system

Abstract

To provide a system.SOLUTION: Provided is a system including: means for importing data of job contents prepared in text form; feeling recognition means for recognizing a user's feeling state; means for a job point that can be automatized among the job contents by using a generative AI on the basis of the imported data and information indicating the recognized feeling sate of the user; and means for presenting to the user the generated job point that can be automatized.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] Companies and individuals who want to promote digital transformation face the challenge of finding it difficult to determine which parts of their own work or the work within their company should be automated or which parts should be made more efficient. [Means for solving the problem]

[0005] To solve this problem, we use generative AI, which takes in text data of business operations and generates automatable business points from that data. Furthermore, by providing answers based on the generated automatable business points, we support the promotion of digital transformation. [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] As one embodiment of the present invention, an interface for inputting business content as text data is provided as a means for importing data in the form of textualized business content. This interface can be in a format where the user directly inputs text. It can also be in a format where an API is used to automatically obtain text data from an existing business management system.

[0029] "Example 2"

[0030] As a generative AI tool, an AI engine is used that generates automatable business points from the imported text data. This AI engine uses natural language processing technology to understand the content of the business from the text data and extracts points that can be automated. Specifically, it analyzes the verbs and nouns in the text data and understands the business flow and procedures they indicate. It then identifies the parts of that that can be automated.

[0031] "Example 3"

[0032] A user interface is provided as a means of responding to the generated automatable business points. This interface displays a list of the generated automatable business points so that the user can refer to them. Specifically, it lists the automatable business points one by one and displays automation suggestions for each business point and the effects of that automation.

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

[0034] "Example 1"

[0035] Step 1: Open an interface where the user can enter their work details as text data.

[0036] Step 2: The user enters the job description as text and presses the send button.

[0037] Step 3: The system takes the text data and sends it to the generative AI.

[0038] "Example 2"

[0039] Step 1: The generative AI receives the text data.

[0040] Step 2: Generative AI uses natural language processing technology to understand the business content from the text data.

[0041] Step 3: Generative AI extracts points that can be automated from the content of the work and generates them as automatable work points.

[0042] "Example 3"

[0043] Step 1: The system displays the generated automatable business points in the user interface.

[0044] Step 2: The user checks the business points that can be automated and proceeds with automating the business based on that.

[0045] Example 1

[0046] 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."

[0047] In conventional business management systems, it was difficult to input business content as text data and then efficiently analyze and summarize it. It was also difficult to automatically generate detailed explanations of business content, which placed a heavy burden on users. This resulted in a lack of progress in business efficiency and automation, and slow improvements to business processes.

[0048] 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.

[0049] In this invention, the server includes an interface means for inputting business content as text data, a means for receiving and saving the input text data, a means for cleaning and formatting the saved data, a means for inputting the cleaned and formatted data into a generative AI model, and a means for returning results obtained from the generative AI model to the user, thereby enabling efficient analysis and automatic generation of summaries and detailed descriptions of business content.

[0050] The "interface means for inputting business details as text data" refers to an interface for a user to input business details in text format, and includes a form on a web browser, a desktop application, or a mobile application.

[0051] The "means for receiving and saving input text data" refers to a means having a function for receiving text data sent from a user and saving it in a storage such as a database.

[0052] "Means for cleaning and formatting stored data" refers to means for analyzing stored text data, cleaning it by removing unnecessary spaces and special characters, and converting it into a format that is easy for the generative AI model to understand.

[0053] "Means for inputting cleaned and formatted data into a generative AI model" means means for inputting cleaned and formatted data into a generative AI model, including the ability to send API requests.

[0054] "Means for returning results obtained from a generative AI model to a user" refers to means for receiving results returned from a generative AI model and returning them to a user, including an interface for displaying the results.

[0055] MODE FOR CARRYING OUT THE INVENTION

[0056] This invention is a system that inputs business content as text data and efficiently analyzes, summarizes, and generates detailed explanations of the data. Specific embodiments of this system are described below.

[0057] Enter business details

[0058] The user uses an interface to enter the job description. This interface can be a form running in a web browser, a dedicated desktop application, or a mobile application. The user enters the job description in a text box and clicks a "Submit" button.

[0059] As a specific example, the user inputs "Today's work included a meeting with client A and the preparation of materials," and presses the send button.

[0060] Receiving and storing data

[0061] The server receives the text data of the business details sent by the user, and stores the received data in a relational database such as MySQL (registered trademark) or PostgreSQL.

[0062] Specifically, the server executes the SQL query "INSERT INTO Business Details (User ID, Date, Details) VALUES (1, '2023-10-01', 'Meeting with Customer A and preparing materials')".

[0063] Data cleaning and formatting

[0064] The server analyzes the stored data and cleans it, removing unnecessary spaces and special characters, etc. It also converts the data into a format that is easy for the generative AI model to understand, for example, by categorizing the work content.

[0065] Specifically, the server converts the text "Meeting with customer A and document creation" into the format "Customer support: Meeting with customer A, Document creation: Document creation."

[0066] Data input to generative AI models

[0067] The server inputs the cleaned and formatted data into a generative AI model, which uses a natural language processing model such as GPT-4 (registered trademark), and sends API requests to the model.

[0068] Specifically, the server sends an HTTP request "POST / generate-summary HTTP / 1.1" to the API of the generated AI model, attaching a JSON payload containing the data.

[0069] Obtaining and displaying results

[0070] The server receives the results returned by the generative AI model, which may be a summary of the task or a detailed description, and then parses and formats the results for return to the user.

[0071] Specifically, the server receives the summary "Meeting with customer A and preparation of materials" and converts it into HTML format for display to the user.

[0072] Returning results to the user

[0073] The server returns the results obtained from the generative AI model to the user, who can view the results through an interface, which can be displayed on a web page or on the application screen.

[0074] Specifically, the server generates HTML containing the content "Summary of today's work: Had a meeting with customer A and prepared materials" and sends it to the user's browser.

[0075] Prompt Sentence Examples

[0076] "Summarize today's work."

[0077] "Summarize this week's work."

[0078] "Please explain in detail the contents of the meeting with Customer A."

[0079] The above is a specific embodiment for carrying out the present invention.

[0080] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0081] Step 1:

[0082] The user enters the job description

[0083] The user uses an interface to input the details of the work. The user enters the details of the work in the text box and clicks the "Send" button. As input, the user enters "Today's work was a meeting with Client A and the preparation of materials." As output, the entered text data is sent to the server.

[0084] Step 2:

[0085] The server receives and stores the data

[0086] The server receives the text data of the work content sent by the user. As input, it receives the text data sent by the user. The server stores the received data in a relational database such as MySQL or PostgreSQL. Specifically, the server executes the SQL query "INSERT INTO Work content (user ID, date, content) VALUES (1, '2023-10-01', 'Meeting with customer A and creating materials')". As output, it obtains the work content stored in the database.

[0087] Step 3:

[0088] The server cleans and formats the data

[0089] The server analyzes the stored data and cleans it by removing unnecessary spaces and special characters. As input, it receives the text data of business operations stored in the database. The server converts the data into a format that is easy for the generative AI model to understand. Specifically, the server converts the text "Meeting with customer A and creating documents" into the format "Customer support: Meeting with customer A, Document creation: Creating documents." The cleaned and formatted data is obtained as output.

[0090] Step 4:

[0091] The server inputs data into the generative AI model

[0092] The server inputs the cleaned and formatted data into the generative AI model. The cleaned and formatted data is used as input. The generative AI model uses a natural language processing model such as GPT-4. Specifically, the server sends an HTTP request "POST / generate-summary HTTP / 1.1" to the generative AI model's API and attaches a JSON payload containing the data. The data is input into the generative AI model as output.

[0093] Step 5:

[0094] The server retrieves the results from the generative AI model

[0095] The server receives the results returned by the generative AI model. As input, it receives the response from the generative AI model. The results may be a summary of the work content or a detailed explanation. Specifically, the server receives the summary "Meeting with customer A and preparation of materials were conducted" and converts this into HTML format for display to the user. As output, it obtains the results obtained from the generative AI model.

[0096] Step 6:

[0097] The server returns the results to the user

[0098] The server returns the results obtained from the generative AI model to the user. The results obtained from the generative AI model are used as input. The user can check the results through the interface. Specifically, the server generates HTML containing the content "Summary of today's work: Held a meeting with customer A and prepared materials" and sends it to the user's browser. The user can check the results as output.

[0099] (Application example 1)

[0100] 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."

[0101] Conventional factory robot management systems have the problem of being difficult to carry out work efficiently, as the process of manually inputting work details and giving instructions to the robots is cumbersome. Furthermore, there was also the problem of insufficient integration with existing work management systems, which hindered progress in automating work and streamlining. This resulted in a decline in productivity throughout the factory and increased costs.

[0102] 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.

[0103] In this invention, the server includes a means for importing text data of work content, a generative AI means for generating automatable work points from the imported data, a means for returning the generated automatable work points, a means for sending the imported work content to a factory robot, and a means for generating instructions for the factory robot to execute the work content. This automates the process from inputting work content to issuing instructions to the robot, improving work efficiency throughout the factory, thereby enabling increased productivity and cost reduction.

[0104] "Means for importing text data of business content" refers to an interface for acquiring text data of business content entered by the user, and an API for automatically acquiring text data from existing business management systems.

[0105] "Generative AI means for generating automatable business points from imported data" refers to artificial intelligence technology that analyzes the text data of imported business content and extracts and generates business points that can be automated.

[0106] "Means for answering generated automatable task points" refers to means for presenting to the user the automatable task points generated by the generative AI means.

[0107] The "means for transmitting the captured work content to the factory robot" refers to a communication means for transmitting the text data of the captured work content to the factory robot.

[0108] "Means for generating instructions for factory robots to execute work content" refers to means for generating specific work instructions based on the work content input by the factory robot and having the robot execute them.

[0109] As an embodiment of the present invention, a factory robot management system is constructed, which includes: means for importing text data of work content, generative AI means for generating automatable work points from the imported data, means for responding with the generated automatable work points, means for transmitting the imported work content to a factory robot, and means for generating instructions for the factory robot to execute the work content.

[0110] Program processing explanation

[0111] Hardware and Software

[0112] Hardware: smartphones, tablets, factory robots

[0113] Software: Python (registered trademark), requests library, API of existing business management system

[0114] Data processing and calculation

[0115] 1. Importing work content: The server acquires the work content entered by the user on a smartphone or tablet as text data. It also automatically acquires text data from existing work management systems via API.

[0116] 2. Generating automatable business points: The server inputs the captured text data into a generative AI model to generate automatable business points. This generative AI model uses natural language processing technology to analyze the business content and extract points for efficient business execution.

[0117] 3. Response of business points: The server presents the generated business points that can be automated to the user, allowing the user to receive specific suggestions for automating and streamlining their business.

[0118] 4. Sending the work content: The server sends the text data of the work content to the factory robot via Wi-Fi or a wired network.

[0119] 5. Instruction generation: The factory robot generates and executes specific work instructions based on the received task content, allowing the robot to complete the task automatically.

[0120] Specific examples

[0121] User input example: A user uses a smartphone to input "Assemble part A."

[0122] Example of retrieving data from API: Retrieve the task content "Inspect part B" from an existing business management system.

[0123] Prompt Sentence Examples

[0124] When a user types "Assemble part A" into their smartphone, send that task to the factory robot.

[0125] Also, obtain the task content "Inspect part B" from the existing task management system and send it to the factory robot in the same way.

[0126] In this way, a system is realized that enables factory robots to perform their tasks efficiently.

[0127] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0128] Step 1:

[0129] The user uses a smartphone or tablet to input the details of the task as text. The input text data is sent to the server. An example of input data is "Assemble part A." The server receives this text data and proceeds to the next processing step.

[0130] Step 2:

[0131] The server automatically retrieves text data of the work content from the existing work management system via API. An example of retrieved data is "Inspect part B." The server receives this data and proceeds to the next processing step.

[0132] Step 3:

[0133] The server inputs the text data acquired in steps 1 and 2 into the generative AI model. The generative AI model uses natural language processing technology to analyze the business content and extract business points that can be automated. For example, from "Assemble part A," it generates "automation points for the assembly process." The generated business points are returned to the server.

[0134] Step 4:

[0135] The server presents the automatable business points returned by the generative AI model to the user, who can then view the suggested business points on the screen of their smartphone or tablet. For example, "automation points for the assembly process" may be displayed.

[0136] Step 5:

[0137] The server sends the text data of the work content that it has captured to the factory robot. The communication method is Wi-Fi or a wired network. An example of the data that is sent is "Assemble part A." The factory robot receives this data and proceeds to the next processing step.

[0138] Step 6:

[0139] The factory robot generates specific work instructions based on the received task content. For example, for the task content "Assemble part A," it generates the instruction "Pick up part A and place it on the assembly line." The generated instructions are stored in the robot's internal system.

[0140] Step 7:

[0141] The factory robot performs the task according to the generated work instructions. For example, based on the instruction "pick up part A and place it on the assembly line," the robot actually picks up part A and places it on the assembly line. When the task is completed, the robot sends a completion report to the server.

[0142] In this way, the process from inputting the work content to giving instructions to the robot and then executing it is automated.

[0143] Example 2

[0144] 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."

[0145] Conventional business process automation systems require manual analysis of business processes and identification of points that can be automated, which is time-consuming and labor-intensive. It is also difficult to accurately grasp the flow and procedures of business processes, which often makes it difficult to achieve efficient automation. Furthermore, there is a lack of specific improvement proposals for business process automation, which hinders progress in improving business efficiency.

[0146] 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.

[0147] In this invention, the server includes a means for importing data in which business content has been converted into text, a means for applying natural language processing technology to the imported data and analyzing verbs and nouns to grasp the flow and procedures of the business, and a generative AI means for generating automatable business points from the grasped flow and procedures of the business. This makes it possible to automatically analyze business content and quickly and accurately identify automatable points.

[0148] "Text data of business content" refers to data in which the procedures and content of business are written in text form.

[0149] A "means for capturing" is a means for inputting user-provided data into the system.

[0150] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0151] "Means for analyzing verbs and nouns" refers to means for extracting verbs and nouns from text data and analyzing the relationships between them.

[0152] "Means for understanding the flow and procedures of work" refers to a means for understanding the progress and procedures of work based on analyzed verbs and nouns.

[0153] "Automable business points" refer to parts or steps within a business process that can be automated.

[0154] "Generative AI methods" are methods that use artificial intelligence technology to analyze captured data and identify business points that can be automated.

[0155] The "answering means" is a means for presenting the generated automatable business points to the user.

[0156] The "function for making improvement suggestions" is a function that makes specific suggestions for automating and streamlining business operations.

[0157] This invention is a system that inputs text data of business operations, analyzes the data, and identifies business operations that can be automated. A specific embodiment of this system will be described below.

[0158] First, the user provides the system with text data of the work content. This data is a written description of the work procedures and content. The user can upload this data through a web interface.

[0159] The server receives the text data provided by the user and stores it in its internal storage. The server then analyzes the text data using natural language processing technology. Specifically, it uses natural language processing libraries such as "spaCy" and "NLTK." This analyzes the sentence structure of the text data and extracts verbs and nouns.

[0160] The server extracts verbs and nouns from the analyzed sentence structure and analyzes their relationships. This clarifies the workflow and procedures. For example, verbs such as "enter" and "create" and nouns such as "sales data," "spreadsheet software," and "report" are extracted.

[0161] Next, the server identifies tasks that can be automated based on the extracted verbs and nouns. For example, it determines that tasks such as "entering sales data into spreadsheet software" and "creating reports" can be automated.

[0162] Finally, the server presents the generated automatable business points to the user, allowing the user to check the business points that can be automated and receive specific suggestions for improvement.

[0163] As a specific example, consider the following text data.

[0164] Example of text data:

[0165] "Enter sales data into a spreadsheet every day and create a report at the end of the week."

[0166] Based on this text data, the server performs the following processing.

[0167] 1. Importing text data:

[0168] The server takes in text data provided by the user, such as "Enter sales data into spreadsheet software every day and create a report on the weekend."

[0169] 2. Application of natural language processing technology:

[0170] The server uses spaCy to analyze the text data.

[0171] 3. Analysis of verbs and nouns:

[0172] The server extracts verbs such as "input" and "create" and nouns such as "sales data," "spreadsheet software," and "report" to grasp the flow of work.

[0173] 4. Identify areas that can be automated:

[0174] The server identifies the parts "enter sales data into spreadsheet software" and "create report" as being automatable.

[0175] Example prompts to input to the generative AI model:

[0176] "Identify the automation aspects of the following task: Enter sales data into a spreadsheet every day and create a report at the end of the week."

[0177] In this way, the server analyzes the text data and identifies points in the work that can be automated. This system makes it possible to automatically analyze work content and quickly and accurately identify points that can be automated.

[0178] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0179] Step 1:

[0180] Importing text data

[0181] Users upload text data of their work through a web interface, and the server receives this data and stores it in its internal storage.

[0182] Input: Text data of business operations

[0183] Output: Text data saved in internal storage

[0184] Specific operation: A user accesses the web interface, selects a text file containing the job description, and presses the upload button. The server receives the uploaded file and stores it in the database.

[0185] Step 2:

[0186] Application of natural language processing technology

[0187] The server applies natural language processing technology to the stored text data. Specifically, it performs grammatical analysis using natural language processing libraries such as "spaCy" and "NLTK."

[0188] Input: Text data stored in internal storage

[0189] Output: Parsed sentence structure data

[0190] Specific operation: The server passes the text data to the "spaCy" parser and performs grammatical analysis, which analyzes the sentence structure of the text data and extracts verbs and nouns.

[0191] Step 3:

[0192] Verb and noun analysis

[0193] The server extracts verbs and nouns from the analyzed sentence structure and analyzes their relationships, thereby clarifying the flow and procedures of work.

[0194] Input: Parsed sentence structure data

[0195] Output: A list of extracted verbs and nouns

[0196] Specific operation: The server lists verbs (e.g., "enter," "create") and nouns (e.g., "sales data," "spreadsheet software," "report") from the sentence structure data and analyzes the relationships between them.

[0197] Step 4:

[0198] Identifying areas where automation is possible

[0199] Based on the extracted verbs and nouns, the server identifies tasks that can be automated, such as routine data entry or periodic report generation.

[0200] Input: A list of extracted verbs and nouns

[0201] Output: A list of business points that can be automated

[0202] Specific operation: The server combines the verb "input" with the noun "sales data" to determine that "entering sales data into a spreadsheet" can be automated. Similarly, it identifies "creating a report" as an automatable step.

[0203] Step 5:

[0204] Presenting business points that can be automated

[0205] The server presents the generated automatable business points to the user, allowing the user to check the business points that can be automated and receive specific suggestions for improvement.

[0206] Input: List of business points that can be automated

[0207] Output: Automated task points presented to the user

[0208] Specific operation: The server displays the business points that can be automated to the user through a web interface. The user checks the presented points and configures automation as necessary.

[0209] (Application example 2)

[0210] 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."

[0211] The work at conventional logistics centers requires a lot of manual work, which makes it inefficient. Furthermore, it is difficult to identify which parts of the work should be automated. This often delays the improvement of work efficiency and the promotion of automation.

[0212] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for importing text data of business content, generative AI means for generating automatable business points from the imported data, means for responding with the generated automatable business points, means for generating an automation proposal based on the generated automatable business points, and means for displaying the generated automation proposal. This makes it possible to identify automation points for business operations at a logistics center and make efficient automation proposals.

[0213] "Text data of work content" refers to data that records work procedures and work content at a logistics center in text format.

[0214] "Means of importing" refers to the means of inputting text data of business content into the system.

[0215] "Generative AI methods" are methods that use artificial intelligence technology to analyze imported text data and generate business points that can be automated.

[0216] "Automable business points" are points that identify parts of a business that can be automated.

[0217] The "answering means" is a means for presenting the generated automatable business points to the user.

[0218] The "means for generating automation proposals" is a means for proposing specific automation methods based on the generated automatable business points.

[0219] The "means for displaying" is a means for visually presenting the generated automated suggestions to the user.

[0220] The system for carrying out the present invention is designed to support the automation of operations in a logistics center. A specific embodiment of the system will be described below.

[0221] System configuration

[0222] The system consists of the following main components:

[0223] 1. A means of importing text data of business operations

[0224] 2. Generative AI methods that generate automatable business points from imported data

[0225] 3. A means of answering the generated automatable business points

[0226] 4. A means for generating automation proposals based on the generated automatable business points

[0227] 5. A way to view the generated automation suggestions

[0228] Hardware and software used

[0229] Hardware: Smartphone

[0230] Software: Python, spaCy (natural language processing library), transformers (generative AI model library)

[0231] Data processing and calculation

[0232] 1. A means of importing text data of business operations

[0233] The user inputs the work procedures and tasks to be performed in the logistics center in text format. For example, the user inputs work details such as "take out the product from the shelf" and "scan the product."

[0234] 2. Generative AI methods that generate automatable business points from imported data

[0235] The server uses spaCy to analyze the imported text data and extract verbs and nouns, thereby understanding the workflow and procedures and identifying points of the business that can be automated.

[0236] 3. A means of answering the generated automatable business points

[0237] The server presents the identified automatable business points to the user. For example, if a business point such as "take out a product from a shelf" is identified, the server displays this to the user.

[0238] 4. A means for generating automation proposals based on the generated automatable business points

[0239] The server uses a generative AI model with a sentiment analysis model to generate automation suggestions. For example, the server inputs a prompt sentence, "Please suggest a way to automate the following task: remove products from shelves," into the generative AI model to generate automation suggestions.

[0240] 5. A way to view the generated automation suggestions

[0241] The server visually presents the generated automation proposal to the user, for example, "We will introduce a system that uses a robotic arm to pick up products from shelves. The robotic arm will work in conjunction with a barcode scanner to pick up accurate products."

[0242] Specific examples

[0243] Input text data:

[0244] Remove the product from the shelf.

[0245] Scan the product.

[0246] Pack the product.

[0247] Print and attach the labels.

[0248] Example prompt for a generative AI model:

[0249] Suggest ways to automate the following tasks: Retrieving items from shelves.

[0250] In this way, a system can be realized that generates specific proposals for streamlining and automating operations within a logistics center.

[0251] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0252] Step 1:

[0253] Users input work procedures and work details in the logistics center in text format. For example, they input work details such as "take out products from shelves" and "scan products." The input text data is sent to the server.

