system

Abstract

To provide a system.SOLUTION: A system comprises: means for automatically detecting a modification point from, say, transaction data, an operation status, a query log, and law revision information; means for transmitting a prompt statement to a generated AI model on the basis of the modification point to generate a concrete modification plan; means for submitting the generated modification plan to a person in charge of the system as an opinion; and means for automatically creating business requirements and system requirements.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional systems, it was necessary for humans to identify areas for improvement based on transaction data, operational status, inquiry logs, legal amendment information, etc., and to create business and system requirements, which required a great deal of time and effort.In addition, humans also had to submit opinions to system personnel, detect operational problems and consider improvement measures, present improvement proposals to development vendors, and plan acceptance test items, all of which required specialized knowledge, making securing and training personnel a challenge. [Means for solving the problem]

[0005] This invention provides a means for automatically detecting points to be improved from transaction data, operational status, inquiry logs, legal amendment information, etc., and automatically creating business requirements and system requirements. It also provides a means for submitting opinions to system personnel, detecting operational problems and considering improvement measures, presenting improvement proposals to development vendors, and automatically planning acceptance test items. This supports all development tasks, making it possible to reduce the number of personnel required and promote business efficiency. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

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

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0027] "Example 1"

[0028] The system of the present invention receives as input transaction data and operational status of internal systems, inquiry logs from ChatBots and other systems, information on legal amendments and regulation revisions, etc. This data is obtained from, for example, a database or cloud storage and sent to the system. The system analyzes this data and automatically detects areas that need improvement. Specifically, it analyzes data patterns and trends to identify system problems and areas that can be improved.

[0029] "Example 2"

[0030] Next, the system automatically creates business and system requirements based on the detected modification points and generates specific modification proposals. The generated modification proposals are then submitted as a recommendation to the system administrator. This recommendation can be submitted, for example, via email or a web-based dashboard.

[0031] "Example 3"

[0032] Furthermore, the system detects operational problems and considers remedial measures. It monitors the system's operating status, and if a problem occurs, it identifies the cause and proposes appropriate remedial measures. The system also presents modification proposals to development vendors and automatically plans acceptance test items. This supports all development tasks, reduces the number of personnel required, and promotes work efficiency.

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

[0034] "Example 1"

[0035] Step 1: The system receives as input transaction data and operational status from internal systems, inquiry logs from ChatBots, etc., and information on legal and regulatory revisions. This data is obtained, for example, from databases or cloud storage, and sent to the system.

[0036] Step 2: The system analyzes this data and automatically detects areas for improvement. Specifically, it analyzes data patterns and trends to identify problems and areas for improvement in the system.

[0037] "Example 2"

[0038] Step 1: The system automatically creates business and system requirements based on the detected modification points and generates specific modification proposals.

[0039] Step 2: The proposed modifications are submitted as a proposal to the system administrator. This proposal can be submitted, for example, via email or a web-based dashboard.

[0040] "Example 3"

[0041] Step 1: The system detects operational problems and considers improvement measures. This involves monitoring the system's operating status, and if a problem occurs, identifying the cause and proposing appropriate improvement measures. Step 2: The system presents improvement proposals to the development vendor and automatically plans acceptance test items. This supports all development tasks, reduces the number of personnel required, and promotes work efficiency.

[0042] Example 1

[0043] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0044] In conventional systems, the task of manually analyzing a variety of data, including transaction data, operational status, inquiry logs, and legal amendment information, to identify areas that need to be improved takes a great deal of time and effort. Furthermore, because the proposal of improvement plans and the planning of acceptance test items based on the analysis results are also done manually, this is inefficient and prone to human error. This leads to issues such as a decrease in the operational efficiency of the system and an increased burden on the personnel in charge.

[0045] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes means for automatically detecting points to be modified from transaction data, operation status, inquiry logs, legal amendment information, etc., means for acquiring data from a database or cloud storage, means for preprocessing the acquired data, means for analyzing the preprocessed data, means for automatically detecting points to be modified based on the analysis results, and means for notifying the detected points to be modified. This enables automatic analysis of data and automatic detection of points to be modified, improving the operational efficiency of the system and reducing the burden on responding personnel.

[0046] "Transaction Data" refers to records of transactions and operations that take place within the system.

[0047] "Operation status" refers to information that indicates the operational status and running status of the system.

[0048] An "inquiry log" refers to data that records the content of inquiries from users and their responses.

[0049] "Legal amendment information" refers to information regarding changes to laws and regulations.

[0050] "Improvement points" refer to problems with the system or areas that need improvement.

[0051] A "database" refers to a system for systematically storing and managing data.

[0052] "Cloud storage" refers to an online storage service for storing data over the Internet.

[0053] "Preprocessing" refers to the process of organizing data and converting it into a format suitable for analysis before conducting data analysis.

[0054] "Analysis" refers to the process of examining data in detail to find patterns and trends.

[0055] "Notification" refers to the act of informing the user of the analysis results and points to be improved.

[0056] MODE FOR CARRYING OUT THE INVENTION

[0057] This invention is a system that automatically analyzes a variety of data such as transaction data, operation status, inquiry logs, and legal amendment information to detect points that need to be improved. A specific embodiment of this system will be described below.

[0058] Data Acquisition

[0059] The server retrieves the necessary data from databases and cloud storage. Specifically, it retrieves transaction data from a MySQL® database and downloads query logs from a storage service. This ensures that the system always has access to the latest data.

[0060] Data Preprocessing

[0061] The server preprocesses the acquired data. This preprocessing includes imputing missing values ​​and normalizing the data. For example, it converts the data into a data frame using the Python (registered trademark) Pandas library and imputes missing values ​​with the mean value. It also normalizes the data and converts it into a format suitable for analysis.

[0062] Data analysis

[0063] The server analyzes the preprocessed data using machine learning algorithms and statistical methods. For example, it uses Scikit-learn (registered trademark) to apply anomaly detection algorithms to identify anomalous transactions. It also uses NLTK, a natural language processing library, to analyze query logs and identify frequently occurring issues.

[0064] Automatic detection of repair points

[0065] The server automatically detects points that need to be improved based on the analysis results. For example, if an abnormal transaction is detected, the details of that transaction are identified and improvement measures are proposed. Also, if the analysis of the inquiry log shows that inquiries about a particular topic are increasing, the server will propose improvements to the system related to that topic.

[0066] Notification of results

[0067] The server notifies the user of the detected points to be fixed. Notifications are made via email or dashboard. For example, the server may send an email to report the analysis results. It may also update the dashboard so that the user can check the analysis results in real time.

[0068] Specific examples

[0069] Example 1: Parsing transaction data

[0070] When a user enters transaction data into the system, the server retrieves the data from a MySQL® database, converts it into a data frame using Pandas, and then applies an anomaly detection algorithm using Scikit-learn® to identify anomalous transactions.

[0071] Example 2: Analyzing ChatBot inquiry logs

[0072] When a user enters a ChatBot inquiry log into the system, the server retrieves the log data from the storage service and analyzes the log using natural language processing libraries such as NLTK and SpaCy, thereby identifying frequently asked topics and issues.

[0073] Prompt Sentence Examples

[0074] Example 1: Parsing transaction data

[0075] "Analyze the following transaction data and detect any anomalous patterns. The data comes from a MySQL® database."

[0076] Example 2: Analyzing ChatBot inquiry logs

[0077] "Analyze the following ChatBot inquiry logs to identify frequently occurring issues. The data is retrieved from a storage service."

[0078] In this way, the server is a system that acquires data, performs preprocessing, analyzes it, automatically detects points that need to be modified, and notifies the user of the results.

[0079] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0080] Step 1: Get the data

[0081] The server retrieves the necessary data from databases and cloud storage. Specifically, it retrieves transaction data from a MySQL (registered trademark) database and downloads query logs from a storage service. As input, it receives database connection information and cloud storage access information, executes SQL queries and API requests, and retrieves data. As output, it saves the retrieved data in local storage.

[0082] Step 2: Preprocessing the data

[0083] The server preprocesses the acquired data. This preprocessing includes imputing missing values ​​and normalizing the data. Specifically, it converts the data into a data frame using the Pandas library and imputes missing values ​​with the mean value. It also normalizes the data and converts it into a format suitable for analysis. It receives the acquired raw data as input and generates preprocessed data as output.

[0084] Step 3: Analyze the data

[0085] The server analyzes the preprocessed data. For the analysis, it uses machine learning algorithms and statistical methods. Specifically, it uses Scikit-learn (registered trademark) to apply anomaly detection algorithms to identify anomalous transactions. It also uses NLTK, a natural language processing library, to analyze query logs and identify frequently occurring issues. It receives the preprocessed data as input and generates analysis results as output.

[0086] Step 4: Automatic detection of repair points

[0087] The server automatically detects points to be improved based on the analysis results. Specifically, if an abnormal transaction is detected, the details of that transaction are identified and improvement measures are proposed. Also, if the analysis of the inquiry log shows an increase in inquiries about a specific topic, the server proposes system improvements related to that topic. The server receives the analysis results as input and identifies points to be improved as output.

[0088] Step 5: Notification of results

[0089] The server notifies the user of the detected modification points. Notifications are made via email or dashboard. Specifically, the analysis results are reported by email. The dashboard is also updated so that the user can check the analysis results in real time. The server receives information about modification points as input, and generates and sends a notification message as output.

[0090] (Application example 1)

[0091] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0092] With conventional systems, it was difficult to comprehensively analyze a wide range of data, including transaction data, operational status, inquiry logs, and legal revision information, and automatically detect areas that needed improvement. Furthermore, there was a lack of means to automatically detect areas that needed optimization in the operation and placement of robots within factories, which hindered efficient operation. This made it difficult to quickly identify and address system problems and areas that could be improved.

[0093] 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.

[0094] In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to the system manager, means for automatically detecting points to be optimized in the operation and placement of robots in the factory, and means for automatically detecting points to be improved by inputting new data. This enables the system to quickly identify problems and areas that can be improved, enabling efficient operation and optimization.

[0095] "Transaction data" refers to data that records transactions and operations that take place within a system.

[0096] "Operation status" is information that indicates the operational status and operating conditions of a system or device.

[0097] An "inquiry log" is data that records the content of inquiries from users and the response history.

[0098] "Legal change information" is information about changes in laws and regulations.

[0099] "Renovation points" refer to areas or parts of systems or equipment that require improvement.

[0100] "Business requirements" refers to the conditions and specifications necessary to carry out a business.

[0101] "System requirements" refers to the technical conditions and specifications that a system must meet.

[0102] "Submitting opinions" is the act of providing suggestions or opinions to the system administrator.

[0103] "Factory robots" refer to automated machinery used in factories.

[0104] "Optimization points for movement and placement" refer to areas for improvement to optimize the movement and placement of the robot.

[0105] "New data" refers to the latest information newly entered into the system.

[0106] The system for carrying out the present invention operates in cooperation with three entities: a server, a terminal, and a user. A specific embodiment of the system will be described below.

[0107] Server Processing

[0108] The server retrieves transaction data, operation status, inquiry logs, legal amendment information, etc. from databases and cloud storage. This data is analyzed on the server, and points for modification are automatically detected. Specifically, the server uses the following software and hardware:

[0109] Software: Python (registered trademark), Pandas, Scikit-learn (registered trademark)

[0110] Hardware: High-performance server machine

[0111] The server analyzes data patterns and trends to identify system problems and areas for improvement. It also runs algorithms to automatically detect optimization points for the operation and placement of robots within the factory. When new data is input, the server analyzes it and automatically detects areas for modification.

[0112] Terminal handling

[0113] The device (e.g., smartphone, tablet, PC) receives the analysis results sent from the server and displays them to the user. The device uses the following software and hardware:

[0114] Software: Web browser, mobile application

[0115] Hardware: Smartphones, tablets, PCs

[0116] The terminal provides an interface for users to check system problems and areas for improvement and take necessary actions. For example, optimization points for the operation and placement of robots within a factory are displayed, and users can change the robot settings based on the results.

[0117] User operations

[0118] Users can check the information sent from the server via their terminal and take necessary actions. As system administrators, users can submit opinions and check business and system requirements. They can also change robot settings based on optimization points for robot operation and placement within the factory.

[0119] Specific examples

[0120] For example, if a robot in a factory frequently stops working, the server can identify the cause and suggest an optimal maintenance schedule. Also, if new safety standards are introduced due to legal changes, the server can adjust the robot's operation based on those standards.

[0121] Prompt Sentence Examples

[0122] "You will be asked to develop an application that analyzes transaction data, operational status, maintenance logs, and legal revision information from robots in factories, and automatically detects optimization points for robot operation and placement. Specifically, the application will have the ability to analyze data patterns and trends, and identify system problems and areas that can be improved."

[0123] In this way, the server, terminal, and user work together to quickly identify problems and areas that can be improved in the system, enabling efficient operation and optimization.

[0124] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0125] Step 1:

[0126] The server retrieves transaction data, operation status, inquiry logs, legal amendment information, etc. from databases and cloud storage. The input is various data sources, and the output is an integrated dataset. Specifically, the server retrieves data using SQL queries and API requests, and integrates the data using the Pandas library.

[0127] Step 2:

[0128] The server preprocesses the integrated dataset. The input is the integrated dataset, and the output is the preprocessed data. Specifically, the server performs missing value imputation, data normalization, and categorical data encoding.

[0129] Step 3:

[0130] The server automatically detects modification points using the preprocessed data. The input is the preprocessed data, and the output is a list of modification points. Specifically, the server trains a machine learning model using the Scikit-learn (registered trademark) library to analyze patterns and trends in the data.

[0131] Step 4:

[0132] The server automatically detects optimization points for the robot's operation and placement within the factory. The input is pre-processed data, and the output is a list of optimization points. Specifically, the server executes an algorithm to identify optimization points for the robot's operation and placement.

[0133] Step 5:

[0134] The server automatically detects modification points by inputting new data. The input is new data, and the output is a list of modification points. Specifically, the server inputs new data into an existing model and predicts modification points.

[0135] Step 6:

[0136] The terminal receives the analysis results sent from the server and displays them to the user. The input is the analysis results from the server, and the output is the display on the user interface. Specifically, the terminal displays the analysis results using a web browser or mobile application.

[0137] Step 7:

[0138] The user checks the information sent from the server through the terminal and takes the necessary action. The input is the analysis results on the terminal, and the output is the user's action. Specifically, the user checks the system's problems and areas that can be improved, and takes action such as changing the robot's settings.

[0139] Example 2

[0140] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0141] In conventional systems, the process of detecting points for improvement from transaction data, operational status, inquiry logs, legal amendment information, etc., and automatically creating business and system requirements was often done manually, resulting in inefficiency. In addition, generating improvement proposals and submitting opinions to system personnel was also done manually, which was time-consuming and labor-intensive, and prone to errors.

[0142] 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 a means for automatically detecting modification points from transaction data, operation status, inquiry logs, legal amendment information, etc., a means for sending a prompt message to the generative AI model based on the modification points and generating a specific modification proposal, a means for submitting the generated modification proposal as a proposal to the system manager, and a means for automatically creating business requirements and system requirements. This makes it possible to automate the process from detecting modification points to generating a modification proposal and submitting a proposal efficiently and accurately.

[0143] "Transaction Data" means data relating to transactions and operations conducted within the system.

[0144] "Operation status" refers to information relating to the operational status and running status of the system.

[0145] An "inquiry log" is a record of inquiries and support requests from users.

[0146] "Legal Change Information" means information about changes to relevant laws and regulations.

[0147] "Modification points" are areas or elements of the system that require improvement or correction.

[0148] A "generative AI model" is a model that has been trained using artificial intelligence to perform a specific task.

[0149] A "prompt" is an instruction entered into a generative AI model to make it perform a specific task.

[0150] "Modification Proposal" means a specific plan or method proposed for improving or modifying a system.

[0151] "Submitting an opinion" is the act of formally submitting a proposal or opinion to the system administrator.

[0152] "Business requirements" are the business requirements and functions that the system must meet.

[0153] "System requirements" are the technical conditions and specifications that a system must meet.

[0154] This invention is a system that automatically detects points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., and automatically creates business requirements and system requirements. Furthermore, it can generate specific improvement proposals using a generative AI model and submit them as suggestions to the system manager.

[0155] Hardware and software used

[0156] server

[0157] The server includes a database, an analytical engine, a generative AI model, and a communication module. The database stores transaction data, operation status, inquiry logs, legal amendment information, and other data. The analytical engine analyzes this data to detect areas that need improvement. The generative AI model used is, for example, GPT-4 (registered trademark) from OpenAI (registered trademark). The communication module sends suggestions to the system administrator via email or a web-based dashboard.

[0158] Terminal

[0159] A terminal is a device through which a user accesses the system. A user uses a web browser to access the system's web application and initiates the automatic creation of business and system requirements.

[0160] User

[0161] The user logs in to the system and starts the automatic creation of business and system requirements. The information entered by the user is sent to the server and analyzed.

[0162] Data processing and calculation

[0163] Detection of repair points

[0164] The server detects points to be modified based on information entered by the user and data automatically collected by the system. For example, if a user enters "I want to add a new customer management function," the server analyzes this information and detects points to be modified related to the customer management function.

[0165] Generate renovation proposals

[0166] The server sends prompts to the generative AI model based on the detected repair points, and the generative AI model generates specific repair proposals based on the prompts and returns them to the server.

[0167] Example prompt sentence:

[0168] Generate business and system requirements for adding new customer management features. Include specific proposed modifications.

[0169] The generative AI model generates the following modifications:

[0170] Business requirements:

[0171] 1. Add the ability to register, update, and delete customer information.

[0172] 2. Add a search function for customer information.

[0173] System requirements:

[0174] 1. Add a new customer table to the database.

[0175] 2. Create an API endpoint to manage customer information.

[0176] 3. Add a customer management screen to the front end.

[0177] Specific renovation proposals:

[0178] 1. Create a SQL script to add a customer table to the PostgreSQL database.

[0179] 2. Use Python® and Flask to create an API endpoint to manage customer information.

[0180] 3. Create a customer management screen using React.

[0181] Submitting opinions

[0182] The server then submits the generated fix proposal to the system administrator as a proposal via email or a web-based dashboard. For example, if the proposal is sent via email, the server uses the SMTP protocol to send the fix proposal to the system administrator.

[0183] Specific behavior:

[0184] The server converts the generated revision plan into an email format.

[0185] The server uses the SMTP protocol to send an email to the system administrator.

[0186] The system administrator receives an email and confirms the proposed modifications.

[0187] In this way, the system can automatically create business and system requirements, generate specific modification proposals, and submit them to the system administrator. This automates the process from detecting modification points to generating modification proposals and submitting opinions, making it possible to carry out the process efficiently and accurately.

[0188] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0189] Step 1:

[0190] A user accesses the system and begins automatically creating business and system requirements.

[0191] Input: User authentication information (username, password)

[0192] Output: User logged in

[0193] Specific behavior:

[0194] The user enters the system URL in a web browser and accesses the login screen.

[0195] The user enters their username and password and clicks the "Login" button.

[0196] The server validates the credentials and logs the user in.

[0197] The user clicks the "Automatically create business and system requirements" button on the dashboard screen.

[0198] Step 2:

[0199] The server receives the user's input and detects the modification points.

[0200] Input: User request (e.g. "I want to add a new customer management function")

[0201] Output: Detected modification points

[0202] Specific behavior:

[0203] The user enters a request into the system and clicks the submit button.

[0204] The server receives the user's input data and stores it in a database.

[0205] The server analyzes the stored data and runs an algorithm to detect points of modification.

[0206] Step 3:

[0207] The server sends a prompt to the generative AI model, which generates specific repair proposals.

[0208] Input: Detected modification point

[0209] Output: Generated modification proposals

[0210] Specific behavior:

[0211] The server connects to the API of the generative AI model and sends a prompt.

[0212] The generative AI model analyzes the prompt text and generates specific repair suggestions.

[0213] The generative AI model returns the generated improvement proposals to the server.

[0214] Example prompt sentence:

[0215] Generate business and system requirements for adding new customer management features. Include specific proposed modifications.

[0216] Step 4:

[0217] The server submits the generated modification plan to the system administrator as a proposal.

