System

Abstract

A system is provided.SOLUTION: This system includes a means for collecting the operation log of an employee, a means for analyzing the collected log and understanding a task process, a means for specifying a task which can be automated based on an analysis result, a means for generating and mounting the AI program of the specified task, and a means for providing the support of AI introduction.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] Increasing the efficiency of business processes is important for modern companies, but Japanese companies in particular are lagging behind other countries in adopting AI technology. This is due to a lack of clarity about the acceptance of new technologies, specific implementation plans, and a lack of a clear vision for how AI will be used in business processes. As a result, many companies are unable to reap the benefits of business automation. Therefore, there is a growing need for systems that can automatically understand business processes and automate them using AI technology. [Means for solving the problem]

[0005] The present invention provides a system that collects and analyzes employee operation logs and communication logs to automatically understand business processes. Specifically, the system includes the following means.

[0006] 1. A means of collecting employee operation logs.

[0007] 2. A means of analyzing collected log data and understanding business processes.

[0008] 3. A means of identifying tasks that can be automated using AI based on analysis results.

[0009] 4. A means of creating and implementing automation programs for the identified tasks.

[0010] 5. Means of providing support for AI adoption.

[0011] Through these measures, we aim to support companies in streamlining their business processes and smoothly introducing AI technology.

[0012] "Employee operation logs" are specific operation data such as keyboard operations, mouse movements, application usage, and file operations performed by employees on their PCs.

[0013] "Collected log data" refers to a series of data recorded on the system, including employee operation logs and communication logs.

[0014] A "business process" is a series of tasks and procedures that are carried out daily within a company, and refers to the steps and activities required to progress a business.

[0015] "Analysis" refers to the process of processing collected log data and using pattern recognition and machine learning to understand the meaning of the data and business processes.

[0016] "AI-enabled tasks" refer to tasks and procedures within a specific business process that can be automated using AI technology.

[0017] An "automation program" is software developed to automatically execute specific business processes using AI technology.

[0018] "Implementation" refers to incorporating the generated automation program into an actual business environment and running it.

[0019] "AI implementation support" refers to services such as consulting, technical assistance, and training provided to help companies smoothly incorporate AI technology into their operations. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] This invention relates to a system that automatically understands business processes within a company and uses generative AI to perform the work on behalf of the company. This system collects and analyzes employee operation logs and communication logs, identifies areas of the work that can be automated using AI, and generates and implements specific automation programs. Specific embodiments are described below.

[0042] Data Collection Phase

[0043] To collect employee operation logs and communication logs, the system operates as follows:

[0044] The server installs special monitoring software on employees' PCs and records logs of keyboard operations, mouse movements, application usage, file operations, and more.

[0045] The terminal transmits the user's operation log to the server in real time.

[0046] The user simply performs normal operations and no special operations are required.

[0047] The server also collects communication logs such as emails, chat tools, and video conferences.

[0048] Data analysis phase

[0049] The collected log data is used to perform analysis to automatically understand business processes.

[0050] The server preprocesses the collected log data and converts it into a format suitable for analysis.

[0051] It uses AI models to analyze data for patterns and frequencies to understand the full picture of business processes, identifying which tasks are routine and which processes have bottlenecks.

[0052] Identifying tasks that can be automated using AI

[0053] Based on the analysis results, we will identify which tasks can be automated using AI.

[0054] The server lists specific tasks (such as data entry, report writing, and automatic email replies) based on the analysis results.

[0055] The AI ​​model evaluates the effectiveness of using AI to perform these tasks.

[0056] Program Development and Implementation Phase

[0057] For tasks identified as amenable to AI, specific automation programs are generated and implemented.

[0058] The server designs and generates an automation program for the specified task.

[0059] Using AI models, technologies such as natural language processing and pattern recognition are incorporated to specifically code the program.

[0060] The generated program is applied to the terminal, automating business processes, such as budget management in the accounting department.

[0061] Implementation support phase

[0062] We provide consulting and technical assistance to support the smooth introduction of AI technology.

[0063] The server provides consulting on the introduction and use of AI technology.

[0064] With support, users can advance the implementation of AI technology and achieve improved business efficiency.

[0065] Specific examples

[0066] For example, the automation of budget management tasks works as follows:

[0067] 1. The server collects data that employees enter into the budget management system and communication logs with each department.

[0068] 2. The AI ​​model analyzes the data and understands the budget management process.

[0069] 3. The server identifies tasks such as inputting, correcting, and confirming budgets as routine tasks that can be automated using AI.

[0070] 4. The server generates a program that automates budget management tasks and applies it to the terminal.

[0071] 5. The user reviews the automated budget control operations and adjusts the output accordingly.

[0072] This will enable companies to streamline their business processes and smoothly introduce AI technology.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The server installs monitoring software on employees' PCs and records operation logs such as keyboard operations, mouse movements, application usage, and file operations.

[0076] Step 2:

[0077] The terminal transmits the user's operation log to the server in real time, which includes organizing the data according to the log format and uploading it to the server.

[0078] Step 3:

[0079] The server preprocesses the collected log data and converts it into a format suitable for analysis, which also includes filtering unnecessary data and filling in missing data.

[0080] Step 4:

[0081] The server inputs the processed log data into an AI model to analyze the overall business process. The AI ​​model uses machine learning algorithms to analyze operation patterns and frequencies and identify business flows.

[0082] Step 5:

[0083] The server extracts tasks that can be automated using AI based on the analysis results. Highly repetitive tasks and routine work are identified and listed as candidates for AI automation.

[0084] Step 6:

[0085] The server designs an automation program for the extracted tasks, which includes creating a flow diagram for each specific task and selecting the necessary algorithms.

[0086] Step 7:

[0087] The server then proceeds with coding and testing based on the designed program. The generated program is also tested in a virtual environment to ensure its compatibility with current operations.

[0088] Step 8:

[0089] The terminal applies the tested automation program to the employee's PC and runs it in the actual work, thereby realizing the automation of work.

[0090] Step 9:

[0091] The server monitors the operation of the automation program to check for any malfunctions, monitors the operation status in real time, and corrects the program as necessary.

[0092] Step 10:

[0093] The server will provide consulting on the introduction and use of AI technology to help users maximize the benefits of AI, as well as provide training programs and answer any questions.

[0094] This series of steps will enable companies to streamline their business processes and smoothly implement AI technology.

[0095] Example 1

[0096] 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."

[0097] Conventional business process automation systems did not adequately collect and analyze a wide range of employee operation logs and communication logs, making it difficult to accurately identify areas of business that could be automated using AI. Furthermore, no effective means were provided for creating and implementing automation programs for identified business processes. As a result, business efficiency did not improve and the benefits of introducing AI technology were limited.

[0098] 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.

[0099] In this invention, the server includes means for collecting employee operation logs, means for preprocessing the collected log data and converting it into a format suitable for analysis, means for analyzing the preprocessed data using a generative model to understand business processes, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing an automation program for the identified tasks, means for applying the automation program on a terminal to automate the business process, and means for providing support for introducing AI, thereby enabling the efficiency of business processes and the smooth introduction of AI technology.

[0100] "Means for collecting employee operation logs" refers to devices or software that monitor keyboard operations, mouse movements, application usage, file operations, etc. on computers used by employees and record this data.

[0101] "Means for preprocessing collected log data and converting it into a format suitable for analysis" refers to a method or device that preprocesses employee operation log and communication log data, such as by cleansing and formatting it, and converts it into structured data suitable for analysis.

[0102] "Means for analyzing preprocessed data using generative models to understand business processes" refers to a method or device that uses machine learning or statistical analysis models to analyze preprocessed data, identify business patterns and frequencies, and understand the overall flow of a process.

[0103] "Means for identifying tasks that can be automated using AI based on analysis results" refers to a method or device that, based on analysis results, extracts and lists routine tasks and tasks that can be automated to improve performance.

[0104] "Means for generating and implementing an automation program for a specified task" refers to a method or device for designing a program to automate a specified task, generating specific code, and applying the program to a system.

[0105] "Means for applying an automation program on a terminal to automate a business process" refers to a method or device that installs and runs a generated automation program on an employee's terminal to automatically perform specified business tasks.

[0106] "Means for providing support for AI implementation" refers to methods or devices that provide consulting, training, and technical support on how to implement and use automation programs, and help users effectively utilize AI technology.

[0107] MODE FOR CARRYING OUT THE INVENTION

[0108] This invention relates to a system that automatically understands business processes within a company and uses generative AI to perform the work. This system collects and analyzes employee operation logs and communication logs, identifies areas of the work that can be automated using AI, and generates and implements specific automation programs. Specific embodiments of this system are described below.

[0109] Data Collection Phase

[0110] The server installs dedicated monitoring software on employees' PCs, such as "Activity Monitor." The monitoring software records logs of keyboard operations, mouse movements, application usage, file operations, and so on.

[0111] The terminal sends the user's operation log to the server in real time. This transmission uses UDP or TCP / IP protocols. When the log reaches a certain size, it is stored in a transmission buffer and transferred to the server.

[0112] Users can simply carry out their normal work without any special operations, such as entering data in Excel, sending emails, and communicating via chat.

[0113] The server also collects communication logs from emails, chat tools (such as Slack and Microsoft Teams), and video conferencing (such as Zoom). This collection is done using API integration and Webhooks.

[0114] Data analysis phase

[0115] The server preprocesses the collected log data and converts it into a format suitable for analysis. For example, it extracts unstructured data using regular expressions and converts it into CSV or JSON format. This process removes noise from the data and retains only the necessary information.

[0116] AI models (such as GPT-4 or TensorFlow) analyze the pre-processed data and analyze the data for patterns and frequencies to understand how business processes are progressing, for example, determining whether a particular task is repeated multiple times.

[0117] Identifying tasks that can be automated using AI

[0118] Based on the AI ​​model's analysis, the server lists routine tasks and bottlenecks, such as data entry and daily report generation.

[0119] The AI ​​model evaluates the effectiveness of using AI for the listed tasks, calculating, for example, how much efficiency will be improved and how much work time will be reduced.

[0120] Program Development and Implementation Phase

[0121] The server generates an automation program based on the evaluation results. Specifically, the program is coded using Python or JavaScript, incorporating natural language processing and pattern recognition to create a program that automates business processes.

[0122] The terminal applies and executes the generated automation program, for example, using the Selenium library to automate browser operations and input or modify budget data.

[0123] Implementation support phase

[0124] The server will provide consulting on how to implement and use AI technology, including creating training video tutorials and FAQs. The server will also respond to user questions and resolve technical issues.

[0125] Users can receive support from the server and smoothly carry out tasks that utilize AI technology, such as checking the operation of the automated programs they have implemented and making adjustments as necessary.

[0126] Specific examples

[0127] For example, the automation of budget management tasks works as follows:

[0128] 1. The server collects data entered into the budget management system and communication logs from each department.

[0129] 2. An AI model (such as BERT) analyzes the collected data and understands the flow of budget requests, approval processes, and coordination work from each department.

[0130] 3. The server identifies tasks such as entering, correcting, and approving budgets as routine tasks that can be automated using AI.

[0131] 4. The server generates a program to automate budget management tasks and applies the script written in Python to the terminal. For example, it uses the Selenium library to automate browser operations and input and modify budget data.

[0132] 5. A user runs the automated budget control process and checks the results, for example, by reviewing the generated budget report and making manual corrections as needed.

[0133] Prompt Sentence Examples

[0134] "Please create a Python script to automate the following business process. The task involves adjusting budgets between departments. Specifically, the task involves forwarding the budget submitted by department A to department B and providing feedback on the evaluation results."

[0135] This will enable companies to improve the efficiency of their business processes and smoothly implement AI technology.

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

[0137] Step 1:

[0138] The server installs monitoring software on employees' PCs, such as "Activity Monitor." After installation, the software records logs of keyboard operations, mouse movements, application usage, file operations, and more.

[0139] Input: Employee's PC, monitoring software

[0140] Output: Installed monitoring software

[0141] Step 2:

[0142] The terminal sends the user's operation log to the server in real time. Data is transferred using UDP or TCP / IP protocols. When the log reaches a certain size, it is stored in a sending buffer and then transferred to the server.

[0143] Input: User operation log

[0144] Output: Operation log sent to the server

[0145] Step 3:

[0146] Users can carry out their daily work as usual. For example, they can enter data in Excel, send and receive emails, and communicate using chat. No special operations are required.

[0147] Input: User's daily routine

[0148] Output: Generated business log

[0149] Step 4:

[0150] The server also collects communication logs from users' email and chat tools, such as Slack and Microsoft Teams, and the video conferencing tool Zoom. This data is collected using API integration and Webhooks.

[0151] Input: emails, chats, video conference logs

[0152] Output: Collected communication logs

[0153] Step 5:

[0154] The server preprocesses the collected log data and converts it into a format suitable for analysis. It extracts unstructured data using regular expressions and converts it into CSV or JSON format. This process removes noise from the data and retains only the necessary information.

[0155] Input: Raw Data Log

[0156] Output: Preprocessed data

[0157] Step 6:

[0158] AI models (such as GPT-4 or TensorFlow) analyze the pre-processed data and analyze it for patterns and frequencies, for example, to identify routine or repeated tasks.

[0159] Input: Preprocessed data

[0160] Output: Identification of work patterns and tasks

[0161] Step 7:

[0162] Based on the analysis results of the AI ​​model, the server lists routine tasks and bottleneck tasks, which clearly shows which tasks can be automated using AI.

[0163] Input: Analysis results of the AI ​​model

[0164] Output: List of tasks that can be automated using AI

[0165] Step 8:

[0166] The AI ​​model evaluates the effect of implementing AI on the listed tasks, calculating, for example, how much the efficiency of the task will improve and how much the work time will be reduced.

[0167] Input: List of tasks that can be automated using AI

[0168] Output: Evaluation of the effectiveness of AI

[0169] Step 9:

[0170] The server then generates automation programs for the identified tasks based on the evaluation results. The automation programs are coded using Python or JavaScript and incorporate natural language processing and pattern recognition.

[0171] Input:Effectiveness evaluation

[0172] Output: Automation program code

[0173] Step 10:

[0174] The terminal applies and executes the generated automation program, for example, using the Selenium library to automate browser operations and input or modify budget data.

[0175] Input: Automation program code

[0176] Output: Automation executed on the terminal

[0177] Step 11:

[0178] Sarver will provide consulting on the implementation and use of AI technology, including creating training video tutorials and FAQs.

[0179] Input: automation programs, user feedback

[0180] Output: Consulting materials, support

[0181] Step 12:

[0182] Users can receive support from the server and smoothly carry out tasks that utilize AI technology, such as checking the operation of the automated programs they have implemented and making adjustments as necessary.

[0183] Input: Consulting materials, support

[0184] Output: Streamlined business processes

[0185] This will enable companies to achieve greater efficiency in their business processes and smoothly implement AI technology.

[0186] (Application example 1)

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

[0188] There is a need to solve problems caused by reduced work efficiency and human error within logistics centers. It is also important to reduce the burden on employees performing the work and improve the accuracy and efficiency of picking work. However, conventional systems cannot fully address these issues, and it requires a lot of time and resources to understand and automate efficient business processes.

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

[0190] In this invention, the server includes means for collecting employee operation logs, means for analyzing the collected log data and understanding business processes, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing automation programs for the identified tasks, means for managing terminals that give instructions for work within the logistics center and collect operation logs, and means for controlling product picking work by logistics robots, thereby enabling efficient and accurate picking work within the logistics center.

[0191] An "employee operation log" is a record of keyboard operations, mouse movements, application use, file operations, etc. performed by employees when performing their work.

[0192] "Collected log data" is a general term for data recorded in the course of business operations, such as employee operation logs and communication logs.

[0193] "Means for understanding business processes" refers to methods and technologies for analyzing collected log data and understanding the flow and characteristics of business processes.

[0194] "AI-enabled tasks" refers to business processes and tasks that can be automated using artificial intelligence technology.

[0195] "Automation Program" means software designed and implemented to automate a specified task.

[0196] "AI implementation support" means providing assistance and consulting related to the implementation and operation of AI technology.

[0197] "Terminal" refers to a device used within a logistics center to issue work instructions and collect operation logs.

[0198] A "logistics center" refers to a facility where goods are stored, managed, and prepared for shipment.

[0199] A "robot" is a mechanical device that automatically performs a set task.

[0200] "Picking work" refers to the work of picking out specified products at a logistics center.

[0201] A "server" refers to a computer system that centrally manages and analyzes data.

[0202] The present invention relates to a system that automatically understands business processes in a logistics center and performs the business processes by utilizing a generation AI. Specific embodiments will be described below.

[0203] Data Collection Phase

[0204] The server installs dedicated monitoring software on terminals in the logistics center and records logs of the picking work performed by workers (users). The terminals track the movements of the workers and send operation logs to the server in real time. The users simply carry out their normal work tasks and no special operations are required. The server also collects control logs for logistics robots. This includes receiving picking lists and the product picking process.

[0205] Data analysis phase

[0206] The server uses the collected log data to perform analysis to automatically understand business processes. Specifically, the server preprocesses the collected log data and converts it into a format suitable for analysis. A generative AI model is used to analyze the patterns and frequency of the data to understand the overall picture of business processes. This allows it to identify which tasks are routine and which processes have bottlenecks.

[0207] Identifying tasks that can be automated using AI

[0208] Based on the analysis results, the server identifies which tasks can be automated using AI. The server then lists specific tasks (such as product picking and shipping preparation) based on the analysis results, and uses a generative AI model to evaluate the effectiveness of implementing these tasks using AI.

[0209] Program Development and Implementation Phase

[0210] The server designs and generates automation programs for the identified tasks. These programs incorporate technologies such as natural language processing and pattern recognition and are specifically coded. The generated programs are applied to terminals and logistics robots, realizing the automation of business processes. For example, picking work within a logistics center can be automated and performed accurately and efficiently.

[0211] Implementation support phase

[0212] The server provides consulting and technical support to support the smooth introduction of AI technology. With this support, users can advance the introduction of AI technology and achieve operational efficiency improvements. As a concrete example, when introducing the new smartphone app "Picking Assist" in a logistics center, the following prompt sentence can be entered:

[0213] Example prompt sentence:

[0214] "Get the picking list, track employee movements and send it to the server"

[0215] This will enable efficient and accurate picking operations within the distribution center, and the system will contribute greatly to the automation and efficiency of operations at distribution centers.

[0216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0217] Step 1:

[0218] The server installs dedicated monitoring software on terminals at the distribution center. The terminals collect user operation logs (e.g., keyboard operations, mouse movements) and send the data to the server in real time. The input is the user's operations, and the output is the collected operation log data.

[0219] Step 2:

[0220] The server preprocesses the collected log data and converts it into a format suitable for analysis. At this stage, the log data is sorted in chronological order and unnecessary data is filtered out. The input is operation log data, and the output is the preprocessed log data.

[0221] Step 3:

[0222] The server uses the preprocessed log data to analyze data patterns and frequencies using a generative AI model. The AI ​​model (e.g., TensorFlow) learns to understand the characteristics of the business process. The input is the preprocessed log data, and the output is the analysis results.

[0223] Step 4:

[0224] Based on the analysis results, the server identifies tasks that can be automated using AI. During this process, the server identifies and lists routine tasks and bottlenecks. The input is the analysis results, and the output is a list of tasks that can be automated using AI.

[0225] Step 5:

[0226] The server generates an automation program for the identified tasks. It incorporates natural language processing and pattern recognition technology and codes the program based on the flow of the target tasks. The input is a list of tasks that can be automated by AI, and the output is the automation program.

[0227] Step 6:

[0228] The server applies the generated program to the terminal and the logistics robot to execute the automation. For example, the logistics robot starts picking products. The input is the automation program, and the output is the automation task being executed.

[0229] Step 7:

[0230] The server monitors the operation of the automation program, detects malfunctions, and automatically corrects them. Log data is analyzed in real time, and when anomalies are detected, the server executes a correction algorithm. The input is the log data from the running automation task, and the output is the corrected operation log.

[0231] Step 8:

[0232] Users receive support in introducing AI to improve operational efficiency within their logistics centers. The server provides consulting information and technical materials for support, helping users to effectively use the new system. The input is the business information that requires support, and the output is specific guidelines and advice for support.

[0233] 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.