[0254] Step 2:

[0255] The server uses spaCy to analyze the received text data. Specifically, it divides the text data into sentences and extracts the verbs and nouns contained in each sentence. This allows the flow and procedures of work to be understood and points of work that can be automated identified. For example, the verb "take out" is extracted from the sentence "Take out the product from the shelf."

[0256] Step 3:

[0257] The server presents the identified automatable task points to the user. For example, if a task point such as "taking out a product from a shelf" is identified, the server displays this to the user. The user confirms the displayed task point.

[0258] Step 4:

[0259] The server generates automation suggestions using a generative AI model based on the identified automatable business points. Specifically, a prompt sentence is input to the generative AI model using a sentiment analysis model. For example, a prompt sentence such as "Please suggest a way to automate the following business task: taking products off shelves" is input to the generative AI model to generate automation suggestions.

[0260] Step 5:

[0261] The server visually presents the generated automation proposal to the user. For example, it displays a proposal such as, "Introduce a system that uses a robotic arm to pick up products from shelves. The robotic arm will work in conjunction with a barcode scanner to pick up accurate products." The user can confirm the displayed automation proposal and execute it as necessary.

[0262] Example 3

[0263] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0264] With conventional business automation systems, it was difficult to simply import text data of business operations, generate automatable business points from that data, and show users specific improvement proposals and effects.In addition, there was a lack of a way for users to easily refer to automatable business points and check detailed information, which often delayed the introduction of business efficiency improvements and automation.

[0265] 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.

[0266] In this invention, the server includes means for importing data that has been converted into textual data on the content of work, generative AI means for generating automatable work points from the imported data, means for responding with the generated automatable work points, user interface means for displaying a list of the generated automatable work points so that the user can refer to them, and means for displaying the effects of automating that work point and specific suggestions when the user selects a specific work point. This allows the user to easily refer to the automatable work points and check their detailed information.

[0267] "Text data of business content" refers to data that records the procedures and content of business as text information.

[0268] "Means for importing" is a function for receiving data from the outside and storing it within the system.

[0269] "Generative AI means" is a function that uses artificial intelligence technology to extract specific information and patterns from input data and generate new data.

[0270] "Automable business points" refer to parts or processes within a business that can be automated.

[0271] "Means of responding" is a function for presenting generated information and data to the user.

[0272] "User interface means" refers to a function that provides a screen and operation means for the user to interact with the system.

[0273] "List display" refers to displaying multiple items in a list format.

[0274] "Making it accessible" means making the information available to users so that they can view it and check for more details if necessary.

[0275] "When selected" refers to the user selecting a specific item by clicking, tapping, or other operations.

[0276] An "effect" refers to the result or influence obtained by a particular action or process.

[0277] A "specific proposal" refers to presenting a concrete solution or improvement plan for a specific problem or issue.

[0278] This invention is a system that takes in text data of business operations, generates automatable business points from that data, and presents specific improvement suggestions and effects to users. Specific embodiments of this system are described below.

[0279] Server Processing

[0280] The server receives business data uploaded by users. This business data can be provided in various formats, such as spreadsheet files, CSV files, data extracted from databases, etc. For example, if a user uploads a file called "BusinessData.xlsx," the server receives the file.

[0281] The server then inputs the received business data into a generative AI model. This generative AI model is built using Tensorflow (registered trademark) and PyTorch and analyzes the data. Specifically, it detects patterns and trends in the business data and identifies business points that can be automated. For example, it analyzes the frequency of data entry and the timing of report generation.

[0282] The server organizes the automatable business points extracted by the generative AI model and sends them to the terminal in data format such as JSON. For example, business points such as "automated data entry" and "automated report generation" are extracted, along with the benefits of each (time savings, cost reduction, etc.).

[0283] Terminal handling

[0284] The terminal receives the business points sent from the server and displays them on the user interface. This user interface is built using HTML, CSS, and JavaScript (registered trademark) and is designed to allow users to operate it intuitively. Specifically, frameworks such as React.js and Vue.js are used to achieve dynamic list display. For example, lists such as "Automated data entry" and "Automated report generation" are displayed.

[0285] User operations

[0286] Users can view the business points that can be automated through the terminal's user interface. When a user clicks on a specific business point, the effects of that automation and specific suggestions are displayed. For example, when the user clicks on "automating data entry," the time-saving and cost-saving effects of that automation are displayed. Specific automation suggestions (e.g., the introduction of specific software or the use of scripts) are also displayed.

[0287] Specific examples

[0288] An example of a prompt sentence to be input into a generative AI model is, "Analyze the business data below and extract business points that can be automated."

[0289] In this way, users can easily refer to business points that can be automated and check the effects and specific proposals. This system is expected to facilitate the introduction of business efficiency and automation, contributing to improved productivity in companies. The flow of the specific processing in Example 3 will be explained using Figure 15.

[0290] Step 1:

[0291] The server receives the business data.

[0292] Input: Business data uploaded by the user (e.g., spreadsheet files, CSV files)

[0293] Specific operation: When a user uploads "Business Data.xlsx", the server receives the file and reads the data.

[0294] Output: Imported business data

[0295] Step 2:

[0296] The server analyzes business data using the generated AI model.

[0297] Input: Imported business data

[0298] How it works: The server inputs the data it reads into a generative AI model, which then analyzes the data using TensorFlow and PyTorch to detect patterns and trends in the business data.

[0299] Output: Automated business operations (e.g., "automated data entry" and "automated report generation")

[0300] Step 3:

[0301] The server extracts the business points that can be automated and sends them to the terminal.

[0302] Input: Automated business points

[0303] Specific operation: The server organizes the automatable business points extracted by the generative AI model and sends them to the terminal in a data format such as JSON.

[0304] Output: Business point data sent to the terminal

[0305] Step 4:

[0306] The terminal displays the received business points on the user interface.

[0307] Input: Business point data sent to the terminal

[0308] Specific operation: The terminal displays the received data in a user interface using React.js. For example, it displays lists such as "Automated data entry" and "Automated report generation."

[0309] Output: List of business points displayed on the user interface

[0310] Step 5:

[0311] The user references the business point and checks the detailed information.

[0312] Input: List of business points displayed on the user interface

[0313] Specific action: The user clicks on a specific business point (e.g., "automate data entry") from the displayed list. Once clicked, the effects of that automation (e.g., time savings, cost reduction) and specific suggestions (e.g., the introduction of specific software or the use of scripts) are displayed.

[0314] Output: Detailed information on the business points confirmed by the user

[0315] In this way, users can easily see which business points can be automated, and see the effects and specific suggestions.

[0316] (Application example 3)

[0317] 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."

[0318] In today's business environment, automation and efficiency of business processes are important issues. However, it is not easy to determine which business processes can be automated and how to automate them effectively. In addition, there is a lack of a way for users to easily understand which business processes can be automated and receive appropriate improvement suggestions based on that information. For this reason, there is a need for a system that can effectively promote business automation and efficiency.

[0319] 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.

[0320] In this invention, the server includes means for importing text data of business content, generative AI means for generating automatable business points from the imported data, means for responding with the generated automatable business points, user interface means for displaying a list of the generated automatable business points so that the user can refer to them, and means for displaying automation suggestions for each business point and the effects of that automation. This allows the user to easily understand the automatable business points and receive appropriate improvement suggestions.

[0321] "Text data of business content" refers to data that records the procedures and content of business in text format.

[0322] "Means for acquiring" refers to a method or device for acquiring data from the outside and storing it within the system.

[0323] "Generative AI means" is a system that has the ability to generate specific information or patterns from data using artificial intelligence.

[0324] "Automable business points" refer to parts or processes within a business that can be automated.

[0325] A "means for responding" is a method or device for providing the generated information or results to the user.

[0326] "User interface means" refers to a function that provides a screen and operation methods for users to interact with the system.

[0327] "List display" refers to displaying multiple items together on one screen or page.

[0328] An "automation proposal" is a proposal that shows specific methods and procedures for automating business processes.

[0329] "Effects of automation" refers to the benefits and results obtained by automating business processes.

[0330] A system for implementing this invention includes a means for importing text data of business content, a generative AI means for generating automatable business points from the imported data, a means for responding with the generated automatable business points, a user interface means for displaying a list of the generated automatable business points so that the user can refer to them, and a means for suggesting automation for each business point and displaying the effects of that automation.

[0331] The server first imports text data of the business operations. This data is extracted from, for example, business procedure manuals or work reports. The imported data is analyzed by a generative AI method, and business points that can be automated are generated. The generative AI method uses natural language processing technology and machine learning algorithms. Specifically, Python libraries such as NLTK and spaCy, and machine learning frameworks such as TensorFlow and PyTorch are used.

[0332] The generated automatable task points are displayed in a list through a user interface. Users can view these points on their smartphone or PC screen. The user interface is built using web technologies such as HTML, CSS, and JavaScript.

[0333] Furthermore, automation suggestions and the effects of such automation are displayed for each task. This allows users to understand specifically which tasks should be automated and how. For example, a suggestion might be displayed such as, "By automating data entry tasks with an RPA tool, you can reduce work time by 50%."

[0334] For example, the following prompts are generated:

[0335] "We propose the introduction of an RPA tool to automate data entry tasks. Using this tool will reduce the time required for tasks by 50% and reduce the error rate."

[0336] In this way, users can easily identify areas of their business that can be automated and receive appropriate improvement suggestions.

[0337] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0338] Step 1:

[0339] The server takes in the text data of the business content.

[0340] Input: Text data such as work procedures and work reports

[0341] Specific operation: The server receives text data of the business details provided by the user via the file upload function or API. The received data is stored in the database.

[0342] Output: Saved text data

[0343] Step 2:

[0344] The server runs a generative AI method that generates automatable business points from the imported data.

[0345] Input: Saved text data

[0346] How it works: The server uses natural language processing (NLTK or spaCy) to analyze text data and understand the business operations. It then uses machine learning algorithms (TensorFlow or PyTorch) to identify business operations that can be automated.

[0347] Output: A list of business points that can be automated

[0348] Step 3:

[0349] The server executes a means for returning the generated automatable task points.

[0350] Input: List of business points that can be automated

[0351] Specific operation: The server obtains a list of business points from the database and passes it to the user interface to provide the generated business points to the user.

[0352] Output: A list of business points displayed in the user interface

[0353] Step 4:

[0354] The terminal displays a list of the generated automatable business points so that the user can refer to them.

[0355] Input: A list of business points passed to the user interface

[0356] Specific operation: The terminal uses HTML, CSS, and JavaScript to display a list of business points on the screen. The user can check these points on the screen of their smartphone or computer.

[0357] Output: A list of business points displayed on the screen

[0358] Step 5:

[0359] The server implements a means for proposing automation for each business point and displaying the effects of that automation.

[0360] Input: List of business points that can be automated

[0361] Specific operation: The server uses the generative AI model to generate automation proposals for each business point. For example, a proposal might be generated such as, "By automating data entry work with an RPA tool, work time can be reduced by 50%." These proposals are then passed to the user interface.

[0362] Output: The automation suggestions and their effects displayed in the user interface

[0363] Step 6:

[0364] The terminal displays automation suggestions and their effects through a user interface.

[0365] Input: Automation suggestions passed to the user interface and their effects

[0366] Specific operation: The device uses HTML, CSS, and JavaScript to display automation suggestions and their effects on the screen. The user can refer to these suggestions to specifically understand which tasks should be automated and how.

[0367] Output: The automation suggestions and their effects displayed on the screen

[0368] 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.

[0369] "Example 1"

[0370] One embodiment of the present invention provides a DX diagnostic system that includes a means for importing text data of business operations, a generative AI means for generating automatable business points from the imported data, and a means for providing the generated automatable business points. This system further incorporates an emotion engine that recognizes user emotions. Specifically, a user inputs business operations as text, and the text data is imported into the system. Next, the generative AI analyzes the text data and identifies business points that can be automated. During this process, the emotion engine recognizes the user's emotions and provides the results to the generative AI. The generative AI takes this emotional information into account to generate automatable business points.

[0371] "Example 2"

[0372] As a concrete example, consider the case where a user inputs "responding to customer inquiries" as a task. From this text data, the generative AI identifies tasks that can be automated, such as "classifying the inquiry content" and "selecting the appropriate response." Meanwhile, the emotion engine recognizes that the user is feeling stressed about this task. Taking this emotional information into consideration, the generative AI makes suggestions for improving the task to reduce stress. For example, by automating "classifying the inquiry content," the AI ​​makes suggestions to reduce the user's workload.

[0373] "Example 3"

[0374] The emotion engine also uses an emotion recognition model to recognize the user's emotional state. This model infers emotions from the user's text and voice input. Specifically, it infers the user's emotional state from the keystroke patterns when the user enters text and the tone of voice when the user enters voice. This emotional information is an important reference for the generative AI when it makes business improvement proposals.

[0375] The processing flow of each embodiment will be described below.

[0376] "Example 1"

[0377] Step 1: The user enters the job description as text.

[0378] Step 2: The system captures the text data.

[0379] Step 3: Generative AI analyzes the text data and identifies areas where tasks can be automated.

[0380] Step 4: The emotion engine recognizes the user's emotions and provides the results to the generative AI.

[0381] Step 5: Generative AI takes emotional information into account to generate automatable task points.

[0382] "Example 2"

[0383] Step 1: The user enters "responding to customer inquiries" as the job description.

[0384] Step 2: Generative AI uses the text data to identify tasks that can be automated, such as classifying the inquiry and selecting the appropriate response.

[0385] Step 3: The emotion engine recognizes that the user is feeling stressed about this task.

[0386] Step 4: Generative AI takes emotional information into account and makes suggestions for work improvements to reduce stress.

[0387] "Example 3"

[0388] Step 1: The emotion engine uses the emotion recognition model to recognize the user's emotional state.

[0389] Step 2: The emotion recognition model estimates emotions from the user's text input, voice input, etc.

[0390] Step 3: This emotional information is provided to the generative AI and used as reference information for business improvement proposals.

[0391] Example 1

[0392] 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."

[0393] Conventional business automation systems have the means to import text data of business operations and generate automatable business points, but do not generate automation points that take the user's emotions into account. As a result, proposals are made that ignore the user's emotions and stress levels, which leads to issues such as insufficient improvement in business efficiency and user satisfaction.

[0394] 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.

[0395] In this invention, the server includes means for importing text data of work content, generative AI means for generating automatable work points from the imported data, means for returning the generated automatable work points, emotion engine means for recognizing user emotions, and means for generating automatable work points in consideration of emotion information from the emotion engine means. This makes it possible to generate automation points that take user emotions into consideration, thereby achieving improved work efficiency and improved user satisfaction.

[0396] "Text data of business content" refers to data that expresses the details and procedures of business as text information.

[0397] "Means of import" refers to the interface or API used to input business details into the system as text data.

[0398] "Generative AI methods" refers to artificial intelligence technology that analyzes imported text data and identifies business points that can be automated.

[0399] "Automable business points" refer to parts or processes within a business that can be automated.

[0400] The "means for replying" refers to a function for notifying the user of the generated automatable business points.

[0401] "Emotion engine means" refers to technology for recognizing a user's emotions and providing that information to generative AI.

[0402] "Emotional information" is data that indicates the user's emotional state, and includes information obtained from input content, input speed, keystroke patterns, and the like.

[0403] This invention is a system that takes in text data of work content, generates automatable work points from that data, and responds to the user. It also has the function of recognizing the user's emotions and generating automatable work points taking that information into consideration.

[0404] Hardware and software used

[0405] Hardware: Servers, user terminals

[0406] Software: Business management system, generative AI, emotion engine, API

[0407] System configuration

[0408] 1. How to import text data of business operations:

[0409] Users use an interface to input their work details. This interface is a form that runs on a web browser, and users enter their work details into text boxes. For example, they might enter specific work details such as "Check email every day at 9:00 and forward important emails to their boss."

[0410] 2. Generative AI methods that generate automatable business points from captured data:

[0411] The server receives text data entered by the user and sends it to the generative AI. The generative AI uses natural language processing technology to analyze the text data and identify points in the business that can be automated. For example, the part "check email every day at 9 o'clock" is identified as an area that can be automated.

[0412] 3. Means of answering the generated automatable business points:

[0413] The server responds to the user with the points of work that can be automated obtained from the generative AI. The user can then view the list of work points that can be automated in a web browser. For example, specific suggestions such as "It is recommended that you automate the task of checking email at 9:00 every day" are displayed.

[0414] 4. Emotion engine means for recognizing user emotions:

[0415] The server sends the emotional data of the user's input to the emotion engine, which then recognizes the user's emotions based on the content, speed, and keystroke patterns of the input. For example, if the user is feeling stressed, that information is provided to the generative AI.

[0416] 5. Means for generating automatable task points taking into account emotion information from emotion engine means:

[0417] The generative AI takes into account the emotional information provided by the emotion engine to optimize tasks that can be automated. For example, if the user is feeling stressed, the generative AI will prioritize automating those tasks.

[0418] Specific examples

[0419] An example of a prompt sentence when a user inputs their job description as text is, "Please input your job description as text. For example, please enter specific job description such as 'Check email every day at 9:00 and forward important emails to your boss.'"

[0420] This system allows users to easily identify areas for automation in their work and improve work efficiency. In addition, by taking user emotions into consideration, more appropriate automation suggestions can be made, which will improve user satisfaction.

[0421] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0422] Step 1:

[0423] The user inputs the business details as text.

[0424] The user uses the interface on the web browser to enter the details of their work in the text box. For example, they can enter specific details such as "Check email every day at 9:00 and forward important emails to their boss." The entered text data is sent to the server by clicking the send button.

[0425] Input: Text data of business content

[0426] Output: Text data sent to the server

[0427] Step 2:

[0428] The server receives the text data and sends it to the generative AI.

[0429] The server receives text data sent by the user. The received data is formatted into a format that can be understood by the generative AI. The formatted data is then sent to the generative AI.

[0430] Input: Text data sent by the user

[0431] Output: Formatted text data sent to the generative AI

[0432] Step 3:

[0433] Generative AI analyzes text data and identifies business points that can be automated.

[0434] Generative AI uses natural language processing technology to analyze text data. During the analysis process, it identifies points in a business process that can be automated. For example, "checking email every day at 9 o'clock" is identified as an area that can be automated.

[0435] Input: Formatted text data

[0436] Output: Automated business points

[0437] Step 4:

[0438] The emotion engine recognizes the user's emotions and provides that information to the generative AI.

[0439] The server sends the emotional data of the user's input to the emotion engine, which then recognizes the emotion from the user's input content, input speed, keystroke patterns, etc. The recognized emotional information is then provided to the generative AI.

[0440] Input: Emotional data when the user inputs

[0441] Output: Emotional information provided to the generative AI

[0442] Step 5:

[0443] Generative AI takes emotional information into account to generate tasks that can be automated.

[0444] The generative AI takes into account the emotional information provided by the emotion engine to optimize tasks that can be automated. For example, if the user is feeling stressed, the generative AI will prioritize automating those tasks.

[0445] Input: Emotional information, automatable business points

[0446] Output: Optimized and automatable business points

[0447] Step 6:

[0448] The server responds to the user with the generated automatable business points.

[0449] The server responds to the user with optimized automatable task points obtained from the generative AI. The user can check the list of automatable task points in a web browser. For example, specific suggestions such as "It is recommended that you automate the task of checking email every day at 9 o'clock" are displayed.

[0450] Input: Optimized and automatable business points

[0451] Output: A list of automatable business points answered by the user

[0452] (Application example 1)

[0453] 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."

[0454] While conventional process automation systems can analyze text data of work content and identify points that can be automated, they face the challenge of making it difficult to automate efficiently while taking into account the emotions of operators.In addition, they lack the functionality to issue specific instructions to robots based on the identified automation points, making it difficult to maximize operational efficiency within factories.

[0455] 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.

[0456] In this invention, the server includes means for importing text data of work content, generative AI means for generating automatable work points from the imported data, means for responding with the generated automatable work points, an emotion engine for recognizing user emotions, means for generating automatable work points taking into account emotion information, and means for issuing instructions to a robot based on the generated automatable work points. This enables efficient work automation that takes into account the emotions of operators, making it possible to maximize work efficiency within a factory.

[0457] "Means for importing text data of business content" refers to an interface that allows users to input business content as text data, and an API that automatically retrieves text data from existing business management systems.

[0458] "Generative AI means that generate automatable business points from input data" refers to artificial intelligence technology that analyzes input text data and identifies business points that can be automated.

[0459] "Means of answering the generated automatable business points" refers to a function for presenting to users the automatable business points identified by generative AI.

[0460] An "emotion engine that recognizes user emotions" refers to technology that analyzes the user's emotional state and provides that information to generative AI.

[0461] "Means for generating automatable task points by taking emotional information into consideration" refers to a function that enables generative AI to identify automatable task points based on emotional information provided by the emotion engine.

[0462] "Means for issuing instructions to robots based on the generated automatable task points" refers to a function for issuing specific instructions to factory robots based on the identified automation points.

[0463] As an embodiment of the present invention, a Factory Automation Assistance System (FAASS) will be described as an example. This system inputs work content as text data, analyzes the data, identifies work points that can be automated, and issues instructions to robots. Furthermore, it uses an emotion engine to recognize the emotions of operators and improve work efficiency.

[0464] Hardware and software used

[0465] Hardware: smartphones, tablets, factory robots

[0466] Software: Business management system API, generative AI models (e.g., GPT-4), emotion engine

[0467] Program processing procedure

[0468] 1. Enter your job description:

[0469] Users enter their work details in text form through the interface of their smartphone or tablet.

[0470] The server uses the business management system API to automatically retrieve existing business data.

[0471] 2. Data Analysis:

[0472] The server uses a generative AI model (e.g., GPT-4) to analyze the input text data.

[0473] The server uses an emotion engine to recognize the user's emotion.

[0474] 3. Identify automation points:

[0475] The server uses generative AI to identify business points that can be automated based on the analysis results and emotional information.

[0476] 4. Giving instructions to the robot:

[0477] The server issues specific instructions to factory robots based on the identified automation points.

[0478] Robots automate tasks according to instructions.

[0479] Specific examples

[0480] Example prompts for generative AI models

[0481] Prompt statement:

[0482] Analyze the following tasks and identify areas that can be automated. Take into account the emotional state of the operators.

[0483] Job Description: Assembling part A, inspecting part B, packaging part C

[0484] Operator emotions: High stress.”

[0485] When this prompt sentence is input into a generative AI model, the model analyzes the work content and identifies specific automation points, such as "assembly of part A can be automated by a robot." By taking emotional information into account, it is possible to prioritize automation of high-stress tasks.

