answer based on the prompt, a means for the server to send the generated answer to the terminal, and a means for the terminal to display the answer to the user. This allows the new agent to quickly and accurately obtain information about the previous agent.

[0892] "New staff" refers to staff who have taken over new duties due to organizational changes, transfers, retirement, etc.

[0893] A "natural language" is a language used by humans on a daily basis, not a specific programming language or technical term.

[0894] A "terminal" is an electronic device such as a computer or smartphone used by a user.

[0895] A "server" is a computer system that receives and processes requests from terminals via a network.

[0896] "Natural language processing technology" is a technology that enables computers to understand and analyze human natural language.

[0897] A "generative AI model" is a model that uses artificial intelligence technology to create appropriate answers or products based on input data.

[0898] A "prompt sentence" is an instruction sentence input to a generative AI model, and is the sentence that serves as the basis for the model to generate an answer.

[0899] An "answer" is information or data that a generative AI model generates based on a prompt.

[0900] "Communication records" are the history of past communications such as emails and chats.

[0901] A "plan" is a future plan or plan recorded on a schedule or calendar.

[0902] "Communication tools" are software that supports communication between users, such as chat apps and messaging services.

[0903] "Created materials" are documents, reports, and other materials created by past staff members.

[0904] Minutes are documents that record the contents of meetings and discussions.

[0905] "Internal and external interactions" are records of communications with other departments within the company and with external business partners.

[0906] This invention is a system that allows new staff to quickly and accurately obtain information about previous staff members. The system involves a process in which a user inputs a question in natural language, and a generative AI model analyzes the question and generates an appropriate answer.

[0907] Hardware and software used

[0908] Device: An electronic device such as a computer or smartphone that a user uses.

[0909] Server: A computer system that receives and processes requests from terminals via a network.

[0910] Natural Language Processing technologies: NLP libraries such as spaCy and NLTK.

[0911] Generative AI models: Generative AI techniques such as OpenAI GPT-3.

[0912] HTTP request libraries: such as Axios or Fetch API.

[0913] Front-end frameworks: such as React or Vue.js.

[0914] Program processing

[0915] 1. The user enters a question

[0916] A user uses a terminal to input a question in natural language.

[0917] Example: A user types "I want to see the materials created by my predecessor" into a browser input field.

[0918] 2. The device sends a question to the server

[0919] The terminal sends the question entered by the user to the server.

[0920] What happens: The device sends a question to the server's API endpoint using an HTTP POST request.

[0921] 3. The server parses the question

[0922] The server analyzes the received question using natural language processing technology.

[0923] What it does: The server uses the spaCy library to analyze the intent of the question and extract important keywords.

[0924] 4. The server sends a prompt to the generative AI model

[0925] The server sends a prompt to the generative AI model based on the analysis results.

[0926] Specific operation: The server generates a prompt sentence saying, "The new employee wants to see the materials created by his predecessor. Please provide a list of the materials created by his predecessor." and sends it to the generative AI model.

[0927] 5. Generative AI models generate answers

[0928] A generative AI model generates appropriate answers based on the prompt.

[0929] Specific behavior: The generative AI model generates the answer, "The list of documents created by the predecessor is as follows: 1. Project plan 2. Progress report 3. Customer service manual."

[0930] 6. The server sends the answer to the device

[0931] The server sends the answer received from the generative AI model to the device.

[0932] Specific operation: The server sends the generated answer to the terminal as an HTTP response.

[0933] 7. The device displays the answer to the user

[0934] The terminal displays the received answer to the user.

[0935] Specific operation: The device displays the text "The list of documents prepared by the predecessor is as follows: 1. Project plan 2. Progress report 3. Customer service manual" in the browser display area.

[0936] In this way, a series of processes is realized in which a new employee inputs a question in natural language, and the generation AI analyzes the question and provides an appropriate answer.

[0937] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0938] Step 1:

[0939] The user enters a question.

[0940] Input: The user types a question in natural language.

[0941] Specific operation: The user types "I want to see the materials created by my predecessor" into the input field of the device's browser.

[0942] Output: The question you typed will be displayed on the terminal.

[0943] Step 2:

[0944] The terminal sends a question to the server.

[0945] Input: The question entered by the user.

[0946] What happens: The device sends a question to the server's API endpoint using an HTTP POST request.

[0947] Output: The question is sent to the server.

[0948] Step 3:

[0949] The server parses the question.

[0950] Input: The question sent from the terminal.

[0951] What it does: The server uses the spaCy library to analyze the intent of the question and extract important keywords.

[0952] Output: Analysis results of the question (e.g. keywords such as "predecessor", "created", "document").

[0953] Step 4:

[0954] The server sends a prompt to the generative AI model.

[0955] Input: The parsed result of the question.

[0956] Specific operation: The server generates a prompt sentence saying, "The new employee wants to see the materials created by his predecessor. Please provide a list of the materials created by his predecessor." and sends it to the generative AI model.

[0957] Output: The prompt sent to the generative AI model.

[0958] Step 5:

[0959] A generative AI model generates the answer.

[0960] Input: The prompt text sent by the server.

[0961] Specific operation: Based on the prompt, the generative AI model generates the answer, "The list of documents created by the predecessor is as follows: 1. Project plan 2. Progress report 3. Customer service manual."

[0962] Output: The generated answer.

[0963] Step 6:

[0964] The server sends the response to the terminal.

[0965] Input: The answer received from the generative AI model.

[0966] Specific operation: The server sends the generated answer to the terminal as an HTTP response.

[0967] Output: The answer sent to the terminal.

[0968] Step 7:

[0969] The terminal displays the answer to the user.

[0970] Input: The answer sent by the server.

[0971] Specific operation: The device displays the text "The list of documents prepared by the predecessor is as follows: 1. Project plan 2. Progress report 3. Customer service manual" in the browser display area.

[0972] Output: The answer displayed to the user.

[0973] (Application example 2)

[0974] Next, a description will be given of Application Example 2 of Form 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."

[0975] When new employees take over work, it is difficult to quickly and efficiently obtain information such as documents prepared by previous employees, minutes of meetings, and internal and external communications. This also creates a problem where new employees in the factory are unable to quickly obtain the information they need to understand how to operate machines and work procedures, resulting in reduced work efficiency.

[0976] 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 a generation AI means for learning all employees' past emails, schedules, communication tools, chats, created documents, etc.; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc.; a means for reducing the time required for handover work and search and retrieval of work history; and a means for a new employee to input questions in natural language to a robot in the factory, where the generation AI analyzes the questions and provides appropriate answers. This enables the new employee to quickly and efficiently obtain the necessary information, improving the handover and work efficiency.

[0977] "All employees" refers to all staff members of a company or organization.

[0978] "Email" refers to digital messages sent and received over the Internet.

[0979] A "datebook" is a tool for managing the schedules of individuals and organizations, and records scheduled meetings and tasks.

[0980] "Communication tools" refers to software and applications for exchanging information, such as chat and video conferencing.

[0981] "Chat" refers to a means of communication for exchanging text messages in real time.

[0982] "Created materials" refers to digital files such as documents and presentations created in the course of business.

[0983] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze input questions and generate appropriate answers.

[0984] "New staff member" refers to a staff member who has been newly assigned to a task within the organization.

[0985] "Former staff member" refers to a staff member who was previously in charge of a particular task.

[0986] "Minutes" refers to a document that records the contents of a meeting or conference.

[0987] "Internal and external interactions" refers to communication with parties both within the company and outside the company.

[0988] "Handover work" refers to the procedures and tasks required to hand over work to the next person in charge.

[0989] "Business history" refers to records of past business operations.

[0990] "Search and retrieval time" refers to the time spent finding needed information.

[0991] "In-plant" means the interior of a facility where manufacturing operations take place.

[0992] A "robot" refers to a mechanical device that performs a specific task automatically.

[0993] "Natural language" refers to a language that humans use on a daily basis.

[0994] "Analysis" refers to the process of analyzing input data and understanding its meaning.

[0995] An "appropriate answer" refers to a response that provides accurate and useful information in response to the entered question.

[0996] A system for implementing this invention has the following configuration: First, a server is equipped with a generation AI means that learns from all employees' past emails, schedules, communication tools, chats, created documents, etc. This generation AI means uses natural language processing technology to analyze input questions and generate appropriate answers.

[0997] When a new employee enters a question using a smartphone or tablet, the question is sent to the server. The server then uses a generative AI tool to analyze the question and search for information such as documents prepared by previous employees, minutes of meetings, and internal and external communications. This allows the new employee to quickly obtain the information they need.

[0998] The same is true when a new employee in a factory inputs a question to a robot in natural language. The robot sends the question to a server, and a generative AI means analyzes the question and provides an appropriate answer. For example, in response to a question such as "I would like to see the operating manual for this machine," the generative AI will display a digital version of the operating manual.

[0999] The hardware used includes factory robots (e.g., FANUC, KUKA, etc.), smartphones, and tablets, while the software used includes the OpenAI API (generative AI model) and Python (programming language).

[1000] As a concrete example, if a new employee asks, "I'd like to see the operating manual for this machine," the generating AI will provide an answer such as, "The digital version of the operating manual is here," and display a link or file.

[1001] An example of a prompt is as follows:

[1002] New employee asks: I'd like to see the operating manual for this machine.

[1003] Generate the appropriate answer.

[1004] In this way, the new staff member can quickly and efficiently obtain the necessary information, improving the handover of work and work efficiency.

[1005] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1006] Step 1:

[1007] The user enters a question using a smartphone or tablet, and the entered question is saved in text format on the device.

[1008] Step 2:

[1009] The terminal sends the entered question to the server, which receives the question and stores it in a database.

[1010] Step 3:

[1011] The server passes the received question to the generation AI means, which uses natural language processing technology to analyze the question and extract relevant keywords and context.

[1012] Step 4:

[1013] The generation AI method searches all employees' past emails, calendars, communication tools, chats, created documents, etc. based on the extracted keywords and context, and obtains related information as search results.

[1014] Step 5:

[1015] The server organizes the information obtained from the generation AI means and generates an appropriate answer, which is stored in text format on the server.

[1016] Step 6:

[1017] The server sends the generated answer to the terminal, which then displays the received answer to the user.

[1018] Step 7:

[1019] The user can check the displayed answers and obtain the necessary information. For example, if a link or file for an operation manual is displayed, the user can click it to view it.

[1020] In this way, users can quickly and efficiently obtain the information they need.

[1021] Example 3

[1022] Next, a description will be given of Example 3 of Form Example 3. 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."

[1023] Previously, handover work and searching and retrieving work history required time and effort, making it difficult for new staff to quickly obtain the necessary information. Furthermore, when work handover occurred due to organizational changes, transfers, or retirement, there was a lack of an efficient way to obtain information about previous staff. This created a risk of disrupting business continuity.

[1024] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1025] In this invention, the server includes a generation AI means for learning all employees' past emails, schedules, communication methods, messages, and documents created, etc., a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc., a means for reducing the time required for handover work and search and retrieval of work history, a means for a user to input an information search request, a means for a terminal to send the user's request to the server, a means for the server to receive the request and generate a prompt for the generation AI model, a means for the server to input the prompt for the generation AI model, a means for the generation AI model to search and extract relevant information from a database, a means for the server to receive results from the generation AI model and send them to the terminal, and a means for the terminal to display the results to the user. This enables a new employee to quickly obtain the information they need and significantly reduces the time required for handover work and search and retrieval of work history.

[1026] "All employees' past emails, schedules, means of communication, messages, documents created, etc." refers to emails sent and received by all employees in the organization in the past, calendars for managing schedules, means of communication used (e.g., telephone, video conference), messages sent and received (e.g., chat apps), and various documents created (e.g., reports, presentation materials).

