System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving authentication information input by a user; means for collecting operation log data of the user; means for analyzing the collected log data and identifying frequent operations and errors; means for automatically generating a business flow based on an analysis result; means for documenting rules and knowledge based on the automatically generated business flow; means for formatting the documented rules and knowledge and generating a business manual; and means for storing the generated manual and allowing the user to access the manual.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The workload during traditional work handovers and when new members join the company is heavy, resulting in problems such as reduced work efficiency and the time required for training. In particular, when experience and rules are dependent on individual employees, it is difficult for new members to start working smoothly, hindering efficient work execution. To address these issues, there is a need for a system that standardizes work and effectively shares individual knowledge. [Means for solving the problem]

[0005] This invention provides a system that receives authentication information entered by a user and collects user operation log data. It also provides a system that analyzes the collected log data and identifies frequently occurring operations and errors. It also provides a system that automatically generates a business flow based on the analysis results, and a system that documents rules and knowledge based on the automatically generated business flow. It also provides a system that formats the documented rules and knowledge, generates a business manual, and stores the generated manual so that users can access it. This system reduces the workload when taking over business or adding new members, enabling efficient business execution.

[0006] "Authentication information" refers to information such as a username and password that a user uses to access a system.

[0007] "Operation log data" is data that records the history of operations (such as button clicks and input contents) performed by a user within the system.

[0008] "Analysis" is the process of identifying frequent patterns of operations and errors based on collected log data.

[0009] A "business flow" is a diagram or document that shows business procedures and processes step by step.

[0010] "Documenting" means organizing the analyzed rules and findings into text and documenting them.

[0011] "Formatting" is the process or form of preparing written content to make it easier to read.

[0012] A "business manual" is a document that contains business procedures, rules, knowledge, etc., and is used for training new members and handing over work.

[0013] A "frequent operation" is an operation that a user frequently performs within the system.

[0014] "Error patterns" refer to the types of errors that frequently occur within a system and the circumstances under which they occur.

[0015] "Storing" means storing the generated business manual in a digital format within the system.

[0016] "Making it accessible to users" means making the generated business manual available for users to easily view or obtain. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The system of the present invention is an integrated log management, analysis, and manual creation system to support users' business operations. This system involves a series of processes, starting with the user logging in by entering authentication information, collecting logs of the user's business operations in real time, analyzing the data, and finally automatically generating a business manual.

[0039] Overall system configuration

[0040] 1. User logs in

[0041] A user accesses the system and enters their username and password.

[0042] The device sends the authentication information to the server.

[0043] The server verifies the credentials and allows the user to log in. If authentication is successful, the user can proceed to the next step.

[0044] 2. The user performs the task, and the device collects the operation log.

[0045] As users go about their daily work, each action within the system is automatically recorded.

[0046] The terminal collects each operation as log data and sends it to the server in real time.

[0047] 3. The server analyzes the log data to identify frequent operations and errors.

[0048] The server analyzes the log data it receives to identify patterns of frequently occurring operations and common errors.

[0049] This analysis often uses data mining techniques and machine learning algorithms.

[0050] 4. The server automatically generates the business flow

[0051] The server automatically generates a specific business flow based on the analysis results.

[0052] The business flow includes the business steps and points to note at each step.

[0053] 5. The server documents the rules and knowledge and generates a manual

[0054] The server documents the necessary rules and knowledge based on the automatically generated business flow.

[0055] Format documented information and generate operational manuals in PDF format.

[0056] 6. The user refers to the generated business manual

[0057] The server stores the generated PDF manual and provides it in a form that is accessible to users.

[0058] The user downloads or views the generated manual.

[0059] Specific examples

[0060] For example, this system comes into play when a new member joins the sales department. The new member first logs in to the system by entering their username and password. After logging in, as the new member begins to operate the sales system, operation logs are collected in real time. The server analyzes the logs and identifies common sales operations and errors. A sales process workflow is then automatically generated based on the analysis results. Based on this workflow, important rules and past knowledge are documented, and a formatted operations manual is generated in PDF format. The new member can download and refer to this manual, allowing them to quickly become familiar with the work. This series of processes significantly reduces the workload when taking over work or when new members join, improving work efficiency.

[0061] In this way, the system of the present invention functions as a powerful tool for improving the work efficiency of users.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] The user enters their username and password on the login screen.

[0065] Step 2:

[0066] The terminal encrypts the entered authentication information and sends it to the server.

[0067] Step 3:

[0068] The server receives the authentication information and checks it against the data in its database.

[0069] If authentication is successful, the server starts a session and returns the user ID, if authentication fails it returns an error message.

[0070] Step 4:

[0071] Users access business applications and begin their daily work.

[0072] Step 5:

[0073] The device records each user operation as log data, such as button clicks and data input.

[0074] Step 6:

[0075] The terminal sends the log data to the server in real time.

[0076] Step 7:

[0077] The server stores the received log data and begins analyzing it.

[0078] Step 8:

[0079] The server analyzes the log data and identifies frequently executed operations (frequent operations).

[0080] Step 9:

[0081] The server analyzes error messages in the log data to identify commonly occurring error patterns.

[0082] Step 10:

[0083] The server automatically generates a workflow based on the identified frequent operations and error patterns. This workflow includes each step and its explanation.

[0084] Step 11:

[0085] Based on the business flow generated by the server, the necessary rules and knowledge are documented in text format.

[0086] Step 12:

[0087] The server formats the documented rules and knowledge to create an easy-to-read business manual.

[0088] Step 13:

[0089] The server saves the generated business manual in PDF format and provides it to the user in an accessible format.

[0090] Step 14:

[0091] The user clicks on the specified link or URL to download or view the generated business manual.

[0092] This series of steps ensures efficient support when taking over business operations or when new members join.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] In today's business environment, many tasks are performed using digital tools, but the recording and analysis of operations, as well as the creation of business manuals, must often be done manually. This makes it difficult to maintain operational efficiency and consistency, and there is insufficient support for new members to quickly become proficient in their work. Furthermore, frequent operational mistakes and errors are often not addressed properly, risking a decline in the overall quality of work. To solve these problems, there is a need for a system that can automatically collect and analyze operational logs, automatically generate business flows, and quickly provide business manuals.

[0096] 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.

[0097] In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation record data, means for analyzing the collected record data and identifying frequently occurring operations and errors, means for automatically generating a workflow based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow, means for formatting the documented rules and knowledge and generating a workflow manual, and means for saving the generated manual and making it accessible to users. This reduces the frequency of operational mistakes and errors, enables new members to quickly become proficient, and improves the efficiency and quality of the entire business.

[0098] A "user" is a person or organization that inputs authentication information to use the system and performs business operations.

[0099] "Authentication information" refers to information such as a username and password that a user enters to log in to a system.

[0100] "Operation record data" is data that records various operations that users perform within the system.

[0101] "Analysis" refers to processing the collected operation record data and identifying frequently occurring operations and error patterns.

[0102] A "workflow" refers to a set of steps or procedures for carrying out a particular task.

[0103] "Rules" are regulations and guidelines for carrying out business.

[0104] "Knowledge" includes information that is useful in performing work and insight gained from past experience.

[0105] "Documentation" means organizing and storing rules and knowledge in written form.

[0106] "Format" means putting a document into a neat form.

[0107] A "business manual" is a documented guidebook that includes business procedures, rules, and knowledge.

[0108] "Saving" refers to storing and preserving the generated business manual in the server.

[0109] "Accessible" means that the business manual is available for viewing and download by the user.

[0110] The present invention is an integrated log management, analysis, and manual creation system designed to support users' work. This system is implemented using the following hardware and software.

[0111] Hardware and software used

[0112] Server: Various cloud services (e.g., AWS EC2 instances)

[0113] Authentication system: OAuth 2.0

[0114] Data collection tool: Logstash

[0115] Data analysis tool: Apache Spark

[0116] Machine learning algorithm: Scikit-learn

[0117] Manual generation tool: LaTeX

[0118] Detailed operation of the system

[0119] 1. User Authentication

[0120] The user accesses the system's login screen and enters authentication information (user name and password). The device encrypts this authentication information using SSL / TLS and sends it to the server.

[0121] The server verifies the authentication information using OAuth 2.0 and, if successful, grants access to the user.

[0122] 2. Collecting business operation logs

[0123] As users perform business operations within the system, each operation is recorded in real time by the terminal.

[0124] The terminal uses a log collection tool (Logstash) to send the recorded operation log to the server.

[0125] 3. Log data analysis

[0126] The operation log data received by the server is analyzed using Apache Spark. The analysis is performed in batch processing using data stored in HDFS.

[0127] Use machine learning algorithms (Scikit-learn) to identify frequent operations and error patterns.

[0128] 4. Automatic generation of business flows

[0129] The server automatically generates a workflow based on the analysis results, which includes the steps and points to note required for the work.

[0130] The generated business flow is saved in XML or YAML format.

[0131] 5. Documentation and manual generation

[0132] The server documents the necessary rules and knowledge based on the business flow, using LaTeX templates.

[0133] The formatted document is output from LaTeX as a business manual in PDF format.

[0134] 6. Provision and reference of manuals

[0135] The server stores the generated PDF manual in the system and provides it to the user in an accessible format.

[0136] The user downloads or views the generated manual from the "Manuals" section of the system.

[0137] Specific examples

[0138] For example, when a new salesperson logs into the system, a log of their work operations is collected in real time. The server analyzes the log and identifies frequently occurring operations and error patterns. Based on the identified patterns, a work manual for the new salesperson is automatically generated. This manual is provided in PDF format, allowing the new employee to refer to it and quickly become familiar with their work.

[0139] Prompt Sentence Examples

[0140] Analyze user business log data using Apache Spark and identify patterns of frequently occurring operations and errors. Explain how to automatically generate business flows in XML format based on the identified patterns and create business manuals in PDF format using LaTeX.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1: User authentication

[0143] Specific description:

[0144] A user accesses the system and enters authentication information (username and password) on the login screen. The device sends this authentication information to the server. The server verifies the authentication information using OAuth 2.0, and if authentication is successful, grants the user access rights.

[0145] Input and Output:

[0146] Input: Username, Password

[0147] Output: Authentication success or failure

[0148] Specific behavior:

[0149] The user opens the system's login page in a browser, enters the username "USER_ID" and password "PASSWORD", and clicks the "Login" button. The device encrypts the authentication information and sends it to the server. The server compares the authentication information with the database, and if authentication is successful, grants the user dashboard access rights.

[0150] Step 2: Collect business operation logs

[0151] Specific description:

[0152] When a user performs a business operation within the system, each operation is recorded in real time. The terminal sends the recorded operation log to the server via a log collection tool (Logstash).

[0153] Input and Output:

[0154] Input: User interaction events (clicks, inputs, window switching, etc.)

[0155] Output: Operation log data

[0156] Specific behavior:

[0157] Every time a user operates a menu in the system, enters a task, or searches, the operation event is recorded in a log file in real time. The terminal uses Logstash to periodically send the recorded log to the server.

[0158] Step 3: Analyze the log data

[0159] Specific description:

[0160] Analyze the log data received by the server. Use Apache Spark to perform batch processing to analyze the data, and use machine learning algorithms (Scikit-learn) to identify frequent operations and error patterns.

[0161] Input and Output:

[0162] Input: Operation log data

[0163] Output: Analysis results (frequent operations, error patterns, etc.)

[0164] Specific behavior:

[0165] The server runs analysis on the log data stored in HDFS using Apache Spark. A machine learning model clusters the data and identifies frequent operations and error patterns. The analysis results are saved in JSON format.

[0166] Step 4: Automatic generation of business flow

[0167] Specific description:

[0168] The server automatically generates a workflow based on the analysis results. This workflow includes specific business steps and points to note. The generated workflow is saved in XML or YAML format.

[0169] Input and Output:

[0170] Input: Analysis results

[0171] Output: Business flow (XML / YAML format)

[0172] Specific behavior:

[0173] The server reads the JSON file containing the analysis results and runs a workflow generation algorithm to systematically summarize the business procedures based on frequently occurring operations and error patterns, and output them in XML or YAML format.

[0174] Step 5: Documentation and manual generation

[0175] Specific description:

[0176] The server documents the necessary rules and knowledge based on the business flow, creates documents in a well-formatted format using LaTeX templates, and generates business manuals in PDF format.

[0177] Input and Output:

[0178] Input: Business flow (XML / YAML format)

[0179] Output: Business manual (PDF format)

[0180] Specific behavior:

[0181] The server parses the business flow XML and reflects the data in the LaTeX template. It then uses the pdflatex command to convert the LaTeX file into PDF format and save it.

[0182] Step 6: Provide and refer to the manual

[0183] Specific description:

[0184] The server saves the generated business manual (PDF format) and provides it in an accessible format to the user. The user downloads or views the manual.

[0185] Input and Output:

[0186] Input: Business Manual (PDF format)

[0187] Output: Download link or view page

[0188] Specific behavior:

[0189] The server saves the generated PDF manual to the file system. The user accesses the "Manuals" section of the system, clicks the "Download" button to download the PDF file, and opens it in a viewer.

[0190] (Application example 1)

[0191] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0192] Modern factories require more efficient machine operation and faster troubleshooting. However, manually analyzing machine operation logs and documenting optimal maintenance procedures and troubleshooting takes a great deal of time and effort. Preparing training materials to quickly familiarize new staff with their work is also a significant burden. Solving these issues and streamlining factory operations is essential.

[0193] 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.

[0194] In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for analyzing the collected log data and identifying frequently occurring operations and errors, means for automatically generating a workflow based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow, means for formatting the documented rules and knowledge and generating a business manual, means for collecting machine operation logs in a factory and automatically generating optimal maintenance procedures and troubleshooting guides based on the analysis results, and means for saving the generated procedures and guides and making them accessible to users, thereby enabling more efficient machine operation and faster troubleshooting.

[0195] A "user" is a person or entity that accesses and operates the system.

[0196] "Authentication information" refers to information such as a user name and password used when a user logs in to a system.

[0197] "Operation log data" refers to a series of behavioral histories and operation information recorded when a user operates the system.

[0198] "Analysis results" refers to information obtained as a result of analyzing collected operation log data using machine learning algorithms, etc.

[0199] A "business flow" refers to a set of steps and procedures required to carry out a specific task.

[0200] "Rules" refer to the regulations and standards that must be followed when carrying out business.

[0201] "Insight" refers to knowledge and wisdom gained from past experiences and data.

[0202] "Documentation" refers to the act of recording and storing information in written form.

[0203] "Format" refers to organizing and structuring documents and data according to a prescribed format.

[0204] A "business manual" is a document that summarizes procedures and precautions for carrying out specific tasks.

[0205] A "machine operation log" is a series of action histories and operation information recorded when a machine in a factory is operated.

[0206] A "maintenance procedure manual" is a document that summarizes the procedures for maintaining and inspecting machinery and equipment.

[0207] A "troubleshooting guide" is a document that provides procedures and measures for resolving machine or system failures and problems.

[0208] The system configuration includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for analyzing the collected log data and identifying frequently occurring operations and errors, means for automatically generating a business flow based on the analysis results, means for documenting rules and knowledge based on the automatically generated business flow, means for formatting the documented rules and knowledge and generating a business manual, means for collecting machine operation logs within a factory and automatically generating optimal maintenance procedures and troubleshooting guides based on the analysis results, and means for saving the generated procedures and guides and making them accessible to users.

[0209] Hardware and Software

[0210] Hardware

[0211] Factory robot: Collects operation logs and executes tasks.

[0212] Computer server: Performs analysis and manual generation of log data.

[0213] software

[0214] Logging library: Uses the Python logging module to collect operation logs in real time.

[0215] Machine learning models: Data mining techniques and machine learning algorithms for log analysis available as Python modules. A concrete example is the hypothetical analyze_logs model.

[0216] PDF generation tool: A tool for formatting and saving documented rules and knowledge in PDF format. An example is the hypothetical generate_manual.

[0217] Processing Description

[0218] Logging in and collecting logs

[0219] The user enters authentication information (username and password) and logs into the system. The terminal sends the authentication information to the server, which then verifies it. If authentication is successful, the user proceeds to the next step. After logging in, when the user begins to operate a robot in the factory, the robot collects log data for each operation and sends it to the server in real time.

[0220] Log analysis

[0221] The server analyzes the collected log data to identify frequently occurring operations and errors. This analysis uses data mining and machine learning algorithms. For example, if a specific error occurs frequently in a product assembly process, analysis is performed to identify the cause and countermeasures.

[0222] Business flow and manual generation

[0223] Based on the analysis results, specific business processes, maintenance procedures, and troubleshooting guides are automatically generated. The documented rules and knowledge are formatted to generate business manuals in PDF format, which are then stored on a server and made accessible to users.

[0224] Specific examples

[0225] For example, this system is useful when new employees are learning how to operate machines in a factory. First, the new employee logs in to the system by entering their username and password. After logging in, collected machine operation logs are analyzed in real time, and an appropriate training manual is automatically generated. By referring to this manual, new employees can quickly become familiar with their work.

[0226] Prompt Sentence Examples

[0227] "Analyze the operation logs of factory robots and identify common problems and their causes."

[0228] As described above, the use of this system makes it possible to improve the efficiency of machine operation and perform quick troubleshooting.

[0229] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0230] Step 1:

[0231] A user accesses the system and enters authentication information (user name and password). The terminal sends this authentication information to the server. The server verifies the received authentication information and confirms that the user is a valid user. If authentication is successful, the user can proceed to the next step. The input is the user name and password, and the output is the authentication success or failure status.

[0232] Step 2:

[0233] When a user starts operating a factory robot, the robot collects operation log data. The terminal sends the collected operation log to a server in real time. The operation log data includes information such as the time, operation details, and error messages. The input is the user's operation, and the output is the operation log data.

[0234] Step 3:

[0235] The server stores the received operation log data in a database and begins analysis. Data mining techniques and machine learning algorithms are used for the analysis. The server identifies patterns of frequently occurring operations and errors. The input is the operation log data, and the output is the analysis results (frequent operations and error patterns).

[0236] Step 4:

[0237] The server automatically generates a specific business flow based on the analysis results. This business flow includes work procedures and points to note. The input is the analysis results, and the output is the automatically generated business flow. Specifically, it includes frequently performed operations and ways to avoid errors.

[0238] Step 5:

[0239] The server documents the necessary rules and knowledge based on the automatically generated business flow. Documentation includes formatting text data and organizing information. The input is the automatically generated business flow, and the output is the documented rules and knowledge.