[0486] The above is a concrete example of application to a factory robot and the program processing procedure. This system enables efficient automation of work processes that take into account the emotions of the operator, maximizing work efficiency within the factory.

[0487] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0488] Step 1:

[0489] Users input their work details as text through the interface of their smartphone or tablet. The input text data is sent to the server. The input data includes details of the work and the operator's emotional information.

[0490] Step 2:

[0491] The server uses the business management system API to automatically retrieve existing business data, which is then integrated with the text data entered by the user to form a dataset for analysis.

[0492] Step 3:

[0493] The server uses a generative AI model (e.g., GPT-4) to analyze the integrated text data. The input for the analysis is text data containing task content and emotional information, and the output is a list of task points that can be automated. The generative AI model understands the task content and identifies the parts that can be automated.

[0494] Step 4:

[0495] The server uses an emotion engine to recognize the user's emotions. The input is the user's emotional information, and the output is the emotional analysis results. The emotion engine evaluates the user's stress level and satisfaction level and provides that information to the generative AI.

[0496] Step 5:

[0497] The server uses generative AI to identify points in tasks that can be automated based on the analysis results and emotional information. The input is the analysis results of the task content and emotional information, and the output is a list of points in tasks that can be automated taking emotions into account. The generative AI takes emotional information into account to identify tasks that should be prioritized for automation.

[0498] Step 6:

[0499] The server issues specific instructions to the factory robots based on the identified automation points. The input is a list of automatable business points, and the output is instructions to the robots. The server generates specific tasks for the robots to perform and sends them to the robots.

[0500] Step 7:

[0501] Robots automate tasks according to instructions received from a server. The input is the instruction from the server, and the output is the task that has been executed. Based on the instructions, the robot automatically performs tasks such as assembling parts, inspecting them, and packaging them.

[0502] These are the specific processing steps of the Factory Automation Assistant System (FAASS). This system enables efficient automation of tasks while taking into account the emotions of the operators, maximizing the efficiency of work within the factory.

[0503] Example 2

[0504] 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."

[0505] Conventional business automation systems are limited to analyzing business processes and extracting points that can be automated, and do not provide business improvement suggestions that take into account the user's emotional state. As a result, there is a lack of specific suggestions to reduce user stress and workload. This has resulted in insufficient improvements in business efficiency and user satisfaction.

[0506] 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.

[0507] In this invention, the server includes means for importing text data of business content, generative AI means for generating automatable business points from the imported data, means for returning the generated automatable business points, emotion analysis means for analyzing the emotional state of the user, and means for making business improvement proposals based on the emotion analysis results, thereby enabling business improvement proposals that take the emotional state of the user into consideration.

[0508] The "means for importing data in the form of text of business content" is a function for importing business content entered by a user into the system as text data.

[0509] "Generative AI means that generates automatable business points from imported data" is a function that uses artificial intelligence technology to analyze imported text data and identify business points that can be automated.

[0510] The "means for answering generated automatable business points" is a function for presenting to the user the automatable business points identified by the generative AI means.

[0511] The "emotion analysis means for analyzing the user's emotional state" is a function for analyzing the user's input data and past work history, and evaluating the emotional state the user feels about the work.

[0512] The "means for making business improvement proposals based on emotion analysis results" is a function for generating specific business improvement proposals to reduce the user's stress, taking into account the user's emotional state obtained by the emotion analysis means.

[0513] This invention is a system that imports and analyzes text data of business operations, identifies business points that can be automated, and proposes business improvement measures that take into account the emotional state of the user. A specific embodiment of this system will be described below.

[0514] System configuration

[0515] Hardware

[0516] This system consists of a server and a user terminal. The server is equipped with a high-performance processor and large-capacity memory, and performs data analysis and generative AI processing. The user terminal provides an interface for users to input their work details.

[0517] software

[0518] The following software is installed on the server:

[0519] 1. Natural language processing libraries: Use natural language processing libraries such as "spaCy" or "NLTK" to analyze text data.

[0520] 2. Generative AI models: Use machine learning models to identify automation points from text data.

[0521] 3. Sentiment Analysis Engine: Use an engine to analyze the user's emotional state.

[0522] System Operation

[0523] Entering text data

[0524] The user inputs the details of the job as text data. For example, the user inputs "responding to inquiries from customers." The user terminal transmits this input data to the server.

[0525] Analysis using natural language processing

[0526] The server analyzes the received text data using a natural language processing library. Specifically, it performs the following processes:

[0527] Tokenization of text data (splitting into words and phrases)

[0528] Part-of-speech tagging (identifying parts of speech such as verbs and nouns)

[0529] Dependency analysis (analyzing the relationships between words)

[0530] Extracting points that can be automated

[0531] Based on the analysis results, the server uses a generative AI model to extract tasks that can be automated, such as classifying the content of inquiries and selecting appropriate responses.

[0532] Emotion analysis

[0533] The server uses a sentiment analysis engine to analyze the user's emotional state. For example, if the user enters "this job is stressful," the sentiment analysis engine determines that the user is experiencing high stress.

[0534] Generate business improvement proposals

[0535] The server generates work improvement proposals to reduce user stress based on the results of the generative AI model and the emotion analysis engine. For example, it could generate a proposal to "reduce the user's workload by automating the classification of inquiry content" and present it to the user.

[0536] Examples of specific examples and prompts

[0537] As a specific example, consider the case where the user inputs "responding to inquiries from customers" as the job content.

[0538] Input: "Responding to customer inquiries"

[0539] Output of generative AI: "Classification of inquiry content" and "Selection of appropriate response"

[0540] Sentiment analysis engine output: "The user is stressed"

[0541] Business improvement proposal: "By automating the classification of inquiries, we can reduce the workload of users."

[0542] An example of a prompt sentence is as follows:

[0543] When a user inputs "responding to customer inquiries" as a task, the generative AI identifies tasks that can be automated, such as "classifying the inquiry" and "selecting the appropriate response." Meanwhile, the sentiment analysis engine recognizes that the user is feeling stressed about this task. Taking this emotional information into consideration, the generative AI makes suggestions for improving the task to reduce stress. For example, by automating "classifying the inquiry," the system makes suggestions to reduce the user's workload.

[0544] In this way, the server analyzes the user's work, identifies points that can be automated, and makes suggestions for improving the work.

[0545] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0546] Step 1:

[0547] The user inputs the details of the job as text data. For example, the user inputs "responding to inquiries from customers." The user terminal transmits this input data to the server.

[0548] Input: Text data of work content (e.g., "Responding to customer inquiries")

[0549] Output: Text data sent to the server

[0550] Step 2:

[0551] The server analyzes the received text data using a natural language processing library. Specifically, it performs the following processes:

[0552] Tokenization of text data (splitting into words and phrases)

[0553] Part-of-speech tagging (identifying parts of speech such as verbs and nouns)

[0554] Dependency analysis (analyzing the relationships between words)

[0555] Input: Text data sent by the user

[0556] Output: Parsed text data (tokenization, part-of-speech tagging, dependency analysis results)

[0557] Step 3:

[0558] Based on the analysis results, the server uses a generative AI model to extract tasks that can be automated, such as classifying the content of inquiries and selecting appropriate responses.

[0559] Input: Parsed text data

[0560] Output: Automated task points (e.g., "classifying inquiry content" and "selecting appropriate responses")

[0561] Step 4:

[0562] The server uses a sentiment analysis engine to analyze the user's emotional state. For example, if the user enters "this job is stressful," the sentiment analysis engine determines that the user is experiencing high stress.

[0563] Input: User input data and past work history

[0564] Output: User's emotional state (e.g., "High Stress")

[0565] Step 5:

[0566] The server generates work improvement proposals to reduce user stress based on the results of the generative AI model and the emotion analysis engine. For example, it could generate a proposal to "reduce the user's workload by automating the classification of inquiry content" and present it to the user.

[0567] Input: Automable task points and user emotional state

[0568] Output: Proposal for business improvement (e.g., "By automating the classification of inquiries, we can reduce the workload of users.")

[0569] In this way, the server analyzes the user's work, identifies points that can be automated, and makes suggestions for improving the work.

[0570] (Application example 2)

[0571] 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."

[0572] Conventional business automation systems can extract points that can be automated from text data of business operations, but they are unable to propose business improvement measures that take into account the user's emotions and stress levels. As a result, while they can improve business efficiency, there are limitations to how much they can reduce user stress and workload. In addition, they lack a function to notify users of the progress of work in real time, making it difficult to manage the progress of work. There is a need to solve these issues.

[0573] 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.

[0574] In this invention, the server includes a means for importing text data of work content, a generative AI means for generating automatable work points from the imported data, a means for responding with the generated automatable work points, a means including an emotion engine for recognizing emotional information and making work improvement proposals, and a means for notifying the progress of automated work in real time. This enables work improvement proposals that take into account the user's emotions and stress level, thereby not only improving work efficiency but also reducing the user's stress and workload. Furthermore, real-time notification of work progress makes it easier to manage work progress.

[0575] "Means for importing text data of business content" refers to means for inputting business content as text data and importing that data into the system.

[0576] "Generative AI means that generates automatable business points from imported data" refers to artificial intelligence means that analyzes imported text data and extracts and generates business points that can be automated.

[0577] The "means for answering the generated automatable task points" is a means for presenting the automatable task points generated by the generative AI means to the user.

[0578] "Means including an emotion engine that recognizes emotion information and makes business improvement proposals" refers to means that includes an emotion engine that analyzes the user's emotion information and makes business improvement proposals based on that information.

[0579] "Means for notifying the user of the progress of an automated task in real time" refers to means for notifying the user of the progress of an automated task in real time.

[0580] A system for implementing this invention includes a means for importing data that has been converted into text about work content, a generative AI means for generating automatable work points from the imported data, a means for responding with the generated automatable work points, a means including an emotion engine that recognizes emotional information and makes suggestions for work improvement, and a means for notifying the progress of automated work in real time.

[0581] Hardware and software used

[0582] Hardware: Smartphone

[0583] Software: Python, spaCy (natural language processing library), sentiment analysis model (transformers library)

[0584] Data processing and calculation

[0585] 1. Enter and import business details

[0586] The user inputs the work content as text data using a smartphone. For example, the user inputs the work content such as "product picking work."

[0587] 2. Creating automatable business points

[0588] The server uses spaCy to analyze the imported text data and extract verbs and nouns, thereby generating task points that can be automated.

[0589] 3. Recognizing emotional information and proposing business improvements

[0590] The server uses a sentiment analysis model to analyze the emotions in the input text data and determine the user's stress level. For example, if the emotion is determined to be "NEGATIVE," the emotion engine will make suggestions for improving the business. Specifically, it will make suggestions such as, "By automating the picking work, we will reduce the workload."

[0591] 4. Real-time notifications of work progress

[0592] The server notifies the user of the progress of the automated tasks in real time, allowing the user to keep track of the progress of the tasks.

[0593] Specific examples

[0594] Entering work content: The user enters "product picking work" as the work content.

[0595] Result of automation point extraction: The server identifies "product picking work" as a business point that can be automated.

[0596] Sentiment analysis result: The server uses the emotion engine to determine "NEGATIVE."

[0597] Business improvement proposal: The server suggests, "By automating the picking process, we can reduce the workload."

[0598] Prompt Sentence Examples

[0599] If a user inputs "picking products" as a task description, the generative AI will use this text data to identify "picking products" as a task that can be automated. Meanwhile, the emotion engine will recognize that the user is feeling stressed about this task. Taking this emotional information into consideration, the generative AI will make suggestions for improving the task to reduce stress. For example, it will suggest automating the "picking task" to reduce the user's workload.

[0600] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0601] Step 1:

[0602] The user inputs the details of the work as text data using a smartphone.

[0603] Input: Text data of the work content (e.g., "Picking products")

[0604] Output: Text data is sent to the server

[0605] Specific operation: The user launches the smartphone application, enters the details of the task in the text input field, and presses the send button.

[0606] Step 2:

[0607] To analyze the text data imported by the server, natural language processing is performed using spaCy.

[0608] Input: Text data sent by the user

[0609] Output: A list of extracted verbs and nouns (e.g., "product," "picking," "work")

[0610] Specific operation: The server receives the text data and uses the spaCy library to tokenize the text and extract verbs and nouns.

[0611] Step 3:

[0612] The server generates automatable business points based on the extracted verbs and nouns.

[0613] Input: A list of extracted verbs and nouns

[0614] Output: Automated business points (e.g., "Automation of picking work")

[0615] Specific operation: The server analyzes the extracted verbs and nouns, identifies business points that can be automated, and creates a list.

[0616] Step 4:

[0617] The server uses a sentiment analysis model to analyze the sentiment of the input text data.

[0618] Input: Text data sent by the user

[0619] Output: Sentiment analysis result (e.g. "NEGATIVE")

[0620] Specific operation: The server inputs the text data into the sentiment analysis model and determines the sentiment using the sentiment analysis model.

[0621] Step 5:

[0622] The server generates business improvement proposals based on the sentiment analysis results.

[0623] Input: Sentiment analysis results, automatable business points

[0624] Output: Proposal for business improvement (e.g., "By automating picking work, we can reduce the workload.")

[0625] Specific operation: The server compares the results of the sentiment analysis with points of work that can be automated and generates work improvement suggestions to reduce the user's stress.

[0626] Step 6:

[0627] The server notifies the user of the generated business improvement proposal.

[0628] Input: Business improvement proposal

[0629] Output: Business improvement proposals displayed on the user's smartphone

[0630] Specific operation: The server sends the generated business improvement proposal to the user's smartphone and displays it as a notification.

[0631] Step 7:

[0632] The server notifies the user of the progress of the automated tasks in real time.

[0633] Input: Automated work progress data

[0634] Output: Progress displayed on the user's smartphone

[0635] Specific operation: The server collects progress data of automated tasks and notifies the user's smartphone in real time.

[0636] Example 3

[0637] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0638] Conventional business automation systems make uniform suggestions without considering the user's emotional state, which often results in suggestions that do not meet the user's needs. Furthermore, they lack a mechanism for effectively utilizing user feedback, which reduces the accuracy and usefulness of the suggestions. This creates the problem of not being able to fully utilize the benefits of business efficiency and automation.

[0639] 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.

[0640] In this invention, the server includes means for importing text data of business content, generative AI means for generating automatable business points from the imported data, means for responding with the generated automatable business points, emotion recognition means for recognizing the emotional state of the user, means for generating business improvement proposals based on the results of the emotion recognition means, and means for displaying the generated business improvement proposals and collecting feedback from the user. This enables customized business improvement proposals that take the user's emotional state into consideration, improving the accuracy and usefulness of the proposals.

[0641] "Text data of business content" refers to data that records the procedures and content of business as text information.

[0642] "Means for importing" refers to means for receiving data from the outside and storing or processing it within the system.

[0643] "Generative AI methods" are methods that use artificial intelligence to analyze data and generate business points that can be automated.

[0644] "Automable business points" refer to parts or processes within a business that can be automated.

[0645] The "means of replying" is a means for providing the generated automatable business points and suggestions to the user.

[0646] An "emotion recognition means" is a means for analyzing a user's input data (text or voice) and estimating the user's emotional state.

[0647] The "means for generating business improvement proposals" is a means for generating proposals for improving business efficiency and automation based on the results of the emotion recognition means.

[0648] The "display means" is a means for displaying the generated business improvement proposals on a user interface.

[0649] "Means for collecting feedback" refers to the means for collecting opinions and reactions from users and reflecting them in the system.

[0650] This invention is a system that imports text data of business operations, generates automatable business points from the imported data, and responds with the generated automatable business points. It also includes a function to recognize the user's emotional state, generate business improvement proposals based on the results, display the proposals, and collect feedback from the user.

[0651] Hardware and software used

[0652] Hardware: Servers (e.g., cloud computing services)

[0653] Software: Emotion recognition models (e.g., emotion analysis engines), generative AI models (e.g., natural language processing engines)

[0654] Specific operation of the system

[0655] User login

[0656] A user accesses the system and enters their username and password on the login screen. The server authenticates them by checking the information against a database. If authentication is successful, the user is redirected to the dashboard.

[0657] Generate automatable business points

[0658] The server analyzes the user's work data and uses a generative AI model to generate automatable work points, taking into account past work history and current work content in the process.

[0659] List of business points

[0660] The server displays a list of the generated automatable business points in the user interface, allowing the user to check the automation proposals for each business point and their effects.

[0661] Performing emotion recognition

[0662] When a user inputs text or voice, the server sends the input data to an emotion recognition model that analyzes keystroke patterns, tone of voice, and other factors to estimate the user's emotional state.

[0663] Generate business improvement proposals

[0664] Based on the emotion recognition results, the server uses a generative AI model to generate business improvement suggestions, which are customized taking into account the user's emotional state.

[0665] Viewing suggestions and user feedback

[0666] The server displays the generated business improvement proposals on a user interface. The user can review the proposals and provide feedback. The feedback is used to generate proposals from the next time onwards.

[0667] Specific examples

[0668] Viewing business points that can be automated

[0669] When a user logs in to the system, the server displays a list of business points such as "automated data entry" and "automated report generation." For each business point, it displays specific benefits such as "By automating this task, work time will be reduced by 30%."

[0670] Use of Emotion Recognition

[0671] When a user types in the text "Can this task really be automated?", the server uses an emotion recognition model to analyze the user's emotional state. For example, it can infer from keystroke patterns that the user is skeptical, and the generative AI model will make suggestions such as, "Should I explain the specific steps for automation in detail?"

[0672] Prompt Sentence Examples

[0673] "Please tell us the benefits of automating data entry."

[0674] "Please explain in more detail how you automate report generation."

[0675] "How much time will I save by automating this task?"

[0676] In this way, the server provides automatable business points through the user interface, grasps the user's emotional state using the emotion recognition model, and the generative AI model makes appropriate business improvement proposals. The flow of the identification process in Example 3 will be described with reference to Figure 21.

[0677] Step 1:

[0678] User login

[0679] A user accesses the system and enters their username and password on the login screen. The server authenticates them by checking the information against a database. If authentication is successful, the user is redirected to the dashboard.

[0680] Input: Username, Password

[0681] Output: Authentication result (success / failure), dashboard screen

[0682] Specific behavior:

[0683] The user enters information into the login form.

[0684] The server queries the database and performs authentication.

[0685] If authentication is successful, redirect the user to the dashboard.

[0686] Step 2:

[0687] Generate automatable business points

[0688] The server analyzes the user's work data and uses a generative AI model to generate automatable work points, taking into account past work history and current work content in the process.

[0689] Input: Business data

[0690] Output: Automated business points

[0691] Specific behavior:

[0692] The server collects the user's business data.

[0693] A generative AI model analyzes the data and extracts business points that can be automated.

[0694] Step 3:

[0695] List of business points

[0696] The server displays a list of the generated automatable business points in the user interface, allowing the user to check the automation proposals for each business point and their effects.

[0697] Input: Automated business points

[0698] Output: List of business points

[0699] Specific behavior:

[0700] The server generates a list of business points.

[0701] Display the list in the user interface.

[0702] Step 4:

[0703] Performing emotion recognition

[0704] When a user inputs text or voice, the server sends the input data to an emotion recognition model that analyzes keystroke patterns, tone of voice, and other factors to estimate the user's emotional state.

[0705] Input: User text input, voice input

[0706] Output: Estimated emotional state

[0707] Specific behavior:

[0708] The user provides input via text or voice.

[0709] The server sends the input data to the emotion recognition model.

[0710] The emotion recognition model estimates the emotional state and returns the results to the server.

[0711] Step 5:

[0712] Generate business improvement proposals

[0713] Based on the emotion recognition results, the server uses a generative AI model to generate business improvement suggestions, which are customized taking into account the user's emotional state.

[0714] Input: Emotional state estimation results, automatable task points

[0715] Output: Business improvement proposals

[0716] Specific behavior:

[0717] The server receives the emotion recognition results.

[0718] A generative AI model generates business improvement proposals.

[0719] Step 6:

[0720] Viewing suggestions and user feedback

[0721] The server displays the generated business improvement proposals on a user interface. The user can review the proposals and provide feedback. The feedback is used to generate proposals from the next time onwards.

[0722] Input: Business improvement suggestions, user feedback

[0723] Output: Improved suggestions, feedback saved

[0724] Specific behavior:

[0725] The server displays the business improvement proposals on the user interface.

[0726] The user reviews the proposal and provides feedback.

[0727] The server collects the feedback and stores it in a database.

[0728] (Application example 3)

[0729] 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."

[0730] Conventional task automation systems can identify points in a task that can be automated, but they have the problem of being unable to propose task improvement measures that take into account the user's emotional state. This can result in insufficient improvements to work efficiency and safety. Furthermore, there is a lack of means to reduce the psychological resistance of users when accepting automation proposals.

[0731] 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.

[0732] In this invention, the server includes means for importing text data of work content, generative AI means for generating automatable work points from the imported data, means for responding with the generated automatable work points, emotion recognition means for recognizing the emotional state of the user, and means for making work improvement suggestions based on the emotion information. This makes it possible to make work improvement suggestions that take the emotional state of the user into consideration, thereby achieving improvements in work efficiency and safety and reducing the psychological resistance of users when accepting automation suggestions.

[0733] "Text data of business content" refers to data that records the procedures and content of business as text information.

[0734] "Means for importing" refers to the functions and devices for acquiring data from the outside and importing it into the system.

[0735] "Generative AI means" refers to functions or devices that use artificial intelligence technology to generate automatable business points from captured data.

[0736] "Automable business points" refer to parts or tasks within a business that can be automated.

[0737] The "answering means" refers to a function or device for presenting the generated automatable business points to the user.

[0738] "Emotion recognition means for recognizing the user's emotional state" refers to a function or device for inferring emotions from the user's voice or text input.

[0739] The "means for proposing business improvement based on emotion information" refers to a function or device for proposing business improvement based on recognized emotion information.

[0740] As an embodiment of the present invention, we will explain an example of a "factory work automation support system" installed on a factory robot. This system imports text data of work content, generates automatable work points from the imported data, and responds with the generated automatable work points. It also recognizes the user's emotional state and makes work improvement suggestions based on the emotional information.

[0741] Hardware and software used

[0742] Hardware:

[0743] Factory robot (with display)

[0744] Microphone (for voice input)

[0745] software:

[0746] Python

[0747] GUI Toolkit

[0748] Emotion Recognition Model

[0749] Business improvement proposal generation algorithm

[0750] Data processing and calculation

[0751] server:

[0752] The server imports text data of the work content. The imported data is analyzed by generative AI to generate work points that can be automated. The generated work points are then sent to the user as answers.