[1027] "Generative AI means" refers to artificial intelligence technology that uses machine learning algorithms to learn from large amounts of data and generate and provide appropriate information in response to user requests.

[1028] "New employee" refers to an employee who has been newly assigned to a specific task within the organization.

[1029] "Former employee" refers to an employee who previously handled a particular task.

[1030] A "prompt sentence" is an instruction sentence input to a generative AI model that describes a specific request for searching and extracting specific information.

[1031] "Database" refers to a system for systematically storing and managing various data within an organization.

[1032] A "server" refers to a computer system that receives and processes requests from clients over a network.

[1033] "Terminal" refers to a device such as a computer or smartphone that is directly operated by a user.

[1034] "User" refers to an individual or member of an organization who uses the system to search for information.

[1035] "Information retrieval request" refers to a request made by a user to a system to obtain specific information.

[1036] "Search and extraction means" refers to the methods and techniques that a generative AI model uses to find and retrieve relevant information from a database based on a prompt.

[1037] This invention is a system for reducing the time required for handovers and searching and retrieving work history. The system uses a generative AI means that learns from all employees' past emails, schedules, communication methods, messages, and documents. A specific embodiment of this system is described below.

[1038] Hardware and software used

[1039] Hardware: High-performance server (e.g. Dell PowerEdge R740)

[1040] Software: Generative AI models (e.g., OpenAI GPT-4), database management systems (e.g., MySQL)

[1041] System configuration

[1042] 1. Server: The server plays a central role in using generative AI models to search and extract information based on user requests.

[1043] 2. Terminal: A terminal is a device through which a user enters information search requests and receives results. Examples include computers and smartphones.

[1044] 3. User: A user is an individual or member of an organization who uses the system to search for information.

[1045] System Operation

[1046] User operations

[1047] Through the terminal interface, the user inputs a request to search for specific information, for example, "I want to see the emails my predecessor exchanged with clients."

[1048] Device behavior

[1049] The terminal receives the user's input and sends the request to the server, where it uses an HTTP request to send data to the server.

[1050] Server Operation

[1051] The server analyzes the request received from the device and generates a prompt to input to the generative AI model, for example, "Show me a list of emails exchanged between my predecessor and the client."

[1052] The server inputs the generated prompt sentence into the generative AI model, which generates a response to the prompt sentence based on pre-trained data.

[1053] The generative AI model analyzes the prompt and searches for and extracts relevant information from a database, such as a list of emails exchanged between a predecessor and a client.

[1054] The server sends the search results received from the generative AI model to the device, again using an HTTP response to send the data.

[1055] Device behavior

[1056] The terminal displays the search results received from the server to the user, who can then check a list of emails exchanged between the predecessor and the client.

[1057] Specific examples

[1058] For example, if a new employee requests to "see the emails exchanged between my predecessor and clients," the device sends this request to the server. The server generates a prompt message saying, "Show me a list of emails exchanged between my predecessor and clients," and inputs this into the generative AI model. The generative AI model searches the database for relevant emails and returns the results to the server. The server then sends the results to the device, which then displays the list of emails to the user.

[1059] In this way, the server, terminal, and user work together to efficiently execute the information search process. This system enables new staff to quickly obtain the necessary information, significantly reducing the time required for handover work and for searching and retrieving work history. The flow of the identification process in the third embodiment will be explained using Figure 15.

[1060] Step 1:

[1061] A user enters a request for information retrieval.

[1062] Through the terminal interface, the user inputs a request to search for specific information, for example, "I want to see the emails my predecessor exchanged with clients."

[1063] Input: User search request

[1064] Output: Search request data sent to the terminal

[1065] Step 2:

[1066] The terminal sends the user's request to the server.

[1067] The terminal receives the user's input and sends the request to the server, where it uses an HTTP request to send data to the server.

[1068] Input: User search request data

[1069] Output: Search request data sent to the server

[1070] Step 3:

[1071] The server receives the request and generates a prompt for the generative AI model.

[1072] The server analyzes the request received from the device and generates a prompt to input to the generative AI model, for example, "Show me a list of emails exchanged between my predecessor and the client."

[1073] Input: Search request data received from the terminal

[1074] Output: Prompt sentence to be input to the generative AI model

[1075] Step 4:

[1076] The server inputs a prompt sentence into the generative AI model.

[1077] The server inputs the generated prompt sentence into the generative AI model, which generates a response to the prompt sentence based on pre-trained data.

[1078] Input: A prompt to be input to the generative AI model

[1079] Output: Search results from a generative AI model

[1080] Step 5:

[1081] A generative AI model searches and extracts relevant information from a database.

[1082] The generative AI model analyzes the prompt and searches for and extracts relevant information from a database, such as a list of emails exchanged between a predecessor and a client.

[1083] Input: prompt statement

[1084] Output: Relevant information extracted from the database

[1085] Step 6:

[1086] The server receives the results from the generative AI model and sends them to the device.

[1087] The server sends the search results received from the generative AI model to the device, again using an HTTP response to send the data.

[1088] Input: Search results from a generative AI model

[1089] Output: Search result data sent to the device

[1090] Step 7:

[1091] The terminal displays the results to the user.

[1092] The terminal displays the search results received from the server to the user, who can then check a list of emails exchanged between the predecessor and the client.

[1093] Input: Search result data received from the server

[1094] Output: The search results that are displayed to the user

[1095] In this way, the server, terminal, and user can work together to efficiently execute the information search process.

[1096] (Application example 3)

[1097] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1098] In logistics centers, it is difficult for new staff to efficiently search and extract past work history and handover information, which results in a significant amount of time spent on handover work and searching and searching work history. In addition, when work handover occurs due to organizational changes, transfers, or retirements, new staff cannot quickly obtain information about previous staff, which can disrupt business continuity.

[1099] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a generation AI means that learns the past emails, schedules, communication tools, chats, created documents, etc. of all employees; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc.; a means for reducing the time required for handover work and search and retrieval of work history; a means for a new employee at a logistics center to efficiently search and extract past work history and handover information; and a means for quickly providing past work history based on a request from the new employee using a generation AI model. This enables the new employee to quickly and efficiently obtain past work history and handover information.

[1100] "Generative AI means" is an artificial intelligence technology that learns from all employees' past emails, schedules, communication tools, chats, created documents, etc., and provides the necessary information.

[1101] "New staff" refers to employees who have taken over new duties due to organizational changes, transfers, retirement, etc.

[1102] "Previous staff member" refers to an employee who took over the duties due to organizational changes, transfer, retirement, etc.

[1103] "Handover" is the process by which a new employee receives work details and related information from the previous employee in order to maintain business continuity.

[1104] "Work history" refers to records of work performed by past personnel and related information.

[1105] A "logistics center" refers to a facility that carries out logistics operations such as storing, sorting, and delivering goods.

[1106] A "generative AI model" is an artificial intelligence model used in generative AI means, which learns from past data and provides information that meets the requirements of new personnel.

[1107] A "prompt sentence" is an instruction sentence entered into a generative AI model to obtain specific information.

[1108] As an embodiment of the present invention, a system is provided that enables a new employee at a logistics center to efficiently search and extract past work history and handover information. Specific embodiments of the system are described below.

[1109] System Configuration

[1110] The system consists of the following main components:

[1111] 1. Generative AI methods: Artificial intelligence technology that learns from all employees' past emails, calendars, communication tools, chats, and created documents.

[1112] 2. New Agent Device: A device (e.g., smartphone, tablet, or PC) used by the new agent to input questions into the generation AI.

[1113] 3. Server: A server for hosting generative AI models and processing data.

[1114] Program processing

[1115] The server does the following:

[1116] 1. Data collection and training: Collect all employees' past emails, calendars, communication tools, chats, and written materials to train the generative AI model. This process uses software such as Python and the OpenAI API.

[1117] 2. Accepting questions: When a new employee enters a question into the terminal, the question is sent to the server.

[1118] 3. Response generation using a generative AI model: The server uses a generative AI model to search past work history based on the new employee's question and quickly provide appropriate information.

[1119] 4. Providing the results: The server sends the generated response to the new agent's terminal and displays it.

[1120] Hardware and software used

[1121] Hardware: Servers, smartphones, tablets, PCs

[1122] Software: Python, OpenAI API

[1123] Specific examples

[1124] When a new employee inputs a request such as "I would like to see the transaction history of my predecessor with a specific client," the generating AI quickly searches for the transaction history with that client and provides it as a list.

[1125] Prompt Sentence Examples

[1126] Search the past business history of a distribution center based on the following request: I want to see the transaction history of my predecessor with a specific client.

[1127] In this way, new staff at the logistics center can quickly and efficiently obtain past work history and handover information.

[1128] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1129] Step 1:

[1130] The server collects all employees' past emails, schedules, communication tools, chats, created documents, etc. This data is used as training data for the generative AI model. The input is various employee data, and the output is a dataset for training the generative AI model. Specifically, it extracts the necessary information from the database and performs preprocessing to create a dataset.

[1131] Step 2:

[1132] The server uses the collected data to train a generative AI model. The input is the dataset created in step 1, and the output is a trained generative AI model. Specifically, it uses Python or the OpenAI API to supply the dataset to the generative AI model and optimizes the model's parameters.

[1133] Step 3:

[1134] The user (new agent) inputs a question to the generation AI from a terminal. The input is the user's question, and the output is a prompt that is sent to the server. Specifically, the user inputs a question through the terminal interface, and the question is formatted as a prompt.

[1135] Step 4:

[1136] The server receives the prompt sent by the user and queries the generative AI model. The input is the user's prompt, and the output is the response from the generative AI model. Specifically, the prompt is input into the generative AI model, and the model searches past work history to generate appropriate information.

[1137] Step 5:

[1138] The server receives the response from the generative AI model and sends it to the user's device. The input is the response from the generative AI model, and the output is the information displayed on the user's device. Specifically, the server formats the response from the generative AI model and sends it to the user's device.

[1139] Step 6:

[1140] The user checks the information displayed on the terminal and obtains the necessary work history and handover information. The input is the information sent from the server, and the output is the work history and handover information obtained by the user. Specifically, the user checks the terminal screen and obtains the necessary information.

[1141] 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.

[1142] "Example 1"

[1143] One embodiment of the present invention combines a system that includes a generation AI that learns from all employees' past emails, schedules, communication tools, chats, and created documents; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, and the like; and a means for reducing the time required for handover work and searching and retrieving work history. This system also includes an emotion engine that recognizes the user's emotions. In this system, the generation AI recognizes the user's emotions and provides information based on their emotional state. Specifically, the system infers the user's emotional state from the text of past emails and chats, and reflects the inference results in the generation AI's learning.

[1144] "Example 2"

[1145] In another embodiment, the emotion engine recognizes the emotional state of the new agent in real time and adjusts the way the generation AI provides information depending on that emotional state. In this embodiment, the generation AI adjusts the way it provides information depending on the new agent's emotional state, such as whether the new agent is feeling stressed or happy. For example, if the new agent is feeling stressed, the generation AI takes that state into account and adjusts the information it provides to be more concise and easy to understand.

[1146] "Example 3"

[1147] In another embodiment, the emotion engine infers the user's emotional state from information such as past emails, schedules, communication tools, chats, and created documents, and reflects the inference results in the learning of the generation AI. In this embodiment, the user's emotional state is inferred from past information and the inference results are reflected in the learning of the generation AI, allowing the new agent to obtain information about past agents and understand the emotional states behind that information. Specifically, the user's emotional state, such as joy, anger, sadness, or surprise, is inferred from the text of past emails and chats, and the inference results are reflected in the learning of the generation AI.

[1148] The processing flow of each embodiment will be described below.