[0234] This invention relates to a system that automatically understands business processes within a company and, by combining it with an emotion engine that recognizes user emotions, utilizes generative AI to perform business processes on behalf of the company. This system collects and analyzes employee operation logs and communication logs, identifies areas of business that can be automated using AI, and generates and implements specific automation programs. It also uses user emotion data to propose improvements to business processes.

[0235] Data Collection Phase

[0236] To collect employee operation logs and communication logs, the system operates as follows:

[0237] The server installs special monitoring software on employees' PCs and records logs of keyboard operations, mouse movements, application usage, file operations, and more.

[0238] The device sends user operation logs to the server in real time, and also collects communication logs such as emails, chats, and video conferences.

[0239] Users only need to carry out their normal work without any special operations.

[0240] Data analysis phase

[0241] The collected log data is used to perform analysis to automatically understand business processes.

[0242] The server preprocesses the collected log data and converts it into a format suitable for analysis, filtering out unnecessary data and filling in missing data.

[0243] AI models analyze data for patterns and frequencies to understand the overall business process, identifying which tasks are routine and which processes have bottlenecks.

[0244] Identifying tasks that can be automated using AI

[0245] Based on the analysis results, we will identify which tasks can be automated using AI.

[0246] Based on the analysis results, the server extracts tasks that can be automated using AI (for example, data entry, report creation, automatic email replies, etc.).

[0247] The AI ​​model evaluates the effectiveness of using AI to perform these tasks.

[0248] Program Development and Implementation Phase

[0249] For tasks identified as amenable to AI, specific automation programs are generated and implemented.

[0250] The server designs and generates automation programs for specified tasks, incorporating technologies such as natural language processing and pattern recognition.

[0251] The terminal then applies the generated program to the user's PC and runs it in the actual work, thereby realizing the automation of the work.

[0252] Introducing the Emotion Engine

[0253] In order to recognize the user's emotions and reflect them in the business process, the following processing is performed.

[0254] The server analyzes the user's operation log and communication log and uses an emotion engine to recognize the user's emotions.

[0255] The AI ​​model will then propose improvements to business processes based on the analysis results of the emotion engine. For example, if a user is feeling stressed, the model will suggest ways to reduce the work that is causing the stress.

[0256] The server uses the emotion data to evaluate the performance of the automated program and adjust it as needed.

[0257] Implementation support phase

[0258] We provide consulting and technical assistance to support the smooth introduction of AI technology.

[0259] The server provides consulting on the introduction and use of AI technology.

[0260] With support, users can advance the implementation of AI technology and achieve improved business efficiency.

[0261] Specific examples

[0262] For example, the automation of budget management tasks works as follows:

[0263] 1. The server collects data that employees enter into the budget management system and communication logs with each department.

[0264] 2. The AI ​​model analyzes the data and understands the budget management process.

[0265] 3. The server identifies tasks such as inputting, correcting, and confirming budgets as routine tasks that can be automated using AI.

[0266] 4. The server generates a program that automates budget management tasks and applies it to the terminal.

[0267] 5. The user reviews the automated budget control operations and adjusts the output accordingly.

[0268] 6. The server analyzes the user's emotional data and, if stress increases, makes suggestions for improvements to reduce work burden.

[0269] In this way, companies can not only streamline their business processes, but also realize a way of working that takes users' emotions into consideration.

[0270] The processing flow will be explained below.

[0271] Step 1:

[0272] The server installs monitoring software on employees' PCs and records operation logs such as keyboard operations, mouse movements, application usage, and file operations.

[0273] Step 2:

[0274] The terminal sends the user's operation log and communication log (email, chat, video conference, etc.) to the server in real time.

[0275] Step 3:

[0276] The server preprocesses the collected log data and converts it into a format suitable for analysis, specifically by cleansing the data, filtering out unnecessary data, and filling in missing data.

[0277] Step 4:

[0278] The server inputs the processed log data into an AI model to analyze the overall business process. The AI ​​model uses machine learning algorithms to analyze operation patterns and frequencies and identify business flows.

[0279] Step 5:

[0280] The server extracts tasks that can be automated using AI based on the analysis results. Highly repetitive tasks and routine work are identified and listed as candidates for AI automation.

[0281] Step 6:

[0282] The server designs an automation program for the extracted tasks, which includes creating a flow diagram for each specific task and selecting the necessary algorithms.

[0283] Step 7:

[0284] The server then proceeds with coding and testing based on the designed program. The generated program is then tested in a virtual environment to verify its compatibility with current operations.

[0285] Step 8:

[0286] The terminal applies the tested automation program to the employee's PC and runs it in the actual work, thereby realizing the automation of work.

[0287] Step 9:

[0288] The server analyzes the user's operation log and communication log and uses an emotion engine to recognize the user's emotions. The emotion data is analyzed to extract stress and satisfaction during operation.

[0289] Step 10:

[0290] The AI ​​model will then propose improvements to business processes based on the analysis results of the emotion engine. For example, if a user is feeling stressed, it will identify the tasks that are causing the stress and make suggestions to reduce the burden.

[0291] Step 11:

[0292] The server uses the emotional data to evaluate the performance of the automated program and adjusts it as needed, so that the program's accuracy improves depending on the user's emotional state.

[0293] Step 12:

[0294] The server provides consulting on the introduction and use of AI technology, and users can receive support to advance the introduction of AI technology and achieve business efficiency.

[0295] This series of steps will enable companies to streamline their business processes and smoothly implement AI technology. Furthermore, analyzing user emotion data will enable companies to provide a more comfortable working environment.

[0296] Example 2

[0297] 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."

[0298] Conventional business process automation systems optimize operations by collecting and analyzing employee operation logs, but lack the functionality to improve operations by reflecting user sentiment. Furthermore, support for implementing AI is insufficient, preventing the full benefits of business automation from being realized. Furthermore, there are inadequate methods for detecting and correcting defects in automation programs, which can lead to numerous problems in actual operation.

[0299] 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 collecting employee operation logs, means for analyzing the collected log data and understanding the business process, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing an automation program for the identified tasks, means for improving the business process using an emotion engine for recognizing user emotions, and means for providing support for introducing AI. This not only enables the efficiency of business processes to be improved, but also enables a work style that takes user emotions into consideration and enables the rapid detection and correction of defects in automation programs.

[0300] An "employee operation log" is a series of data such as keyboard operations, mouse movements, application usage, and file operations that occur when an employee performs their work.

[0301] "Log data analysis" refers to understanding business processes by preprocessing collected operation logs and analyzing patterns and frequencies.

[0302] "Tasks that can be automated using AI" are tasks that can be automated based on the results of analysis, such as routine tasks such as data entry, report creation, and automatic email replies.

[0303] An "automation program" is a program designed and generated to efficiently carry out a specific task, incorporating technologies such as natural language processing and pattern recognition.

[0304] An "emotion engine" is an algorithm or software that analyzes a user's operation log and communication log to recognize the user's emotions.

[0305] "Improvement of business processes" means proposing and implementing work environments and workflows to reduce stress based on user emotional data recognized by the emotion engine.

[0306] "AI implementation support" refers to activities that provide consulting and technical guidance to support the smooth implementation of AI technology, ensuring that AI systems operate effectively.

[0307] "Communication logs" are data related to communications between employees, such as emails, chats, and video conferences.

[0308] "Fault detection and correction" is the process of monitoring the behavior of an automated program while it is running, and quickly finding and correcting problems.

[0309] This invention relates to a system that automatically understands business processes within a company and, by combining it with an emotion engine that recognizes user emotions, utilizes generative AI to perform business processes on behalf of the company. This system collects and analyzes employee operation logs and communication logs, identifies areas of business that can be automated using AI, and generates and implements specific automation programs. It also uses user emotion data to propose improvements to business processes.

[0310] First, the server remotely installs dedicated monitoring software on employees' PCs. This monitoring software records logs of keyboard operations, mouse movements, application usage, file operations, and more. The terminal then sends the user's operation logs to the server in real time. It also collects communication logs such as emails, chats, and video conferences. During this time, the user simply carries out their normal work duties without any special operations being required.

[0311] The server then preprocesses the collected log data and converts it into a format suitable for analysis. Specifically, it removes duplicate data, filters out noisy data, and fills in missing data. Based on this preprocessed data, the AI ​​model analyzes the patterns and frequency of business processes to identify which tasks are routine and which processes have bottlenecks.

[0312] The server then extracts tasks that can be automated using AI (for example, data entry, automatic report generation, and automatic replies to standard emails) based on the analysis results. The AI ​​model evaluates the degree to which efficiency improvements can be expected by automating the extracted tasks using AI. The server then designs and generates automation programs for the identified tasks. These programs incorporate natural language processing and pattern recognition technologies.

[0313] The terminal executes the automation program sent from the server and applies it to the on-site PC. This allows the automated tasks to be executed on the user's PC. The user can then check the results of the installed automation program and make adjustments as necessary.

[0314] Furthermore, the server uses an emotion engine that analyzes user operation logs and communication logs to recognize user emotions. The AI ​​model then proposes improvements to business processes based on the emotion data recognized by the emotion engine. For example, it might automate stressful tasks or propose new workflows to reduce work burdens. The server uses the emotion data to evaluate the performance of the automation program and make adjustments if necessary.

[0315] Finally, the server provides consulting and technical support related to the introduction of AI technology. With this support, users can smoothly implement AI technology. Specifically, they can apply automation programs based on the provided guidelines to achieve business efficiency.

[0316] As a concrete example, consider the automation of budget management tasks. The server collects data entered by employees into the budget management system and communication logs with each department. The AI ​​model analyzes this data and understands the budget management process. The server identifies tasks such as entering, correcting, and finalizing the budget as routine tasks that can be automated using AI. The server generates a program to automate the budget management tasks and applies it to the device. The user reviews the automated budget management tasks and adjusts the output as appropriate. The server analyzes the user's emotional data and, if stress levels increase, suggests improvements to reduce the workload.

[0317] An example of a prompt is, "Please describe the program processing for this system based on the following steps. Please provide detailed processing for analyzing business processes, identifying areas that can be automated using AI, and generating and implementing an automation program."

[0318] By using this system, companies can simultaneously streamline their business processes and manage employee emotions.

[0319] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0320] Step 1:

[0321] The server remotely installs dedicated monitoring software on employees' PCs. This monitoring software records log data such as keyboard operations, mouse movements, application usage, and file operations. The input is various operational data of employees, and the output is the recorded operation log data.

[0322] Step 2:

[0323] The device sends the user's operation log to the server in real time. It also collects communication logs such as emails, chats, and video conferences and sends them to the server. The input is the collected operation log and communication log, and the output is the log data sent to the server.

[0324] Step 3:

[0325] The server preprocesses the collected log data and converts it into a format suitable for analysis. Specifically, it removes duplicate data, filters out noisy data, and fills in missing data. The input is raw log data, and the output is a preprocessed, clean dataset.

[0326] Step 4:

[0327] The server inputs the preprocessed data into the AI ​​model to analyze the patterns and frequency of business processes. The AI ​​model analyzes the data and identifies routine tasks and bottlenecks in business processes. The input is the preprocessed dataset, and the output is the analysis results of the business processes.

[0328] Step 5:

[0329] The server identifies tasks that can be automated using AI based on the analysis results. These include data entry, automatic report generation, and automatic replies to standard emails. The input is the analysis results of the business process, and the output is a list of tasks that can be automated using AI.

[0330] Step 6:

[0331] The server designs and generates an automation program for the identified tasks. This program incorporates technologies such as natural language processing and pattern recognition. The input is a list of tasks that can be automated by AI, and the output is the automation program.

[0332] Step 7:

[0333] The terminal executes the automation program sent from the server and applies it to the on-site PC. This causes the automated task to be executed on the user's PC. The input is the generated automation program, and the output is the result of the task.

[0334] Step 8:

[0335] Users check the results of the implemented automation program and make corrections or adjustments as necessary. The input is the automated business result, and the output is the corrected or adjusted business result.

[0336] Step 9:

[0337] The server recognizes the user's emotions using an emotion engine that analyzes the user's operation log and communication log. The input is the operation log and communication log, and the output is the user's emotion data.

[0338] Step 10:

[0339] The AI ​​model makes suggestions for improving business processes based on the emotional data recognized by the emotion engine. For example, it might automate stressful tasks or suggest new workflows to reduce work burdens. The input is emotional data, and the output is improvement suggestions.

[0340] Step 11:

[0341] The server uses the emotion data to evaluate the performance of the automated program and make adjustments if necessary. The inputs are the emotion data and performance data of the automated program, and the output is the adjusted automated program.

[0342] Step 12:

[0343] The server provides consulting and technical support related to the introduction of AI technology. The user receives the provided support to smoothly introduce AI technology and achieve business efficiency. The input is a request for consulting or technical support, and the output is the provided support and guidelines.

[0344] An example of a prompt is, "Please describe the program processing for this system based on the following steps. Please provide detailed processing for analyzing business processes, identifying areas that can be automated using AI, and generating and implementing an automation program."

[0345] (Application example 2)

[0346] 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."

[0347] With conventional business automation systems, it was difficult to improve business processes while taking into account the emotions of workers, leading to problems such as stress and reduced productivity. Furthermore, when automating work within a factory, there was an issue of not being able to operate business flexibly in conjunction with the emotions of workers.

[0348] 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 collecting employee operation logs, means for analyzing the collected log data and understanding the business process, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing automation programs for the identified tasks, means for collecting worker emotion data and proposing improvements to the business process, and means for providing support for introducing AI. This enables flexible business operations that take into account the emotions of workers while improving work efficiency.

[0349] The "means for collecting employee operation logs" refers to a means for recording operation information when workers perform their work and storing it in a database.

[0350] "Means for analyzing collected log data and understanding business processes" refers to a means for analyzing recorded operation logs to understand the flow, frequency, and bottlenecks of business operations.

[0351] "Means for identifying tasks that can be automated using AI based on analysis results" refers to means for identifying which tasks are suitable for automation based on the results of analyzing log data.

[0352] "Means for generating and implementing automation programs for identified tasks" refers to means for designing and creating programs to automate identified tasks and applying them to actual systems.

[0353] "Means for collecting worker emotional data and proposing improvements to work processes" refers to means for analyzing the emotional state of workers and proposing ways to optimize work processes based on the results.

[0354] "Means for providing support for AI implementation" refers to means for providing necessary support and consulting when implementing AI technology.

[0355] The system for carrying out the present invention is configured by combining the following means.

[0356] 1. Data Collection Phase

[0357] The server installs dedicated monitoring software on employees' PCs to collect employee operation and communication logs. This software records logs of keyboard operations, mouse movements, application usage, file operations, etc. It also collects communication logs such as emails, chats, and video conferences.

[0358] The terminal sends the collected operation log to the server in real time. The user only needs to carry out their normal work; no special operations are required.

[0359] 2. Data analysis phase

[0360] The server preprocesses the collected log data and converts it into a format suitable for analysis, filtering out unnecessary data and filling in missing data.

[0361] AI models analyze data for patterns and frequencies to understand the overall business process, identifying which tasks are routine and which processes have bottlenecks.

[0362] 3. Identifying tasks that can be automated using AI

[0363] Based on the analysis results, the server extracts tasks that can be automated using AI (e.g., data entry, report creation, automatic email replies, etc.).

[0364] The AI ​​model evaluates the effectiveness of using AI to perform these tasks.

[0365] 4. Program Generation and Implementation Phase

[0366] The server designs and generates automation programs for specified tasks, incorporating technologies such as natural language processing and pattern recognition.

[0367] The terminal applies the generated program to the user's terminal and runs it in actual business.

[0368] 5. Introducing the Emotion Engine

[0369] The server analyzes the user's operation log and communication log and recognizes the user's emotions using an emotion engine.

[0370] The AI ​​model will then propose improvements to business processes based on the analysis results of the emotion engine. For example, if a user is feeling stressed, the model will suggest ways to reduce the work that is causing the stress.

[0371] The server uses the emotion data to evaluate the performance of the automated program and adjust it as needed.

[0372] Specific examples

[0373] scenario

[0374] Take the example of sorting products on a conveyor belt in a factory.

[0375] 1. Use smartphones or tablets to collect worker operation logs (product handling records, quantity records, etc.).

[0376] 2. Keep a communication log when workers use video conferencing or chat tools.

[0377] 3. The server performs preprocessing and performs pattern analysis on the log data.

[0378] 4. Use natural language processing technology to analyze emotions from workers' chat content.

[0379] 5. Identify the routine parts of the sorting process and determine whether they can be automated using AI.

[0380] 6. Generate a robot control program that automates sorting based on product type and quantity.

[0381] 7. The program is applied to the robot, and the actual sorting work is carried out automatically.

[0382] 8. If workers are stressed, adjust sorting assignments to reduce their workload.

[0383] 9. Based on the suggested improvements, fine-tune the robot's control program in real time.

[0384] In this way, work efficiency within the factory can be significantly improved, and flexible business operations can be carried out in accordance with the emotional state of the workers.

[0385] Examples of prompt statements

[0386] Design a generative AI system that automatically understands business processes using employee operation logs and communication logs and combines them with an emotion engine. Identify areas of business that can be automated using AI, then generate and implement automation programs. Also, propose improvements to business processes using user emotion data.

[0387] Specifically, we will implement a system that will provide robots working in factories with suggestions for improving work efficiency and operations based on the emotions and work logs of workers. Factory workers will use smartphones, tablets, and head-mounted displays to collect and analyze operation logs in real time and generate automation programs to be applied to the robots.

[0388] This system will enable automation of work processes within factories and companies and improve work processes by taking into account the emotions of workers.

[0389] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0390] Step 1: Data collection

[0391] Subject: Server

[0392] Description: The server collects employee operation logs and communication logs. Operation logs include keyboard operations, mouse movements, information on applications used, file operations, etc. This data is collected in real time through dedicated monitoring software and sent to the server. Communication logs include records of emails, chats, and video conferences.

[0393] Input: Employee operation log and communication log

[0394] Output: Collected operational and communication data

[0395] Step 2: Data Preprocessing

[0396] Subject: Server

[0397] Description: The server preprocesses the collected log data. Specifically, it filters the data, removes unnecessary data, imputes missing values, and converts the data into a format suitable for analysis.

[0398] Input: Collected operational and communication data

[0399] Output: Preprocessed data

[0400] Step 3: Data analysis

[0401] Subject: Server, AI model

[0402] Description: The server inputs the preprocessed data into the AI ​​model to analyze business processes. The AI ​​model analyzes the patterns and frequency of the data to understand the overall picture of the business. This analysis identifies routine tasks and bottleneck processes.

[0403] Input: Preprocessed data

[0404] Output: Business process analysis results

[0405] Step 4: Identifying tasks that can be automated using AI

[0406] Subject: Server, AI model

[0407] Description: The server extracts tasks that can be automated using AI based on the analysis results. The AI ​​model evaluates the degree to which automating the tasks will improve efficiency. This evaluation identifies specific tasks to automate.

[0408] Input: Business process analysis results

[0409] Output: List of tasks that can be automated using AI and evaluation results of efficiency effects

[0410] Step 5: Generate the automation program

[0411] Subject: Server

[0412] Description: The server generates an automation program for the specified business. This program incorporates technologies such as natural language processing and pattern recognition and is designed according to the specific business content. The generated program is converted into an executable format.

[0413] Input: List of tasks that can be automated using AI and evaluation results of efficiency effects

[0414] Output: Automation program

[0415] Step 6: Implement the program

[0416] Subject: Terminal

[0417] Description: The terminal applies the generated automation program to the user's terminal and runs it in actual work. Here, the application status of the program is monitored and adjustments are made as necessary.

[0418] Input: Automation program

[0419] Output: Automated tasks

[0420] Step 7: Collect emotion data

[0421] Subject: Server

[0422] Description: The server collects user emotional data. The emotional data is obtained by analyzing operation logs and communication logs. An emotion engine is used to recognize emotional states such as stress and satisfaction.

[0423] Input: User operation log and communication log

[0424] Output: Emotion data

[0425] Step 8: Analyze emotion data and propose improvements

[0426] Subject: Server, AI model

[0427] Description: The server uses collected emotional data to make suggestions for improving work processes. The AI ​​model analyzes the emotional data and identifies factors that increase stress or decrease satisfaction. Suggested improvements include adjusting work assignments or changing processes.

[0428] Input: Emotion data

[0429] Output: Business process improvement proposals

[0430] Step 9: Implementing improvement proposals

[0431] Subject: Terminal

[0432] Description: The terminal applies the proposed improvements and optimizes the business process, fine-tuning programs and changing work assignments to improve business efficiency in real time.