[0240] Step 6:

[0241] The server generates business manuals, maintenance procedures, and troubleshooting guides based on documented rules and knowledge. This is done using a PDF generation tool. The input is the documented rules and knowledge, and the output is a business manual in PDF format.

[0242] Step 7:

[0243] The generated business manuals and procedures are stored on the server and can be accessed by users. Users can log in to the system and download or view these documents. The input is a business manual in PDF format, and the output is available for users to view or download.

[0244] Step 8:

[0245] For example, if a user operates a factory robot and inputs a prompt such as "Analyze the factory robot's operation log and identify frequent problems and their causes," the system will perform an analysis and generate a document containing improvement suggestions based on the results. The input is the prompt, and the output is a document containing the analysis results and improvement suggestions.

[0246] 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.

[0247] The system of the present invention not only collects and analyzes user operation log data during work, but also combines it with an emotion engine that recognizes user emotions to provide more comprehensive business support. This system begins with the user logging in by entering their authentication information, and includes a series of processes that collect the user's business operations and emotional information in real time, analyzes the data, and automatically generates a business manual that includes the business flow and points to note regarding emotions.

[0248] Overall system configuration

[0249] 1. User logs in

[0250] 1. A user accesses the system and enters their username and password.

[0251] 2. The device sends the authentication information to the server.

[0252] 3. The server verifies the authentication information and allows the user to log in. If authentication is successful, it returns the user ID.

[0253] 2. The user performs the task, and the device collects the operation log and emotion data.

[0254] 1. As users go about their daily work, each operation within the system is automatically recorded.

[0255] 2. The device collects user operation log data and sends it to the server in real time.

[0256] 3. The emotion engine collects the user's emotions (for example, joy, anger, sadness, or happiness through facial recognition or voice analysis).

[0257] 4. The device sends the collected emotion data to the server.

[0258] 3. The server analyzes the operation log data and emotion data.

[0259] 1. The server saves the received operation log data and emotion data and begins analysis.

[0260] 2. The server analyzes the operation log data and identifies frequently performed operations (frequent operations).

[0261] 3. The server analyzes the emotion data and identifies patterns of emotion change.

[0262] 4. The server automatically generates a workflow based on frequently occurring operations, error patterns, and emotion patterns. This workflow includes each step and its explanation.

[0263] 4. The server documents the business flow and emotional rules and knowledge, and generates a manual.

[0264] 1. The server documents rules and knowledge in text format based on the automatically generated business flow.

[0265] 2. Add emotional caveats to your server documentation.

[0266] 3. The server formats the documented rules and knowledge to create an easy-to-read business manual.

[0267] 5. The user refers to the generated business manual

[0268] 1. The server saves the generated business manual in PDF format and provides it to the user in an accessible format.

[0269] 2. The user clicks on the specified link or URL to download or view the generated business manual.

[0270] Specific examples

[0271] For example, this system comes into play when a new member joins the customer support department. The new member first logs in to the system by entering their username and password. After logging in, as the new member begins to operate the support system, the emotion engine collects emotional data along with the operation log. The server analyzes this data and identifies frequently occurring support operations, error patterns, and emotional patterns. Based on the analysis results, a support process workflow is automatically generated, and an operations manual, including points to note regarding emotions, is generated in PDF format. The new member can download and refer to this manual, allowing them to quickly become familiar with the work. This series of processes significantly reduces the workload when taking over work or when new members join, improving work efficiency.

[0272] In this way, the system of the present invention is a powerful tool for improving the user's work efficiency, and by combining it with an emotion engine, it realizes more human-friendly work support.

[0273] The processing flow will be explained below.

[0274] Step 1:

[0275] A user accesses the system and enters their username and password.

[0276] Step 2:

[0277] The terminal encrypts the entered authentication information and sends it to the server.

[0278] Step 3:

[0279] The server receives the authentication information and checks it against the data in its database.

[0280] If authentication is successful, the server starts a session and returns the user ID, if authentication fails it returns an error message.

[0281] Step 4:

[0282] Users access business applications and begin their daily work.

[0283] Step 5:

[0284] The device records each user operation as log data, such as button clicks and data input.

[0285] Step 6:

[0286] The emotion engine recognizes the user's emotions and collects emotional data through facial recognition and voice analysis.

[0287] Step 7:

[0288] The log data and emotion data collected by the device are sent to the server in real time.

[0289] Step 8:

[0290] The server stores the received log data and emotion data and begins analysis.

[0291] Step 9:

[0292] The server analyzes the log data and identifies frequently executed operations (frequent operations).

[0293] Step 10:

[0294] The server analyzes the emotion data and identifies emotion patterns based on changes in emotion.

[0295] Step 11:

[0296] The server automatically generates a workflow based on frequently occurring operations and emotion patterns. This workflow includes each step and its explanation.

[0297] Step 12:

[0298] The server documents the necessary rules and knowledge in text format based on the automatically generated business flow.

[0299] Step 13:

[0300] The server adds emotional warnings to the documented content to create an easy-to-read business manual.

[0301] Step 14:

[0302] The server saves the generated business manual in PDF format and provides it to the user in an accessible format.

[0303] Step 15:

[0304] The user clicks on the specified link or URL to download or view the generated business manual.

[0305] This series of steps reduces the workload when handing over tasks or when new members join, improving work efficiency. The generated manual also includes points to be aware of from an emotional perspective, reducing user stress and enabling smoother work execution.

[0306] Example 2

[0307] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0308] While conventional business support systems are capable of collecting user operation log data, they lack the ability to recognize and analyze user emotions, limiting their ability to improve business efficiency and optimize user experience. Furthermore, because they are unable to automatically generate emotion-based workflows or create manuals, it is difficult for new members to take over tasks, resulting in a heavy workload.

[0309] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for collecting emotion data, means for analyzing the collected log data and emotion data to identify frequently occurring operations, errors, and changes in emotion, means for automatically generating a workflow based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow, means for formatting the documented rules and knowledge and generating a business manual, and means for saving the generated manual and making it accessible to the user. This enables a comprehensive analysis of the user's operation status and emotional state, enabling efficient and user-friendly business support.

[0310] "Authentication information" refers to information that a user enters to verify their identity when accessing a system, and typically includes a username and password.

[0311] "Operation log data" is recorded data that is generated when a user operates the system, and includes the type, time, and frequency of the operation.

[0312] "Emotion data" refers to data that reflects the user's emotional state, and includes facial expression analysis using face recognition and emotion determination using voice analysis.

[0313] "Analysis" is the process of identifying meanings and patterns based on collected data, and involves the use of statistical analysis methods and machine learning algorithms.

[0314] A "business flow" is a series of steps that show the procedures and steps of a business, and includes operating procedures and accompanying explanations.

[0315] "Rules" describe the norms and standards that must be followed in the performance of business.

[0316] "Knowledge" is a description of the specialized knowledge and know-how required to carry out a business.

[0317] "Documentation" is the process of writing and recording information in text form, formatted for easy reading.

[0318] A "business manual" is a document that compiles business procedures, rules, and knowledge, and provides users with guidelines to refer to when performing their work.

[0319] The system of this invention collects and analyzes user operation log data and emotion data to provide comprehensive business support, and automatically generates business flows and business manuals based on this data. This system is implemented using the following hardware and software.

[0320] Hardware used

[0321] 1. Terminal: A device such as a computer or smartphone that is operated by a user.

[0322] 2. Server: A server for storing data, analyzing, and generating manuals.

[0323] 3. Camera: A device for collecting emotional data through facial recognition of the user.

[0324] 4. Microphone: A device for analyzing the user's voice and collecting emotional data.

[0325] Software used

[0326] 1. Authentication system: Software that manages user authentication information and controls access to a system.

[0327] 2. Log collection software: Software for recording user operation log data.

[0328] 3. Emotion Engine: Software that uses facial recognition and voice analysis to collect user emotional data.

[0329] 4. Data analysis software: Software for analyzing collected data and automatically generating business flows.

[0330] 5. Document generation software: Software that documents business flows, rules, and knowledge and generates business manuals.

[0331] 6. PDF generation software: Software for saving documented content in PDF format.

[0332] A natural language description of what the system does

[0333] This system first receives the authentication information (user name and password) entered by the user, and the server authenticates the user by checking the information against a database. If authentication is successful, the server returns the user ID.

[0334] When a task begins, the device automatically records the user's operations and generates operation log data. This data is sent to the server in real time. At the same time, the emotion engine uses the camera and microphone to collect the user's emotional data. For example, it uses facial recognition and voice analysis to determine whether the user is expressing emotions such as joy, anger, or sadness. The device also sends this emotional data to the server in real time.

[0335] The server stores the received operation log data and emotion data in a database and begins analysis. This analysis identifies frequently occurring operations, error patterns, and patterns of emotional changes. Based on the analysis results, an efficient workflow is automatically generated. This workflow includes specific operation procedures and related explanations.

[0336] The server then documents the rules and knowledge in text format based on the automatically generated workflow. It also adds emotional warnings, such as how to deal with stress caused by a particular operation.

[0337] Finally, the documented information is saved in PDF format and made accessible to users, who can click on the provided link to download or view the operation manual.

[0338] Specific examples

[0339] For example, this system is extremely useful when a new member joins the customer support department. The new member first accesses the system and enters their username and password. The terminal sends the authentication information to the server, which then performs authentication. If authentication is successful, the new member begins work. At that time, the terminal collects operation log data, and the emotion engine collects emotion data. The collected data is sent to the server in real time, where it is analyzed to identify frequently occurring operations, error patterns, and changes in emotion.

[0340] For example, frequently performed operations such as searching for customer information or updating a support ticket are identified. Furthermore, if a user feels stressed while interacting with a customer, the timing is identified. Based on this, a work flow is automatically generated, and a work manual is created that includes notes on emotional responses. New members can download and refer to this manual to quickly become familiar with their work.

[0341] Example prompts for generative AI models

[0342] "Please explain the system that collects and analyzes operation logs and emotional data when users perform their work. Also, please provide details on the contents of the work manual that will be generated."

[0343] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0344] Step 1:

[0345] A user accesses the system and enters a username and password. The entered authentication information (username and password) is input data, which the terminal receives and sends to the server.

[0346] Step 2:

[0347] The server receives the authentication information sent from the terminal and compares it with the database. Previous registration information is stored in the database, and by comparing the authentication information, it is possible to confirm whether the user is a legitimate user. If authenticated, the server returns the user ID and notifies the terminal that authentication was successful. This output includes the authentication result (success / failure) and the user ID.

[0348] Step 3:

[0349] A user starts their work and performs various operations within the system, which generates operation log data. Specific actions include a user searching for customer information or updating a support ticket. Details of each operation are recorded as input data.

[0350] Step 4:

[0351] The terminal collects the generated operation log data and sends it to the server in real time. The input data is the user's operation log, and the log data includes the type of operation, timestamp, and content. The terminal sends this to the server.

[0352] Step 5:

[0353] The emotion engine uses the camera and microphone to collect the user's emotion data. The emotion engine performs facial and voice analysis to identify the user's emotions (e.g., joy, anger, sadness, and happiness). The emotion data is recorded as input data.

[0354] Step 6:

[0355] The device sends the collected emotion data to the server in real time. The input data is emotion data generated by the emotion engine, and this data includes the type, duration, and intensity of the emotion. The device then sends this to the server.

[0356] Step 7:

[0357] The server stores the received operation log data and emotion data in a database. The input data are operation log data and emotion data, which are then stored in a database.

[0358] Step 8:

[0359] The server analyzes the stored operation log data to identify frequently occurring operations and error patterns. Data mining and machine learning algorithms are used for the analysis to identify frequently occurring operations, errors, time periods, etc. The output is a list of frequently occurring operations and error patterns.

[0360] Step 9:

[0361] The server analyzes the stored emotion data and identifies patterns of emotion change. Statistical methods and machine learning algorithms are used for the analysis to identify emotion changes, peaks, and emotional chains. The output is a list of emotion patterns.

[0362] Step 10:

[0363] The server automatically generates a workflow based on frequently occurring operations, error patterns, and emotion patterns. The workflow includes specific operation procedures, error handling methods, and emotional precautions. The output is visualized data such as a workflow diagram.

[0364] Step 11:

[0365] The server documents business procedures, rules, and knowledge in text format based on the automatically generated business flow. Documentation requires specialized knowledge, and details are described, for example, "When performing operation A, perform steps 1-2-3." The output is a draft of the business manual.

[0366] Step 12:

[0367] The server formats the documented content and creates a PDF business manual. Formatting includes layout adjustment and formatting, and the final output is a PDF business manual that can be easily referenced by users.

[0368] Step 13:

[0369] The server saves the generated business manual in storage and makes it accessible to users. Access permissions are set in the storage, and users can access the manual through a specific link or URL. The output is an access link to the manual.

[0370] Step 14:

[0371] The user clicks on the provided link or URL to download or view the generated business manual. The user can refer to this manual to learn more about the business and improve work efficiency. The output is an improvement in the user's business knowledge.

[0372] (Application example 2)

[0373] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0374] In modern work, there is a growing demand for enhanced work support by simultaneously collecting and analyzing user operation log data and emotional data. However, current systems are specialized in collecting and analyzing operation log data, making it difficult to consider the user's emotional state. As a result, users' stress and emotional problems may affect their work efficiency. Furthermore, comprehensive work support has not been achieved because emotional care advice cannot be incorporated into the work flow.

[0375] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for collecting user emotion data, means for analyzing the collected log data and emotion data and identifying frequently occurring operations, errors, and emotion patterns, means for automatically generating a workflow and emotional care advice based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow and emotional care advice, means for formatting the documented rules and knowledge and generating a business manual, and means for saving the generated manual and making it accessible to the user. This enables comprehensive analysis of the user's operation log data and emotion data to enable efficient and user-friendly business support.

[0376] "Authentication information" refers to personal identification information such as a username and password that a user enters when accessing a system.

[0377] "Operation log data" is data that records the history of operations and actions performed by users on the system.

[0378] "Emotion data" is information that reflects the user's emotional state, and is data obtained using technologies such as facial recognition and voice analysis.

[0379] "Frequent operations" is a term that refers to operations or actions that users perform repeatedly within a system.

[0380] "Error" refers to any malfunction or problem that a user may encounter while operating the system.

[0381] An "emotion pattern" refers to a change in the user's emotional state that has a certain tendency or regularity.

[0382] "Business flow" refers to a series of steps and processes for carrying out business, shown in diagrams or text.

[0383] "Emotional care advice" refers to advice or instructions provided based on the user's emotional state while working, with the aim of reducing stress and increasing motivation for the user.

[0384] "Rules and knowledge" refers to the rules for carrying out business and knowledge based on past experience.

[0385] "Documentation" refers to the process of organizing collected information and transcribing it into an easy-to-read format.

[0386] "Formatting" refers to the process of adjusting the form and appearance of a document.

[0387] A "business manual" refers to a document that summarizes procedures, rules, and points to note for carrying out business.

[0388] The system of this invention generates comprehensive workflow and emotional care advice by collecting and analyzing user operation log data and emotional data. This system is implemented based on the following configuration and procedures.

[0389] Program processing

[0390] 1. Login Process

[0391] A user accesses the system and enters authentication information such as a username and password. The terminal sends this authentication information to the server, which then authenticates the user. At this time, a user ID is generated and returned to the user.

[0392] 2. Collecting operation logs and emotion data

[0393] When a user performs a task, the device collects the user's operation log data in real time and sends it to the server. At the same time, the emotion engine collects the user's emotion data using the device's built-in camera and microphone. The emotion data is analyzed using face recognition and voice analysis technology (e.g., OpenCV, Face Recognition library).

[0394] 3. Data Analysis

[0395] The server stores and analyzes the operation log data and emotion data it receives. It identifies frequent operations and error patterns from the operation log data, and identifies patterns of emotional changes from the emotion data. This is done using emotion analysis libraries such as EmotionDetector.

[0396] 4. Generation of workflow and emotional care advice

[0397] The server automatically generates business flows and emotional care advice based on the analysis results, and documents the contents of the automatically generated business flows and emotional care advice using the GuideGenerator library.

[0398] 5. Creating and saving the manual

[0399] The server formats the documented rules and knowledge and generates a work manual. The generated manual and emotional care advice are saved in PDF format and made accessible to users, who can download or view it.

[0400] Specific examples

[0401] For example, imagine a scenario in which a new factory worker is assigned to a production line, puts on smart glasses, and logs in to the system. As the worker begins to work, the system collects and analyzes their operation logs and emotional data in real time. It provides guidance for unfamiliar operations and displays advice on how to relax if the worker feels stressed. Finally, a detailed operation guide and emotional care advice are automatically generated based on the analysis results and provided in PDF format.

[0402] Prompt Sentence Examples

[0403] "A new worker is assigned to a factory line and begins work wearing smart glasses. An application records the worker's actions and analyzes their emotional state in real time. The application displays guidance for unfamiliar operations and provides instructions to relax if the worker feels stressed. Please explain the specific operations and effects of the application."

[0404] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0405] Step 1:

[0406] A user accesses the system and enters authentication information such as a username and password. The terminal sends this authentication information as input data to the server. The server verifies the authentication information, and if authentication is successful, generates and outputs a user ID.

[0407] Step 2:

[0408] When a user performs a task, the terminal records each user operation in real time as operation log data, including the type of operation and a timestamp. The recorded operation log data is then sent from the terminal to the server.

[0409] Step 3:

[0410] The emotion engine collects the user's emotion data using the device's built-in camera and microphone, using face recognition and voice analysis technologies (e.g., OpenCV, Face Recognition library), and transmits the acquired emotion data from the device to the server.

[0411] Step 4:

[0412] The server stores the received operation log data and emotion data as input data and performs analysis. It identifies frequently occurring operations and error patterns from the operation log data, and identifies patterns of emotion change from the emotion data. As a result of the analysis, it outputs information on frequently occurring operations, error patterns, and emotion patterns.

[0413] Step 5:

[0414] The server automatically generates business flows and emotional care advice based on the analysis results. Using the GuideGenerator library, the server generates and documents business flows and emotional care advice using the analyzed data as input. The generated business flows and emotional care advice are then output.

[0415] Step 6:

[0416] The server formats the documented rules and knowledge and generates a business manual, which is saved as a PDF file.

[0417] Step 7:

[0418] The generated manuals and emotional care advice are stored on a server and made available for users to access, download, or view. Users can click on a link or URL to download the file and view the contents.