[1149] "Example 1"

[1150] Step 1: The generative AI learns all employees' past emails, schedules, communication tools, chats, created documents, etc.

[1151] Step 2: The new agent asks the generation AI a question.

[1152] Step 3: The generation AI provides documents created by past staff members, minutes of meetings, internal and external communications, etc.

[1153] Step 4: The emotion engine recognizes the user's emotions, and the generation AI provides information based on that emotional state.

[1154] "Example 2"

[1155] Step 1: The emotion engine recognizes the new contact's emotional state in real time.

[1156] Step 2: Adjust how the generative AI presents information depending on that emotional state.

[1157] "Example 3"

[1158] Step 1: The emotion engine estimates the user's emotional state from information such as past emails, schedules, communication tools, chats, and created documents.

[1159] Step 2: The estimation results are reflected in the learning of the generative AI.

[1160] Step 3: The new agent obtains information about the previous agent and also understands the emotional state behind that information.

[1161] Example 1

[1162] Next, a description will be given of Example 1 of Form 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."

[1163] In conventional work handover systems, it is difficult for new staff to quickly and efficiently obtain information about previous staff, and there are problems with the handover process and searching and retrieving work history, which requires a lot of time and effort.In addition, information provided does not take into account the user's emotional state, making it impossible to reduce user stress and dissatisfaction.

[1164] 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.

[1165] In this invention, the server includes a generation AI means for learning all employees' past emails, schedules, communication tools, conversation records, and created documents; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, meeting records, and internal and external communications; a means for reducing the time required for handover work and search and retrieval of work history; a means for incorporating an emotion engine that recognizes user emotions; and a means for reflecting the emotion engine's inference results in the generation AI's learning data. This allows new employees to quickly and efficiently obtain the information they need, significantly reducing the time required for handover work and search and retrieval of work history. Furthermore, information is provided according to the user's emotional state, reducing user stress and frustration.

[1166] "Generative AI" is an artificial intelligence that studies all employees' past emails, schedules, communication tools, conversation records, created documents, etc., and analyzes and learns using natural language processing technology.

[1167] An "emotion engine" is an analytical model for recognizing a user's emotions and estimating their emotional state.

[1168] "Email" means a digital message sent or received over the Internet.

[1169] A "datebook" is a tool for managing the schedules of individuals and organizations.

[1170] "Communication tools" are software and platforms that enable people in remote locations to communicate, such as email, chat, and video conferencing.

[1171] A "dialogue transcript" is a log of a conversation that takes place through a chat or messaging application.

[1172] "Created materials" refers to digital content such as documents, reports, and presentation materials created in connection with business.

[1173] "New staff" refers to employees who have taken over new duties due to organizational changes, transfers, retirement, etc.

[1174] A "former employee" is an employee who was previously in charge of a particular task.

[1175] A "meeting record" is a document that records the contents of a meeting and the main points of discussion.

[1176] "Internal and external interactions" refers to all communications between parties both within and outside the organization.

[1177] "Handover work" refers to the procedures and information sharing required to transfer work to a new person in charge.

[1178] "Business history" refers to records and history information of business operations that have been carried out in the past.

[1179] This invention is a system that uses a generative AI that learns from all employees' past emails, schedules, communication tools, conversation records, created materials, etc. A specific embodiment of this system will be described below.

[1180] 1. Data Collection and Preprocessing

[1181] The server collects data such as all employees' past emails, schedules, communication tools, conversation records, and created materials. Specifically, emails are obtained from a general mail server (e.g., IMAP server), schedules are obtained from a calendar service (e.g., Google Calendar API), and communication tools and conversation records are obtained from messaging platforms (e.g., Slack API, Microsoft Teams API).

[1182] The collected data is pre-processed by the server, which includes cleaning the text data, tokenizing it, removing stop words, stemming, etc. For example, unnecessary HTML tags are removed from the body of an email, and words are split into parts to make it easier to analyze.

[1183] 2. Data analysis and generative AI training

[1184] The server analyzes the preprocessed data, using natural language processing techniques to extract and correlate various pieces of information. Specifically, it extracts important keywords and topics from email content and analyzes calendar details to find related events.

[1185] The server then uses the parsed data to train a generative AI model, typically a natural language processing model like the Transformer model or BERT, to learn information about a specific project, for example, by combining historical email and calendar data.

[1186] 3. Incorporating an Emotional Engine

[1187] The server incorporates an emotion engine into the generative AI that recognizes the user's emotions. Specifically, it uses an emotion analysis model to estimate the user's emotional state from the text of past emails and conversation records. For example, if the user is feeling stressed, that emotional state can be detected.

[1188] The emotion engine's inference results are reflected in the generation AI's training data. This enables the generation AI to provide information tailored to the user's emotional state. For example, a user in a stressed state will be given priority for simple, easy-to-understand information.

[1189] 4. Information provision

[1190] The user uses a terminal to input a question to the generation AI. Specifically, they input a prompt sentence. For example, they might input, "I'd like to see the progress report for project X last year."

[1191] The server uses generative AI to search for and provide appropriate information in response to the user's question. It selects the most appropriate information based on the learned data and the inferences of the emotion engine. For example, it searches for progress reports related to Project X and provides them to the user.

[1192] Examples of specific examples and prompts

[1193] Specific examples

[1194] A new employee asks the generating AI, "I'd like to see the progress report for project X from last year."

[1195] The generative AI searches past emails, conversation records, and created documents to provide progress reports related to Project X.

[1196] If the emotion engine detects that a new employee is stressed, the generative AI will prioritize providing concise, easy-to-understand information.

[1197] Prompt Sentence Examples

[1198] "I'd like to see last year's progress report on Project X."

[1199] "Please tell me about the interactions your previous representatives had with Client Y."

[1200] "I'd like to check the minutes of a recent meeting."

[1201] This system allows users to quickly and efficiently obtain the information they need, significantly reducing the time required for handovers and searching through work history. It also provides information tailored to the user's emotional state, reducing stress and frustration.

[1202] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1203] Step 1:

[1204] Data collection

[1205] The server collects data such as all employees' past emails, schedules, communication tools, conversation records, and created materials. Specifically, emails are obtained from a general mail server (e.g., IMAP server), schedules are obtained from a calendar service (e.g., Google Calendar API), and communication tools and conversation records are obtained from messaging platforms (e.g., Slack API, Microsoft Teams API).

[1206] Input: Email, calendar, communication tools, conversation records, created materials

[1207] Output: Raw data collected

[1208] Step 2:

[1209] Data Preprocessing

[1210] The server preprocesses the collected data, which includes cleaning the text data, tokenizing it, removing stop words, stemming, etc. For example, it removes unnecessary HTML tags from the body of an email and splits words into a format that is easier to analyze.

[1211] Input: Raw data collected

[1212] Output: Preprocessed data

[1213] Step 3:

[1214] Data analysis

[1215] The server analyzes the preprocessed data, using natural language processing techniques to extract and correlate various pieces of information. Specifically, it extracts important keywords and topics from email content and analyzes calendar details to find related events.

[1216] Input: Preprocessed data

[1217] Output: Parsed data (keywords, topics, event associations)

[1218] Step 4:

[1219] Generative AI training

[1220] The server uses the analyzed data to train a generative AI model, typically a natural language processing model like the Transformer model or BERT, to learn information about a specific project, for example, by combining historical email and calendar data.

[1221] Input: Parsed data

[1222] Output: Trained generative AI model

[1223] Step 5:

[1224] emotion recognition

[1225] The server incorporates an emotion engine into the generative AI that recognizes the user's emotions. Specifically, it uses an emotion analysis model to estimate the user's emotional state from the text of past emails and conversation records. For example, if the user is feeling stressed, that emotional state can be detected.

[1226] Input: Past emails, conversation records

[1227] Output: Estimated emotional state

[1228] Step 6:

[1229] Reflecting emotional data

[1230] The server reflects the emotion engine's estimation results in the learning data of the generation AI, enabling the generation AI to provide information according to the user's emotional state.

[1231] Input: Estimated emotional state, trained generative AI model

[1232] Output: Generative AI model that reflects emotional state

[1233] Step 7:

[1234] User question input

[1235] The user uses a terminal to input a question to the generation AI. Specifically, they input a prompt sentence. For example, they might input, "I'd like to see the progress report for project X last year."

[1236] Input: prompt statement

[1237] Output: User question

[1238] Step 8:

[1239] Information search and provision

[1240] The server uses generative AI to search for and provide appropriate information in response to the user's question. It selects the most appropriate information based on the learned data and the inferences of the emotion engine. For example, it searches for progress reports related to Project X and provides them to the user.

[1241] Input: User questions, generative AI model reflecting emotional state

[1242] Output: Search results (progress reports and other relevant information)

[1243] In this way, users can quickly and efficiently obtain the information they need, significantly reducing the time required for handovers and searching through work history. Furthermore, information is provided according to the user's emotional state, reducing stress and frustration for the user.

[1244] (Application example 1)

[1245] Next, a description will be given of Application Example 1 of Form 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."

[1246] In conventional factory work handover systems, it was difficult for new workers to efficiently retrieve past work history and communication logs, and it took a great deal of time and effort to search for handover work and work history. In addition, information provided did not take into account the emotional state of the workers, which led to the problem of stress and fatigue accumulating.

[1247] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes: a generation AI means for learning all employees' past emails, schedules, communication tools, chats, and created documents; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes, internal and external communications, and the like; a means for reducing the time required for handover work and work history search and retrieval; an emotion engine means for recognizing the worker's emotional state and providing information based on that emotional state; and a means for learning the past work history, schedules, communication logs, created documents, and the like of factory workers to support the new worker in efficiently taking over work. This allows the new worker to efficiently acquire past work history and communication logs, significantly reducing the time and effort required for handover work and work history search. Furthermore, providing information that takes the worker's emotional state into consideration reduces stress and fatigue.

[1248] "Generative AI" is an artificial intelligence that studies all employees' past emails, schedules, communication tools, chats, created documents, etc., and analyzes and learns using natural language processing technology.

[1249] "New personnel" refers to workers or employees who have taken over new duties due to organizational changes, transfers, retirement, etc.

[1250] An "emotion engine" is a technology that recognizes the user's emotional state and provides information according to that emotional state.

[1251] "Work history" is a detailed record of work that has been done in the past, and includes information such as the work content, date and time, and person in charge.

[1252] A "communication log" is a record of past interactions that have taken place through email, chat, communication tools, etc.

[1253] "Handover work" refers to the process of communicating the information and procedures necessary to hand over work to a new person in charge.

[1254] "Work history" is a record of work that has been done in the past, and includes information such as the work content, date and time, and person in charge.

[1255] "Information provision" is the act of using generative AI and emotion engines to provide users with the information they need appropriately.

[1256] A system for implementing the present invention has the following configuration.

[1257] System configuration

[1258] 1. Data collection methods:

[1259] The server collects past emails, schedules, communication tools, chats, and documents created by factory workers. The hardware used is a server and database, and the software used is Python, SQL, and natural language processing libraries (e.g., NLTK, spaCy).

[1260] 2. Data analysis and learning methods:

[1261] The server analyzes the collected data using natural language processing technology and trains the generative AI model. The hardware used is a GPU-equipped server, and the software used is TensorFlow and PyTorch.

[1262] 3. Emotion recognition means:

[1263] The server uses an emotion engine to recognize the emotional state of the worker. The hardware used is a camera and a microphone, and the software used is OpenCV and a speech recognition library (e.g., Google Speech-to-Text).

[1264] 4. Information provision method:

[1265] When a new worker inputs a question on the server, the generative AI provides appropriate information based on past data. The way the information is presented is adjusted depending on the worker's emotional state. The hardware used is a tablet and a display, and the software used is Flask (a web application framework).