[0433] Input: Business process improvement proposal

[0434] Output: Improved business processes

[0435] 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.

[0436] 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.

[0437] 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.

[0438] [Second embodiment]

[0439] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0440] 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.

[0441] 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).

[0442] 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.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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."

[0451] This invention relates to a system that automatically understands business processes within a company and uses generative AI to perform the work on behalf of the company. This system collects and analyzes employee operation logs and communication logs, identifies areas of the work that can be automated using AI, and generates and implements specific automation programs. Specific embodiments are described below.

[0452] Data Collection Phase

[0453] To collect employee operation logs and communication logs, the system operates as follows:

[0454] The server installs special monitoring software on employees' PCs and records logs of keyboard operations, mouse movements, application usage, file operations, and more.

[0455] The terminal transmits the user's operation log to the server in real time.

[0456] The user simply performs normal operations and no special operations are required.

[0457] The server also collects communication logs such as emails, chat tools, and video conferences.

[0458] Data analysis phase

[0459] The collected log data is used to perform analysis to automatically understand business processes.

[0460] The server preprocesses the collected log data and converts it into a format suitable for analysis.

[0461] It uses AI models to analyze data for patterns and frequencies to understand the full picture of business processes, identifying which tasks are routine and which processes have bottlenecks.

[0462] Identifying tasks that can be automated using AI

[0463] Based on the analysis results, we will identify which tasks can be automated using AI.

[0464] The server lists specific tasks (such as data entry, report writing, and automatic email replies) based on the analysis results.

[0465] The AI ​​model evaluates the effectiveness of using AI to perform these tasks.

[0466] Program Development and Implementation Phase

[0467] For tasks identified as amenable to AI, specific automation programs are generated and implemented.

[0468] The server designs and generates an automation program for the specified task.

[0469] Using AI models, technologies such as natural language processing and pattern recognition are incorporated to specifically code the program.

[0470] The generated program is applied to the terminal, automating business processes, such as budget management in the accounting department.

[0471] Implementation support phase

[0472] We provide consulting and technical assistance to support the smooth introduction of AI technology.

[0473] The server provides consulting on the introduction and use of AI technology.

[0474] With support, users can advance the implementation of AI technology and achieve improved business efficiency.

[0475] Specific examples

[0476] For example, the automation of budget management tasks works as follows:

[0477] 1. The server collects data that employees enter into the budget management system and communication logs with each department.

[0478] 2. The AI ​​model analyzes the data and understands the budget management process.

[0479] 3. The server identifies tasks such as inputting, correcting, and confirming budgets as routine tasks that can be automated using AI.

[0480] 4. The server generates a program that automates budget management tasks and applies it to the terminal.

[0481] 5. The user reviews the automated budget control operations and adjusts the output accordingly.

[0482] This will enable companies to streamline their business processes and smoothly introduce AI technology.

[0483] The processing flow will be explained below.

[0484] Step 1:

[0485] The server installs monitoring software on employees' PCs and records operation logs such as keyboard operations, mouse movements, application usage, and file operations.

[0486] Step 2:

[0487] The terminal transmits the user's operation log to the server in real time, which includes organizing the data according to the log format and uploading it to the server.

[0488] Step 3:

[0489] The server preprocesses the collected log data and converts it into a format suitable for analysis, which also includes filtering unnecessary data and filling in missing data.

[0490] Step 4:

[0491] The server inputs the processed log data into an AI model to analyze the overall business process. The AI ​​model uses machine learning algorithms to analyze operation patterns and frequencies and identify business flows.

[0492] Step 5:

[0493] The server extracts tasks that can be automated using AI based on the analysis results. Highly repetitive tasks and routine work are identified and listed as candidates for AI automation.

[0494] Step 6:

[0495] The server designs an automation program for the extracted tasks, which includes creating a flow diagram for each specific task and selecting the necessary algorithms.

[0496] Step 7:

[0497] The server then proceeds with coding and testing based on the designed program. The generated program is also tested in a virtual environment to ensure its compatibility with current operations.

[0498] Step 8:

[0499] The terminal applies the tested automation program to the employee's PC and runs it in the actual work, thereby realizing the automation of work.

[0500] Step 9:

[0501] The server monitors the operation of the automation program to check for any malfunctions, monitors the operation status in real time, and corrects the program as necessary.

[0502] Step 10:

[0503] The server will provide consulting on the introduction and use of AI technology to help users maximize the benefits of AI, as well as provide training programs and answer any questions.

[0504] This series of steps will enable companies to streamline their business processes and smoothly implement AI technology.

[0505] Example 1

[0506] 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."

[0507] Conventional business process automation systems did not adequately collect and analyze a wide range of employee operation logs and communication logs, making it difficult to accurately identify areas of business that could be automated using AI. Furthermore, no effective means were provided for creating and implementing automation programs for identified business processes. As a result, business efficiency did not improve and the benefits of introducing AI technology were limited.

[0508] 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.

[0509] In this invention, the server includes means for collecting employee operation logs, means for preprocessing the collected log data and converting it into a format suitable for analysis, means for analyzing the preprocessed data using a generative model to understand business processes, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing an automation program for the identified tasks, means for applying the automation program on a terminal to automate the business process, and means for providing support for introducing AI, thereby enabling the efficiency of business processes and the smooth introduction of AI technology.

[0510] "Means for collecting employee operation logs" refers to devices or software that monitor keyboard operations, mouse movements, application usage, file operations, etc. on computers used by employees and record this data.

[0511] "Means for preprocessing collected log data and converting it into a format suitable for analysis" refers to a method or device that preprocesses employee operation log and communication log data, such as by cleansing and formatting it, and converts it into structured data suitable for analysis.

[0512] "Means for analyzing preprocessed data using generative models to understand business processes" refers to a method or device that uses machine learning or statistical analysis models to analyze preprocessed data, identify business patterns and frequencies, and understand the overall flow of a process.

[0513] "Means for identifying tasks that can be automated using AI based on analysis results" refers to a method or device that, based on analysis results, extracts and lists routine tasks and tasks that can be automated to improve performance.

[0514] "Means for generating and implementing an automation program for a specified task" refers to a method or device for designing a program to automate a specified task, generating specific code, and applying the program to a system.

[0515] "Means for applying an automation program on a terminal to automate a business process" refers to a method or device that installs and runs a generated automation program on an employee's terminal to automatically perform specified business tasks.

[0516] "Means for providing support for AI implementation" refers to methods or devices that provide consulting, training, and technical support on how to implement and use automation programs, and help users effectively utilize AI technology.

[0517] MODE FOR CARRYING OUT THE INVENTION

[0518] This invention relates to a system that automatically understands business processes within a company and uses generative AI to perform the work. This system collects and analyzes employee operation logs and communication logs, identifies areas of the work that can be automated using AI, and generates and implements specific automation programs. Specific embodiments of this system are described below.

[0519] Data Collection Phase

[0520] The server installs dedicated monitoring software on employees' PCs, such as "Activity Monitor." The monitoring software records logs of keyboard operations, mouse movements, application usage, file operations, and so on.

[0521] The terminal sends the user's operation log to the server in real time. This transmission uses UDP or TCP / IP protocols. When the log reaches a certain size, it is stored in a transmission buffer and transferred to the server.

[0522] Users can simply carry out their normal work without any special operations, such as entering data in Excel, sending emails, and communicating via chat.

[0523] The server also collects communication logs from emails, chat tools (such as Slack and Microsoft Teams), and video conferencing (such as Zoom). This collection is done using API integration and Webhooks.

[0524] Data analysis phase

[0525] The server preprocesses the collected log data and converts it into a format suitable for analysis. For example, it extracts unstructured data using regular expressions and converts it into CSV or JSON format. This process removes noise from the data and retains only the necessary information.

[0526] AI models (such as GPT-4 or TensorFlow) analyze the pre-processed data and analyze the data for patterns and frequencies to understand how business processes are progressing, for example, determining whether a particular task is repeated multiple times.

[0527] Identifying tasks that can be automated using AI

[0528] Based on the AI ​​model's analysis, the server lists routine tasks and bottlenecks, such as data entry and daily report generation.

[0529] The AI ​​model evaluates the effectiveness of using AI for the listed tasks, calculating, for example, how much efficiency will be improved and how much work time will be reduced.

[0530] Program Development and Implementation Phase

[0531] The server generates an automation program based on the evaluation results. Specifically, the program is coded using Python or JavaScript, incorporating natural language processing and pattern recognition to create a program that automates business processes.

[0532] The terminal applies and executes the generated automation program, for example, using the Selenium library to automate browser operations and input or modify budget data.

[0533] Implementation support phase

[0534] The server will provide consulting on how to implement and use AI technology, including creating training video tutorials and FAQs. The server will also respond to user questions and resolve technical issues.

[0535] Users can receive support from the server and smoothly carry out tasks that utilize AI technology, such as checking the operation of the automated programs they have implemented and making adjustments as necessary.

[0536] Specific examples

[0537] For example, the automation of budget management tasks works as follows:

[0538] 1. The server collects data entered into the budget management system and communication logs from each department.

[0539] 2. An AI model (such as BERT) analyzes the collected data and understands the flow of budget requests, approval processes, and coordination work from each department.

[0540] 3. The server identifies tasks such as entering, correcting, and approving budgets as routine tasks that can be automated using AI.

[0541] 4. The server generates a program to automate budget management tasks and applies the script written in Python to the terminal. For example, it uses the Selenium library to automate browser operations and input and modify budget data.

[0542] 5. A user runs the automated budget control process and checks the results, for example, by reviewing the generated budget report and making manual corrections as needed.

[0543] Prompt Sentence Examples

[0544] "Please create a Python script to automate the following business process. The task involves adjusting budgets between departments. Specifically, the task involves forwarding the budget submitted by department A to department B and providing feedback on the evaluation results."

[0545] This will enable companies to improve the efficiency of their business processes and smoothly implement AI technology.

[0546] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0547] Step 1:

[0548] The server installs monitoring software on employees' PCs, such as "Activity Monitor." After installation, the software records logs of keyboard operations, mouse movements, application usage, file operations, and more.

[0549] Input: Employee's PC, monitoring software

[0550] Output: Installed monitoring software

[0551] Step 2:

[0552] The terminal sends the user's operation log to the server in real time. Data is transferred using UDP or TCP / IP protocols. When the log reaches a certain size, it is stored in a sending buffer and then transferred to the server.

[0553] Input: User operation log

[0554] Output: Operation log sent to the server

[0555] Step 3:

[0556] Users can carry out their daily work as usual. For example, they can enter data in Excel, send and receive emails, and communicate using chat. No special operations are required.

[0557] Input: User's daily routine

[0558] Output: Generated business log

[0559] Step 4:

[0560] The server also collects communication logs from users' email and chat tools, such as Slack and Microsoft Teams, and the video conferencing tool Zoom. This data is collected using API integration and Webhooks.

[0561] Input: emails, chats, video conference logs

[0562] Output: Collected communication logs

[0563] Step 5:

[0564] The server preprocesses the collected log data and converts it into a format suitable for analysis. It extracts unstructured data using regular expressions and converts it into CSV or JSON format. This process removes noise from the data and retains only the necessary information.

[0565] Input: Raw Data Log

[0566] Output: Preprocessed data

[0567] Step 6:

[0568] AI models (such as GPT-4 or TensorFlow) analyze the pre-processed data and analyze it for patterns and frequencies, for example, to identify routine or repeated tasks.

[0569] Input: Preprocessed data

[0570] Output: Identification of work patterns and tasks

[0571] Step 7:

[0572] Based on the analysis results of the AI ​​model, the server lists routine tasks and bottleneck tasks, which clearly shows which tasks can be automated using AI.

[0573] Input: Analysis results of the AI ​​model

[0574] Output: List of tasks that can be automated using AI

[0575] Step 8:

[0576] The AI ​​model evaluates the effect of implementing AI on the listed tasks, calculating, for example, how much the efficiency of the task will improve and how much the work time will be reduced.

[0577] Input: List of tasks that can be automated using AI

[0578] Output: Evaluation of the effectiveness of AI

[0579] Step 9:

[0580] The server then generates automation programs for the identified tasks based on the evaluation results. The automation programs are coded using Python or JavaScript and incorporate natural language processing and pattern recognition.

[0581] Input:Effectiveness evaluation

[0582] Output: Automation program code

[0583] Step 10:

[0584] The terminal applies and executes the generated automation program, for example, using the Selenium library to automate browser operations and input or modify budget data.

[0585] Input: Automation program code

[0586] Output: Automation executed on the terminal

[0587] Step 11:

[0588] Sarver will provide consulting on the implementation and use of AI technology, including creating training video tutorials and FAQs.

[0589] Input: automation programs, user feedback

[0590] Output: Consulting materials, support

[0591] Step 12:

[0592] Users can receive support from the server and smoothly carry out tasks that utilize AI technology, such as checking the operation of the automated programs they have implemented and making adjustments as necessary.

[0593] Input: Consulting materials, support

[0594] Output: Streamlined business processes

[0595] This will enable companies to achieve greater efficiency in their business processes and smoothly implement AI technology.

[0596] (Application example 1)

[0597] 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."

[0598] There is a need to solve problems caused by reduced work efficiency and human error within logistics centers. It is also important to reduce the burden on employees performing the work and improve the accuracy and efficiency of picking work. However, conventional systems cannot fully address these issues, and it requires a lot of time and resources to understand and automate efficient business processes.

[0599] 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.

[0600] In this invention, the server includes means for collecting employee operation logs, means for analyzing the collected log data and understanding business processes, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing automation programs for the identified tasks, means for managing terminals that give instructions for work within the logistics center and collect operation logs, and means for controlling product picking work by logistics robots, thereby enabling efficient and accurate picking work within the logistics center.

[0601] An "employee operation log" is a record of keyboard operations, mouse movements, application use, file operations, etc. performed by employees when performing their work.

[0602] "Collected log data" is a general term for data recorded in the course of business operations, such as employee operation logs and communication logs.

[0603] "Means for understanding business processes" refers to methods and technologies for analyzing collected log data and understanding the flow and characteristics of business processes.

[0604] "AI-enabled tasks" refers to business processes and tasks that can be automated using artificial intelligence technology.

[0605] "Automation Program" means software designed and implemented to automate a specified task.

[0606] "AI implementation support" means providing assistance and consulting related to the implementation and operation of AI technology.

[0607] "Terminal" refers to a device used within a logistics center to issue work instructions and collect operation logs.

[0608] A "logistics center" refers to a facility where goods are stored, managed, and prepared for shipment.

[0609] A "robot" is a mechanical device that automatically performs a set task.

[0610] "Picking work" refers to the work of picking out specified products at a logistics center.

[0611] A "server" refers to a computer system that centrally manages and analyzes data.

[0612] The present invention relates to a system that automatically understands business processes in a logistics center and performs the business processes by utilizing a generation AI. Specific embodiments will be described below.

[0613] Data Collection Phase

[0614] The server installs dedicated monitoring software on terminals in the logistics center and records logs of the picking work performed by workers (users). The terminals track the movements of the workers and send operation logs to the server in real time. The users simply carry out their normal work tasks and no special operations are required. The server also collects control logs for logistics robots. This includes receiving picking lists and the product picking process.

[0615] Data analysis phase

[0616] The server uses the collected log data to perform analysis to automatically understand business processes. Specifically, the server preprocesses the collected log data and converts it into a format suitable for analysis. A generative AI model is used to analyze the patterns and frequency of the data to understand the overall picture of business processes. This allows it to identify which tasks are routine and which processes have bottlenecks.

[0617] Identifying tasks that can be automated using AI

[0618] Based on the analysis results, the server identifies which tasks can be automated using AI. The server then lists specific tasks (such as product picking and shipping preparation) based on the analysis results, and uses a generative AI model to evaluate the effectiveness of implementing these tasks using AI.

[0619] Program Development and Implementation Phase

[0620] The server designs and generates automation programs for the identified tasks. These programs incorporate technologies such as natural language processing and pattern recognition and are specifically coded. The generated programs are applied to terminals and logistics robots, realizing the automation of business processes. For example, picking work within a logistics center can be automated and performed accurately and efficiently.

[0621] Implementation support phase

[0622] The server provides consulting and technical support to support the smooth introduction of AI technology. With this support, users can advance the introduction of AI technology and achieve operational efficiency improvements. As a concrete example, when introducing the new smartphone app "Picking Assist" in a logistics center, the following prompt sentence can be entered:

[0623] Example prompt sentence:

[0624] "Get the picking list, track employee movements and send it to the server"

[0625] This will enable efficient and accurate picking operations within the distribution center, and the system will contribute greatly to the automation and efficiency of operations at distribution centers.

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

[0627] Step 1:

[0628] The server installs dedicated monitoring software on terminals at the distribution center. The terminals collect user operation logs (e.g., keyboard operations, mouse movements) and send the data to the server in real time. The input is the user's operations, and the output is the collected operation log data.

[0629] Step 2:

[0630] The server preprocesses the collected log data and converts it into a format suitable for analysis. At this stage, the log data is sorted in chronological order and unnecessary data is filtered out. The input is operation log data, and the output is the preprocessed log data.

[0631] Step 3:

[0632] The server uses the preprocessed log data to analyze data patterns and frequencies using a generative AI model. The AI ​​model (e.g., TensorFlow) learns to understand the characteristics of the business process. The input is the preprocessed log data, and the output is the analysis results.

[0633] Step 4:

[0634] Based on the analysis results, the server identifies tasks that can be automated using AI. During this process, the server identifies and lists routine tasks and bottlenecks. The input is the analysis results, and the output is a list of tasks that can be automated using AI.

[0635] Step 5:

[0636] The server generates an automation program for the identified tasks. It incorporates natural language processing and pattern recognition technology and codes the program based on the flow of the target tasks. The input is a list of tasks that can be automated by AI, and the output is the automation program.

[0637] Step 6:

[0638] The server applies the generated program to the terminal and the logistics robot to execute the automation. For example, the logistics robot starts picking products. The input is the automation program, and the output is the automation task being executed.

[0639] Step 7:

[0640] The server monitors the operation of the automation program, detects malfunctions, and automatically corrects them. Log data is analyzed in real time, and when anomalies are detected, the server executes a correction algorithm. The input is the log data from the running automation task, and the output is the corrected operation log.

[0641] Step 8:

[0642] Users receive support in introducing AI to improve operational efficiency within their logistics centers. The server provides consulting information and technical materials for support, helping users to effectively use the new system. The input is the business information that requires support, and the output is specific guidelines and advice for support.

[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] This invention relates to a system that automatically understands business processes within a company and, by combining it with an emotion engine that recognizes user emotions, utilizes generative AI to perform business processes on behalf of the company. This system collects and analyzes employee operation logs and communication logs, identifies areas of business that can be automated using AI, and generates and implements specific automation programs. It also uses user emotion data to propose improvements to business processes.

[0645] Data Collection Phase

[0646] To collect employee operation logs and communication logs, the system operates as follows:

[0647] The server installs special monitoring software on employees' PCs and records logs of keyboard operations, mouse movements, application usage, file operations, and more.

[0648] The device sends user operation logs to the server in real time, and also collects communication logs such as emails, chats, and video conferences.

[0649] Users only need to carry out their normal work without any special operations.

[0650] Data analysis phase

[0651] The collected log data is used to perform analysis to automatically understand business processes.

[0652] The server preprocesses the collected log data and converts it into a format suitable for analysis, filtering out unnecessary data and filling in missing data.

[0653] AI models analyze data for patterns and frequencies to understand the overall business process, identifying which tasks are routine and which processes have bottlenecks.

[0654] Identifying tasks that can be automated using AI

[0655] Based on the analysis results, we will identify which tasks can be automated using AI.

[0656] Based on the analysis results, the server extracts tasks that can be automated using AI (for example, data entry, report creation, automatic email replies, etc.).

[0657] The AI ​​model evaluates the effectiveness of using AI to perform these tasks.

[0658] Program Development and Implementation Phase

[0659] For tasks identified as amenable to AI, specific automation programs are generated and implemented.

[0660] The server designs and generates automation programs for specified tasks, incorporating technologies such as natural language processing and pattern recognition.

[0661] The terminal then applies the generated program to the user's PC and runs it in the actual work, thereby realizing the automation of the work.