[0753] Device:

[0754] The terminal (factory robot) receives the user's voice and text input and recognizes the user's emotional state using an emotion recognition means. The recognized emotional information is sent to the server, and business improvement suggestions are made based on the emotional information.

[0755] User:

[0756] Users can check which business processes can be automated through the factory robot's display and receive suggestions for business improvement based on the robot's emotional state.

[0757] Specific examples

[0758] For example, if a worker says, "This task takes a long time," the emotion recognition model will detect "stress." Based on this, the factory robot will suggest, "Automating this task will save time."

[0759] Example prompt sentence:

[0760] If a user says "This task takes time" via voice input, the emotion recognition model will detect "stress" and make automated suggestions.

[0761] In this way, factory robots can recognize the emotional state of workers and make appropriate suggestions for improving work, thereby improving work efficiency and safety.

[0762] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0763] Step 1:

[0764] The server takes in the text data of the business content. As input, it receives data that records the business procedures and content as text information and stores it in the system. As output, the taken-in text data is passed to the generative AI means.

[0765] Step 2:

[0766] The server generates automatable task points from the imported data. As input, it receives the text data imported in step 1, and the generative AI means analyzes it. As data processing, it uses natural language processing technology to analyze the task content and identify the parts that can be automated. As output, it generates automatable task points.

[0767] Step 3:

[0768] The server responds with the generated automatable business points. As input, it receives the automatable business points generated in step 2 and generates data to present to the user. As output, it displays the automatable business points to the user.

[0769] Step 4:

[0770] The terminal receives voice or text input from the user. As input, it receives data in which the user expresses emotions through voice or text and passes this to the emotion recognition means. As output, the voice or text data is passed to the emotion recognition means.

[0771] Step 5:

[0772] The device recognizes the user's emotional state using an emotion recognition means. The input is the voice and text data received in step 4, analyzed, and the emotion recognition model estimates the emotion. The data is processed by analyzing the tone of the voice and the keystroke pattern of the text. The output is the user's emotional state.

[0773] Step 6:

[0774] The server makes business improvement proposals based on the emotional information. As input, it receives the emotional information recognized in step 5, and the generative AI means generates business improvement proposals. As data processing, it applies an algorithm to generate business improvement proposals that take the emotional information into account. As output, business improvement proposals that take the emotional information into account are displayed to the user.

[0775] Step 7:

[0776] The user checks the factory robot's display for business improvement suggestions based on the automatable business points and emotional state. As input, the data generated in Steps 3 and 6 is received and displayed on the display. As output, the user checks the suggestions and implements automation or improvement of business processes as necessary.

[0777] 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.

[0778] 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 making a neural network perform deep learning. A prompt containing 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 the data format of voice data, text data, etc.

[0779] Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0780] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0781] 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.

[0782] [Second embodiment]

[0783] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0784] 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.

[0785] 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).

[0786] 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.

[0787] 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.

[0788] 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).

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0794] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0795] "Example 1"

[0796] As one embodiment of the present invention, an interface for inputting business content as text data is provided as a means for importing data in the form of textualized business content. This interface can be in a format where the user directly inputs text. It can also be in a format where an API is used to automatically obtain text data from an existing business management system.

[0797] "Example 2"

[0798] As a generative AI tool, an AI engine is used that generates automatable business points from the imported text data. This AI engine uses natural language processing technology to understand the content of the business from the text data and extracts points that can be automated. Specifically, it analyzes the verbs and nouns in the text data and understands the business flow and procedures they indicate. It then identifies the parts of that that can be automated.

[0799] "Example 3"

[0800] A user interface is provided as a means of responding to the generated automatable business points. This interface displays a list of the generated automatable business points so that the user can refer to them. Specifically, it lists the automatable business points one by one and displays automation suggestions for each business point and the effects of that automation.

[0801] The processing flow of each embodiment will be described below.

[0802] "Example 1"

[0803] Step 1: Open an interface where the user can enter their work details as text data.

[0804] Step 2: The user enters the job description as text and presses the send button.

[0805] Step 3: The system takes the text data and sends it to the generative AI.

[0806] "Example 2"

[0807] Step 1: The generative AI receives the text data.

[0808] Step 2: Generative AI uses natural language processing technology to understand the business content from the text data.

[0809] Step 3: Generative AI extracts points that can be automated from the content of the work and generates them as automatable work points.

[0810] "Example 3"

[0811] Step 1: The system displays the generated automatable business points in the user interface.

[0812] Step 2: The user checks the business points that can be automated and proceeds with automating the business based on that.

[0813] Example 1

[0814] 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."

[0815] In conventional business management systems, it was difficult to input business content as text data and then efficiently analyze and summarize it. It was also difficult to automatically generate detailed explanations of business content, which placed a heavy burden on users. This resulted in a lack of progress in business efficiency and automation, and slow improvements to business processes.

[0816] 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.

[0817] In this invention, the server includes an interface means for inputting business content as text data, a means for receiving and saving the input text data, a means for cleaning and formatting the saved data, a means for inputting the cleaned and formatted data into a generative AI model, and a means for returning results obtained from the generative AI model to the user, thereby enabling efficient analysis and automatic generation of summaries and detailed descriptions of business content.

[0818] The "interface means for inputting business details as text data" refers to an interface for a user to input business details in text format, and includes a form on a web browser, a desktop application, or a mobile application.

[0819] The "means for receiving and saving input text data" refers to a means having a function for receiving text data sent from a user and saving it in a storage such as a database.

[0820] "Means for cleaning and formatting stored data" refers to means for analyzing stored text data, cleaning it by removing unnecessary spaces and special characters, and converting it into a format that is easy for the generative AI model to understand.

[0821] "Means for inputting cleaned and formatted data into a generative AI model" means means for inputting cleaned and formatted data into a generative AI model, including the ability to send API requests.

[0822] "Means for returning results obtained from a generative AI model to a user" refers to means for receiving results returned from a generative AI model and returning them to a user, including an interface for displaying the results.

[0823] MODE FOR CARRYING OUT THE INVENTION

[0824] This invention is a system that inputs business content as text data and efficiently analyzes, summarizes, and generates detailed explanations of the data. Specific embodiments of this system are described below.

[0825] Enter business details

[0826] The user uses an interface to enter the job description. This interface can be a form running in a web browser, a dedicated desktop application, or a mobile application. The user enters the job description in a text box and clicks a "Submit" button.

[0827] As a specific example, the user inputs "Today's work included a meeting with client A and the preparation of materials," and presses the send button.

[0828] Receiving and storing data

[0829] The server receives the text data of the business details sent by the user, and the received data is stored in a relational database such as MySQL or PostgreSQL.

[0830] Specifically, the server executes the SQL query "INSERT INTO Business Details (User ID, Date, Details) VALUES (1, '2023-10-01', 'Meeting with Customer A and preparing materials')".

[0831] Data cleaning and formatting

[0832] The server analyzes the stored data and cleans it, removing unnecessary spaces and special characters, etc. It also converts the data into a format that is easy for the generative AI model to understand, for example, by categorizing the work content.

[0833] Specifically, the server converts the text "Meeting with customer A and document creation" into the format "Customer support: Meeting with customer A, Document creation: Document creation."

[0834] Data input to generative AI models

[0835] The server inputs the cleaned and formatted data into a generative AI model, which uses a natural language processing model such as GPT-4, and sends an API request to the model.

[0836] Specifically, the server sends an HTTP request "POST / generate-summary HTTP / 1.1" to the API of the generated AI model, attaching a JSON payload containing the data.

[0837] Obtaining and displaying results

[0838] The server receives the results returned by the generative AI model, which may be a summary of the task or a detailed description, and then parses and formats the results for return to the user.

[0839] Specifically, the server receives the summary "Meeting with customer A and preparation of materials" and converts it into HTML format for display to the user.

[0840] Returning results to the user

[0841] The server returns the results obtained from the generative AI model to the user, who can view the results through an interface, which can be displayed on a web page or on the application screen.

[0842] Specifically, the server generates HTML containing the content "Summary of today's work: Had a meeting with customer A and prepared materials" and sends it to the user's browser.

[0843] Prompt Sentence Examples

[0844] "Summarize today's work."

[0845] "Summarize this week's work."

[0846] "Please explain in detail the contents of the meeting with Customer A."

[0847] The above is a specific embodiment for carrying out the present invention.

[0848] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0849] Step 1:

[0850] The user enters the job description

[0851] The user uses an interface to input the details of the work. The user enters the details of the work in the text box and clicks the "Send" button. As input, the user enters "Today's work was a meeting with Client A and the preparation of materials." As output, the entered text data is sent to the server.

[0852] Step 2:

[0853] The server receives and stores the data

[0854] The server receives the text data of the work content sent by the user. As input, it receives the text data sent by the user. The server stores the received data in a relational database such as MySQL or PostgreSQL. Specifically, the server executes the SQL query "INSERT INTO Work content (user ID, date, content) VALUES (1, '2023-10-01', 'Meeting with customer A and creating materials')". As output, it obtains the work content stored in the database.

[0855] Step 3:

[0856] The server cleans and formats the data

[0857] The server analyzes the stored data and cleans it by removing unnecessary spaces and special characters. As input, it receives the text data of business operations stored in the database. The server converts the data into a format that is easy for the generative AI model to understand. Specifically, the server converts the text "Meeting with customer A and creating documents" into the format "Customer support: Meeting with customer A, Document creation: Creating documents." The cleaned and formatted data is obtained as output.

[0858] Step 4:

[0859] The server inputs data into the generative AI model

[0860] The server inputs the cleaned and formatted data into the generative AI model. The cleaned and formatted data is used as input. The generative AI model uses a natural language processing model such as GPT-4. Specifically, the server sends an HTTP request "POST / generate-summary HTTP / 1.1" to the generative AI model's API and attaches a JSON payload containing the data. The data is input into the generative AI model as output.

[0861] Step 5:

[0862] The server retrieves the results from the generative AI model

[0863] The server receives the results returned by the generative AI model. As input, it receives the response from the generative AI model. The results may be a summary of the work content or a detailed explanation. Specifically, the server receives the summary "Meeting with customer A and preparation of materials were conducted" and converts this into HTML format for display to the user. As output, it obtains the results obtained from the generative AI model.

[0864] Step 6:

[0865] The server returns the results to the user

[0866] The server returns the results obtained from the generative AI model to the user. The results obtained from the generative AI model are used as input. The user can check the results through the interface. Specifically, the server generates HTML containing the content "Summary of today's work: Held a meeting with customer A and prepared materials" and sends it to the user's browser. The user can check the results as output.

[0867] (Application example 1)

[0868] 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."

[0869] Conventional factory robot management systems have the problem of being difficult to carry out work efficiently, as the process of manually inputting work details and giving instructions to the robots is cumbersome. Furthermore, there was also the problem of insufficient integration with existing work management systems, which hindered progress in automating work and streamlining. This resulted in a decline in productivity throughout the factory and increased costs.

[0870] 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.

[0871] In this invention, the server includes a means for importing text data of work content, a generative AI means for generating automatable work points from the imported data, a means for returning the generated automatable work points, a means for sending the imported work content to a factory robot, and a means for generating instructions for the factory robot to execute the work content. This automates the process from inputting work content to issuing instructions to the robot, improving work efficiency throughout the factory, thereby enabling increased productivity and cost reduction.

[0872] "Means for importing text data of business content" refers to an interface for acquiring text data of business content entered by the user, and an API for automatically acquiring text data from existing business management systems.

[0873] "Generative AI means for generating automatable business points from imported data" refers to artificial intelligence technology that analyzes the text data of imported business content and extracts and generates business points that can be automated.

[0874] "Means for answering generated automatable task points" refers to means for presenting to the user the automatable task points generated by the generative AI means.

[0875] The "means for transmitting the captured work content to the factory robot" refers to a communication means for transmitting the text data of the captured work content to the factory robot.

[0876] "Means for generating instructions for factory robots to execute work content" refers to means for generating specific work instructions based on the work content input by the factory robot and having the robot execute them.

[0877] As an embodiment of the present invention, a factory robot management system is constructed, which includes: means for importing text data of work content, generative AI means for generating automatable work points from the imported data, means for responding with the generated automatable work points, means for transmitting the imported work content to a factory robot, and means for generating instructions for the factory robot to execute the work content.

[0878] Program processing explanation

[0879] Hardware and Software

[0880] Hardware: smartphones, tablets, factory robots

[0881] Software: Python, requests library, API of existing business management system

[0882] Data processing and calculation

[0883] 1. Importing work content: The server acquires the work content entered by the user on a smartphone or tablet as text data. It also automatically acquires text data from existing work management systems via API.

[0884] 2. Generating automatable business points: The server inputs the captured text data into a generative AI model to generate automatable business points. This generative AI model uses natural language processing technology to analyze the business content and extract points for efficient business execution.

[0885] 3. Response of business points: The server presents the generated business points that can be automated to the user, allowing the user to receive specific suggestions for automating and streamlining their business.

[0886] 4. Sending the work content: The server sends the text data of the work content to the factory robot via Wi-Fi or a wired network.

[0887] 5. Instruction generation: The factory robot generates and executes specific work instructions based on the received task content, allowing the robot to complete the task automatically.

[0888] Specific examples

[0889] User input example: A user uses a smartphone to input "Assemble part A."

[0890] Example of retrieving data from API: Retrieve the task content "Inspect part B" from an existing business management system.

[0891] Prompt Sentence Examples

[0892] When a user types "Assemble part A" into their smartphone, send that task to the factory robot.

[0893] Also, obtain the task content "Inspect part B" from the existing task management system and send it to the factory robot in the same way.

[0894] In this way, a system is realized that enables factory robots to perform their tasks efficiently.

[0895] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0896] Step 1:

[0897] The user uses a smartphone or tablet to input the details of the task as text. The input text data is sent to the server. An example of input data is "Assemble part A." The server receives this text data and proceeds to the next processing step.

[0898] Step 2:

[0899] The server automatically retrieves text data of the work content from the existing work management system via API. An example of retrieved data is "Inspect part B." The server receives this data and proceeds to the next processing step.

[0900] Step 3:

[0901] The server inputs the text data acquired in steps 1 and 2 into the generative AI model. The generative AI model uses natural language processing technology to analyze the business content and extract business points that can be automated. For example, from "Assemble part A," it generates "automation points for the assembly process." The generated business points are returned to the server.

[0902] Step 4:

[0903] The server presents the automatable business points returned by the generative AI model to the user, who can then view the suggested business points on the screen of their smartphone or tablet. For example, "automation points for the assembly process" may be displayed.

[0904] Step 5:

[0905] The server sends the text data of the work content that it has captured to the factory robot. The communication method is Wi-Fi or a wired network. An example of the data that is sent is "Assemble part A." The factory robot receives this data and proceeds to the next processing step.

[0906] Step 6:

[0907] The factory robot generates specific work instructions based on the received task content. For example, for the task content "Assemble part A," it generates the instruction "Pick up part A and place it on the assembly line." The generated instructions are stored in the robot's internal system.

[0908] Step 7:

[0909] The factory robot performs the task according to the generated work instructions. For example, based on the instruction "pick up part A and place it on the assembly line," the robot actually picks up part A and places it on the assembly line. When the task is completed, the robot sends a completion report to the server.

[0910] In this way, the process from inputting the work content to giving instructions to the robot and then executing it is automated.

[0911] Example 2

[0912] 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."

[0913] Conventional business process automation systems require manual analysis of business processes and identification of points that can be automated, which is time-consuming and labor-intensive. It is also difficult to accurately grasp the flow and procedures of business processes, which often makes it difficult to achieve efficient automation. Furthermore, there is a lack of specific improvement proposals for business process automation, which hinders progress in improving business efficiency.

[0914] 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.

[0915] In this invention, the server includes a means for importing data in which business content has been converted into text, a means for applying natural language processing technology to the imported data and analyzing verbs and nouns to grasp the flow and procedures of the business, and a generative AI means for generating automatable business points from the grasped flow and procedures of the business. This makes it possible to automatically analyze business content and quickly and accurately identify automatable points.

[0916] "Text data of business content" refers to data in which the procedures and content of business are written in text form.

[0917] A "means for capturing" is a means for inputting user-provided data into the system.

[0918] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0919] "Means for analyzing verbs and nouns" refers to means for extracting verbs and nouns from text data and analyzing the relationships between them.

[0920] "Means for understanding the flow and procedures of work" refers to a means for understanding the progress and procedures of work based on analyzed verbs and nouns.

[0921] "Automable business points" refer to parts or steps within a business process that can be automated.

[0922] "Generative AI methods" are methods that use artificial intelligence technology to analyze captured data and identify business points that can be automated.

[0923] The "answering means" is a means for presenting the generated automatable business points to the user.

[0924] The "function for making improvement suggestions" is a function that makes specific suggestions for automating and streamlining business operations.

[0925] This invention is a system that inputs text data of business operations, analyzes the data, and identifies business operations that can be automated. A specific embodiment of this system will be described below.

[0926] First, the user provides the system with text data of the work content. This data is a written description of the work procedures and content. The user can upload this data through a web interface.

[0927] The server receives the text data provided by the user and stores it in its internal storage. The server then analyzes the text data using natural language processing technology. Specifically, it uses natural language processing libraries such as "spaCy" and "NLTK." This analyzes the sentence structure of the text data and extracts verbs and nouns.

[0928] The server extracts verbs and nouns from the analyzed sentence structure and analyzes their relationships. This clarifies the workflow and procedures. For example, verbs such as "enter" and "create" and nouns such as "sales data," "spreadsheet software," and "report" are extracted.

[0929] Next, the server identifies tasks that can be automated based on the extracted verbs and nouns. For example, it determines that tasks such as "entering sales data into spreadsheet software" and "creating reports" can be automated.

[0930] Finally, the server presents the generated automatable business points to the user, allowing the user to check the business points that can be automated and receive specific suggestions for improvement.

[0931] As a specific example, consider the following text data.

[0932] Example of text data:

[0933] "Enter sales data into a spreadsheet every day and create a report at the end of the week."

[0934] Based on this text data, the server performs the following processing.

[0935] 1. Importing text data:

[0936] The server takes in text data provided by the user, such as "Enter sales data into spreadsheet software every day and create a report on the weekend."

[0937] 2. Application of natural language processing technology:

[0938] The server uses spaCy to analyze the text data.

[0939] 3. Analysis of verbs and nouns:

[0940] The server extracts verbs such as "input" and "create" and nouns such as "sales data," "spreadsheet software," and "report" to grasp the flow of work.

[0941] 4. Identify areas that can be automated:

[0942] The server identifies the parts "enter sales data into spreadsheet software" and "create report" as being automatable.

[0943] Example prompts to input to the generative AI model:

[0944] "Identify the automation aspects of the following task: Enter sales data into a spreadsheet every day and create a report at the end of the week."

[0945] In this way, the server analyzes the text data and identifies points in the work that can be automated. This system makes it possible to automatically analyze work content and quickly and accurately identify points that can be automated.

[0946] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0947] Step 1:

[0948] Importing text data

[0949] Users upload text data of their work through a web interface, and the server receives this data and stores it in its internal storage.

[0950] Input: Text data of business operations

[0951] Output: Text data saved in internal storage

[0952] Specific operation: A user accesses the web interface, selects a text file containing the job description, and presses the upload button. The server receives the uploaded file and stores it in the database.

[0953] Step 2:

[0954] Application of natural language processing technology

[0955] The server applies natural language processing technology to the stored text data. Specifically, it performs grammatical analysis using natural language processing libraries such as "spaCy" and "NLTK."

[0956] Input: Text data stored in internal storage

[0957] Output: Parsed sentence structure data

[0958] Specific operation: The server passes the text data to the "spaCy" parser and performs grammatical analysis, which analyzes the sentence structure of the text data and extracts verbs and nouns.

[0959] Step 3:

[0960] Verb and noun analysis

[0961] The server extracts verbs and nouns from the analyzed sentence structure and analyzes their relationships, thereby clarifying the flow and procedures of work.

[0962] Input: Parsed sentence structure data

[0963] Output: A list of extracted verbs and nouns

[0964] Specific operation: The server lists verbs (e.g., "enter," "create") and nouns (e.g., "sales data," "spreadsheet software," "report") from the sentence structure data and analyzes the relationships between them.

[0965] Step 4:

[0966] Identifying areas where automation is possible

[0967] Based on the extracted verbs and nouns, the server identifies tasks that can be automated, such as routine data entry or periodic report generation.

[0968] Input: A list of extracted verbs and nouns

[0969] Output: A list of business points that can be automated

[0970] Specific operation: The server combines the verb "input" with the noun "sales data" to determine that "entering sales data into a spreadsheet" can be automated. Similarly, it identifies "creating a report" as an automatable step.

[0971] Step 5:

[0972] Presenting business points that can be automated

[0973] The server presents the generated automatable business points to the user, allowing the user to check the business points that can be automated and receive specific suggestions for improvement.

[0974] Input: List of business points that can be automated

[0975] Output: Automated task points presented to the user

[0976] Specific operation: The server displays the business points that can be automated to the user through a web interface. The user checks the presented points and configures automation as necessary.

[0977] (Application example 2)

[0978] 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."

[0979] The work at conventional logistics centers requires a lot of manual work, which makes it inefficient. Furthermore, it is difficult to identify which parts of the work should be automated. This often delays the improvement of work efficiency and the promotion of automation.

[0980] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for importing text data of business content, generative AI means for generating automatable business points from the imported data, means for responding with the generated automatable business points, means for generating an automation proposal based on the generated automatable business points, and means for displaying the generated automation proposal. This makes it possible to identify automation points for business operations at a logistics center and make efficient automation proposals.

[0981] "Text data of work content" refers to data that records work procedures and work content at a logistics center in text format.

[0982] "Means of importing" refers to the means of inputting text data of business content into the system.

[0983] "Generative AI methods" are methods that use artificial intelligence technology to analyze imported text data and generate business points that can be automated.

[0984] "Automable business points" are points that identify parts of a business that can be automated.

[0985] The "answering means" is a means for presenting the generated automatable business points to the user.

[0986] The "means for generating automation proposals" is a means for proposing specific automation methods based on the generated automatable business points.

[0987] The "means for displaying" is a means for visually presenting the generated automated suggestions to the user.