[1266] Specific examples

[1267] For example, if a new worker asks, "What tasks did you perform during yesterday's shift?", the server uses the generative AI model to search past work history and provide detailed tasks. If the worker is feeling stressed, the server will provide advice such as, "I recommend you take a break to relax."

[1268] Prompt Sentence Examples

[1269] Please tell me what work you did during yesterday's shift.

[1270] In this way, the server allows new workers to take over tasks efficiently and quickly obtain work history and communication logs.In addition, by using an emotion engine, it is possible to provide information according to the worker's emotional state, thereby reducing stress and fatigue.

[1271] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1272] Step 1:

[1273] The server collects past emails, schedules, communication tools, chats, and created documents of factory workers. The input is data from various data sources (email server, schedule management system, chat logs, etc.), and the output is the storage of the collected data in a database. Specifically, it uses Python scripts to retrieve data from each data source and uses SQL to store it in the database.

[1274] Step 2:

[1275] The server analyzes the collected data using natural language processing technology and trains the generative AI model. The input is past emails, schedules, communication logs, created documents, etc. stored in the database, and the output is the trained generative AI model. Specifically, the data is analyzed using Python and natural language processing libraries (NLTK, spaCy), and the generative AI model is trained using TensorFlow or PyTorch.

[1276] Step 3:

[1277] The server uses an emotion engine to recognize the emotional state of the worker. The input is the worker's video and audio data acquired from the camera and microphone, and the output is the recognized emotional state. Specifically, it analyzes the video data using OpenCV, converts the audio data to text using Google Speech-to-Text, and applies the emotion recognition algorithm.

[1278] Step 4:

[1279] When a new worker inputs a question on the server, the generative AI provides appropriate information based on past data. The input is the question (prompt) from the new worker, and the output is the answer generated by the generative AI model. In concrete terms, a web application is built using Flask, and the question input by the user is passed to the generative AI model, which then generates and displays the answer.

[1280] Step 5:

[1281] The server adjusts the way it provides information depending on the emotional state. The input is the recognized emotional state and the answer generated by the generative AI model, and the output is the adjusted way of providing information. Specifically, if the emotional state indicates stress, it will perform processing such as adding advice to relax.

[1282] Step 6:

[1283] The terminal displays the information provided by the server to the new worker. The input is the information sent from the server, and the output is the information displayed on the terminal's display. Specific operations include building an interface to display information on a tablet or display.

[1284] Example 2

[1285] Next, a description will be given of Example 2 of Form 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."

[1286] In conventional work handover systems, it was difficult for new staff to quickly and accurately obtain information about previous staff members, and handover work and searching and retrieving work history required a great deal of time and effort. Furthermore, information provided was not tailored to the emotional state of the new staff member, which could cause stress and confusion.

[1287] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a generation AI means for learning the past emails, schedules, communication tools, dialogue records, prepared documents, etc. of all employees; a means for the new staff member to ask the generation AI a question to obtain prepared documents, meeting records, internal and external communications, etc., of previous staff members; a means for the new staff member to input a question in natural language and for the generation AI to analyze the question and generate an appropriate answer; a means for recognizing the emotional state of the new staff member in real time and adjusting the way the generation AI provides information depending on the emotional state; and a means for reducing the time required for handover work and searching and retrieving work history. This enables the new staff member to quickly and accurately obtain information about previous staff members and provide appropriate information according to their emotional state.

[1288] "All employees' past emails, schedules, communication tools, conversation records, documents created, etc." refers to emails sent and received by all employees in the organization in the past, schedules used for schedule management, tools used for business contact and communication, chat and message records, and various documents created in connection with work.

[1289] "Generative AI means" refers to a system that uses artificial intelligence technology to learn the past data of all employees and generate appropriate answers to questions in natural language.

[1290] "New employee" refers to an employee who has taken over a new role within an organization.

[1291] "Former employees" refer to employees who previously held specific roles within an organization.

[1292] "Entering a question in natural language" means that the user enters a question in a language that they use on a daily basis (e.g., Japanese, English).

[1293] "Analyzing questions and generating appropriate answers" refers to the process of analyzing input natural language questions and generating optimal answers based on their content.

[1294] "Real-time emotional state recognition" refers to detecting the user's current emotions (e.g., stress, joy) in real time and adjusting the system's behavior based on that information.

[1295] "Adjusting the way information is presented" refers to changing the content and format of the information provided depending on the user's emotional state.

[1296] "Reducing the time required for handover work and searching and retrieving work history" refers to shortening the time required for handover work and searching and retrieving work history by enabling new staff to quickly and efficiently obtain information about previous staff.

[1297] This invention is a system for enabling a new employee to quickly and efficiently obtain information about previous employees. This system includes a generation AI means for learning the past emails, schedules, communication tools, conversation records, and prepared documents of all employees, a means for the new employee to ask the generation AI questions to obtain prepared documents, meeting records, internal and external communications, etc. of previous employees, a means for the new employee to input questions in natural language and for the generation AI to analyze the questions and generate appropriate answers, a means for recognizing the emotional state of the new employee in real time and adjusting the way the generation AI provides information depending on that emotional state, and a means for reducing the time required for handover work and searching and retrieving work history.

[1298] Hardware and software used

[1299] Hardware:

[1300] Server equipped with high-performance CPU and GPU

[1301] software:

[1302] Natural language processing libraries (e.g., spaCy, NLTK)

[1303] Generative AI models (e.g., GPT-4)

[1304] Program processing

[1305] The server receives natural language questions entered by users on their devices. It then analyzes the questions using a natural language processing library to extract keywords and context. It then inputs the analysis results as prompts to the generative AI model to generate appropriate answers.

[1306] Specific examples

[1307] For example, if a new employee asks, "I'd like to see the materials created by my predecessor," the server analyzes this question and extracts the keyword "materials created by my predecessor." Next, it inputs a prompt to the generative AI model saying, "Please provide a list of materials created by my predecessor," and the generative AI model generates a list of materials created by the predecessor.

[1308] Example prompt sentence:

[1309] "Please provide a list of materials prepared by your predecessor."

[1310] In another embodiment, when an emotion engine is used, the server recognizes the emotional state of the new agent in real time. The emotion engine analyzes the emotional state of the new agent using facial recognition technology and voice analysis technology. For example, if the new agent is feeling stressed, the server inputs a prompt to the generative AI model such as, "Please provide concise and easy-to-understand information to the new agent who is feeling stressed," and the generative AI model provides information appropriate to the state.

[1311] Example prompt (when using the emotion engine):

[1312] "Provide concise, easy-to-understand information for stressed new reps."

[1313] In this way, the server can provide appropriate information depending on the new agent's questions and emotional state.

[1314] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1315] Step 1:

[1316] The user inputs a question in natural language using the input interface of the terminal. For example, the user might input, "I would like to see the materials created by my predecessor." The input question is temporarily stored in the terminal's memory.

[1317] Step 2:

[1318] The terminal sends the question entered by the user to the server. At this time, the question is converted into JSON format and sent to the server via an HTTP request. The input is the user's question, and the output is the JSON data sent to the server.

[1319] Step 3:

[1320] The server parses the received JSON data and analyzes the question using a natural language processing library (e.g., spaCy, NLTK). Specifically, it analyzes the grammatical structure of the question and extracts keywords and context. The input is JSON data, and the output is the parsed keywords and context.

[1321] Step 4:

[1322] The server generates a prompt for the generative AI model (e.g., GPT-4) based on the analysis results. For example, it generates a prompt such as, "Please provide a list of materials created by your predecessor." The input is the analyzed keywords and context, and the output is the generated prompt.

[1323] Step 5:

[1324] The server inputs the generated prompt into the generative AI model and generates an appropriate answer. The generative AI model generates a list of materials created by the predecessor based on the prompt and returns it to the server. The input is the prompt, and the output is the generated answer.

[1325] Step 6:

[1326] The server converts the answer received from the generative AI model into JSON format and sends it to the terminal via an HTTP response. The input is the generated answer, and the output is the JSON data sent to the terminal.

[1327] Step 7:

[1328] The terminal analyzes the received JSON data and displays a list of documents created by the predecessor to the user. For example, links such as "Document 1.pdf" and "Document 2.docx" are displayed on the screen. The input is the JSON data received from the server, and the output is the information displayed to the user.

[1329] In this way, the user can easily check the materials created by his / her predecessor.

[1330] (Application example 2)

[1331] Next, a description will be given of Application Example 2 of Form 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."

[1332] When a new employee takes over a task, it is difficult to quickly and accurately obtain documents, minutes, and internal and external communications prepared by the previous employee. Furthermore, if the new employee is under stress, it can become even more difficult to obtain information. This results in a significant amount of time spent on handover work and searching and retrieving work history, which can lead to a decline in productivity.

[1333] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1334] In this invention, the server includes a generation AI means for learning all employees' past emails, schedules, communication tools, chats, created documents, etc., a means for the new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc., a means for reducing the time required for handover work and searching and retrieving work history, an emotion engine means for recognizing the emotional state of the new employee in real time, and a means for the generation AI to adjust the method of providing information according to the emotional state. This not only enables the new employee to quickly and accurately obtain the information they need, but also makes it possible to provide appropriate information according to their emotional state.

[1335] "Generative AI means" is an artificial intelligence technology that learns from all employees' past emails, schedules, communication tools, chats, created documents, etc., and generates appropriate answers when a new employee inputs a question in natural language.

[1336] "New staff member" refers to an employee who has taken over new duties due to organizational changes, transfer, retirement, etc.

[1337] The "emotion engine means" is a technology that recognizes the emotional state of new staff in real time, and the generation AI adjusts the way information is provided based on that emotional state.

[1338] The "means for adjusting the method of providing information" refers to a technology that appropriately changes the format and content of the information provided by the generating AI depending on the emotional state of the new representative.

[1339] "Means to reduce the time required for handover work and searching and retrieving work history" refers to technology that reduces the time required for handover work and searching and retrieving work history by using generative AI to quickly and accurately obtain documents prepared by previous staff members, minutes of meetings, internal and external communications, etc.

[1340] The system for implementing the present invention includes a generating AI means, an emotion engine means, and a means for adjusting an information provision method. Specific embodiments are described below.

[1341] System Configuration

[1342] The server uses OpenAI's API as its generative AI tool, and the Emotion Recognition library as its emotion engine tool. These software packages are installed on factory robots, allowing them to provide appropriate answers to new employees' questions.

[1343] Hardware

[1344] Factory robots are equipped with the following hardware:

[1345] Microphone: To capture the new agent's voice input.

[1346] Camera: To recognize the facial expressions of new recruits in real time.

[1347] software

[1348] The server uses the following software:

[1349] OpenAI API: As a generative AI tool, it generates appropriate answers to questions entered in natural language.

[1350] Emotion Recognition Library: An emotion engine means to recognize the emotional state of new contacts in real time.

[1351] Data processing and calculation

[1352] 1. Acquisition of voice input: The new agent inputs a question into the robot. The voice data is acquired through the microphone and converted into text data.

[1353] 2. Question analysis: Generative AI means analyzes the text data and generates appropriate answers.

[1354] 3. Recognizing emotional state: The facial expression data of the new agent is acquired through the camera, and the emotion engine means analyzes the data to recognize the emotional state.

[1355] 4. Adjustment of information provision method: Adjust the format and content of the information provided by the generative AI means depending on the emotional state.

[1356] Specific examples

[1357] If a new employee asks, "Please tell me how to operate this machine," the following process will be followed:

[1358] 1. Voice input is acquired through the microphone and converted into text data.

[1359] 2. The generative AI means analyzes the question "Please tell me how to operate this machine" and generates an appropriate answer.