[0662] Introducing the Emotion Engine

[0663] In order to recognize the user's emotions and reflect them in the business process, the following processing is performed.

[0664] The server analyzes the user's operation log and communication log and uses an emotion engine to recognize the user's emotions.

[0665] The AI ​​model will then propose improvements to business processes based on the analysis results of the emotion engine. For example, if a user is feeling stressed, the model will suggest ways to reduce the work that is causing the stress.

[0666] The server uses the emotion data to evaluate the performance of the automated program and adjust it as needed.

[0667] Implementation support phase

[0668] We provide consulting and technical assistance to support the smooth introduction of AI technology.

[0669] The server provides consulting on the introduction and use of AI technology.

[0670] With support, users can advance the implementation of AI technology and achieve improved business efficiency.

[0671] Specific examples

[0672] For example, the automation of budget management tasks works as follows:

[0673] 1. The server collects data that employees enter into the budget management system and communication logs with each department.

[0674] 2. The AI ​​model analyzes the data and understands the budget management process.

[0675] 3. The server identifies tasks such as inputting, correcting, and confirming budgets as routine tasks that can be automated using AI.

[0676] 4. The server generates a program that automates budget management tasks and applies it to the terminal.

[0677] 5. The user reviews the automated budget control operations and adjusts the output accordingly.

[0678] 6. The server analyzes the user's emotional data and, if stress increases, makes suggestions for improvements to reduce work burden.

[0679] In this way, companies can not only streamline their business processes, but also realize a way of working that takes users' emotions into consideration.

[0680] The processing flow will be explained below.

[0681] Step 1:

[0682] The server installs monitoring software on employees' PCs and records operation logs such as keyboard operations, mouse movements, application usage, and file operations.

[0683] Step 2:

[0684] The terminal sends the user's operation log and communication log (email, chat, video conference, etc.) to the server in real time.

[0685] Step 3:

[0686] The server preprocesses the collected log data and converts it into a format suitable for analysis, specifically by cleansing the data, filtering out unnecessary data, and filling in missing data.

[0687] Step 4:

[0688] The server inputs the processed log data into an AI model to analyze the overall business process. The AI ​​model uses machine learning algorithms to analyze operation patterns and frequencies and identify business flows.

[0689] Step 5:

[0690] The server extracts tasks that can be automated using AI based on the analysis results. Highly repetitive tasks and routine work are identified and listed as candidates for AI automation.

[0691] Step 6:

[0692] The server designs an automation program for the extracted tasks, which includes creating a flow diagram for each specific task and selecting the necessary algorithms.

[0693] Step 7:

[0694] The server then proceeds with coding and testing based on the designed program. The generated program is then tested in a virtual environment to verify its compatibility with current operations.

[0695] Step 8:

[0696] The terminal applies the tested automation program to the employee's PC and runs it in the actual work, thereby realizing the automation of work.

[0697] Step 9:

[0698] The server analyzes the user's operation log and communication log and uses an emotion engine to recognize the user's emotions. The emotion data is analyzed to extract stress and satisfaction during operation.

[0699] Step 10:

[0700] The AI ​​model will then propose improvements to business processes based on the analysis results of the emotion engine. For example, if a user is feeling stressed, it will identify the tasks that are causing the stress and make suggestions to reduce the burden.

[0701] Step 11:

[0702] The server uses the emotional data to evaluate the performance of the automated program and adjusts it as needed, so that the program's accuracy improves depending on the user's emotional state.

[0703] Step 12:

[0704] The server provides consulting on the introduction and use of AI technology, and users can receive support to advance the introduction of AI technology and achieve business efficiency.

[0705] This series of steps will enable companies to streamline their business processes and smoothly implement AI technology. Furthermore, analyzing user emotion data will enable companies to provide a more comfortable working environment.

[0706] Example 2

[0707] 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."

[0708] Conventional business process automation systems optimize operations by collecting and analyzing employee operation logs, but lack the functionality to improve operations by reflecting user sentiment. Furthermore, support for implementing AI is insufficient, preventing the full benefits of business automation from being realized. Furthermore, there are inadequate methods for detecting and correcting defects in automation programs, which can lead to numerous problems in actual operation.

[0709] 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 collecting employee operation logs, means for analyzing the collected log data and understanding the business process, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing an automation program for the identified tasks, means for improving the business process using an emotion engine for recognizing user emotions, and means for providing support for introducing AI. This not only enables the efficiency of business processes to be improved, but also enables a work style that takes user emotions into consideration and enables the rapid detection and correction of defects in automation programs.

[0710] An "employee operation log" is a series of data such as keyboard operations, mouse movements, application usage, and file operations that occur when an employee performs their work.

[0711] "Log data analysis" refers to understanding business processes by preprocessing collected operation logs and analyzing patterns and frequencies.

[0712] "Tasks that can be automated using AI" are tasks that can be automated based on the results of analysis, such as routine tasks such as data entry, report creation, and automatic email replies.

[0713] An "automation program" is a program designed and generated to efficiently carry out a specific task, incorporating technologies such as natural language processing and pattern recognition.

[0714] An "emotion engine" is an algorithm or software that analyzes a user's operation log and communication log to recognize the user's emotions.

[0715] "Improvement of business processes" means proposing and implementing work environments and workflows to reduce stress based on user emotional data recognized by the emotion engine.

[0716] "AI implementation support" refers to activities that provide consulting and technical guidance to support the smooth implementation of AI technology, ensuring that AI systems operate effectively.

[0717] "Communication logs" are data related to communications between employees, such as emails, chats, and video conferences.

[0718] "Fault detection and correction" is the process of monitoring the behavior of an automated program while it is running, and quickly finding and correcting problems.

[0719] This invention relates to a system that automatically understands business processes within a company and, by combining it with an emotion engine that recognizes user emotions, utilizes generative AI to perform business processes on behalf of the company. This system collects and analyzes employee operation logs and communication logs, identifies areas of business that can be automated using AI, and generates and implements specific automation programs. It also uses user emotion data to propose improvements to business processes.

[0720] First, the server remotely installs dedicated monitoring software on employees' PCs. This monitoring software records logs of keyboard operations, mouse movements, application usage, file operations, and more. The terminal then sends the user's operation logs to the server in real time. It also collects communication logs such as emails, chats, and video conferences. During this time, the user simply carries out their normal work duties without any special operations being required.

[0721] The server then preprocesses the collected log data and converts it into a format suitable for analysis. Specifically, it removes duplicate data, filters out noisy data, and fills in missing data. Based on this preprocessed data, the AI ​​model analyzes the patterns and frequency of business processes to identify which tasks are routine and which processes have bottlenecks.

[0722] The server then extracts tasks that can be automated using AI (for example, data entry, automatic report generation, and automatic replies to standard emails) based on the analysis results. The AI ​​model evaluates the degree to which efficiency improvements can be expected by automating the extracted tasks using AI. The server then designs and generates automation programs for the identified tasks. These programs incorporate natural language processing and pattern recognition technologies.

[0723] The terminal executes the automation program sent from the server and applies it to the on-site PC. This allows the automated tasks to be executed on the user's PC. The user can then check the results of the installed automation program and make adjustments as necessary.

[0724] Furthermore, the server uses an emotion engine that analyzes user operation logs and communication logs to recognize user emotions. The AI ​​model then proposes improvements to business processes based on the emotion data recognized by the emotion engine. For example, it might automate stressful tasks or propose new workflows to reduce work burdens. The server uses the emotion data to evaluate the performance of the automation program and make adjustments if necessary.

[0725] Finally, the server provides consulting and technical support related to the introduction of AI technology. With this support, users can smoothly implement AI technology. Specifically, they can apply automation programs based on the provided guidelines to achieve business efficiency.

[0726] As a concrete example, consider the automation of budget management tasks. The server collects data entered by employees into the budget management system and communication logs with each department. The AI ​​model analyzes this data and understands the budget management process. The server identifies tasks such as entering, correcting, and finalizing the budget as routine tasks that can be automated using AI. The server generates a program to automate the budget management tasks and applies it to the device. The user reviews the automated budget management tasks and adjusts the output as appropriate. The server analyzes the user's emotional data and, if stress levels increase, suggests improvements to reduce the workload.

[0727] An example of a prompt is, "Please describe the program processing for this system based on the following steps. Please provide detailed processing for analyzing business processes, identifying areas that can be automated using AI, and generating and implementing an automation program."

[0728] By using this system, companies can simultaneously streamline their business processes and manage employee emotions.

[0729] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0730] Step 1:

[0731] The server remotely installs dedicated monitoring software on employees' PCs. This monitoring software records log data such as keyboard operations, mouse movements, application usage, and file operations. The input is various operational data of employees, and the output is the recorded operation log data.

[0732] Step 2:

[0733] The device sends the user's operation log to the server in real time. It also collects communication logs such as emails, chats, and video conferences and sends them to the server. The input is the collected operation log and communication log, and the output is the log data sent to the server.

[0734] Step 3:

[0735] The server preprocesses the collected log data and converts it into a format suitable for analysis. Specifically, it removes duplicate data, filters out noisy data, and fills in missing data. The input is raw log data, and the output is a preprocessed, clean dataset.

[0736] Step 4:

[0737] The server inputs the preprocessed data into the AI ​​model to analyze the patterns and frequency of business processes. The AI ​​model analyzes the data and identifies routine tasks and bottlenecks in business processes. The input is the preprocessed dataset, and the output is the analysis results of the business processes.

[0738] Step 5:

[0739] The server identifies tasks that can be automated using AI based on the analysis results. These include data entry, automatic report generation, and automatic replies to standard emails. The input is the analysis results of the business process, and the output is a list of tasks that can be automated using AI.

[0740] Step 6:

[0741] The server designs and generates an automation program for the identified tasks. This program incorporates technologies such as natural language processing and pattern recognition. The input is a list of tasks that can be automated by AI, and the output is the automation program.

[0742] Step 7:

[0743] The terminal executes the automation program sent from the server and applies it to the on-site PC. This causes the automated task to be executed on the user's PC. The input is the generated automation program, and the output is the result of the task.

[0744] Step 8:

[0745] Users check the results of the implemented automation program and make corrections or adjustments as necessary. The input is the automated business result, and the output is the corrected or adjusted business result.

[0746] Step 9:

[0747] The server recognizes the user's emotions using an emotion engine that analyzes the user's operation log and communication log. The input is the operation log and communication log, and the output is the user's emotion data.

[0748] Step 10:

[0749] The AI ​​model makes suggestions for improving business processes based on the emotional data recognized by the emotion engine. For example, it might automate stressful tasks or suggest new workflows to reduce work burdens. The input is emotional data, and the output is improvement suggestions.

[0750] Step 11:

[0751] The server uses the emotion data to evaluate the performance of the automated program and make adjustments if necessary. The inputs are the emotion data and performance data of the automated program, and the output is the adjusted automated program.

[0752] Step 12:

[0753] The server provides consulting and technical support related to the introduction of AI technology. The user receives the provided support to smoothly introduce AI technology and achieve business efficiency. The input is a request for consulting or technical support, and the output is the provided support and guidelines.

[0754] An example of a prompt is, "Please describe the program processing for this system based on the following steps. Please provide detailed processing for analyzing business processes, identifying areas that can be automated using AI, and generating and implementing an automation program."

[0755] (Application example 2)

[0756] 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."

[0757] With conventional business automation systems, it was difficult to improve business processes while taking into account the emotions of workers, leading to problems such as stress and reduced productivity. Furthermore, when automating work within a factory, there was an issue of not being able to operate business flexibly in conjunction with the emotions of workers.

[0758] 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 collecting employee operation logs, means for analyzing the collected log data and understanding the business process, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing automation programs for the identified tasks, means for collecting worker emotion data and proposing improvements to the business process, and means for providing support for introducing AI. This enables flexible business operations that take into account the emotions of workers while improving work efficiency.

[0759] The "means for collecting employee operation logs" refers to a means for recording operation information when workers perform their work and storing it in a database.

[0760] "Means for analyzing collected log data and understanding business processes" refers to a means for analyzing recorded operation logs to understand the flow, frequency, and bottlenecks of business operations.

[0761] "Means for identifying tasks that can be automated using AI based on analysis results" refers to means for identifying which tasks are suitable for automation based on the results of analyzing log data.

[0762] "Means for generating and implementing automation programs for identified tasks" refers to means for designing and creating programs to automate identified tasks and applying them to actual systems.

[0763] "Means for collecting worker emotional data and proposing improvements to work processes" refers to means for analyzing the emotional state of workers and proposing ways to optimize work processes based on the results.

[0764] "Means for providing support for AI implementation" refers to means for providing necessary support and consulting when implementing AI technology.

[0765] The system for carrying out the present invention is configured by combining the following means.

[0766] 1. Data Collection Phase

[0767] The server installs dedicated monitoring software on employees' PCs to collect employee operation and communication logs. This software records logs of keyboard operations, mouse movements, application usage, file operations, etc. It also collects communication logs such as emails, chats, and video conferences.

[0768] The terminal sends the collected operation log to the server in real time. The user only needs to carry out their normal work; no special operations are required.

[0769] 2. Data analysis phase

[0770] The server preprocesses the collected log data and converts it into a format suitable for analysis, filtering out unnecessary data and filling in missing data.

[0771] AI models analyze data for patterns and frequencies to understand the overall business process, identifying which tasks are routine and which processes have bottlenecks.

[0772] 3. Identifying tasks that can be automated using AI

[0773] Based on the analysis results, the server extracts tasks that can be automated using AI (e.g., data entry, report creation, automatic email replies, etc.).

[0774] The AI ​​model evaluates the effectiveness of using AI to perform these tasks.

[0775] 4. Program Generation and Implementation Phase

[0776] The server designs and generates automation programs for specified tasks, incorporating technologies such as natural language processing and pattern recognition.

[0777] The terminal applies the generated program to the user's terminal and runs it in actual business.

[0778] 5. Introducing the Emotion Engine

[0779] The server analyzes the user's operation log and communication log and recognizes the user's emotions using an emotion engine.

[0780] The AI ​​model will then propose improvements to business processes based on the analysis results of the emotion engine. For example, if a user is feeling stressed, the model will suggest ways to reduce the work that is causing the stress.

[0781] The server uses the emotion data to evaluate the performance of the automated program and adjust it as needed.

[0782] Specific examples

[0783] scenario

[0784] Take the example of sorting products on a conveyor belt in a factory.

[0785] 1. Use smartphones or tablets to collect worker operation logs (product handling records, quantity records, etc.).

[0786] 2. Keep a communication log when workers use video conferencing or chat tools.

[0787] 3. The server performs preprocessing and performs pattern analysis on the log data.

[0788] 4. Use natural language processing technology to analyze emotions from workers' chat content.

[0789] 5. Identify the routine parts of the sorting process and determine whether they can be automated using AI.

[0790] 6. Generate a robot control program that automates sorting based on product type and quantity.

[0791] 7. The program is applied to the robot, and the actual sorting work is carried out automatically.

[0792] 8. If workers are stressed, adjust sorting assignments to reduce their workload.

[0793] 9. Based on the suggested improvements, fine-tune the robot's control program in real time.

[0794] In this way, work efficiency within the factory can be significantly improved, and flexible business operations can be carried out in accordance with the emotional state of the workers.

[0795] Examples of prompt statements

[0796] Design a generative AI system that automatically understands business processes using employee operation logs and communication logs and combines them with an emotion engine. Identify areas of business that can be automated using AI, then generate and implement automation programs. Also, propose improvements to business processes using user emotion data.

[0797] Specifically, we will implement a system that will provide robots working in factories with suggestions for improving work efficiency and operations based on the emotions and work logs of workers. Factory workers will use smartphones, tablets, and head-mounted displays to collect and analyze operation logs in real time and generate automation programs to be applied to the robots.

[0798] This system will enable automation of work processes within factories and companies and improve work processes by taking into account the emotions of workers.

[0799] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0800] Step 1: Data collection

[0801] Subject: Server

[0802] Description: The server collects employee operation logs and communication logs. Operation logs include keyboard operations, mouse movements, information on applications used, file operations, etc. This data is collected in real time through dedicated monitoring software and sent to the server. Communication logs include records of emails, chats, and video conferences.

[0803] Input: Employee operation log and communication log

[0804] Output: Collected operational and communication data

[0805] Step 2: Data Preprocessing

[0806] Subject: Server

[0807] Description: The server preprocesses the collected log data. Specifically, it filters the data, removes unnecessary data, imputes missing values, and converts the data into a format suitable for analysis.

[0808] Input: Collected operational and communication data

[0809] Output: Preprocessed data

[0810] Step 3: Data analysis

[0811] Subject: Server, AI model

[0812] Description: The server inputs the preprocessed data into the AI ​​model to analyze business processes. The AI ​​model analyzes the patterns and frequency of the data to understand the overall picture of the business. This analysis identifies routine tasks and bottleneck processes.

[0813] Input: Preprocessed data

[0814] Output: Business process analysis results

[0815] Step 4: Identifying tasks that can be automated using AI

[0816] Subject: Server, AI model

[0817] Description: The server extracts tasks that can be automated using AI based on the analysis results. The AI ​​model evaluates the degree to which automating the tasks will improve efficiency. This evaluation identifies specific tasks to automate.

[0818] Input: Business process analysis results

[0819] Output: List of tasks that can be automated using AI and evaluation results of efficiency effects

[0820] Step 5: Generate the automation program

[0821] Subject: Server

[0822] Description: The server generates an automation program for the specified business. This program incorporates technologies such as natural language processing and pattern recognition and is designed according to the specific business content. The generated program is converted into an executable format.

[0823] Input: List of tasks that can be automated using AI and evaluation results of efficiency effects

[0824] Output: Automation program

[0825] Step 6: Implement the program

[0826] Subject: Terminal

[0827] Description: The terminal applies the generated automation program to the user's terminal and runs it in actual work. Here, the application status of the program is monitored and adjustments are made as necessary.

[0828] Input: Automation program

[0829] Output: Automated tasks

[0830] Step 7: Collect emotion data

[0831] Subject: Server

[0832] Description: The server collects user emotional data. The emotional data is obtained by analyzing operation logs and communication logs. An emotion engine is used to recognize emotional states such as stress and satisfaction.

[0833] Input: User operation log and communication log

[0834] Output: Emotion data

[0835] Step 8: Analyze emotion data and propose improvements

[0836] Subject: Server, AI model

[0837] Description: The server uses collected emotional data to make suggestions for improving work processes. The AI ​​model analyzes the emotional data and identifies factors that increase stress or decrease satisfaction. Suggested improvements include adjusting work assignments or changing processes.

[0838] Input: Emotion data

[0839] Output: Business process improvement proposals

[0840] Step 9: Implementing improvement proposals

[0841] Subject: Terminal

[0842] Description: The terminal applies the proposed improvements and optimizes the business process, fine-tuning programs and changing work assignments to improve business efficiency in real time.

[0843] Input: Business process improvement proposal

[0844] Output: Improved business processes

[0845] 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.

[0846] 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.

[0847] 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.

[0848] [Third embodiment]

[0849] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0850] 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.

[0851] 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).

[0852] 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.

[0853] 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.

[0854] 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).

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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."

[0861] This invention relates to a system that automatically understands business processes within a company and uses generative AI to perform the work on behalf of the company. This system collects and analyzes employee operation logs and communication logs, identifies areas of the work that can be automated using AI, and generates and implements specific automation programs. Specific embodiments are described below.

[0862] Data Collection Phase

[0863] To collect employee operation logs and communication logs, the system operates as follows:

[0864] The server installs special monitoring software on employees' PCs and records logs of keyboard operations, mouse movements, application usage, file operations, and more.

[0865] The terminal transmits the user's operation log to the server in real time.

[0866] The user simply performs normal operations and no special operations are required.

[0867] The server also collects communication logs such as emails, chat tools, and video conferences.

[0868] Data analysis phase

[0869] The collected log data is used to perform analysis to automatically understand business processes.

[0870] The server preprocesses the collected log data and converts it into a format suitable for analysis.

[0871] It uses AI models to analyze data for patterns and frequencies to understand the full picture of business processes, identifying which tasks are routine and which processes have bottlenecks.

[0872] Identifying tasks that can be automated using AI

[0873] Based on the analysis results, we will identify which tasks can be automated using AI.

[0874] The server lists specific tasks (such as data entry, report writing, and automatic email replies) based on the analysis results.

[0875] The AI ​​model evaluates the effectiveness of using AI to perform these tasks.