[0419] 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.

[0420] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0421] 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.

[0422] [Second embodiment]

[0423] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0435] The system of the present invention is an integrated log management, analysis, and manual creation system to support users' business operations. This system involves a series of processes, starting with the user logging in by entering authentication information, collecting logs of the user's business operations in real time, analyzing the data, and finally automatically generating a business manual.

[0436] Overall system configuration

[0437] 1. User logs in

[0438] A user accesses the system and enters their username and password.

[0439] The device sends the authentication information to the server.

[0440] The server verifies the credentials and allows the user to log in. If authentication is successful, the user can proceed to the next step.

[0441] 2. The user performs the task, and the device collects the operation log.

[0442] As users go about their daily work, each action within the system is automatically recorded.

[0443] The terminal collects each operation as log data and sends it to the server in real time.

[0444] 3. The server analyzes the log data to identify frequent operations and errors.

[0445] The server analyzes the log data it receives to identify patterns of frequently occurring operations and common errors.

[0446] This analysis often uses data mining techniques and machine learning algorithms.

[0447] 4. The server automatically generates the business flow

[0448] The server automatically generates a specific business flow based on the analysis results.

[0449] The business flow includes the business steps and points to note at each step.

[0450] 5. The server documents the rules and knowledge and generates a manual

[0451] The server documents the necessary rules and knowledge based on the automatically generated business flow.

[0452] Format documented information and generate operational manuals in PDF format.

[0453] 6. The user refers to the generated business manual

[0454] The server stores the generated PDF manual and provides it in a form that is accessible to users.

[0455] The user downloads or views the generated manual.

[0456] Specific examples

[0457] For example, this system comes into play when a new member joins the sales department. The new member first logs in to the system by entering their username and password. After logging in, as the new member begins to operate the sales system, operation logs are collected in real time. The server analyzes the logs and identifies common sales operations and errors. A sales process workflow is then automatically generated based on the analysis results. Based on this workflow, important rules and past knowledge are documented, and a formatted operations manual is generated in PDF format. The new member can download and refer to this manual, allowing them to quickly become familiar with the work. This series of processes significantly reduces the workload when taking over work or when new members join, improving work efficiency.

[0458] In this way, the system of the present invention functions as a powerful tool for improving the work efficiency of users.

[0459] The processing flow will be explained below.

[0460] Step 1:

[0461] The user enters their username and password on the login screen.

[0462] Step 2:

[0463] The terminal encrypts the entered authentication information and sends it to the server.

[0464] Step 3:

[0465] The server receives the authentication information and checks it against the data in its database.

[0466] If authentication is successful, the server starts a session and returns the user ID, if authentication fails it returns an error message.

[0467] Step 4:

[0468] Users access business applications and begin their daily work.

[0469] Step 5:

[0470] The device records each user operation as log data, such as button clicks and data input.

[0471] Step 6:

[0472] The terminal sends the log data to the server in real time.

[0473] Step 7:

[0474] The server stores the received log data and begins analyzing it.

[0475] Step 8:

[0476] The server analyzes the log data and identifies frequently executed operations (frequent operations).

[0477] Step 9:

[0478] The server analyzes error messages in the log data to identify commonly occurring error patterns.

[0479] Step 10:

[0480] The server automatically generates a workflow based on the identified frequent operations and error patterns. This workflow includes each step and its explanation.

[0481] Step 11:

[0482] Based on the business flow generated by the server, the necessary rules and knowledge are documented in text format.

[0483] Step 12:

[0484] The server formats the documented rules and knowledge to create an easy-to-read business manual.

[0485] Step 13:

[0486] The server saves the generated business manual in PDF format and provides it to the user in an accessible format.

[0487] Step 14:

[0488] The user clicks on the specified link or URL to download or view the generated business manual.

[0489] This series of steps ensures efficient support when taking over business operations or when new members join.

[0490] Example 1

[0491] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0492] In today's business environment, many tasks are performed using digital tools, but the recording and analysis of operations, as well as the creation of business manuals, must often be done manually. This makes it difficult to maintain operational efficiency and consistency, and there is insufficient support for new members to quickly become proficient in their work. Furthermore, frequent operational mistakes and errors are often not addressed properly, risking a decline in the overall quality of work. To solve these problems, there is a need for a system that can automatically collect and analyze operational logs, automatically generate business flows, and quickly provide business manuals.

[0493] 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.

[0494] In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation record data, means for analyzing the collected record data and identifying frequently occurring operations and errors, means for automatically generating a workflow based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow, means for formatting the documented rules and knowledge and generating a workflow manual, and means for saving the generated manual and making it accessible to users. This reduces the frequency of operational mistakes and errors, enables new members to quickly become proficient, and improves the efficiency and quality of the entire business.

[0495] A "user" is a person or organization that inputs authentication information to use the system and performs business operations.

[0496] "Authentication information" refers to information such as a username and password that a user enters to log in to a system.

[0497] "Operation record data" is data that records various operations that users perform within the system.

[0498] "Analysis" refers to processing the collected operation record data and identifying frequently occurring operations and error patterns.

[0499] A "workflow" refers to a set of steps or procedures for carrying out a particular task.

[0500] "Rules" are regulations and guidelines for carrying out business.

[0501] "Knowledge" includes information that is useful in performing work and insight gained from past experience.

[0502] "Documentation" means organizing and storing rules and knowledge in written form.

[0503] "Format" means putting a document into a neat form.

[0504] A "business manual" is a documented guidebook that includes business procedures, rules, and knowledge.

[0505] "Saving" refers to storing and preserving the generated business manual in the server.

[0506] "Accessible" means that the business manual is available for viewing and download by the user.

[0507] The present invention is an integrated log management, analysis, and manual creation system designed to support users' work. This system is implemented using the following hardware and software.

[0508] Hardware and software used

[0509] Server: Various cloud services (e.g., AWS EC2 instances)

[0510] Authentication system: OAuth 2.0

[0511] Data collection tool: Logstash

[0512] Data analysis tool: Apache Spark

[0513] Machine learning algorithm: Scikit-learn

[0514] Manual generation tool: LaTeX

[0515] Detailed operation of the system

[0516] 1. User Authentication

[0517] The user accesses the system's login screen and enters authentication information (user name and password). The device encrypts this authentication information using SSL / TLS and sends it to the server.

[0518] The server verifies the authentication information using OAuth 2.0 and, if successful, grants access to the user.

[0519] 2. Collecting business operation logs

[0520] As users perform business operations within the system, each operation is recorded in real time by the terminal.

[0521] The terminal uses a log collection tool (Logstash) to send the recorded operation log to the server.

[0522] 3. Log data analysis

[0523] The operation log data received by the server is analyzed using Apache Spark. The analysis is performed in batch processing using data stored in HDFS.

[0524] Use machine learning algorithms (Scikit-learn) to identify frequent operations and error patterns.

[0525] 4. Automatic generation of business flows

[0526] The server automatically generates a workflow based on the analysis results, which includes the steps and points to note required for the work.

[0527] The generated business flow is saved in XML or YAML format.

[0528] 5. Documentation and manual generation

[0529] The server documents the necessary rules and knowledge based on the business flow, using LaTeX templates.

[0530] The formatted document is output from LaTeX as a business manual in PDF format.

[0531] 6. Provision and reference of manuals

[0532] The server stores the generated PDF manual in the system and provides it to the user in an accessible format.

[0533] The user downloads or views the generated manual from the "Manuals" section of the system.

[0534] Specific examples

[0535] For example, when a new salesperson logs into the system, a log of their work operations is collected in real time. The server analyzes the log and identifies frequently occurring operations and error patterns. Based on the identified patterns, a work manual for the new salesperson is automatically generated. This manual is provided in PDF format, allowing the new employee to refer to it and quickly become familiar with their work.

[0536] Prompt Sentence Examples

[0537] Analyze user business log data using Apache Spark and identify patterns of frequently occurring operations and errors. Explain how to automatically generate business flows in XML format based on the identified patterns and create business manuals in PDF format using LaTeX.

[0538] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0539] Step 1: User authentication

[0540] Specific description:

[0541] A user accesses the system and enters authentication information (username and password) on the login screen. The device sends this authentication information to the server. The server verifies the authentication information using OAuth 2.0, and if authentication is successful, grants the user access rights.

[0542] Input and Output:

[0543] Input: Username, Password

[0544] Output: Authentication success or failure

[0545] Specific behavior:

[0546] The user opens the system's login page in a browser, enters the username "USER_ID" and password "PASSWORD", and clicks the "Login" button. The device encrypts the authentication information and sends it to the server. The server compares the authentication information with the database, and if authentication is successful, grants the user dashboard access rights.

[0547] Step 2: Collect business operation logs

[0548] Specific description:

[0549] When a user performs a business operation within the system, each operation is recorded in real time. The terminal sends the recorded operation log to the server via a log collection tool (Logstash).

[0550] Input and Output:

[0551] Input: User interaction events (clicks, inputs, window switching, etc.)

[0552] Output: Operation log data

[0553] Specific behavior:

[0554] Every time a user operates a menu in the system, enters a task, or searches, the operation event is recorded in a log file in real time. The terminal uses Logstash to periodically send the recorded log to the server.

[0555] Step 3: Analyze the log data

[0556] Specific description:

[0557] Analyze the log data received by the server. Use Apache Spark to perform batch processing to analyze the data, and use machine learning algorithms (Scikit-learn) to identify frequent operations and error patterns.

[0558] Input and Output:

[0559] Input: Operation log data

[0560] Output: Analysis results (frequent operations, error patterns, etc.)

[0561] Specific behavior:

[0562] The server runs analysis on the log data stored in HDFS using Apache Spark. A machine learning model clusters the data and identifies frequent operations and error patterns. The analysis results are saved in JSON format.

[0563] Step 4: Automatic generation of business flow

[0564] Specific description:

[0565] The server automatically generates a workflow based on the analysis results. This workflow includes specific business steps and points to note. The generated workflow is saved in XML or YAML format.

[0566] Input and Output:

[0567] Input: Analysis results

[0568] Output: Business flow (XML / YAML format)

[0569] Specific behavior:

[0570] The server reads the JSON file containing the analysis results and runs a workflow generation algorithm to systematically summarize the business procedures based on frequently occurring operations and error patterns, and output them in XML or YAML format.

[0571] Step 5: Documentation and manual generation

[0572] Specific description:

[0573] The server documents the necessary rules and knowledge based on the business flow, creates documents in a well-formatted format using LaTeX templates, and generates business manuals in PDF format.

[0574] Input and Output:

[0575] Input: Business flow (XML / YAML format)

[0576] Output: Business manual (PDF format)

[0577] Specific behavior:

[0578] The server parses the business flow XML and reflects the data in the LaTeX template. It then uses the pdflatex command to convert the LaTeX file into PDF format and save it.

[0579] Step 6: Provide and refer to the manual

[0580] Specific description:

[0581] The server saves the generated business manual (PDF format) and provides it in an accessible format to the user. The user downloads or views the manual.

[0582] Input and Output:

[0583] Input: Business Manual (PDF format)

[0584] Output: Download link or view page

[0585] Specific behavior:

[0586] The server saves the generated PDF manual to the file system. The user accesses the "Manuals" section of the system, clicks the "Download" button to download the PDF file, and opens it in a viewer.

[0587] (Application example 1)

[0588] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0589] Modern factories require more efficient machine operation and faster troubleshooting. However, manually analyzing machine operation logs and documenting optimal maintenance procedures and troubleshooting takes a great deal of time and effort. Preparing training materials to quickly familiarize new staff with their work is also a significant burden. Solving these issues and streamlining factory operations is essential.

[0590] 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.

[0591] In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for analyzing the collected log data and identifying frequently occurring operations and errors, means for automatically generating a workflow based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow, means for formatting the documented rules and knowledge and generating a business manual, means for collecting machine operation logs in a factory and automatically generating optimal maintenance procedures and troubleshooting guides based on the analysis results, and means for saving the generated procedures and guides and making them accessible to users, thereby enabling more efficient machine operation and faster troubleshooting.

[0592] A "user" is a person or entity that accesses and operates the system.

[0593] "Authentication information" refers to information such as a user name and password used when a user logs in to a system.

[0594] "Operation log data" refers to a series of behavioral histories and operation information recorded when a user operates the system.

[0595] "Analysis results" refers to information obtained as a result of analyzing collected operation log data using machine learning algorithms, etc.

[0596] A "business flow" refers to a set of steps and procedures required to carry out a specific task.

[0597] "Rules" refer to the regulations and standards that must be followed when carrying out business.

[0598] "Insight" refers to knowledge and wisdom gained from past experiences and data.

[0599] "Documentation" refers to the act of recording and storing information in written form.

[0600] "Format" refers to organizing and structuring documents and data according to a prescribed format.

[0601] A "business manual" is a document that summarizes procedures and precautions for carrying out specific tasks.

[0602] A "machine operation log" is a series of action histories and operation information recorded when a machine in a factory is operated.

[0603] A "maintenance procedure manual" is a document that summarizes the procedures for maintaining and inspecting machinery and equipment.

[0604] A "troubleshooting guide" is a document that provides procedures and measures for resolving machine or system failures and problems.

[0605] The system configuration includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for analyzing the collected log data and identifying frequently occurring operations and errors, means for automatically generating a business flow based on the analysis results, means for documenting rules and knowledge based on the automatically generated business flow, means for formatting the documented rules and knowledge and generating a business manual, means for collecting machine operation logs within a factory and automatically generating optimal maintenance procedures and troubleshooting guides based on the analysis results, and means for saving the generated procedures and guides and making them accessible to users.

[0606] Hardware and Software

[0607] Hardware

[0608] Factory robot: Collects operation logs and executes tasks.

[0609] Computer server: Performs analysis and manual generation of log data.

[0610] software

[0611] Logging library: Uses the Python logging module to collect operation logs in real time.

[0612] Machine learning models: Data mining techniques and machine learning algorithms for log analysis available as Python modules. A concrete example is the hypothetical analyze_logs model.

[0613] PDF generation tool: A tool for formatting and saving documented rules and knowledge in PDF format. An example is the hypothetical generate_manual.

[0614] Processing Description

[0615] Logging in and collecting logs

[0616] The user enters authentication information (username and password) and logs into the system. The terminal sends the authentication information to the server, which then verifies it. If authentication is successful, the user proceeds to the next step. After logging in, when the user begins to operate a robot in the factory, the robot collects log data for each operation and sends it to the server in real time.

[0617] Log analysis

[0618] The server analyzes the collected log data to identify frequently occurring operations and errors. This analysis uses data mining and machine learning algorithms. For example, if a specific error occurs frequently in a product assembly process, analysis is performed to identify the cause and countermeasures.

[0619] Business flow and manual generation

[0620] Based on the analysis results, specific business processes, maintenance procedures, and troubleshooting guides are automatically generated. The documented rules and knowledge are formatted to generate business manuals in PDF format, which are then stored on a server and made accessible to users.

[0621] Specific examples

[0622] For example, this system is useful when new employees are learning how to operate machines in a factory. First, the new employee logs in to the system by entering their username and password. After logging in, collected machine operation logs are analyzed in real time, and an appropriate training manual is automatically generated. By referring to this manual, new employees can quickly become familiar with their work.

[0623] Prompt Sentence Examples

[0624] "Analyze the operation logs of factory robots and identify common problems and their causes."

[0625] As described above, the use of this system makes it possible to improve the efficiency of machine operation and perform quick troubleshooting.

[0626] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0627] Step 1:

[0628] A user accesses the system and enters authentication information (user name and password). The terminal sends this authentication information to the server. The server verifies the received authentication information and confirms that the user is a valid user. If authentication is successful, the user can proceed to the next step. The input is the user name and password, and the output is the authentication success or failure status.

[0629] Step 2:

[0630] When a user starts operating a factory robot, the robot collects operation log data. The terminal sends the collected operation log to a server in real time. The operation log data includes information such as the time, operation details, and error messages. The input is the user's operation, and the output is the operation log data.

[0631] Step 3:

[0632] The server stores the received operation log data in a database and begins analysis. Data mining techniques and machine learning algorithms are used for the analysis. The server identifies patterns of frequently occurring operations and errors. The input is the operation log data, and the output is the analysis results (frequent operations and error patterns).

[0633] Step 4:

[0634] The server automatically generates a specific business flow based on the analysis results. This business flow includes work procedures and points to note. The input is the analysis results, and the output is the automatically generated business flow. Specifically, it includes frequently performed operations and ways to avoid errors.

[0635] Step 5:

[0636] The server documents the necessary rules and knowledge based on the automatically generated business flow. Documentation includes formatting text data and organizing information. The input is the automatically generated business flow, and the output is the documented rules and knowledge.

[0637] Step 6:

[0638] The server generates business manuals, maintenance procedures, and troubleshooting guides based on documented rules and knowledge. This is done using a PDF generation tool. The input is the documented rules and knowledge, and the output is a business manual in PDF format.

[0639] Step 7:

[0640] The generated business manuals and procedures are stored on the server and can be accessed by users. Users can log in to the system and download or view these documents. The input is a business manual in PDF format, and the output is available for users to view or download.

[0641] Step 8:

[0642] For example, if a user operates a factory robot and inputs a prompt such as "Analyze the factory robot's operation log and identify frequent problems and their causes," the system will perform an analysis and generate a document containing improvement suggestions based on the results. The input is the prompt, and the output is a document containing the analysis results and improvement suggestions.

[0643] 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.

[0644] The system of the present invention not only collects and analyzes user operation log data during work, but also combines it with an emotion engine that recognizes user emotions to provide more comprehensive business support. This system begins with the user logging in by entering their authentication information, and includes a series of processes that collect the user's business operations and emotional information in real time, analyzes the data, and automatically generates a business manual that includes the business flow and points to note regarding emotions.

[0645] Overall system configuration

[0646] 1. User logs in

[0647] 1. A user accesses the system and enters their username and password.

[0648] 2. The device sends the authentication information to the server.

[0649] 3. The server verifies the authentication information and allows the user to log in. If authentication is successful, it returns the user ID.

[0650] 2. The user performs the task, and the device collects the operation log and emotion data.

[0651] 1. As users go about their daily work, each operation within the system is automatically recorded.

[0652] 2. The device collects user operation log data and sends it to the server in real time.

[0653] 3. The emotion engine collects the user's emotions (for example, joy, anger, sadness, or happiness through facial recognition or voice analysis).

[0654] 4. The device sends the collected emotion data to the server.