[1360] 3. Facial expression data of the new staff member is acquired through the camera, and the emotion engine means analyzes the data and recognizes that the new staff member is feeling stressed.

[1361] 4. Tailor the information provided by the generative AI means to a concise and easy-to-understand format depending on the emotional state.

[1362] Prompt Sentence Examples

[1363] If a new agent asks, "Please tell me how to operate this machine," and the emotion engine recognizes that the agent is stressed, the generative AI will adjust the response as follows:

[1364] Example prompt sentence:

[1365] New employee: Please tell me how to operate this machine.

[1366] Emotion engine: Recognizing stress states.

[1367] Generative AI: Short answer: To operate this machine, first turn it on, then press the start button.

[1368] In this way, adjusting the way information is presented depending on the emotional state of the new employee can help them acquire information more efficiently.

[1369] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1370] Step 1:

[1371] The user inputs a question to the robot. The input is in the form of voice, and the voice data is picked up through the robot's microphone. The voice data is converted into text data by speech recognition software. The input is voice data, and the output is text data.

[1372] Step 2:

[1373] The server sends text data to the generation AI means. The generation AI means analyzes the text data and generates an appropriate answer. The input is text data, and the output is the generated answer text.

[1374] Step 3:

[1375] The terminal (robot) acquires facial expression data of the user through a camera. The facial expression data is acquired in the form of an image and sent to the emotion engine means. The input is image data, and the output is emotional state data.

[1376] Step 4:

[1377] The server receives the emotional state data analyzed by the emotion engine means. The emotion engine means analyzes the image data and recognizes the user's emotional state. The input is the image data and the output is the emotional state data.

[1378] Step 5:

[1379] The server adjusts the format and content of the information provided by the generating AI means based on the emotional state data. For example, if the user is feeling stressed, the server converts the generated answer text into a concise and easy-to-understand format. The input is the emotional state data and the generated answer text, and the output is the adjusted answer text.

[1380] Step 6:

[1381] The terminal (robot) provides the user with the adjusted answer text. The user receives the answer through voice output or a display. The input is the adjusted answer text, and the output is information provided to the user.

[1382] Example 3

[1383] Next, a description will be given of Example 3 of Form Example 3. 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."

[1384] Traditional handover procedures and searching and searching work history were time-consuming, making it difficult for new staff to quickly obtain information about previous staff. Furthermore, it was not possible to understand the emotional state of previous staff from past work history or communication history, making it difficult to grasp the emotions behind the information. This hindered work efficiency and smooth handover.

[1385] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1386] In this invention, the server includes a generation AI means for learning the past emails, schedules, communication tools, conversation records, created documents, etc. of all employees, a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc., a means for reducing the time required for handover work and searching and retrieving work history, a means for inferring emotional states from the text of past emails and conversation records and reflecting the inference results in the learning of the generation AI, and a means for a new employee to understand the emotional states behind the information of previous employees. This allows the new employee to quickly obtain information about previous employees and also understand the emotional states behind that information.

[1387] "Generative AI" is an artificial intelligence technology that learns from past data and provides appropriate information in response to user questions.

[1388] "Email" means a digital message sent or received over the Internet.

[1389] A "datebook" is a digital tool for managing a user's schedule and appointments.

[1390] A "communication tool" is software or an application that allows users to exchange messages and information with each other.

[1391] "Dialogue records" are data that store the history of chats and messages between users.

[1392] "Created materials" are digital files such as documents and presentations created by users for business purposes.

[1393] "Emotional state" refers to a user's psychological state, such as joy, anger, sadness, or surprise.

[1394] An "emotion engine" is software for estimating a user's emotional state from text data.

[1395] A "new person in charge" is someone who has taken over new duties due to organizational changes, transfers, retirement, etc.

[1396] The "previous person in charge" is the person who was in charge of the work before the work was handed over to the new person in charge.

[1397] This invention is a system for reducing handover and work history search and retrieval time. The system uses a generative AI model to learn from past data and provides the ability to efficiently search and extract the information needed by new staff.

[1398] The server collects all employees' past emails, schedules, communication tools, conversation records, created documents, etc. and stores this data in a database. Specifically, the following hardware and software are used:

[1399] Data collection:

[1400] The server retrieves emails using the IMAP or POP3 protocol. For example, it retrieves emails using the Gmail API, retrieves calendar data using the Google Calendar API, retrieves conversation records using the Slack API or Microsoft Teams API, and retrieves documents using the Google Drive API or OneDrive API.

[1401] Data preprocessing:

[1402] The server preprocesses the collected data, specifically cleaning, tokenizing, and normalizing the text data using natural language processing libraries (e.g., NLTK, spaCy).

[1403] Training generative AI models:

[1404] The server uses the preprocessed data to train a generative AI model (e.g., GPT-3, BERT), which then understands the user's past work and communication history.

[1405] Emotion estimation:

[1406] The server uses an emotion engine to estimate the user's emotional state from the text of past emails and conversation records. Specifically, it performs emotion analysis using natural language processing libraries (e.g., TextBlob, VADER), and reflects the results in the training of the generative AI model.

[1407] When a new employee searches for specific information, the terminal uses the generative AI model to quickly search for and extract the information. Specifically, the user inputs a prompt into the terminal, and the terminal sends the prompt to the generative AI model. The generative AI model searches for the relevant information and returns the results to the terminal. The terminal then displays the search results to the user.

[1408] For example, if a new employee requests to see the emails exchanged between the previous employee and the client, the following prompt might be used:

[1409] Example prompt sentence:

[1410] "Please show me a list of emails my predecessor exchanged with clients."

[1411] In response to this prompt, the generative AI model quickly searches and extracts a list of relevant emails and provides them to the new person in charge. As a specific example of emotion estimation, when estimating a user's emotional state from the text of past emails and conversation records, the following prompt is used:

[1412] Example prompt sentence:

[1413] "Please infer the user's emotional state from the text of this email."

[1414] In response to this prompt, the emotion engine estimates the user's emotional state and reflects the results in the learning of the generative AI model. This allows the new agent to understand not only the information of previous agents but also the emotional state behind that information. The flow of the identification process in Example 3 is explained using Figure 21.

[1415] Step 1:

[1416] Data collection

[1417] The server collects data such as users' emails, calendars, communication tools, conversation records, and created materials. Specifically, it retrieves emails using the IMAP and POP3 protocols, retrieves calendar data using the Google Calendar API, retrieves conversation records using the Slack API and Microsoft Teams API, and retrieves created materials using the Google Drive API and OneDrive API.

[1418] Input: User account information

[1419] Output: Collected data (emails, schedules, conversation logs, and documents)

[1420] Step 2:

[1421] Data Preprocessing

[1422] The server preprocesses the collected data. Specifically, it uses natural language processing libraries (e.g., NLTK, spaCy) to clean, tokenize, and normalize the text data. For example, it removes unnecessary HTML tags and special characters to make the text data clean.

[1423] Input: Collected data

[1424] Output: Preprocessed data

[1425] Step 3:

[1426] Training generative AI models

[1427] The server uses the preprocessed data to train a generative AI model (e.g., GPT-3, BERT). Specifically, the preprocessed data is divided into training data and test data, and the generative AI model is trained using the training data. During this process, the model understands the user's past work history and communication history.

[1428] Input: Preprocessed data

[1429] Output: A trained generative AI model

[1430] Step 4:

[1431] Emotion estimation

[1432] The server uses an emotion engine to estimate the user's emotional state from the text of past emails and conversation records. Specifically, it performs emotion analysis using natural language processing libraries (e.g., TextBlob, VADER), and reflects the results in the training of the generative AI model.

[1433] Input: Preprocessed text data

[1434] Output: Estimated emotional state

[1435] Step 5:

[1436] Information Search and Extraction

[1437] When a new employee searches for specific information, the terminal uses the generative AI model to quickly search for and extract the information. Specifically, the user inputs a prompt into the terminal, and the terminal sends the prompt to the generative AI model. The generative AI model searches for the relevant information and returns the results to the terminal. The terminal then displays the search results to the user.

[1438] Input: Prompt text (e.g., "Please display a list of emails exchanged between my predecessor and clients.")

[1439] Output: Search results (list of matching emails)

[1440] Step 6:

[1441] Displaying the results

[1442] The device displays the search results returned by the generative AI model to the user, including a list of relevant emails and estimated emotional states.

[1443] Input: Search results, estimated emotional state

[1444] Output: Information displayed to the user (list of emails, estimated emotional state)

[1445] (Application example 3)

[1446] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1447] Traditional handover procedures and searching and searching work history were time-consuming, making it difficult for new staff to quickly obtain information about previous staff. It was also difficult to understand the emotional state of previous staff, making it difficult to grasp the background and intentions of the work. This resulted in reduced work efficiency and a decline in the quality of the handover.

[1448] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1449] In this invention, the server includes a generation AI means for learning the past emails, schedules, communication tools, chats, created documents, etc. of all employees, a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc., a means for reducing the time required for handover work and searching and retrieving work history, a means for quickly searching and extracting past work history and communication history, and a means for an emotion engine to estimate the emotional state of the worker from information such as past emails, schedules, chats, etc. and reflect the results in the learning of the generation AI. This enables a new employee to quickly obtain information about previous employees and also understand the emotional states behind that information.

[1450] "Generative AI" is an artificial intelligence technology that learns from past data and quickly searches for and extracts the information needed by new staff.

[1451] A "new person in charge" is someone who has taken over new duties due to organizational changes, transfers, retirement, etc.

[1452] A "former person in charge" is a person who was previously in charge of a particular task.

[1453] The "emotion engine" is a technology that estimates a worker's emotional state from information such as past emails, schedules, and chats.

[1454] "Work history" is a record of past tasks and work.

[1455] "Communication history" is a record of past emails, chats, and other interactions.

[1456] "Search and retrieval" is the act of finding and retrieving specific information from a database or record.

[1457] "Handover work" refers to a series of procedures and tasks required to hand over work to a new employee.

[1458] "Business history" is a detailed record of past business operations.

[1459] A system for carrying out this invention is configured as follows: The server includes a generation AI means for learning the past emails, schedules, communication tools, chats, created documents, etc. of all employees, a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc., a means for reducing the time required for handover work and searching and retrieving work history, a means for quickly searching and extracting past work history and communication history, and a means for an emotion engine to estimate the emotional state of the worker from information such as past emails, schedules, chats, etc., and reflect the results in the learning of the generation AI.

[1460] Program processing explanation

[1461] The server first collects all employees' past emails, schedules, communication tools, chats, created documents, etc., and trains the generative AI model. This generative AI model is built using OpenAI's API. When a new employee needs specific information, they input a prompt to the generative AI. For example, they input a prompt such as, "Please tell me the specific work steps performed by my predecessor." Based on this prompt, the generative AI quickly searches and extracts past work history and communication history, and provides the required information.

[1462] Furthermore, the emotion engine estimates the emotional state of the worker from information such as past emails, schedules, and chats. This emotion engine is built using, for example, the sentiment_analysis library. The emotional state estimated by the emotion engine is reflected in the learning of the generative AI. This allows new workers to understand the emotional states of previous workers when they were working.

[1463] Specific examples

[1464] For example, if a new employee wants to know the specific work steps performed by their predecessor, they can enter the following prompt into the generation AI:

[1465] Prompt statement:

[1466] What specific work steps did your predecessor take?

[1467] Based on this prompt, the generation AI quickly searches and extracts relevant information from past work history and provides it to the new worker. The emotion engine also estimates the emotional state associated with the work procedure and provides this to the new worker. This allows the new worker to efficiently take over the work while understanding the emotional state of the previous worker.