[0876] Program Development and Implementation Phase

[0877] For tasks identified as amenable to AI, specific automation programs are generated and implemented.

[0878] The server designs and generates an automation program for the specified task.

[0879] Using AI models, technologies such as natural language processing and pattern recognition are incorporated to specifically code the program.

[0880] The generated program is applied to the terminal, automating business processes, such as budget management in the accounting department.

[0881] Implementation support phase

[0882] We provide consulting and technical assistance to support the smooth introduction of AI technology.

[0883] The server provides consulting on the introduction and use of AI technology.

[0884] With support, users can advance the implementation of AI technology and achieve improved business efficiency.

[0885] Specific examples

[0886] For example, the automation of budget management tasks works as follows:

[0887] 1. The server collects data that employees enter into the budget management system and communication logs with each department.

[0888] 2. The AI ​​model analyzes the data and understands the budget management process.

[0889] 3. The server identifies tasks such as inputting, correcting, and confirming budgets as routine tasks that can be automated using AI.

[0890] 4. The server generates a program that automates budget management tasks and applies it to the terminal.

[0891] 5. The user reviews the automated budget control operations and adjusts the output accordingly.

[0892] This will enable companies to streamline their business processes and smoothly introduce AI technology.

[0893] The processing flow will be explained below.

[0894] Step 1:

[0895] The server installs monitoring software on employees' PCs and records operation logs such as keyboard operations, mouse movements, application usage, and file operations.

[0896] Step 2:

[0897] The terminal transmits the user's operation log to the server in real time, which includes organizing the data according to the log format and uploading it to the server.

[0898] Step 3:

[0899] The server preprocesses the collected log data and converts it into a format suitable for analysis, which also includes filtering unnecessary data and filling in missing data.

[0900] Step 4:

[0901] The server inputs the processed log data into an AI model to analyze the overall business process. The AI ​​model uses machine learning algorithms to analyze operation patterns and frequencies and identify business flows.

[0902] Step 5:

[0903] The server extracts tasks that can be automated using AI based on the analysis results. Highly repetitive tasks and routine work are identified and listed as candidates for AI automation.

[0904] Step 6:

[0905] The server designs an automation program for the extracted tasks, which includes creating a flow diagram for each specific task and selecting the necessary algorithms.

[0906] Step 7:

[0907] The server then proceeds with coding and testing based on the designed program. The generated program is also tested in a virtual environment to ensure its compatibility with current operations.

[0908] Step 8:

[0909] The terminal applies the tested automation program to the employee's PC and runs it in the actual work, thereby realizing the automation of work.

[0910] Step 9:

[0911] The server monitors the operation of the automation program to check for any malfunctions, monitors the operation status in real time, and corrects the program as necessary.

[0912] Step 10:

[0913] The server will provide consulting on the introduction and use of AI technology to help users maximize the benefits of AI, as well as provide training programs and answer any questions.

[0914] This series of steps will enable companies to streamline their business processes and smoothly implement AI technology.

[0915] Example 1

[0916] 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."

[0917] Conventional business process automation systems did not adequately collect and analyze a wide range of employee operation logs and communication logs, making it difficult to accurately identify areas of business that could be automated using AI. Furthermore, no effective means were provided for creating and implementing automation programs for identified business processes. As a result, business efficiency did not improve and the benefits of introducing AI technology were limited.

[0918] 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.

[0919] In this invention, the server includes means for collecting employee operation logs, means for preprocessing the collected log data and converting it into a format suitable for analysis, means for analyzing the preprocessed data using a generative model to understand business processes, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing an automation program for the identified tasks, means for applying the automation program on a terminal to automate the business process, and means for providing support for introducing AI, thereby enabling the efficiency of business processes and the smooth introduction of AI technology.

[0920] "Means for collecting employee operation logs" refers to devices or software that monitor keyboard operations, mouse movements, application usage, file operations, etc. on computers used by employees and record this data.

[0921] "Means for preprocessing collected log data and converting it into a format suitable for analysis" refers to a method or device that preprocesses employee operation log and communication log data, such as by cleansing and formatting it, and converts it into structured data suitable for analysis.

[0922] "Means for analyzing preprocessed data using generative models to understand business processes" refers to a method or device that uses machine learning or statistical analysis models to analyze preprocessed data, identify business patterns and frequencies, and understand the overall flow of a process.

[0923] "Means for identifying tasks that can be automated using AI based on analysis results" refers to a method or device that, based on analysis results, extracts and lists routine tasks and tasks that can be automated to improve performance.

[0924] "Means for generating and implementing an automation program for a specified task" refers to a method or device for designing a program to automate a specified task, generating specific code, and applying the program to a system.

[0925] "Means for applying an automation program on a terminal to automate a business process" refers to a method or device that installs and runs a generated automation program on an employee's terminal to automatically perform specified business tasks.

[0926] "Means for providing support for AI implementation" refers to methods or devices that provide consulting, training, and technical support on how to implement and use automation programs, and help users effectively utilize AI technology.

[0927] MODE FOR CARRYING OUT THE INVENTION

[0928] This invention relates to a system that automatically understands business processes within a company and uses generative AI to perform the work. This system collects and analyzes employee operation logs and communication logs, identifies areas of the work that can be automated using AI, and generates and implements specific automation programs. Specific embodiments of this system are described below.

[0929] Data Collection Phase

[0930] The server installs dedicated monitoring software on employees' PCs, such as "Activity Monitor." The monitoring software records logs of keyboard operations, mouse movements, application usage, file operations, and so on.

[0931] The terminal sends the user's operation log to the server in real time. This transmission uses UDP or TCP / IP protocols. When the log reaches a certain size, it is stored in a transmission buffer and transferred to the server.

[0932] Users can simply carry out their normal work without any special operations, such as entering data in Excel, sending emails, and communicating via chat.

[0933] The server also collects communication logs from emails, chat tools (such as Slack and Microsoft Teams), and video conferencing (such as Zoom). This collection is done using API integration and Webhooks.

[0934] Data analysis phase

[0935] The server preprocesses the collected log data and converts it into a format suitable for analysis. For example, it extracts unstructured data using regular expressions and converts it into CSV or JSON format. This process removes noise from the data and retains only the necessary information.

[0936] AI models (such as GPT-4 or TensorFlow) analyze the pre-processed data and analyze the data for patterns and frequencies to understand how business processes are progressing, for example, determining whether a particular task is repeated multiple times.

[0937] Identifying tasks that can be automated using AI

[0938] Based on the AI ​​model's analysis, the server lists routine tasks and bottlenecks, such as data entry and daily report generation.

[0939] The AI ​​model evaluates the effectiveness of using AI for the listed tasks, calculating, for example, how much efficiency will be improved and how much work time will be reduced.

[0940] Program Development and Implementation Phase

[0941] The server generates an automation program based on the evaluation results. Specifically, the program is coded using Python or JavaScript, incorporating natural language processing and pattern recognition to create a program that automates business processes.

[0942] The terminal applies and executes the generated automation program, for example, using the Selenium library to automate browser operations and input or modify budget data.

[0943] Implementation support phase

[0944] The server will provide consulting on how to implement and use AI technology, including creating training video tutorials and FAQs. The server will also respond to user questions and resolve technical issues.

[0945] Users can receive support from the server and smoothly carry out tasks that utilize AI technology, such as checking the operation of the automated programs they have implemented and making adjustments as necessary.

[0946] Specific examples

[0947] For example, the automation of budget management tasks works as follows:

[0948] 1. The server collects data entered into the budget management system and communication logs from each department.

[0949] 2. An AI model (such as BERT) analyzes the collected data and understands the flow of budget requests, approval processes, and coordination work from each department.

[0950] 3. The server identifies tasks such as entering, correcting, and approving budgets as routine tasks that can be automated using AI.

[0951] 4. The server generates a program to automate budget management tasks and applies the script written in Python to the terminal. For example, it uses the Selenium library to automate browser operations and input and modify budget data.

[0952] 5. A user runs the automated budget control process and checks the results, for example, by reviewing the generated budget report and making manual corrections as needed.

[0953] Prompt Sentence Examples

[0954] "Please create a Python script to automate the following business process. The task involves adjusting budgets between departments. Specifically, the task involves forwarding the budget submitted by department A to department B and providing feedback on the evaluation results."

[0955] This will enable companies to improve the efficiency of their business processes and smoothly implement AI technology.

[0956] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0957] Step 1:

[0958] The server installs monitoring software on employees' PCs, such as "Activity Monitor." After installation, the software records logs of keyboard operations, mouse movements, application usage, file operations, and more.

[0959] Input: Employee's PC, monitoring software

[0960] Output: Installed monitoring software

[0961] Step 2:

[0962] The terminal sends the user's operation log to the server in real time. Data is transferred using UDP or TCP / IP protocols. When the log reaches a certain size, it is stored in a sending buffer and then transferred to the server.

[0963] Input: User operation log

[0964] Output: Operation log sent to the server

[0965] Step 3:

[0966] Users can carry out their daily work as usual. For example, they can enter data in Excel, send and receive emails, and communicate using chat. No special operations are required.

[0967] Input: User's daily routine

[0968] Output: Generated business log

[0969] Step 4:

[0970] The server also collects communication logs from users' email and chat tools, such as Slack and Microsoft Teams, and the video conferencing tool Zoom. This data is collected using API integration and Webhooks.

[0971] Input: emails, chats, video conference logs

[0972] Output: Collected communication logs

[0973] Step 5:

[0974] The server preprocesses the collected log data and converts it into a format suitable for analysis. It extracts unstructured data using regular expressions and converts it into CSV or JSON format. This process removes noise from the data and retains only the necessary information.

[0975] Input: Raw Data Log

[0976] Output: Preprocessed data

[0977] Step 6:

[0978] AI models (such as GPT-4 or TensorFlow) analyze the pre-processed data and analyze it for patterns and frequencies, for example, to identify routine or repeated tasks.

[0979] Input: Preprocessed data

[0980] Output: Identification of work patterns and tasks

[0981] Step 7:

[0982] Based on the analysis results of the AI ​​model, the server lists routine tasks and bottleneck tasks, which clearly shows which tasks can be automated using AI.

[0983] Input: Analysis results of the AI ​​model

[0984] Output: List of tasks that can be automated using AI

[0985] Step 8:

[0986] The AI ​​model evaluates the effect of implementing AI on the listed tasks, calculating, for example, how much the efficiency of the task will improve and how much the work time will be reduced.

[0987] Input: List of tasks that can be automated using AI

[0988] Output: Evaluation of the effectiveness of AI

[0989] Step 9:

[0990] The server then generates automation programs for the identified tasks based on the evaluation results. The automation programs are coded using Python or JavaScript and incorporate natural language processing and pattern recognition.

[0991] Input:Effectiveness evaluation

[0992] Output: Automation program code

[0993] Step 10:

[0994] The terminal applies and executes the generated automation program, for example, using the Selenium library to automate browser operations and input or modify budget data.

[0995] Input: Automation program code

[0996] Output: Automation executed on the terminal

[0997] Step 11:

[0998] Sarver will provide consulting on the implementation and use of AI technology, including creating training video tutorials and FAQs.

[0999] Input: automation programs, user feedback

[1000] Output: Consulting materials, support

[1001] Step 12:

[1002] Users can receive support from the server and smoothly carry out tasks that utilize AI technology, such as checking the operation of the automated programs they have implemented and making adjustments as necessary.

[1003] Input: Consulting materials, support

[1004] Output: Streamlined business processes

[1005] This will enable companies to achieve greater efficiency in their business processes and smoothly implement AI technology.

[1006] (Application example 1)

[1007] 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."

[1008] There is a need to solve problems caused by reduced work efficiency and human error within logistics centers. It is also important to reduce the burden on employees performing the work and improve the accuracy and efficiency of picking work. However, conventional systems cannot fully address these issues, and it requires a lot of time and resources to understand and automate efficient business processes.

[1009] 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.

[1010] In this invention, the server includes means for collecting employee operation logs, means for analyzing the collected log data and understanding business processes, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing automation programs for the identified tasks, means for managing terminals that give instructions for work within the logistics center and collect operation logs, and means for controlling product picking work by logistics robots, thereby enabling efficient and accurate picking work within the logistics center.

[1011] An "employee operation log" is a record of keyboard operations, mouse movements, application use, file operations, etc. performed by employees when performing their work.

[1012] "Collected log data" is a general term for data recorded in the course of business operations, such as employee operation logs and communication logs.

[1013] "Means for understanding business processes" refers to methods and technologies for analyzing collected log data and understanding the flow and characteristics of business processes.

[1014] "AI-enabled tasks" refers to business processes and tasks that can be automated using artificial intelligence technology.

[1015] "Automation Program" means software designed and implemented to automate a specified task.

[1016] "AI implementation support" means providing assistance and consulting related to the implementation and operation of AI technology.

[1017] "Terminal" refers to a device used within a logistics center to issue work instructions and collect operation logs.

[1018] A "logistics center" refers to a facility where goods are stored, managed, and prepared for shipment.

[1019] A "robot" is a mechanical device that automatically performs a set task.

[1020] "Picking work" refers to the work of picking out specified products at a logistics center.

[1021] A "server" refers to a computer system that centrally manages and analyzes data.

[1022] The present invention relates to a system that automatically understands business processes in a logistics center and performs the business processes by utilizing a generation AI. Specific embodiments will be described below.

[1023] Data Collection Phase

[1024] The server installs dedicated monitoring software on terminals in the logistics center and records logs of the picking work performed by workers (users). The terminals track the movements of the workers and send operation logs to the server in real time. The users simply carry out their normal work tasks and no special operations are required. The server also collects control logs for logistics robots. This includes receiving picking lists and the product picking process.

[1025] Data analysis phase

[1026] The server uses the collected log data to perform analysis to automatically understand business processes. Specifically, the server preprocesses the collected log data and converts it into a format suitable for analysis. A generative AI model is used to analyze the patterns and frequency of the data to understand the overall picture of business processes. This allows it to identify which tasks are routine and which processes have bottlenecks.

[1027] Identifying tasks that can be automated using AI

[1028] Based on the analysis results, the server identifies which tasks can be automated using AI. The server then lists specific tasks (such as product picking and shipping preparation) based on the analysis results, and uses a generative AI model to evaluate the effectiveness of implementing these tasks using AI.

[1029] Program Development and Implementation Phase

[1030] The server designs and generates automation programs for the identified tasks. These programs incorporate technologies such as natural language processing and pattern recognition and are specifically coded. The generated programs are applied to terminals and logistics robots, realizing the automation of business processes. For example, picking work within a logistics center can be automated and performed accurately and efficiently.

[1031] Implementation support phase

[1032] The server provides consulting and technical support to support the smooth introduction of AI technology. With this support, users can advance the introduction of AI technology and achieve operational efficiency improvements. As a concrete example, when introducing the new smartphone app "Picking Assist" in a logistics center, the following prompt sentence can be entered:

[1033] Example prompt sentence:

[1034] "Get the picking list, track employee movements and send it to the server"

[1035] This will enable efficient and accurate picking operations within the distribution center, and the system will contribute greatly to the automation and efficiency of operations at distribution centers.

[1036] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1037] Step 1:

[1038] The server installs dedicated monitoring software on terminals at the distribution center. The terminals collect user operation logs (e.g., keyboard operations, mouse movements) and send the data to the server in real time. The input is the user's operations, and the output is the collected operation log data.

[1039] Step 2:

[1040] The server preprocesses the collected log data and converts it into a format suitable for analysis. At this stage, the log data is sorted in chronological order and unnecessary data is filtered out. The input is operation log data, and the output is the preprocessed log data.

[1041] Step 3:

[1042] The server uses the preprocessed log data to analyze data patterns and frequencies using a generative AI model. The AI ​​model (e.g., TensorFlow) learns to understand the characteristics of the business process. The input is the preprocessed log data, and the output is the analysis results.

[1043] Step 4:

[1044] Based on the analysis results, the server identifies tasks that can be automated using AI. During this process, the server identifies and lists routine tasks and bottlenecks. The input is the analysis results, and the output is a list of tasks that can be automated using AI.

[1045] Step 5:

[1046] The server generates an automation program for the identified tasks. It incorporates natural language processing and pattern recognition technology and codes the program based on the flow of the target tasks. The input is a list of tasks that can be automated by AI, and the output is the automation program.

[1047] Step 6:

[1048] The server applies the generated program to the terminal and the logistics robot to execute the automation. For example, the logistics robot starts picking products. The input is the automation program, and the output is the automation task being executed.

[1049] Step 7:

[1050] The server monitors the operation of the automation program, detects malfunctions, and automatically corrects them. Log data is analyzed in real time, and when anomalies are detected, the server executes a correction algorithm. The input is the log data from the running automation task, and the output is the corrected operation log.

[1051] Step 8:

[1052] Users receive support in introducing AI to improve operational efficiency within their logistics centers. The server provides consulting information and technical materials for support, helping users to effectively use the new system. The input is the business information that requires support, and the output is specific guidelines and advice for support.

[1053] 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.

[1054] This invention relates to a system that automatically understands business processes within a company and, by combining it with an emotion engine that recognizes user emotions, utilizes generative AI to perform business processes on behalf of the company. This system collects and analyzes employee operation logs and communication logs, identifies areas of business that can be automated using AI, and generates and implements specific automation programs. It also uses user emotion data to propose improvements to business processes.

[1055] Data Collection Phase

[1056] To collect employee operation logs and communication logs, the system operates as follows:

[1057] The server installs special monitoring software on employees' PCs and records logs of keyboard operations, mouse movements, application usage, file operations, and more.

[1058] The device sends user operation logs to the server in real time, and also collects communication logs such as emails, chats, and video conferences.

[1059] Users only need to carry out their normal work without any special operations.

[1060] Data analysis phase

[1061] The collected log data is used to perform analysis to automatically understand business processes.

[1062] The server preprocesses the collected log data and converts it into a format suitable for analysis, filtering out unnecessary data and filling in missing data.

[1063] AI models analyze data for patterns and frequencies to understand the overall business process, identifying which tasks are routine and which processes have bottlenecks.

[1064] Identifying tasks that can be automated using AI

[1065] Based on the analysis results, we will identify which tasks can be automated using AI.

[1066] Based on the analysis results, the server extracts tasks that can be automated using AI (for example, data entry, report creation, automatic email replies, etc.).

[1067] The AI ​​model evaluates the effectiveness of using AI to perform these tasks.

[1068] Program Development and Implementation Phase

[1069] For tasks identified as amenable to AI, specific automation programs are generated and implemented.

[1070] The server designs and generates automation programs for specified tasks, incorporating technologies such as natural language processing and pattern recognition.

[1071] The terminal then applies the generated program to the user's PC and runs it in the actual work, thereby realizing the automation of the work.

[1072] Introducing the Emotion Engine

[1073] In order to recognize the user's emotions and reflect them in the business process, the following processing is performed.

[1074] The server analyzes the user's operation log and communication log and uses an emotion engine to recognize the user's emotions.

[1075] The AI ​​model will then propose improvements to business processes based on the analysis results of the emotion engine. For example, if a user is feeling stressed, the model will suggest ways to reduce the work that is causing the stress.

[1076] The server uses the emotion data to evaluate the performance of the automated program and adjust it as needed.

[1077] Implementation support phase

[1078] We provide consulting and technical assistance to support the smooth introduction of AI technology.

[1079] The server provides consulting on the introduction and use of AI technology.

[1080] With support, users can advance the implementation of AI technology and achieve improved business efficiency.

[1081] Specific examples

[1082] For example, the automation of budget management tasks works as follows:

[1083] 1. The server collects data that employees enter into the budget management system and communication logs with each department.

[1084] 2. The AI ​​model analyzes the data and understands the budget management process.

[1085] 3. The server identifies tasks such as inputting, correcting, and confirming budgets as routine tasks that can be automated using AI.

[1086] 4. The server generates a program that automates budget management tasks and applies it to the terminal.

[1087] 5. The user reviews the automated budget control operations and adjusts the output accordingly.

[1088] 6. The server analyzes the user's emotional data and, if stress increases, makes suggestions for improvements to reduce work burden.

[1089] In this way, companies can not only streamline their business processes, but also realize a way of working that takes users' emotions into consideration.

[1090] The processing flow will be explained below.

[1091] Step 1:

[1092] The server installs monitoring software on employees' PCs and records operation logs such as keyboard operations, mouse movements, application usage, and file operations.