[0218] Input: Generated modification proposal

[0219] Output: Submitted feedback to the system administrator

[0220] Specific behavior:

[0221] The server converts the generated revision plan into an email format.

[0222] The server uses the SMTP protocol to send an email to the system administrator.

[0223] The system administrator receives an email and confirms the proposed modifications.

[0224] In this way, the system can automatically create business and system requirements, generate specific modification proposals, and submit them to the system administrator. This automates the process from detecting modification points to generating modification proposals and submitting opinions, making it possible to carry out the process efficiently and accurately.

[0225] (Application example 2)

[0226] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0227] While conventional systems can detect points that need to be improved based on transaction data and operational status, they are unable to monitor robot operation logs in real time, automatically generate business and system requirements when an abnormality is detected, and quickly notify system personnel of specific improvement plans. This has resulted in slow responses when an abnormality occurs, and has led to issues such as insufficient improvement of work efficiency and reduction of personnel required.

[0228] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0229] In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to a system manager, means for monitoring the robot's operation log in real time, means for creating business requirements and system requirements when an abnormality is detected, and means for submitting the created improvement plan to the system manager via email or a web-based dashboard. This makes it possible to quickly and automatically create a countermeasure when a robot abnormality occurs and notify the system manager.

[0230] "Transaction Data" refers to information relating to transactions and operations conducted within the System.

[0231] "Operation status" refers to information that indicates the operational status and operating conditions of systems and equipment.

[0232] "Inquiry log" refers to data that records the history of inquiries and requests from users and systems.

[0233] "Legal Change Information" refers to information regarding changes in relevant laws and regulations.

[0234] "Modification points" refer to areas of systems or equipment that require improvement or correction.

[0235] "Business requirements" refers to the conditions and specifications necessary to carry out a business.

[0236] "System requirements" refers to the technical conditions and specifications that a system must meet.

[0237] "Submitting opinions" refers to the act of submitting suggestions or opinions to the system administrator.

[0238] "Robot operation log" refers to data that records the robot's operations and operating conditions.

[0239] "Real-time monitoring" refers to monitoring the ongoing situation immediately.

[0240] "When an abnormality is detected" refers to when a state that deviates from normal operation is detected.

[0241] "Email" means electronic messages sent and received over the Internet.

[0242] "Web-based Dashboard" means an information display screen accessible through a web browser.

[0243] The system for implementing this invention is configured as follows: The server has a means for automatically detecting points that need to be modified from transaction data, operation status, inquiry logs, legal amendment information, etc. This allows the system's operational status to be constantly monitored and necessary modifications to be quickly identified.

[0244] Furthermore, the server has a means for automatically creating business and system requirements. This allows specific business and system requirements to be automatically generated based on the detected modification points. The generated requirements are submitted as a suggestion to the system manager. The suggestion can be submitted via email or a web-based dashboard.

[0245] The server also has a means of monitoring the robot's operation log in real time. This allows the robot's operating status to be constantly monitored, and any abnormalities detected can be dealt with immediately. If an abnormality is detected, the server generates business and system requirements and creates a specific modification plan. The generated modification plan is submitted to the system administrator via email or a web-based dashboard.

[0246] This system is implemented using a program written in Python (registered trademark). Specifically, the smtplib library is used to implement a function for sending emails. The server acquires the robot's operation logs and runs an algorithm to detect abnormalities. If an abnormality is detected, business and system requirements are generated and a repair plan is created. This information is notified to the system administrator via email or a web-based dashboard.

[0247] For example, if a "Motor malfunction" is detected in the robot's operation log, the server will suggest "replacement of the motor" and notify the system administrator by email. In this way, it is possible to quickly and automatically generate countermeasures when a robot malfunction occurs and notify the system administrator.

[0248] Examples of prompt sentences include the following:

[0249] Create a Python (registered trademark) program that will automatically generate business requirements and system requirements, create specific repair plans, and notify the system administrator when an abnormality is detected in the robot's operation log. The condition for detecting an abnormality is assumed to be when the log status is "error."

[0250] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0251] Step 1:

[0252] The server collects transaction data, operation status, inquiry logs, legal amendment information, etc. These data are necessary to understand the operational status of the system. The input includes various log data and legal amendment information, and the output is stored in the server.

[0253] Step 2:

[0254] The server analyzes the acquired data and automatically detects points to be fixed. Specifically, it uses an algorithm to detect outliers and patterns to identify which parts of the system need to be fixed. The input includes the data acquired in step 1, and the output generates a list of points to be fixed.

[0255] Step 3:

[0256] The server automatically creates business and system requirements based on the detected modification points. To do this, it uses a generative AI model and prompts to generate specific requirements as input. The output is a document of business and system requirements.

[0257] Step 4:

[0258] The server submits the generated business and system requirements to the system administrator as a proposal via email or a web-based dashboard. The input includes the requirements document generated in step 3, and the output includes a notification sent to the system administrator.

[0259] Step 5:

[0260] The server monitors the robot's operation logs in real time. This involves a process of continuously receiving and analyzing the log data sent from the robot. The input includes the robot's operation logs, and the output includes the analysis results.

[0261] Step 6:

[0262] If the server detects an anomaly in the robot's operation log, it generates business requirements and system requirements. Specifically, it uses an anomaly detection algorithm to identify the location where the anomaly occurred and generates requirements based on that. The input includes the log data analyzed in step 5, and the output is a requirements document that corresponds to the anomaly.

[0263] Step 7:

[0264] The server then submits the generated remediation plan to the system administrator via email or a web-based dashboard, allowing the administrator to quickly review and implement the plan. The input includes the remediation plan generated in step 6, and the output includes the notification sent to the system administrator.

[0265] Example 3

[0266] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0267] In modern system operations, it is extremely important to quickly detect operational problems and propose appropriate remedial measures. However, in conventional systems, problem detection, cause identification, and remedial measures are often done manually, which not only takes time to respond but also consumes a lot of human resources. In addition, because proposals for modifications to development vendors and the planning of acceptance test items are also done manually, it is inefficient and prone to errors. This makes stable system operation difficult and poses the problem of hindering business efficiency.

[0268] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for monitoring the operation status of the system in real time, a means for detecting problems and identifying their causes, and a means for proposing improvements based on the identified causes. This makes it possible to quickly detect operational problems and automatically propose appropriate improvements. In addition, the system also presents improvement proposals to development vendors and automatically plans acceptance test items, thereby reducing the number of personnel required and improving work efficiency.

[0269] "Transaction data" is data related to a series of operations or transactions that take place within a system.

[0270] "Operational status" refers to information about the operating status and performance of a system.

[0271] An "inquiry log" is a record of inquiries and support requests from users.

[0272] "Legal Change Information" means information about changes to relevant laws and regulations.

[0273] "Modification points" are areas of the system that require improvement or correction.

[0274] "Business requirements" are the business conditions and needs that the system must meet.

[0275] "System requirements" are the technical conditions and specifications that a system must meet.

[0276] "Submitting opinions" is the act of providing suggestions or opinions to the system administrator.

[0277] "Real-time monitoring" means monitoring the operating status of a system immediately.

[0278] "Detecting problems" means discovering abnormalities or errors that occur within a system.

[0279] "Identifying the cause" means identifying the root cause of a detected problem.

[0280] "Proposing improvements" means presenting ways to improve the system based on the identified causes.

[0281] "Proposing modifications" means making specific proposals for correcting or improving the system.

[0282] "Automatically planning acceptance test items" means automatically creating test items to be performed after a system is modified.

[0283] This invention is a system that monitors the system's operating status in real time, detects problems, identifies their causes, and proposes appropriate remediation measures. Furthermore, by presenting modification proposals to development vendors and automatically planning acceptance test items, it supports all development tasks, reduces the number of personnel required, and promotes work efficiency.

[0284] The server uses monitoring tools such as Prometheus and Grafana to monitor the system's operation in real time, collecting data such as log data, performance metrics, and network traffic to detect abnormal values ​​and error messages.

[0285] The server uses the Python (registered trademark) Pandas library and the R dplyr package to analyze the collected data, thereby detecting problems when CPU usage exceeds a certain threshold or when specific error logs occur frequently.

[0286] The server uses log analysis tools such as Elasticsearch (registered trademark) and Splunk to identify the cause of detected problems. For example, if a specific process is consuming excessive resources, the server analyzes detailed logs of that process to identify the cause.

[0287] Based on the identified causes, the server uses a generative AI model to suggest appropriate remediation measures, such as code changes or system setting adjustments to optimize processes that are consuming excessive resources.

[0288] Based on the proposed improvements, the server presents a proposal for repairs to the development vendor, generating documentation including specific code changes and configuration change procedures. This documentation is automatically generated using a generative AI model.

[0289] The server automatically creates acceptance test items based on the proposed modifications. For example, it generates test cases to verify that the modified system operates correctly. These test cases are automatically generated using a generative AI model.

[0290] The terminal provides an interface for users to check the system status. Users can check the system's operating status and details of problems through the terminal. The terminal can be accessed using a web browser or a dedicated desktop application.

[0291] The user checks the improvement measures provided by the system and implements them as necessary. The user also checks the modification plans and acceptance test items suggested by the system and provides feedback to the development vendor.

[0292] Examples:

[0293] For example, while monitoring the system's operation status, the server detects that CPU usage has exceeded 90%. The server uses Prometheus to detect this abnormal value. Next, the server performs detailed log analysis using Elasticsearch® to identify that a specific process is consuming excessive resources. The server then uses a generative AI model to propose code changes to optimize the process that is consuming excessive resources. For example, the server makes a specific suggestion such as, "Improve the memory management of function B to fix the memory leak in process A." Based on the proposed improvement, the server then presents a modification proposal to the development vendor. For example, the server generates a document containing, "Specific code modification steps to improve memory management for function B." Finally, the server automatically creates acceptance test items based on the modification proposal. For example, the server generates a test case to verify that memory management for function B has been improved."

[0294] Example prompt sentence:

[0295] "Monitor the system's operating status, identify the cause when the CPU utilization rate exceeds 90%, and propose a remedial measure." The flow of the identification process in the third embodiment will be described with reference to FIG.

[0296] Step 1: Monitor system activity

[0297] The server monitors the system's operating status in real time. As input, it collects data such as log data, performance metrics, and network traffic. This is done using monitoring tools such as Prometheus and Grafana. The server analyzes the collected data to detect abnormal values ​​and error messages. As output, it generates a list of abnormal values ​​and error messages.

[0298] Step 2: Detect the problem

[0299] The server analyzes the collected data and detects outliers and error messages. It uses the log data and performance metrics collected in step 1 as input. For analysis, it uses the Pandas library in Python (registered trademark) and the dplyr package in R. It generates a list of detected problems as output. Specifically, it detects problems when CPU usage exceeds a certain threshold or when specific error logs occur frequently.

[0300] Step 3: Identify the cause

[0301] The server performs detailed log analysis to identify the causes of the detected problems. The list of problems detected in step 2 is used as input. A log analysis tool such as Elasticsearch (registered trademark) or Splunk is used for the analysis. A list of identified causes is generated as output. Specifically, if a specific process is consuming excessive resources, the detailed log of that process is analyzed to identify the cause.

[0302] Step 4: Propose improvements

[0303] The server then suggests appropriate remediation actions based on the identified causes. As input, it uses the list of causes identified in step 3. It uses a generative AI model to generate optimal remediation actions. As output, it generates a list of remediation actions. Specific actions include suggesting code changes or system setting adjustments to optimize processes that are consuming excessive resources.

[0304] Step 5: Present your renovation proposal

[0305] The server presents a proposed fix to the development vendor based on the proposed improvements. As input, it uses the list of improvements generated in step 4. It uses a generative AI model to generate a document containing specific steps for code changes and configuration changes. As output, it generates a document of the proposed fix. As a specific operation, it generates a document containing "specific code change steps to improve memory management for function B."

[0306] Step 6: Automatic planning of acceptance test items

[0307] The server automatically creates acceptance test cases based on the proposed modifications. It uses the modification document generated in step 5 as input. It uses a generative AI model to generate test cases. It generates a list of acceptance test cases as output. Specifically, it generates a test case to verify that memory management for function B has been improved.

[0308] (Application example 3)

[0309] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0310] With conventional systems, it is difficult to automatically detect points that need to be improved based on transaction data and operational status, and it is also time-consuming to automatically create business and system requirements. In addition, there is a lack of efficient means to support all development tasks, such as providing opinions to system personnel, detecting operational problems, proposing improvements, presenting improvement proposals to development vendors, and automatically planning acceptance test items. As a result, reductions in the number of personnel and improvements in work efficiency have not been fully achieved. Furthermore, there is a need to monitor the operational status of robots operating in factories in real time and respond quickly when problems occur.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0312] In this invention, the server includes a means for automatically detecting points to be improved based on transaction data, operational status, inquiry logs, legal amendment information, etc.; a means for automatically creating business requirements and system requirements; a means for submitting opinions to system personnel; a means for acquiring robot operation data; a means for detecting problems; a means for proposing improvements; a means for presenting improvement proposals; and a means for automatically planning acceptance test items. This allows for real-time monitoring of the system's operational status and rapid response when problems occur. It also efficiently supports all development tasks, reducing the number of personnel required and promoting business efficiency.

[0313] "Transaction data" is data that records information about transactions and operations that take place within the system.

[0314] "Operation status" is information that indicates how a system or device is currently operating.

[0315] An "inquiry log" is data that records inquiries and requests from users to the system.

[0316] "Legal Change Information" means information about changes to relevant laws and regulations.

[0317] "Modification points" are areas in a system or process that require improvement or correction.

[0318] "Business requirements" define the conditions and functions necessary to carry out a business.

[0319] "System requirements" define the technical conditions and functions that a system must meet.

[0320] "Submitting opinions" means providing suggestions or opinions to the system administrator.

[0321] "Robot operation data" refers to data related to the operating status and performance of robots operating within a factory.

[0322] "Means for detecting problems" are methods for detecting abnormalities or errors that occur during the operation of a system or robot.

[0323] "Means for proposing improvements" are methods for proposing appropriate solutions to detected problems.

[0324] A "method of proposing modifications" is a method of making specific proposals for improving or modifying a system or process.

[0325] "Acceptance test items" define the content and conditions of tests to be conducted after a system or process is modified.

[0326] "Automatic planning means" refers to a method for automatically creating plans or proposals based on specific conditions or data.

[0327] The system for carrying out this invention consists of three main elements: a server, a terminal, and a user. The server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to the system manager, means for acquiring robot operation data, means for detecting problems, means for proposing improvements, means for presenting improvement proposals, and means for automatically planning acceptance test items.

[0328] The server runs a program using Python (registered trademark) and uses the requests library to make HTTP requests. The server obtains operational data from the robots operating in the factory via API, analyzes that data, and detects problems. For any problems detected, it proposes appropriate improvements and modifications, and automatically creates acceptance test items.

[0329] The terminals are mobile devices such as smartphones and tablets that display information provided by the server in real time. Users can monitor the system's operational status through the terminals and check proposed improvements and modifications.

[0330] As a concrete example, consider a scenario in which the operational status of a robot "robot_123" operating in a factory is monitored. The server obtains operational data for "robot_123" via an API and analyzes that data to detect problems. For example, if error code "E001" is detected, the server will suggest an improvement measure such as "Check the power supply" and present "Upgrade the power supply unit" as a suggested repair. It will also automatically create an acceptance test item such as "Test the stability of the power supply."

[0331] An example of a prompt to input to a generative AI model is as follows:

[0332] "Monitor the operational status of robots operating in the factory, and if a problem occurs, identify the cause and propose appropriate remedial measures. You will also be responsible for presenting improvement proposals to development vendors and automatically planning acceptance test items."

[0333] In this way, by linking servers, terminals, and users, it is possible to monitor the system's operational status in real time and respond quickly when problems occur. It also efficiently supports all development tasks, reduces the number of personnel required, and promotes work efficiency.

[0334] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0335] Step 1:

[0336] The server obtains operational data from robots operating in the factory. Specifically, the server requests the robot's operational data through an API and receives the obtained data in JSON format. The input is the robot's ID, and the output is the robot's operational data. To process the data, the server analyzes the obtained JSON data and extracts the necessary information.

[0337] Step 2:

[0338] The server analyzes the acquired operational data and detects problems. Specifically, it checks each data point in the operational data to detect error codes and abnormal values. The input is the operational data, and the output is a list of detected problems. During data calculations, the server checks the status of each data point to determine whether it contains an error code.

[0339] Step 3:

[0340] The server proposes solutions to the detected problems. Specifically, it lists predefined solutions based on the detected error code. The input is a list of detected problems, and the output is a list of solutions. For data processing, the server retrieves the error codes and corresponding solutions from a database and lists them.

[0341] Step 4:

[0342] The server presents fix proposals for detected problems. Specifically, it proposes fix proposals for systems and processes based on error codes. The input is a list of detected problems, and the output is a list of fix proposals. For data processing, the server retrieves the error codes and corresponding fix proposals from a database and creates a list.

[0343] Step 5:

[0344] The server automatically creates acceptance test items for the detected problems. Specifically, it defines the content and conditions of the acceptance test based on the error code. The input is a list of detected problems, and the output is a list of acceptance test items. For data processing, the server retrieves the error codes and corresponding acceptance test items from the database and creates a list.

[0345] Step 6:

[0346] The terminal displays information provided by the server in real time. Specifically, it displays operational data received from the server, detected problems, improvement measures, modification proposals, and acceptance test items to the user. The input is information from the server, and the output is what is displayed to the user. As part of data processing, the terminal formats the received information so that it can be displayed in an appropriate format.

[0347] Step 7:

[0348] Users monitor the system's operational status through their terminals and check proposed improvements and modifications. Specifically, they understand the system's operational status based on the information displayed on the terminals and take necessary action. The input is the information displayed on the terminals, and the output is the user's judgment and response. As a data calculation, users make decisions based on the displayed information.

[0349] 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.

[0350] "Example 1"

[0351] One embodiment of the present invention is a system that incorporates an emotion engine. This system recognizes a user's emotion and adjusts the system's operation based on that emotion. Specifically, it includes an emotion engine for recognizing the user's emotion and a control unit for adjusting the system's operation based on that emotion. The emotion engine estimates the user's emotion from, for example, the user's tone of voice, facial expression, and choice of words. The control unit adjusts the system's operation based on the output from the emotion engine. For example, if the user feels angry, the system adjusts its operation to respond more politely.

[0352] "Example 2"

[0353] Another embodiment of the present invention is a system that incorporates an emotion engine. This system recognizes a user's emotion and adjusts the system's operation based on that emotion. Specifically, it includes an emotion engine for recognizing the user's emotion and a control unit for adjusting the system's operation based on that emotion. The emotion engine infers the user's emotion from, for example, the user's tone of voice, facial expression, and choice of words. The control unit adjusts the system's operation based on the output from the emotion engine. For example, if the system senses that the user is happy, it adjusts its operation to respond in a way that shares that joy.

[0354] "Example 3"

[0355] Furthermore, another embodiment of the present invention is a system that incorporates an emotion engine. This system recognizes a user's emotion and adjusts the system's operation based on that emotion. Specifically, it includes an emotion engine for recognizing the user's emotion and a control unit for adjusting the system's operation based on that emotion. The emotion engine infers the user's emotion from, for example, the user's tone of voice, facial expression, and choice of words. The control unit adjusts the system's operation based on the output from the emotion engine. For example, if the user feels surprised, the system adjusts its operation to respond in a way that alleviates the surprise.

[0356] The processing flow of each embodiment will be described below.

[0357] "Example 1"

[0358] Step 1: The emotion engine works to infer emotions from the user's tone of voice, facial expressions, choice of words, etc.

[0359] Step 2: The control unit receives the output from the emotion engine.

[0360] Step 3: The control unit adjusts the system's behavior based on the received emotion. For example, if the control unit senses that the user is angry, the system adjusts its behavior to respond more politely.

[0361] "Example 2"

[0362] Step 1: The emotion engine works to infer emotions from the user's tone of voice, facial expressions, choice of words, etc.

[0363] Step 2: The control unit receives the output from the emotion engine.