[1468] The hardware used includes servers and PCs, and the software used includes the OpenAI API and sentiment_analysis library, which effectively utilizes generative AI models and emotion engines to significantly reduce the time required for handovers and searching and retrieving work history.

[1469] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1470] Step 1:

[1471] The server collects all employees' past emails, schedules, communication tools, chats, created documents, etc. This data is input into the generative AI model for learning. The input data includes email content, schedule details, chat logs, created documents, etc., and the output is a trained generative AI model. Specifically, it extracts the necessary information from the database and feeds it into the generative AI model.

[1472] Step 2:

[1473] The user (new employee) inputs a prompt that requires specific information from the generation AI. For example, the user might input a prompt such as, "Please tell me the specific work steps that your predecessor performed." The input is the prompt, and the output is the answer returned by the generation AI. Specifically, the user inputs the prompt using a terminal and sends it to the server.

[1474] Step 3:

[1475] The server uses a generative AI model to quickly search and extract past work history and communication history based on the input prompt. The input is the prompt and a trained generative AI model, and the output is the searched and extracted information. Specifically, the generative AI model queries the database and retrieves the relevant information.

[1476] Step 4:

[1477] The server uses an emotion engine to estimate the worker's emotional state from the retrieved and extracted information. The input is the retrieved and extracted information, and the output is the estimated emotional state. Specifically, the emotion engine analyzes the text data and classifies the emotional state.

[1478] Step 5:

[1479] The server reflects the estimation results of the emotion engine in the generative AI model. The input is the estimated emotional state, and the output is a generative AI model that reflects the emotional state. Specifically, it updates the parameters of the generative AI model and has it learn the emotional state.

[1480] Step 6:

[1481] The server provides the new agent with the output results of the generative AI model and the inference results of the emotion engine. The input is the output results of the generative AI model and the inference results of the emotion engine, and the output is the information provided to the new agent. Specifically, the server sends the information to the terminal, and the user views it.

[1482] Step 7:

[1483] The user (new worker) uses the provided information to understand the work procedures and emotional state of the previous worker and efficiently take over the work. The input is information provided by the server, and the output is the new worker's understanding and execution of the work. Specifically, the user checks the information on their device and reflects it in the actual work.

[1484] 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.

[1485] 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> ) 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.

[1486] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1487] 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.

[1488] [Third embodiment]

[1489] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1490] 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.

[1491] 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).

[1492] 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.

[1493] 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.

[1494] 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).

[1495] 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.

[1496] 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.

[1497] 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.

[1498] 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.

[1499] 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.

[1500] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1501] "Example 1"

[1502] One embodiment of the present invention uses a generation AI that learns from all employees' past emails, schedules, communication tools, chats, created documents, etc. This generation AI uses natural language processing technology to analyze and learn from various types of information. Specifically, it analyzes the content of past emails, schedule details, communication tool and chat logs, the content of created documents, etc., and correlates each piece of information to learn.

[1503] "Example 2"

[1504] Natural language processing technology is used as a means for new staff to ask questions to the generation AI. Specifically, the new staff inputs a question in natural language, and the generation AI analyzes the question and generates an appropriate answer. For example, in response to a question such as "I would like to see the materials created by my predecessor," the generation AI provides a list of materials created by the predecessor.

[1505] "Example 3"

[1506] As a means of reducing the time required for handover work and searching and retrieving work history, the generation AI provides a function to efficiently search and extract the information it has learned. Specifically, the generation AI quickly searches and extracts the information needed by the new employee based on the information it has learned. For example, in response to a request to "see the emails exchanged between the predecessor and clients," the generation AI quickly provides a list of emails exchanged between the predecessor and clients.

[1507] The processing flow of each embodiment will be described below.

[1508] "Example 1"

[1509] Step 1: The generation AI obtains all employees' past emails, schedules, communication tools, chats, created documents, etc.

[1510] Step 2: Analyze the various pieces of information acquired using natural language processing technology, and learn by associating each piece of information.

[1511] Step 3: Based on the learned information, the information required by the new staff member is quickly searched and extracted.

[1512] "Example 2"

[1513] Step 1: New agent types question in natural language.

[1514] Step 2: The generative AI analyzes the question.

[1515] Step 3: Based on the analysis results, generate an appropriate answer and provide it to the new contact.

[1516] "Example 3"

[1517] Step 1: The new contact person enters a search and retrieval request for the information they need.

[1518] Step 2: Based on the information learned by the generation AI, it quickly searches for and extracts information that corresponds to the request.

[1519] Step 3: Provide the searched and extracted information to the new contact.

[1520] Example 1

[1521] Next, a description will be given of Example 1 of Form 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."

[1522] With conventional business handover systems, it was difficult for new employees to quickly and accurately obtain information about previous employees when organizational changes, transfers, or retirements occurred. Furthermore, there was a lack of a way to efficiently analyze, correlate, and learn from vast amounts of data, such as past emails, schedules, communication tools, chats, and created documents, which meant that handover work and searching and retrieving work history took a great deal of time and effort.

[1523] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a generation AI means for learning the past emails, schedules, communication tools, dialogue records, created documents, etc. of all employees; a means for a new person in charge to ask the generation AI questions to obtain documents created by previous people, meeting records, internal and external communications, etc.; a means for reducing the time required for handover work and search and retrieval of work history; a means for preprocessing collected data and training a generation AI model; a means for evaluating the performance of the generation AI model; and a means for providing information according to user requests using the generation AI model. This enables a new person in charge to quickly and accurately obtain information about previous people, significantly reducing the time required for handover work and search and retrieval of work history.

[1524] The "generative AI means" is a means of studying all employees' past emails, schedules, communication tools, conversation records, documents created, etc., and analyzing and learning using natural language processing technology.

[1525] "New staff" refers to staff who have taken over new duties due to organizational changes, transfers, retirement, etc.

[1526] "Previous person in charge" refers to the person in charge before taking over the duties due to organizational changes, transfer, retirement, etc.

[1527] "Created documents" refers to documents created by employees in the course of their work, and specifically includes reports, presentation materials, plans, etc.

[1528] "Meeting records" refers to documents that record the contents of a meeting, including minutes and meeting notes.

[1529] "Internal and external interactions" refers to communications that employees have with internal and external parties in the course of their work, including email, chat, conference calls, etc.

[1530] "Handover work" refers to a series of tasks required to hand over work to a new employee, including providing past work details and related materials.

[1531] "Search and retrieval time for business history" refers to the time required to search and retrieve information related to past business.

[1532] "Means for preprocessing collected data" refers to means for converting collected data into a format that is easy to analyze, and specifically includes text normalization, tokenization, data filtering, etc.

[1533] A "means for training a generative AI model" is a means for training a generative AI model using preprocessed data.

[1534] "Means for evaluating the performance of a generative AI model" refers to means for evaluating the performance of a generative AI model using evaluation indicators such as precision, recall, and F1 score.

[1535] "Means for providing information in response to a user's request using a generative AI model" refers to means for generating and providing appropriate information using a generative AI model based on a prompt sentence entered by a user.

[1536] This invention is a system that uses a generative AI model that learns from all employees' past emails, schedules, communication tools, conversation records, documents created, etc., allowing new employees to quickly and accurately obtain information about previous employees, significantly reducing the time required for handover work and searching and retrieving work history.

[1537] 1. Program Generation

[1538] The server collects all employees' past emails, schedules, communication tools, conversation records, documents, etc., and generates a generative AI model that learns from this data. This generative AI model analyzes and learns from various types of information using natural language processing technology.

[1539] 2. Data analysis and learning

[1540] The server analyzes and learns data using the following hardware and software:

[1541] Hardware: A server equipped with a powerful GPU (e.g., a powerful graphics processing unit)

[1542] Software: Natural language processing libraries (e.g., natural language processing libraries), database management systems (e.g., database management systems)

[1543] The server analyzes the following data:

[1544] Email: Analyze the content of past emails to extract important keywords and topics.

[1545] Calendar: Analyzes calendar details to learn meeting and event patterns.

[1546] Communication tools and conversation logs: Analyze logs to identify frequently used phrases and topics.

[1547] Created documents: Analyze the content of created documents and extract relevant information.

[1548] The server correlates this information, learns from it, and updates the generative AI model.

[1549] 3. Examples of concrete examples and prompts

[1550] Below are some specific examples of how users can use generative AI models.

[1551] Examples:

[1552] When a user asks a generative AI model to "create an agenda for the next project meeting," the model analyzes past project meeting agendas, related emails, and conversation records to generate an appropriate agenda.

[1553] Example prompt sentence:

[1554] "I'd like you to create an agenda for our next project meeting, using past meeting agendas, relevant emails, and transcripts."

[1555] In this way, the generative AI model leverages past data to provide information that meets the user's needs.

[1556] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1557] Step 1: Data collection

[1558] The server collects data on all employees' past emails, schedules, communication tools, conversation records, documents created, etc. Specifically, it obtains information from the following data sources:

[1559] Input: Internal email server, calendar management system, communication tool logs, file server

[1560] Output: raw data collected

[1561] Specific operation: The server retrieves "all employee emails from January 1, 2022 to December 31, 2022" from the email server, retrieves employee schedule information from the calendar management system, and retrieves conversation records and created documents from communication tool logs and file servers.

[1562] Step 2: Data Preprocessing

[1563] The server preprocesses the collected data. Specifically, it performs the following processes:

[1564] Input: Raw data collected

[1565] Output: Preprocessed data

[1566] Specific operation: The server extracts the subject, body, sender, and recipient from the acquired email data, removes unnecessary HTML tags, and tokenizes the text data into words and phrases, eliminating unnecessary and duplicate data.

[1567] Step 3: Model training

[1568] The server uses the preprocessed data to train the generative AI model. Specifically, training is performed using the following steps:

[1569] Input: Preprocessed data

[1570] Output: Trained generative AI model

[1571] What it does: The server splits the data into training data and test data, trains a generative AI model using the training data, builds the model using a natural language processing library, and tunes hyperparameters to optimize the model's performance.

[1572] Step 4: Model evaluation

[1573] The server evaluates the performance of the generative AI model using test data. Specifically, it uses the following evaluation indicators:

[1574] Input: Test data, trained generative AI model

[1575] Output: Model evaluation results (precision, recall, F1 score)

[1576] Specific operation: The server inputs test data into the generative AI model, compares the model's prediction results with the actual data, and calculates evaluation metrics such as precision, recall, and F1 score to evaluate the model's performance.

[1577] Step 5: Use the model

[1578] The user uses the generative AI model to perform a specific task by entering a prompt like the following:

[1579] Input: User prompt (e.g., "Please create an agenda for our next project meeting.")

[1580] Output: The information generated (e.g., the agenda)

[1581] How it works: When a user types, "I want an agenda for my next project meeting," the server uses the generative AI model to analyze past project meeting agendas, related emails, and conversation records to generate an appropriate agenda. The generated agenda is then displayed on the user's device.

[1582] In this way, specific processing is performed at each step, and information is provided according to the user's request using a generative AI model.

[1583] (Application example 1)

[1584] Next, a description will be given of Application Example 1 of Form 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."

[1585] With conventional work handover systems, it was difficult for new staff to efficiently obtain information from previous staff, resulting in the time-consuming process of handover work and searching and retrieving work history. Furthermore, there was a lack of appropriate procedure suggestions to improve work efficiency within the factory, making it difficult to optimize work.

[1586] 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.

[1587] In this invention, the server includes a generation AI means for learning all employees' past emails, schedules, communication tools, chats, and created documents; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes, internal and external communications, etc.; a means for reducing the time required to search and retrieve handover work and work history; a means for learning past work logs, schedules, communication tool logs, created work instructions, etc., and proposing optimal work procedures to improve work efficiency within the factory; and a means for issuing work instructions in real time. This allows new employees to quickly obtain information about previous employees and significantly reduces the time required to search and retrieve handover work and work history. Furthermore, optimal procedures are proposed to improve work efficiency within the factory, thereby achieving work optimization.