[0988] The system for carrying out the present invention is designed to support the automation of operations in a logistics center. A specific embodiment of the system will be described below.

[0989] System configuration

[0990] The system consists of the following main components:

[0991] 1. A means of importing text data of business operations

[0992] 2. Generative AI methods that generate automatable business points from imported data

[0993] 3. A means of answering the generated automatable business points

[0994] 4. A means for generating automation proposals based on the generated automatable business points

[0995] 5. A way to view the generated automation suggestions

[0996] Hardware and software used

[0997] Hardware: Smartphone

[0998] Software: Python, spaCy (natural language processing library), transformers (generative AI model library)

[0999] Data processing and calculation

[1000] 1. A means of importing text data of business operations

[1001] The user inputs the work procedures and tasks to be performed in the logistics center in text format. For example, the user inputs work details such as "take out the product from the shelf" and "scan the product."

[1002] 2. Generative AI methods that generate automatable business points from imported data

[1003] The server uses spaCy to analyze the imported text data and extract verbs and nouns, thereby understanding the workflow and procedures and identifying points of the business that can be automated.

[1004] 3. A means of answering the generated automatable business points

[1005] The server presents the identified automatable business points to the user. For example, if a business point such as "take out a product from a shelf" is identified, the server displays this to the user.

[1006] 4. A means for generating automation proposals based on the generated automatable business points

[1007] The server uses a generative AI model with a sentiment analysis model to generate automation suggestions. For example, the server inputs a prompt sentence, "Please suggest a way to automate the following task: remove products from shelves," into the generative AI model to generate automation suggestions.

[1008] 5. A way to view the generated automation suggestions

[1009] The server visually presents the generated automation proposal to the user, for example, "We will introduce a system that uses a robotic arm to pick up products from shelves. The robotic arm will work in conjunction with a barcode scanner to pick up accurate products."

[1010] Specific examples

[1011] Input text data:

[1012] Remove the product from the shelf.

[1013] Scan the product.

[1014] Pack the product.

[1015] Print and attach the labels.

[1016] Example prompt for a generative AI model:

[1017] Suggest ways to automate the following tasks: Retrieving items from shelves.

[1018] In this way, a system can be realized that generates specific proposals for streamlining and automating operations within a logistics center.

[1019] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1020] Step 1:

[1021] Users input work procedures and work details in the logistics center in text format. For example, they input work details such as "take out products from shelves" and "scan products." The input text data is sent to the server.

[1022] Step 2:

[1023] The server uses spaCy to analyze the received text data. Specifically, it divides the text data into sentences and extracts the verbs and nouns contained in each sentence. This allows the flow and procedures of work to be understood and points of work that can be automated identified. For example, the verb "take out" is extracted from the sentence "Take out the product from the shelf."

[1024] Step 3:

[1025] The server presents the identified automatable task points to the user. For example, if a task point such as "taking out a product from a shelf" is identified, the server displays this to the user. The user confirms the displayed task point.

[1026] Step 4:

[1027] The server generates automation suggestions using a generative AI model based on the identified automatable business points. Specifically, a prompt sentence is input to the generative AI model using a sentiment analysis model. For example, a prompt sentence such as "Please suggest a way to automate the following business task: taking products off shelves" is input to the generative AI model to generate automation suggestions.

[1028] Step 5:

[1029] The server visually presents the generated automation proposal to the user. For example, it displays a proposal such as, "Introduce a system that uses a robotic arm to pick up products from shelves. The robotic arm will work in conjunction with a barcode scanner to pick up accurate products." The user can confirm the displayed automation proposal and execute it as necessary.

[1030] Example 3

[1031] 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."

[1032] With conventional business automation systems, it was difficult to simply import text data of business operations, generate automatable business points from that data, and show users specific improvement proposals and effects.In addition, there was a lack of a way for users to easily refer to automatable business points and check detailed information, which often delayed the introduction of business efficiency improvements and automation.

[1033] 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.

[1034] In this invention, the server includes means for importing data that has been converted into textual data on the content of work, generative AI means for generating automatable work points from the imported data, means for responding with the generated automatable work points, user interface means for displaying a list of the generated automatable work points so that the user can refer to them, and means for displaying the effects of automating that work point and specific suggestions when the user selects a specific work point. This allows the user to easily refer to the automatable work points and check their detailed information.

[1035] "Text data of business content" refers to data that records the procedures and content of business as text information.

[1036] "Means for importing" is a function for receiving data from the outside and storing it within the system.

[1037] "Generative AI means" is a function that uses artificial intelligence technology to extract specific information and patterns from input data and generate new data.

[1038] "Automable business points" refer to parts or processes within a business that can be automated.

[1039] "Means of responding" is a function for presenting generated information and data to the user.

[1040] "User interface means" refers to a function that provides a screen and operation means for the user to interact with the system.

[1041] "List display" refers to displaying multiple items in a list format.

[1042] "Making it accessible" means making the information available to users so that they can view it and check for more details if necessary.

[1043] "When selected" refers to the user selecting a specific item by clicking, tapping, or other operations.

[1044] "Effect" refers to the result or influence obtained by a particular action or process.

[1045] A "specific proposal" refers to presenting a concrete solution or improvement plan for a specific problem or issue.

[1046] This invention is a system that takes in text data of business operations, generates automatable business points from that data, and presents specific improvement suggestions and effects to users. Specific embodiments of this system are described below.

[1047] Server Processing

[1048] The server receives business data uploaded by users. This business data can be provided in various formats, such as spreadsheet files, CSV files, data extracted from databases, etc. For example, if a user uploads a file called "BusinessData.xlsx," the server receives the file.

[1049] The server then inputs the received business data into a generative AI model. This generative AI model is built using TensorFlow and PyTorch and analyzes the data. Specifically, it detects patterns and trends in the business data and identifies business points that can be automated. For example, it analyzes the frequency of data entry and the timing of report generation.

[1050] The server organizes the automatable business points extracted by the generative AI model and sends them to the terminal in data format such as JSON. For example, business points such as "automated data entry" and "automated report generation" are extracted, along with the benefits of each (time savings, cost reduction, etc.).

[1051] Terminal handling

[1052] The terminal receives the business points sent from the server and displays them on the user interface. This user interface is built using HTML, CSS, and JavaScript and is designed to be intuitive for users. Specifically, frameworks such as React.js and Vue.js are used to achieve dynamic list display. For example, lists such as "Automated data entry" and "Automated report generation" are displayed.

[1053] User operations

[1054] Users can view the business points that can be automated through the terminal's user interface. When a user clicks on a specific business point, the effects of that automation and specific suggestions are displayed. For example, when the user clicks on "automating data entry," the time-saving and cost-saving effects of that automation are displayed. Specific automation suggestions (e.g., the introduction of specific software or the use of scripts) are also displayed.

[1055] Specific examples

[1056] An example of a prompt sentence to be input into a generative AI model is, "Analyze the business data below and extract business points that can be automated."

[1057] In this way, users can easily refer to business points that can be automated and check the effects and specific proposals. This system is expected to facilitate the introduction of business efficiency and automation, contributing to improved productivity in companies. The flow of the specific processing in Example 3 will be explained using Figure 15.

[1058] Step 1:

[1059] The server receives the business data.

[1060] Input: Business data uploaded by the user (e.g., spreadsheet files, CSV files)

[1061] Specific operation: When a user uploads "Business Data.xlsx", the server receives the file and reads the data.

[1062] Output: Imported business data

[1063] Step 2:

[1064] The server analyzes business data using the generated AI model.

[1065] Input: Imported business data

[1066] How it works: The server inputs the data it reads into a generative AI model, which then analyzes the data using TensorFlow and PyTorch to detect patterns and trends in the business data.

[1067] Output: Automated business operations (e.g., "automated data entry" and "automated report generation")

[1068] Step 3:

[1069] The server extracts the business points that can be automated and sends them to the terminal.

[1070] Input: Automated business points

[1071] Specific operation: The server organizes the automatable business points extracted by the generative AI model and sends them to the terminal in a data format such as JSON.

[1072] Output: Business point data sent to the terminal

[1073] Step 4:

[1074] The terminal displays the received business points on the user interface.

[1075] Input: Business point data sent to the terminal

[1076] Specific operation: The terminal displays the received data in a user interface using React.js. For example, it displays lists such as "Automated data entry" and "Automated report generation."

[1077] Output: List of business points displayed on the user interface

[1078] Step 5:

[1079] The user references the business point and checks the detailed information.

[1080] Input: List of business points displayed on the user interface

[1081] Specific action: The user clicks on a specific business point (e.g., "automate data entry") from the displayed list. Once clicked, the effects of that automation (e.g., time savings, cost reduction) and specific suggestions (e.g., the introduction of specific software or the use of scripts) are displayed.

[1082] Output: Detailed information on the business points confirmed by the user

[1083] In this way, users can easily see which business points can be automated, and see the effects and specific suggestions.

[1084] (Application example 3)

[1085] 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."

[1086] In today's business environment, automation and efficiency of business processes are important issues. However, it is not easy to determine which business processes can be automated and how to automate them effectively. In addition, there is a lack of a way for users to easily understand which business processes can be automated and receive appropriate improvement suggestions based on that information. For this reason, there is a need for a system that can effectively promote business automation and efficiency.

[1087] 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.

[1088] In this invention, the server includes means for importing text data of business content, generative AI means for generating automatable business points from the imported data, means for responding with the generated automatable business points, user interface means for displaying a list of the generated automatable business points so that the user can refer to them, and means for displaying automation suggestions for each business point and the effects of that automation. This allows the user to easily understand the automatable business points and receive appropriate improvement suggestions.

[1089] "Text data of business content" refers to data that records the procedures and content of business in text format.

[1090] "Means for acquiring" refers to a method or device for acquiring data from the outside and storing it within the system.

[1091] "Generative AI means" is a system that has the ability to generate specific information or patterns from data using artificial intelligence.

[1092] "Automable business points" refer to parts or processes within a business that can be automated.

[1093] A "means for responding" is a method or device for providing the generated information or results to the user.

[1094] "User interface means" refers to a function that provides a screen and operation methods for users to interact with the system.

[1095] "List display" refers to displaying multiple items together on one screen or page.

[1096] An "automation proposal" is a proposal that shows specific methods and procedures for automating business processes.

[1097] "Effects of automation" refers to the benefits and results obtained by automating business processes.

[1098] A system for implementing this invention includes a means for importing text data of business content, a generative AI means for generating automatable business points from the imported data, a means for responding with the generated automatable business points, a user interface means for displaying a list of the generated automatable business points so that the user can refer to them, and a means for suggesting automation for each business point and displaying the effects of that automation.

[1099] The server first imports text data of the business operations. This data is extracted from, for example, business procedure manuals or work reports. The imported data is analyzed by a generative AI method, and business points that can be automated are generated. The generative AI method uses natural language processing technology and machine learning algorithms. Specifically, Python libraries such as NLTK and spaCy, and machine learning frameworks such as TensorFlow and PyTorch are used.

[1100] The generated automatable task points are displayed in a list through a user interface. Users can view these points on their smartphone or PC screen. The user interface is built using web technologies such as HTML, CSS, and JavaScript.

[1101] Furthermore, automation suggestions and the effects of such automation are displayed for each task. This allows users to understand specifically which tasks should be automated and how. For example, a suggestion might be displayed such as, "By automating data entry tasks with an RPA tool, you can reduce work time by 50%."

[1102] For example, the following prompts are generated:

[1103] "We propose the introduction of an RPA tool to automate data entry tasks. Using this tool will reduce the time required for tasks by 50% and reduce the error rate."

[1104] In this way, users can easily identify areas of their business that can be automated and receive appropriate improvement suggestions.

[1105] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1106] Step 1:

[1107] The server takes in the text data of the business content.

[1108] Input: Text data such as work procedures and work reports

[1109] Specific operation: The server receives text data of the business details provided by the user via the file upload function or API. The received data is stored in the database.

[1110] Output: Saved text data

[1111] Step 2:

[1112] The server runs a generative AI method that generates automatable business points from the imported data.

[1113] Input: Saved text data

[1114] How it works: The server uses natural language processing (NLTK or spaCy) to analyze text data and understand the business operations. It then uses machine learning algorithms (TensorFlow or PyTorch) to identify business operations that can be automated.

[1115] Output: A list of business points that can be automated

[1116] Step 3:

[1117] The server executes a means for returning the generated automatable task points.

[1118] Input: List of business points that can be automated

[1119] Specific operation: The server obtains a list of business points from the database and passes it to the user interface to provide the generated business points to the user.

[1120] Output: A list of business points displayed in the user interface

[1121] Step 4:

[1122] The terminal displays a list of the generated automatable business points so that the user can refer to them.

[1123] Input: A list of business points passed to the user interface

[1124] Specific operation: The terminal uses HTML, CSS, and JavaScript to display a list of business points on the screen. The user can check these points on the screen of their smartphone or computer.

[1125] Output: A list of business points displayed on the screen

[1126] Step 5:

[1127] The server implements a means for proposing automation for each business point and displaying the effects of that automation.

[1128] Input: List of business points that can be automated

[1129] Specific operation: The server uses the generative AI model to generate automation proposals for each business point. For example, a proposal might be generated such as, "By automating data entry work with an RPA tool, work time can be reduced by 50%." These proposals are then passed to the user interface.

[1130] Output: The automation suggestions and their effects displayed in the user interface

[1131] Step 6:

[1132] The terminal displays automation suggestions and their effects through a user interface.

[1133] Input: Automation suggestions passed to the user interface and their effects

[1134] Specific operation: The device uses HTML, CSS, and JavaScript to display automation suggestions and their effects on the screen. The user can refer to these suggestions to specifically understand which tasks should be automated and how.

[1135] Output: The automation suggestions and their effects displayed on the screen

[1136] 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.

[1137] "Example 1"

[1138] One embodiment of the present invention provides a DX diagnostic system that includes a means for importing text data of business operations, a generative AI means for generating automatable business points from the imported data, and a means for providing the generated automatable business points. This system further incorporates an emotion engine that recognizes user emotions. Specifically, a user inputs business operations as text, and the text data is imported into the system. Next, the generative AI analyzes the text data and identifies business points that can be automated. During this process, the emotion engine recognizes the user's emotions and provides the results to the generative AI. The generative AI takes this emotional information into account to generate automatable business points.

[1139] "Example 2"

[1140] As a concrete example, consider the case where a user inputs "responding to customer inquiries" as a task. From this text data, the generative AI identifies tasks that can be automated, such as "classifying the inquiry content" and "selecting the appropriate response." Meanwhile, the emotion engine recognizes that the user is feeling stressed about this task. Taking this emotional information into consideration, the generative AI makes suggestions for improving the task to reduce stress. For example, by automating "classifying the inquiry content," the AI ​​makes suggestions to reduce the user's workload.

[1141] "Example 3"

[1142] The emotion engine also uses an emotion recognition model to recognize the user's emotional state. This model infers emotions from the user's text and voice input. Specifically, it infers the user's emotional state from the keystroke patterns when the user enters text and the tone of voice when the user enters voice. This emotional information is an important reference for the generative AI when it makes business improvement proposals.

[1143] The processing flow of each embodiment will be described below.

[1144] "Example 1"

[1145] Step 1: The user enters the job description as text.

[1146] Step 2: The system captures the text data.

[1147] Step 3: Generative AI analyzes the text data and identifies areas where tasks can be automated.

[1148] Step 4: The emotion engine recognizes the user's emotions and provides the results to the generative AI.

[1149] Step 5: Generative AI takes emotional information into account to generate automatable task points.

[1150] "Example 2"

[1151] Step 1: The user enters "responding to customer inquiries" as the job description.

[1152] Step 2: Generative AI uses the text data to identify tasks that can be automated, such as classifying the inquiry and selecting the appropriate response.

[1153] Step 3: The emotion engine recognizes that the user is feeling stressed about this task.

[1154] Step 4: Generative AI takes emotional information into account and makes suggestions for work improvements to reduce stress.

[1155] "Example 3"

[1156] Step 1: The emotion engine uses the emotion recognition model to recognize the user's emotional state.

[1157] Step 2: The emotion recognition model estimates emotions from the user's text input, voice input, etc.

[1158] Step 3: This emotional information is provided to the generative AI and used as reference information for business improvement proposals.

[1159] Example 1

[1160] 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."

[1161] Conventional business automation systems have the means to import text data of business operations and generate automatable business points, but do not generate automation points that take the user's emotions into account. As a result, proposals are made that ignore the user's emotions and stress levels, which leads to issues such as insufficient improvement in business efficiency and user satisfaction.

[1162] 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.

[1163] In this invention, the server includes means for importing text data of work content, generative AI means for generating automatable work points from the imported data, means for returning the generated automatable work points, emotion engine means for recognizing user emotions, and means for generating automatable work points in consideration of emotion information from the emotion engine means. This makes it possible to generate automation points that take user emotions into consideration, thereby achieving improved work efficiency and improved user satisfaction.

[1164] "Text data of business content" refers to data that expresses the details and procedures of business as text information.

[1165] "Means of import" refers to the interface or API used to input business details into the system as text data.

[1166] "Generative AI methods" refers to artificial intelligence technology that analyzes imported text data and identifies business points that can be automated.

[1167] "Automable business points" refer to parts or processes within a business that can be automated.

[1168] The "means for replying" refers to a function for notifying the user of the generated automatable business points.

[1169] "Emotion engine means" refers to technology for recognizing a user's emotions and providing that information to generative AI.

[1170] "Emotional information" is data that indicates the user's emotional state, and includes information obtained from input content, input speed, keystroke patterns, and the like.

[1171] This invention is a system that takes in text data of work content, generates automatable work points from that data, and responds to the user. It also has the function of recognizing the user's emotions and generating automatable work points taking that information into consideration.

[1172] Hardware and software used

[1173] Hardware: Servers, user terminals

[1174] Software: Business management system, generative AI, emotion engine, API

[1175] System configuration

[1176] 1. How to import text data of business operations:

[1177] Users use an interface to input their work details. This interface is a form that runs on a web browser, and users enter their work details into text boxes. For example, they might enter specific work details such as "Check email every day at 9:00 and forward important emails to their boss."

[1178] 2. Generative AI methods that generate automatable business points from captured data:

[1179] The server receives text data entered by the user and sends it to the generative AI. The generative AI uses natural language processing technology to analyze the text data and identify points in the business that can be automated. For example, the part "check email every day at 9 o'clock" is identified as an area that can be automated.

[1180] 3. Means of answering the generated automatable business points:

[1181] The server responds to the user with the points of work that can be automated obtained from the generative AI. The user can then view the list of work points that can be automated in a web browser. For example, specific suggestions such as "It is recommended that you automate the task of checking email at 9:00 every day" are displayed.

[1182] 4. Emotion engine means for recognizing user emotions:

[1183] The server sends the emotional data of the user's input to the emotion engine, which then recognizes the user's emotions based on the content, speed, and keystroke patterns of the input. For example, if the user is feeling stressed, that information is provided to the generative AI.

[1184] 5. Means for generating automatable task points taking into account emotion information from emotion engine means:

[1185] The generative AI takes into account the emotional information provided by the emotion engine to optimize tasks that can be automated. For example, if the user is feeling stressed, the generative AI will prioritize automating those tasks.

[1186] Specific examples

[1187] An example of a prompt sentence when a user inputs their job description as text is, "Please input your job description as text. For example, please enter specific job description such as 'Check email every day at 9:00 and forward important emails to your boss.'"

[1188] This system allows users to easily identify areas for automation in their work and improve work efficiency. In addition, by taking user emotions into consideration, more appropriate automation suggestions can be made, which will improve user satisfaction.

[1189] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1190] Step 1:

[1191] The user inputs the business details as text.

[1192] The user uses the interface on the web browser to enter the details of their work in the text box. For example, they can enter specific details such as "Check email every day at 9:00 and forward important emails to their boss." The entered text data is sent to the server by clicking the send button.

[1193] Input: Text data of business content

[1194] Output: Text data sent to the server

[1195] Step 2:

[1196] The server receives the text data and sends it to the generative AI.

[1197] The server receives text data sent by the user. The received data is formatted into a format that can be understood by the generative AI. The formatted data is then sent to the generative AI.

[1198] Input: Text data sent by the user

[1199] Output: Formatted text data sent to the generative AI

[1200] Step 3:

[1201] Generative AI analyzes text data and identifies business points that can be automated.

[1202] Generative AI uses natural language processing technology to analyze text data. During the analysis process, it identifies points in a business process that can be automated. For example, "checking email every day at 9 o'clock" is identified as an area that can be automated.

[1203] Input: Formatted text data

[1204] Output: Automated business points

[1205] Step 4:

[1206] The emotion engine recognizes the user's emotions and provides that information to the generative AI.

[1207] The server sends the emotional data of the user's input to the emotion engine, which then recognizes the emotion from the user's input content, input speed, keystroke patterns, etc. The recognized emotional information is then provided to the generative AI.

[1208] Input: Emotional data when the user inputs

[1209] Output: Emotional information provided to the generative AI

[1210] Step 5:

[1211] Generative AI takes emotional information into account to generate tasks that can be automated.

[1212] The generative AI takes into account the emotional information provided by the emotion engine to optimize tasks that can be automated. For example, if the user is feeling stressed, the generative AI will prioritize automating those tasks.

[1213] Input: Emotional information, automatable business points

[1214] Output: Optimized and automatable business points

[1215] Step 6:

[1216] The server responds to the user with the generated automatable business points.

[1217] The server responds to the user with optimized automatable task points obtained from the generative AI. The user can check the list of automatable task points in a web browser. For example, specific suggestions such as "It is recommended that you automate the task of checking email every day at 9 o'clock" are displayed.

[1218] Input: Optimized and automatable business points

[1219] Output: A list of automatable business points answered by the user

[1220] (Application example 1)

[1221] 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."

[1222] While conventional process automation systems can analyze text data of work content and identify points that can be automated, they face the challenge of making it difficult to automate efficiently while taking into account the emotions of operators.In addition, they lack the functionality to issue specific instructions to robots based on the identified automation points, making it difficult to maximize operational efficiency within factories.

[1223] 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.

[1224] In this invention, the server includes means for importing text data of work content, generative AI means for generating automatable work points from the imported data, means for responding with the generated automatable work points, an emotion engine for recognizing user emotions, means for generating automatable work points taking into account emotion information, and means for issuing instructions to a robot based on the generated automatable work points. This enables efficient work automation that takes into account the emotions of operators, making it possible to maximize work efficiency within a factory.