[0364] Step 3: The control unit adjusts the system's behavior based on the received emotion. For example, if the control unit senses that the user is happy, the system adjusts its behavior to respond in a way that shares that happiness.

[0365] "Example 3"

[0366] Step 1: The emotion engine works to infer emotions from the user's tone of voice, facial expressions, choice of words, etc.

[0367] Step 2: The control unit receives the output from the emotion engine.

[0368] Step 3: The control unit adjusts the system's behavior based on the received emotion. For example, if the user feels surprised, the system adjusts its behavior to respond in a way that alleviates the surprise.

[0369] Example 1

[0370] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0371] In conventional systems, it was necessary to manually identify areas for improvement from large amounts of data such as transaction data and inquiry logs, which was time-consuming and labor-intensive. Furthermore, the system was unable to respond in a way that took user feelings into consideration, which could lead to a decline in user satisfaction. Furthermore, there was an issue of operational efficiency being hindered by insufficient efforts to detect operational problems, consider improvement measures, and streamline development tasks.

[0372] 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.

[0373] In this invention, the server includes a means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., a means for automatically creating business requirements and system requirements, a means for submitting opinions to system personnel, a means for recognizing user emotions, and a means for adjusting system operation based on the recognized emotions. This enables automatic analysis of data and rapid detection of points to be improved, realizing appropriate responses according to user emotions. It also improves business efficiency by detecting operational problems, supporting the consideration of improvement measures, and streamlining development tasks.

[0374] "Transaction Data" refers to records of transactions and operations that take place within the system.

[0375] "Operation status" refers to information that indicates the operational status and running status of the system.

[0376] An "inquiry log" refers to data that records the content of inquiries from users and their responses.

[0377] "Legal amendment information" refers to information regarding changes to laws and regulations.

[0378] "Means for automatically detecting points for improvement" refers to a function that automatically identifies problems in the system and areas that need improvement.

[0379] "Means for automatically creating business requirements and system requirements" refers to the function of automatically generating business requirements and system specifications.

[0380] "Means for submitting opinions to system personnel" refers to the function of notifying system personnel of proposals for system modifications and improvements.

[0381] "Means for recognizing the user's emotions" refers to the function of inferring emotions from the user's tone of voice, facial expressions, choice of words, etc.

[0382] "Means for adjusting system behavior based on recognized emotions" refers to a function that changes the system's response or behavior depending on the user's emotions.

[0383] This invention is a system that receives as input transaction data, operational status, inquiry logs, legal amendment information, etc. from an internal system, analyzes this data, and automatically detects points that need to be improved. It also includes a function to recognize user emotions and adjust the system's operation based on those emotions.

[0384] Data Acquisition and Input

[0385] The server retrieves transaction data and query logs from a database (e.g., MySQL®) or cloud storage (e.g., AWS® S3). For example, it retrieves data using an SQL query such as "SELECT FROM transactions WHERE date >= '2023-01-01'". The retrieved data is sent to the system in JSON format.

[0386] Data analysis and automatic detection of repair points

[0387] The server analyzes the data using the Python® Pandas library. For example, it reads the data using "df = pd.read_json(data)". It then uses a machine learning model (e.g., Scikit-learn®) to analyze the data for patterns and trends. For example, it makes predictions using "model.predict(df)" to detect outliers and trends.

[0388] Emotion Engine Operation

[0389] When a user uses the system, the device (e.g., PC or smartphone) sends the user's tone of voice, facial expression, and choice of words to the emotion engine. The emotion engine estimates the user's emotions using voice analysis (e.g., Google® Cloud Speech-to-Text) and facial expression analysis (e.g., OpenCV). For example, "if the user's voice tone is high and their facial expression is stern, anger is detected."

[0390] Adjusting system operation

[0391] The control unit adjusts the system's behavior based on the output from the emotion engine (e.g., anger, joy, sadness). For example, "If the user is angry, make the response message more polite." Specifically, a polite message such as "Sorry for keeping you waiting. Here is your recent transaction history" is displayed.

[0392] Examples and prompts

[0393] For example, when a user inquires of the system, "Tell me my recent transaction history," the specific operation is as follows.

[0394] 1. The server executes the query "SELECT FROM transactions WHERE date >= '2023-01-01'" from the database to retrieve transaction data.

[0395] 2. The server sends the acquired data to the analysis module to detect outliers and trends.

[0396] 3. When a user makes an inquiry through the device, the device sends the user's tone of voice and facial expressions to the emotion engine.

[0397] 4. The emotion engine detects anger from the user's high-pitched voice and stern facial expression.

[0398] 5. The control unit changes the response message based on the output from the emotion engine to "Sorry for the wait. Here is your recent transaction history."

[0399] In this way, the system responds appropriately to the user's emotions.

[0400] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0401] Step 1:

[0402] The server retrieves transaction data, operation status, inquiry logs, and legal change information from databases and cloud storage. Specifically, it extracts the necessary data from the database using SQL queries and retrieves data from cloud storage via APIs. The input is the connection information and query for the database or cloud storage, and the output is the retrieved data.

[0403] Step 2:

[0404] The server sends the acquired data to the analysis module. The data is sent in JSON format. The input is the data acquired in step 1, and the output is the data sent to the analysis module. Specifically, the data is sent using an HTTP request.

[0405] Step 3:

[0406] The server parses the data using the Python (registered trademark) Pandas library. Specifically, it reads the data using "df = pd.read_json(data)" and processes it as a data frame. The input is the data sent in step 2, and the output is the parsed data frame.

[0407] Step 4:

[0408] The server uses a machine learning model (e.g., Scikit-learn®) to analyze the data for patterns and trends. Specifically, it performs predictions (e.g., "model.predict(df)") and detects outliers and trends. The input is the data frame analyzed in step 3, and the output is the prediction results.

[0409] Step 5:

[0410] When a user uses the system, the device transmits the user's tone of voice, facial expressions, and choice of words to the emotion engine. Specifically, it uses a microphone and camera to capture audio and video and transmits them to the emotion engine. The input is the user's audio and video data, and the output is the data transmitted to the emotion engine.

[0411] Step 6:

[0412] The emotion engine uses voice analysis and facial expression analysis to estimate the user's emotions. Specifically, it converts voice data into text and analyzes facial expression data to estimate emotions. The input is the voice and video data sent in step 5, and the output is the estimated emotion data.

[0413] Step 7:

[0414] The control unit adjusts the system's behavior based on the output from the emotion engine. Specifically, if the user is angry, the response message will be more polite. The input is the emotion data estimated in step 6, and the output is the adjusted response message.

[0415] Step 8:

[0416] The system responds appropriately based on the user's emotions. Specifically, it displays a polite message such as, "Sorry for the wait. Here is your recent transaction history." The input is the response message adjusted in step 7, and the output is the message displayed to the user.

[0417] (Application example 1)

[0418] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0419] Conventional systems can automatically detect points for improvement by analyzing transaction data, operational status, inquiry logs, legal amendment information, etc., but they lack the ability to recognize customer emotions and adjust the system's behavior based on those emotions, making it difficult to improve the quality of customer service. Furthermore, they lack the ability to suggest ways to respond based on customer emotions, which can lead to inconsistent responses from store staff.

[0420] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to the system staff, means for recognizing customer emotions and adjusting system operation based on those emotions, and means for proposing a response method based on the customer emotions. This makes it possible to respond in a way that takes customer emotions into consideration, thereby improving customer satisfaction and ensuring consistency in the responses of store staff.

[0421] "Transaction data" refers to data that records transactions and operations that take place within a system.

[0422] "Operation status" is information that indicates the operational status and progress of a system or business.

[0423] An "inquiry log" is data that records the content of inquiries from users and the history of responses.

[0424] "Legal amendment information" is information regarding changes to laws and regulations.

[0425] "Improvement points" refer to areas where improvements are needed in systems or business processes.

[0426] "Business requirements" refers to the conditions and specifications necessary to carry out a business.

[0427] "System requirements" refers to the technical conditions and specifications that a system must meet.

[0428] "Submitting opinions" is the act of providing suggestions or opinions to the system administrator.

[0429] "Customer emotions" refers to the emotional state that a customer feels, such as joy, anger, or sadness.

[0430] "Adjusting the system's behavior" refers to changing the system's behavior in response to customer emotions.

[0431] "Suggesting a response method" refers to presenting an appropriate response method based on the customer's feelings.

[0432] The following system configuration will be described as an embodiment of the present invention.

[0433] System Configuration

[0434] The system consists of the following main components:

[0435] 1. Server: Analyzes transaction data, operation status, inquiry logs, legal amendment information, etc., and automatically detects areas that need to be improved.

[0436] 2. Terminals: Devices such as smartphones and smart glasses used by store staff recognize customers' emotions in real time and suggest ways to respond based on those emotions.

[0437] 3. Users: Store staff and customers who use the system.

[0438] Hardware and software used

[0439] Hardware: Smartphone, smart glasses, camera, microphone

[0440] Software: OpenCV (camera feed processing), EmotionRecognizer (emotion recognition), TransactionAnalyzer (transaction data analysis), ChatbotLogAnalyzer (chatbot log analysis)

[0441] Data processing and calculation

[0442] Server Processing

[0443] The server retrieves transaction data, operation status, inquiry logs, legal amendment information, etc. from databases and cloud storage and analyzes this data. Specifically, it analyzes data patterns and trends to identify system problems and areas that can be improved. Based on the analysis results, it automatically creates business requirements and system requirements and submits opinions to the system manager.

[0444] Terminal handling

[0445] The device uses a camera and microphone to recognize customer emotions in real time. EmotionRecognizer is used to estimate emotions from the customer's tone of voice, facial expressions, and choice of words. Based on the estimated emotions, the system adjusts its behavior and suggests ways to respond to the customer.

[0446] Specific examples

[0447] For example, when a customer visits a store and begins a conversation with a staff member, the device's camera and microphone capture the customer's facial expressions and voice. If EmotionRecognizer analyzes this data and determines that the customer is angry, the device will notify the staff member to "please try to be polite." The server also analyzes transaction data and inquiry logs to identify areas for system improvement.

[0448] Prompt Sentence Examples

[0449] Develop an application that recognizes customer emotions and suggests ways to respond based on those emotions. Use camera and audio feeds to recognize emotions, and analyze transaction data and chatbot logs to find areas for improvement. If the customer is angry, say "Please be polite," and if they're happy, say "Keep it up."

[0450] In this way, it is possible to respond in a way that takes into account the feelings of the customer, thereby improving customer satisfaction and ensuring consistency in the responses of store staff.

[0451] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0452] Step 1:

[0453] The server retrieves transaction data, operational status, inquiry logs, legal change information, etc. from databases and cloud storage. This data serves as input for identifying system issues and areas for improvement. Database queries and APIs are used to retrieve the data and prepare it for analysis.

[0454] Step 2:

[0455] The server analyzes the acquired data and analyzes patterns and trends. Specifically, it detects anomalies in transaction data and extracts frequently occurring keywords from query logs. This identifies problems with the system and areas that can be improved. The analysis results are output as suggestions for improvement.

[0456] Step 3:

[0457] The server automatically creates business and system requirements based on the analysis results. These include areas that need to be modified in the system and new functions that should be added. The generated business and system requirements are output as a recommendation to the system manager.

[0458] Step 4:

[0459] The device uses a camera and microphone to recognize customer emotions in real time. Using EmotionRecognizer, it infers emotions from the customer's tone of voice, facial expressions, and word choice. The input data is the camera feed and audio feed, and the output is the estimated customer emotion.

[0460] Step 5:

[0461] The device adjusts the system's behavior based on the estimated customer's emotions. For example, if the system determines that the customer is angry, it changes its behavior to be more polite, suggesting ways to respond, and notifying staff.

[0462] Step 6:

[0463] The device suggests ways to respond based on the customer's emotions. For example, if the customer is angry, it will notify them by saying, "Please try to be polite," and if the customer is happy, it will notify them by saying, "Please continue to respond in this manner." The input is the estimated emotion, and the output is a suggested way to respond.

[0464] Step 7:

[0465] The server identifies areas for improvement in the system based on the results of analyzing transaction data and inquiry logs, and submits suggestions to the system administrator. This allows for efficient system modifications. The input is the analysis results, and the output is the content of the suggestions.

[0466] In this way, it is possible to respond in a way that takes into account the feelings of the customer, thereby improving customer satisfaction and ensuring consistency in the responses of store staff.

[0467] Example 2

[0468] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0469] In conventional systems, it was difficult to automatically detect points that needed improvement from transaction data, operational status, inquiry logs, legal amendment information, etc., and to automatically create business and system requirements. Furthermore, because system operation was not adjusted based on user feedback or user sentiment, business efficiency and user experience were not sufficiently improved. Furthermore, there was a lack of means to generate and submit improvement proposals using generative AI models. These issues need to be resolved.

[0470] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0471] In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to system personnel, means for recognizing user emotions using an emotion engine, means for adjusting system operation based on the recognized emotions, means for generating improvement proposals using a generative AI model, and means for submitting the generated improvement proposals via email or a web-based dashboard, thereby enabling business efficiency improvement and an improved user experience.

[0472] "Transaction Data" means data relating to transactions and operations conducted within the System.

[0473] "Operational status" refers to information about the operational status and performance of a system.

[0474] An "inquiry log" is a record of inquiries and support requests from users.

[0475] "Legal Change Information" means information about changes to relevant laws and regulations.

[0476] "Improvement points" are areas or problems that require improvement in the system.

[0477] "Business requirements" are the conditions and functions necessary to carry out a business.

[0478] "System requirements" are the technical conditions and specifications that a system must meet.

[0479] "Submitting an opinion" is the act of submitting a proposal or opinion to the system administrator.

[0480] An "emotion engine" is software or hardware for recognizing a user's emotions.

[0481] A "generative AI model" is a model that uses artificial intelligence to generate new information and suggestions from data.

[0482] "Email" is a means of sending and receiving messages over the Internet.

[0483] A "web-based dashboard" is an information display accessible through a web browser.

[0484] MODE FOR CARRYING OUT THE INVENTION

[0485] This invention is a system that automatically detects modification points from transaction data, operational status, inquiry logs, legal amendment information, etc., and automatically creates business and system requirements. It also includes a function that recognizes user emotions using an emotion engine and adjusts system operation based on those emotions. Furthermore, it uses a generative AI model to generate modification proposals, which are then submitted via email or a web-based dashboard.

[0486] Hardware and software used

[0487] Server: A high-performance server is used to run a program that automatically creates business and system requirements.

[0488] Device: Use a device equipped with an emotion engine (e.g., smartphone, tablet).

[0489] Emotion engine: Uses software (e.g., Microsoft® Azure® Emotion API) to recognize user emotions.

[0490] Control Unit: A software module is used to adjust the behavior of the system based on the output of the emotion engine.

[0491] Generative AI model: OpenAI's GPT-3 (registered trademark) model is used to generate improvement suggestions.

[0492] Program processing explanation

[0493] 1. The server collects system log data. It periodically reads the system log files and stores them in a database. For example, it reads the log files every day at 2:00 AM and stores them in a MySQL (registered trademark) database.

[0494] 2. The server analyzes the collected log data and detects areas for improvement. It runs a script to analyze the log data and detects performance degradation and error messages. For example, it uses a Python script to analyze the log data and count the frequency of error messages.

[0495] 3. The server generates specific repair proposals based on the detected repair points. It uses a generative AI model to propose solutions to the detected problems. For example, it uses OpenAI's GPT-3 model to generate the cause of the error message and its solution.

[0496] 4. The server submits the generated modification proposal to the system administrator as a suggestion. The server sends the generated modification proposal to the system administrator by email. For example, the server uses the SMTP protocol to send an email containing the generated modification proposal.

[0497] A system that combines emotion engines

[0498] 1. The device analyzes the user's tone of voice and facial expressions using an emotion engine. It uses a microphone and camera to capture the user's voice and facial expressions and input them into the emotion engine. For example, when the user speaks, the microphone captures the user's voice and the camera captures their facial expressions.

[0499] 2. The device sends the output of the emotion engine to the control unit. The emotion engine's analysis results are sent to the control unit, and instructions are received to adjust the system's operation. For example, the emotion engine's output is sent to the control unit in JSON format.

[0500] 3. The device's control unit adjusts the system's behavior based on the user's emotions. Based on instructions from the control unit, the device executes appropriate actions according to the user's emotions. For example, if the device recognizes that the user is happy, it plays cheerful music.

[0501] Prompt Sentence Examples

[0502] "Analyze the system log data, identify the cause of the performance degradation, and generate specific fixes. Then, send the fixes to the system administrator via email."

[0503] "Recognize emotions from the user's tone of voice and facial expressions, and adjust the system's behavior based on those emotions. For example, if you sense the user is happy, play upbeat music."

[0504] The system is designed to improve operational efficiency and enhance the user experience.

[0505] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0506] Automated generation of business and system requirements

[0507] Step 1:

[0508] The server collects system log data.

[0509] Input: System log file

[0510] Specific operation: The server reads the log file at 2:00 AM every day and stores it in a MySQL database.

[0511] Output: Log data stored in a database

[0512] Step 2:

[0513] The server analyzes the collected log data and detects points that need to be modified.

[0514] Input: Log data stored in the database

[0515] Specific operation: The server uses a Python script to analyze the log data and count the frequency of error messages.

[0516] Output: Detected modification points

[0517] Step 3:

[0518] The server generates a specific repair plan based on the detected repair points.

[0519] Input: Detected modification point

[0520] What it does: The server uses OpenAI's GPT-3 model to generate the cause of the error message and its solution.

[0521] Output: Generated modification plan

[0522] Step 4:

[0523] The server submits the generated modification plan as a suggestion to the system manager.

[0524] Input: Generated renovation proposal

[0525] Specific operation: The server uses the SMTP protocol to send an email containing the generated revision proposal.

[0526] Output: Email sent to system contact

[0527] A system that combines emotion engines

[0528] Step 1:

[0529] The device uses an emotion engine to analyze the user's tone of voice and facial expressions.

[0530] Input: User's voice and facial expressions

[0531] Specific operation: The device captures audio with a microphone and captures facial expressions with a camera.

[0532] Output: Analysis results by the emotion engine

[0533] Step 2:

[0534] The terminal transmits the output of the emotion engine to the control unit.

[0535] Input: Analysis results by emotion engine

[0536] Specific operation: The terminal sends the output of the emotion engine in JSON format to the control unit.

[0537] Output: Analysis results sent to the control unit

[0538] Step 3:

[0539] The control unit of the terminal adjusts the operation of the system based on the emotion.

[0540] Input: Analysis results sent to the control unit

[0541] Specific behavior: If the device recognizes that the user is happy, it plays upbeat music.

[0542] Output: System behavior according to user's emotions

[0543] The system is designed to improve operational efficiency and enhance the user experience.

[0544] (Application example 2)

[0545] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0546] Conventional systems can automatically detect areas for improvement based on transaction data, operational status, inquiry logs, legal revision information, etc., but they cannot recognize user emotions and adjust system operations based on those emotions. It is also difficult to propose customer service methods based on customer emotions or automatically generate business and system requirements from emotional and video data. This has prevented systems from achieving sufficient improvements in customer satisfaction and operational efficiency.

[0547] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0548] In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to the system manager, an emotion engine for recognizing the user's emotions, a control unit for adjusting the system operation based on the emotions, means for analyzing the customer's facial expressions and tone of voice in real time to recognize emotions, means for proposing customer service methods based on the recognized emotions, and means for automatically creating business requirements and system requirements from emotion data and video data. This enables flexible responses based on customer emotions, improving customer satisfaction and streamlining operations.

[0549] "Transaction Data" refers to information relating to transactions and operations conducted within the System.

[0550] "Operational status" refers to information about the operational status of the system and the processes that are running.

[0551] "Inquiry log" refers to data that records the content of inquiries from users and the history of responses to those inquiries.

[0552] "Legal Change Information" refers to information regarding changes in relevant laws and regulations.

[0553] "Modification points" refer to areas of the system that require improvement or correction.

[0554] "Business requirements" refer to the conditions and functions necessary to carry out business.

[0555] "System requirements" refers to the technical conditions and specifications that a system must meet.

[0556] "Submitting opinions" refers to submitting suggestions or opinions to the system administrator.