[1588] "Generative AI" is an artificial intelligence that studies all employees' past emails, schedules, communication tools, chats, created documents, etc., and uses natural language processing technology to analyze the information, associate it, and learn.

[1589] "New staff member" refers to an employee who has taken over new duties due to organizational changes, transfer, retirement, etc.

[1590] "Former employee" refers to an employee who was previously in charge of a particular task.

[1591] A "work log" is data that records the history of work performed in a factory.

[1592] "Schedule" is information that shows employees' plans and plans.

[1593] "Communication tools" are tools used for communicating and sharing information between employees, and include chat and email.

[1594] "Prepared materials" refers to documents, reports, and other materials created by employees in the course of their work.

[1595] A "work instruction" is a document that describes work procedures and instructions within a factory.

[1596] "Real-time work instructions" refers to the generation AI grasping the situation within the factory in real time and providing instructions on the spot on the optimal work procedures.

[1597] "Handover" is the process of conveying the job content and related information from the previous person in charge to the new person in charge.

[1598] "Search and retrieval time for business history" is the time required to search for information on past business operations and find the required data.

[1599] A system for implementing this invention is configured as follows: The server is equipped with a generation AI that learns from all employees' past emails, schedules, communication tools, chats, created documents, etc. This generation AI uses natural language processing technology to analyze and learn from various types of information. Specifically, it analyzes the content of past emails, schedule details, communication tool and chat logs, the content of created documents, etc., and correlates each piece of information to learn.

[1600] By asking the generated AI questions, new staff can obtain documents prepared by previous staff, minutes of meetings, internal and external communications, etc. This can significantly reduce the time spent on handover work and searching and retrieving work history.

[1601] Furthermore, to improve the efficiency of work within the factory, the generative AI learns from past work logs, schedules, communication tool logs, created work instructions, etc., and proposes optimal work procedures. Providing work instructions in real time can improve work efficiency within the factory.

[1602] As a specific example, the following prompt sentence could be input to the generative AI model:

[1603] Please suggest the best procedure based on the following data:

[1604] 2023-10-01 09:00:00: Email - Part A is out of stock.

[1605] 2023-10-01 10:00:00: Schedule - Assembly work for part B.

[1606] 2023-10-01 11:00:00: Chat - Part C requires quality inspection.

[1607] 2023-10-01 12:00:00: Created materials - Assembly instructions for part D.

[1608] The server runs a Python program and inputs prompts to the generation AI using OpenAI's API. The generation AI then proposes optimal work procedures based on the input prompts. This allows new workers to quickly acquire information about previous workers, improving work efficiency within the factory.

[1609] The hardware used is a computer installed on a factory robot, and the software uses Python and the OpenAI API, including Pandas, a Python data analysis library, and NLTK, a natural language processing library.

[1610] This system allows new employees to quickly obtain information about previous employees, significantly reducing the time it takes to take over and to search and find work history. It also suggests optimal procedures to improve work efficiency within the factory, resulting in work optimization.

[1611] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1612] Step 1:

[1613] The server reads all employees' past emails, schedules, communication tools, chats, created documents, etc. from CSV files. This obtains various data as a data frame. The input is a CSV file, and the output is a data frame.

[1614] Step 2:

[1615] The server combines the read data into a single data frame. Specifically, it uses the Pandas library to combine each data frame vertically. The input is multiple data frames, and the output is the combined data frame.

[1616] Step 3:

[1617] The server creates a prompt sentence to be input to the generation AI based on the integrated data frame. Specifically, it converts each row of data into text format, arranges it in chronological order, and generates a prompt sentence. The input is the integrated data frame, and the output is the prompt sentence.

[1618] Step 4:

[1619] The server sends the generated prompt to OpenAI's API and obtains the optimal work procedure from the generation AI. Specifically, it uses OpenAI's Completion endpoint to input the prompt and receive the generated text. The input is the prompt, and the output is the generated work procedure.

[1620] Step 5:

[1621] The server displays the generated work procedure to the new worker. Specifically, it displays the generated text on a user interface so that the worker can check it. The input is the generated work procedure, and the output is the information displayed on the user interface.

[1622] Step 6:

[1623] The user performs the work in the factory based on the displayed work procedure. Specifically, the user performs the necessary work according to the procedure proposed by the generation AI. The input is the displayed work procedure, and the output is the performed work.

[1624] Example 2

[1625] Next, a description will be given of Example 2 of Form 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."

[1626] When taking over work due to organizational changes, transfers, retirement, etc., it is difficult for new staff to quickly and accurately obtain information about previous staff. With conventional methods, the handover process and searching and retrieving work history takes a lot of time and effort, resulting in a decrease in productivity.

[1627] 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.

[1628] In this invention, the server includes a means for a new agent to input a question in natural language, a means for the terminal to send the question to the server, a means for the server to analyze the question using natural language processing technology, a means for the server to send a prompt to the generative AI model based on the analysis result, a means for the generative AI model to generate an answer based on the prompt, a means for the server to send the generated answer to the terminal, and a means for the terminal to display the answer to the user. This allows the new agent to quickly and accurately obtain information about the previous agent.

[1629] "New staff" refers to staff who have taken over new duties due to organizational changes, transfers, retirement, etc.

[1630] A "natural language" is a language used by humans on a daily basis, not a specific programming language or technical term.

[1631] A "terminal" is an electronic device such as a computer or smartphone used by a user.

[1632] A "server" is a computer system that receives and processes requests from terminals via a network.

[1633] "Natural language processing technology" is a technology that enables computers to understand and analyze human natural language.

[1634] A "generative AI model" is a model that uses artificial intelligence technology to create appropriate answers or products based on input data.

[1635] A "prompt sentence" is an instruction sentence input to a generative AI model, and is the sentence that serves as the basis for the model to generate an answer.

[1636] An "answer" is information or data that a generative AI model generates based on a prompt.

[1637] "Communication records" are the history of past communications such as emails and chats.

[1638] A "plan" is a future plan or plan recorded on a schedule or calendar.

[1639] "Communication tools" are software that supports communication between users, such as chat apps and messaging services.

[1640] "Created materials" are documents, reports, and other materials created by past staff members.

[1641] Minutes are documents that record the contents of meetings and discussions.

[1642] "Internal and external interactions" are records of communications with other departments within the company and with external business partners.

[1643] This invention is a system that allows new staff to quickly and accurately obtain information about previous staff members. The system involves a process in which a user inputs a question in natural language, and a generative AI model analyzes the question and generates an appropriate answer.

[1644] Hardware and software used

[1645] Device: An electronic device such as a computer or smartphone that a user uses.

[1646] Server: A computer system that receives and processes requests from terminals via a network.

[1647] Natural Language Processing technologies: NLP libraries such as spaCy and NLTK.

[1648] Generative AI models: Generative AI techniques such as OpenAI GPT-3.

[1649] HTTP request libraries: such as Axios or Fetch API.

[1650] Front-end frameworks: such as React or Vue.js.

[1651] Program processing

[1652] 1. The user enters a question

[1653] A user uses a terminal to input a question in natural language.

[1654] Example: A user types "I want to see the materials created by my predecessor" into a browser input field.

[1655] 2. The device sends a question to the server

[1656] The terminal sends the question entered by the user to the server.

[1657] What happens: The device sends a question to the server's API endpoint using an HTTP POST request.

[1658] 3. The server parses the question

[1659] The server analyzes the received question using natural language processing technology.

[1660] What it does: The server uses the spaCy library to analyze the intent of the question and extract important keywords.

[1661] 4. The server sends a prompt to the generative AI model

[1662] The server sends a prompt to the generative AI model based on the analysis results.

[1663] Specific operation: The server generates a prompt sentence saying, "The new employee wants to see the materials created by his predecessor. Please provide a list of the materials created by his predecessor." and sends it to the generative AI model.

[1664] 5. Generative AI models generate answers

[1665] A generative AI model generates appropriate answers based on the prompt.

[1666] Specific behavior: The generative AI model generates the answer, "The list of documents created by the predecessor is as follows: 1. Project plan 2. Progress report 3. Customer service manual."

[1667] 6. The server sends the answer to the device

[1668] The server sends the answer received from the generative AI model to the device.

[1669] Specific operation: The server sends the generated answer to the terminal as an HTTP response.

[1670] 7. The device displays the answer to the user

[1671] The terminal displays the received answer to the user.

[1672] Specific operation: The device displays the text "The list of documents prepared by the predecessor is as follows: 1. Project plan 2. Progress report 3. Customer service manual" in the browser display area.

[1673] In this way, a series of processes is realized in which a new employee inputs a question in natural language, and the generation AI analyzes the question and provides an appropriate answer.

[1674] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1675] Step 1:

[1676] The user enters a question.

[1677] Input: The user types a question in natural language.

[1678] Specific operation: The user types "I want to see the materials created by my predecessor" into the input field of the device's browser.

[1679] Output: The question you typed will be displayed on the terminal.

[1680] Step 2:

[1681] The terminal sends a question to the server.

[1682] Input: The question entered by the user.

[1683] What happens: The device sends a question to the server's API endpoint using an HTTP POST request.

[1684] Output: The question is sent to the server.

[1685] Step 3:

[1686] The server parses the question.

[1687] Input: The question sent from the terminal.

[1688] What it does: The server uses the spaCy library to analyze the intent of the question and extract important keywords.

[1689] Output: Analysis results of the question (e.g. keywords such as "predecessor", "created", "document").

[1690] Step 4:

[1691] The server sends a prompt to the generative AI model.

[1692] Input: The parsed result of the question.

[1693] Specific operation: The server generates a prompt sentence saying, "The new employee wants to see the materials created by his predecessor. Please provide a list of the materials created by his predecessor." and sends it to the generative AI model.

[1694] Output: The prompt sent to the generative AI model.

[1695] Step 5:

[1696] A generative AI model generates the answer.

[1697] Input: The prompt text sent by the server.

[1698] Specific operation: Based on the prompt, the generative AI model generates the answer, "The list of documents created by the predecessor is as follows: 1. Project plan 2. Progress report 3. Customer service manual."

[1699] Output: The generated answer.

[1700] Step 6:

[1701] The server sends the response to the terminal.

[1702] Input: The answer received from the generative AI model.

[1703] Specific operation: The server sends the generated answer to the terminal as an HTTP response.

[1704] Output: The answer sent to the terminal.

[1705] Step 7:

[1706] The terminal displays the answer to the user.

[1707] Input: The answer sent by the server.

[1708] Specific operation: The device displays the text "The list of documents prepared by the predecessor is as follows: 1. Project plan 2. Progress report 3. Customer service manual" in the browser display area.

[1709] Output: The answer displayed to the user.

[1710] (Application example 2)

[1711] Next, a description will be given of Application Example 2 of Form 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."

[1712] When new employees take over work, it is difficult to quickly and efficiently obtain information such as documents prepared by previous employees, minutes of meetings, and internal and external communications. This also creates a problem where new employees in the factory are unable to quickly obtain the information they need to understand how to operate machines and work procedures, resulting in reduced work efficiency.

[1713] 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 a generation AI means for learning all employees' past emails, schedules, communication tools, chats, created documents, etc.; a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc.; a means for reducing the time required for handover work and search and retrieval of work history; and a means for a new employee to input questions in natural language to a robot in the factory, where the generation AI analyzes the questions and provides appropriate answers. This enables the new employee to quickly and efficiently obtain the necessary information, improving the handover and work efficiency.

[1714] "All employees" refers to all staff members of a company or organization.