[1225] "Means for importing text data of business content" refers to an interface that allows users to input business content as text data, and an API that automatically retrieves text data from existing business management systems.

[1226] "Generative AI means that generate automatable business points from input data" refers to artificial intelligence technology that analyzes input text data and identifies business points that can be automated.

[1227] "Means of answering the generated automatable business points" refers to a function for presenting to users the automatable business points identified by generative AI.

[1228] An "emotion engine that recognizes user emotions" refers to technology that analyzes the user's emotional state and provides that information to generative AI.

[1229] "Means for generating automatable task points by taking emotional information into consideration" refers to a function that enables generative AI to identify automatable task points based on emotional information provided by the emotion engine.

[1230] "Means for issuing instructions to robots based on the generated automatable task points" refers to a function for issuing specific instructions to factory robots based on the identified automation points.

[1231] As an embodiment of the present invention, a Factory Automation Assistance System (FAASS) will be described as an example. This system inputs work content as text data, analyzes the data, identifies work points that can be automated, and issues instructions to robots. Furthermore, it uses an emotion engine to recognize the emotions of operators and improve work efficiency.

[1232] Hardware and software used

[1233] Hardware: smartphones, tablets, factory robots

[1234] Software: Business management system API, generative AI models (e.g., GPT-4), emotion engine

[1235] Program processing procedure

[1236] 1. Enter your job description:

[1237] Users enter their work details in text form through the interface of their smartphone or tablet.

[1238] The server uses the business management system API to automatically retrieve existing business data.

[1239] 2. Data Analysis:

[1240] The server uses a generative AI model (e.g., GPT-4) to analyze the input text data.

[1241] The server uses an emotion engine to recognize the user's emotion.

[1242] 3. Identify automation points:

[1243] The server uses generative AI to identify business points that can be automated based on the analysis results and emotional information.

[1244] 4. Giving instructions to the robot:

[1245] The server issues specific instructions to factory robots based on the identified automation points.

[1246] Robots automate tasks according to instructions.

[1247] Specific examples

[1248] Example prompts for generative AI models

[1249] Prompt statement:

[1250] Analyze the following tasks and identify areas that can be automated. Take into account the emotional state of the operators.

[1251] Job Description: Assembling part A, inspecting part B, packaging part C

[1252] Operator emotions: High stress.”

[1253] When this prompt sentence is input into a generative AI model, the model analyzes the work content and identifies specific automation points, such as "assembly of part A can be automated by a robot." By taking emotional information into account, it is possible to prioritize automation of high-stress tasks.

[1254] The above is a concrete example of application to a factory robot and the program processing procedure. This system enables efficient automation of work processes that take into account the emotions of the operator, maximizing work efficiency within the factory.

[1255] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1256] Step 1:

[1257] Users input their work details as text through the interface of their smartphone or tablet. The input text data is sent to the server. The input data includes details of the work and the operator's emotional information.

[1258] Step 2:

[1259] The server uses the business management system API to automatically retrieve existing business data, which is then integrated with the text data entered by the user to form a dataset for analysis.

[1260] Step 3:

[1261] The server uses a generative AI model (e.g., GPT-4) to analyze the integrated text data. The input for the analysis is text data containing task content and emotional information, and the output is a list of task points that can be automated. The generative AI model understands the task content and identifies the parts that can be automated.

[1262] Step 4:

[1263] The server uses an emotion engine to recognize the user's emotions. The input is the user's emotional information, and the output is the emotional analysis results. The emotion engine evaluates the user's stress level and satisfaction level and provides that information to the generative AI.

[1264] Step 5:

[1265] The server uses generative AI to identify points in tasks that can be automated based on the analysis results and emotional information. The input is the analysis results of the task content and emotional information, and the output is a list of points in tasks that can be automated taking emotions into account. The generative AI takes emotional information into account to identify tasks that should be prioritized for automation.

[1266] Step 6:

[1267] The server issues specific instructions to the factory robots based on the identified automation points. The input is a list of automatable business points, and the output is instructions to the robots. The server generates specific tasks for the robots to perform and sends them to the robots.

[1268] Step 7:

[1269] Robots automate tasks according to instructions received from a server. The input is the instruction from the server, and the output is the task that has been executed. Based on the instructions, the robot automatically performs tasks such as assembling parts, inspecting them, and packaging them.

[1270] These are the specific processing steps of the Factory Automation Assistant System (FAASS). This system enables efficient automation of tasks while taking into account the emotions of the operators, maximizing the efficiency of work within the factory.

[1271] Example 2

[1272] 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."

[1273] Conventional business automation systems are limited to analyzing business processes and extracting points that can be automated, and do not provide business improvement suggestions that take into account the user's emotional state. As a result, there is a lack of specific suggestions to reduce user stress and workload. This has resulted in insufficient improvements in business efficiency and user satisfaction.

[1274] 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.

[1275] In this invention, the server includes means for importing text data of business content, generative AI means for generating automatable business points from the imported data, means for returning the generated automatable business points, emotion analysis means for analyzing the emotional state of the user, and means for making business improvement proposals based on the emotion analysis results, thereby enabling business improvement proposals that take the emotional state of the user into consideration.

[1276] The "means for importing data in the form of text of business content" is a function for importing business content entered by a user into the system as text data.

[1277] "Generative AI means that generates automatable business points from imported data" is a function that uses artificial intelligence technology to analyze imported text data and identify business points that can be automated.

[1278] The "means for answering generated automatable business points" is a function for presenting to the user the automatable business points identified by the generative AI means.

[1279] The "emotion analysis means for analyzing the user's emotional state" is a function for analyzing the user's input data and past work history, and evaluating the emotional state the user feels about the work.

[1280] The "means for making business improvement proposals based on emotion analysis results" is a function for generating specific business improvement proposals to reduce the user's stress, taking into account the user's emotional state obtained by the emotion analysis means.

[1281] This invention is a system that imports and analyzes text data of business operations, identifies business points that can be automated, and proposes business improvement measures that take into account the emotional state of the user. A specific embodiment of this system will be described below.

[1282] System configuration

[1283] Hardware

[1284] This system consists of a server and a user terminal. The server is equipped with a high-performance processor and large-capacity memory, and performs data analysis and generative AI processing. The user terminal provides an interface for users to input their work details.

[1285] software

[1286] The following software is installed on the server:

[1287] 1. Natural language processing libraries: Use natural language processing libraries such as "spaCy" or "NLTK" to analyze text data.

[1288] 2. Generative AI models: Use machine learning models to identify automatable business processes from text data.

[1289] 3. Sentiment Analysis Engine: Use an engine to analyze the user's emotional state.

[1290] System Operation

[1291] Entering text data

[1292] The user inputs the details of the job as text data. For example, the user inputs "responding to inquiries from customers." The user terminal transmits this input data to the server.

[1293] Analysis using natural language processing

[1294] The server analyzes the received text data using a natural language processing library. Specifically, it performs the following processes:

[1295] Tokenization of text data (splitting into words and phrases)

[1296] Part-of-speech tagging (identifying parts of speech such as verbs and nouns)

[1297] Dependency analysis (analyzing the relationships between words)

[1298] Extracting points that can be automated

[1299] Based on the analysis results, the server uses a generative AI model to extract tasks that can be automated, such as classifying the content of inquiries and selecting appropriate responses.

[1300] Emotion analysis

[1301] The server uses a sentiment analysis engine to analyze the user's emotional state. For example, if the user enters "this job is stressful," the sentiment analysis engine determines that the user is experiencing high stress.

[1302] Generate business improvement proposals

[1303] The server generates work improvement proposals to reduce user stress based on the results of the generative AI model and the emotion analysis engine. For example, it could generate a proposal to "reduce the user's workload by automating the classification of inquiry content" and present it to the user.

[1304] Examples of specific examples and prompts

[1305] As a specific example, consider the case where the user inputs "responding to inquiries from customers" as the job content.

[1306] Input: "Responding to customer inquiries"

[1307] Output of generative AI: "Classification of inquiry content" and "Selection of appropriate response"

[1308] Sentiment analysis engine output: "The user is stressed"

[1309] Business improvement proposal: "By automating the classification of inquiries, we can reduce the workload of users."

[1310] An example of a prompt sentence is as follows:

[1311] When a user inputs "responding to customer inquiries" as a task, the generative AI identifies tasks that can be automated, such as "classifying the inquiry" and "selecting the appropriate response." Meanwhile, the sentiment analysis engine recognizes that the user is feeling stressed about this task. Taking this emotional information into consideration, the generative AI makes suggestions for improving the task to reduce stress. For example, by automating "classifying the inquiry," the system makes suggestions to reduce the user's workload.

[1312] In this way, the server analyzes the user's work, identifies points that can be automated, and makes suggestions for improving the work.

[1313] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1314] Step 1:

[1315] The user inputs the details of the job as text data. For example, the user inputs "responding to inquiries from customers." The user terminal transmits this input data to the server.

[1316] Input: Text data of work content (e.g., "Responding to customer inquiries")

[1317] Output: Text data sent to the server

[1318] Step 2:

[1319] The server analyzes the received text data using a natural language processing library. Specifically, it performs the following processes:

[1320] Tokenization of text data (splitting into words and phrases)

[1321] Part-of-speech tagging (identifying parts of speech such as verbs and nouns)

[1322] Dependency analysis (analyzing the relationships between words)

[1323] Input: Text data sent by the user

[1324] Output: Parsed text data (tokenization, part-of-speech tagging, dependency analysis results)

[1325] Step 3:

[1326] Based on the analysis results, the server uses a generative AI model to extract tasks that can be automated, such as classifying the content of inquiries and selecting appropriate responses.

[1327] Input: Parsed text data

[1328] Output: Automated task points (e.g., "classifying inquiry content" and "selecting appropriate responses")

[1329] Step 4:

[1330] The server uses a sentiment analysis engine to analyze the user's emotional state. For example, if the user enters "this job is stressful," the sentiment analysis engine determines that the user is experiencing high stress.

[1331] Input: User input data and past work history

[1332] Output: User's emotional state (e.g., "High Stress")

[1333] Step 5:

[1334] The server generates work improvement proposals to reduce user stress based on the results of the generative AI model and the emotion analysis engine. For example, it could generate a proposal to "reduce the user's workload by automating the classification of inquiry content" and present it to the user.

[1335] Input: Automable task points and user emotional state

[1336] Output: Proposal for business improvement (e.g., "By automating the classification of inquiries, we can reduce the workload of users.")

[1337] In this way, the server analyzes the user's work, identifies points that can be automated, and makes suggestions for improving the work.

[1338] (Application example 2)

[1339] 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."

[1340] Conventional business automation systems can extract points that can be automated from text data of business operations, but they are unable to propose business improvement measures that take into account the user's emotions and stress levels. As a result, while they can improve business efficiency, there are limitations to how much they can reduce user stress and workload. In addition, they lack a function to notify users of the progress of work in real time, making it difficult to manage the progress of work. There is a need to solve these issues.

[1341] 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.

[1342] In this invention, the server includes a means for importing text data of work content, a generative AI means for generating automatable work points from the imported data, a means for responding with the generated automatable work points, a means including an emotion engine for recognizing emotional information and making work improvement proposals, and a means for notifying the progress of automated work in real time. This enables work improvement proposals that take into account the user's emotions and stress level, thereby not only improving work efficiency but also reducing the user's stress and workload. Furthermore, real-time notification of work progress makes it easier to manage work progress.

[1343] "Means for importing text data of business content" refers to means for inputting business content as text data and importing that data into the system.

[1344] "Generative AI means that generates automatable business points from imported data" refers to artificial intelligence means that analyzes imported text data and extracts and generates business points that can be automated.

[1345] The "means for answering the generated automatable task points" is a means for presenting the automatable task points generated by the generative AI means to the user.

[1346] "Means including an emotion engine that recognizes emotion information and makes business improvement proposals" refers to means that includes an emotion engine that analyzes the user's emotion information and makes business improvement proposals based on that information.

[1347] "Means for notifying the user of the progress of an automated task in real time" refers to means for notifying the user of the progress of an automated task in real time.

[1348] A system for implementing this invention includes a means for importing data that has been converted into text about work content, a generative AI means for generating automatable work points from the imported data, a means for responding with the generated automatable work points, a means including an emotion engine that recognizes emotional information and makes suggestions for work improvement, and a means for notifying the progress of automated work in real time.

[1349] Hardware and software used

[1350] Hardware: Smartphone

[1351] Software: Python, spaCy (natural language processing library), sentiment analysis model

[1352] Data processing and calculation

[1353] 1. Enter and import business details

[1354] The user inputs the work content as text data using a smartphone. For example, the user inputs the work content such as "product picking work."

[1355] 2. Creating automatable business points

[1356] The server uses spaCy to analyze the imported text data and extract verbs and nouns, thereby generating task points that can be automated.

[1357] 3. Recognizing emotional information and proposing business improvements

[1358] The server uses a sentiment analysis model to analyze the emotions in the input text data and determine the user's stress level. For example, if the emotion is determined to be "NEGATIVE," the emotion engine will make suggestions for improving the business. Specifically, it will make suggestions such as, "By automating the picking work, we will reduce the workload."

[1359] 4. Real-time notifications of work progress

[1360] The server notifies the user of the progress of the automated tasks in real time, allowing the user to keep track of the progress of the tasks.

[1361] Specific examples

[1362] Entering work content: The user enters "product picking work" as the work content.

[1363] Result of automation point extraction: The server identifies "product picking work" as a business point that can be automated.

[1364] Sentiment analysis result: The server uses the emotion engine to determine "NEGATIVE."

[1365] Business improvement proposal: The server suggests, "By automating the picking process, we can reduce the workload."

[1366] Prompt Sentence Examples

[1367] If a user inputs "picking products" as a task description, the generative AI will use this text data to identify "picking products" as a task that can be automated. Meanwhile, the emotion engine will recognize that the user is feeling stressed about this task. Taking this emotional information into consideration, the generative AI will make suggestions for improving the task to reduce stress. For example, it will suggest automating the "picking task" to reduce the user's workload.

[1368] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1369] Step 1:

[1370] The user inputs the details of the work as text data using a smartphone.

[1371] Input: Text data of the work content (e.g., "Picking products")

[1372] Output: Text data is sent to the server

[1373] Specific operation: The user launches the smartphone application, enters the details of the task in the text input field, and presses the send button.

[1374] Step 2:

[1375] To analyze the text data imported by the server, natural language processing is performed using spaCy.

[1376] Input: Text data sent by the user

[1377] Output: A list of extracted verbs and nouns (e.g., "product," "picking," "work")

[1378] Specific operation: The server receives the text data and uses the spaCy library to tokenize the text and extract verbs and nouns.

[1379] Step 3:

[1380] The server generates automatable business points based on the extracted verbs and nouns.

[1381] Input: A list of extracted verbs and nouns

[1382] Output: Automated business points (e.g., "Automation of picking work")

[1383] Specific operation: The server analyzes the extracted verbs and nouns, identifies business points that can be automated, and creates a list.

[1384] Step 4:

[1385] The server uses a sentiment analysis model to analyze the sentiment of the input text data.

[1386] Input: Text data sent by the user

[1387] Output: Sentiment analysis result (e.g. "NEGATIVE")

[1388] Specific operation: The server inputs the text data into the sentiment analysis model and determines the sentiment using the sentiment analysis model.

[1389] Step 5:

[1390] The server generates business improvement proposals based on the sentiment analysis results.

[1391] Input: Sentiment analysis results, automatable business points

[1392] Output: Proposal for business improvement (e.g., "By automating picking work, we can reduce the workload.")

[1393] Specific operation: The server compares the results of the sentiment analysis with points of work that can be automated and generates work improvement suggestions to reduce the user's stress.

[1394] Step 6:

[1395] The server notifies the user of the generated business improvement proposal.

[1396] Input: Business improvement proposal

[1397] Output: Business improvement proposals displayed on the user's smartphone

[1398] Specific operation: The server sends the generated business improvement proposal to the user's smartphone and displays it as a notification.

[1399] Step 7:

[1400] The server notifies the user of the progress of the automated tasks in real time.

[1401] Input: Automated work progress data

[1402] Output: Progress displayed on the user's smartphone

[1403] Specific operation: The server collects progress data of automated tasks and notifies the user's smartphone in real time.

[1404] Example 3

[1405] 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."

[1406] Conventional business automation systems make uniform suggestions without considering the user's emotional state, which often results in suggestions that do not meet the user's needs. Furthermore, they lack a mechanism for effectively utilizing user feedback, which reduces the accuracy and usefulness of the suggestions. This creates the problem of not being able to fully utilize the benefits of business efficiency and automation.

[1407] 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.

[1408] In this invention, the server includes means for importing text data of business content, generative AI means for generating automatable business points from the imported data, means for responding with the generated automatable business points, emotion recognition means for recognizing the emotional state of the user, means for generating business improvement proposals based on the results of the emotion recognition means, and means for displaying the generated business improvement proposals and collecting feedback from the user. This enables customized business improvement proposals that take the user's emotional state into consideration, improving the accuracy and usefulness of the proposals.

[1409] "Text data of business content" refers to data that records the procedures and content of business as text information.

[1410] "Means for importing" refers to means for receiving data from the outside and storing or processing it within the system.

[1411] "Generative AI methods" are methods that use artificial intelligence to analyze data and generate business points that can be automated.

[1412] "Automable business points" refer to parts or processes within a business that can be automated.

[1413] The "means of replying" is a means for providing the generated automatable business points and suggestions to the user.

[1414] An "emotion recognition means" is a means for analyzing a user's input data (text or voice) and estimating the user's emotional state.

[1415] The "means for generating business improvement proposals" is a means for generating proposals for improving business efficiency and automation based on the results of the emotion recognition means.

[1416] The "display means" is a means for displaying the generated business improvement proposals on a user interface.

[1417] "Means for collecting feedback" refers to the means for collecting opinions and reactions from users and reflecting them in the system.

[1418] This invention is a system that imports text data of business operations, generates automatable business points from the imported data, and responds with the generated automatable business points. It also includes a function to recognize the user's emotional state, generate business improvement proposals based on the results, display the proposals, and collect feedback from the user.

[1419] Hardware and software used

[1420] Hardware: Servers (e.g., cloud computing services)

[1421] Software: Emotion recognition models (e.g., emotion analysis engines), generative AI models (e.g., natural language processing engines)

[1422] Specific operation of the system

[1423] User login

[1424] A user accesses the system and enters their username and password on the login screen. The server authenticates them by checking the information against a database. If authentication is successful, the user is redirected to the dashboard.

[1425] Generate automatable business points

[1426] The server analyzes the user's work data and uses a generative AI model to generate automatable work points, taking into account past work history and current work content in the process.

[1427] List of business points

[1428] The server displays a list of the generated automatable business points in the user interface, allowing the user to check the automation proposals for each business point and their effects.

[1429] Performing emotion recognition

[1430] When a user inputs text or voice, the server sends the input data to an emotion recognition model that analyzes keystroke patterns, tone of voice, and other factors to estimate the user's emotional state.

[1431] Generate business improvement proposals

[1432] Based on the emotion recognition results, the server uses a generative AI model to generate business improvement suggestions, which are customized taking into account the user's emotional state.

[1433] Viewing suggestions and user feedback

[1434] The server displays the generated business improvement proposals on a user interface. The user can review the proposals and provide feedback. The feedback is used to generate proposals from the next time onwards.

[1435] Specific examples

[1436] Viewing business points that can be automated

[1437] When a user logs in to the system, the server displays a list of business points such as "automated data entry" and "automated report generation." For each business point, it displays specific benefits such as "By automating this task, work time will be reduced by 30%."

[1438] Use of Emotion Recognition

[1439] When a user types in the text "Can this task really be automated?", the server uses an emotion recognition model to analyze the user's emotional state. For example, it can infer from keystroke patterns that the user is skeptical, and the generative AI model will make suggestions such as, "Should I explain the specific steps for automation in detail?"

[1440] Prompt Sentence Examples

[1441] "Please tell us the benefits of automating data entry."

[1442] "Please explain in more detail how you automate report generation."

[1443] "How much time will I save by automating this task?"

[1444] In this way, the server provides automatable business points through the user interface, grasps the user's emotional state using the emotion recognition model, and the generative AI model makes appropriate business improvement proposals. The flow of the identification process in Example 3 will be described with reference to Figure 21.

[1445] Step 1:

[1446] User login

[1447] A user accesses the system and enters their username and password on the login screen. The server authenticates them by checking the information against a database. If authentication is successful, the user is redirected to the dashboard.

[1448] Input: Username, Password

[1449] Output: Authentication result (success / failure), dashboard screen

[1450] Specific behavior:

[1451] The user enters information into the login form.

[1452] The server queries the database and performs authentication.

[1453] If authentication is successful, redirect the user to the dashboard.

[1454] Step 2:

[1455] Generate automatable business points

[1456] The server analyzes the user's work data and uses a generative AI model to generate automatable work points, taking into account past work history and current work content in the process.

[1457] Input: Business data

[1458] Output: Automated business points

[1459] Specific behavior:

[1460] The server collects the user's business data.

[1461] A generative AI model analyzes the data and extracts business points that can be automated.

[1462] Step 3:

[1463] List of business points

[1464] The server displays a list of the generated automatable business points in the user interface, allowing the user to check the automation proposals for each business point and their effects.

[1465] Input: Automated business points

[1466] Output: List of business points

[1467] Specific behavior:

[1468] The server generates a list of business points.

[1469] Display the list in the user interface.

[1470] Step 4:

[1471] Performing emotion recognition

[1472] When a user inputs text or voice, the server sends the input data to an emotion recognition model that analyzes keystroke patterns, tone of voice, and other factors to estimate the user's emotional state.

[1473] Input: User text input, voice input

[1474] Output: Estimated emotional state

[1475] Specific behavior:

[1476] The user provides input via text or voice.

[1477] The server sends the input data to the emotion recognition model.

[1478] The emotion recognition model estimates the emotional state and returns the results to the server.

[1479] Step 5:

[1480] Generate business improvement proposals

[1481] Based on the emotion recognition results, the server uses a generative AI model to generate business improvement suggestions, which are customized taking into account the user's emotional state.

[1482] Input: Emotional state estimation results, automatable task points

[1483] Output: Business improvement proposals

[1484] Specific behavior:

[1485] The server receives the emotion recognition results.

[1486] A generative AI model generates business improvement proposals.