[1093] Step 2:

[1094] The terminal sends the user's operation log and communication log (email, chat, video conference, etc.) to the server in real time.

[1095] Step 3:

[1096] The server preprocesses the collected log data and converts it into a format suitable for analysis, specifically by cleansing the data, filtering out unnecessary data, and filling in missing data.

[1097] Step 4:

[1098] The server inputs the processed log data into an AI model to analyze the overall business process. The AI ​​model uses machine learning algorithms to analyze operation patterns and frequencies and identify business flows.

[1099] Step 5:

[1100] The server extracts tasks that can be automated using AI based on the analysis results. Highly repetitive tasks and routine work are identified and listed as candidates for AI automation.

[1101] Step 6:

[1102] The server designs an automation program for the extracted tasks, which includes creating a flow diagram for each specific task and selecting the necessary algorithms.

[1103] Step 7:

[1104] The server then proceeds with coding and testing based on the designed program. The generated program is then tested in a virtual environment to verify its compatibility with current operations.

[1105] Step 8:

[1106] The terminal applies the tested automation program to the employee's PC and runs it in the actual work, thereby realizing the automation of work.

[1107] Step 9:

[1108] The server analyzes the user's operation log and communication log and uses an emotion engine to recognize the user's emotions. The emotion data is analyzed to extract stress and satisfaction during operation.

[1109] Step 10:

[1110] The AI ​​model will then propose improvements to business processes based on the analysis results of the emotion engine. For example, if a user is feeling stressed, it will identify the tasks that are causing the stress and make suggestions to reduce the burden.

[1111] Step 11:

[1112] The server uses the emotional data to evaluate the performance of the automated program and adjusts it as needed, so that the program's accuracy improves depending on the user's emotional state.

[1113] Step 12:

[1114] The server provides consulting on the introduction and use of AI technology, and users can receive support to advance the introduction of AI technology and achieve business efficiency.

[1115] This series of steps will enable companies to streamline their business processes and smoothly implement AI technology. Furthermore, analyzing user emotion data will enable companies to provide a more comfortable working environment.

[1116] Example 2

[1117] 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."

[1118] Conventional business process automation systems optimize operations by collecting and analyzing employee operation logs, but lack the functionality to improve operations by reflecting user sentiment. Furthermore, support for implementing AI is insufficient, preventing the full benefits of business automation from being realized. Furthermore, there are inadequate methods for detecting and correcting defects in automation programs, which can lead to numerous problems in actual operation.

[1119] 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 collecting employee operation logs, means for analyzing the collected log data and understanding the business process, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing an automation program for the identified tasks, means for improving the business process using an emotion engine for recognizing user emotions, and means for providing support for introducing AI. This not only enables the efficiency of business processes to be improved, but also enables a work style that takes user emotions into consideration and enables the rapid detection and correction of defects in automation programs.

[1120] An "employee operation log" is a series of data such as keyboard operations, mouse movements, application usage, and file operations that occur when an employee performs their work.

[1121] "Log data analysis" refers to understanding business processes by preprocessing collected operation logs and analyzing patterns and frequencies.

[1122] "Tasks that can be automated using AI" are tasks that can be automated based on the results of analysis, such as routine tasks such as data entry, report creation, and automatic email replies.

[1123] An "automation program" is a program designed and generated to efficiently carry out a specific task, incorporating technologies such as natural language processing and pattern recognition.

[1124] An "emotion engine" is an algorithm or software that analyzes a user's operation log and communication log to recognize the user's emotions.

[1125] "Improvement of business processes" means proposing and implementing work environments and workflows to reduce stress based on user emotional data recognized by the emotion engine.

[1126] "AI implementation support" refers to activities that provide consulting and technical guidance to support the smooth implementation of AI technology, ensuring that AI systems operate effectively.

[1127] "Communication logs" are data related to communications between employees, such as emails, chats, and video conferences.

[1128] "Fault detection and correction" is the process of monitoring the behavior of an automated program while it is running, and quickly finding and correcting problems.

[1129] This invention relates to a system that automatically understands business processes within a company and, by combining it with an emotion engine that recognizes user emotions, utilizes generative AI to perform business processes on behalf of the company. This system collects and analyzes employee operation logs and communication logs, identifies areas of business that can be automated using AI, and generates and implements specific automation programs. It also uses user emotion data to propose improvements to business processes.

[1130] First, the server remotely installs dedicated monitoring software on employees' PCs. This monitoring software records logs of keyboard operations, mouse movements, application usage, file operations, and more. The terminal then sends the user's operation logs to the server in real time. It also collects communication logs such as emails, chats, and video conferences. During this time, the user simply carries out their normal work duties without any special operations being required.

[1131] The server then preprocesses the collected log data and converts it into a format suitable for analysis. Specifically, it removes duplicate data, filters out noisy data, and fills in missing data. Based on this preprocessed data, the AI ​​model analyzes the patterns and frequency of business processes to identify which tasks are routine and which processes have bottlenecks.

[1132] The server then extracts tasks that can be automated using AI (for example, data entry, automatic report generation, and automatic replies to standard emails) based on the analysis results. The AI ​​model evaluates the degree to which efficiency improvements can be expected by automating the extracted tasks using AI. The server then designs and generates automation programs for the identified tasks. These programs incorporate natural language processing and pattern recognition technologies.

[1133] The terminal executes the automation program sent from the server and applies it to the on-site PC. This allows the automated tasks to be executed on the user's PC. The user can then check the results of the installed automation program and make adjustments as necessary.

[1134] Furthermore, the server uses an emotion engine that analyzes user operation logs and communication logs to recognize user emotions. The AI ​​model then proposes improvements to business processes based on the emotion data recognized by the emotion engine. For example, it might automate stressful tasks or propose new workflows to reduce work burdens. The server uses the emotion data to evaluate the performance of the automation program and make adjustments if necessary.

[1135] Finally, the server provides consulting and technical support related to the introduction of AI technology. With this support, users can smoothly implement AI technology. Specifically, they can apply automation programs based on the provided guidelines to achieve business efficiency.

[1136] As a concrete example, consider the automation of budget management tasks. The server collects data entered by employees into the budget management system and communication logs with each department. The AI ​​model analyzes this data and understands the budget management process. The server identifies tasks such as entering, correcting, and finalizing the budget as routine tasks that can be automated using AI. The server generates a program to automate the budget management tasks and applies it to the device. The user reviews the automated budget management tasks and adjusts the output as appropriate. The server analyzes the user's emotional data and, if stress levels increase, suggests improvements to reduce the workload.

[1137] An example of a prompt is, "Please describe the program processing for this system based on the following steps. Please provide detailed processing for analyzing business processes, identifying areas that can be automated using AI, and generating and implementing an automation program."

[1138] By using this system, companies can simultaneously streamline their business processes and manage employee emotions.

[1139] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1140] Step 1:

[1141] The server remotely installs dedicated monitoring software on employees' PCs. This monitoring software records log data such as keyboard operations, mouse movements, application usage, and file operations. The input is various operational data of employees, and the output is the recorded operation log data.

[1142] Step 2:

[1143] The device sends the user's operation log to the server in real time. It also collects communication logs such as emails, chats, and video conferences and sends them to the server. The input is the collected operation log and communication log, and the output is the log data sent to the server.

[1144] Step 3:

[1145] The server preprocesses the collected log data and converts it into a format suitable for analysis. Specifically, it removes duplicate data, filters out noisy data, and fills in missing data. The input is raw log data, and the output is a preprocessed, clean dataset.

[1146] Step 4:

[1147] The server inputs the preprocessed data into the AI ​​model to analyze the patterns and frequency of business processes. The AI ​​model analyzes the data and identifies routine tasks and bottlenecks in business processes. The input is the preprocessed dataset, and the output is the analysis results of the business processes.

[1148] Step 5:

[1149] The server identifies tasks that can be automated using AI based on the analysis results. These include data entry, automatic report generation, and automatic replies to standard emails. The input is the analysis results of the business process, and the output is a list of tasks that can be automated using AI.

[1150] Step 6:

[1151] The server designs and generates an automation program for the identified tasks. This program incorporates technologies such as natural language processing and pattern recognition. The input is a list of tasks that can be automated by AI, and the output is the automation program.

[1152] Step 7:

[1153] The terminal executes the automation program sent from the server and applies it to the on-site PC. This causes the automated task to be executed on the user's PC. The input is the generated automation program, and the output is the result of the task.

[1154] Step 8:

[1155] Users check the results of the implemented automation program and make corrections or adjustments as necessary. The input is the automated business result, and the output is the corrected or adjusted business result.

[1156] Step 9:

[1157] The server recognizes the user's emotions using an emotion engine that analyzes the user's operation log and communication log. The input is the operation log and communication log, and the output is the user's emotion data.

[1158] Step 10:

[1159] The AI ​​model makes suggestions for improving business processes based on the emotional data recognized by the emotion engine. For example, it might automate stressful tasks or suggest new workflows to reduce work burdens. The input is emotional data, and the output is improvement suggestions.

[1160] Step 11:

[1161] The server uses the emotion data to evaluate the performance of the automated program and make adjustments if necessary. The inputs are the emotion data and performance data of the automated program, and the output is the adjusted automated program.

[1162] Step 12:

[1163] The server provides consulting and technical support related to the introduction of AI technology. The user receives the provided support to smoothly introduce AI technology and achieve business efficiency. The input is a request for consulting or technical support, and the output is the provided support and guidelines.

[1164] An example of a prompt is, "Please describe the program processing for this system based on the following steps. Please provide detailed processing for analyzing business processes, identifying areas that can be automated using AI, and generating and implementing an automation program."

[1165] (Application example 2)

[1166] 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."

[1167] With conventional business automation systems, it was difficult to improve business processes while taking into account the emotions of workers, leading to problems such as stress and reduced productivity. Furthermore, when automating work within a factory, there was an issue of not being able to operate business flexibly in conjunction with the emotions of workers.

[1168] 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 collecting employee operation logs, means for analyzing the collected log data and understanding the business process, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing automation programs for the identified tasks, means for collecting worker emotion data and proposing improvements to the business process, and means for providing support for introducing AI. This enables flexible business operations that take into account the emotions of workers while improving work efficiency.

[1169] The "means for collecting employee operation logs" refers to a means for recording operation information when workers perform their work and storing it in a database.

[1170] "Means for analyzing collected log data and understanding business processes" refers to a means for analyzing recorded operation logs to understand the flow, frequency, and bottlenecks of business operations.

[1171] "Means for identifying tasks that can be automated using AI based on analysis results" refers to means for identifying which tasks are suitable for automation based on the results of analyzing log data.

[1172] "Means for generating and implementing automation programs for identified tasks" refers to means for designing and creating programs to automate identified tasks and applying them to actual systems.

[1173] "Means for collecting worker emotional data and proposing improvements to work processes" refers to means for analyzing the emotional state of workers and proposing ways to optimize work processes based on the results.

[1174] "Means for providing support for AI implementation" refers to means for providing necessary support and consulting when implementing AI technology.

[1175] The system for carrying out the present invention is configured by combining the following means.

[1176] 1. Data Collection Phase

[1177] The server installs dedicated monitoring software on employees' PCs to collect employee operation and communication logs. This software records logs of keyboard operations, mouse movements, application usage, file operations, etc. It also collects communication logs such as emails, chats, and video conferences.

[1178] The terminal sends the collected operation log to the server in real time. The user only needs to carry out their normal work; no special operations are required.

[1179] 2. Data analysis phase

[1180] The server preprocesses the collected log data and converts it into a format suitable for analysis, filtering out unnecessary data and filling in missing data.

[1181] AI models analyze data for patterns and frequencies to understand the overall business process, identifying which tasks are routine and which processes have bottlenecks.

[1182] 3. Identifying tasks that can be automated using AI

[1183] Based on the analysis results, the server extracts tasks that can be automated using AI (e.g., data entry, report creation, automatic email replies, etc.).

[1184] The AI ​​model evaluates the effectiveness of using AI to perform these tasks.

[1185] 4. Program Generation and Implementation Phase

[1186] The server designs and generates automation programs for specified tasks, incorporating technologies such as natural language processing and pattern recognition.

[1187] The terminal applies the generated program to the user's terminal and runs it in actual business.

[1188] 5. Introducing the Emotion Engine

[1189] The server analyzes the user's operation log and communication log and recognizes the user's emotions using an emotion engine.

[1190] The AI ​​model will then propose improvements to business processes based on the analysis results of the emotion engine. For example, if a user is feeling stressed, the model will suggest ways to reduce the work that is causing the stress.

[1191] The server uses the emotion data to evaluate the performance of the automated program and adjust it as needed.

[1192] Specific examples

[1193] scenario

[1194] Take the example of sorting products on a conveyor belt in a factory.

[1195] 1. Use smartphones or tablets to collect worker operation logs (product handling records, quantity records, etc.).

[1196] 2. Keep a communication log when workers use video conferencing or chat tools.

[1197] 3. The server performs preprocessing and performs pattern analysis on the log data.

[1198] 4. Use natural language processing technology to analyze emotions from workers' chat content.

[1199] 5. Identify the routine parts of the sorting process and determine whether they can be automated using AI.

[1200] 6. Generate a robot control program that automates sorting based on product type and quantity.

[1201] 7. The program is applied to the robot, and the actual sorting work is carried out automatically.

[1202] 8. If workers are stressed, adjust sorting assignments to reduce their workload.

[1203] 9. Based on the suggested improvements, fine-tune the robot's control program in real time.

[1204] In this way, work efficiency within the factory can be significantly improved, and flexible business operations can be carried out in accordance with the emotional state of the workers.

[1205] Examples of prompt statements

[1206] Design a generative AI system that automatically understands business processes using employee operation logs and communication logs and combines them with an emotion engine. Identify areas of business that can be automated using AI, then generate and implement automation programs. Also, propose improvements to business processes using user emotion data.

[1207] Specifically, we will implement a system that will provide robots working in factories with suggestions for improving work efficiency and operations based on the emotions and work logs of workers. Factory workers will use smartphones, tablets, and head-mounted displays to collect and analyze operation logs in real time and generate automation programs to be applied to the robots.

[1208] This system will enable automation of work processes within factories and companies and improve work processes by taking into account the emotions of workers.

[1209] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1210] Step 1: Data collection

[1211] Subject: Server

[1212] Description: The server collects employee operation logs and communication logs. Operation logs include keyboard operations, mouse movements, information on applications used, file operations, etc. This data is collected in real time through dedicated monitoring software and sent to the server. Communication logs include records of emails, chats, and video conferences.

[1213] Input: Employee operation log and communication log

[1214] Output: Collected operational and communication data

[1215] Step 2: Data Preprocessing

[1216] Subject: Server

[1217] Description: The server preprocesses the collected log data. Specifically, it filters the data, removes unnecessary data, imputes missing values, and converts the data into a format suitable for analysis.

[1218] Input: Collected operational and communication data

[1219] Output: Preprocessed data

[1220] Step 3: Data analysis

[1221] Subject: Server, AI model

[1222] Description: The server inputs the preprocessed data into the AI ​​model to analyze business processes. The AI ​​model analyzes the patterns and frequency of the data to understand the overall picture of the business. This analysis identifies routine tasks and bottleneck processes.

[1223] Input: Preprocessed data

[1224] Output: Business process analysis results

[1225] Step 4: Identifying tasks that can be automated using AI

[1226] Subject: Server, AI model

[1227] Description: The server extracts tasks that can be automated using AI based on the analysis results. The AI ​​model evaluates the degree to which automating the tasks will improve efficiency. This evaluation identifies specific tasks to automate.

[1228] Input: Business process analysis results

[1229] Output: List of tasks that can be automated using AI and evaluation results of efficiency effects

[1230] Step 5: Generate the automation program

[1231] Subject: Server

[1232] Description: The server generates an automation program for the specified business. This program incorporates technologies such as natural language processing and pattern recognition and is designed according to the specific business content. The generated program is converted into an executable format.

[1233] Input: List of tasks that can be automated using AI and evaluation results of efficiency effects

[1234] Output: Automation program

[1235] Step 6: Implement the program

[1236] Subject: Terminal

[1237] Description: The terminal applies the generated automation program to the user's terminal and runs it in actual work. Here, the application status of the program is monitored and adjustments are made as necessary.

[1238] Input: Automation program

[1239] Output: Automated tasks

[1240] Step 7: Collect emotion data

[1241] Subject: Server

[1242] Description: The server collects user emotional data. The emotional data is obtained by analyzing operation logs and communication logs. An emotion engine is used to recognize emotional states such as stress and satisfaction.

[1243] Input: User operation log and communication log

[1244] Output: Emotion data

[1245] Step 8: Analyze emotion data and propose improvements

[1246] Subject: Server, AI model

[1247] Description: The server uses collected emotional data to make suggestions for improving work processes. The AI ​​model analyzes the emotional data and identifies factors that increase stress or decrease satisfaction. Suggested improvements include adjusting work assignments or changing processes.

[1248] Input: Emotion data

[1249] Output: Business process improvement proposals

[1250] Step 9: Implementing improvement proposals

[1251] Subject: Terminal

[1252] Description: The terminal applies the proposed improvements and optimizes the business process, fine-tuning programs and changing work assignments to improve business efficiency in real time.

[1253] Input: Business process improvement proposal

[1254] Output: Improved business processes

[1255] 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.

[1256] 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.

[1257] 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.

[1258] [Fourth embodiment]

[1259] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1260] 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.

[1261] 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).

[1262] 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.

[1263] 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.

[1264] 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).

[1265] 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.

[1266] 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.

[1267] 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.

[1268] 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.

[1269] 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.

[1270] 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.

[1271] 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."

[1272] This invention relates to a system that automatically understands business processes within a company and uses generative AI to perform the work on behalf of the company. This system collects and analyzes employee operation logs and communication logs, identifies areas of the work that can be automated using AI, and generates and implements specific automation programs. Specific embodiments are described below.

[1273] Data Collection Phase

[1274] To collect employee operation logs and communication logs, the system operates as follows:

[1275] The server installs special monitoring software on employees' PCs and records logs of keyboard operations, mouse movements, application usage, file operations, and more.

[1276] The terminal transmits the user's operation log to the server in real time.

[1277] The user simply performs normal operations and no special operations are required.

[1278] The server also collects communication logs such as emails, chat tools, and video conferences.

[1279] Data analysis phase

[1280] The collected log data is used to perform analysis to automatically understand business processes.

[1281] The server preprocesses the collected log data and converts it into a format suitable for analysis.

[1282] It uses AI models to analyze data for patterns and frequencies to understand the full picture of business processes, identifying which tasks are routine and which processes have bottlenecks.

[1283] Identifying tasks that can be automated using AI

[1284] Based on the analysis results, we will identify which tasks can be automated using AI.

[1285] The server lists specific tasks (such as data entry, report writing, and automatic email replies) based on the analysis results.

[1286] The AI ​​model evaluates the effectiveness of using AI to perform these tasks.

[1287] Program Development and Implementation Phase

[1288] For tasks identified as amenable to AI, specific automation programs are generated and implemented.

[1289] The server designs and generates an automation program for the specified task.

[1290] Using AI models, technologies such as natural language processing and pattern recognition are incorporated to specifically code the program.

[1291] The generated program is applied to the terminal, automating business processes, such as budget management in the accounting department.

[1292] Implementation support phase

[1293] We provide consulting and technical assistance to support the smooth introduction of AI technology.

[1294] The server provides consulting on the introduction and use of AI technology.

[1295] With support, users can advance the implementation of AI technology and achieve improved business efficiency.

[1296] Specific examples

[1297] For example, the automation of budget management tasks works as follows:

[1298] 1. The server collects data that employees enter into the budget management system and communication logs with each department.

[1299] 2. The AI ​​model analyzes the data and understands the budget management process.

[1300] 3. The server identifies tasks such as inputting, correcting, and confirming budgets as routine tasks that can be automated using AI.

[1301] 4. The server generates a program that automates budget management tasks and applies it to the terminal.

[1302] 5. The user reviews the automated budget control operations and adjusts the output accordingly.

[1303] This will enable companies to streamline their business processes and smoothly introduce AI technology.

[1304] The processing flow will be explained below.

[1305] Step 1:

[1306] The server installs monitoring software on employees' PCs and records operation logs such as keyboard operations, mouse movements, application usage, and file operations.

[1307] Step 2:

[1308] The terminal transmits the user's operation log to the server in real time, which includes organizing the data according to the log format and uploading it to the server.