[0557] An "emotion engine" refers to software or algorithms that recognize a user's emotions.

[0558] The "controller" refers to the part that adjusts the system's operation based on the output from the emotion engine.

[0559] "Facial expression analysis" refers to the technology of analyzing a user's facial expressions from camera footage and recognizing their emotions.

[0560] "Voice tone analysis" refers to technology that analyzes the tone of a user's voice and recognizes their emotions.

[0561] "Customer service suggestion" refers to proposing an appropriate customer service method based on the recognized emotions.

[0562] "Emotion data" refers to information about the user's emotions.

[0563] "Video data" refers to video information captured by a camera.

[0564] A system for implementing this invention is configured as follows: The server has means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc. It also has means for automatically creating business requirements and system requirements and means for submitting opinions to system personnel. It also includes an emotion engine for recognizing user emotions and a control unit for adjusting system operation based on those emotions.

[0565] This system allows store clerks wearing smart glasses to analyze customers' facial expressions and tone of voice in real time to recognize their emotions. It also includes a means to suggest ways to serve customers based on the recognized emotions, and a means to automatically generate business and system requirements from emotion data and video data.

[0566] Hardware and software used

[0567] Hardware: Smart glasses (with built-in camera and display), computer (for data processing)

[0568] Software: OpenCV (image processing library), EmotionRecognizer (emotion recognition library), SystemRequirementsGenerator (business and system requirements generation library)

[0569] Data processing and calculation

[0570] The server processes the video data captured by the camera in real time using OpenCV. It uses EmotionRecognizer to recognize customer emotions from the video data. It then proposes appropriate ways to serve customers based on the recognized emotions. It also uses SystemRequirementsGenerator to automatically generate business and system requirements from the emotion and video data.

[0571] Specific examples

[0572] For example, if a customer is smiling and looking at a product, the smart glasses' display will say, "The customer is happy. Try to be more kind." If the customer looks dissatisfied, the display will say, "The customer is angry. Try to stay calm and resolve the issue."

[0573] Prompt Sentence Examples

[0574] "Develop a smart glasses application that analyzes a customer's facial expressions and tone of voice, recognizes their emotions, and suggests appropriate ways to serve them. Include a function that suggests a friendly response if the customer is happy, and a calm response if the customer is angry. Also, add a function that automatically generates business and system requirements based on emotion data."

[0575] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0576] Step 1:

[0577] The server acquires video data in real time through the smart glasses' camera. The input is video data including the customer's facial expressions and movements. The output is frames of video data. Specifically, the camera captures the customer's face and sends the video to the server.

[0578] Step 2:

[0579] The server processes the acquired video data in real time using OpenCV and performs face detection. The input is the frame of video data acquired in step 1. The output is the position information of the detected face. Specifically, the face detection algorithm identifies the face in the video frame and outputs its position as coordinates.

[0580] Step 3:

[0581] The server uses EmotionRecognizer to analyze the detected facial expressions and recognize emotions. The input is the face position information obtained in step 2 and the video data frame. The output is the recognized emotion (e.g., joy, anger, sadness). Specifically, the facial expression analysis algorithm analyzes the facial features and infers the emotion.

[0582] Step 4:

[0583] The server proposes an appropriate customer service method based on the recognized emotion. The input is the emotion data obtained in step 3. The output is a customer service method suggestion (e.g., "The customer is happy. Let's try to be even more friendly."). Specifically, the server searches a database for a customer service method that corresponds to the emotion data and displays it on the smart glasses' display.

[0584] Step 5:

[0585] The server automatically generates business requirements and system requirements from the emotion data and video data. The input is the emotion data obtained in step 3 and the video data acquired in step 1. The output is the automatically generated business requirements and system requirements. Specifically, the SystemRequirementsGenerator analyzes the emotion data and video data and generates the necessary business requirements and system requirements.

[0586] Step 6:

[0587] The server submits the generated business requirements and system requirements to the system administrator. The input is the business requirements and system requirements generated in step 5. The output is a submission to the system administrator (e.g., email or a web-based dashboard). Specific operations include converting the generated requirements into an appropriate format and notifying the system administrator.

[0588] Example 3

[0589] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0590] Conventional systems lack the ability to detect operational problems, propose solutions, or recognize user emotions and adjust system behavior accordingly, resulting in poor system efficiency and a poor user experience. Furthermore, while there is a demand for more efficient development tasks and fewer personnel, there is a lack of automation methods to achieve this.

[0591] The identification processing by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., a means for automatically creating business requirements and system requirements, a means for submitting opinions to the system manager, a means for monitoring the system operation status in real time, a means for detecting anomalies and identifying their causes, a means for proposing solutions to the identified problems, a means for recognizing user emotions, and a means for adjusting the system operation based on the recognized emotions. This makes it possible to quickly detect operational problems, propose appropriate solutions, and further adjust the system operation according to the user's emotions. This improves system efficiency and user experience.

[0592] "Transaction Data" refers to information relating to transactions and operations conducted within the System.

[0593] "Operational status" refers to information about the system's operating status and performance.

[0594] "Inquiry log" refers to records of inquiries and support requests from users.

[0595] "Legal Change Information" refers to information regarding changes in relevant laws and regulations.

[0596] "Means for automatically detecting points for improvement" refers to a function that automatically identifies problems in the system and areas that need improvement.

[0597] "Means for automatically creating business requirements and system requirements" refers to the function of automatically generating business needs and system specifications.

[0598] "Means for submitting opinions to system personnel" refers to the function of providing improvement suggestions and opinions to system administrators and operations personnel.

[0599] "Means for monitoring the system's operating status in real time" refers to the function of monitoring the system's operating status in real time and detecting abnormalities.

[0600] "Means for detecting abnormalities and identifying their causes" refers to the function of detecting abnormalities in the system and identifying their causes.

[0601] "Means for proposing improvements to identified problems" refers to the function of proposing appropriate solutions to detected problems.

[0602] "Means for recognizing the user's emotions" refers to the function of inferring emotions from the user's tone of voice, facial expressions, choice of words, etc.

[0603] "Means for adjusting the system's behavior based on the recognized emotion" refers to a function for changing the system's behavior in response to the user's emotion.

[0604] This invention is a system that monitors the system's operating status, identifies the cause of any problems that may arise, and proposes appropriate solutions. It also includes a function to recognize the user's emotions and adjust the system's operation based on those emotions.

[0605] Hardware and software used

[0606] server

[0607] The server is the main computing resource for monitoring the system's operating status in real time and detecting abnormalities. Specifically, the following tools are used:

[0608] Monitoring tools: Prometheus, Grafana

[0609] Data analysis tools: ELK stack (Elasticsearch (registered trademark), Logstash (registered trademark), Kibana)

[0610] Terminal

[0611] A terminal is a device through which a user interacts with the system, including PCs, smartphones, tablets, etc.

[0612] Emotion Engine

[0613] The emotion engine is software that recognizes the user's emotions. Specifically, it uses the following APIs:

[0614] Emotion Recognition API: Microsoft® Azure® Emotion API, Google® Cloud Natural Language API

[0615] Control unit

[0616] The control unit is a software module for coordinating the behavior of the system based on the output from the emotion engine.

[0617] Data processing and calculation

[0618] Server Processing

[0619] The server monitors the system's operating status in real time, specifically by collecting performance metrics such as CPU usage, memory usage, and network traffic, using monitoring tools such as Prometheus and Grafana.

[0620] If an anomaly is detected, the server analyzes the log data to identify the cause, for example, discovering that a particular process is consuming excessive resources, using the ELK stack (Elasticsearch®, Logstash®, Kibana).

[0621] For any identified problems, the server will suggest appropriate remedial measures, such as restarting processes that are consuming excessive resources or changing settings.

[0622] Terminal handling

[0623] The device captures the user's tone of voice, facial expressions, and word choice using hardware such as a microphone and camera.

[0624] Emotion engine processing

[0625] The emotion engine analyzes data acquired from the device and estimates the user's emotions. For example, it identifies the user's emotional state, such as whether they are surprised, angry, or happy. This is done using the Microsoft® Azure® Emotion API and the Google® Cloud Natural Language API.

[0626] Control unit processing

[0627] The control unit adjusts the system's behavior based on the output from the emotion engine. For example, if the system senses that the user is surprised, it will respond to alleviate the surprise. Specifically, it may display a message that gives the user a sense of security or perform optimizations to improve the system's response speed.

[0628] Specific examples

[0629] Operational problem detection: The server detects a sudden spike in CPU usage, analyzes log data to identify that a specific process is the cause, and then proposes a remedial measure to limit the process's resource usage.

[0630] Example of use of emotion engine: If a user speaks to the terminal in an angry tone, saying "Why is it so slow?", the emotion engine will detect the anger and the control unit will perform optimizations to improve the system's response speed.

[0631] Prompt Sentence Examples

[0632] "Please explain how the system should respond if a user expresses anger towards the system."

[0633] In this way, the system detects operational problems, considers improvement measures, and adjusts its operations based on the user's feelings, thereby providing a more efficient and user-friendly environment. The flow of the identification process in the third embodiment will be described with reference to Fig. 21.

[0634] Step 1:

[0635] The server monitors the system's operating status in real time. Specifically, it collects performance metrics such as CPU usage, memory usage, and network traffic. This is done using monitoring tools such as Prometheus and Grafana. Various system performance data is input, and this data is displayed in real time on a monitoring dashboard as output.

[0636] Step 2:

[0637] The server analyzes the collected performance data and detects anomalies. For example, it issues an alert if CPU usage exceeds 80%. The input is the performance data collected in step 1, and the output is an anomaly detection alert. Specifically, the anomaly detection algorithm analyzes the data and issues an alert if the threshold is exceeded.

[0638] Step 3:

[0639] When an anomaly is detected, the server analyzes the log data to identify the cause. For example, it discovers that a specific process is consuming excessive resources. The inputs are an anomaly detection alert and log data, and the output is the identification of the cause of the anomaly. Specifically, the log data is analyzed using a log analysis tool (for example, the ELK stack).

[0640] Step 4:

[0641] The server then proposes remediation measures for the identified issues, such as restarting a process that is consuming too many resources or changing a configuration. The input is the cause of the anomaly, and the output is a proposed remediation. The specific actions are based on historical data and best practices.

[0642] Step 5:

[0643] The server automatically generates fix proposals for the development vendor based on the identified issues. For example, it proposes fixes for specific parts of the code or the addition of new features. The input is a proposed improvement, and the output is a generated fix proposal. Specifically, the fix proposal generation algorithm runs and generates a specific fix proposal.

[0644] Step 6:

[0645] The server automatically creates acceptance test cases based on the proposed modifications. For example, it generates test cases to verify whether a new function works properly. The input is the proposed modifications, and the output is the generated acceptance test cases. Specifically, the test case generation algorithm runs and generates specific test cases.

[0646] Step 7:

[0647] The device captures the user's tone of voice, facial expressions, and choice of words. This is done using hardware such as a microphone and camera. The input is the user's voice data and video data, and the output is sent to the emotion engine. Specific operations include voice recognition and video analysis.

[0648] Step 8:

[0649] The emotion engine analyzes data acquired from the device and estimates the user's emotions. For example, it identifies emotional states such as whether the user is surprised, angry, or happy. The inputs include audio and video data, and the output is an estimated emotion result. Specifically, an emotion recognition algorithm is run to identify the emotional state.

[0650] Step 9:

[0651] The control unit adjusts the system's behavior based on the output from the emotion engine. For example, if the user feels surprised, the system responds to alleviate that surprise. The input is the emotion estimation result, and the output is the adjustment of the system's behavior. Specific actions include displaying a message that gives the user a sense of security, or optimizing the system to improve response speed.

[0652] (Application example 3)

[0653] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0654] In conventional systems, operational problem detection and improvement proposals are often done manually, resulting in inefficiency. Furthermore, it is difficult to recognize customer emotions in real time and take appropriate action based on that, making improving customer satisfaction a challenge. Furthermore, there is a need for a system that can quickly detect store operation problems and propose improvement measures.

[0655] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0656] In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to system personnel, an emotion engine for recognizing customer emotions, a control unit for adjusting system operation based on output from the emotion engine, and means for detecting problems in store operation and proposing improvement measures. This makes it possible to automatically detect operational problems and quickly propose improvement measures, and to take appropriate measures based on customer emotions, thereby improving customer satisfaction.

[0657] "Transaction Data" refers to information relating to transactions and operations conducted within the System.

[0658] "Operation status" refers to information that indicates the operational status and operating status of the system.

[0659] "Inquiry log" refers to data that records the content of inquiries from users and response history.

[0660] "Legal Change Information" refers to information regarding changes in relevant laws and regulations.

[0661] "Modification points" refer to areas of the system that require improvement or correction.

[0662] "Business requirements" refers to the conditions and specifications necessary to carry out a business.

[0663] "System requirements" refers to the technical conditions and specifications that a system must meet.

[0664] "Submitting opinions" refers to the act of providing suggestions or opinions to the system administrator.

[0665] An "emotion engine" refers to software or algorithms that recognize a user's emotions.

[0666] "Controller" refers to the hardware and software used to manage and regulate the operation of the system.

[0667] "Store operation issues" refers to challenges and obstacles that arise in the operation of a store.

[0668] "Improvement measures" refer to specific methods or means for solving detected problems.

[0669] A system for implementing this invention includes means for automatically detecting points to be improved from transaction data, operational status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to system personnel, an emotion engine for recognizing customer emotions, a control unit for adjusting the operation of the system based on output from the emotion engine, and means for detecting problems in store operations and proposing improvements.

[0670] System Program

[0671] The server collects transaction data and operational status, analyzes this data, and automatically detects areas that need improvement. Specifically, it retrieves the necessary information from the database and uses an algorithm to identify problem areas. Next, it automatically creates business requirements and system requirements and submits recommendations to the system manager.

[0672] The device (e.g., smart glasses) is equipped with a camera and microphone to analyze the customer's facial expressions and tone of voice in real time. This allows an emotion engine to recognize the customer's emotions and adjust the system's behavior based on that output. For example, if the customer is dissatisfied, the device will display a notification to the store clerk urging them to be more polite.

[0673] The server also detects problems in store operations and proposes solutions. This includes an algorithm that analyzes operational data and identifies problem areas. It automatically generates solutions for detected problems and notifies the system administrator.

[0674] Hardware and software used

[0675] Hardware: Smart glasses (camera, microphone), server

[0676] Software: OpenCV (camera image analysis), EmotionEngine (emotion recognition), ProblemDetector (problem detection), ImprovementSuggester (improvement suggestion)

[0677] Specific examples

[0678] For example, if a customer in a store asks about a product but looks a little dissatisfied, the smart glasses will recognize the customer's dissatisfaction and notify the store clerk, "The customer seems dissatisfied. Please respond politely." In addition, if the server analyzes the store's operational data and detects a problem with inventory management, it will suggest "updating the inventory management system" as a remedial measure.

[0679] Prompt Sentence Examples

[0680] Create a program that analyzes customer facial expressions and tone of voice to recognize emotions. Add functionality to suggest appropriate responses to store associates based on the recognized emotions. Also include functionality to detect problems in store operations and suggest solutions.

[0681] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0682] Step 1:

[0683] The server retrieves transaction data, operation status, inquiry logs, legal amendment information, etc. from the database. This data is analyzed and points to be modified are automatically detected. The input is various data, and the output is a list of points to be modified. Specifically, an algorithm is used to detect abnormal values ​​and patterns in the data and identify areas that require modification.

[0684] Step 2:

[0685] The server automatically creates business and system requirements based on the modification points. The input is a list of modification points, and the output is a document of business and system requirements. Specifically, the server generates requirements using a template and fills in the necessary information.

[0686] Step 3:

[0687] The server notifies the system manager of the generated business requirements and system requirements and submits their opinions. The input is a requirements document, and the output is a notification message. Specifically, the server sends information to the manager via email or a notification system.

[0688] Step 4:

[0689] The device (smart glasses) captures the customer's facial expressions and tone of voice in real time using a camera and microphone. The input is camera video and audio data, and the output is the analysis results. Specifically, the video is analyzed using OpenCV, and emotions are recognized using EmotionEngine.

[0690] Step 5:

[0691] The terminal adjusts the system's behavior based on the recognized emotion. The input is the emotion analysis result, and the output is a notification message to the store clerk. Specifically, if the emotion is "dissatisfied," the terminal displays a message to the store clerk saying, "The customer is dissatisfied. Please respond politely."

[0692] Step 6:

[0693] The server analyzes store operation data and detects problems. The input is operation data, and the output is a list of problems. Specifically, it uses data analysis algorithms to evaluate the operation data and identify anomalies and problem areas.

[0694] Step 7:

[0695] The server proposes solutions to the detected problems. The input is a list of problems, and the output is a document proposing solutions. Specifically, it selects appropriate solutions from a database of solutions and notifies the system administrator.

[0696] Step 8:

[0697] The server saves all processing results as logs for later reference. The input is the output data of each step, and the output is a log file. Specifically, the processing results are recorded in chronological order, allowing them to be searched and analyzed as needed.

[0698] 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.

[0699] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0700] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0701] 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.

[0702] [Second embodiment]

[0703] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0704] 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.

[0705] 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).

[0706] 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.

[0707] 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.

[0708] 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).

[0709] 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.

[0710] 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.

[0711] 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.

[0712] 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.

[0713] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0714] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0715] "Example 1"

[0716] The system of the present invention receives as input transaction data and operational status of internal systems, inquiry logs from ChatBots and other systems, information on legal amendments and regulation revisions, etc. This data is obtained from, for example, a database or cloud storage and sent to the system. The system analyzes this data and automatically detects areas that need improvement. Specifically, it analyzes data patterns and trends to identify system problems and areas that can be improved.

[0717] "Example 2"

[0718] Next, the system automatically creates business and system requirements based on the detected modification points and generates specific modification proposals. The generated modification proposals are then submitted as a recommendation to the system administrator. This recommendation can be submitted, for example, via email or a web-based dashboard.

[0719] "Example 3"

[0720] Furthermore, the system detects operational problems and considers remedial measures. It monitors the system's operating status, and if a problem occurs, it identifies the cause and proposes appropriate remedial measures. The system also presents modification proposals to development vendors and automatically plans acceptance test items. This supports all development tasks, reduces the number of personnel required, and promotes work efficiency.

[0721] The processing flow of each embodiment will be described below.

[0722] "Example 1"

[0723] Step 1: The system receives as input transaction data and operational status from internal systems, inquiry logs from ChatBots, etc., and information on legal and regulatory revisions. This data is obtained, for example, from databases or cloud storage, and sent to the system.

[0724] Step 2: The system analyzes this data and automatically detects areas for improvement. Specifically, it analyzes data patterns and trends to identify problems and areas that can be improved in the system.

[0725] "Example 2"

[0726] Step 1: The system automatically creates business and system requirements based on the detected modification points and generates specific modification proposals.

[0727] Step 2: The proposed modifications are submitted as a proposal to the system administrator. This proposal can be submitted, for example, via email or a web-based dashboard.

[0728] "Example 3"

[0729] Step 1: The system detects operational problems and considers improvement measures. This involves monitoring the system's operating status, and if a problem occurs, identifying the cause and proposing appropriate improvement measures. Step 2: The system presents improvement proposals to the development vendor and automatically plans acceptance test items. This supports all development tasks, reduces the number of personnel required, and promotes work efficiency.

[0730] Example 1

[0731] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0732] In conventional systems, the task of manually analyzing a variety of data, including transaction data, operational status, inquiry logs, and legal amendment information, to identify areas that need to be improved takes a great deal of time and effort. Furthermore, because the proposal of improvement plans and the planning of acceptance test items based on the analysis results are also done manually, this is inefficient and prone to human error. This leads to issues such as a decrease in the operational efficiency of the system and an increased burden on the personnel in charge.

[0733] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes means for automatically detecting points to be modified from transaction data, operation status, inquiry logs, legal amendment information, etc., means for acquiring data from a database or cloud storage, means for preprocessing the acquired data, means for analyzing the preprocessed data, means for automatically detecting points to be modified based on the analysis results, and means for notifying the detected points to be modified. This enables automatic analysis of data and automatic detection of points to be modified, improving the operational efficiency of the system and reducing the burden on responding personnel.