[1487] Step 6:

[1488] Viewing suggestions and user feedback

[1489] The server displays the generated business improvement proposals on a user interface. The user can review the proposals and provide feedback. The feedback is used to generate proposals from the next time onwards.

[1490] Input: Business improvement suggestions, user feedback

[1491] Output: Improved suggestions, feedback saved

[1492] Specific behavior:

[1493] The server displays the business improvement proposals on the user interface.

[1494] The user reviews the proposal and provides feedback.

[1495] The server collects the feedback and stores it in a database.

[1496] (Application example 3)

[1497] 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."

[1498] Conventional task automation systems can identify points in a task that can be automated, but they have the problem of being unable to propose task improvement measures that take into account the user's emotional state. This can result in insufficient improvements to work efficiency and safety. Furthermore, there is a lack of means to reduce the psychological resistance of users when accepting automation proposals.

[1499] 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.

[1500] In this invention, the server includes means for importing text data of work content, generative AI means for generating automatable work points from the imported data, means for responding with the generated automatable work points, emotion recognition means for recognizing the emotional state of the user, and means for making work improvement suggestions based on the emotion information. This makes it possible to make work improvement suggestions that take the emotional state of the user into consideration, thereby achieving improvements in work efficiency and safety and reducing the psychological resistance of users when accepting automation suggestions.

[1501] "Text data of business content" refers to data that records the procedures and content of business as text information.

[1502] "Means for importing" refers to the functions and devices for acquiring data from the outside and importing it into the system.

[1503] "Generative AI means" refers to functions or devices that use artificial intelligence technology to generate automatable business points from captured data.

[1504] "Automable business points" refer to parts or tasks within a business that can be automated.

[1505] The "answering means" refers to a function or device for presenting the generated automatable business points to the user.

[1506] "Emotion recognition means for recognizing the user's emotional state" refers to a function or device for inferring emotions from the user's voice or text input.

[1507] The "means for proposing business improvement based on emotion information" refers to a function or device for proposing business improvement based on recognized emotion information.

[1508] As an embodiment of the present invention, we will explain an example of a "factory work automation support system" installed on a factory robot. This system imports text data of work content, generates automatable work points from the imported data, and responds with the generated automatable work points. It also recognizes the user's emotional state and makes work improvement suggestions based on the emotional information.

[1509] Hardware and software used

[1510] Hardware:

[1511] Factory robot (with display)

[1512] Microphone (for voice input)

[1513] software:

[1514] Python

[1515] GUI Toolkit

[1516] Emotion Recognition Model

[1517] Business improvement proposal generation algorithm

[1518] Data processing and calculation

[1519] server:

[1520] The server imports text data of the work content. The imported data is analyzed by generative AI to generate work points that can be automated. The generated work points are then sent to the user as answers.

[1521] Device:

[1522] The terminal (factory robot) receives the user's voice and text input and recognizes the user's emotional state using an emotion recognition means. The recognized emotional information is sent to the server, and business improvement suggestions are made based on the emotional information.

[1523] User:

[1524] Users can check which business processes can be automated through the factory robot's display and receive suggestions for business improvement based on the robot's emotional state.

[1525] Specific examples

[1526] For example, if a worker says, "This task takes a long time," the emotion recognition model will detect "stress." Based on this, the factory robot will suggest, "Automating this task will save time."

[1527] Example prompt sentence:

[1528] If a user says "This task takes time" via voice input, the emotion recognition model will detect "stress" and make automated suggestions.

[1529] In this way, factory robots can recognize the emotional state of workers and make appropriate suggestions for improving work, thereby improving work efficiency and safety.

[1530] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1531] Step 1:

[1532] The server takes in the text data of the business content. As input, it receives data that records the business procedures and content as text information and stores it in the system. As output, the taken-in text data is passed to the generative AI means.

[1533] Step 2:

[1534] The server generates automatable task points from the imported data. As input, it receives the text data imported in step 1, and the generative AI means analyzes it. As data processing, it uses natural language processing technology to analyze the task content and identify the parts that can be automated. As output, it generates automatable task points.

[1535] Step 3:

[1536] The server responds with the generated automatable business points. As input, it receives the automatable business points generated in step 2 and generates data to present to the user. As output, it displays the automatable business points to the user.

[1537] Step 4:

[1538] The terminal receives voice or text input from the user. As input, it receives data in which the user expresses emotions through voice or text and passes this to the emotion recognition means. As output, the voice or text data is passed to the emotion recognition means.

[1539] Step 5:

[1540] The device recognizes the user's emotional state using an emotion recognition means. The input is the voice and text data received in step 4, analyzed, and the emotion recognition model estimates the emotion. The data is processed by analyzing the tone of the voice and the keystroke pattern of the text. The output is the user's emotional state.

[1541] Step 6:

[1542] The server makes business improvement proposals based on the emotional information. As input, it receives the emotional information recognized in step 5, and the generative AI means generates business improvement proposals. As data processing, it applies an algorithm to generate business improvement proposals that take the emotional information into account. As output, business improvement proposals that take the emotional information into account are displayed to the user.

[1543] Step 7:

[1544] The user checks the factory robot's display for business improvement suggestions based on the automatable business points and emotional state. As input, the data generated in Steps 3 and 6 is received and displayed on the display. As output, the user checks the suggestions and implements automation or improvement of business processes as necessary.

[1545] 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.

[1546] 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.

[1547] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1548] 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.

[1549] [Third embodiment]

[1550] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1551] 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.

[1552] 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).

[1553] 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.

[1554] 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.

[1555] 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).

[1556] 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.

[1557] 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.

[1558] 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.

[1559] 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.

[1560] 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.

[1561] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1562] "Example 1"

[1563] As one embodiment of the present invention, an interface for inputting business content as text data is provided as a means for importing data in the form of textualized business content. This interface can be in a format where the user directly inputs text. It can also be in a format where an API is used to automatically obtain text data from an existing business management system.

[1564] "Example 2"

[1565] As a generative AI tool, an AI engine is used that generates automatable business points from the imported text data. This AI engine uses natural language processing technology to understand the content of the business from the text data and extracts points that can be automated. Specifically, it analyzes the verbs and nouns in the text data and understands the business flow and procedures they indicate. It then identifies the parts of that that can be automated.

[1566] "Example 3"

[1567] A user interface is provided as a means of responding to the generated automatable business points. This interface displays a list of the generated automatable business points so that the user can refer to them. Specifically, it lists the automatable business points one by one and displays automation suggestions for each business point and the effects of that automation.

[1568] The processing flow of each embodiment will be described below.

[1569] "Example 1"

[1570] Step 1: Open an interface where the user can enter their work details as text data.

[1571] Step 2: The user enters the job description as text and presses the send button.

[1572] Step 3: The system takes the text data and sends it to the generative AI.

[1573] "Example 2"

[1574] Step 1: The generative AI receives the text data.

[1575] Step 2: Generative AI uses natural language processing technology to understand the business content from the text data.

[1576] Step 3: Generative AI extracts points that can be automated from the content of the work and generates them as automatable work points.

[1577] "Example 3"

[1578] Step 1: The system displays the generated automatable business points in the user interface.

[1579] Step 2: The user checks the business points that can be automated and proceeds with automating the business based on that.

[1580] Example 1

[1581] 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."

[1582] In conventional business management systems, it was difficult to input business content as text data and then efficiently analyze and summarize it. It was also difficult to automatically generate detailed explanations of business content, which placed a heavy burden on users. This resulted in a lack of progress in business efficiency and automation, and slow improvements to business processes.

[1583] 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.

[1584] In this invention, the server includes an interface means for inputting business content as text data, a means for receiving and saving the input text data, a means for cleaning and formatting the saved data, a means for inputting the cleaned and formatted data into a generative AI model, and a means for returning results obtained from the generative AI model to the user, thereby enabling efficient analysis and automatic generation of summaries and detailed descriptions of business content.

[1585] The "interface means for inputting business details as text data" refers to an interface for a user to input business details in text format, and includes a form on a web browser, a desktop application, or a mobile application.

[1586] The "means for receiving and saving input text data" refers to a means having a function for receiving text data sent from a user and saving it in a storage such as a database.

[1587] "Means for cleaning and formatting stored data" refers to means for analyzing stored text data, cleaning it by removing unnecessary spaces and special characters, and converting it into a format that is easy for the generative AI model to understand.

[1588] "Means for inputting cleaned and formatted data into a generative AI model" means means for inputting cleaned and formatted data into a generative AI model, including the ability to send API requests.

[1589] "Means for returning results obtained from a generative AI model to a user" refers to means for receiving results returned from a generative AI model and returning them to a user, including an interface for displaying the results.

[1590] MODE FOR CARRYING OUT THE INVENTION

[1591] This invention is a system that inputs business content as text data and efficiently analyzes, summarizes, and generates detailed explanations of the data. Specific embodiments of this system are described below.

[1592] Enter business details

[1593] The user uses an interface to enter the job description. This interface can be a form running in a web browser, a dedicated desktop application, or a mobile application. The user enters the job description in a text box and clicks a "Submit" button.

[1594] As a specific example, the user inputs "Today's work included a meeting with client A and the preparation of materials," and presses the send button.

[1595] Receiving and storing data

[1596] The server receives the text data of the business details sent by the user, and the received data is stored in a relational database such as MySQL or PostgreSQL.

[1597] Specifically, the server executes the SQL query "INSERT INTO Business Details (User ID, Date, Details) VALUES (1, '2023-10-01', 'Meeting with Customer A and preparing materials')".

[1598] Data cleaning and formatting

[1599] The server analyzes the stored data and cleans it, removing unnecessary spaces and special characters, etc. It also converts the data into a format that is easy for the generative AI model to understand, for example, by categorizing the work content.

[1600] Specifically, the server converts the text "Meeting with customer A and document creation" into the format "Customer support: Meeting with customer A, Document creation: Document creation."

[1601] Data input to generative AI models

[1602] The server inputs the cleaned and formatted data into a generative AI model, which uses a natural language processing model such as GPT-4, and sends an API request to the model.

[1603] Specifically, the server sends an HTTP request "POST / generate-summary HTTP / 1.1" to the API of the generated AI model, attaching a JSON payload containing the data.

[1604] Obtaining and displaying results

[1605] The server receives the results returned by the generative AI model, which may be a summary of the task or a detailed description, and then parses and formats the results for return to the user.

[1606] Specifically, the server receives the summary "Meeting with customer A and preparation of materials" and converts it into HTML format for display to the user.

[1607] Returning results to the user

[1608] The server returns the results obtained from the generative AI model to the user, who can view the results through an interface, which can be displayed on a web page or on the application screen.

[1609] Specifically, the server generates HTML containing the content "Summary of today's work: Had a meeting with customer A and prepared materials" and sends it to the user's browser.

[1610] Prompt Sentence Examples

[1611] "Summarize today's work."

[1612] "Summarize this week's work."

[1613] "Please explain in detail the contents of the meeting with Customer A."

[1614] The above is a specific embodiment for carrying out the present invention.

[1615] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1616] Step 1:

[1617] The user enters the job description

[1618] The user uses an interface to input the details of the work. The user enters the details of the work in the text box and clicks the "Send" button. As input, the user enters "Today's work was a meeting with Client A and the preparation of materials." As output, the entered text data is sent to the server.

[1619] Step 2:

[1620] The server receives and stores the data

[1621] The server receives the text data of the work content sent by the user. As input, it receives the text data sent by the user. The server stores the received data in a relational database such as MySQL or PostgreSQL. Specifically, the server executes the SQL query "INSERT INTO Work content (user ID, date, content) VALUES (1, '2023-10-01', 'Meeting with customer A and creating materials')". As output, it obtains the work content stored in the database.

[1622] Step 3:

[1623] The server cleans and formats the data

[1624] The server analyzes the stored data and cleans it by removing unnecessary spaces and special characters. As input, it receives the text data of business operations stored in the database. The server converts the data into a format that is easy for the generative AI model to understand. Specifically, the server converts the text "Meeting with customer A and creating documents" into the format "Customer support: Meeting with customer A, Document creation: Creating documents." The cleaned and formatted data is obtained as output.

[1625] Step 4:

[1626] The server inputs data into the generative AI model

[1627] The server inputs the cleaned and formatted data into the generative AI model. The cleaned and formatted data is used as input. The generative AI model uses a natural language processing model such as GPT-4. Specifically, the server sends an HTTP request "POST / generate-summary HTTP / 1.1" to the generative AI model's API and attaches a JSON payload containing the data. The data is input into the generative AI model as output.

[1628] Step 5:

[1629] The server retrieves the results from the generative AI model

[1630] The server receives the results returned by the generative AI model. As input, it receives the response from the generative AI model. The results may be a summary of the work content or a detailed explanation. Specifically, the server receives the summary "Meeting with customer A and preparation of materials were conducted" and converts this into HTML format for display to the user. As output, it obtains the results obtained from the generative AI model.

[1631] Step 6:

[1632] The server returns the results to the user

[1633] The server returns the results obtained from the generative AI model to the user. The results obtained from the generative AI model are used as input. The user can check the results through the interface. Specifically, the server generates HTML containing the content "Summary of today's work: Held a meeting with customer A and prepared materials" and sends it to the user's browser. The user can check the results as output.

[1634] (Application example 1)

[1635] 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."

[1636] Conventional factory robot management systems have the problem of being difficult to carry out work efficiently, as the process of manually inputting work details and giving instructions to the robots is cumbersome. Furthermore, there was also the problem of insufficient integration with existing work management systems, which hindered progress in automating work and streamlining. This resulted in a decline in productivity throughout the factory and increased costs.

[1637] 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.

[1638] In this invention, the server includes a means for importing text data of work content, a generative AI means for generating automatable work points from the imported data, a means for returning the generated automatable work points, a means for sending the imported work content to a factory robot, and a means for generating instructions for the factory robot to execute the work content. This automates the process from inputting work content to issuing instructions to the robot, improving work efficiency throughout the factory, thereby enabling increased productivity and cost reduction.

[1639] "Means for importing text data of business content" refers to an interface for acquiring text data of business content entered by the user, and an API for automatically acquiring text data from existing business management systems.

[1640] "Generative AI means for generating automatable business points from imported data" refers to artificial intelligence technology that analyzes the text data of imported business content and extracts and generates business points that can be automated.

[1641] "Means for answering generated automatable task points" refers to means for presenting to the user the automatable task points generated by the generative AI means.

[1642] The "means for transmitting the captured work content to the factory robot" refers to a communication means for transmitting the text data of the captured work content to the factory robot.

[1643] "Means for generating instructions for factory robots to execute work content" refers to means for generating specific work instructions based on the work content input by the factory robot and having the robot execute them.

[1644] As an embodiment of the present invention, a factory robot management system is constructed, which includes: means for importing text data of work content, generative AI means for generating automatable work points from the imported data, means for responding with the generated automatable work points, means for transmitting the imported work content to a factory robot, and means for generating instructions for the factory robot to execute the work content.

[1645] Program processing explanation

[1646] Hardware and Software

[1647] Hardware: smartphones, tablets, factory robots

[1648] Software: Python, requests library, API of existing business management system

[1649] Data processing and calculation

[1650] 1. Importing work content: The server acquires the work content entered by the user on a smartphone or tablet as text data. It also automatically acquires text data from existing work management systems via API.

[1651] 2. Generating automatable business points: The server inputs the captured text data into a generative AI model to generate automatable business points. This generative AI model uses natural language processing technology to analyze the business content and extract points for efficient business execution.

[1652] 3. Response of business points: The server presents the generated business points that can be automated to the user, allowing the user to receive specific suggestions for automating and streamlining their business.

[1653] 4. Sending the work content: The server sends the text data of the work content to the factory robot via Wi-Fi or a wired network.

[1654] 5. Instruction generation: The factory robot generates and executes specific work instructions based on the received task content, allowing the robot to complete the task automatically.

[1655] Specific examples

[1656] User input example: A user uses a smartphone to input "Assemble part A."

[1657] Example of retrieving data from API: Retrieve the task content "Inspect part B" from an existing business management system.

[1658] Prompt Sentence Examples

[1659] When a user types "Assemble part A" into their smartphone, send that task to the factory robot.

[1660] Also, obtain the task content "Inspect part B" from the existing task management system and send it to the factory robot in the same way.

[1661] In this way, a system is realized that enables factory robots to perform their tasks efficiently.

[1662] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1663] Step 1:

[1664] The user uses a smartphone or tablet to input the details of the task as text. The input text data is sent to the server. An example of input data is "Assemble part A." The server receives this text data and proceeds to the next processing step.

[1665] Step 2:

[1666] The server automatically retrieves text data of the work content from the existing work management system via API. An example of retrieved data is "Inspect part B." The server receives this data and proceeds to the next processing step.

[1667] Step 3:

[1668] The server inputs the text data acquired in steps 1 and 2 into the generative AI model. The generative AI model uses natural language processing technology to analyze the business content and extract business points that can be automated. For example, from "Assemble part A," it generates "automation points for the assembly process." The generated business points are returned to the server.

[1669] Step 4:

[1670] The server presents the automatable business points returned by the generative AI model to the user, who can then view the suggested business points on the screen of their smartphone or tablet. For example, "automation points for the assembly process" may be displayed.

[1671] Step 5:

[1672] The server sends the text data of the work content that it has captured to the factory robot. The communication method is Wi-Fi or a wired network. An example of the data that is sent is "Assemble part A." The factory robot receives this data and proceeds to the next processing step.

[1673] Step 6:

[1674] The factory robot generates specific work instructions based on the received task content. For example, for the task content "Assemble part A," it generates the instruction "Pick up part A and place it on the assembly line." The generated instructions are stored in the robot's internal system.

[1675] Step 7:

[1676] The factory robot performs the task according to the generated work instructions. For example, based on the instruction "pick up part A and place it on the assembly line," the robot actually picks up part A and places it on the assembly line. When the task is completed, the robot sends a completion report to the server.

[1677] In this way, the process from inputting the work content to giving instructions to the robot and then executing it is automated.

[1678] Example 2

[1679] 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."

[1680] Conventional business process automation systems require manual analysis of business processes and identification of points that can be automated, which is time-consuming and labor-intensive. It is also difficult to accurately grasp the flow and procedures of business processes, which often makes it difficult to achieve efficient automation. Furthermore, there is a lack of specific improvement proposals for business process automation, which hinders progress in improving business efficiency.

[1681] 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.

[1682] In this invention, the server includes a means for importing data in which business content has been converted into text, a means for applying natural language processing technology to the imported data and analyzing verbs and nouns to grasp the flow and procedures of the business, and a generative AI means for generating automatable business points from the grasped flow and procedures of the business. This makes it possible to automatically analyze business content and quickly and accurately identify automatable points.

[1683] "Text data of business content" refers to data in which the procedures and content of business are written in text form.

[1684] A "means for capturing" is a means for inputting user-provided data into the system.

[1685] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[1686] "Means for analyzing verbs and nouns" refers to means for extracting verbs and nouns from text data and analyzing the relationships between them.

[1687] "Means for understanding the flow and procedures of work" refers to a means for understanding the progress and procedures of work based on analyzed verbs and nouns.

[1688] "Automable business points" refer to parts or steps within a business process that can be automated.

[1689] "Generative AI methods" are methods that use artificial intelligence technology to analyze captured data and identify business points that can be automated.

[1690] The "answering means" is a means for presenting the generated automatable business points to the user.

[1691] The "function for making improvement suggestions" is a function that makes specific suggestions for automating and streamlining business operations.

[1692] This invention is a system that inputs text data of business operations, analyzes the data, and identifies business operations that can be automated. A specific embodiment of this system will be described below.

[1693] First, the user provides the system with text data of the work content. This data is a written description of the work procedures and content. The user can upload this data through a web interface.

[1694] The server receives the text data provided by the user and stores it in its internal storage. The server then analyzes the text data using natural language processing technology. Specifically, it uses natural language processing libraries such as "spaCy" and "NLTK." This analyzes the sentence structure of the text data and extracts verbs and nouns.

[1695] The server extracts verbs and nouns from the analyzed sentence structure and analyzes their relationships. This clarifies the workflow and procedures. For example, verbs such as "enter" and "create" and nouns such as "sales data," "spreadsheet software," and "report" are extracted.

[1696] Next, the server identifies tasks that can be automated based on the extracted verbs and nouns. For example, it determines that tasks such as "entering sales data into spreadsheet software" and "creating reports" can be automated.

[1697] Finally, the server presents the generated automatable business points to the user, allowing the user to check the business points that can be automated and receive specific suggestions for improvement.

[1698] As a specific example, consider the following text data.

[1699] Example of text data:

[1700] "Enter sales data into a spreadsheet every day and create a report at the end of the week."

[1701] Based on this text data, the server performs the following processing.

[1702] 1. Importing text data:

[1703] The server takes in text data provided by the user, such as "Enter sales data into spreadsheet software every day and create a report on the weekend."

[1704] 2. Application of natural language processing technology:

[1705] The server uses spaCy to analyze the text data.

[1706] 3. Analysis of verbs and nouns:

[1707] The server extracts verbs such as "input" and "create" and nouns such as "sales data," "spreadsheet software," and "report" to grasp the flow of work.

[1708] 4. Identify areas that can be automated:

[1709] The server identifies the parts "enter sales data into spreadsheet software" and "create report" as being automatable.

[1710] Example prompts to input to the generative AI model:

[1711] "Identify the automation aspects of the following task: Enter sales data into a spreadsheet every day and create a report at the end of the week."

[1712] In this way, the server analyzes the text data and identifies points in the work that can be automated. This system makes it possible to automatically analyze work content and quickly and accurately identify points that can be automated.

[1713] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1714] Step 1:

[1715] Importing text data

[1716] Users upload text data of their work through a web interface, and the server receives this data and stores it in its internal storage.

[1717] Input: Text data of business operations

[1718] Output: Text data saved in internal storage

[1719] Specific operation: A user accesses the web interface, selects a text file containing the job description, and presses the upload button. The server receives the uploaded file and stores it in the database.

[1720] Step 2:

[1721] Application of natural language processing technology

[1722] The server applies natural language processing technology to the stored text data. Specifically, it performs grammatical analysis using natural language processing libraries such as "spaCy" and "NLTK."

[1723] Input: Text data stored in internal storage

[1724] Output: Parsed sentence structure data

[1725] Specific operation: The server passes the text data to the "spaCy" parser and performs grammatical analysis, which analyzes the sentence structure of the text data and extracts verbs and nouns.

[1726] Step 3:

[1727] Verb and noun analysis

[1728] The server extracts verbs and nouns from the analyzed sentence structure and analyzes their relationships, thereby clarifying the flow and procedures of work.