[1309] Step 3:

[1310] The server preprocesses the collected log data and converts it into a format suitable for analysis, which also includes filtering unnecessary data and filling in missing data.

[1311] Step 4:

[1312] The server inputs the processed log data into an AI model to analyze the overall business process. The AI ​​model uses machine learning algorithms to analyze operation patterns and frequencies and identify business flows.

[1313] Step 5:

[1314] The server extracts tasks that can be automated using AI based on the analysis results. Highly repetitive tasks and routine work are identified and listed as candidates for AI automation.

[1315] Step 6:

[1316] The server designs an automation program for the extracted tasks, which includes creating a flow diagram for each specific task and selecting the necessary algorithms.

[1317] Step 7:

[1318] The server then proceeds with coding and testing based on the designed program. The generated program is also tested in a virtual environment to ensure its compatibility with current operations.

[1319] Step 8:

[1320] The terminal applies the tested automation program to the employee's PC and runs it in the actual work, thereby realizing the automation of work.

[1321] Step 9:

[1322] The server monitors the operation of the automation program to check for any malfunctions, monitors the operation status in real time, and corrects the program as necessary.

[1323] Step 10:

[1324] The server will provide consulting on the introduction and use of AI technology to help users maximize the benefits of AI, as well as provide training programs and answer any questions.

[1325] This series of steps will enable companies to streamline their business processes and smoothly implement AI technology.

[1326] Example 1

[1327] 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."

[1328] Conventional business process automation systems did not adequately collect and analyze a wide range of employee operation logs and communication logs, making it difficult to accurately identify areas of business that could be automated using AI. Furthermore, no effective means were provided for creating and implementing automation programs for identified business processes. As a result, business efficiency did not improve and the benefits of introducing AI technology were limited.

[1329] 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.

[1330] In this invention, the server includes means for collecting employee operation logs, means for preprocessing the collected log data and converting it into a format suitable for analysis, means for analyzing the preprocessed data using a generative model to understand business processes, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing an automation program for the identified tasks, means for applying the automation program on a terminal to automate the business process, and means for providing support for introducing AI, thereby enabling the efficiency of business processes and the smooth introduction of AI technology.

[1331] "Means for collecting employee operation logs" refers to devices or software that monitor keyboard operations, mouse movements, application usage, file operations, etc. on computers used by employees and record this data.

[1332] "Means for preprocessing collected log data and converting it into a format suitable for analysis" refers to a method or device that preprocesses employee operation log and communication log data, such as by cleansing and formatting it, and converts it into structured data suitable for analysis.

[1333] "Means for analyzing preprocessed data using generative models to understand business processes" refers to a method or device that uses machine learning or statistical analysis models to analyze preprocessed data, identify business patterns and frequencies, and understand the overall flow of a process.

[1334] "Means for identifying tasks that can be automated using AI based on analysis results" refers to a method or device that, based on analysis results, extracts and lists routine tasks and tasks that can be automated to improve performance.

[1335] "Means for generating and implementing an automation program for a specified task" refers to a method or device for designing a program to automate a specified task, generating specific code, and applying the program to a system.

[1336] "Means for applying an automation program on a terminal to automate a business process" refers to a method or device that installs and runs a generated automation program on an employee's terminal to automatically perform specified business tasks.

[1337] "Means for providing support for AI implementation" refers to methods or devices that provide consulting, training, and technical support on how to implement and use automation programs, and help users effectively utilize AI technology.

[1338] MODE FOR CARRYING OUT THE INVENTION

[1339] This invention relates to a system that automatically understands business processes within a company and uses generative AI to perform the work. This system collects and analyzes employee operation logs and communication logs, identifies areas of the work that can be automated using AI, and generates and implements specific automation programs. Specific embodiments of this system are described below.

[1340] Data Collection Phase

[1341] The server installs dedicated monitoring software on employees' PCs, such as "Activity Monitor." The monitoring software records logs of keyboard operations, mouse movements, application usage, file operations, and so on.

[1342] The terminal sends the user's operation log to the server in real time. This transmission uses UDP or TCP / IP protocols. When the log reaches a certain size, it is stored in a transmission buffer and transferred to the server.

[1343] Users can simply carry out their normal work without any special operations, such as entering data in Excel, sending emails, and communicating via chat.

[1344] The server also collects communication logs from emails, chat tools (such as Slack and Microsoft Teams), and video conferencing (such as Zoom). This collection is done using API integration and Webhooks.

[1345] Data analysis phase

[1346] The server preprocesses the collected log data and converts it into a format suitable for analysis. For example, it extracts unstructured data using regular expressions and converts it into CSV or JSON format. This process removes noise from the data and retains only the necessary information.

[1347] AI models (such as GPT-4 or TensorFlow) analyze the pre-processed data and analyze the data for patterns and frequencies to understand how business processes are progressing, for example, determining whether a particular task is repeated multiple times.

[1348] Identifying tasks that can be automated using AI

[1349] Based on the AI ​​model's analysis, the server lists routine tasks and bottlenecks, such as data entry and daily report generation.

[1350] The AI ​​model evaluates the effectiveness of using AI for the listed tasks, calculating, for example, how much efficiency will be improved and how much work time will be reduced.

[1351] Program Development and Implementation Phase

[1352] The server generates an automation program based on the evaluation results. Specifically, the program is coded using Python or JavaScript, incorporating natural language processing and pattern recognition to create a program that automates business processes.

[1353] The terminal applies and executes the generated automation program, for example, using the Selenium library to automate browser operations and input or modify budget data.

[1354] Implementation support phase

[1355] The server will provide consulting on how to implement and use AI technology, including creating training video tutorials and FAQs. The server will also respond to user questions and resolve technical issues.

[1356] Users can receive support from the server and smoothly carry out tasks that utilize AI technology, such as checking the operation of the automated programs they have implemented and making adjustments as necessary.

[1357] Specific examples

[1358] For example, the automation of budget management tasks works as follows:

[1359] 1. The server collects data entered into the budget management system and communication logs from each department.

[1360] 2. An AI model (such as BERT) analyzes the collected data and understands the flow of budget requests, approval processes, and coordination work from each department.

[1361] 3. The server identifies tasks such as entering, correcting, and approving budgets as routine tasks that can be automated using AI.

[1362] 4. The server generates a program to automate budget management tasks and applies the script written in Python to the terminal. For example, it uses the Selenium library to automate browser operations and input and modify budget data.

[1363] 5. A user runs the automated budget control process and checks the results, for example, by reviewing the generated budget report and making manual corrections as needed.

[1364] Prompt Sentence Examples

[1365] "Please create a Python script to automate the following business process. The task involves adjusting budgets between departments. Specifically, the task involves forwarding the budget submitted by department A to department B and providing feedback on the evaluation results."

[1366] This will enable companies to improve the efficiency of their business processes and smoothly implement AI technology.

[1367] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1368] Step 1:

[1369] The server installs monitoring software on employees' PCs, such as "Activity Monitor." After installation, the software records logs of keyboard operations, mouse movements, application usage, file operations, and more.

[1370] Input: Employee's PC, monitoring software

[1371] Output: Installed monitoring software

[1372] Step 2:

[1373] The terminal sends the user's operation log to the server in real time. Data is transferred using UDP or TCP / IP protocols. When the log reaches a certain size, it is stored in a sending buffer and then transferred to the server.

[1374] Input: User operation log

[1375] Output: Operation log sent to the server

[1376] Step 3:

[1377] Users can carry out their daily work as usual. For example, they can enter data in Excel, send and receive emails, and communicate using chat. No special operations are required.

[1378] Input: User's daily routine

[1379] Output: Generated business log

[1380] Step 4:

[1381] The server also collects communication logs from users' email and chat tools, such as Slack and Microsoft Teams, and the video conferencing tool Zoom. This data is collected using API integration and Webhooks.

[1382] Input: emails, chats, video conference logs

[1383] Output: Collected communication logs

[1384] Step 5:

[1385] The server preprocesses the collected log data and converts it into a format suitable for analysis. It extracts unstructured data using regular expressions and converts it into CSV or JSON format. This process removes noise from the data and retains only the necessary information.

[1386] Input: Raw Data Log

[1387] Output: Preprocessed data

[1388] Step 6:

[1389] AI models (such as GPT-4 or TensorFlow) analyze the pre-processed data and analyze it for patterns and frequencies, for example, to identify routine or repeated tasks.

[1390] Input: Preprocessed data

[1391] Output: Identification of work patterns and tasks

[1392] Step 7:

[1393] Based on the analysis results of the AI ​​model, the server lists routine tasks and bottleneck tasks, which clearly shows which tasks can be automated using AI.

[1394] Input: Analysis results of the AI ​​model

[1395] Output: List of tasks that can be automated using AI

[1396] Step 8:

[1397] The AI ​​model evaluates the effect of implementing AI on the listed tasks, calculating, for example, how much the efficiency of the task will improve and how much the work time will be reduced.

[1398] Input: List of tasks that can be automated using AI

[1399] Output: Evaluation of the effectiveness of AI

[1400] Step 9:

[1401] The server then generates automation programs for the identified tasks based on the evaluation results. The automation programs are coded using Python or JavaScript and incorporate natural language processing and pattern recognition.

[1402] Input:Effectiveness evaluation

[1403] Output: Automation program code

[1404] Step 10:

[1405] The terminal applies and executes the generated automation program, for example, using the Selenium library to automate browser operations and input or modify budget data.

[1406] Input: Automation program code

[1407] Output: Automation executed on the terminal

[1408] Step 11:

[1409] Sarver will provide consulting on the implementation and use of AI technology, including creating training video tutorials and FAQs.

[1410] Input: automation programs, user feedback

[1411] Output: Consulting materials, support

[1412] Step 12:

[1413] Users can receive support from the server and smoothly carry out tasks that utilize AI technology, such as checking the operation of the automated programs they have implemented and making adjustments as necessary.

[1414] Input: Consulting materials, support

[1415] Output: Streamlined business processes

[1416] This will enable companies to achieve greater efficiency in their business processes and smoothly implement AI technology.

[1417] (Application example 1)

[1418] 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."

[1419] There is a need to solve problems caused by reduced work efficiency and human error within logistics centers. It is also important to reduce the burden on employees performing the work and improve the accuracy and efficiency of picking work. However, conventional systems cannot fully address these issues, and it requires a lot of time and resources to understand and automate efficient business processes.

[1420] 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.

[1421] In this invention, the server includes means for collecting employee operation logs, means for analyzing the collected log data and understanding business processes, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing automation programs for the identified tasks, means for managing terminals that give instructions for work within the logistics center and collect operation logs, and means for controlling product picking work by logistics robots, thereby enabling efficient and accurate picking work within the logistics center.

[1422] An "employee operation log" is a record of keyboard operations, mouse movements, application use, file operations, etc. performed by employees when performing their work.

[1423] "Collected log data" is a general term for data recorded in the course of business operations, such as employee operation logs and communication logs.

[1424] "Means for understanding business processes" refers to methods and technologies for analyzing collected log data and understanding the flow and characteristics of business processes.

[1425] "AI-enabled tasks" refers to business processes and tasks that can be automated using artificial intelligence technology.

[1426] "Automation Program" means software designed and implemented to automate a specified task.

[1427] "AI implementation support" means providing assistance and consulting related to the implementation and operation of AI technology.

[1428] "Terminal" refers to a device used within a logistics center to issue work instructions and collect operation logs.

[1429] A "logistics center" refers to a facility where goods are stored, managed, and prepared for shipment.

[1430] A "robot" is a mechanical device that automatically performs a set task.

[1431] "Picking work" refers to the work of picking out specified products at a logistics center.

[1432] A "server" refers to a computer system that centrally manages and analyzes data.

[1433] The present invention relates to a system that automatically understands business processes in a logistics center and performs the business processes by utilizing a generation AI. Specific embodiments will be described below.

[1434] Data Collection Phase

[1435] The server installs dedicated monitoring software on terminals in the logistics center and records logs of the picking work performed by workers (users). The terminals track the movements of the workers and send operation logs to the server in real time. The users simply carry out their normal work tasks and no special operations are required. The server also collects control logs for logistics robots. This includes receiving picking lists and the product picking process.

[1436] Data analysis phase

[1437] The server uses the collected log data to perform analysis to automatically understand business processes. Specifically, the server preprocesses the collected log data and converts it into a format suitable for analysis. A generative AI model is used to analyze the patterns and frequency of the data to understand the overall picture of business processes. This allows it to identify which tasks are routine and which processes have bottlenecks.

[1438] Identifying tasks that can be automated using AI

[1439] Based on the analysis results, the server identifies which tasks can be automated using AI. The server then lists specific tasks (such as product picking and shipping preparation) based on the analysis results, and uses a generative AI model to evaluate the effectiveness of implementing these tasks using AI.

[1440] Program Development and Implementation Phase

[1441] The server designs and generates automation programs for the identified tasks. These programs incorporate technologies such as natural language processing and pattern recognition and are specifically coded. The generated programs are applied to terminals and logistics robots, realizing the automation of business processes. For example, picking work within a logistics center can be automated and performed accurately and efficiently.

[1442] Implementation support phase

[1443] The server provides consulting and technical support to support the smooth introduction of AI technology. With this support, users can advance the introduction of AI technology and achieve operational efficiency improvements. As a concrete example, when introducing the new smartphone app "Picking Assist" in a logistics center, the following prompt sentence can be entered:

[1444] Example prompt sentence:

[1445] "Get the picking list, track employee movements and send it to the server"

[1446] This will enable efficient and accurate picking operations within the distribution center, and the system will contribute greatly to the automation and efficiency of operations at distribution centers.

[1447] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1448] Step 1:

[1449] The server installs dedicated monitoring software on terminals at the distribution center. The terminals collect user operation logs (e.g., keyboard operations, mouse movements) and send the data to the server in real time. The input is the user's operations, and the output is the collected operation log data.

[1450] Step 2:

[1451] The server preprocesses the collected log data and converts it into a format suitable for analysis. At this stage, the log data is sorted in chronological order and unnecessary data is filtered out. The input is operation log data, and the output is the preprocessed log data.

[1452] Step 3:

[1453] The server uses the preprocessed log data to analyze data patterns and frequencies using a generative AI model. The AI ​​model (e.g., TensorFlow) learns to understand the characteristics of the business process. The input is the preprocessed log data, and the output is the analysis results.

[1454] Step 4:

[1455] Based on the analysis results, the server identifies tasks that can be automated using AI. During this process, the server identifies and lists routine tasks and bottlenecks. The input is the analysis results, and the output is a list of tasks that can be automated using AI.

[1456] Step 5:

[1457] The server generates an automation program for the identified tasks. It incorporates natural language processing and pattern recognition technology and codes the program based on the flow of the target tasks. The input is a list of tasks that can be automated by AI, and the output is the automation program.

[1458] Step 6:

[1459] The server applies the generated program to the terminal and the logistics robot to execute the automation. For example, the logistics robot starts picking products. The input is the automation program, and the output is the automation task being executed.

[1460] Step 7:

[1461] The server monitors the operation of the automation program, detects malfunctions, and automatically corrects them. Log data is analyzed in real time, and when anomalies are detected, the server executes a correction algorithm. The input is the log data from the running automation task, and the output is the corrected operation log.

[1462] Step 8:

[1463] Users receive support in introducing AI to improve operational efficiency within their logistics centers. The server provides consulting information and technical materials for support, helping users to effectively use the new system. The input is the business information that requires support, and the output is specific guidelines and advice for support.

[1464] 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.

[1465] This invention relates to a system that automatically understands business processes within a company and, by combining it with an emotion engine that recognizes user emotions, utilizes generative AI to perform business processes on behalf of the company. This system collects and analyzes employee operation logs and communication logs, identifies areas of business that can be automated using AI, and generates and implements specific automation programs. It also uses user emotion data to propose improvements to business processes.

[1466] Data Collection Phase

[1467] To collect employee operation logs and communication logs, the system operates as follows:

[1468] The server installs special monitoring software on employees' PCs and records logs of keyboard operations, mouse movements, application usage, file operations, and more.

[1469] The device sends user operation logs to the server in real time, and also collects communication logs such as emails, chats, and video conferences.

[1470] Users only need to carry out their normal work without any special operations.

[1471] Data analysis phase

[1472] The collected log data is used to perform analysis to automatically understand business processes.

[1473] The server preprocesses the collected log data and converts it into a format suitable for analysis, filtering out unnecessary data and filling in missing data.

[1474] AI models analyze data for patterns and frequencies to understand the overall business process, identifying which tasks are routine and which processes have bottlenecks.

[1475] Identifying tasks that can be automated using AI

[1476] Based on the analysis results, we will identify which tasks can be automated using AI.

[1477] Based on the analysis results, the server extracts tasks that can be automated using AI (for example, data entry, report creation, automatic email replies, etc.).

[1478] The AI ​​model evaluates the effectiveness of using AI to perform these tasks.

[1479] Program Development and Implementation Phase

[1480] For tasks identified as amenable to AI, specific automation programs are generated and implemented.

[1481] The server designs and generates automation programs for specified tasks, incorporating technologies such as natural language processing and pattern recognition.

[1482] The terminal then applies the generated program to the user's PC and runs it in the actual work, thereby realizing the automation of the work.

[1483] Introducing the Emotion Engine

[1484] In order to recognize the user's emotions and reflect them in the business process, the following processing is performed.

[1485] The server analyzes the user's operation log and communication log and uses an emotion engine to recognize the user's emotions.

[1486] The AI ​​model will then propose improvements to business processes based on the analysis results of the emotion engine. For example, if a user is feeling stressed, the model will suggest ways to reduce the work that is causing the stress.

[1487] The server uses the emotion data to evaluate the performance of the automated program and adjust it as needed.

[1488] Implementation support phase

[1489] We provide consulting and technical assistance to support the smooth introduction of AI technology.

[1490] The server provides consulting on the introduction and use of AI technology.

[1491] With support, users can advance the implementation of AI technology and achieve improved business efficiency.

[1492] Specific examples

[1493] For example, the automation of budget management tasks works as follows:

[1494] 1. The server collects data that employees enter into the budget management system and communication logs with each department.

[1495] 2. The AI ​​model analyzes the data and understands the budget management process.

[1496] 3. The server identifies tasks such as inputting, correcting, and confirming budgets as routine tasks that can be automated using AI.

[1497] 4. The server generates a program that automates budget management tasks and applies it to the terminal.

[1498] 5. The user reviews the automated budget control operations and adjusts the output accordingly.

[1499] 6. The server analyzes the user's emotional data and, if stress increases, makes suggestions for improvements to reduce work burden.

[1500] In this way, companies can not only streamline their business processes, but also realize a way of working that takes users' emotions into consideration.

[1501] The processing flow will be explained below.

[1502] Step 1:

[1503] The server installs monitoring software on employees' PCs and records operation logs such as keyboard operations, mouse movements, application usage, and file operations.

[1504] Step 2:

[1505] The terminal sends the user's operation log and communication log (email, chat, video conference, etc.) to the server in real time.

[1506] Step 3:

[1507] The server preprocesses the collected log data and converts it into a format suitable for analysis, specifically by cleansing the data, filtering out unnecessary data, and filling in missing data.

[1508] Step 4:

[1509] The server inputs the processed log data into an AI model to analyze the overall business process. The AI ​​model uses machine learning algorithms to analyze operation patterns and frequencies and identify business flows.

[1510] Step 5:

[1511] The server extracts tasks that can be automated using AI based on the analysis results. Highly repetitive tasks and routine work are identified and listed as candidates for AI automation.

[1512] Step 6:

[1513] The server designs an automation program for the extracted tasks, which includes creating a flow diagram for each specific task and selecting the necessary algorithms.

[1514] Step 7:

[1515] The server then proceeds with coding and testing based on the designed program. The generated program is then tested in a virtual environment to verify its compatibility with current operations.

[1516] Step 8:

[1517] The terminal applies the tested automation program to the employee's PC and runs it in the actual work, thereby realizing the automation of work.

[1518] Step 9:

[1519] The server analyzes the user's operation log and communication log and uses an emotion engine to recognize the user's emotions. The emotion data is analyzed to extract stress and satisfaction during operation.

[1520] Step 10:

[1521] The AI ​​model will then propose improvements to business processes based on the analysis results of the emotion engine. For example, if a user is feeling stressed, it will identify the tasks that are causing the stress and make suggestions to reduce the burden.