[0655] 3. The server analyzes the operation log data and emotion data.

[0656] 1. The server saves the received operation log data and emotion data and begins analysis.

[0657] 2. The server analyzes the operation log data and identifies frequently performed operations (frequent operations).

[0658] 3. The server analyzes the emotion data and identifies patterns of emotion change.

[0659] 4. The server automatically generates a workflow based on frequently occurring operations, error patterns, and emotion patterns. This workflow includes each step and its explanation.

[0660] 4. The server documents the business flow and emotional rules and knowledge, and generates a manual.

[0661] 1. The server documents rules and knowledge in text format based on the automatically generated business flow.

[0662] 2. Add emotional caveats to your server documentation.

[0663] 3. The server formats the documented rules and knowledge to create an easy-to-read business manual.

[0664] 5. The user refers to the generated business manual

[0665] 1. The server saves the generated business manual in PDF format and provides it to the user in an accessible format.

[0666] 2. The user clicks on the specified link or URL to download or view the generated business manual.

[0667] Specific examples

[0668] For example, this system comes into play when a new member joins the customer support department. The new member first logs in to the system by entering their username and password. After logging in, as the new member begins to operate the support system, the emotion engine collects emotional data along with the operation log. The server analyzes this data and identifies frequently occurring support operations, error patterns, and emotional patterns. Based on the analysis results, a support process workflow is automatically generated, and an operations manual, including points to note regarding emotions, is generated in PDF format. The new member can download and refer to this manual, allowing them to quickly become familiar with the work. This series of processes significantly reduces the workload when taking over work or when new members join, improving work efficiency.

[0669] In this way, the system of the present invention is a powerful tool for improving the user's work efficiency, and by combining it with an emotion engine, it realizes more human-friendly work support.

[0670] The processing flow will be explained below.

[0671] Step 1:

[0672] A user accesses the system and enters their username and password.

[0673] Step 2:

[0674] The terminal encrypts the entered authentication information and sends it to the server.

[0675] Step 3:

[0676] The server receives the authentication information and checks it against the data in its database.

[0677] If authentication is successful, the server starts a session and returns the user ID, if authentication fails it returns an error message.

[0678] Step 4:

[0679] Users access business applications and begin their daily work.

[0680] Step 5:

[0681] The device records each user operation as log data, such as button clicks and data input.

[0682] Step 6:

[0683] The emotion engine recognizes the user's emotions and collects emotional data through facial recognition and voice analysis.

[0684] Step 7:

[0685] The log data and emotion data collected by the device are sent to the server in real time.

[0686] Step 8:

[0687] The server stores the received log data and emotion data and begins analysis.

[0688] Step 9:

[0689] The server analyzes the log data and identifies frequently executed operations (frequent operations).

[0690] Step 10:

[0691] The server analyzes the emotion data and identifies emotion patterns based on changes in emotion.

[0692] Step 11:

[0693] The server automatically generates a workflow based on frequently occurring operations and emotion patterns. This workflow includes each step and its explanation.

[0694] Step 12:

[0695] The server documents the necessary rules and knowledge in text format based on the automatically generated business flow.

[0696] Step 13:

[0697] The server adds emotional warnings to the documented content to create an easy-to-read business manual.

[0698] Step 14:

[0699] The server saves the generated business manual in PDF format and provides it to the user in an accessible format.

[0700] Step 15:

[0701] The user clicks on the specified link or URL to download or view the generated business manual.

[0702] This series of steps reduces the workload when handing over tasks or when new members join, improving work efficiency. The generated manual also includes points to be aware of from an emotional perspective, reducing user stress and enabling smoother work execution.

[0703] Example 2

[0704] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0705] While conventional business support systems are capable of collecting user operation log data, they lack the ability to recognize and analyze user emotions, limiting their ability to improve business efficiency and optimize user experience. Furthermore, because they are unable to automatically generate emotion-based workflows or create manuals, it is difficult for new members to take over tasks, resulting in a heavy workload.

[0706] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for collecting emotion data, means for analyzing the collected log data and emotion data to identify frequently occurring operations, errors, and changes in emotion, means for automatically generating a workflow based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow, means for formatting the documented rules and knowledge and generating a business manual, and means for saving the generated manual and making it accessible to the user. This enables a comprehensive analysis of the user's operation status and emotional state, enabling efficient and user-friendly business support.

[0707] "Authentication information" refers to information that a user enters to verify their identity when accessing a system, and typically includes a username and password.

[0708] "Operation log data" is recorded data that is generated when a user operates the system, and includes the type, time, and frequency of the operation.

[0709] "Emotion data" refers to data that reflects the user's emotional state, and includes facial expression analysis using face recognition and emotion determination using voice analysis.

[0710] "Analysis" is the process of identifying meanings and patterns based on collected data, and involves the use of statistical analysis methods and machine learning algorithms.

[0711] A "business flow" is a series of steps that show the procedures and steps of a business, and includes operating procedures and accompanying explanations.

[0712] "Rules" describe the norms and standards that must be followed in the performance of business.

[0713] "Knowledge" is a description of the specialized knowledge and know-how required to carry out a business.

[0714] "Documentation" is the process of writing and recording information in text form, formatted for easy reading.

[0715] A "business manual" is a document that compiles business procedures, rules, and knowledge, and provides users with guidelines to refer to when performing their work.

[0716] The system of this invention collects and analyzes user operation log data and emotion data to provide comprehensive business support, and automatically generates business flows and business manuals based on this data. This system is implemented using the following hardware and software.

[0717] Hardware used

[0718] 1. Terminal: A device such as a computer or smartphone that is operated by a user.

[0719] 2. Server: A server for storing data, analyzing, and generating manuals.

[0720] 3. Camera: A device for collecting emotional data through facial recognition of the user.

[0721] 4. Microphone: A device for analyzing the user's voice and collecting emotional data.

[0722] Software used

[0723] 1. Authentication system: Software that manages user authentication information and controls access to a system.

[0724] 2. Log collection software: Software for recording user operation log data.

[0725] 3. Emotion Engine: Software that uses facial recognition and voice analysis to collect user emotional data.

[0726] 4. Data analysis software: Software for analyzing collected data and automatically generating business flows.

[0727] 5. Document generation software: Software that documents business flows, rules, and knowledge and generates business manuals.

[0728] 6. PDF generation software: Software for saving documented content in PDF format.

[0729] A natural language description of what the system does

[0730] This system first receives the authentication information (user name and password) entered by the user, and the server authenticates the user by checking the information against a database. If authentication is successful, the server returns the user ID.

[0731] When a task begins, the device automatically records the user's operations and generates operation log data. This data is sent to the server in real time. At the same time, the emotion engine uses the camera and microphone to collect the user's emotional data. For example, it uses facial recognition and voice analysis to determine whether the user is expressing emotions such as joy, anger, or sadness. The device also sends this emotional data to the server in real time.

[0732] The server stores the received operation log data and emotion data in a database and begins analysis. This analysis identifies frequently occurring operations, error patterns, and patterns of emotional changes. Based on the analysis results, an efficient workflow is automatically generated. This workflow includes specific operation procedures and related explanations.

[0733] The server then documents the rules and knowledge in text format based on the automatically generated workflow. It also adds emotional warnings, such as how to deal with stress caused by a particular operation.

[0734] Finally, the documented information is saved in PDF format and made accessible to users, who can click on the provided link to download or view the operation manual.

[0735] Specific examples

[0736] For example, this system is extremely useful when a new member joins the customer support department. The new member first accesses the system and enters their username and password. The terminal sends the authentication information to the server, which then performs authentication. If authentication is successful, the new member begins work. At that time, the terminal collects operation log data, and the emotion engine collects emotion data. The collected data is sent to the server in real time, where it is analyzed to identify frequently occurring operations, error patterns, and changes in emotion.

[0737] For example, frequently performed operations such as searching for customer information or updating a support ticket are identified. Furthermore, if a user feels stressed while interacting with a customer, the timing is identified. Based on this, a work flow is automatically generated, and a work manual is created that includes notes on emotional responses. New members can download and refer to this manual to quickly become familiar with their work.

[0738] Example prompts for generative AI models

[0739] "Please explain the system that collects and analyzes operation logs and emotional data when users perform their work. Also, please provide details on the contents of the work manual that will be generated."

[0740] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0741] Step 1:

[0742] A user accesses the system and enters a username and password. The entered authentication information (username and password) is input data, which the terminal receives and sends to the server.

[0743] Step 2:

[0744] The server receives the authentication information sent from the terminal and compares it with the database. Previous registration information is stored in the database, and by comparing the authentication information, it is possible to confirm whether the user is a legitimate user. If authenticated, the server returns the user ID and notifies the terminal that authentication was successful. This output includes the authentication result (success / failure) and the user ID.

[0745] Step 3:

[0746] A user starts their work and performs various operations within the system, which generates operation log data. Specific actions include a user searching for customer information or updating a support ticket. Details of each operation are recorded as input data.

[0747] Step 4:

[0748] The terminal collects the generated operation log data and sends it to the server in real time. The input data is the user's operation log, and the log data includes the type of operation, timestamp, and content. The terminal sends this to the server.

[0749] Step 5:

[0750] The emotion engine uses the camera and microphone to collect the user's emotion data. The emotion engine performs facial and voice analysis to identify the user's emotions (e.g., joy, anger, sadness, and happiness). The emotion data is recorded as input data.

[0751] Step 6:

[0752] The device sends the collected emotion data to the server in real time. The input data is emotion data generated by the emotion engine, and this data includes the type, duration, and intensity of the emotion. The device then sends this to the server.

[0753] Step 7:

[0754] The server stores the received operation log data and emotion data in a database. The input data are operation log data and emotion data, which are then stored in a database.

[0755] Step 8:

[0756] The server analyzes the stored operation log data to identify frequently occurring operations and error patterns. Data mining and machine learning algorithms are used for the analysis to identify frequently occurring operations, errors, time periods, etc. The output is a list of frequently occurring operations and error patterns.

[0757] Step 9:

[0758] The server analyzes the stored emotion data and identifies patterns of emotion change. Statistical methods and machine learning algorithms are used for the analysis to identify emotion changes, peaks, and emotional chains. The output is a list of emotion patterns.

[0759] Step 10:

[0760] The server automatically generates a workflow based on frequently occurring operations, error patterns, and emotion patterns. The workflow includes specific operation procedures, error handling methods, and emotional precautions. The output is visualized data such as a workflow diagram.

[0761] Step 11:

[0762] The server documents business procedures, rules, and knowledge in text format based on the automatically generated business flow. Documentation requires specialized knowledge, and details are described, for example, "When performing operation A, perform steps 1-2-3." The output is a draft of the business manual.

[0763] Step 12:

[0764] The server formats the documented content and creates a PDF business manual. Formatting includes layout adjustment and formatting, and the final output is a PDF business manual that can be easily referenced by users.

[0765] Step 13:

[0766] The server saves the generated business manual in storage and makes it accessible to users. Access permissions are set in the storage, and users can access the manual through a specific link or URL. The output is an access link to the manual.

[0767] Step 14:

[0768] The user clicks on the provided link or URL to download or view the generated business manual. The user can refer to this manual to learn more about the business and improve work efficiency. The output is an improvement in the user's business knowledge.

[0769] (Application example 2)

[0770] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0771] In modern work, there is a growing demand for enhanced work support by simultaneously collecting and analyzing user operation log data and emotional data. However, current systems are specialized in collecting and analyzing operation log data, making it difficult to consider the user's emotional state. As a result, users' stress and emotional problems may affect their work efficiency. Furthermore, comprehensive work support has not been achieved because emotional care advice cannot be incorporated into the work flow.

[0772] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for collecting user emotion data, means for analyzing the collected log data and emotion data and identifying frequently occurring operations, errors, and emotion patterns, means for automatically generating a workflow and emotional care advice based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow and emotional care advice, means for formatting the documented rules and knowledge and generating a business manual, and means for saving the generated manual and making it accessible to the user. This enables comprehensive analysis of the user's operation log data and emotion data to enable efficient and user-friendly business support.

[0773] "Authentication information" refers to personal identification information such as a username and password that a user enters when accessing a system.

[0774] "Operation log data" is data that records the history of operations and actions performed by users on the system.

[0775] "Emotion data" is information that reflects the user's emotional state, and is data obtained using technologies such as facial recognition and voice analysis.

[0776] "Frequent operations" is a term that refers to operations or actions that users perform repeatedly within a system.

[0777] "Error" refers to any malfunction or problem that a user may encounter while operating the system.

[0778] An "emotion pattern" refers to a change in the user's emotional state that has a certain tendency or regularity.

[0779] "Business flow" refers to a series of steps and processes for carrying out business, shown in diagrams or text.

[0780] "Emotional care advice" refers to advice or instructions provided based on the user's emotional state while working, with the aim of reducing stress and increasing motivation for the user.

[0781] "Rules and knowledge" refers to the rules for carrying out business and knowledge based on past experience.

[0782] "Documentation" refers to the process of organizing collected information and transcribing it into an easy-to-read format.

[0783] "Formatting" refers to the process of adjusting the form and appearance of a document.

[0784] A "business manual" refers to a document that summarizes procedures, rules, and points to note for carrying out business.

[0785] The system of this invention generates comprehensive workflow and emotional care advice by collecting and analyzing user operation log data and emotional data. This system is implemented based on the following configuration and procedures.

[0786] Program processing

[0787] 1. Login Process

[0788] A user accesses the system and enters authentication information such as a username and password. The terminal sends this authentication information to the server, which then authenticates the user. At this time, a user ID is generated and returned to the user.

[0789] 2. Collecting operation logs and emotion data

[0790] When a user performs a task, the device collects the user's operation log data in real time and sends it to the server. At the same time, the emotion engine collects the user's emotion data using the device's built-in camera and microphone. The emotion data is analyzed using face recognition and voice analysis technology (e.g., OpenCV, Face Recognition library).

[0791] 3. Data Analysis

[0792] The server stores and analyzes the operation log data and emotion data it receives. It identifies frequent operations and error patterns from the operation log data, and identifies patterns of emotional changes from the emotion data. This is done using emotion analysis libraries such as EmotionDetector.

[0793] 4. Generation of workflow and emotional care advice

[0794] The server automatically generates business flows and emotional care advice based on the analysis results, and documents the contents of the automatically generated business flows and emotional care advice using the GuideGenerator library.

[0795] 5. Creating and saving the manual

[0796] The server formats the documented rules and knowledge and generates a work manual. The generated manual and emotional care advice are saved in PDF format and made accessible to users, who can download or view it.

[0797] Specific examples

[0798] For example, imagine a scenario in which a new factory worker is assigned to a production line, puts on smart glasses, and logs in to the system. As the worker begins to work, the system collects and analyzes their operation logs and emotional data in real time. It provides guidance for unfamiliar operations and displays advice on how to relax if the worker feels stressed. Finally, a detailed operation guide and emotional care advice are automatically generated based on the analysis results and provided in PDF format.

[0799] Prompt Sentence Examples

[0800] "A new worker is assigned to a factory line and begins work wearing smart glasses. An application records the worker's actions and analyzes their emotional state in real time. The application displays guidance for unfamiliar operations and provides instructions to relax if the worker feels stressed. Please explain the specific operations and effects of the application."

[0801] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0802] Step 1:

[0803] A user accesses the system and enters authentication information such as a username and password. The terminal sends this authentication information as input data to the server. The server verifies the authentication information, and if authentication is successful, generates and outputs a user ID.

[0804] Step 2:

[0805] When a user performs a task, the terminal records each user operation in real time as operation log data, including the type of operation and a timestamp. The recorded operation log data is then sent from the terminal to the server.

[0806] Step 3:

[0807] The emotion engine collects the user's emotion data using the device's built-in camera and microphone, using face recognition and voice analysis technologies (e.g., OpenCV, Face Recognition library), and transmits the acquired emotion data from the device to the server.

[0808] Step 4:

[0809] The server stores the received operation log data and emotion data as input data and performs analysis. It identifies frequently occurring operations and error patterns from the operation log data, and identifies patterns of emotion change from the emotion data. As a result of the analysis, it outputs information on frequently occurring operations, error patterns, and emotion patterns.

[0810] Step 5:

[0811] The server automatically generates business flows and emotional care advice based on the analysis results. Using the GuideGenerator library, the server generates and documents business flows and emotional care advice using the analyzed data as input. The generated business flows and emotional care advice are then output.

[0812] Step 6:

[0813] The server formats the documented rules and knowledge and generates a business manual, which is saved as a PDF file.

[0814] Step 7:

[0815] The generated manuals and emotional care advice are stored on a server and made available for users to access, download, or view. Users can click on a link or URL to download the file and view the contents.

[0816] 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.

[0817] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0818] 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.

[0819] [Third embodiment]

[0820] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0821] 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.

[0822] 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).

[0823] 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.

[0824] 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.

[0825] 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).

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0832] The system of the present invention is an integrated log management, analysis, and manual creation system to support users' business operations. This system involves a series of processes, starting with the user logging in by entering authentication information, collecting logs of the user's business operations in real time, analyzing the data, and finally automatically generating a business manual.

[0833] Overall system configuration

[0834] 1. User logs in

[0835] A user accesses the system and enters their username and password.

[0836] The device sends the authentication information to the server.

[0837] The server verifies the credentials and allows the user to log in. If authentication is successful, the user can proceed to the next step.

[0838] 2. The user performs the task, and the device collects the operation log.

[0839] As users go about their daily work, each action within the system is automatically recorded.

[0840] The terminal collects each operation as log data and sends it to the server in real time.

[0841] 3. The server analyzes the log data to identify frequent operations and errors.

[0842] The server analyzes the log data it receives to identify patterns of frequently occurring operations and common errors.

[0843] This analysis often uses data mining techniques and machine learning algorithms.

[0844] 4. The server automatically generates the business flow

[0845] The server automatically generates a specific business flow based on the analysis results.

[0846] The business flow includes the business steps and points to note at each step.

[0847] 5. The server documents the rules and knowledge and generates a manual

[0848] The server documents the necessary rules and knowledge based on the automatically generated business flow.

[0849] Format documented information and generate operational manuals in PDF format.

[0850] 6. The user refers to the generated business manual

[0851] The server stores the generated PDF manual and provides it in a form that is accessible to users.

[0852] The user downloads or views the generated manual.