[0734] "Transaction Data" refers to records of transactions and operations that take place within the system.

[0735] "Operation status" refers to information that indicates the operational status and running status of the system.

[0736] An "inquiry log" refers to data that records the content of inquiries from users and their responses.

[0737] "Legal amendment information" refers to information regarding changes to laws and regulations.

[0738] "Improvement points" refer to problems with the system or areas that need improvement.

[0739] A "database" refers to a system for systematically storing and managing data.

[0740] "Cloud storage" refers to an online storage service for storing data over the Internet.

[0741] "Preprocessing" refers to the process of organizing data and converting it into a format suitable for analysis before conducting data analysis.

[0742] "Analysis" refers to the process of examining data in detail to find patterns and trends.

[0743] "Notification" refers to the act of informing the user of the analysis results and points to be improved.

[0744] MODE FOR CARRYING OUT THE INVENTION

[0745] This invention is a system that automatically analyzes a variety of data such as transaction data, operation status, inquiry logs, and legal amendment information to detect points that need to be improved. A specific embodiment of this system will be described below.

[0746] Data Acquisition

[0747] The server retrieves the necessary data from databases and cloud storage. Specifically, it retrieves transaction data from a MySQL® database and downloads query logs from a storage service. This ensures that the system always has access to the latest data.

[0748] Data Preprocessing

[0749] The server preprocesses the acquired data. This preprocessing includes imputing missing values ​​and normalizing the data. For example, it converts the data into a data frame using the Python (registered trademark) Pandas library and imputes missing values ​​with the mean value. It also normalizes the data and converts it into a format suitable for analysis.

[0750] Data analysis

[0751] The server analyzes the preprocessed data using machine learning algorithms and statistical methods. For example, it uses Scikit-learn (registered trademark) to apply anomaly detection algorithms to identify anomalous transactions. It also uses NLTK, a natural language processing library, to analyze query logs and identify frequently occurring issues.

[0752] Automatic detection of repair points

[0753] The server automatically detects points that need to be improved based on the analysis results. For example, if an abnormal transaction is detected, the details of that transaction are identified and improvement measures are proposed. Also, if the analysis of the inquiry log shows that inquiries about a particular topic are increasing, the server will propose improvements to the system related to that topic.

[0754] Notification of results

[0755] The server notifies the user of the detected points to be fixed. Notifications are made via email or dashboard. For example, the server may send an email to report the analysis results. It may also update the dashboard so that the user can check the analysis results in real time.

[0756] Specific examples

[0757] Example 1: Parsing transaction data

[0758] When a user enters transaction data into the system, the server retrieves the data from a MySQL® database, converts it into a data frame using Pandas, and then applies an anomaly detection algorithm using Scikit-learn® to identify anomalous transactions.

[0759] Example 2: Analyzing ChatBot inquiry logs

[0760] When a user enters a ChatBot inquiry log into the system, the server retrieves the log data from the storage service and analyzes the log using natural language processing libraries such as NLTK and SpaCy, thereby identifying frequently asked topics and issues.

[0761] Prompt Sentence Examples

[0762] Example 1: Parsing transaction data

[0763] "Analyze the following transaction data and detect any anomalous patterns. The data comes from a MySQL® database."

[0764] Example 2: Analyzing ChatBot inquiry logs

[0765] "Analyze the following ChatBot inquiry logs to identify frequently occurring issues. The data is retrieved from a storage service."

[0766] In this way, the server is a system that acquires data, performs preprocessing, analyzes it, automatically detects points that need to be modified, and notifies the user of the results.

[0767] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0768] Step 1: Get the data

[0769] The server retrieves the necessary data from databases and cloud storage. Specifically, it retrieves transaction data from a MySQL (registered trademark) database and downloads query logs from a storage service. As input, it receives database connection information and cloud storage access information, executes SQL queries and API requests, and retrieves data. As output, it saves the retrieved data in local storage.

[0770] Step 2: Preprocessing the data

[0771] The server preprocesses the acquired data. This preprocessing includes imputing missing values ​​and normalizing the data. Specifically, it converts the data into a data frame using the Pandas library and imputes missing values ​​with the mean value. It also normalizes the data and converts it into a format suitable for analysis. It receives the acquired raw data as input and generates preprocessed data as output.

[0772] Step 3: Analyze the data

[0773] The server analyzes the preprocessed data. For the analysis, it uses machine learning algorithms and statistical methods. Specifically, it uses Scikit-learn (registered trademark) to apply anomaly detection algorithms to identify anomalous transactions. It also uses NLTK, a natural language processing library, to analyze query logs and identify frequently occurring issues. It receives the preprocessed data as input and generates analysis results as output.

[0774] Step 4: Automatic detection of repair points

[0775] The server automatically detects points to be improved based on the analysis results. Specifically, if an abnormal transaction is detected, the details of that transaction are identified and improvement measures are proposed. Also, if the analysis of the inquiry log shows an increase in inquiries about a specific topic, the server proposes system improvements related to that topic. The server receives the analysis results as input and identifies points to be improved as output.

[0776] Step 5: Notification of results

[0777] The server notifies the user of the detected modification points. Notifications are made via email or dashboard. Specifically, the analysis results are reported by email. The dashboard is also updated so that the user can check the analysis results in real time. The server receives information about modification points as input, and generates and sends a notification message as output.

[0778] (Application example 1)

[0779] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0780] With conventional systems, it was difficult to comprehensively analyze a wide range of data, including transaction data, operational status, inquiry logs, and legal revision information, and automatically detect areas that needed improvement. Furthermore, there was a lack of means to automatically detect areas that needed optimization in the operation and placement of robots within factories, which hindered efficient operation. This made it difficult to quickly identify and address system problems and areas that could be improved.

[0781] 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.

[0782] In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to the system manager, means for automatically detecting points to be optimized in the operation and placement of robots in the factory, and means for automatically detecting points to be improved by inputting new data. This enables the system to quickly identify problems and areas that can be improved, enabling efficient operation and optimization.

[0783] "Transaction data" refers to data that records transactions and operations that take place within a system.

[0784] "Operation status" is information that indicates the operational status and operating conditions of a system or device.

[0785] An "inquiry log" is data that records the content of inquiries from users and the response history.

[0786] "Legal change information" is information about changes in laws and regulations.

[0787] "Renovation points" refer to areas or parts of systems or equipment that require improvement.

[0788] "Business requirements" refers to the conditions and specifications necessary to carry out a business.

[0789] "System requirements" refers to the technical conditions and specifications that a system must meet.

[0790] "Submitting an opinion" is the act of providing a suggestion or opinion to the system administrator.

[0791] "Factory robots" refer to automated machinery used in factories.

[0792] "Optimization points for movement and placement" refer to areas for improvement to optimize the movement and placement of the robot.

[0793] "New data" refers to the latest information newly entered into the system.

[0794] The system for carrying out the present invention operates in cooperation with three entities: a server, a terminal, and a user. A specific embodiment of the system will be described below.

[0795] Server Processing

[0796] The server retrieves transaction data, operation status, inquiry logs, legal amendment information, etc. from databases and cloud storage. This data is analyzed on the server, and points for modification are automatically detected. Specifically, the server uses the following software and hardware:

[0797] Software: Python (registered trademark), Pandas, Scikit-learn (registered trademark)

[0798] Hardware: High-performance server machine

[0799] The server analyzes data patterns and trends to identify system problems and areas for improvement. It also runs algorithms to automatically detect optimization points for the operation and placement of robots within the factory. When new data is input, the server analyzes it and automatically detects areas for modification.

[0800] Terminal handling

[0801] The device (e.g., smartphone, tablet, PC) receives the analysis results sent from the server and displays them to the user. The device uses the following software and hardware:

[0802] Software: Web browser, mobile application

[0803] Hardware: Smartphones, tablets, PCs

[0804] The terminal provides an interface for users to check system problems and areas for improvement and take necessary actions. For example, optimization points for the operation and placement of robots within a factory are displayed, and users can change the robot settings based on the results.

[0805] User operations

[0806] Users can check the information sent from the server via their terminal and take necessary actions. As system administrators, users can submit opinions and check business and system requirements. They can also change robot settings based on optimization points for robot operation and placement within the factory.

[0807] Specific examples

[0808] For example, if a robot in a factory frequently stops working, the server can identify the cause and suggest an optimal maintenance schedule. Also, if new safety standards are introduced due to legal changes, the server can adjust the robot's operation based on those standards.

[0809] Prompt Sentence Examples

[0810] "You will be asked to develop an application that analyzes transaction data, operational status, maintenance logs, and legal revision information from robots in factories, and automatically detects optimization points for robot operation and placement. Specifically, the application will have the ability to analyze data patterns and trends, and identify system problems and areas that can be improved."

[0811] In this way, the server, terminal, and user work together to quickly identify problems and areas that can be improved in the system, enabling efficient operation and optimization.

[0812] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0813] Step 1:

[0814] The server retrieves transaction data, operation status, inquiry logs, legal amendment information, etc. from databases and cloud storage. The input is various data sources, and the output is an integrated dataset. Specifically, the server retrieves data using SQL queries and API requests, and integrates the data using the Pandas library.

[0815] Step 2:

[0816] The server preprocesses the integrated dataset. The input is the integrated dataset, and the output is the preprocessed data. Specifically, the server performs missing value imputation, data normalization, and categorical data encoding.

[0817] Step 3:

[0818] The server automatically detects modification points using the preprocessed data. The input is the preprocessed data, and the output is a list of modification points. Specifically, the server trains a machine learning model using the Scikit-learn (registered trademark) library to analyze patterns and trends in the data.

[0819] Step 4:

[0820] The server automatically detects optimization points for the robot's operation and placement within the factory. The input is pre-processed data, and the output is a list of optimization points. Specifically, the server executes an algorithm to identify optimization points for the robot's operation and placement.

[0821] Step 5:

[0822] The server automatically detects modification points by inputting new data. The input is new data, and the output is a list of modification points. Specifically, the server inputs new data into an existing model and predicts modification points.

[0823] Step 6:

[0824] The terminal receives the analysis results sent from the server and displays them to the user. The input is the analysis results from the server, and the output is the display on the user interface. Specifically, the terminal displays the analysis results using a web browser or mobile application.

[0825] Step 7:

[0826] The user checks the information sent from the server through the terminal and takes the necessary action. The input is the analysis results on the terminal, and the output is the user's action. Specifically, the user checks the system's problems and areas that can be improved, and takes action such as changing the robot's settings.

[0827] Example 2

[0828] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0829] In conventional systems, the process of detecting points for improvement from transaction data, operational status, inquiry logs, legal amendment information, etc., and automatically creating business and system requirements was often done manually, resulting in inefficiency. In addition, generating improvement proposals and submitting opinions to system personnel was also done manually, which was time-consuming and labor-intensive, and prone to errors.

[0830] 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 a means for automatically detecting modification points from transaction data, operation status, inquiry logs, legal amendment information, etc., a means for sending a prompt message to the generative AI model based on the modification points and generating a specific modification proposal, a means for submitting the generated modification proposal as a proposal to the system manager, and a means for automatically creating business requirements and system requirements. This makes it possible to automate the process from detecting modification points to generating a modification proposal and submitting a proposal efficiently and accurately.

[0831] "Transaction Data" means data relating to transactions and operations conducted within the system.

[0832] "Operation status" refers to information relating to the operational status and running status of the system.

[0833] An "inquiry log" is a record of inquiries and support requests from users.

[0834] "Legal Change Information" means information about changes to relevant laws and regulations.

[0835] "Modification points" are areas or elements of the system that require improvement or correction.

[0836] A "generative AI model" is a model that has been trained using artificial intelligence to perform a specific task.

[0837] A "prompt" is an instruction entered into a generative AI model to make it perform a specific task.

[0838] "Modification Proposal" means a specific plan or method proposed for improving or modifying a system.

[0839] "Submitting an opinion" is the act of formally submitting a proposal or opinion to the system administrator.

[0840] "Business requirements" are the business requirements and functions that the system must meet.

[0841] "System requirements" are the technical conditions and specifications that a system must meet.

[0842] This invention is a system that automatically detects points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., and automatically creates business requirements and system requirements. Furthermore, it can generate specific improvement proposals using a generative AI model and submit them as suggestions to the system manager.

[0843] Hardware and software used

[0844] server

[0845] The server includes a database, analytical engine, generative AI model, and communication module. The database stores transaction data, operation status, inquiry logs, legal amendment information, and other data. The analytical engine analyzes this data to detect areas that need improvement. OpenAI's GPT-4 is used as the generative AI model, for example. The communication module sends suggestions to the system administrator via email or a web-based dashboard.

[0846] Terminal

[0847] A terminal is a device through which a user accesses the system. A user uses a web browser to access the system's web application and initiates the automatic creation of business and system requirements.

[0848] User

[0849] The user logs in to the system and starts the automatic creation of business and system requirements. The information entered by the user is sent to the server and analyzed.

[0850] Data processing and calculation

[0851] Detection of repair points

[0852] The server detects points to be modified based on information entered by the user and data automatically collected by the system. For example, if a user enters "I want to add a new customer management function," the server analyzes this information and detects points to be modified related to the customer management function.

[0853] Generate renovation proposals

[0854] The server sends prompts to the generative AI model based on the detected repair points, and the generative AI model generates specific repair proposals based on the prompts and returns them to the server.

[0855] Example prompt sentence:

[0856] Generate business and system requirements for adding new customer management features. Include specific proposed modifications.

[0857] The generative AI model generates the following modifications:

[0858] Business requirements:

[0859] 1. Add the ability to register, update, and delete customer information.

[0860] 2. Add a search function for customer information.

[0861] System requirements:

[0862] 1. Add a new customer table to the database.

[0863] 2. Create an API endpoint to manage customer information.

[0864] 3. Add a customer management screen to the front end.

[0865] Specific renovation proposals:

[0866] 1. Create a SQL script to add a customer table to the PostgreSQL database.

[0867] 2. Use Python® and Flask to create an API endpoint to manage customer information.

[0868] 3. Create a customer management screen using React.

[0869] Submitting opinions

[0870] The server then submits the generated proposed fixes to the system administrator as a proposal via email or a web-based dashboard. For example, if the proposal is sent via email, the server uses the SMTP protocol to send the proposed fixes to the system administrator.

[0871] Specific behavior:

[0872] The server converts the generated revision plan into an email format.

[0873] The server uses the SMTP protocol to send an email to the system administrator.

[0874] The system administrator receives an email and confirms the proposed modifications.

[0875] In this way, the system can automatically create business and system requirements, generate specific modification proposals, and submit them to the system administrator. This automates the process from detecting modification points to generating modification proposals and submitting opinions, making it possible to carry out the process efficiently and accurately.

[0876] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0877] Step 1:

[0878] A user accesses the system and begins automatically creating business and system requirements.

[0879] Input: User authentication information (username, password)

[0880] Output: User is logged in

[0881] Specific behavior:

[0882] The user enters the system URL in a web browser and accesses the login screen.

[0883] The user enters their username and password and clicks the "Login" button.

[0884] The server validates the credentials and logs the user in.

[0885] The user clicks the "Automatically create business and system requirements" button on the dashboard screen.

[0886] Step 2:

[0887] The server receives the user's input and detects the modification points.

[0888] Input: User request (e.g. "I want to add a new customer management function")

[0889] Output: Detected modification points

[0890] Specific behavior:

[0891] The user enters a request into the system and clicks the submit button.

[0892] The server receives the user's input data and stores it in a database.

[0893] The server analyzes the stored data and runs an algorithm to detect points of modification.

[0894] Step 3:

[0895] The server sends a prompt to the generative AI model, which generates specific repair proposals.

[0896] Input: Detected modification point

[0897] Output: Generated modification proposal

[0898] Specific behavior:

[0899] The server connects to the API of the generative AI model and sends a prompt.

[0900] The generative AI model analyzes the prompt text and generates specific repair suggestions.

[0901] The generative AI model returns the generated improvement proposals to the server.

[0902] Example prompt sentence:

[0903] Generate business and system requirements for adding new customer management features. Include specific proposed modifications.

[0904] Step 4:

[0905] The server submits the generated modification plan to the system administrator as a proposal.

[0906] Input: Generated modification proposal

[0907] Output: Submitted feedback to the system administrator

[0908] Specific behavior:

[0909] The server converts the generated revision plan into an email format.

[0910] The server uses the SMTP protocol to send an email to the system administrator.

[0911] The system administrator receives an email and confirms the proposed modifications.

[0912] In this way, the system can automatically create business and system requirements, generate specific modification proposals, and submit them to the system administrator. This automates the process from detecting modification points to generating modification proposals and submitting opinions, making it possible to carry out the process efficiently and accurately.

[0913] (Application example 2)

[0914] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0915] While conventional systems can detect points that need to be improved based on transaction data and operational status, they are unable to monitor robot operation logs in real time, automatically generate business and system requirements when an abnormality is detected, and quickly notify system personnel of specific improvement plans. This has resulted in slow responses when an abnormality occurs, and has led to issues such as insufficient improvement of work efficiency and reduction of personnel required.

[0916] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0917] In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to a system manager, means for monitoring the robot's operation log in real time, means for creating business requirements and system requirements when an abnormality is detected, and means for submitting the created improvement plan to the system manager via email or a web-based dashboard. This makes it possible to quickly and automatically create a countermeasure when a robot abnormality occurs and notify the system manager.

[0918] "Transaction Data" refers to information relating to transactions and operations conducted within the System.

[0919] "Operation status" refers to information that indicates the operational status and operating conditions of systems and equipment.

[0920] "Inquiry log" refers to data that records the history of inquiries and requests from users and systems.

[0921] "Legal Change Information" refers to information regarding changes in relevant laws and regulations.

[0922] "Modification points" refer to areas of systems or equipment that require improvement or correction.

[0923] "Business requirements" refers to the conditions and specifications necessary to carry out a business.

[0924] "System requirements" refers to the technical conditions and specifications that a system must meet.

[0925] "Submitting opinions" refers to the act of submitting suggestions or opinions to the system administrator.

[0926] "Robot operation log" refers to data that records the robot's operations and operating conditions.

[0927] "Real-time monitoring" refers to monitoring the ongoing situation immediately.

[0928] "When an abnormality is detected" refers to when a state that deviates from normal operation is detected.

[0929] "Email" means electronic messages sent and received over the Internet.

[0930] "Web-based Dashboard" means an information display screen accessible through a web browser.

[0931] The system for implementing this invention is configured as follows: The server has a means for automatically detecting points that need to be modified from transaction data, operation status, inquiry logs, legal amendment information, etc. This allows the system's operational status to be constantly monitored and necessary modifications to be quickly identified.

[0932] Furthermore, the server has a means for automatically creating business and system requirements. This allows specific business and system requirements to be automatically generated based on the detected modification points. The generated requirements are submitted as a suggestion to the system manager. The suggestion can be submitted via email or a web-based dashboard.

[0933] The server also has a means of monitoring the robot's operation log in real time. This allows the robot's operating status to be constantly monitored, and any abnormalities detected can be dealt with immediately. If an abnormality is detected, the server generates business and system requirements and creates a specific modification plan. The generated modification plan is submitted to the system administrator via email or a web-based dashboard.

[0934] This system is implemented using a program written in Python (registered trademark). Specifically, the smtplib library is used to implement a function for sending emails. The server acquires the robot's operation logs and runs an algorithm to detect abnormalities. If an abnormality is detected, business and system requirements are generated and a repair plan is created. This information is notified to the system administrator via email or a web-based dashboard.

[0935] For example, if a "Motor malfunction" is detected in the robot's operation log, the server will suggest "replacement of the motor" and notify the system administrator by email. In this way, it is possible to quickly and automatically generate countermeasures when a robot malfunction occurs and notify the system administrator.

[0936] Examples of prompt sentences include the following:

[0937] Create a Python (registered trademark) program that will automatically generate business requirements and system requirements, create specific repair plans, and notify the system administrator when an abnormality is detected in the robot's operation log. The condition for detecting an abnormality is assumed to be when the log status is "error."