[1522] Step 11:

[1523] The server uses the emotional data to evaluate the performance of the automated program and adjusts it as needed, so that the program's accuracy improves depending on the user's emotional state.

[1524] Step 12:

[1525] The server provides consulting on the introduction and use of AI technology, and users can receive support to advance the introduction of AI technology and achieve business efficiency.

[1526] This series of steps will enable companies to streamline their business processes and smoothly implement AI technology. Furthermore, analyzing user emotion data will enable companies to provide a more comfortable working environment.

[1527] Example 2

[1528] 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."

[1529] Conventional business process automation systems optimize operations by collecting and analyzing employee operation logs, but lack the functionality to improve operations by reflecting user sentiment. Furthermore, support for implementing AI is insufficient, preventing the full benefits of business automation from being realized. Furthermore, there are inadequate methods for detecting and correcting defects in automation programs, which can lead to numerous problems in actual operation.

[1530] 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 collecting employee operation logs, means for analyzing the collected log data and understanding the business process, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing an automation program for the identified tasks, means for improving the business process using an emotion engine for recognizing user emotions, and means for providing support for introducing AI. This not only enables the efficiency of business processes to be improved, but also enables a work style that takes user emotions into consideration and enables the rapid detection and correction of defects in automation programs.

[1531] An "employee operation log" is a series of data such as keyboard operations, mouse movements, application usage, and file operations that occur when an employee performs their work.

[1532] "Log data analysis" refers to understanding business processes by preprocessing collected operation logs and analyzing patterns and frequencies.

[1533] "Tasks that can be automated using AI" are tasks that can be automated based on the results of analysis, such as routine tasks such as data entry, report creation, and automatic email replies.

[1534] An "automation program" is a program designed and generated to efficiently carry out a specific task, incorporating technologies such as natural language processing and pattern recognition.

[1535] An "emotion engine" is an algorithm or software that analyzes a user's operation log and communication log to recognize the user's emotions.

[1536] "Improvement of business processes" means proposing and implementing work environments and workflows to reduce stress based on user emotional data recognized by the emotion engine.

[1537] "AI implementation support" refers to activities that provide consulting and technical guidance to support the smooth implementation of AI technology, ensuring that AI systems operate effectively.

[1538] "Communication logs" are data related to communications between employees, such as emails, chats, and video conferences.

[1539] "Fault detection and correction" is the process of monitoring the behavior of an automated program while it is running, and quickly finding and correcting problems.

[1540] This invention relates to a system that automatically understands business processes within a company and, by combining it with an emotion engine that recognizes user emotions, utilizes generative AI to perform business processes on behalf of the company. This system collects and analyzes employee operation logs and communication logs, identifies areas of business that can be automated using AI, and generates and implements specific automation programs. It also uses user emotion data to propose improvements to business processes.

[1541] First, the server remotely installs dedicated monitoring software on employees' PCs. This monitoring software records logs of keyboard operations, mouse movements, application usage, file operations, and more. The terminal then sends the user's operation logs to the server in real time. It also collects communication logs such as emails, chats, and video conferences. During this time, the user simply carries out their normal work duties without any special operations being required.

[1542] The server then preprocesses the collected log data and converts it into a format suitable for analysis. Specifically, it removes duplicate data, filters out noisy data, and fills in missing data. Based on this preprocessed data, the AI ​​model analyzes the patterns and frequency of business processes to identify which tasks are routine and which processes have bottlenecks.

[1543] The server then extracts tasks that can be automated using AI (for example, data entry, automatic report generation, and automatic replies to standard emails) based on the analysis results. The AI ​​model evaluates the degree to which efficiency improvements can be expected by automating the extracted tasks using AI. The server then designs and generates automation programs for the identified tasks. These programs incorporate natural language processing and pattern recognition technologies.

[1544] The terminal executes the automation program sent from the server and applies it to the on-site PC. This allows the automated tasks to be executed on the user's PC. The user can then check the results of the installed automation program and make adjustments as necessary.

[1545] Furthermore, the server uses an emotion engine that analyzes user operation logs and communication logs to recognize user emotions. The AI ​​model then proposes improvements to business processes based on the emotion data recognized by the emotion engine. For example, it might automate stressful tasks or propose new workflows to reduce work burdens. The server uses the emotion data to evaluate the performance of the automation program and make adjustments if necessary.

[1546] Finally, the server provides consulting and technical support related to the introduction of AI technology. With this support, users can smoothly implement AI technology. Specifically, they can apply automation programs based on the provided guidelines to achieve business efficiency.

[1547] As a concrete example, consider the automation of budget management tasks. The server collects data entered by employees into the budget management system and communication logs with each department. The AI ​​model analyzes this data and understands the budget management process. The server identifies tasks such as entering, correcting, and finalizing the budget as routine tasks that can be automated using AI. The server generates a program to automate the budget management tasks and applies it to the device. The user reviews the automated budget management tasks and adjusts the output as appropriate. The server analyzes the user's emotional data and, if stress levels increase, suggests improvements to reduce the workload.

[1548] An example of a prompt is, "Please describe the program processing for this system based on the following steps. Please provide detailed processing for analyzing business processes, identifying areas that can be automated using AI, and generating and implementing an automation program."

[1549] By using this system, companies can simultaneously streamline their business processes and manage employee emotions.

[1550] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1551] Step 1:

[1552] The server remotely installs dedicated monitoring software on employees' PCs. This monitoring software records log data such as keyboard operations, mouse movements, application usage, and file operations. The input is various operational data of employees, and the output is the recorded operation log data.

[1553] Step 2:

[1554] The device sends the user's operation log to the server in real time. It also collects communication logs such as emails, chats, and video conferences and sends them to the server. The input is the collected operation log and communication log, and the output is the log data sent to the server.

[1555] Step 3:

[1556] The server preprocesses the collected log data and converts it into a format suitable for analysis. Specifically, it removes duplicate data, filters out noisy data, and fills in missing data. The input is raw log data, and the output is a preprocessed, clean dataset.

[1557] Step 4:

[1558] The server inputs the preprocessed data into the AI ​​model to analyze the patterns and frequency of business processes. The AI ​​model analyzes the data and identifies routine tasks and bottlenecks in business processes. The input is the preprocessed dataset, and the output is the analysis results of the business processes.

[1559] Step 5:

[1560] The server identifies tasks that can be automated using AI based on the analysis results. These include data entry, automatic report generation, and automatic replies to standard emails. The input is the analysis results of the business process, and the output is a list of tasks that can be automated using AI.

[1561] Step 6:

[1562] The server designs and generates an automation program for the identified tasks. This program incorporates technologies such as natural language processing and pattern recognition. The input is a list of tasks that can be automated by AI, and the output is the automation program.

[1563] Step 7:

[1564] The terminal executes the automation program sent from the server and applies it to the on-site PC. This causes the automated task to be executed on the user's PC. The input is the generated automation program, and the output is the result of the task.

[1565] Step 8:

[1566] Users check the results of the implemented automation program and make corrections or adjustments as necessary. The input is the automated business result, and the output is the corrected or adjusted business result.

[1567] Step 9:

[1568] The server recognizes the user's emotions using an emotion engine that analyzes the user's operation log and communication log. The input is the operation log and communication log, and the output is the user's emotion data.

[1569] Step 10:

[1570] The AI ​​model makes suggestions for improving business processes based on the emotional data recognized by the emotion engine. For example, it might automate stressful tasks or suggest new workflows to reduce work burdens. The input is emotional data, and the output is improvement suggestions.

[1571] Step 11:

[1572] The server uses the emotion data to evaluate the performance of the automated program and make adjustments if necessary. The inputs are the emotion data and performance data of the automated program, and the output is the adjusted automated program.

[1573] Step 12:

[1574] The server provides consulting and technical support related to the introduction of AI technology. The user receives the provided support to smoothly introduce AI technology and achieve business efficiency. The input is a request for consulting or technical support, and the output is the provided support and guidelines.

[1575] An example of a prompt is, "Please describe the program processing for this system based on the following steps. Please provide detailed processing for analyzing business processes, identifying areas that can be automated using AI, and generating and implementing an automation program."

[1576] (Application example 2)

[1577] 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."

[1578] With conventional business automation systems, it was difficult to improve business processes while taking into account the emotions of workers, leading to problems such as stress and reduced productivity. Furthermore, when automating work within a factory, there was an issue of not being able to operate business flexibly in conjunction with the emotions of workers.

[1579] 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 collecting employee operation logs, means for analyzing the collected log data and understanding the business process, means for identifying tasks that can be automated using AI based on the analysis results, means for generating and implementing automation programs for the identified tasks, means for collecting worker emotion data and proposing improvements to the business process, and means for providing support for introducing AI. This enables flexible business operations that take into account the emotions of workers while improving work efficiency.

[1580] The "means for collecting employee operation logs" refers to a means for recording operation information when workers perform their work and storing it in a database.

[1581] "Means for analyzing collected log data and understanding business processes" refers to a means for analyzing recorded operation logs to understand the flow, frequency, and bottlenecks of business operations.

[1582] "Means for identifying tasks that can be automated using AI based on analysis results" refers to means for identifying which tasks are suitable for automation based on the results of analyzing log data.

[1583] "Means for generating and implementing automation programs for identified tasks" refers to means for designing and creating programs to automate identified tasks and applying them to actual systems.

[1584] "Means for collecting worker emotional data and proposing improvements to work processes" refers to means for analyzing the emotional state of workers and proposing ways to optimize work processes based on the results.

[1585] "Means for providing support for AI implementation" refers to means for providing necessary support and consulting when implementing AI technology.

[1586] The system for carrying out the present invention is configured by combining the following means.

[1587] 1. Data Collection Phase

[1588] The server installs dedicated monitoring software on employees' PCs to collect employee operation and communication logs. This software records logs of keyboard operations, mouse movements, application usage, file operations, etc. It also collects communication logs such as emails, chats, and video conferences.

[1589] The terminal sends the collected operation log to the server in real time. The user only needs to carry out their normal work; no special operations are required.

[1590] 2. Data analysis phase

[1591] The server preprocesses the collected log data and converts it into a format suitable for analysis, filtering out unnecessary data and filling in missing data.

[1592] AI models analyze data for patterns and frequencies to understand the overall business process, identifying which tasks are routine and which processes have bottlenecks.

[1593] 3. Identifying tasks that can be automated using AI

[1594] Based on the analysis results, the server extracts tasks that can be automated using AI (e.g., data entry, report creation, automatic email replies, etc.).

[1595] The AI ​​model evaluates the effectiveness of using AI to perform these tasks.

[1596] 4. Program Generation and Implementation Phase

[1597] The server designs and generates automation programs for specified tasks, incorporating technologies such as natural language processing and pattern recognition.

[1598] The terminal applies the generated program to the user's terminal and runs it in actual business.

[1599] 5. Introducing the Emotion Engine

[1600] The server analyzes the user's operation log and communication log and recognizes the user's emotions using an emotion engine.

[1601] The AI ​​model will then propose improvements to business processes based on the analysis results of the emotion engine. For example, if a user is feeling stressed, the model will suggest ways to reduce the work that is causing the stress.

[1602] The server uses the emotion data to evaluate the performance of the automated program and adjust it as needed.

[1603] Specific examples

[1604] scenario

[1605] Take the example of sorting products on a conveyor belt in a factory.

[1606] 1. Use smartphones or tablets to collect worker operation logs (product handling records, quantity records, etc.).

[1607] 2. Keep a communication log when workers use video conferencing or chat tools.

[1608] 3. The server performs preprocessing and performs pattern analysis on the log data.

[1609] 4. Use natural language processing technology to analyze emotions from workers' chat content.

[1610] 5. Identify the routine parts of the sorting process and determine whether they can be automated using AI.

[1611] 6. Generate a robot control program that automates sorting based on product type and quantity.

[1612] 7. The program is applied to the robot, and the actual sorting work is carried out automatically.

[1613] 8. If workers are stressed, adjust sorting assignments to reduce their workload.

[1614] 9. Based on the suggested improvements, fine-tune the robot's control program in real time.

[1615] In this way, work efficiency within the factory can be significantly improved, and flexible business operations can be carried out in accordance with the emotional state of the workers.

[1616] Examples of prompt statements

[1617] Design a generative AI system that automatically understands business processes using employee operation logs and communication logs and combines them with an emotion engine. Identify areas of business that can be automated using AI, then generate and implement automation programs. Also, propose improvements to business processes using user emotion data.

[1618] Specifically, we will implement a system that will provide robots working in factories with suggestions for improving work efficiency and operations based on the emotions and work logs of workers. Factory workers will use smartphones, tablets, and head-mounted displays to collect and analyze operation logs in real time and generate automation programs to be applied to the robots.

[1619] This system will enable automation of work processes within factories and companies and improve work processes by taking into account the emotions of workers.

[1620] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1621] Step 1: Data collection

[1622] Subject: Server

[1623] Description: The server collects employee operation logs and communication logs. Operation logs include keyboard operations, mouse movements, information on applications used, file operations, etc. This data is collected in real time through dedicated monitoring software and sent to the server. Communication logs include records of emails, chats, and video conferences.

[1624] Input: Employee operation log and communication log

[1625] Output: Collected operational and communication data

[1626] Step 2: Data Preprocessing

[1627] Subject: Server

[1628] Description: The server preprocesses the collected log data. Specifically, it filters the data, removes unnecessary data, imputes missing values, and converts the data into a format suitable for analysis.

[1629] Input: Collected operational and communication data

[1630] Output: Preprocessed data

[1631] Step 3: Data analysis

[1632] Subject: Server, AI model

[1633] Description: The server inputs the preprocessed data into the AI ​​model to analyze business processes. The AI ​​model analyzes the patterns and frequency of the data to understand the overall picture of the business. This analysis identifies routine tasks and bottleneck processes.

[1634] Input: Preprocessed data

[1635] Output: Business process analysis results

[1636] Step 4: Identifying tasks that can be automated using AI

[1637] Subject: Server, AI model

[1638] Description: The server extracts tasks that can be automated using AI based on the analysis results. The AI ​​model evaluates the degree to which automating the tasks will improve efficiency. This evaluation identifies specific tasks to automate.

[1639] Input: Business process analysis results

[1640] Output: List of tasks that can be automated using AI and evaluation results of efficiency effects

[1641] Step 5: Generate the automation program

[1642] Subject: Server

[1643] Description: The server generates an automation program for the specified business. This program incorporates technologies such as natural language processing and pattern recognition and is designed according to the specific business content. The generated program is converted into an executable format.

[1644] Input: List of tasks that can be automated using AI and evaluation results of efficiency effects

[1645] Output: Automation program

[1646] Step 6: Implement the program

[1647] Subject: Terminal

[1648] Description: The terminal applies the generated automation program to the user's terminal and runs it in actual work. Here, the application status of the program is monitored and adjustments are made as necessary.

[1649] Input: Automation program

[1650] Output: Automated tasks

[1651] Step 7: Collect emotion data

[1652] Subject: Server

[1653] Description: The server collects user emotional data. The emotional data is obtained by analyzing operation logs and communication logs. An emotion engine is used to recognize emotional states such as stress and satisfaction.

[1654] Input: User operation log and communication log

[1655] Output: Emotion data

[1656] Step 8: Analyze emotion data and propose improvements

[1657] Subject: Server, AI model

[1658] Description: The server uses collected emotional data to make suggestions for improving work processes. The AI ​​model analyzes the emotional data and identifies factors that increase stress or decrease satisfaction. Suggested improvements include adjusting work assignments or changing processes.

[1659] Input: Emotion data

[1660] Output: Business process improvement proposals

[1661] Step 9: Implementing improvement proposals

[1662] Subject: Terminal

[1663] Description: The terminal applies the proposed improvements and optimizes the business process, fine-tuning programs and changing work assignments to improve business efficiency in real time.

[1664] Input: Business process improvement proposal

[1665] Output: Improved business processes

[1666] 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.

[1667] 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.

[1668] 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.

[1669] 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.

[1670] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1671] 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.

[1672] 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).

[1673] 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.

[1674] 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."

[1675] 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.

[1676] 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).

[1677] 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.

[1678] 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.

[1679] 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.

[1680] 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.

[1681] 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.

[1682] 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.

[1683] 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.

[1684] 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.

[1685] 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.

[1686] 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.

[1687] The following is further disclosed regarding the above embodiment.

[1688] (Claim 1)

[1689] A means of collecting employee operation logs;

[1690] A means of analyzing collected log data and understanding business processes,

[1691] A means to identify tasks that can be automated using AI based on the analysis results, and

[1692] A means for generating and implementing an automation program for the identified tasks;

[1693] A means of providing support for AI adoption,

[1694] A system including:

[1695] (Claim 2)

[1696] 2. The system according to claim 1, further comprising means for collecting and analyzing employee communication logs.

[1697] (Claim 3)

[1698] 10. The system of claim 1, further comprising means for monitoring the operation of the automated program and detecting and correcting malfunctions.

[1699] "Example 1"

[1700] (Claim 1)

[1701] A means of collecting employee operation logs;

[1702] A means of preprocessing the collected log data and converting it into a format suitable for analysis;

[1703] A means of analyzing the pre-processed data and understanding business processes using generative models;

[1704] A means to identify tasks that can be automated using AI based on the analysis results, and

[1705] A means for generating and implementing an automation program for the identified tasks;

[1706] A means for applying automation programs on the terminal to automate business processes;

[1707] A means of providing support for AI adoption,

[1708] A system including:

[1709] (Claim 2)

[1710] 2. The system according to claim 1, further comprising means for collecting and analyzing employee communication logs.

[1711] (Claim 3)

[1712] 10. The system of claim 1, further comprising means for monitoring the operation of the automated program and detecting and correcting malfunctions.

[1713] "Application Example 1"

[1714] (Claim 1)

[1715] A means of collecting employee operation logs;

[1716] A means of analyzing collected log data and understanding business processes,

[1717] A means to identify tasks that can be automated using AI based on the analysis results, and

[1718] A means for generating and implementing an automation program for the identified tasks;

[1719] A means of providing support for AI adoption,

[1720] a means for managing a terminal that issues work instructions and collects operation logs within the logistics center;

[1721] A means for controlling the product picking operation by the logistics robot;

[1722] A system including:

[1723] (Claim 2)

[1724] 2. The system according to claim 1, further comprising means for collecting and analyzing employee communication logs.

[1725] (Claim 3)

[1726] 10. The system of claim 1, further comprising means for monitoring the operation of the automated program and detecting and correcting malfunctions.

[1727] "Example 2: Combining Emotion Engines"

[1728] (Claim 1)

[1729] A means of collecting employee operation logs;

[1730] A means of analyzing collected log data and understanding business processes,

[1731] A means to identify tasks that can be automated using AI based on the analysis results, and

[1732] A means for generating and implementing an automation program for the identified tasks;

[1733] A means for improving a business process using an emotion engine for recognizing user emotions;

[1734] A means of providing support for AI adoption,

[1735] A system including:

[1736] (Claim 2)

[1737] 2. The system according to claim 1, further comprising means for collecting and analyzing employee communication logs.

[1738] (Claim 3)

[1739] 10. The system of claim 1, further comprising means for monitoring the operation of the automated program and detecting and correcting malfunctions.

[1740] "Application example 2 when combining emotion engines"

[1741] (Claim 1)

[1742] A means of collecting employee operation logs;

[1743] A means of analyzing collected log data and understanding business processes,

[1744] A means to identify tasks that can be automated using AI based on the analysis results, and

[1745] A means for generating and implementing an automation program for the identified tasks;

[1746] A means of collecting worker emotion data and proposing improvements to work processes;

[1747] A means of providing support for AI adoption,

[1748] A system including:

[1749] (Claim 2)

[1750] 2. The system according to claim 1, further comprising means for collecting and analyzing employee communication logs.

[1751] (Claim 3)

[1752] 10. The system of claim 1, further comprising means for monitoring the operation of the automated program and detecting and correcting malfunctions. [Explanation of symbols]

[1753] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting employee operation logs; A means of analyzing collected log data and understanding business processes, A means to identify tasks that can be automated using AI based on the analysis results, and A means for generating and implementing an automation program for the identified business; A means of providing support for AI adoption, A system including:

2. 2. The system according to claim 1, further comprising means for collecting and analyzing employee communication logs.

3. 10. The system of claim 1, further comprising means for monitoring the operation of the automated program and detecting and correcting malfunctions.

Images