[0853] Specific examples

[0854] For example, this system comes into play when a new member joins the sales department. The new member first logs in to the system by entering their username and password. After logging in, as the new member begins to operate the sales system, operation logs are collected in real time. The server analyzes the logs and identifies common sales operations and errors. A sales process workflow is then automatically generated based on the analysis results. Based on this workflow, important rules and past knowledge are documented, and a formatted operations manual is generated in PDF format. The new member can download and refer to this manual, allowing them to quickly become familiar with the work. This series of processes significantly reduces the workload when taking over work or when new members join, improving work efficiency.

[0855] In this way, the system of the present invention functions as a powerful tool for improving the work efficiency of users.

[0856] The processing flow will be explained below.

[0857] Step 1:

[0858] The user enters their username and password on the login screen.

[0859] Step 2:

[0860] The terminal encrypts the entered authentication information and sends it to the server.

[0861] Step 3:

[0862] The server receives the authentication information and checks it against the data in its database.

[0863] If authentication is successful, the server starts a session and returns the user ID, if authentication fails it returns an error message.

[0864] Step 4:

[0865] Users access business applications and begin their daily work.

[0866] Step 5:

[0867] The device records each user operation as log data, such as button clicks and data input.

[0868] Step 6:

[0869] The terminal sends the log data to the server in real time.

[0870] Step 7:

[0871] The server stores the received log data and begins analyzing it.

[0872] Step 8:

[0873] The server analyzes the log data and identifies frequently executed operations (frequent operations).

[0874] Step 9:

[0875] The server analyzes error messages in the log data to identify commonly occurring error patterns.

[0876] Step 10:

[0877] The server automatically generates a workflow based on the identified frequent operations and error patterns. This workflow includes each step and its explanation.

[0878] Step 11:

[0879] Based on the business flow generated by the server, the necessary rules and knowledge are documented in text format.

[0880] Step 12:

[0881] The server formats the documented rules and knowledge to create an easy-to-read business manual.

[0882] Step 13:

[0883] The server saves the generated business manual in PDF format and provides it to the user in an accessible format.

[0884] Step 14:

[0885] The user clicks on the specified link or URL to download or view the generated business manual.

[0886] This series of steps ensures efficient support when taking over business operations or when new members join.

[0887] Example 1

[0888] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0889] In today's business environment, many tasks are performed using digital tools, but the recording and analysis of operations, as well as the creation of business manuals, must often be done manually. This makes it difficult to maintain operational efficiency and consistency, and there is insufficient support for new members to quickly become proficient in their work. Furthermore, frequent operational mistakes and errors are often not addressed properly, risking a decline in the overall quality of work. To solve these problems, there is a need for a system that can automatically collect and analyze operational logs, automatically generate business flows, and quickly provide business manuals.

[0890] 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.

[0891] In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation record data, means for analyzing the collected record data and identifying frequently occurring operations and errors, means for automatically generating a workflow based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow, means for formatting the documented rules and knowledge and generating a workflow manual, and means for saving the generated manual and making it accessible to users. This reduces the frequency of operational mistakes and errors, enables new members to quickly become proficient, and improves the efficiency and quality of the entire business.

[0892] A "user" is a person or organization that inputs authentication information to use the system and performs business operations.

[0893] "Authentication information" refers to information such as a username and password that a user enters to log in to a system.

[0894] "Operation record data" is data that records various operations that users perform within the system.

[0895] "Analysis" refers to processing the collected operation record data and identifying frequently occurring operations and error patterns.

[0896] A "workflow" refers to a set of steps or procedures for carrying out a particular task.

[0897] "Rules" are regulations and guidelines for carrying out business.

[0898] "Knowledge" includes information that is useful in performing work and insight gained from past experience.

[0899] "Documentation" means organizing and storing rules and knowledge in written form.

[0900] "Format" means putting a document into a neat form.

[0901] A "business manual" is a documented guidebook that includes business procedures, rules, and knowledge.

[0902] "Saving" refers to storing and preserving the generated business manual in the server.

[0903] "Accessible" means that the business manual is available for viewing and download by the user.

[0904] The present invention is an integrated log management, analysis, and manual creation system designed to support users' work. This system is implemented using the following hardware and software.

[0905] Hardware and software used

[0906] Server: Various cloud services (e.g., AWS EC2 instances)

[0907] Authentication system: OAuth 2.0

[0908] Data collection tool: Logstash

[0909] Data analysis tool: Apache Spark

[0910] Machine learning algorithm: Scikit-learn

[0911] Manual generation tool: LaTeX

[0912] Detailed operation of the system

[0913] 1. User Authentication

[0914] The user accesses the system's login screen and enters authentication information (user name and password). The device encrypts this authentication information using SSL / TLS and sends it to the server.

[0915] The server verifies the authentication information using OAuth 2.0 and, if successful, grants access to the user.

[0916] 2. Collecting business operation logs

[0917] As users perform business operations within the system, each operation is recorded in real time by the terminal.

[0918] The terminal uses a log collection tool (Logstash) to send the recorded operation log to the server.

[0919] 3. Log data analysis

[0920] The operation log data received by the server is analyzed using Apache Spark. The analysis is performed in batch processing using data stored in HDFS.

[0921] Use machine learning algorithms (Scikit-learn) to identify frequent operations and error patterns.

[0922] 4. Automatic generation of business flows

[0923] The server automatically generates a workflow based on the analysis results, which includes the steps and points to note required for the work.

[0924] The generated business flow is saved in XML or YAML format.

[0925] 5. Documentation and manual generation

[0926] The server documents the necessary rules and knowledge based on the business flow, using LaTeX templates.

[0927] The formatted document is output from LaTeX as a business manual in PDF format.

[0928] 6. Provision and reference of manuals

[0929] The server stores the generated PDF manual in the system and provides it to the user in an accessible format.

[0930] The user downloads or views the generated manual from the "Manuals" section of the system.

[0931] Specific examples

[0932] For example, when a new salesperson logs into the system, a log of their work operations is collected in real time. The server analyzes the log and identifies frequently occurring operations and error patterns. Based on the identified patterns, a work manual for the new salesperson is automatically generated. This manual is provided in PDF format, allowing the new employee to refer to it and quickly become familiar with their work.

[0933] Prompt Sentence Examples

[0934] Analyze user business log data using Apache Spark and identify patterns of frequently occurring operations and errors. Explain how to automatically generate business flows in XML format based on the identified patterns and create business manuals in PDF format using LaTeX.

[0935] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0936] Step 1: User authentication

[0937] Specific description:

[0938] A user accesses the system and enters authentication information (username and password) on the login screen. The device sends this authentication information to the server. The server verifies the authentication information using OAuth 2.0, and if authentication is successful, grants the user access rights.

[0939] Input and Output:

[0940] Input: Username, Password

[0941] Output: Authentication success or failure

[0942] Specific behavior:

[0943] The user opens the system's login page in a browser, enters the username "USER_ID" and password "PASSWORD", and clicks the "Login" button. The device encrypts the authentication information and sends it to the server. The server compares the authentication information with the database, and if authentication is successful, grants the user dashboard access rights.

[0944] Step 2: Collect business operation logs

[0945] Specific description:

[0946] When a user performs a business operation within the system, each operation is recorded in real time. The terminal sends the recorded operation log to the server via a log collection tool (Logstash).

[0947] Input and Output:

[0948] Input: User interaction events (clicks, inputs, window switching, etc.)

[0949] Output: Operation log data

[0950] Specific behavior:

[0951] Every time a user operates a menu in the system, enters a task, or searches, the operation event is recorded in a log file in real time. The terminal uses Logstash to periodically send the recorded log to the server.

[0952] Step 3: Analyze the log data

[0953] Specific description:

[0954] Analyze the log data received by the server. Use Apache Spark to perform batch processing to analyze the data, and use machine learning algorithms (Scikit-learn) to identify frequent operations and error patterns.

[0955] Input and Output:

[0956] Input: Operation log data

[0957] Output: Analysis results (frequent operations, error patterns, etc.)

[0958] Specific behavior:

[0959] The server runs analysis on the log data stored in HDFS using Apache Spark. A machine learning model clusters the data and identifies frequent operations and error patterns. The analysis results are saved in JSON format.

[0960] Step 4: Automatic generation of business flow

[0961] Specific description:

[0962] The server automatically generates a workflow based on the analysis results. This workflow includes specific business steps and points to note. The generated workflow is saved in XML or YAML format.

[0963] Input and Output:

[0964] Input: Analysis results

[0965] Output: Business flow (XML / YAML format)

[0966] Specific behavior:

[0967] The server reads the JSON file containing the analysis results and runs a workflow generation algorithm to systematically summarize the business procedures based on frequently occurring operations and error patterns, and output them in XML or YAML format.

[0968] Step 5: Documentation and manual generation

[0969] Specific description:

[0970] The server documents the necessary rules and knowledge based on the business flow, creates documents in a well-formatted format using LaTeX templates, and generates business manuals in PDF format.

[0971] Input and Output:

[0972] Input: Business flow (XML / YAML format)

[0973] Output: Business manual (PDF format)

[0974] Specific behavior:

[0975] The server parses the business flow XML and reflects the data in the LaTeX template. It then uses the pdflatex command to convert the LaTeX file into PDF format and save it.

[0976] Step 6: Provide and refer to the manual

[0977] Specific description:

[0978] The server saves the generated business manual (PDF format) and provides it in an accessible format to the user. The user downloads or views the manual.

[0979] Input and Output:

[0980] Input: Business Manual (PDF format)

[0981] Output: Download link or view page

[0982] Specific behavior:

[0983] The server saves the generated PDF manual to the file system. The user accesses the "Manuals" section of the system, clicks the "Download" button to download the PDF file, and opens it in a viewer.

[0984] (Application example 1)

[0985] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0986] Modern factories require more efficient machine operation and faster troubleshooting. However, manually analyzing machine operation logs and documenting optimal maintenance procedures and troubleshooting takes a great deal of time and effort. Preparing training materials to quickly familiarize new staff with their work is also a significant burden. Solving these issues and streamlining factory operations is essential.

[0987] 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.

[0988] In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for analyzing the collected log data and identifying frequently occurring operations and errors, means for automatically generating a workflow based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow, means for formatting the documented rules and knowledge and generating a business manual, means for collecting machine operation logs in a factory and automatically generating optimal maintenance procedures and troubleshooting guides based on the analysis results, and means for saving the generated procedures and guides and making them accessible to users, thereby enabling more efficient machine operation and faster troubleshooting.

[0989] A "user" is a person or entity that accesses and operates the system.

[0990] "Authentication information" refers to information such as a user name and password used when a user logs in to a system.

[0991] "Operation log data" refers to a series of behavioral histories and operation information recorded when a user operates the system.

[0992] "Analysis results" refers to information obtained as a result of analyzing collected operation log data using machine learning algorithms, etc.

[0993] A "business flow" refers to a set of steps and procedures required to carry out a specific task.

[0994] "Rules" refer to the regulations and standards that must be followed when carrying out business.

[0995] "Insight" refers to knowledge and wisdom gained from past experiences and data.

[0996] "Documentation" refers to the act of recording and storing information in written form.

[0997] "Format" refers to organizing and structuring documents and data according to a prescribed format.

[0998] A "business manual" is a document that summarizes procedures and precautions for carrying out specific tasks.

[0999] A "machine operation log" is a series of action histories and operation information recorded when a machine in a factory is operated.

[1000] A "maintenance procedure manual" is a document that summarizes the procedures for maintaining and inspecting machinery and equipment.

[1001] A "troubleshooting guide" is a document that provides procedures and measures for resolving machine or system failures and problems.

[1002] The system configuration includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for analyzing the collected log data and identifying frequently occurring operations and errors, means for automatically generating a business flow based on the analysis results, means for documenting rules and knowledge based on the automatically generated business flow, means for formatting the documented rules and knowledge and generating a business manual, means for collecting machine operation logs within a factory and automatically generating optimal maintenance procedures and troubleshooting guides based on the analysis results, and means for saving the generated procedures and guides and making them accessible to users.

[1003] Hardware and Software

[1004] Hardware

[1005] Factory robot: Collects operation logs and executes tasks.

[1006] Computer server: Performs analysis and manual generation of log data.

[1007] software

[1008] Logging library: Uses the Python logging module to collect operation logs in real time.

[1009] Machine learning models: Data mining techniques and machine learning algorithms for log analysis available as Python modules. A concrete example is the hypothetical analyze_logs model.

[1010] PDF generation tool: A tool for formatting and saving documented rules and knowledge in PDF format. An example is the hypothetical generate_manual.

[1011] Processing Description

[1012] Logging in and collecting logs

[1013] The user enters authentication information (username and password) and logs into the system. The terminal sends the authentication information to the server, which then verifies it. If authentication is successful, the user proceeds to the next step. After logging in, when the user begins to operate a robot in the factory, the robot collects log data for each operation and sends it to the server in real time.

[1014] Log analysis

[1015] The server analyzes the collected log data to identify frequently occurring operations and errors. This analysis uses data mining and machine learning algorithms. For example, if a specific error occurs frequently in a product assembly process, analysis is performed to identify the cause and countermeasures.

[1016] Business flow and manual generation

[1017] Based on the analysis results, specific business processes, maintenance procedures, and troubleshooting guides are automatically generated. The documented rules and knowledge are formatted to generate business manuals in PDF format, which are then stored on a server and made accessible to users.

[1018] Specific examples

[1019] For example, this system is useful when new employees are learning how to operate machines in a factory. First, the new employee logs in to the system by entering their username and password. After logging in, collected machine operation logs are analyzed in real time, and an appropriate training manual is automatically generated. By referring to this manual, new employees can quickly become familiar with their work.

[1020] Prompt Sentence Examples

[1021] "Analyze the operation logs of factory robots and identify common problems and their causes."

[1022] As described above, the use of this system makes it possible to improve the efficiency of machine operation and perform quick troubleshooting.

[1023] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1024] Step 1:

[1025] A user accesses the system and enters authentication information (user name and password). The terminal sends this authentication information to the server. The server verifies the received authentication information and confirms that the user is a valid user. If authentication is successful, the user can proceed to the next step. The input is the user name and password, and the output is the authentication success or failure status.

[1026] Step 2:

[1027] When a user starts operating a factory robot, the robot collects operation log data. The terminal sends the collected operation log to a server in real time. The operation log data includes information such as the time, operation details, and error messages. The input is the user's operation, and the output is the operation log data.

[1028] Step 3:

[1029] The server stores the received operation log data in a database and begins analysis. Data mining techniques and machine learning algorithms are used for the analysis. The server identifies patterns of frequently occurring operations and errors. The input is the operation log data, and the output is the analysis results (frequent operations and error patterns).

[1030] Step 4:

[1031] The server automatically generates a specific business flow based on the analysis results. This business flow includes work procedures and points to note. The input is the analysis results, and the output is the automatically generated business flow. Specifically, it includes frequently performed operations and ways to avoid errors.

[1032] Step 5:

[1033] The server documents the necessary rules and knowledge based on the automatically generated business flow. Documentation includes formatting text data and organizing information. The input is the automatically generated business flow, and the output is the documented rules and knowledge.

[1034] Step 6:

[1035] The server generates business manuals, maintenance procedures, and troubleshooting guides based on documented rules and knowledge. This is done using a PDF generation tool. The input is the documented rules and knowledge, and the output is a business manual in PDF format.

[1036] Step 7:

[1037] The generated business manuals and procedures are stored on the server and can be accessed by users. Users can log in to the system and download or view these documents. The input is a business manual in PDF format, and the output is available for users to view or download.

[1038] Step 8:

[1039] For example, if a user operates a factory robot and inputs a prompt such as "Analyze the factory robot's operation log and identify frequent problems and their causes," the system will perform an analysis and generate a document containing improvement suggestions based on the results. The input is the prompt, and the output is a document containing the analysis results and improvement suggestions.

[1040] 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.

[1041] The system of the present invention not only collects and analyzes user operation log data during work, but also combines it with an emotion engine that recognizes user emotions to provide more comprehensive business support. This system begins with the user logging in by entering their authentication information, and includes a series of processes that collect the user's business operations and emotional information in real time, analyzes the data, and automatically generates a business manual that includes the business flow and points to note regarding emotions.

[1042] Overall system configuration

[1043] 1. User logs in

[1044] 1. A user accesses the system and enters their username and password.

[1045] 2. The device sends the authentication information to the server.

[1046] 3. The server verifies the authentication information and allows the user to log in. If authentication is successful, it returns the user ID.

[1047] 2. The user performs the task, and the device collects the operation log and emotion data.

[1048] 1. As users go about their daily work, each operation within the system is automatically recorded.

[1049] 2. The device collects user operation log data and sends it to the server in real time.

[1050] 3. The emotion engine collects the user's emotions (for example, joy, anger, sadness, or happiness through facial recognition or voice analysis).

[1051] 4. The device sends the collected emotion data to the server.

[1052] 3. The server analyzes the operation log data and emotion data.

[1053] 1. The server saves the received operation log data and emotion data and begins analysis.

[1054] 2. The server analyzes the operation log data and identifies frequently performed operations (frequent operations).

[1055] 3. The server analyzes the emotion data and identifies patterns of emotion change.

[1056] 4. The server automatically generates a workflow based on frequently occurring operations, error patterns, and emotion patterns. This workflow includes each step and its explanation.

[1057] 4. The server documents the business flow and emotional rules and knowledge, and generates a manual.

[1058] 1. The server documents rules and knowledge in text format based on the automatically generated business flow.

[1059] 2. Add emotional caveats to your server documentation.

[1060] 3. The server formats the documented rules and knowledge to create an easy-to-read business manual.

[1061] 5. The user refers to the generated business manual

[1062] 1. The server saves the generated business manual in PDF format and provides it to the user in an accessible format.

[1063] 2. The user clicks on the specified link or URL to download or view the generated business manual.

[1064] Specific examples

[1065] For example, this system comes into play when a new member joins the customer support department. The new member first logs in to the system by entering their username and password. After logging in, as the new member begins to operate the support system, the emotion engine collects emotional data along with the operation log. The server analyzes this data and identifies frequently occurring support operations, error patterns, and emotional patterns. Based on the analysis results, a support process workflow is automatically generated, and an operations manual, including points to note regarding emotions, is generated in PDF format. The new member can download and refer to this manual, allowing them to quickly become familiar with the work. This series of processes significantly reduces the workload when taking over work or when new members join, improving work efficiency.

[1066] In this way, the system of the present invention is a powerful tool for improving the user's work efficiency, and by combining it with an emotion engine, it realizes more human-friendly work support.

[1067] The processing flow will be explained below.