[1729] Input: Parsed sentence structure data

[1730] Output: A list of extracted verbs and nouns

[1731] Specific operation: The server lists verbs (e.g., "enter," "create") and nouns (e.g., "sales data," "spreadsheet software," "report") from the sentence structure data and analyzes the relationships between them.

[1732] Step 4:

[1733] Identifying areas where automation is possible

[1734] Based on the extracted verbs and nouns, the server identifies tasks that can be automated, such as routine data entry or periodic report generation.

[1735] Input: A list of extracted verbs and nouns

[1736] Output: A list of business points that can be automated

[1737] Specific operation: The server combines the verb "input" with the noun "sales data" to determine that "entering sales data into a spreadsheet" can be automated. Similarly, it identifies "creating a report" as an automatable step.

[1738] Step 5:

[1739] Presenting business points that can be automated

[1740] The server presents the generated automatable business points to the user, allowing the user to check the business points that can be automated and receive specific suggestions for improvement.

[1741] Input: List of business points that can be automated

[1742] Output: Automated task points presented to the user

[1743] Specific operation: The server displays the business points that can be automated to the user through a web interface. The user checks the presented points and configures automation as necessary.

[1744] (Application example 2)

[1745] 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."

[1746] The work at conventional logistics centers requires a lot of manual work, which makes it inefficient. Furthermore, it is difficult to identify which parts of the work should be automated. This often delays the improvement of work efficiency and the promotion of automation.

[1747] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for importing text data of business content, generative AI means for generating automatable business points from the imported data, means for responding with the generated automatable business points, means for generating an automation proposal based on the generated automatable business points, and means for displaying the generated automation proposal. This makes it possible to identify automation points for business operations at a logistics center and make efficient automation proposals.

[1748] "Text data of work content" refers to data that records work procedures and work content at a logistics center in text format.

[1749] "Means of importing" refers to the means of inputting text data of business content into the system.

[1750] "Generative AI methods" are methods that use artificial intelligence technology to analyze imported text data and generate business points that can be automated.

[1751] "Automable business points" are points that identify parts of a business that can be automated.

[1752] The "answering means" is a means for presenting the generated automatable business points to the user.

[1753] The "means for generating automation proposals" is a means for proposing specific automation methods based on the generated automatable business points.

[1754] The "means for displaying" is a means for visually presenting the generated automated suggestions to the user.

[1755] The system for carrying out the present invention is designed to support the automation of operations in a logistics center. A specific embodiment of the system will be described below.

[1756] System configuration

[1757] The system consists of the following main components:

[1758] 1. A means of importing text data of business operations

[1759] 2. Generative AI methods that generate automatable business points from imported data

[1760] 3. A means of answering the generated automatable business points

[1761] 4. A means for generating automation proposals based on the generated automatable business points

[1762] 5. A way to view the generated automation suggestions

[1763] Hardware and software used

[1764] Hardware: Smartphone

[1765] Software: Python, spaCy (natural language processing library), transformers (generative AI model library)

[1766] Data processing and calculation

[1767] 1. A means of importing text data of business operations

[1768] The user inputs the work procedures and tasks to be performed in the logistics center in text format. For example, the user inputs work details such as "take out the product from the shelf" and "scan the product."

[1769] 2. Generative AI methods that generate automatable business points from imported data

[1770] The server uses spaCy to analyze the imported text data and extract verbs and nouns, thereby understanding the workflow and procedures and identifying points of the business that can be automated.

[1771] 3. A means of answering the generated automatable business points

[1772] The server presents the identified automatable business points to the user. For example, if a business point such as "take out a product from a shelf" is identified, the server displays this to the user.

[1773] 4. A means for generating automation proposals based on the generated automatable business points

[1774] The server uses a generative AI model with a sentiment analysis model to generate automation suggestions. For example, the server inputs a prompt sentence, "Please suggest a way to automate the following task: remove products from shelves," into the generative AI model to generate automation suggestions.

[1775] 5. A way to view the generated automation suggestions

[1776] The server visually presents the generated automation proposal to the user, for example, "We will introduce a system that uses a robotic arm to pick up products from shelves. The robotic arm will work in conjunction with a barcode scanner to pick up accurate products."

[1777] Specific examples

[1778] Input text data:

[1779] Remove the product from the shelf.

[1780] Scan the product.

[1781] Pack the product.

[1782] Print and attach the labels.

[1783] Example prompt for a generative AI model:

[1784] Suggest ways to automate the following tasks: Retrieving items from shelves.

[1785] In this way, a system can be realized that generates specific proposals for streamlining and automating operations within a logistics center.

[1786] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1787] Step 1:

[1788] Users input work procedures and work details in the logistics center in text format. For example, they input work details such as "take out products from shelves" and "scan products." The input text data is sent to the server.

[1789] Step 2:

[1790] The server uses spaCy to analyze the received text data. Specifically, it divides the text data into sentences and extracts the verbs and nouns contained in each sentence. This allows the flow and procedures of work to be understood and points of work that can be automated identified. For example, the verb "take out" is extracted from the sentence "Take out the product from the shelf."

[1791] Step 3:

[1792] The server presents the identified automatable task points to the user. For example, if a task point such as "taking out a product from a shelf" is identified, the server displays this to the user. The user confirms the displayed task point.

[1793] Step 4:

[1794] The server generates automation suggestions using a generative AI model based on the identified automatable business points. Specifically, a prompt sentence is input to the generative AI model using a sentiment analysis model. For example, a prompt sentence such as "Please suggest a way to automate the following business task: taking products off shelves" is input to the generative AI model to generate automation suggestions.

[1795] Step 5:

[1796] The server visually presents the generated automation proposal to the user. For example, it displays a proposal such as, "Introduce a system that uses a robotic arm to pick up products from shelves. The robotic arm will work in conjunction with a barcode scanner to pick up accurate products." The user can confirm the displayed automation proposal and execute it as necessary.

[1797] Example 3

[1798] 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."

[1799] With conventional business automation systems, it was difficult to simply import text data of business operations, generate automatable business points from that data, and show users specific improvement proposals and effects.In addition, there was a lack of a way for users to easily refer to automatable business points and check detailed information, which often delayed the introduction of business efficiency improvements and automation.

[1800] 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.

[1801] In this invention, the server includes means for importing data that has been converted into textual data on the content of work, generative AI means for generating automatable work points from the imported data, means for responding with the generated automatable work points, user interface means for displaying a list of the generated automatable work points so that the user can refer to them, and means for displaying the effects of automating that work point and specific suggestions when the user selects a specific work point. This allows the user to easily refer to the automatable work points and check their detailed information.

[1802] "Text data of business content" refers to data that records the procedures and content of business as text information.

[1803] "Means for importing" is a function for receiving data from the outside and storing it within the system.

[1804] "Generative AI means" is a function that uses artificial intelligence technology to extract specific information and patterns from input data and generate new data.

[1805] "Automable business points" refer to parts or processes within a business that can be automated.

[1806] "Means of responding" is a function for presenting generated information and data to the user.

[1807] "User interface means" refers to a function that provides a screen and operation means for the user to interact with the system.

[1808] "List display" refers to displaying multiple items in a list format.

[1809] "Making it accessible" means making the information available to users so that they can view it and check for more details if necessary.

[1810] "When selected" refers to the user selecting a specific item by clicking, tapping, or other operations.

[1811] An "effect" refers to the result or influence obtained by a particular action or process.

[1812] A "specific proposal" refers to presenting a concrete solution or improvement plan for a specific problem or issue.

[1813] This invention is a system that takes in text data of business operations, generates automatable business points from that data, and presents specific improvement suggestions and effects to users. Specific embodiments of this system are described below.

[1814] Server Processing

[1815] The server receives business data uploaded by users. This business data can be provided in various formats, such as spreadsheet files, CSV files, data extracted from databases, etc. For example, if a user uploads a file called "BusinessData.xlsx," the server receives the file.

[1816] The server then inputs the received business data into a generative AI model. This generative AI model is built using TensorFlow and PyTorch and analyzes the data. Specifically, it detects patterns and trends in the business data and identifies business points that can be automated. For example, it analyzes the frequency of data entry and the timing of report generation.

[1817] The server organizes the automatable business points extracted by the generative AI model and sends them to the terminal in data format such as JSON. For example, business points such as "automated data entry" and "automated report generation" are extracted, along with the benefits of each (time savings, cost reduction, etc.).

[1818] Terminal handling

[1819] The terminal receives the business points sent from the server and displays them on the user interface. This user interface is built using HTML, CSS, and JavaScript and is designed to be intuitive for users. Specifically, frameworks such as React.js and Vue.js are used to achieve dynamic list display. For example, lists such as "Automated data entry" and "Automated report generation" are displayed.

[1820] User operations

[1821] Users can view the business points that can be automated through the terminal's user interface. When a user clicks on a specific business point, the effects of that automation and specific suggestions are displayed. For example, when the user clicks on "automating data entry," the time-saving and cost-saving effects of that automation are displayed. Specific automation suggestions (e.g., the introduction of specific software or the use of scripts) are also displayed.

[1822] Specific examples

[1823] An example of a prompt sentence to be input into a generative AI model is, "Analyze the business data below and extract business points that can be automated."

[1824] In this way, users can easily refer to business points that can be automated and check the effects and specific proposals. This system is expected to facilitate the introduction of business efficiency and automation, contributing to improved productivity in companies. The flow of the specific processing in Example 3 will be explained using Figure 15.

[1825] Step 1:

[1826] The server receives the business data.

[1827] Input: Business data uploaded by the user (e.g., spreadsheet files, CSV files)

[1828] Specific operation: When a user uploads "Business Data.xlsx", the server receives the file and reads the data.

[1829] Output: Imported business data

[1830] Step 2:

[1831] The server analyzes business data using the generated AI model.

[1832] Input: Imported business data

[1833] How it works: The server inputs the data it reads into a generative AI model, which then analyzes the data using TensorFlow and PyTorch to detect patterns and trends in the business data.

[1834] Output: Automated business operations (e.g., "automated data entry" and "automated report generation")

[1835] Step 3:

[1836] The server extracts the business points that can be automated and sends them to the terminal.

[1837] Input: Automated business points

[1838] Specific operation: The server organizes the automatable business points extracted by the generative AI model and sends them to the terminal in a data format such as JSON.

[1839] Output: Business point data sent to the terminal

[1840] Step 4:

[1841] The terminal displays the received business points on the user interface.

[1842] Input: Business point data sent to the terminal

[1843] Specific operation: The terminal displays the received data in a user interface using React.js. For example, it displays lists such as "Automated data entry" and "Automated report generation."

[1844] Output: List of business points displayed on the user interface

[1845] Step 5:

[1846] The user references the business point and checks the detailed information.

[1847] Input: List of business points displayed on the user interface

[1848] Specific action: The user clicks on a specific business point (e.g., "automate data entry") from the displayed list. Once clicked, the effects of that automation (e.g., time savings, cost reduction) and specific suggestions (e.g., the introduction of specific software or the use of scripts) are displayed.

[1849] Output: Detailed information on the business points confirmed by the user

[1850] In this way, users can easily see which business points can be automated, and see the effects and specific suggestions.

[1851] (Application example 3)

[1852] 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."

[1853] In today's business environment, automation and efficiency of business processes are important issues. However, it is not easy to determine which business processes can be automated and how to automate them effectively. In addition, there is a lack of a way for users to easily understand which business processes can be automated and receive appropriate improvement suggestions based on that information. For this reason, there is a need for a system that can effectively promote business automation and efficiency.

[1854] 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.

[1855] In this invention, the server includes means for importing text data of business content, generative AI means for generating automatable business points from the imported data, means for responding with the generated automatable business points, user interface means for displaying a list of the generated automatable business points so that the user can refer to them, and means for displaying automation suggestions for each business point and the effects of that automation. This allows the user to easily understand the automatable business points and receive appropriate improvement suggestions.

[1856] "Text data of business content" refers to data that records the procedures and content of business in text format.

[1857] "Means for acquiring" refers to a method or device for acquiring data from the outside and storing it within the system.

[1858] "Generative AI means" is a system that has the ability to generate specific information or patterns from data using artificial intelligence.

[1859] "Automable business points" refer to parts or processes within a business that can be automated.

[1860] A "means for responding" is a method or device for providing the generated information or results to the user.

[1861] "User interface means" refers to a function that provides a screen and operation methods for users to interact with the system.

[1862] "List display" refers to displaying multiple items together on one screen or page.

[1863] An "automation proposal" is a proposal that shows specific methods and procedures for automating business processes.

[1864] "Effects of automation" refers to the benefits and results obtained by automating business processes.

[1865] A system for implementing this invention includes a means for importing text data of business content, a generative AI means for generating automatable business points from the imported data, a means for responding with the generated automatable business points, a user interface means for displaying a list of the generated automatable business points so that the user can refer to them, and a means for suggesting automation for each business point and displaying the effects of that automation.

[1866] The server first imports text data of the business operations. This data is extracted from, for example, business procedure manuals or work reports. The imported data is analyzed by a generative AI method, and business points that can be automated are generated. The generative AI method uses natural language processing technology and machine learning algorithms. Specifically, Python libraries such as NLTK and spaCy, and machine learning frameworks such as TensorFlow and PyTorch are used.

[1867] The generated automatable task points are displayed in a list through a user interface. Users can view these points on their smartphone or PC screen. The user interface is built using web technologies such as HTML, CSS, and JavaScript.

[1868] Furthermore, automation suggestions and the effects of such automation are displayed for each task. This allows users to understand specifically which tasks should be automated and how. For example, a suggestion might be displayed such as, "By automating data entry tasks with an RPA tool, you can reduce work time by 50%."

[1869] For example, the following prompts are generated:

[1870] "We propose the introduction of an RPA tool to automate data entry tasks. Using this tool will reduce the time required for tasks by 50% and reduce the error rate."

[1871] In this way, users can easily identify areas of their business that can be automated and receive appropriate improvement suggestions.

[1872] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1873] Step 1:

[1874] The server takes in the text data of the business content.

[1875] Input: Text data such as work procedures and work reports

[1876] Specific operation: The server receives text data of the business details provided by the user via the file upload function or API. The received data is stored in the database.

[1877] Output: Saved text data

[1878] Step 2:

[1879] The server runs a generative AI method that generates automatable business points from the imported data.

[1880] Input: Saved text data

[1881] How it works: The server uses natural language processing (NLTK or spaCy) to analyze text data and understand the business operations. It then uses machine learning algorithms (TensorFlow or PyTorch) to identify business operations that can be automated.

[1882] Output: A list of business points that can be automated

[1883] Step 3:

[1884] The server executes a means for returning the generated automatable task points.

[1885] Input: List of business points that can be automated

[1886] Specific operation: The server obtains a list of business points from the database and passes it to the user interface to provide the generated business points to the user.

[1887] Output: A list of business points displayed in the user interface

[1888] Step 4:

[1889] The terminal displays a list of the generated automatable business points so that the user can refer to them.

[1890] Input: A list of business points passed to the user interface

[1891] Specific operation: The terminal uses HTML, CSS, and JavaScript to display a list of business points on the screen. The user can check these points on the screen of their smartphone or computer.

[1892] Output: A list of business points displayed on the screen

[1893] Step 5:

[1894] The server implements a means for proposing automation for each business point and displaying the effects of that automation.

[1895] Input: List of business points that can be automated

[1896] Specific operation: The server uses the generative AI model to generate automation proposals for each business point. For example, a proposal might be generated such as, "By automating data entry work with an RPA tool, work time can be reduced by 50%." These proposals are then passed to the user interface.

[1897] Output: The automation suggestions and their effects displayed in the user interface

[1898] Step 6:

[1899] The terminal displays automation suggestions and their effects through a user interface.

[1900] Input: Automation suggestions passed to the user interface and their effects

[1901] Specific operation: The device uses HTML, CSS, and JavaScript to display automation suggestions and their effects on the screen. The user can refer to these suggestions to specifically understand which tasks should be automated and how.

[1902] Output: The automation suggestions and their effects displayed on the screen

[1903] 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.

[1904] "Example 1"

[1905] One embodiment of the present invention provides a DX diagnostic system that includes a means for importing text data of business operations, a generative AI means for generating automatable business points from the imported data, and a means for providing the generated automatable business points. This system further incorporates an emotion engine that recognizes user emotions. Specifically, a user inputs business operations as text, and the text data is imported into the system. Next, the generative AI analyzes the text data and identifies business points that can be automated. During this process, the emotion engine recognizes the user's emotions and provides the results to the generative AI. The generative AI takes this emotional information into account to generate automatable business points.

[1906] "Example 2"

[1907] As a concrete example, consider the case where a user inputs "responding to customer inquiries" as a task. From this text data, the generative AI identifies tasks that can be automated, such as "classifying the inquiry content" and "selecting the appropriate response." Meanwhile, the emotion engine recognizes that the user is feeling stressed about this task. Taking this emotional information into consideration, the generative AI makes suggestions for improving the task to reduce stress. For example, by automating "classifying the inquiry content," the AI ​​makes suggestions to reduce the user's workload.

[1908] "Example 3"

[1909] The emotion engine also uses an emotion recognition model to recognize the user's emotional state. This model infers emotions from the user's text and voice input. Specifically, it infers the user's emotional state from the keystroke patterns when the user enters text and the tone of voice when the user enters voice. This emotional information is an important reference for the generative AI when it makes business improvement proposals.

[1910] The processing flow of each embodiment will be described below.

[1911] "Example 1"

[1912] Step 1: The user enters the job description as text.

[1913] Step 2: The system captures the text data.

[1914] Step 3: Generative AI analyzes the text data and identifies areas where tasks can be automated.

[1915] Step 4: The emotion engine recognizes the user's emotions and provides the results to the generative AI.

[1916] Step 5: Generative AI takes emotional information into account to generate automatable task points.

[1917] "Example 2"

[1918] Step 1: The user enters "responding to customer inquiries" as the job description.

[1919] Step 2: Generative AI uses the text data to identify tasks that can be automated, such as classifying the inquiry and selecting the appropriate response.

[1920] Step 3: The emotion engine recognizes that the user is feeling stressed about this task.

[1921] Step 4: Generative AI takes emotional information into account and makes suggestions for work improvements to reduce stress.

[1922] "Example 3"

[1923] Step 1: The emotion engine uses the emotion recognition model to recognize the user's emotional state.

[1924] Step 2: The emotion recognition model estimates emotions from the user's text input, voice input, etc.

[1925] Step 3: This emotional information is provided to the generative AI and used as reference information for business improvement proposals.

[1926] Example 1

[1927] 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."

[1928] Conventional business automation systems have the means to import text data of business operations and generate automatable business points, but do not generate automation points that take the user's emotions into account. As a result, proposals are made that ignore the user's emotions and stress levels, which leads to issues such as insufficient improvement in business efficiency and user satisfaction.

[1929] 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.

[1930] In this invention, the server includes means for importing text data of work content, generative AI means for generating automatable work points from the imported data, means for returning the generated automatable work points, emotion engine means for recognizing user emotions, and means for generating automatable work points in consideration of emotion information from the emotion engine means. This makes it possible to generate automation points that take user emotions into consideration, thereby achieving improved work efficiency and improved user satisfaction.

[1931] "Text data of business content" refers to data that expresses the details and procedures of business as text information.

[1932] "Means of import" refers to the interface or API used to input business details into the system as text data.

[1933] "Generative AI methods" refers to artificial intelligence technology that analyzes imported text data and identifies business points that can be automated.

[1934] "Automable business points" refer to parts or processes within a business that can be automated.

[1935] The "means for replying" refers to a function for notifying the user of the generated automatable business points.

[1936] "Emotion engine means" refers to technology for recognizing a user's emotions and providing that information to generative AI.

[1937] "Emotional information" is data that indicates the user's emotional state, and includes information obtained from input content, input speed, keystroke patterns, and the like.

[1938] This invention is a system that takes in text data of work content, generates automatable work points from that data, and responds to the user. It also has the function of recognizing the user's emotions and generating automatable work points taking that information into consideration.

[1939] Hardware and software used

[1940] Hardware: Servers, user terminals

[1941] Software: Business management system, generative AI, emotion engine, API

[1942] System configuration

[1943] 1. How to import text data of business operations:

[1944] Users use an interface to input their work details. This interface is a form that runs on a web browser, and users enter their work details into text boxes. For example, they might enter specific work details such as "Check email every day at 9:00 and forward important emails to their boss."

[1945] 2. Generative AI methods that generate automatable business points from captured data:

[1946] The server receives text data entered by the user and sends it to the generative AI. The generative AI uses natural language processing technology to analyze the text data and identify points in the business that can be automated. For example, the part "check email every day at 9 o'clock" is identified as an area that can be automated.

[1947] 3. Means of answering the generated automatable business points:

[1948] The server responds to the user with the points of work that can be automated obtained from the generative AI. The user can then view the list of work points that can be automated in a web browser. For example, specific suggestions such as "It is recommended that you automate the task of checking email at 9:00 every day" are displayed.

[1949] 4. Emotion engine means for recognizing user emotions:

[1950] The server sends the emotional data of the user's input to the emotion engine, which then recognizes the user's emotions based on the content, speed, and keystroke patterns of the input. For example, if the user is feeling stressed, that information is provided to the generative AI.

[1951] 5. Means for generating automatable task points taking into account emotion information from emotion engine means:

[1952] The generative AI takes into account the emotional information provided by the emotion engine to optimize tasks that can be automated. For example, if the user is feeling stressed, the generative AI will prioritize automating those tasks.

[1953] Specific examples

[1954] An example of a prompt sentence when a user inputs their job description as text is, "Please input your job description as text. For example, please enter specific job description such as 'Check email every day at 9:00 and forward important emails to your boss.'"

[1955] This system allows users to easily identify areas for automation in their work and improve work efficiency. In a...

Claims

[Claim 1] A means of importing text data of business content, emotion recognition means for recognizing an emotional state of a user by analyzing at least one of a user's tone of voice, text input speed, and keystroke pattern using an emotion recognition model; a means for generating a prompt sentence that instructs the user to identify an automated task point in the task content, taking into consideration the emotional state of the user, based on the captured data and the recognized information indicating the emotional state of the user; and A means for inputting the prompt sentence into a generative AI model to cause the generative AI model to generate the automatable task points; means for presenting the generated automatable task points to the user; a means for generating a task to be performed by a robot based on the generated automatable task points and transmitting the task to the robot; A system including:

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