[0938] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0939] Step 1:

[0940] The server collects transaction data, operation status, inquiry logs, legal amendment information, etc. These data are necessary to understand the operational status of the system. The input includes various log data and legal amendment information, and the output is stored in the server.

[0941] Step 2:

[0942] The server analyzes the acquired data and automatically detects points to be fixed. Specifically, it uses an algorithm to detect outliers and patterns to identify which parts of the system need to be fixed. The input includes the data acquired in step 1, and the output generates a list of points to be fixed.

[0943] Step 3:

[0944] The server automatically creates business and system requirements based on the detected modification points. To do this, it uses a generative AI model and prompts to generate specific requirements as input. The output is a document of business and system requirements.

[0945] Step 4:

[0946] The server submits the generated business and system requirements to the system administrator as a proposal via email or a web-based dashboard. The input includes the requirements document generated in step 3, and the output includes a notification sent to the system administrator.

[0947] Step 5:

[0948] The server monitors the robot's operation logs in real time. This involves a process of continuously receiving and analyzing the log data sent from the robot. The input includes the robot's operation logs, and the output includes the analysis results.

[0949] Step 6:

[0950] If the server detects an anomaly in the robot's operation log, it generates business requirements and system requirements. Specifically, it uses an anomaly detection algorithm to identify the location where the anomaly occurred and generates requirements based on that. The input includes the log data analyzed in step 5, and the output is a requirements document that corresponds to the anomaly.

[0951] Step 7:

[0952] The server then submits the generated remediation plan to the system administrator via email or a web-based dashboard, allowing the administrator to quickly review and implement the plan. The input includes the remediation plan generated in step 6, and the output includes the notification sent to the system administrator.

[0953] Example 3

[0954] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0955] In modern system operations, it is extremely important to quickly detect operational problems and propose appropriate remedial measures. However, in conventional systems, problem detection, cause identification, and remedial measures are often done manually, which not only takes time to respond but also consumes a lot of human resources. In addition, because proposals for modifications to development vendors and the planning of acceptance test items are also done manually, it is inefficient and prone to errors. This makes stable system operation difficult and poses the problem of hindering business efficiency.

[0956] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for monitoring the operation status of the system in real time, a means for detecting problems and identifying their causes, and a means for proposing improvements based on the identified causes. This makes it possible to quickly detect operational problems and automatically propose appropriate improvements. In addition, the system also presents improvement proposals to development vendors and automatically plans acceptance test items, thereby reducing the number of personnel required and improving work efficiency.

[0957] "Transaction data" is data related to a series of operations or transactions that take place within a system.

[0958] "Operational status" refers to information about the operating status and performance of a system.

[0959] An "inquiry log" is a record of inquiries and support requests from users.

[0960] "Legal Change Information" means information about changes to relevant laws and regulations.

[0961] "Modification points" are areas of the system that require improvement or correction.

[0962] "Business requirements" are the business conditions and needs that the system must meet.

[0963] "System requirements" are the technical conditions and specifications that a system must meet.

[0964] "Submitting opinions" is the act of providing suggestions or opinions to the system administrator.

[0965] "Real-time monitoring" means monitoring the operating status of a system immediately.

[0966] "Detecting problems" means discovering abnormalities or errors that occur within a system.

[0967] "Identifying the cause" means identifying the root cause of a detected problem.

[0968] "Proposing improvements" means presenting ways to improve the system based on the identified causes.

[0969] "Proposing modifications" means making specific proposals for correcting or improving the system.

[0970] "Automatically planning acceptance test items" means automatically creating test items to be performed after a system is modified.

[0971] This invention is a system that monitors the system's operating status in real time, detects problems, identifies their causes, and proposes appropriate remediation measures. Furthermore, by presenting modification proposals to development vendors and automatically planning acceptance test items, it supports all development tasks, reduces the number of personnel required, and promotes work efficiency.

[0972] The server uses monitoring tools such as Prometheus and Grafana to monitor the system's operation in real time, collecting data such as log data, performance metrics, and network traffic to detect abnormal values ​​and error messages.

[0973] The server uses the Python (registered trademark) Pandas library and the R dplyr package to analyze the collected data, thereby detecting problems when CPU usage exceeds a certain threshold or when specific error logs occur frequently.

[0974] The server uses log analysis tools such as Elasticsearch (registered trademark) and Splunk to identify the cause of detected problems. For example, if a specific process is consuming excessive resources, the server analyzes detailed logs of that process to identify the cause.

[0975] Based on the identified causes, the server uses a generative AI model to suggest appropriate remediation measures, such as code changes or system setting adjustments to optimize processes that are consuming excessive resources.

[0976] Based on the proposed improvements, the server presents a proposal for repairs to the development vendor, generating documentation including specific code changes and configuration change procedures. This documentation is automatically generated using a generative AI model.

[0977] The server automatically creates acceptance test items based on the proposed modifications. For example, it generates test cases to verify that the modified system operates correctly. These test cases are automatically generated using a generative AI model.

[0978] The terminal provides an interface for users to check the system status. Users can check the system's operating status and details of problems through the terminal. The terminal can be accessed using a web browser or a dedicated desktop application.

[0979] The user checks the improvement measures provided by the system and implements them as necessary. The user also checks the modification plans and acceptance test items suggested by the system and provides feedback to the development vendor.

[0980] Examples:

[0981] For example, while monitoring the system's operation status, the server detects that CPU usage has exceeded 90%. The server uses Prometheus to detect this abnormal value. Next, the server performs detailed log analysis using Elasticsearch® to identify that a specific process is consuming excessive resources. The server then uses a generative AI model to propose code changes to optimize the process that is consuming excessive resources. For example, the server makes a specific suggestion such as, "Improve the memory management of function B to fix the memory leak in process A." Based on the proposed improvement, the server then presents a modification proposal to the development vendor. For example, the server generates a document containing, "Specific code modification steps to improve memory management for function B." Finally, the server automatically creates acceptance test items based on the modification proposal. For example, the server generates a test case to verify that memory management for function B has been improved."

[0982] Example prompt sentence:

[0983] "Monitor the system's operating status, identify the cause when the CPU utilization rate exceeds 90%, and propose a remedial measure." The flow of the identification process in the third embodiment will be described with reference to FIG.

[0984] Step 1: Monitor system activity

[0985] The server monitors the system's operating status in real time. As input, it collects data such as log data, performance metrics, and network traffic. This is done using monitoring tools such as Prometheus and Grafana. The server analyzes the collected data to detect abnormal values ​​and error messages. As output, it generates a list of abnormal values ​​and error messages.

[0986] Step 2: Detect the problem

[0987] The server analyzes the collected data and detects outliers and error messages. It uses the log data and performance metrics collected in step 1 as input. For analysis, it uses the Pandas library in Python (registered trademark) and the dplyr package in R. It generates a list of detected problems as output. Specifically, it detects problems when CPU usage exceeds a certain threshold or when specific error logs occur frequently.

[0988] Step 3: Identify the cause

[0989] The server performs detailed log analysis to identify the causes of the detected problems. The list of problems detected in step 2 is used as input. A log analysis tool such as Elasticsearch (registered trademark) or Splunk is used for the analysis. A list of identified causes is generated as output. Specifically, if a specific process is consuming excessive resources, the detailed log of that process is analyzed to identify the cause.

[0990] Step 4: Propose improvements

[0991] The server then suggests appropriate remediation actions based on the identified causes. As input, it uses the list of causes identified in step 3. It uses a generative AI model to generate optimal remediation actions. As output, it generates a list of remediation actions. Specific actions include suggesting code changes or system setting adjustments to optimize processes that are consuming excessive resources.

[0992] Step 5: Present your renovation proposal

[0993] The server presents a proposed fix to the development vendor based on the proposed improvements. As input, it uses the list of improvements generated in step 4. It uses a generative AI model to generate a document containing specific steps for code changes and configuration changes. As output, it generates a document of the proposed fix. As a specific operation, it generates a document containing "specific code change steps to improve memory management for function B."

[0994] Step 6: Automatic planning of acceptance test items

[0995] The server automatically creates acceptance test cases based on the proposed modifications. It uses the modification document generated in step 5 as input. It uses a generative AI model to generate test cases. It generates a list of acceptance test cases as output. Specifically, it generates a test case to verify that memory management for function B has been improved.

[0996] (Application example 3)

[0997] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0998] With conventional systems, it is difficult to automatically detect points that need to be improved based on transaction data and operational status, and it is also time-consuming to automatically create business and system requirements. In addition, there is a lack of efficient means to support all development tasks, such as providing opinions to system personnel, detecting operational problems, proposing improvements, presenting improvement proposals to development vendors, and automatically planning acceptance test items. As a result, reductions in the number of personnel and improvements in work efficiency have not been fully achieved. Furthermore, there is a need to monitor the operational status of robots operating in factories in real time and respond quickly when problems occur.

[0999] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1000] In this invention, the server includes a means for automatically detecting points to be improved based on transaction data, operational status, inquiry logs, legal amendment information, etc.; a means for automatically creating business requirements and system requirements; a means for submitting opinions to system personnel; a means for acquiring robot operation data; a means for detecting problems; a means for proposing improvements; a means for presenting improvement proposals; and a means for automatically planning acceptance test items. This allows for real-time monitoring of the system's operational status and rapid response when problems occur. It also efficiently supports all development tasks, reducing the number of personnel required and promoting business efficiency.

[1001] "Transaction data" is data that records information about transactions and operations that take place within the system.

[1002] "Operation status" is information that indicates how a system or device is currently operating.

[1003] An "inquiry log" is data that records inquiries and requests from users to the system.

[1004] "Legal Change Information" means information about changes to relevant laws and regulations.

[1005] "Modification points" are areas in a system or process that require improvement or correction.

[1006] "Business requirements" define the conditions and functions necessary to carry out a business.

[1007] "System requirements" define the technical conditions and functions that a system must meet.

[1008] "Submitting opinions" means providing suggestions or opinions to the system administrator.

[1009] "Robot operation data" refers to data related to the operating status and performance of robots operating within a factory.

[1010] "Means for detecting problems" are methods for detecting abnormalities or errors that occur during the operation of a system or robot.

[1011] "Means for proposing improvements" are methods for proposing appropriate solutions to detected problems.

[1012] A "method of proposing modifications" is a method of making specific proposals for improving or modifying a system or process.

[1013] "Acceptance test items" define the content and conditions of tests to be conducted after a system or process is modified.

[1014] "Automatic planning means" refers to a method for automatically creating plans or proposals based on specific conditions or data.

[1015] The system for carrying out this invention consists of three main elements: a server, a terminal, and a user. The server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to the system manager, means for acquiring robot operation data, means for detecting problems, means for proposing improvements, means for presenting improvement proposals, and means for automatically planning acceptance test items.

[1016] The server runs a program using Python (registered trademark) and uses the requests library to make HTTP requests. The server obtains operational data from the robots operating in the factory via API, analyzes that data, and detects problems. For any problems detected, it proposes appropriate improvements and modifications, and automatically creates acceptance test items.

[1017] The terminals are mobile devices such as smartphones and tablets that display information provided by the server in real time. Users can monitor the system's operational status through the terminals and check proposed improvements and modifications.

[1018] As a concrete example, consider a scenario in which the operational status of a robot "robot_123" operating in a factory is monitored. The server obtains operational data for "robot_123" via an API and analyzes that data to detect problems. For example, if error code "E001" is detected, the server will suggest an improvement measure such as "Check the power supply" and present "Upgrade the power supply unit" as a suggested repair. It will also automatically create an acceptance test item such as "Test the stability of the power supply."

[1019] An example of a prompt to input to a generative AI model is as follows:

[1020] "Monitor the operational status of robots operating in the factory, and if a problem occurs, identify the cause and propose appropriate remedial measures. You will also be responsible for presenting improvement proposals to development vendors and automatically planning acceptance test items."

[1021] In this way, by linking servers, terminals, and users, it is possible to monitor the system's operational status in real time and respond quickly when problems occur. It also efficiently supports all development tasks, reduces the number of personnel required, and promotes work efficiency.

[1022] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1023] Step 1:

[1024] The server obtains operational data from robots operating in the factory. Specifically, the server requests the robot's operational data through an API and receives the obtained data in JSON format. The input is the robot's ID, and the output is the robot's operational data. To process the data, the server analyzes the obtained JSON data and extracts the necessary information.

[1025] Step 2:

[1026] The server analyzes the acquired operational data and detects problems. Specifically, it checks each data point in the operational data to detect error codes and abnormal values. The input is the operational data, and the output is a list of detected problems. During data calculations, the server checks the status of each data point to determine whether it contains an error code.

[1027] Step 3:

[1028] The server proposes solutions to the detected problems. Specifically, it lists predefined solutions based on the detected error code. The input is a list of detected problems, and the output is a list of solutions. For data processing, the server retrieves the error codes and corresponding solutions from a database and lists them.

[1029] Step 4:

[1030] The server presents fix proposals for detected problems. Specifically, it proposes fix proposals for systems and processes based on error codes. The input is a list of detected problems, and the output is a list of fix proposals. For data processing, the server retrieves the error codes and corresponding fix proposals from a database and creates a list.

[1031] Step 5:

[1032] The server automatically creates acceptance test items for the detected problems. Specifically, it defines the content and conditions of the acceptance test based on the error code. The input is a list of detected problems, and the output is a list of acceptance test items. For data processing, the server retrieves the error codes and corresponding acceptance test items from the database and creates a list.

[1033] Step 6:

[1034] The terminal displays information provided by the server in real time. Specifically, it displays operational data received from the server, detected problems, improvement measures, modification proposals, and acceptance test items to the user. The input is information from the server, and the output is what is displayed to the user. As part of data processing, the terminal formats the received information so that it can be displayed in an appropriate format.

[1035] Step 7:

[1036] Users monitor the system's operational status through their terminals and check proposed improvements and modifications. Specifically, they understand the system's operational status based on the information displayed on the terminals and take necessary action. The input is the information displayed on the terminals, and the output is the user's judgment and response. As a data calculation, users make decisions based on the displayed information.

[1037] 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.

[1038] "Example 1"

[1039] One embodiment of the present invention is a system that incorporates an emotion engine. This system recognizes a user's emotion and adjusts the system's operation based on that emotion. Specifically, it includes an emotion engine for recognizing the user's emotion and a control unit for adjusting the system's operation based on that emotion. The emotion engine estimates the user's emotion from, for example, the user's tone of voice, facial expression, and choice of words. The control unit adjusts the system's operation based on the output from the emotion engine. For example, if the user feels angry, the system adjusts its operation to respond more politely.

[1040] "Example 2"

[1041] Another embodiment of the present invention is a system that incorporates an emotion engine. This system recognizes a user's emotion and adjusts the system's operation based on that emotion. Specifically, it includes an emotion engine for recognizing the user's emotion and a control unit for adjusting the system's operation based on that emotion. The emotion engine infers the user's emotion from, for example, the user's tone of voice, facial expression, and choice of words. The control unit adjusts the system's operation based on the output from the emotion engine. For example, if the system senses that the user is happy, it adjusts its operation to respond in a way that shares that joy.

[1042] "Example 3"

[1043] Furthermore, another embodiment of the present invention is a system that incorporates an emotion engine. This system recognizes a user's emotion and adjusts the system's operation based on that emotion. Specifically, it includes an emotion engine for recognizing the user's emotion and a control unit for adjusting the system's operation based on that emotion. The emotion engine infers the user's emotion from, for example, the user's tone of voice, facial expression, and choice of words. The control unit adjusts the system's operation based on the output from the emotion engine. For example, if the user feels surprised, the system adjusts its operation to respond in a way that alleviates the surprise.

[1044] The processing flow of each embodiment will be described below.

[1045] "Example 1"

[1046] Step 1: The emotion engine works to infer emotions from the user's tone of voice, facial expressions, choice of words, etc.

[1047] Step 2: The control unit receives the output from the emotion engine.

[1048] Step 3: The control unit adjusts the system's behavior based on the received emotion. For example,

[1049] If the system senses that the user is angry, it adjusts its behavior to be more polite.

[1050] "Example 2"

[1051] Step 1: The emotion engine works to infer emotions from the user's tone of voice, facial expressions, choice of words, etc.

[1052] Step 2: The control unit receives the output from the emotion engine.

[1053] Step 3: The control unit adjusts the system's behavior based on the received emotion. For example, if the control unit senses that the user is happy, the system adjusts its behavior to respond in a way that shares that happiness.

[1054] "Example 3"

[1055] Step 1: The emotion engine works to infer emotions from the user's tone of voice, facial expressions, choice of words, etc.

[1056] Step 2: The control unit receives the output from the emotion engine.

[1057] Step 3: The control unit adjusts the system's behavior based on the received emotion. For example, if the user feels surprised, the system adjusts its behavior to respond in a way that alleviates the surprise.

[1058] Example 1

[1059] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1060] In conventional systems, it was necessary to manually identify areas for improvement from large amounts of data such as transaction data and inquiry logs, which was time-consuming and labor-intensive. Furthermore, the system was unable to respond in a way that took user feelings into consideration, which could lead to a decline in user satisfaction. Furthermore, there was an issue of operational efficiency being hindered by insufficient efforts to detect operational problems, consider improvement measures, and streamline development tasks.

[1061] 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.

[1062] In this invention, the server includes a means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., a means for automatically creating business requirements and system requirements, a means for submitting opinions to system personnel, a means for recognizing user emotions, and a means for adjusting system operation based on the recognized emotions. This enables automatic analysis of data and rapid detection of points to be improved, realizing appropriate responses according to user emotions. It also improves business efficiency by detecting operational problems, supporting the consideration of improvement measures, and streamlining development tasks.

[1063] "Transaction Data" refers to records of transactions and operations that take place within the system.

[1064] "Operation status" refers to information that indicates the operational status and running status of the system.

[1065] An "inquiry log" refers to data that records the content of inquiries from users and their responses.

[1066] "Legal amendment information" refers to information regarding changes to laws and regulations.

[1067] "Means for automatically detecting points for improvement" refers to a function that automatically identifies problems in the system and areas that need improvement.

[1068] "Means for automatically creating business requirements and system requirements" refers to the function of automatically generating business requirements and system specifications.

[1069] "Means for submitting opinions to system personnel" refers to the function of notifying system personnel of proposals for system modifications and improvements.

[1070] "Means for recognizing the user's emotions" refers to the function of inferring emotions from the user's tone of voice, facial expressions, choice of words, etc.

[1071] "Means for adjusting system behavior based on recognized emotions" refers to a function that changes the system's response or behavior depending on the user's emotions.

[1072] This invention is a system that receives as input transaction data, operational status, inquiry logs, legal amendment information, etc. from an internal system, analyzes this data, and automatically detects points that need to be improved. It also includes a function to recognize user emotions and adjust the system's operation based on those emotions.

[1073] Data Acquisition and Input

[1074] The server retrieves transaction data and query logs from a database (e.g., MySQL (registered trademark)) or cloud storage (e.g., AWS S3). For example, it retrieves data using an SQL query such as "SELECT FROM transactions WHERE date >= '2023-01-01'". The retrieved data is sent to the system in JSON format.

[1075] Data analysis and automatic detection of repair points

[1076] The server analyzes the data using the Python® Pandas library. For example, it reads the data using "df = pd.read_json(data)". It then uses a machine learning model (e.g., Scikit-learn®) to analyze the data for patterns and trends. For example, it makes predictions using "model.predict(df)" to detect outliers and trends.

[1077] Emotion Engine Operation

[1078] When a user uses the system, the device (e.g., PC or smartphone) sends the user's tone of voice, facial expression, and choice of words to the emotion engine. The emotion engine uses voice analysis (and facial expression analysis) to estimate the user's emotions. For example, "if the user's voice tone is high and their facial expression is stern, anger is detected."

[1079] Adjusting system operation

[1080] The control unit adjusts the system's behavior based on the output from the emotion engine (e.g., anger, joy, sadness). For example, "If the user is angry, make the response message more polite." Specifically, a polite message such as "Sorry for keeping you waiting. Here is your recent transaction history" is displayed.

[1081] Examples and prompts

[1082] For example, when a user inquires of the system, "Tell me my recent transaction history," the specific operation is as follows.

[1083] 1. The server executes the query "SELECT FROM transactions WHERE date >= '2023-01-01'" from the database to retrieve transaction data.