[1068] Step 1:

[1069] A user accesses the system and enters their username and password.

[1070] Step 2:

[1071] The terminal encrypts the entered authentication information and sends it to the server.

[1072] Step 3:

[1073] The server receives the authentication information and checks it against the data in its database.

[1074] If authentication is successful, the server starts a session and returns the user ID, if authentication fails it returns an error message.

[1075] Step 4:

[1076] Users access business applications and begin their daily work.

[1077] Step 5:

[1078] The device records each user operation as log data, such as button clicks and data input.

[1079] Step 6:

[1080] The emotion engine recognizes the user's emotions and collects emotional data through facial recognition and voice analysis.

[1081] Step 7:

[1082] The log data and emotion data collected by the device are sent to the server in real time.

[1083] Step 8:

[1084] The server stores the received log data and emotion data and begins analysis.

[1085] Step 9:

[1086] The server analyzes the log data and identifies frequently executed operations (frequent operations).

[1087] Step 10:

[1088] The server analyzes the emotion data and identifies emotion patterns based on changes in emotion.

[1089] Step 11:

[1090] The server automatically generates a workflow based on frequently occurring operations and emotion patterns. This workflow includes each step and its explanation.

[1091] Step 12:

[1092] The server documents the necessary rules and knowledge in text format based on the automatically generated business flow.

[1093] Step 13:

[1094] The server adds emotional warnings to the documented content to create an easy-to-read business manual.

[1095] Step 14:

[1096] The server saves the generated business manual in PDF format and provides it to the user in an accessible format.

[1097] Step 15:

[1098] The user clicks on the specified link or URL to download or view the generated business manual.

[1099] This series of steps reduces the workload when handing over tasks or when new members join, improving work efficiency. The generated manual also includes points to be aware of from an emotional perspective, reducing user stress and enabling smoother work execution.

[1100] Example 2

[1101] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1102] While conventional business support systems are capable of collecting user operation log data, they lack the ability to recognize and analyze user emotions, limiting their ability to improve business efficiency and optimize user experience. Furthermore, because they are unable to automatically generate emotion-based workflows or create manuals, it is difficult for new members to take over tasks, resulting in a heavy workload.

[1103] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for collecting emotion data, means for analyzing the collected log data and emotion data to identify frequently occurring operations, errors, and changes in emotion, means for automatically generating a workflow based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow, means for formatting the documented rules and knowledge and generating a business manual, and means for saving the generated manual and making it accessible to the user. This enables a comprehensive analysis of the user's operation status and emotional state, enabling efficient and user-friendly business support.

[1104] "Authentication information" refers to information that a user enters to verify their identity when accessing a system, and typically includes a username and password.

[1105] "Operation log data" is recorded data that is generated when a user operates the system, and includes the type, time, and frequency of the operation.

[1106] "Emotion data" refers to data that reflects the user's emotional state, and includes facial expression analysis using face recognition and emotion determination using voice analysis.

[1107] "Analysis" is the process of identifying meanings and patterns based on collected data, and involves the use of statistical analysis methods and machine learning algorithms.

[1108] A "business flow" is a series of steps that show the procedures and steps of a business, and includes operating procedures and accompanying explanations.

[1109] "Rules" describe the norms and standards that must be followed in the performance of business.

[1110] "Knowledge" is a description of the specialized knowledge and know-how required to carry out a business.

[1111] "Documentation" is the process of writing and recording information in text form, formatted for easy reading.

[1112] A "business manual" is a document that compiles business procedures, rules, and knowledge, and provides users with guidelines to refer to when performing their work.

[1113] The system of this invention collects and analyzes user operation log data and emotion data to provide comprehensive business support, and automatically generates business flows and business manuals based on this data. This system is implemented using the following hardware and software.

[1114] Hardware used

[1115] 1. Terminal: A device such as a computer or smartphone that is operated by a user.

[1116] 2. Server: A server for storing data, analyzing, and generating manuals.

[1117] 3. Camera: A device for collecting emotional data through facial recognition of the user.

[1118] 4. Microphone: A device for analyzing the user's voice and collecting emotional data.

[1119] Software used

[1120] 1. Authentication system: Software that manages user authentication information and controls access to a system.

[1121] 2. Log collection software: Software for recording user operation log data.

[1122] 3. Emotion Engine: Software that uses facial recognition and voice analysis to collect user emotional data.

[1123] 4. Data analysis software: Software for analyzing collected data and automatically generating business flows.

[1124] 5. Document generation software: Software that documents business flows, rules, and knowledge and generates business manuals.

[1125] 6. PDF generation software: Software for saving documented content in PDF format.

[1126] A natural language description of what the system does

[1127] This system first receives the authentication information (user name and password) entered by the user, and the server authenticates the user by checking the information against a database. If authentication is successful, the server returns the user ID.

[1128] When a task begins, the device automatically records the user's operations and generates operation log data. This data is sent to the server in real time. At the same time, the emotion engine uses the camera and microphone to collect the user's emotional data. For example, it uses facial recognition and voice analysis to determine whether the user is expressing emotions such as joy, anger, or sadness. The device also sends this emotional data to the server in real time.

[1129] The server stores the received operation log data and emotion data in a database and begins analysis. This analysis identifies frequently occurring operations, error patterns, and patterns of emotional changes. Based on the analysis results, an efficient workflow is automatically generated. This workflow includes specific operation procedures and related explanations.

[1130] The server then documents the rules and knowledge in text format based on the automatically generated workflow. It also adds emotional warnings, such as how to deal with stress caused by a particular operation.

[1131] Finally, the documented information is saved in PDF format and made accessible to users, who can click on the provided link to download or view the operation manual.

[1132] Specific examples

[1133] For example, this system is extremely useful when a new member joins the customer support department. The new member first accesses the system and enters their username and password. The terminal sends the authentication information to the server, which then performs authentication. If authentication is successful, the new member begins work. At that time, the terminal collects operation log data, and the emotion engine collects emotion data. The collected data is sent to the server in real time, where it is analyzed to identify frequently occurring operations, error patterns, and changes in emotion.

[1134] For example, frequently performed operations such as searching for customer information or updating a support ticket are identified. Furthermore, if a user feels stressed while interacting with a customer, the timing is identified. Based on this, a work flow is automatically generated, and a work manual is created that includes notes on emotional responses. New members can download and refer to this manual to quickly become familiar with their work.

[1135] Example prompts for generative AI models

[1136] "Please explain the system that collects and analyzes operation logs and emotional data when users perform their work. Also, please provide details on the contents of the work manual that will be generated."

[1137] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1138] Step 1:

[1139] A user accesses the system and enters a username and password. The entered authentication information (username and password) is input data, which the terminal receives and sends to the server.

[1140] Step 2:

[1141] The server receives the authentication information sent from the terminal and compares it with the database. Previous registration information is stored in the database, and by comparing the authentication information, it is possible to confirm whether the user is a legitimate user. If authenticated, the server returns the user ID and notifies the terminal that authentication was successful. This output includes the authentication result (success / failure) and the user ID.

[1142] Step 3:

[1143] A user starts their work and performs various operations within the system, which generates operation log data. Specific actions include a user searching for customer information or updating a support ticket. Details of each operation are recorded as input data.

[1144] Step 4:

[1145] The terminal collects the generated operation log data and sends it to the server in real time. The input data is the user's operation log, and the log data includes the type of operation, timestamp, and content. The terminal sends this to the server.

[1146] Step 5:

[1147] The emotion engine uses the camera and microphone to collect the user's emotion data. The emotion engine performs facial and voice analysis to identify the user's emotions (e.g., joy, anger, sadness, and happiness). The emotion data is recorded as input data.

[1148] Step 6:

[1149] The device sends the collected emotion data to the server in real time. The input data is emotion data generated by the emotion engine, and this data includes the type, duration, and intensity of the emotion. The device then sends this to the server.

[1150] Step 7:

[1151] The server stores the received operation log data and emotion data in a database. The input data are operation log data and emotion data, which are then stored in a database.

[1152] Step 8:

[1153] The server analyzes the stored operation log data to identify frequently occurring operations and error patterns. Data mining and machine learning algorithms are used for the analysis to identify frequently occurring operations, errors, time periods, etc. The output is a list of frequently occurring operations and error patterns.

[1154] Step 9:

[1155] The server analyzes the stored emotion data and identifies patterns of emotion change. Statistical methods and machine learning algorithms are used for the analysis to identify emotion changes, peaks, and emotional chains. The output is a list of emotion patterns.

[1156] Step 10:

[1157] The server automatically generates a workflow based on frequently occurring operations, error patterns, and emotion patterns. The workflow includes specific operation procedures, error handling methods, and emotional precautions. The output is visualized data such as a workflow diagram.

[1158] Step 11:

[1159] The server documents business procedures, rules, and knowledge in text format based on the automatically generated business flow. Documentation requires specialized knowledge, and details are described, for example, "When performing operation A, perform steps 1-2-3." The output is a draft of the business manual.

[1160] Step 12:

[1161] The server formats the documented content and creates a PDF business manual. Formatting includes layout adjustment and formatting, and the final output is a PDF business manual that can be easily referenced by users.

[1162] Step 13:

[1163] The server saves the generated business manual in storage and makes it accessible to users. Access permissions are set in the storage, and users can access the manual through a specific link or URL. The output is an access link to the manual.

[1164] Step 14:

[1165] The user clicks on the provided link or URL to download or view the generated business manual. The user can refer to this manual to learn more about the business and improve work efficiency. The output is an improvement in the user's business knowledge.

[1166] (Application example 2)

[1167] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1168] In modern work, there is a growing demand for enhanced work support by simultaneously collecting and analyzing user operation log data and emotional data. However, current systems are specialized in collecting and analyzing operation log data, making it difficult to consider the user's emotional state. As a result, users' stress and emotional problems may affect their work efficiency. Furthermore, comprehensive work support has not been achieved because emotional care advice cannot be incorporated into the work flow.

[1169] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for collecting user emotion data, means for analyzing the collected log data and emotion data and identifying frequently occurring operations, errors, and emotion patterns, means for automatically generating a workflow and emotional care advice based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow and emotional care advice, means for formatting the documented rules and knowledge and generating a business manual, and means for saving the generated manual and making it accessible to the user. This enables comprehensive analysis of the user's operation log data and emotion data to enable efficient and user-friendly business support.

[1170] "Authentication information" refers to personal identification information such as a username and password that a user enters when accessing a system.

[1171] "Operation log data" is data that records the history of operations and actions performed by users on the system.

[1172] "Emotion data" is information that reflects the user's emotional state, and is data obtained using technologies such as facial recognition and voice analysis.

[1173] "Frequent operations" is a term that refers to operations or actions that users perform repeatedly within a system.

[1174] "Error" refers to any malfunction or problem that a user may encounter while operating the system.

[1175] An "emotion pattern" refers to a change in the user's emotional state that has a certain tendency or regularity.

[1176] "Business flow" refers to a series of steps and processes for carrying out business, shown in diagrams or text.

[1177] "Emotional care advice" refers to advice or instructions provided based on the user's emotional state while working, with the aim of reducing stress and increasing motivation for the user.

[1178] "Rules and knowledge" refers to the rules for carrying out business and knowledge based on past experience.

[1179] "Documentation" refers to the process of organizing collected information and transcribing it into an easy-to-read format.

[1180] "Formatting" refers to the process of adjusting the form and appearance of a document.

[1181] A "business manual" refers to a document that summarizes procedures, rules, and points to note for carrying out business.

[1182] The system of this invention generates comprehensive workflow and emotional care advice by collecting and analyzing user operation log data and emotional data. This system is implemented based on the following configuration and procedures.

[1183] Program processing

[1184] 1. Login Process

[1185] A user accesses the system and enters authentication information such as a username and password. The terminal sends this authentication information to the server, which then authenticates the user. At this time, a user ID is generated and returned to the user.

[1186] 2. Collecting operation logs and emotion data

[1187] When a user performs a task, the device collects the user's operation log data in real time and sends it to the server. At the same time, the emotion engine collects the user's emotion data using the device's built-in camera and microphone. The emotion data is analyzed using face recognition and voice analysis technology (e.g., OpenCV, Face Recognition library).

[1188] 3. Data Analysis

[1189] The server stores and analyzes the operation log data and emotion data it receives. It identifies frequent operations and error patterns from the operation log data, and identifies patterns of emotional changes from the emotion data. This is done using emotion analysis libraries such as EmotionDetector.

[1190] 4. Generation of workflow and emotional care advice

[1191] The server automatically generates business flows and emotional care advice based on the analysis results, and documents the contents of the automatically generated business flows and emotional care advice using the GuideGenerator library.

[1192] 5. Creating and saving the manual

[1193] The server formats the documented rules and knowledge and generates a work manual. The generated manual and emotional care advice are saved in PDF format and made accessible to users, who can download or view it.

[1194] Specific examples

[1195] For example, imagine a scenario in which a new factory worker is assigned to a production line, puts on smart glasses, and logs in to the system. As the worker begins to work, the system collects and analyzes their operation logs and emotional data in real time. It provides guidance for unfamiliar operations and displays advice on how to relax if the worker feels stressed. Finally, a detailed operation guide and emotional care advice are automatically generated based on the analysis results and provided in PDF format.

[1196] Prompt Sentence Examples

[1197] "A new worker is assigned to a factory line and begins work wearing smart glasses. An application records the worker's actions and analyzes their emotional state in real time. The application displays guidance for unfamiliar operations and provides instructions to relax if the worker feels stressed. Please explain the specific operations and effects of the application."

[1198] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1199] Step 1:

[1200] A user accesses the system and enters authentication information such as a username and password. The terminal sends this authentication information as input data to the server. The server verifies the authentication information, and if authentication is successful, generates and outputs a user ID.

[1201] Step 2:

[1202] When a user performs a task, the terminal records each user operation in real time as operation log data, including the type of operation and a timestamp. The recorded operation log data is then sent from the terminal to the server.

[1203] Step 3:

[1204] The emotion engine collects the user's emotion data using the device's built-in camera and microphone, using face recognition and voice analysis technologies (e.g., OpenCV, Face Recognition library), and transmits the acquired emotion data from the device to the server.

[1205] Step 4:

[1206] The server stores the received operation log data and emotion data as input data and performs analysis. It identifies frequently occurring operations and error patterns from the operation log data, and identifies patterns of emotion change from the emotion data. As a result of the analysis, it outputs information on frequently occurring operations, error patterns, and emotion patterns.

[1207] Step 5:

[1208] The server automatically generates business flows and emotional care advice based on the analysis results. Using the GuideGenerator library, the server generates and documents business flows and emotional care advice using the analyzed data as input. The generated business flows and emotional care advice are then output.

[1209] Step 6:

[1210] The server formats the documented rules and knowledge and generates a business manual, which is saved as a PDF file.

[1211] Step 7:

[1212] The generated manuals and emotional care advice are stored on a server and made available for users to access, download, or view. Users can click on a link or URL to download the file and view the contents.

[1213] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1214] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1215] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1216] [Fourth embodiment]

[1217] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1218] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1219] 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).

[1220] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1221] 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.

[1222] 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).

[1223] 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.

[1224] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1225] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1226] 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.

[1227] 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.

[1228] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1229] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1230] The system of the present invention is an integrated log management, analysis, and manual creation system to support users' business operations. This system involves a series of processes, starting with the user logging in by entering authentication information, collecting logs of the user's business operations in real time, analyzing the data, and finally automatically generating a business manual.

[1231] Overall system configuration

[1232] 1. User logs in

[1233] A user accesses the system and enters their username and password.

[1234] The device sends the authentication information to the server.

[1235] The server verifies the credentials and allows the user to log in. If authentication is successful, the user can proceed to the next step.

[1236] 2. The user performs the task, and the device collects the operation log.

[1237] As users go about their daily work, each action within the system is automatically recorded.

[1238] The terminal collects each operation as log data and sends it to the server in real time.

[1239] 3. The server analyzes the log data to identify frequent operations and errors.

[1240] The server analyzes the log data it receives to identify patterns of frequently occurring operations and common errors.

[1241] This analysis often uses data mining techniques and machine learning algorithms.

[1242] 4. The server automatically generates the business flow

[1243] The server automatically generates a specific business flow based on the analysis results.

[1244] The business flow includes the business steps and points to note at each step.

[1245] 5. The server documents the rules and knowledge and generates a manual

[1246] The server documents the necessary rules and knowledge based on the automatically generated business flow.

[1247] Format documented information and generate operational manuals in PDF format.

[1248] 6. The user refers to the generated business manual

[1249] The server stores the generated PDF manual and provides it in a form that is accessible to users.

[1250] The user downloads or views the generated manual.

[1251] Specific examples

[1252] For example, this system comes into play when a new member joins the sales department. The new member first logs in to the system by entering their username and password. After logging in, as the new member begins to operate the sales system, operation logs are collected in real time. The server analyzes the logs and identifies common sales operations and errors. A sales process workflow is then automatically generated based on the analysis results. Based on this workflow, important rules and past knowledge are documented, and a formatted operations manual is generated in PDF format. The new member can download and refer to this manual, allowing them to quickly become familiar with the work. This series of processes significantly reduces the workload when taking over work or when new members join, improving work efficiency.

[1253] In this way, the system of the present invention functions as a powerful tool for improving the work efficiency of users.

[1254] The processing flow will be explained below.

[1255] Step 1:

[1256] The user enters their username and password on the login screen.

[1257] Step 2:

[1258] The terminal encrypts the entered authentication information and sends it to the server.

[1259] Step 3:

[1260] The server receives the authentication information and checks it against the data in its database.

[1261] If authentication is successful, the server starts a session and returns the user ID, if authentication fails it returns an error message.

[1262] Step 4:

[1263] Users access business applications and begin their daily work.

[1264] Step 5:

[1265] The device records each user operation as log data, such as button clicks and data input.

[1266] Step 6:

[1267] The terminal sends the log data to the server in real time.

[1268] Step 7:

[1269] The server stores the received log data and begins analyzing it.

[1270] Step 8:

[1271] The server analyzes the log data and identifies frequently executed operations (frequent operations).

[1272] Step 9:

[1273] The server analyzes error messages in the log data to identify commonly occurring error patterns.

[1274] Step 10:

[1275] The server automatically generates a workflow based on the identified frequent operations and error patterns. This workflow includes each step and its explanation.

[1276] Step 11:

[1277] Based on the business flow generated by the server, the necessary rules and knowledge are documented in text format.