[1715] "Email" refers to digital messages sent and received over the Internet.

[1716] A "datebook" is a tool for managing the schedules of individuals and organizations, and records scheduled meetings and tasks.

[1717] "Communication tools" refers to software and applications for exchanging information, such as chat and video conferencing.

[1718] "Chat" refers to a means of communication for exchanging text messages in real time.

[1719] "Created materials" refers to digital files such as documents and presentations created in the course of business.

[1720] "Generative AI" refers to artificial intelligence that uses natural language processing technology to analyze input questions and generate appropriate answers.

[1721] "New staff member" refers to a staff member who has been newly assigned to a task within the organization.

[1722] "Former staff member" refers to a staff member who was previously in charge of a particular task.

[1723] "Minutes" refers to a document that records the contents of a meeting or conference.

[1724] "Internal and external interactions" refers to communication with parties both within the company and outside the company.

[1725] "Handover work" refers to the procedures and tasks required to hand over work to the next person in charge.

[1726] "Business history" refers to records of past business operations.

[1727] "Search and retrieval time" refers to the time spent finding needed information.

[1728] "In-plant" means the interior of a facility where manufacturing operations take place.

[1729] A "robot" refers to a mechanical device that performs a specific task automatically.

[1730] "Natural language" refers to a language that humans use on a daily basis.

[1731] "Analysis" refers to the process of analyzing input data and understanding its meaning.

[1732] An "appropriate answer" refers to a response that provides accurate and useful information in response to the entered question.

[1733] A system for implementing this invention has the following configuration: First, a server is equipped with a generation AI means that learns from all employees' past emails, schedules, communication tools, chats, created documents, etc. This generation AI means uses natural language processing technology to analyze input questions and generate appropriate answers.

[1734] When a new employee enters a question using a smartphone or tablet, the question is sent to the server. The server then uses a generative AI tool to analyze the question and search for information such as documents prepared by previous employees, minutes of meetings, and internal and external communications. This allows the new employee to quickly obtain the information they need.

[1735] The same is true when a new employee in a factory inputs a question to a robot in natural language. The robot sends the question to a server, and a generative AI means analyzes the question and provides an appropriate answer. For example, in response to a question such as "I would like to see the operating manual for this machine," the generative AI will display a digital version of the operating manual.

[1736] The hardware used includes factory robots (e.g., FANUC, KUKA, etc.), smartphones, and tablets, while the software used includes the OpenAI API (generative AI model) and Python (programming language).

[1737] As a concrete example, if a new employee asks, "I'd like to see the operating manual for this machine," the generating AI will provide an answer such as, "The digital version of the operating manual is here," and display a link or file.

[1738] An example of a prompt is as follows:

[1739] New employee asks: I'd like to see the operating manual for this machine.

[1740] Generate the appropriate answer.

[1741] In this way, the new staff member can quickly and efficiently obtain the necessary information, improving the handover of work and work efficiency.

[1742] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1743] Step 1:

[1744] The user enters a question using a smartphone or tablet, and the entered question is saved in text format on the device.

[1745] Step 2:

[1746] The terminal sends the entered question to the server, which receives the question and stores it in a database.

[1747] Step 3:

[1748] The server passes the received question to the generation AI means, which uses natural language processing technology to analyze the question and extract relevant keywords and context.

[1749] Step 4:

[1750] The generation AI method searches all employees' past emails, calendars, communication tools, chats, created documents, etc. based on the extracted keywords and context, and obtains related information as search results.

[1751] Step 5:

[1752] The server organizes the information obtained from the generation AI means and generates an appropriate answer, which is stored in text format on the server.

[1753] Step 6:

[1754] The server sends the generated answer to the terminal, which then displays the received answer to the user.

[1755] Step 7:

[1756] The user can check the displayed answers and obtain the necessary information. For example, if a link or file for an operation manual is displayed, the user can click it to view it.

[1757] In this way, users can quickly and efficiently obtain the information they need.

[1758] Example 3

[1759] Next, a third embodiment of the third embodiment will be described. 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."

[1760] Previously, handover work and searching and retrieving work history required time and effort, making it difficult for new staff to quickly obtain the necessary information. Furthermore, when work handover occurred due to organizational changes, transfers, or retirement, there was a lack of an efficient way to obtain information about previous staff. This created a risk of disrupting business continuity.

[1761] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1762] In this invention, the server includes a generation AI means for learning all employees' past emails, schedules, communication methods, messages, and documents created, etc., a means for a new employee to ask the generation AI questions to obtain documents created by previous employees, minutes of meetings, internal and external communications, etc., a means for reducing the time required for handover work and search and retrieval of work history, a means for a user to input an information search request, a means for a terminal to send the user's request to the server, a means for the server to receive the request and generate a prompt for the generation AI model, a means for the server to input the prompt for the generation AI model, a means for the generation AI model to search and extract relevant information from a database, a means for the server to receive results from the generation AI model and send them to the terminal, and a means for the terminal to display the results to the user. This enables a new employee to quickly obtain the information they need and significantly reduces the time required for handover work and search and retrieval of work history.

[1763] "All employees' past emails, schedules, means of communication, messages, documents created, etc." refers to emails sent and received by all employees in the organization in the past, calendars for managing schedules, means of communication used (e.g., telephone, video conference), messages sent and received (e.g., chat apps), and various documents created (e.g., reports, presentation materials).

[1764] "Generative AI means" refers to artificial intelligence technology that uses machine learning algorithms to learn from large amounts of data and generate and provide appropriate information in response to user requests.

[1765] "New employee" refers to an employee who has been newly assigned to a specific task within the organization.

[1766] "Former employee" refers to an employee who previously handled a particular task.

[1767] A "prompt sentence" is an instruction sentence input to a generative AI model that describes a specific request for searching and extracting specific information.

[1768] "Database" refers to a system for systematically storing and managing various data within an organization.

[1769] A "server" refers to a computer system that receives and processes requests from clients over a network.

[1770] "Terminal" refers to a device such as a computer or smartphone that is directly operated by a user.

[1771] "User" refers to an individual or member of an organization who uses the system to search for information.

[1772] "Information retrieval request" refers to a request made by a user to a system to obtain specific information.

[1773] "Search and extraction means" refers to the methods and techniques that a generative AI model uses to find and retrieve relevant information from a database based on a prompt.

[1774] This invention is a system for reducing the time required for handovers and searching and retrieving work history. The system uses a generative AI means that learns from all employees' past emails, schedules, communication methods, messages, and documents. A specific embodiment of this system is described below.

[1775] Hardware and software used

[1776] Hardware: High-performance server (e.g. Dell PowerEdge R740)

[1777] Software: Generative AI models (e.g., OpenAI GPT-4), database management systems (e.g., MySQL)

[1778] System configuration

[1779] 1. Server: The server plays a central role in using generative AI models to search and extract information based on user requests.

[1780] 2. Terminal: A terminal is a device through which a user enters information search requests and receives results. Examples include computers and smartphones.

[1781] 3. User: A user is an individual or member of an organization who uses the system to search for information.

[1782] System Operation

[1783] User operations

[1784] Through the terminal interface, the user inputs a request to search for specific information, for example, "I want to see the emails my predecessor exchanged with clients."

[1785] Device behavior

[1786] The terminal receives the user's input and sends the request to the server, where it uses an HTTP request to send data to the server.

[1787] Server Operation

[1788] The server analyzes the request received from the device and generates a prompt to input to the generative AI model, for example, "Show me a list of emails exchanged between my predecessor and the client."

[1789] The server inputs the generated prompt sentence into the generative AI model, which generates a response to the prompt sentence based on pre-trained data.

[1790] The generative AI model analyzes the prompt and searches for and extracts relevant information from a database, such as a list of emails exchanged between a predecessor and a client.

[1791] The server sends the search results received from the generative AI model to the device, again using an HTTP response to send the data.

[1792] Device behavior

[1793] The terminal displays the search results received from the server to the user, who can then check a list of emails exchanged between the predecessor and the client.

[1794] Specific examples

[1795] For example, if a new employee requests to "see the emails exchanged between my predecessor and clients," the device sends this request to the server. The server generates a prompt message saying, "Show me a list of emails exchanged between my predecessor and clients," and inputs this into the generative AI model. The generative AI model searches the database for relevant emails and returns the results to the server. The server then sends the results to the device, which then displays the list of emails to the user.

[1796] In this way, the server, terminal, and user work together to efficiently execute the information search process. This system enables new staff to quickly obtain the necessary information, significantly reducing the time required for handover work and for searching and retrieving work history. The flow of the identification process in the third embodiment will be explained using Figure 15.

[1797] Step 1:

[1798] A user enters a request for information retrieval.

[1799] Through the terminal interface, the user inputs a request to search for specific information, for example, "I want to see the emails my predecessor exchanged with clients."

[1800] Input: User search request

[1801] Output: Search request data sent to the terminal

[1802] Step 2:

[1803] The terminal sends the user's request to the server.

[1804] The terminal receives the user's input and sends the request to the server, where it uses an HTTP request to send data to the server.

[1805] Input: User search request data

[1806] Output: Search request data sent to the server

[1807] Step 3:

[1808] The server receives the request and generates a prompt for the generative AI model.

[1809] The server analyzes the request received from the device and generates a prompt to input to the generative AI model, for example, "Show me a list of emails exchanged between my predecessor and the client."

[1810] Input: Search request data received from the terminal

[1811] Output: Prompt sentence to be input to the generative AI model

[1812] Step 4:

[1813] The server inputs a prompt sentence into the generative AI model.

[1814] The server inputs the generated prompt sentence into the generative AI model, which generates a response to the prompt sentence based on pre-trained data.

[1815] Input: A prompt to be input to the generative AI model

[1816] Output: Search results from a generative AI model

[1817] Step 5:

[1818] A generative AI model searches and extracts relevant information from a database.

[1819] The generative AI model analyzes the prompt and searches for and extracts relevant information from a database, such as a list of emails exchanged between a predecessor and a client.

[1820] Input: prompt statement

[1821] Output: Relevant information extracted from the database

[1822] Step 6:

[1823] The server receives the results from the generative AI model and sends them to the device.

[1824] The server sends the search results received from the generative AI model to the device, again using an HTTP response to send the data.

[1825] Input: Search results from a generative AI model

[1826] Output: Search result data sent to the device

[1827] Step 7:

[1828] The terminal displays the results to the user.

[1829] The terminal displays the search results received from the server to the user, who can then check a list of emails exchanged between the predecessor and the client.

[1830] Input: Search result data received from the server

[1831] Output: The search results that are displayed to the user

[1832] In this way, the server, terminal, and user can work together to efficiently execute the information search process.

[1833] (Application example 3)

[1834] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1835] In logistics centers, it is difficult for new staff to efficiently search and extract past work history and handover information, which results in a significant amount of time spent on handover work and searching and searching work history. In addition, when work handover occurs due to organizational changes, transfers, or retirements, new staff cannot quickly obtain information about previous staff, which can disrupt business continuity.

[1836] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, t...

Claims

1. A means for learning the contents of all employees' past emails, schedules, dialogue records, documents created, and chats using a generative AI model; An emotion engine that recognizes the emotions of new staff members; A means for generating an answer to a question of a new agent by inputting a specific prompt sentence into a generative AI model; An adjustment means for adjusting the content of the answer in accordance with the emotion; Including, The system provides answers to the questions including documents prepared by past personnel, meeting records, and information about internal and external communications.

2. 2. The system of claim 1, further comprising means for assisting new personnel in taking over the work of previous personnel.

3. 2. The system according to claim 1, wherein said adjustment means adjusts the content of said answer to be simple and easy to understand if the new staff member is feeling stressed.

Images