[1084] 2. The server sends the acquired data to the analysis module to detect outliers and trends.

[1085] 3. When a user makes an inquiry through the device, the device sends the user's tone of voice and facial expressions to the emotion engine.

[1086] 4. The emotion engine detects anger from the user's high-pitched voice and stern facial expression.

[1087] 5. The control unit changes the response message based on the output from the emotion engine to "Sorry for the wait. Here is your recent transaction history."

[1088] In this way, the system responds appropriately to the user's emotions.

[1089] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1090] Step 1:

[1091] The server retrieves transaction data, operation status, inquiry logs, and legal change information from databases and cloud storage. Specifically, it extracts the necessary data from the database using SQL queries and retrieves data from cloud storage via APIs. The input is the connection information and query for the database or cloud storage, and the output is the retrieved data.

[1092] Step 2:

[1093] The server sends the acquired data to the analysis module. The data is sent in JSON format. The input is the data acquired in step 1, and the output is the data sent to the analysis module. Specifically, the data is sent using an HTTP request.

[1094] Step 3:

[1095] The server parses the data using the Python (registered trademark) Pandas library. Specifically, it reads the data using "df = pd.read_json(data)" and processes it as a data frame. The input is the data sent in step 2, and the output is the parsed data frame.

[1096] Step 4:

[1097] The server uses a machine learning model (e.g., Scikit-learn®) to analyze the data for patterns and trends. Specifically, it performs predictions (e.g., "model.predict(df)") and detects outliers and trends. The input is the data frame analyzed in step 3, and the output is the prediction results.

[1098] Step 5:

[1099] When a user uses the system, the device transmits the user's tone of voice, facial expressions, and choice of words to the emotion engine. Specifically, it uses a microphone and camera to capture audio and video and transmits them to the emotion engine. The input is the user's audio and video data, and the output is the data transmitted to the emotion engine.

[1100] Step 6:

[1101] The emotion engine uses voice analysis and facial expression analysis to estimate the user's emotions. Specifically, it converts voice data into text and analyzes facial expression data to estimate emotions. The input is the voice and video data sent in step 5, and the output is the estimated emotion data.

[1102] Step 7:

[1103] The control unit adjusts the system's behavior based on the output from the emotion engine. Specifically, if the user is angry, the response message will be more polite. The input is the emotion data estimated in step 6, and the output is the adjusted response message.

[1104] Step 8:

[1105] The system responds appropriately based on the user's emotions. Specifically, it displays a polite message such as, "Sorry for the wait. Here is your recent transaction history." The input is the response message adjusted in step 7, and the output is the message displayed to the user.

[1106] (Application example 1)

[1107] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1108] Conventional systems can automatically detect points for improvement by analyzing transaction data, operational status, inquiry logs, legal amendment information, etc., but they lack the ability to recognize customer emotions and adjust the system's behavior based on those emotions, making it difficult to improve the quality of customer service. Furthermore, they lack the ability to suggest ways to respond based on customer emotions, which can lead to inconsistent responses from store staff.

[1109] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to the system staff, means for recognizing customer emotions and adjusting system operation based on those emotions, and means for proposing a response method based on the customer emotions. This makes it possible to respond in a way that takes customer emotions into consideration, thereby improving customer satisfaction and ensuring consistency in the responses of store staff.

[1110] "Transaction data" refers to data that records transactions and operations that take place within a system.

[1111] "Operation status" is information that indicates the operational status and progress of a system or business.

[1112] An "inquiry log" is data that records the content of inquiries from users and the history of responses.

[1113] "Legal amendment information" is information regarding changes to laws and regulations.

[1114] "Improvement points" refer to areas where improvements are needed in systems or business processes.

[1115] "Business requirements" refers to the conditions and specifications necessary to carry out a business.

[1116] "System requirements" refers to the technical conditions and specifications that a system must meet.

[1117] "Submitting opinions" is the act of providing suggestions or opinions to the system administrator.

[1118] "Customer emotions" refers to the emotional state that a customer feels, such as joy, anger, or sadness.

[1119] "Adjusting the system's behavior" refers to changing the system's behavior in response to customer emotions.

[1120] "Suggesting a response method" refers to presenting an appropriate response method based on the customer's feelings.

[1121] The following system configuration will be described as an embodiment of the present invention.

[1122] System Configuration

[1123] The system consists of the following main components:

[1124] 1. Server: Analyzes transaction data, operation status, inquiry logs, legal amendment information, etc., and automatically detects areas that need to be improved.

[1125] 2. Terminals: Devices such as smartphones and smart glasses used by store staff recognize customers' emotions in real time and suggest ways to respond based on those emotions.

[1126] 3. Users: Store staff and customers who use the system.

[1127] Hardware and software used

[1128] Hardware: Smartphone, smart glasses, camera, microphone

[1129] Software: OpenCV (camera feed processing), EmotionRecognizer (emotion recognition), TransactionAnalyzer (transaction data analysis), ChatbotLogAnalyzer (chatbot log analysis)

[1130] Data processing and calculation

[1131] Server Processing

[1132] The server retrieves transaction data, operation status, inquiry logs, legal amendment information, etc. from databases and cloud storage and analyzes this data. Specifically, it analyzes data patterns and trends to identify system problems and areas that can be improved. Based on the analysis results, it automatically creates business requirements and system requirements and submits opinions to the system manager.

[1133] Terminal handling

[1134] The device uses a camera and microphone to recognize customer emotions in real time. EmotionRecognizer is used to estimate emotions from the customer's tone of voice, facial expressions, and choice of words. Based on the estimated emotions, the system adjusts its behavior and suggests ways to respond to the customer.

[1135] Specific examples

[1136] For example, when a customer visits a store and begins a conversation with a staff member, the device's camera and microphone capture the customer's facial expressions and voice. If EmotionRecognizer analyzes this data and determines that the customer is angry, the device will notify the staff member to "please try to be polite." The server also analyzes transaction data and inquiry logs to identify areas for system improvement.

[1137] Prompt Sentence Examples

[1138] Develop an application that recognizes customer emotions and suggests ways to respond based on those emotions. Use camera and audio feeds to recognize emotions, and analyze transaction data and chatbot logs to find areas for improvement. If the customer is angry, say "Please be polite," and if they're happy, say "Keep it up."

[1139] In this way, it is possible to respond in a way that takes into account the feelings of the customer, thereby improving customer satisfaction and ensuring consistency in the responses of store staff.

[1140] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1141] Step 1:

[1142] The server retrieves transaction data, operational status, inquiry logs, legal change information, etc. from databases and cloud storage. This data serves as input for identifying system issues and areas for improvement. Database queries and APIs are used to retrieve the data and prepare it for analysis.

[1143] Step 2:

[1144] The server analyzes the acquired data and analyzes patterns and trends. Specifically, it detects anomalies in transaction data and extracts frequently occurring keywords from query logs. This identifies problems with the system and areas that can be improved. The analysis results are output as suggestions for improvement.

[1145] Step 3:

[1146] The server automatically creates business and system requirements based on the analysis results. These include areas that need to be modified in the system and new functions that should be added. The generated business and system requirements are output as a recommendation to the system manager.

[1147] Step 4:

[1148] The device uses a camera and microphone to recognize customer emotions in real time. Using EmotionRecognizer, it infers emotions from the customer's tone of voice, facial expressions, and word choice. The input data is the camera feed and audio feed, and the output is the estimated customer emotion.

[1149] Step 5:

[1150] The device adjusts the system's behavior based on the estimated customer's emotions. For example, if the system determines that the customer is angry, it changes its behavior to be more polite, suggesting ways to respond, and notifying staff.

[1151] Step 6:

[1152] The device suggests ways to respond based on the customer's emotions. For example, if the customer is angry, it will notify them by saying, "Please try to be polite," and if the customer is happy, it will notify them by saying, "Please continue to respond in this manner." The input is the estimated emotion, and the output is a suggested way to respond.

[1153] Step 7:

[1154] The server identifies areas for improvement in the system based on the results of analyzing transaction data and inquiry logs, and submits suggestions to the system administrator. This allows for efficient system modifications. The input is the analysis results, and the output is the content of the suggestions.

[1155] In this way, it is possible to respond in a way that takes into account the feelings of the customer, thereby improving customer satisfaction and ensuring consistency in the responses of store staff.

[1156] Example 2

[1157] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1158] In conventional systems, it was difficult to automatically detect points that needed improvement from transaction data, operational status, inquiry logs, legal amendment information, etc., and to automatically create business and system requirements. Furthermore, because system operation was not adjusted based on user feedback or user sentiment, business efficiency and user experience were not sufficiently improved. Furthermore, there was a lack of means to generate and submit improvement proposals using generative AI models. These issues need to be resolved.

[1159] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1160] In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to system personnel, means for recognizing user emotions using an emotion engine, means for adjusting system operation based on the recognized emotions, means for generating improvement proposals using a generative AI model, and means for submitting the generated improvement proposals via email or a web-based dashboard, thereby enabling business efficiency improvement and an improved user experience.

[1161] "Transaction Data" means data relating to transactions and operations conducted within the System.

[1162] "Operational status" refers to information about the operational status and performance of a system.

[1163] An "inquiry log" is a record of inquiries and support requests from users.

[1164] "Legal Change Information" means information about changes to relevant laws and regulations.

[1165] "Improvement points" are areas or problems that require improvement in the system.

[1166] "Business requirements" are the conditions and functions necessary to carry out a business.

[1167] "System requirements" are the technical conditions and specifications that a system must meet.

[1168] "Submitting an opinion" is the act of submitting a proposal or opinion to the system administrator.

[1169] An "emotion engine" is software or hardware for recognizing a user's emotions.

[1170] A "generative AI model" is a model that uses artificial intelligence to generate new information and suggestions from data.

[1171] "Email" is a means of sending and receiving messages over the Internet.

[1172] A "web-based dashboard" is an information display accessible through a web browser.

[1173] MODE FOR CARRYING OUT THE INVENTION

[1174] This invention is a system that automatically detects modification points from transaction data, operational status, inquiry logs, legal amendment information, etc., and automatically creates business and system requirements. It also includes a function that recognizes user emotions using an emotion engine and adjusts system operation based on those emotions. Furthermore, it uses a generative AI model to generate modification proposals, which are then submitted via email or a web-based dashboard.

[1175] Hardware and software used

[1176] Server: A high-performance server is used to run a program that automatically creates business and system requirements.

[1177] Device: Use a device equipped with an emotion engine (e.g., smartphone, tablet).

[1178] Emotion engine: Uses software (e.g., Microsoft® Azure® Emotion API) to recognize user emotions.

[1179] Control Unit: A software module is used to adjust the behavior of the system based on the output of the emotion engine.

[1180] Generative AI model: OpenAI's GPT-3 model is used to generate improvement suggestions.

[1181] Program processing explanation

[1182] 1. The server collects system log data. It periodically reads the system log files and stores them in a database. For example, it reads the log files every day at 2:00 AM and stores them in a MySQL (registered trademark) database.

[1183] 2. The server analyzes the collected log data and detects areas for improvement. It runs a script to analyze the log data and detects performance degradation and error messages. For example, it uses a Python script to analyze the log data and count the frequency of error messages.

[1184] 3. The server generates specific repair proposals based on the detected repair points. It uses a generative AI model to propose solutions to the detected problems. For example, it uses OpenAI's GPT-3 model to generate the cause of the error message and its solution.

[1185] 4. The server submits the generated modification proposal to the system administrator as a suggestion. The server sends the generated modification proposal to the system administrator by email. For example, the server uses the SMTP protocol to send an email containing the generated modification proposal.

[1186] A system that combines emotion engines

[1187] 1. The device analyzes the user's tone of voice and facial expressions using an emotion engine. It uses a microphone and camera to capture the user's voice and facial expressions and input them into the emotion engine. For example, when the user speaks, the microphone captures the user's voice and the camera captures their facial expressions.

[1188] 2. The device sends the output of the emotion engine to the control unit. The emotion engine's analysis results are sent to the control unit, and instructions are received to adjust the system's operation. For example, the emotion engine's output is sent to the control unit in JSON format.

[1189] 3. The device's control unit adjusts the system's behavior based on the user's emotions. Based on instructions from the control unit, the device executes appropriate actions according to the user's emotions. For example, if the device recognizes that the user is happy, it plays cheerful music.

[1190] Prompt Sentence Examples

[1191] "Analyze the system log data, identify the cause of the performance degradation, and generate specific fixes. Then, send the fixes to the system administrator via email."

[1192] "Recognize emotions from the user's tone of voice and facial expressions, and adjust the system's behavior based on those emotions. For example, if you sense the user is happy, play upbeat music."

[1193] The system is designed to improve operational efficiency and enhance the user experience.

[1194] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1195] Automated generation of business and system requirements

[1196] Step 1:

[1197] The server collects system log data.

[1198] Input: System log file

[1199] Specific operation: The server reads the log file at 2:00 AM every day and stores it in a MySQL database.

[1200] Output: Log data stored in a database

[1201] Step 2:

[1202] The server analyzes the collected log data and detects points that need to be modified.

[1203] Input: Log data stored in the database

[1204] Specific operation: The server uses a Python script to analyze the log data and count the frequency of error messages.

[1205] Output: Detected modification points

[1206] Step 3:

[1207] The server generates a specific repair plan based on the detected repair points.

[1208] Input: Detected modification point

[1209] What it does: The server uses OpenAI's GPT-3 model to generate the cause of the error message and its solution.

[1210] Output: Generated modification plan

[1211] Step 4:

[1212] The server submits the generated modification plan as a suggestion to the system manager.

[1213] Input: Generated renovation proposal

[1214] Specific operation: The server uses the SMTP protocol to send an email containing the generated revision proposal.

[1215] Output: Email sent to system contact

[1216] A system that combines emotion engines

[1217] Step 1:

[1218] The device uses an emotion engine to analyze the user's tone of voice and facial expressions.

[1219] Input: User's voice and facial expressions

[1220] Specific operation: The device captures audio with a microphone and captures facial expressions with a camera.

[1221] Output: Analysis results by the emotion engine

[1222] Step 2:

[1223] The terminal transmits the output of the emotion engine to the control unit.

[1224] Input: Analysis results by emotion engine

[1225] Specific operation: The terminal sends the output of the emotion engine in JSON format to the control unit.

[1226] Output: Analysis results sent to the control unit

[1227] Step 3:

[1228] The control unit of the terminal adjusts the operation of the system based on the emotion.

[1229] Input: Analysis results sent to the control unit

[1230] Specific behavior: If the device recognizes that the user is happy, it plays upbeat music.

[1231] Output: System behavior according to user's emotions

[1232] The system is designed to improve operational efficiency and enhance the user experience.

[1233] (Application example 2)

[1234] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1235] Conventional systems can automatically detect areas for improvement based on transaction data, operational status, inquiry logs, legal revision information, etc., but they cannot recognize user emotions and adjust system operations based on those emotions. It is also difficult to propose customer service methods based on customer emotions or automatically generate business and system requirements from emotional and video data. This has prevented systems from achieving sufficient improvements in customer satisfaction and operational efficiency.

[1236] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1237] In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to the system manager, an emotion engine for recognizing the user's emotions, a control unit for adjusting the system operation based on the emotions, means for analyzing the customer's facial expressions and tone of voice in real time to recognize emotions, means for proposing customer service methods based on the recognized emotions, and means for automatically creating business requirements and system requirements from emotion data and video data. This enables flexible responses based on customer emotions, improving customer satisfaction and streamlining operations.

[1238] "Transaction Data" refers to information relating to transactions and operations conducted within the System.

[1239] "Operational status" refers to information about the operational status of the system and the processes that are running.

[1240] "Inquiry log" refers to data that records the content of inquiries from users and the history of responses to those inquiries.

[1241] "Legal Change Information" refers to information regarding changes in relevant laws and regulations.

[1242] "Modification points" refer to areas of the system that require improvement or correction.

[1243] "Business requirements" refer to the conditions and functions necessary to carry out business.

[1244] "System requirements" refers to the technical conditions and specifications that a system must meet.

[1245] "Submitting opinions" refers to submitting suggestions or opinions to the system administrator.

[1246] An "emotion engine" refers to software or algorithms that recognize a user's emotions.

[1247] The "controller" refers to the part that adjusts the system's operation based on the output from the emotion engine.

[1248] "Facial expression analysis" refers to the technology of analyzing a user's facial expressions from camera footage and recognizing their emotions.

[1249] "Voice tone analysis" refers to technology that analyzes the tone of a user's voice and recognizes their emotions.

[1250] "Customer service suggestion" refers to proposing an appropriate customer service method based on the recognized emotions.

[1251] "Emotion data" refers to information about the user's emotions.

[1252] "Video data" refers to video information captured by a camera.

[1253] A system for implementing this invention is configured as follows: The server has means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc. It also has means for automatically creating business requirements and system requirements and means for submitting opinions to system personnel. It also includes an emotion engine for recognizing user emotions and a control unit for adjusting system operation based on those emotions.

[1254] This system allows store clerks wearing smart glasses to analyze customers' facial expressions and tone of voice in real time to recognize their emotions. It also includes a means to suggest ways to serve customers based on the recognized emotions, and a means to automatically generate business and system requirements from emotion data and video data.

[1255] Hardware and software used

[1256] Hardware: Smart glasses (with built-in camera and display), computer (for data processing)

[1257] Software: OpenCV (image processing library), EmotionRecognizer (emotion recognition library), SystemRequirementsGenerator (business and system requirements generation library)

[1258] Data processing and calculation

[1259] The server processes the video data captured by the camera in real time using OpenCV. It uses EmotionRecognizer to recognize customer emotions from the video data. It then proposes appropriate ways to serve customers based on the recognized emotions. It also uses SystemRequirementsGenerator to automatically generate business and system requirements from the emotion and video data.

[1260] Specific examples

[1261] For example, if a customer is smiling and looking at a product, the smart glasses' display will say, "The customer is happy. Try to be more kind." If the customer looks dissatisfied, the display will say, "The customer is angry. Try to stay calm and resolve the issue."

[1262] Prompt Sentence Examples

[1263] "Develop a smart glasses application that analyzes a customer's facial expressions and tone of voice, recognizes their emotions, and suggests appropriate ways to serve them. Include a function that suggests a friendly response if the customer is happy, and a calm response if the customer is angry. Also, add a function that automatically generates business and system requirements based on emotion data."

[1264] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1265] Step 1:

[1266] The server acquires video data in real time through the smart glasses' camera. The input is video data including the customer's facial expressions and movements. The output is frames of video data. Specifically, the camera captures the customer's face and sends the video to the server.

[1267] Step 2:

[1268] The server processes the acquired video data in real time using OpenCV and performs face detection. The input is the frame of video data acquired in step 1. The output is the position information of the detected face. Specifically, the face detection algorithm identifies the face in the video frame and outputs its position as coordinates.

[1269] Step 3:

[1270] The server uses EmotionRecognizer to analyze the detected facial expressions and recognize emotions. The input is the face position information obtained in step 2 and the video data frame. The output is the recognized emotion (e.g., joy, anger, sadness). Specifically, the facial expression analysis algorithm analyzes the facial features and infers the emotion.

[1271] Step 4:

[1272] The server proposes an appropriate customer service method based on the recognized emotion. The input is the emotion data obtained in step 3. The output is a customer service method suggestion (e.g., "The customer is happy. Let's try to be even more friendly."). Specifically, the server searches a database for a customer service method that corresponds to the emotion data and displays it on the smart glasses' display.

[1273] Step 5:

[1274] The server automatically generates business requirements and system requirements from the emotion data and video data. The input is the emotion data obtained in step 3 and the video data acquired in step 1. The output is the automatically generated business requirements and system requirements. Specifically, the SystemRequirementsGenerator analyzes the emotion data and video data and generates the necessary business requirements and system requirements.

[1275] Step 6:

[1276] The server submits the generated business requirements and system requirements to the system administrator. The input is the business requirements and system requirements generated in step 5. The output is a submission to the system administrator (e.g., email or a web-based dashboard). Specific operations include converting the generated requirements into an appropriate format and notifying the system administrator.