[1278] Step 12:

[1279] The server formats the documented rules and knowledge to create an easy-to-read business manual.

[1280] Step 13:

[1281] The server saves the generated business manual in PDF format and provides it to the user in an accessible format.

[1282] Step 14:

[1283] The user clicks on the specified link or URL to download or view the generated business manual.

[1284] This series of steps ensures efficient support when taking over business operations or when new members join.

[1285] Example 1

[1286] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1287] In today's business environment, many tasks are performed using digital tools, but the recording and analysis of operations, as well as the creation of business manuals, must often be done manually. This makes it difficult to maintain operational efficiency and consistency, and there is insufficient support for new members to quickly become proficient in their work. Furthermore, frequent operational mistakes and errors are often not addressed properly, risking a decline in the overall quality of work. To solve these problems, there is a need for a system that can automatically collect and analyze operational logs, automatically generate business flows, and quickly provide business manuals.

[1288] 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.

[1289] In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation record data, means for analyzing the collected record data and identifying frequently occurring operations and errors, means for automatically generating a workflow based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow, means for formatting the documented rules and knowledge and generating a workflow manual, and means for saving the generated manual and making it accessible to users. This reduces the frequency of operational mistakes and errors, enables new members to quickly become proficient, and improves the efficiency and quality of the entire business.

[1290] A "user" is a person or organization that inputs authentication information to use the system and performs business operations.

[1291] "Authentication information" refers to information such as a username and password that a user enters to log in to a system.

[1292] "Operation record data" is data that records various operations that users perform within the system.

[1293] "Analysis" refers to processing the collected operation record data and identifying frequently occurring operations and error patterns.

[1294] A "workflow" refers to a set of steps or procedures for carrying out a particular task.

[1295] "Rules" are regulations and guidelines for carrying out business.

[1296] "Knowledge" includes information that is useful in performing work and insight gained from past experience.

[1297] "Documentation" means organizing and storing rules and knowledge in written form.

[1298] "Format" means putting a document into a neat form.

[1299] A "business manual" is a documented guidebook that includes business procedures, rules, and knowledge.

[1300] "Saving" refers to storing and preserving the generated business manual in the server.

[1301] "Accessible" means that the business manual is available for viewing and download by the user.

[1302] The present invention is an integrated log management, analysis, and manual creation system designed to support users' work. This system is implemented using the following hardware and software.

[1303] Hardware and software used

[1304] Server: Various cloud services (e.g., AWS EC2 instances)

[1305] Authentication system: OAuth 2.0

[1306] Data collection tool: Logstash

[1307] Data analysis tool: Apache Spark

[1308] Machine learning algorithm: Scikit-learn

[1309] Manual generation tool: LaTeX

[1310] Detailed operation of the system

[1311] 1. User Authentication

[1312] The user accesses the system's login screen and enters authentication information (user name and password). The device encrypts this authentication information using SSL / TLS and sends it to the server.

[1313] The server verifies the authentication information using OAuth 2.0 and, if successful, grants access to the user.

[1314] 2. Collecting business operation logs

[1315] As users perform business operations within the system, each operation is recorded in real time by the terminal.

[1316] The terminal uses a log collection tool (Logstash) to send the recorded operation log to the server.

[1317] 3. Log data analysis

[1318] The operation log data received by the server is analyzed using Apache Spark. The analysis is performed in batch processing using data stored in HDFS.

[1319] Use machine learning algorithms (Scikit-learn) to identify frequent operations and error patterns.

[1320] 4. Automatic generation of business flows

[1321] The server automatically generates a workflow based on the analysis results, which includes the steps and points to note required for the work.

[1322] The generated business flow is saved in XML or YAML format.

[1323] 5. Documentation and manual generation

[1324] The server documents the necessary rules and knowledge based on the business flow, using LaTeX templates.

[1325] The formatted document is output from LaTeX as a business manual in PDF format.

[1326] 6. Provision and reference of manuals

[1327] The server stores the generated PDF manual in the system and provides it to the user in an accessible format.

[1328] The user downloads or views the generated manual from the "Manuals" section of the system.

[1329] Specific examples

[1330] For example, when a new salesperson logs into the system, a log of their work operations is collected in real time. The server analyzes the log and identifies frequently occurring operations and error patterns. Based on the identified patterns, a work manual for the new salesperson is automatically generated. This manual is provided in PDF format, allowing the new employee to refer to it and quickly become familiar with their work.

[1331] Prompt Sentence Examples

[1332] Analyze user business log data using Apache Spark and identify patterns of frequently occurring operations and errors. Explain how to automatically generate business flows in XML format based on the identified patterns and create business manuals in PDF format using LaTeX.

[1333] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1334] Step 1: User authentication

[1335] Specific description:

[1336] A user accesses the system and enters authentication information (username and password) on the login screen. The device sends this authentication information to the server. The server verifies the authentication information using OAuth 2.0, and if authentication is successful, grants the user access rights.

[1337] Input and Output:

[1338] Input: Username, Password

[1339] Output: Authentication success or failure

[1340] Specific behavior:

[1341] The user opens the system's login page in a browser, enters the username "USER_ID" and password "PASSWORD", and clicks the "Login" button. The device encrypts the authentication information and sends it to the server. The server compares the authentication information with the database, and if authentication is successful, grants the user dashboard access rights.

[1342] Step 2: Collect business operation logs

[1343] Specific description:

[1344] When a user performs a business operation within the system, each operation is recorded in real time. The terminal sends the recorded operation log to the server via a log collection tool (Logstash).

[1345] Input and Output:

[1346] Input: User interaction events (clicks, inputs, window switching, etc.)

[1347] Output: Operation log data

[1348] Specific behavior:

[1349] Every time a user operates a menu in the system, enters a task, or searches, the operation event is recorded in a log file in real time. The terminal uses Logstash to periodically send the recorded log to the server.

[1350] Step 3: Analyze the log data

[1351] Specific description:

[1352] Analyze the log data received by the server. Use Apache Spark to perform batch processing to analyze the data, and use machine learning algorithms (Scikit-learn) to identify frequent operations and error patterns.

[1353] Input and Output:

[1354] Input: Operation log data

[1355] Output: Analysis results (frequent operations, error patterns, etc.)

[1356] Specific behavior:

[1357] The server runs analysis on the log data stored in HDFS using Apache Spark. A machine learning model clusters the data and identifies frequent operations and error patterns. The analysis results are saved in JSON format.

[1358] Step 4: Automatic generation of business flow

[1359] Specific description:

[1360] The server automatically generates a workflow based on the analysis results. This workflow includes specific business steps and points to note. The generated workflow is saved in XML or YAML format.

[1361] Input and Output:

[1362] Input: Analysis results

[1363] Output: Business flow (XML / YAML format)

[1364] Specific behavior:

[1365] The server reads the JSON file containing the analysis results and runs a workflow generation algorithm to systematically summarize the business procedures based on frequently occurring operations and error patterns, and output them in XML or YAML format.

[1366] Step 5: Documentation and manual generation

[1367] Specific description:

[1368] The server documents the necessary rules and knowledge based on the business flow, creates documents in a well-formatted format using LaTeX templates, and generates business manuals in PDF format.

[1369] Input and Output:

[1370] Input: Business flow (XML / YAML format)

[1371] Output: Business manual (PDF format)

[1372] Specific behavior:

[1373] The server parses the business flow XML and reflects the data in the LaTeX template. It then uses the pdflatex command to convert the LaTeX file into PDF format and save it.

[1374] Step 6: Provide and refer to the manual

[1375] Specific description:

[1376] The server saves the generated business manual (PDF format) and provides it in an accessible format to the user. The user downloads or views the manual.

[1377] Input and Output:

[1378] Input: Business Manual (PDF format)

[1379] Output: Download link or view page

[1380] Specific behavior:

[1381] The server saves the generated PDF manual to the file system. The user accesses the "Manuals" section of the system, clicks the "Download" button to download the PDF file, and opens it in a viewer.

[1382] (Application example 1)

[1383] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1384] Modern factories require more efficient machine operation and faster troubleshooting. However, manually analyzing machine operation logs and documenting optimal maintenance procedures and troubleshooting takes a great deal of time and effort. Preparing training materials to quickly familiarize new staff with their work is also a significant burden. Solving these issues and streamlining factory operations is essential.

[1385] 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.

[1386] In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for analyzing the collected log data and identifying frequently occurring operations and errors, means for automatically generating a workflow based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow, means for formatting the documented rules and knowledge and generating a business manual, means for collecting machine operation logs in a factory and automatically generating optimal maintenance procedures and troubleshooting guides based on the analysis results, and means for saving the generated procedures and guides and making them accessible to users, thereby enabling more efficient machine operation and faster troubleshooting.

[1387] A "user" is a person or entity that accesses and operates the system.

[1388] "Authentication information" refers to information such as a user name and password used when a user logs in to a system.

[1389] "Operation log data" refers to a series of behavioral histories and operation information recorded when a user operates the system.

[1390] "Analysis results" refers to information obtained as a result of analyzing collected operation log data using machine learning algorithms, etc.

[1391] A "business flow" refers to a set of steps and procedures required to carry out a specific task.

[1392] "Rules" refer to the regulations and standards that must be followed when carrying out business.

[1393] "Insight" refers to knowledge and wisdom gained from past experiences and data.

[1394] "Documentation" refers to the act of recording and storing information in written form.

[1395] "Format" refers to organizing and structuring documents and data according to a prescribed format.

[1396] A "business manual" is a document that summarizes procedures and precautions for carrying out specific tasks.

[1397] A "machine operation log" is a series of action histories and operation information recorded when a machine in a factory is operated.

[1398] A "maintenance procedure manual" is a document that summarizes the procedures for maintaining and inspecting machinery and equipment.

[1399] A "troubleshooting guide" is a document that provides procedures and measures for resolving machine or system failures and problems.

[1400] The system configuration includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for analyzing the collected log data and identifying frequently occurring operations and errors, means for automatically generating a business flow based on the analysis results, means for documenting rules and knowledge based on the automatically generated business flow, means for formatting the documented rules and knowledge and generating a business manual, means for collecting machine operation logs within a factory and automatically generating optimal maintenance procedures and troubleshooting guides based on the analysis results, and means for saving the generated procedures and guides and making them accessible to users.

[1401] Hardware and Software

[1402] Hardware

[1403] Factory robot: Collects operation logs and executes tasks.

[1404] Computer server: Performs analysis and manual generation of log data.

[1405] software

[1406] Logging library: Uses the Python logging module to collect operation logs in real time.

[1407] Machine learning models: Data mining techniques and machine learning algorithms for log analysis available as Python modules. A concrete example is the hypothetical analyze_logs model.

[1408] PDF generation tool: A tool for formatting and saving documented rules and knowledge in PDF format. An example is the hypothetical generate_manual.

[1409] Processing Description

[1410] Logging in and collecting logs

[1411] The user enters authentication information (username and password) and logs into the system. The terminal sends the authentication information to the server, which then verifies it. If authentication is successful, the user proceeds to the next step. After logging in, when the user begins to operate a robot in the factory, the robot collects log data for each operation and sends it to the server in real time.

[1412] Log analysis

[1413] The server analyzes the collected log data to identify frequently occurring operations and errors. This analysis uses data mining and machine learning algorithms. For example, if a specific error occurs frequently in a product assembly process, analysis is performed to identify the cause and countermeasures.

[1414] Business flow and manual generation

[1415] Based on the analysis results, specific business processes, maintenance procedures, and troubleshooting guides are automatically generated. The documented rules and knowledge are formatted to generate business manuals in PDF format, which are then stored on a server and made accessible to users.

[1416] Specific examples

[1417] For example, this system is useful when new employees are learning how to operate machines in a factory. First, the new employee logs in to the system by entering their username and password. After logging in, collected machine operation logs are analyzed in real time, and an appropriate training manual is automatically generated. By referring to this manual, new employees can quickly become familiar with their work.

[1418] Prompt Sentence Examples

[1419] "Analyze the operation logs of factory robots and identify common problems and their causes."

[1420] As described above, the use of this system makes it possible to improve the efficiency of machine operation and perform quick troubleshooting.

[1421] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1422] Step 1:

[1423] A user accesses the system and enters authentication information (user name and password). The terminal sends this authentication information to the server. The server verifies the received authentication information and confirms that the user is a valid user. If authentication is successful, the user can proceed to the next step. The input is the user name and password, and the output is the authentication success or failure status.

[1424] Step 2:

[1425] When a user starts operating a factory robot, the robot collects operation log data. The terminal sends the collected operation log to a server in real time. The operation log data includes information such as the time, operation details, and error messages. The input is the user's operation, and the output is the operation log data.

[1426] Step 3:

[1427] The server stores the received operation log data in a database and begins analysis. Data mining techniques and machine learning algorithms are used for the analysis. The server identifies patterns of frequently occurring operations and errors. The input is the operation log data, and the output is the analysis results (frequent operations and error patterns).

[1428] Step 4:

[1429] The server automatically generates a specific business flow based on the analysis results. This business flow includes work procedures and points to note. The input is the analysis results, and the output is the automatically generated business flow. Specifically, it includes frequently performed operations and ways to avoid errors.

[1430] Step 5:

[1431] The server documents the necessary rules and knowledge based on the automatically generated business flow. Documentation includes formatting text data and organizing information. The input is the automatically generated business flow, and the output is the documented rules and knowledge.

[1432] Step 6:

[1433] The server generates business manuals, maintenance procedures, and troubleshooting guides based on documented rules and knowledge. This is done using a PDF generation tool. The input is the documented rules and knowledge, and the output is a business manual in PDF format.

[1434] Step 7:

[1435] The generated business manuals and procedures are stored on the server and can be accessed by users. Users can log in to the system and download or view these documents. The input is a business manual in PDF format, and the output is available for users to view or download.

[1436] Step 8:

[1437] For example, if a user operates a factory robot and inputs a prompt such as "Analyze the factory robot's operation log and identify frequent problems and their causes," the system will perform an analysis and generate a document containing improvement suggestions based on the results. The input is the prompt, and the output is a document containing the analysis results and improvement suggestions.

[1438] 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.

[1439] The system of the present invention not only collects and analyzes user operation log data during work, but also combines it with an emotion engine that recognizes user emotions to provide more comprehensive business support. This system begins with the user logging in by entering their authentication information, and includes a series of processes that collect the user's business operations and emotional information in real time, analyzes the data, and automatically generates a business manual that includes the business flow and points to note regarding emotions.

[1440] Overall system configuration

[1441] 1. User logs in

[1442] 1. A user accesses the system and enters their username and password.

[1443] 2. The device sends the authentication information to the server.

[1444] 3. The server verifies the authentication information and allows the user to log in. If authentication is successful, it returns the user ID.

[1445] 2. The user performs the task, and the device collects the operation log and emotion data.

[1446] 1. As users go about their daily work, each operation within the system is automatically recorded.

[1447] 2. The device collects user operation log data and sends it to the server in real time.

[1448] 3. The emotion engine collects the user's emotions (for example, joy, anger, sadness, or happiness through facial recognition or voice analysis).

[1449] 4. The device sends the collected emotion data to the server.

[1450] 3. The server analyzes the operation log data and emotion data.

[1451] 1. The server saves the received operation log data and emotion data and begins analysis.

[1452] 2. The server analyzes the operation log data and identifies frequently performed operations (frequent operations).

[1453] 3. The server analyzes the emotion data and identifies patterns of emotion change.

[1454] 4. The server automatically generates a workflow based on frequently occurring operations, error patterns, and emotion patterns. This workflow includes each step and its explanation.

[1455] 4. The server documents the business flow and emotional rules and knowledge, and generates a manual.

[1456] 1. The server documents rules and knowledge in text format based on the automatically generated business flow.

[1457] 2. Add emotional caveats to your server documentation.

[1458] 3. The server formats the documented rules and knowledge to create an easy-to-read business manual.

[1459] 5. The user refers to the generated business manual

[1460] 1. The server saves the generated business manual in PDF format and provides it to the user in an accessible format.

[1461] 2. The user clicks on the specified link or URL to download or view the generated business manual.

[1462] Specific examples

[1463] For example, this system comes into play when a new member joins the customer support department. The new member first logs in to the system by entering their username and password. After logging in, as the new member begins to operate the support system, the emotion engine collects emotional data along with the operation log. The server analyzes this data and identifies frequently occurring support operations, error patterns, and emotional patterns. Based on the analysis results, a support process workflow is automatically generated, and an operations manual, including points to note regarding emotions, is generated in PDF format. The new member can download and refer to this manual, allowing them to quickly become familiar with the work. This series of processes significantly reduces the workload when taking over work or when new members join, improving work efficiency.

[1464] In this way, the system of the present invention is a powerful tool for improving the user's work efficiency, and by combining it with an emotion engine, it realizes more human-friendly work support.

[1465] The processing flow will be explained below.

[1466] Step 1:

[1467] A user accesses the system and enters their username and password.

[1468] Step 2:

[1469] The terminal encrypts the entered authentication information and sends it to the server.

[1470] Step 3:

[1471] The server receives the authentication information and checks it against the data in its database.

[1472] If authentication is successful, the server starts a session and returns the user ID, if authentication fails it returns an error message.

[1473] Step 4:

[1474] Users access business applications and begin their daily work.

[1475] Step 5:

[1476] The device records each user operation as log data, such as button clicks and data input.

[1477] Step 6:

[1478] The emotion engine recognizes the user's emotions and collects emotional data through facial recognition and voice analysis.

[1479] Step 7:

[1480] The log data and emotion data collected by the device are sent to the server in real time.

[1481] Step 8:

[1482] The server stores the received log data and emotion data and begins analysis.

[1483] Step 9:

[1484] The server analyzes the log data and identifies frequently executed operations (frequent operations).

[1485] Step 10:

[1486] The server analyzes the emotion data and identifies emotion patterns based on changes in emotion.

[1487] Step 11:

[1488] The server automatically generates a workflow based on frequently occurring operations and emotion patterns. This workflow includes each step and its explanation.

[1489] Step 12:

[1490] The server documents the necessary rules and knowledge in text format based on the automatically generated business flow.

[1491] Step 13:

[1492] The server adds emotional warnings to the documented content to create an easy-to-read business manual.

[1493] Step 14:

[1494] The server saves the generated business manual in PDF format and provides it to the user in an accessible format.

[1495] Step 15:

[1496] The user clicks on the specified link or URL to download or view the generated business manual.

[1497] This series of steps reduces the workload when handing over tasks or when new members join, improving work efficiency. The generated manual also includes points to be aware of from an emotional perspective, reducing user stress and enabling smoother work execution.

[1498] Example 2

[1499] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1500] While conventional business support systems are capable of collecting user operation log data, they lack the ability to recognize and analyze user emotions, limiting their ability to improve business efficiency and optimize user experience. Furthermore, because they are unable to automatically generate emotion-based workflows or create manuals, it is difficult for new members to take over tasks, resulting in a heavy workload.

[1501] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for collecting emotion data, means for analyzing the collected log data and emotion data to identify frequently occurring operations, errors, and changes in emotion, means for automatically generating a workflow based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow, means for formatting the documented rules and knowledge and generating a business manual, and means for saving the generated manual and making it accessible to the user. This enables a comprehensive analysis of the user's operation status and emotional state, enabling efficient and user-friendly business support.

[1502] "Authentication information" refers to information that a user enters to verify their identity when accessing a system, and typically includes a username and password.

[1503] "Operation log data" is recorded data that is generated when a user operates the system, and includes the type, time, and frequency of the operation.

[1504] "Emotion data" refers to data that reflects the user's emotional state, and includes facial expression analysis using face recognition and emotion determination using voice analysis.

[1505] "Analysis" is the process of identifying meanings and patterns based on collected data, and involves the use of statistical analysis methods and machine learning algorithms.

[1506] A "business flow" is a series of steps that show the procedures and steps of a business, and includes operating procedures and accompanying explanations.

[1507] "Rules" describe the norms and standards that must be followed in the performance of business.

[1508] "Knowledge" is a description of the specialized knowledge and know-how required to carry out a business.

[1509] "Documentation" is the process of writing and recording information in text form, formatted for easy reading.

[1510] A "business manual" is a document that compiles business procedures, rules, and knowledge, and provides users with guidelines to refer to when performing their work.

[1511] The system of this invention collects and analyzes user operation log data and emotion data to provide comprehensive business support, and automatically generates business flows and business manuals based on this data. This system is implemented using the following hardware and software.

[1512] Hardware used

[1513] 1. Terminal: A device such as a computer or smartphone that is operated by a user.

[1514] 2. Server: A server for storing data, analyzing, and generating manuals.

[1515] 3. Camera: A device for collecting emotional data through facial recognition of the user.

[1516] 4. Microphone: A device for analyzing the user's voice and collecting emotional data.

[1517] Software used

[1518] 1. Authentication system: Software that manages user authentication information and controls access to a system.

[1519] 2. Log collection software: Software for recording user operation log data.

[1520] 3. Emotion Engine: Software that uses facial recognition and voice analysis to collect user emotional data.

[1521] 4. Data analysis software: Software for analyzing collected data and automatically generating business flows.

[1522] 5. Document generation software: Software that documents business flows, rules, and knowledge and generates business manuals.

[1523] 6. PDF generation software: Software for saving documented content in PDF format.

[1524] A natural language description of what the system does

[1525] This system first receives the authentication information (user name and password) entered by the user, and the server authenticates the user by checking the information against a database. If authentication is successful, the server returns the user ID.

[1526] When a task begins, the device automatically records the user's operations and generates operation log data. This data is sent to the server in real time. At the same time, the emotion engine uses the camera and microphone to collect the user's emotional data. For example, it uses facial recognition and voice analysis to determine whether the user is expressing emotions such as joy, anger, or sadness. The device also sends this emotional data to the server in real time.

[1527] The server stores the received operation log data and emotion data in a database and begins analysis. This analysis identifies frequently occurring operations, error patterns, and patterns of emotional changes. Based on the analysis results, an efficient workflow is automatically generated. This workflow includes specific operation procedures and related explanations.

[1528] The server then documents the rules and knowledge in text format based on the automatically generated workflow. It also adds emotional warnings, such as how to deal with stress caused by a particular operation.

[1529] Finally, the documented information is saved in PDF format and made accessible to users, who can click on the provided link to download or view the operation manual.

[1530] Specific examples

[1531] For example, this system is extremely useful when a new member joins the customer support department. The new member first accesses the system and enters their username and password. The terminal sends the authentication information to the server, which then performs authentication. If authentication is successful, the new member begins work. At that time, the terminal collects operation log data, and the emotion engine collects emotion data. The collected data is sent to the server in real time, where it is analyzed to identify frequently occurring operations, error patterns, and changes in emotion.

[1532] For example, frequently performed operations such as searching for customer information or updating a support ticket are identified. Furthermore, if a user feels stressed while interacting with a customer, the timing is identified. Based on this, a work flow is automatically generated, and a work manual is created that includes notes on emotional responses. New members can download and refer to this manual to quickly become familiar with their work.

[1533] Example prompts for generative AI models

[1534] "Please explain the system that collects and analyzes operation logs and emotional data when users perform their work. Also, please provide details on the contents of the work manual that will be generated."

[1535] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1536] Step 1:

[1537] A user accesses the system and enters a username and password. The entered authentication information (username and password) is input data, which the terminal receives and sends to the server.

[1538] Step 2:

[1539] The server receives the authentication information sent from the terminal and compares it with the database. Previous registration information is stored in the database, and by comparing the authentication information, it is possible to confirm whether the user is a legitimate user. If authenticated, the server returns the user ID and notifies the terminal that authentication was successful. This output includes the authentication result (success / failure) and the user ID.

[1540] Step 3:

[1541] A user starts their work and performs various operations within the system, which generates operation log data. Specific actions include a user searching for customer information or updating a support ticket. Details of each operation are recorded as input data.

[1542] Step 4:

[1543] The terminal collects the generated operation log data and sends it to the server in real time. The input data is the user's operation log, and the log data includes the type of operation, timestamp, and content. The terminal sends this to the server.

[1544] Step 5:

[1545] The emotion engine uses the camera and microphone to collect the user's emotion data. The emotion engine performs facial and voice analysis to identify the user's emotions (e.g., joy, anger, sadness, and happiness). The emotion data is recorded as input data.

[1546] Step 6:

[1547] The device sends the collected emotion data to the server in real time. The input data is emotion data generated by the emotion engine, and this data includes the type, duration, and intensity of the emotion. The device then sends this to the server.

[1548] Step 7:

[1549] The server stores the received operation log data and emotion data in a database. The input data are operation log data and emotion data, which are then stored in a database.

[1550] Step 8:

[1551] The server analyzes the stored operation log data to identify frequently occurring operations and error patterns. Data mining and machine learning algorithms are used for the analysis to identify frequently occurring operations, errors, time periods, etc. The output is a list of frequently occurring operations and error patterns.

[1552] Step 9:

[1553] The server analyzes the stored emotion data and identifies patterns of emotion change. Statistical methods and machine learning algorithms are used for the analysis to identify emotion changes, peaks, and emotional chains. The output is a list of emotion patterns.

[1554] Step 10:

[1555] The server automatically generates a workflow based on frequently occurring operations, error patterns, and emotion patterns. The workflow includes specific operation procedures, error handling methods, and emotional precautions. The output is visualized data such as a workflow diagram.

[1556] Step 11:

[1557] The server documents business procedures, rules, and knowledge in text format based on the automatically generated business flow. Documentation requires specialized knowledge, and details are described, for example, "When performing operation A, perform steps 1-2-3." The output is a draft of the business manual.

[1558] Step 12:

[1559] The server formats the documented content and creates a PDF business manual. Formatting includes layout adjustment and formatting, and the final output is a PDF business manual that can be easily referenced by users.

[1560] Step 13:

[1561] The server saves the generated business manual in storage and makes it accessible to users. Access permissions are set in the storage, and users can access the manual through a specific link or URL. The output is an access link to the manual.

[1562] Step 14:

[1563] The user clicks on the provided link or URL to download or view the generated business manual. The user can refer to this manual to learn more about the business and improve work efficiency. The output is an improvement in the user's business knowledge.

[1564] (Application example 2)

[1565] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1566] In modern work, there is a growing demand for enhanced work support by simultaneously collecting and analyzing user operation log data and emotional data. However, current systems are specialized in collecting and analyzing operation log data, making it difficult to consider the user's emotional state. As a result, users' stress and emotional problems may affect their work efficiency. Furthermore, comprehensive work support has not been achieved because emotional care advice cannot be incorporated into the work flow.

[1567] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving authentication information entered by a user, means for collecting user operation log data, means for collecting user emotion data, means for analyzing the collected log data and emotion data and identifying frequently occurring operations, errors, and emotion patterns, means for automatically generating a workflow and emotional care advice based on the analysis results, means for documenting rules and knowledge based on the automatically generated workflow and emotional care advice, means for formatting the documented rules and knowledge and generating a business manual, and means for saving the generated manual and making it accessible to the user. This enables comprehensive analysis of the user's operation log data and emotion data to enable efficient and user-friendly business support.

[1568] "Authentication information" refers to personal identification information such as a username and password that a user enters when accessing a system.

[1569] "Operation log data" is data that records the history of operations and actions performed by users on the system.

[1570] "Emotion data" is information that reflects the user's emotional state, and is data obtained using technologies such as facial recognition and voice analysis.

[1571] "Frequent operations" is a term that refers to operations or actions that users perform repeatedly within a system.

[1572] "Error" refers to any malfunction or problem that a user may encounter while operating the system.

[1573] An "emotion pattern" refers to a change in the user's emotional state that has a certain tendency or regularity.

[1574] "Business flow" refers to a series of steps and processes for carrying out business, shown in diagrams or text.

[1575] "Emotional care advice" refers to advice or instructions provided based on the user's emotional state while working, with the aim of reducing stress and increasing motivation for the user.

[1576] "Rules and knowledge" refers to the rules for carrying out business and knowledge based on past experience.

[1577] "Documentation" refers to the process of organizing collected information and transcribing it into an easy-to-read format.

[1578] "Formatting" refers to the process of adjusting the form and appearance of a document.

[1579] A "business manual" refers to a document that summarizes procedures, rules, and points to note for carrying out business.

[1580] The system of this invention generates comprehensive workflow and emotional care advice by collecting and analyzing user operation log data and emotional data. This system is implemented based on the following configuration and procedures.

[1581] Program processing

[1582] 1. Login Process

[1583] A user accesses the system and enters authentication information such as a username and password. The terminal sends this authentication information to the server, which then authenticates the user. At this time, a user ID is generated and returned to the user.

[1584] 2. Collecting operation logs and emotion data

[1585] When a user performs a task, the device collects the user's operation log data in real time and sends it to the server. At the same time, the emotion engine collects the user's emotion data using the device's built-in camera and microphone. The emotion data is analyzed using face recognition and voice analysis technology (e.g., OpenCV, Face Recognition library).

[1586] 3. Data Analysis

[1587] The server stores and analyzes the operation log data and emotion data it receives. It identifies frequent operations and error patterns from the operation log data, and identifies patterns of emotional changes from the emotion data. This is done using emotion analysis libraries such as EmotionDetector.

[1588] 4. Generation of workflow and emotional care advice

[1589] The server automatically generates business flows and emotional care advice based on the analysis results, and documents the contents of the automatically generated business flows and emotional care advice using the GuideGenerator library.

[1590] 5. Creating and saving the manual

[1591] The server formats the documented rules and knowledge and generates a work manual. The generated manual and emotional care advice are saved in PDF format and made accessible to users, who can download or view it.

[1592] Specific examples

[1593] For example, imagine a scenario in which a new factory worker is assigned to a production line, puts on smart glasses, and logs in to the system. As the worker begins to work, the system collects and analyzes their operation logs and emotional data in real time. It provides guidance for unfamiliar operations and displays advice on how to relax if the worker feels stressed. Finally, a detailed operation guide and emotional care advice are automatically generated based on the analysis results and provided in PDF format.

[1594] Prompt Sentence Examples

[1595] "A new worker is assigned to a factory line and begins work wearing smart glasses. An application records the worker's actions and analyzes their emotional state in real time. The application displays guidance for unfamiliar operations and provides instructions to relax if the worker feels stressed. Please explain the specific operations and effects of the application."

[1596] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1597] Step 1:

[1598] A user accesses the system and enters authentication information such as a username and password. The terminal sends this authentication information as input data to the server. The server verifies the authentication information, and if authentication is successful, generates and outputs a user ID.

[1599] Step 2:

[1600] When a user performs a task, the terminal records each user operation in real time as operation log data, including the type of operation and a timestamp. The recorded operation log data is then sent from the terminal to the server.

[1601] Step 3:

[1602] The emotion engine collects the user's emotion data using the device's built-in camera and microphone, using face recognition and voice analysis technologies (e.g., OpenCV, Face Recognition library), and transmits the acquired emotion data from the device to the server.

[1603] Step 4:

[1604] The server stores the received operation log data and emotion data as input data and performs analysis. It identifies frequently occurring operations and error patterns from the operation log data, and identifies patterns of emotion change from the emotion data. As a result of the analysis, it outputs information on frequently occurring operations, error patterns, and emotion patterns.

[1605] Step 5:

[1606] The server automatically generates business flows and emotional care advice based on the analysis results. Using the GuideGenerator library, the server generates and documents business flows and emotional care advice using the analyzed data as input. The generated business flows and emotional care advice are then output.

[1607] Step 6:

[1608] The server formats the documented rules and knowledge and generates a business manual, which is saved as a PDF file.

[1609] Step 7:

[1610] The generated manuals and emotional care advice are stored on a server and made available for users to access, download, or view. Users can click on a link or URL to download the file and view the contents.

[1611] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1612] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1613] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1614] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1615] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1616] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1617] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1618] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1619] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1620] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1621] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1622] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1623] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1624] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1625] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1626] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1627] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1628] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1629] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1630] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1631] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1632] The following is further disclosed regarding the above embodiment.

[1633] (Claim 1)

[1634] means for receiving authentication information entered by a user;

[1635] A means for collecting user operation log data;

[1636] A means of analyzing the collected log data and identifying frequently occurring operations and errors,

[1637] A means for automatically generating a workflow based on the analysis results;

[1638] A means to document rules and findings based on automatically generated business flows, and

[1639] A means to format documented rules and knowledge and generate business manuals;

[1640] The system includes a means for storing the generated manual and making it accessible to users.

[1641] (Claim 2)

[1642] The system according to claim 1, wherein a business flow including frequently occurring operations and errors is generated based on the analysis results of the collected operation log data.

[1643] (Claim 3)

[1644] 2. The system according to claim 1, further comprising means for enabling a user to download or view the generated business manual.

[1645] "Example 1"

[1646] (Claim 1)

[1647] means for receiving authentication information entered by a user;

[1648] A means for collecting user operation record data;

[1649] A method for analyzing the collected record data and identifying frequently occurring operations and errors;

[1650] A means for automatically generating a workflow based on the analysis results;

[1651] A means to document rules and knowledge based on automatically generated workflows,

[1652] A means to format documented rules and knowledge and generate business guides;

[1653] The system includes a means for storing the generated guide and making it accessible to users.

[1654] (Claim 2)

[1655] The system according to claim 1, which generates a business flow including frequently occurring operations and errors based on the analysis results of the collected operation record data.

[1656] (Claim 3)

[1657] 10. The system of claim 1, further comprising means for enabling a user to download or view the generated business manual.

[1658] "Application Example 1"

[1659] (Claim 1)

[1660] means for receiving authentication information entered by a user;

[1661] A means for collecting user operation log data;

[1662] A means of analyzing the collected log data and identifying frequently occurring operations and errors,

[1663] A means for automatically generating a workflow based on the analysis results;

[1664] A means to document rules and findings based on automatically generated business flows, and

[1665] A means to format documented rules and knowledge and generate business manuals;

[1666] A means to collect machine operation logs within a factory and automatically generate optimal maintenance procedures and troubleshooting guides based on the analysis results;

[1667] A system that includes a means for storing generated procedures and guides and making them accessible to users.

[1668] (Claim 2)

[1669] 2. The system according to claim 1, wherein an optimal business flow is generated based on the analysis results of the collected operation log data.

[1670] (Claim 3)

[1671] 2. The system according to claim 1, further comprising means for enabling a user to download or view the generated operation manual and maintenance procedure manual.

[1672] "Example 2: Combining Emotion Engines"

[1673] (Claim 1)

[1674] means for receiving authentication information entered by a user;

[1675] A means for collecting user operation log data;

[1676] a means for collecting emotion data;

[1677] A method for analyzing collected log data and emotion data to identify frequently occurring operations, errors, and changes in emotion;

[1678] A means for automatically generating a workflow based on the analysis results;

[1679] A means to document rules and findings based on automatically generated business flows, and

[1680] A means to format documented rules and knowledge and generate business manuals;

[1681] The system includes a means for storing the generated manual and making it accessible to users.

[1682] (Claim 2)

[1683] The system according to claim 1, which generates a business flow including frequently occurring operations, errors, and changes in emotions based on the analysis results of the collected operation log data and emotion data.

[1684] (Claim 3)

[1685] 2. The system according to claim 1, further comprising means for enabling a user to download or view the generated business manual.

[1686] "Application example 2 when combining emotion engines"

[1687] (Claim 1)

[1688] means for receiving authentication information entered by a user;

[1689] A means for collecting user operation log data;

[1690] means for collecting user emotion data;

[1691] A means for analyzing the collected log data and emotion data to identify frequently occurring operations, errors, and emotion patterns;

[1692] A means for automatically generating work flows and emotional care advice based on the analysis results;

[1693] A means to document rules and findings based on automatically generated workflows and emotional care advice;

[1694] A means to format documented rules and knowledge and generate business manuals;

[1695] The system includes a means for storing the generated manual and making it accessible to users.

[1696] (Claim 2)

[1697] The system according to claim 1, wherein a business flow including emotional care advice is generated based on the analysis results of the collected operation log data and emotional data.

[1698] (Claim 3)

[1699] 10. The system of claim 1, further comprising means for enabling a user to download or view the generated work manual and emotional care advice. [Explanation of symbols]

[1700] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving authentication information entered by a user; A means for collecting user operation log data; A means of analyzing the collected log data and identifying frequently occurring operations and errors, A means for automatically generating a workflow based on the analysis results; A means to document rules and findings based on automatically generated business flows, and A means to format documented rules and knowledge and generate business manuals; The system includes a means for storing the generated manual and making it accessible to users.

2. 2. The system according to claim 1, wherein a business flow including frequently occurring operations and errors is generated based on the analysis results of the collected operation log data.

3. 2. The system according to claim 1, further comprising means for enabling a user to download or view the generated business manual.

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