[1277] Example 3

[1278] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1279] Conventional systems lack the ability to detect operational problems, propose solutions, or recognize user emotions and adjust system behavior accordingly, resulting in poor system efficiency and a poor user experience. Furthermore, while there is a demand for more efficient development tasks and fewer personnel, there is a lack of automation methods to achieve this.

[1280] The identification processing by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., a means for automatically creating business requirements and system requirements, a means for submitting opinions to the system manager, a means for monitoring the system operation status in real time, a means for detecting anomalies and identifying their causes, a means for proposing solutions to the identified problems, a means for recognizing user emotions, and a means for adjusting the system operation based on the recognized emotions. This makes it possible to quickly detect operational problems, propose appropriate solutions, and further adjust the system operation according to the user's emotions. This improves system efficiency and user experience.

[1281] "Transaction Data" refers to information relating to transactions and operations conducted within the System.

[1282] "Operational status" refers to information about the system's operating status and performance.

[1283] "Inquiry log" refers to records of inquiries and support requests from users.

[1284] "Legal Change Information" refers to information regarding changes in relevant laws and regulations.

[1285] "Means for automatically detecting points for improvement" refers to a function that automatically identifies problems in the system and areas that need improvement.

[1286] "Means for automatically creating business requirements and system requirements" refers to the function of automatically generating business needs and system specifications.

[1287] "Means for submitting opinions to system personnel" refers to the function of providing improvement suggestions and opinions to system administrators and operations personnel.

[1288] "Means for monitoring the system's operating status in real time" refers to the function of monitoring the system's operating status in real time and detecting abnormalities.

[1289] "Means for detecting abnormalities and identifying their causes" refers to the function of detecting abnormalities in the system and identifying their causes.

[1290] "Means for proposing improvements to identified problems" refers to the function of proposing appropriate solutions to detected problems.

[1291] "Means for recognizing the user's emotions" refers to the function of inferring emotions from the user's tone of voice, facial expressions, choice of words, etc.

[1292] "Means for adjusting the system's behavior based on the recognized emotion" refers to a function for changing the system's behavior in response to the user's emotion.

[1293] This invention is a system that monitors the system's operating status, identifies the cause of any problems that may arise, and proposes appropriate solutions. It also includes a function to recognize the user's emotions and adjust the system's operation based on those emotions.

[1294] Hardware and software used

[1295] server

[1296] The server is the main computing resource for monitoring the system's operating status in real time and detecting abnormalities. Specifically, the following tools are used:

[1297] Monitoring tools: Prometheus, Grafana

[1298] Data analysis tools: ELK stack (Elasticsearch (registered trademark), Logstash (registered trademark), Kibana)

[1299] Terminal

[1300] A terminal is a device through which a user interacts with the system, including PCs, smartphones, tablets, etc.

[1301] Emotion Engine

[1302] The emotion engine is software that recognizes the user's emotions. Specifically, it uses the following APIs:

[1303] Emotion Recognition API: Microsoft® Azure® Emotion API, Google® Cloud Natural Language API

[1304] Control unit

[1305] The control unit is a software module for coordinating the behavior of the system based on the output from the emotion engine.

[1306] Data processing and calculation

[1307] Server Processing

[1308] The server monitors the system's operating status in real time, specifically by collecting performance metrics such as CPU usage, memory usage, and network traffic, using monitoring tools such as Prometheus and Grafana.

[1309] If an anomaly is detected, the server analyzes the log data to identify the cause, for example, discovering that a particular process is consuming excessive resources, using the ELK stack (Elasticsearch®, Logstash®, Kibana).

[1310] For any identified problems, the server will suggest appropriate remedial measures, such as restarting processes that are consuming excessive resources or changing settings.

[1311] Terminal handling

[1312] The device captures the user's tone of voice, facial expressions, and word choice using hardware such as a microphone and camera.

[1313] Emotion engine processing

[1314] The emotion engine analyzes data acquired from the device and estimates the user's emotions. For example, it identifies the user's emotional state, such as whether they are surprised, angry, or happy. This is done using the Microsoft® Azure® Emotion API and the Google® Cloud Natural Language API.

[1315] Control unit processing

[1316] The control unit adjusts the system's behavior based on the output from the emotion engine. For example, if the system senses that the user is surprised, it will respond to alleviate the surprise. Specifically, it may display a message that gives the user a sense of security or perform optimizations to improve the system's response speed.

[1317] Specific examples

[1318] Operational problem detection: The server detects a sudden spike in CPU usage, analyzes log data to identify that a specific process is the cause, and then proposes a remedial measure to limit the process's resource usage.

[1319] Example of use of emotion engine: If a user speaks to the terminal in an angry tone, saying "Why is it so slow?", the emotion engine will detect the anger and the control unit will perform optimizations to improve the system's response speed.

[1320] Prompt Sentence Examples

[1321] "Please explain how the system should respond if a user expresses anger towards the system."

[1322] In this way, the system detects operational problems, considers improvement measures, and adjusts its operations based on the user's feelings, thereby providing a more efficient and user-friendly environment. The flow of the identification process in the third embodiment will be described with reference to Fig. 21.

[1323] Step 1:

[1324] The server monitors the system's operating status in real time. Specifically, it collects performance metrics such as CPU usage, memory usage, and network traffic. This is done using monitoring tools such as Prometheus and Grafana. Various system performance data is input, and this data is displayed in real time on a monitoring dashboard as output.

[1325] Step 2:

[1326] The server analyzes the collected performance data and detects anomalies. For example, it issues an alert if CPU usage exceeds 80%. The input is the performance data collected in step 1, and the output is an anomaly detection alert. Specifically, the anomaly detection algorithm analyzes the data and issues an alert if the threshold is exceeded.

[1327] Step 3:

[1328] When an anomaly is detected, the server analyzes the log data to identify the cause. For example, it discovers that a specific process is consuming excessive resources. The inputs are an anomaly detection alert and log data, and the output is the identification of the cause of the anomaly. Specifically, the log data is analyzed using a log analysis tool (for example, the ELK stack).

[1329] Step 4:

[1330] The server then proposes remediation measures for the identified issues, such as restarting a process that is consuming too many resources or changing a configuration. The input is the cause of the anomaly, and the output is a proposed remediation. The specific actions are based on historical data and best practices.

[1331] Step 5:

[1332] The server automatically generates fix proposals for the development vendor based on the identified issues. For example, it proposes fixes for specific parts of the code or the addition of new features. The input is a proposed improvement, and the output is a generated fix proposal. Specifically, the fix proposal generation algorithm runs and generates a specific fix proposal.

[1333] Step 6:

[1334] The server automatically creates acceptance test cases based on the proposed modifications. For example, it generates test cases to verify whether a new function works properly. The input is the proposed modifications, and the output is the generated acceptance test cases. Specifically, the test case generation algorithm runs and generates specific test cases.

[1335] Step 7:

[1336] The device captures the user's tone of voice, facial expressions, and choice of words. This is done using hardware such as a microphone and camera. The input is the user's voice data and video data, and the output is sent to the emotion engine. Specific operations include voice recognition and video analysis.

[1337] Step 8:

[1338] The emotion engine analyzes data acquired from the device and estimates the user's emotions. For example, it identifies emotional states such as whether the user is surprised, angry, or happy. The inputs include audio and video data, and the output is an estimated emotion result. Specifically, an emotion recognition algorithm is run to identify the emotional state.

[1339] Step 9:

[1340] The control unit adjusts the system's behavior based on the output from the emotion engine. For example, if the user feels surprised, the system responds to alleviate that surprise. The input is the emotion estimation result, and the output is the adjustment of the system's behavior. Specific actions include displaying a message that gives the user a sense of security, or optimizing the system to improve response speed.

[1341] (Application example 3)

[1342] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1343] In conventional systems, operational problem detection and improvement proposals are often done manually, resulting in inefficiency. Furthermore, it is difficult to recognize customer emotions in real time and take appropriate action based on that, making improving customer satisfaction a challenge. Furthermore, there is a need for a system that can quickly detect store operation problems and propose improvement measures.

[1344] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1345] In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to system personnel, an emotion engine for recognizing customer emotions, a control unit for adjusting system operation based on output from the emotion engine, and means for detecting problems in store operation and proposing improvement measures. This makes it possible to automatically detect operational problems and quickly propose improvement measures, and to take appropriate measures based on customer emotions, thereby improving customer satisfaction.

[1346] "Transaction Data" refers to information relating to transactions and operations conducted within the System.

[1347] "Operation status" refers to information that indicates the operational status and operating status of the system.

[1348] "Inquiry log" refers to data that records the content of inquiries from users and response history.

[1349] "Legal Change Information" refers to information regarding changes in relevant laws and regulations.

[1350] "Modification points" refer to areas of the system that require improvement or correction.

[1351] "Business requirements" refers to the conditions and specifications necessary to carry out a business.

[1352] "System requirements" refers to the technical conditions and specifications that a system must meet.

[1353] "Submitting opinions" refers to the act of providing suggestions or opinions to the system administrator.

[1354] An "emotion engine" refers to software or algorithms that recognize a user's emotions.

[1355] "Controller" refers to the hardware and software used to manage and regulate the operation of the system.

[1356] "Store operation issues" refers to challenges and obstacles that arise in the operation of a store.

[1357] "Improvement measures" refer to specific methods or means for solving detected problems.

[1358] A system for implementing this invention includes means for automatically detecting points to be improved from transaction data, operational status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to system personnel, an emotion engine for recognizing customer emotions, a control unit for adjusting the operation of the system based on output from the emotion engine, and means for detecting problems in store operations and proposing improvements.

[1359] System Program

[1360] The server collects transaction data and operational status, analyzes this data, and automatically detects areas that need improvement. Specifically, it retrieves the necessary information from the database and uses an algorithm to identify problem areas. Next, it automatically creates business requirements and system requirements and submits recommendations to the system manager.

[1361] The device (e.g., smart glasses) is equipped with a camera and microphone to analyze the customer's facial expressions and tone of voice in real time. This allows an emotion engine to recognize the customer's emotions and adjust the system's behavior based on that output. For example, if the customer is dissatisfied, the device will display a notification to the store clerk urging them to be more polite.

[1362] The server also detects problems in store operations and proposes solutions. This includes an algorithm that analyzes operational data and identifies problem areas. It automatically generates solutions for detected problems and notifies the system administrator.

[1363] Hardware and software used

[1364] Hardware: Smart glasses (camera, microphone), server

[1365] Software: OpenCV (camera image analysis), EmotionEngine (emotion recognition), ProblemDetector (problem detection), ImprovementSuggester (improvement suggestion)

[1366] Specific examples

[1367] For example, if a customer in a store asks about a product but looks a little dissatisfied, the smart glasses will recognize the customer's dissatisfaction and notify the store clerk, "The customer seems dissatisfied. Please respond politely." In addition, if the server analyzes the store's operational data and detects a problem with inventory management, it will suggest "updating the inventory management system" as a remedial measure.

[1368] Prompt Sentence Examples

[1369] Create a program that analyzes customer facial expressions and tone of voice to recognize emotions. Add functionality to suggest appropriate responses to store associates based on the recognized emotions. Also include functionality to detect problems in store operations and suggest solutions.

[1370] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1371] Step 1:

[1372] The server retrieves transaction data, operation status, inquiry logs, legal amendment information, etc. from the database. This data is analyzed and points to be modified are automatically detected. The input is various data, and the output is a list of points to be modified. Specifically, an algorithm is used to detect abnormal values ​​and patterns in the data and identify areas that require modification.

[1373] Step 2:

[1374] The server automatically creates business and system requirements based on the modification points. The input is a list of modification points, and the output is a document of business and system requirements. Specifically, the server generates requirements using a template and fills in the necessary information.

[1375] Step 3:

[1376] The server notifies the system manager of the generated business requirements and system requirements and submits their opinions. The input is a requirements document, and the output is a notification message. Specifically, the server sends information to the manager via email or a notification system.

[1377] Step 4:

[1378] The device (smart glasses) captures the customer's facial expressions and tone of voice in real time using a camera and microphone. The input is camera video and audio data, and the output is the analysis results. Specifically, the video is analyzed using OpenCV, and emotions are recognized using EmotionEngine.

[1379] Step 5:

[1380] The terminal adjusts the system's behavior based on the recognized emotion. The input is the emotion analysis result, and the output is a notification message to the store clerk. Specifically, if the emotion is "dissatisfied," the terminal displays a message to the store clerk saying, "The customer is dissatisfied. Please respond politely."

[1381] Step 6:

[1382] The server analyzes store operation data and detects problems. The input is operation data, and the output is a list of problems. Specifically, it uses data analysis algorithms to evaluate the operation data and identify anomalies and problem areas.

[1383] Step 7:

[1384] The server proposes solutions to the detected problems. The input is a list of problems, and the output is a document proposing solutions. Specifically, it selects appropriate solutions from a database of solutions and notifies the system administrator.

[1385] Step 8:

[1386] The server saves all processing results as logs for later reference. The input is the output data of each step, and the output is a log file. Specifically, the processing results are recorded in chronological order, allowing them to be searched and analyzed as needed.

[1387] 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.

[1388] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1389] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1390] 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.

[1391] [Third embodiment]

[1392] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1393] 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.

[1394] 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.

[1395] The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and 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).

[1396] 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.

[1397] 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.

[1398] 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).

[1399] 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.

[1400] 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.

[1401] 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.

[1402] 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.

[1403] 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.

[1404] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1405] "Example 1"

[1406] The system of the present invention receives as input transaction data and operational status of internal systems, inquiry logs from ChatBots and other systems, information on legal amendments and regulation revisions, etc. This data is obtained from, for example, a database or cloud storage and sent to the system. The system analyzes this data and automatically detects areas that need improvement. Specifically, it analyzes data patterns and trends to identify system problems and areas that can be improved.

[1407] "Example 2"

[1408] Next, the system automatically creates business and system requirements based on the detected modification points and generates specific modification proposals. The generated modification proposals are then submitted as a recommendation to the system administrator. This recommendation can be submitted, for example, via email or a web-based dashboard.

[1409] "Example 3"

[1410] Furthermore, the system detects operational problems and considers remedial measures. It monitors the system's operating status, and if a problem occurs, it identifies the cause and proposes appropriate remedial measures. The system also presents modification proposals to development vendors and automatically plans acceptance test items. This supports all development tasks, reduces the number of personnel required, and promotes work efficiency.

[1411] The processing flow of each embodiment will be described below.

[1412] "Example 1"

[1413] Step 1: The system receives as input transaction data and operational status from internal systems, inquiry logs from ChatBots, etc., and information on legal and regulatory revisions. This data is obtained, for example, from databases or cloud storage, and sent to the system.

[1414] Step 2: The system analyzes this data and automatically detects areas for improvement. Specifically, it analyzes data patterns and trends to identify problems and areas for improvement in the system.

[1415] "Example 2"

[1416] Step 1: The system automatically creates business and system requirements based on the detected modification points and generates specific modification proposals.

[1417] Step 2: The proposed modifications are submitted as a proposal to the system administrator. This proposal can be submitted, for example, via email or a web-based dashboard.

[1418] "Example 3"

[1419] Step 1: The system detects operational problems and considers improvement measures. This involves monitoring the system's operating status, and if a problem occurs, identifying the cause and proposing appropriate improvement measures. Step 2: The system presents improvement proposals to the development vendor and automatically plans acceptance test items. This supports all development tasks, reduces the number of personnel required, and promotes work efficiency.

[1420] Example 1

[1421] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1422] In conventional systems, the task of manually analyzing a variety of data, including transaction data, operational status, inquiry logs, and legal amendment information, to identify areas that need to be improved takes a great deal of time and effort. Furthermore, because the proposal of improvement plans and the planning of acceptance test items based on the analysis results are also done manually, this is inefficient and prone to human error. This leads to issues such as a decrease in the operational efficiency of the system and an increased burden on the personnel in charge.

[1423] The identification process by the identification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes means for automatically detecting points to be modified from transaction data, operation status, inquiry logs, legal amendment information, etc., means for acquiring data from a database or cloud storage, means for preprocessing the acquired data, means for analyzing the preprocessed data, means for automatically detecting points to be modified based on the analysis results, and means for notifying the detected points to be modified. This enables automatic analysis of data and automatic detection of points to be modified, improving the operational efficiency of the system and reducing the burden on responding personnel.

[1424] "Transaction Data" refers to records of transactions and operations that take place within the system.

[1425] "Operation status" refers to information that indicates the operational status and running status of the system.

[1426] An "inquiry log" refers to data that records the content of inquiries from users and their responses.

[1427] "Legal amendment information" refers to information regarding changes to laws and regulations.

[1428] "Improvement points" refer to problems with the system or areas that need improvement.

[1429] A "database" refers to a system for systematically storing and managing data.

[1430] "Cloud storage" refers to an online storage service for storing data over the Internet.

[1431] "Preprocessing" refers to the process of organizing data and converting it into a format suitable for analysis before conducting data analysis.

[1432] "Analysis" refers to the process of examining data in detail to find patterns and trends.

[1433] "Notification" refers to the act of informing the user of the analysis results and points to be improved.

[1434] MODE FOR CARRYING OUT THE INVENTION

[1435] This invention is a system that automatically analyzes a variety of data such as transaction data, operation status, inquiry logs, and legal amendment information to detect points that need to be improved. A specific embodiment of this system will be described below.

[1436] Data Acquisition

[1437] The server retrieves the necessary data from databases and cloud storage. Specifically, it retrieves transaction data from a MySQL® database and downloads query logs from a storage service. This ensures that the system always has access to the latest data.

[1438] Data Preprocessing

[1439] The server preprocesses the acquired data. This preprocessing includes imputing missing values ​​and normalizing the data. For example, it converts the data into a data frame using the Python (registered trademark) Pandas library and imputes missing values ​​with the mean value. It also normalizes the data and converts it into a format suitable for analysis.

[1440] Data analysis

[1441] The server analyzes the preprocessed data using machine learning algorithms and statistical methods. For example, it uses Scikit-learn (registered trademark) to apply anomaly detection algorithms to identify anomalous transactions. It also uses NLTK, a natural language processing library, to analyze query logs and identify frequently occurring issues.

[1442] Automatic detection of repair points

[1443] The server automatically detects points that need to be improved based on the analysis results. For example, if an abnormal transaction is detected, the details of that transaction are identified and improvement measures are proposed. Also, if the analysis of the inquiry log shows that inquiries about a particular topic are increasing, the server will propose improvements to the system related to that topic.

[1444] Notification of results

[1445] The server notifies the user of the detected points to be fixed. Notifications are made via email or dashboard. For example, the server may send an email to report the analysis results. It may also update the dashboard so that the user can check the analysis results in real time.

[1446] Specific examples

[1447] Example 1: Parsing transaction data

[1448] When a user enters transaction data into the system, the server retrieves the data from a MySQL® database, converts it into a data frame using Pandas, and then applies an anomaly detection algorithm using Scikit-learn® to identify anomalous transactions.

[1449] Example 2: Analyzing ChatBot inquiry logs

[1450] When a user enters a ChatBot inquiry log into the system, the server retrieves the log data from the storage service and analyzes the log using natural language processing libraries such as NLTK and SpaCy, thereby identifying frequently asked topics and issues.

[1451] Prompt Sentence Examples

[1452] Example 1: Parsing transaction data

[1453] "Analyze the following transaction data and detect any anomalous patterns. The data comes from a MySQL® database."

[1454] Example 2: Analyzing ChatBot inquiry logs

[1455] "Analyze the following ChatBot inquiry logs to identify frequently occurring issues. The data is retrieved from a storage service."

[1456] In this way, the server is a system that acquires data, performs preprocessing, analyzes it, automatically detects points that need to be modified, and notifies the user of the results.

[1457] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1458] Step 1: Get the data

[1459] The server retrieves the necessary data from databases and cloud storage. Specifically, it retrieves transaction data from a MySQL (registered trademark) database and downloads query logs from a storage service. As input, it receives database connection information and cloud storage access information, executes SQL queries and API requests, and retrieves data. As output, it saves the retrieved data in local storage.

[1460] Step 2: Preprocessing the data

[1461] The server preprocesses the acquired data. This preprocessing includes imputing missing values ​​and normalizing the data. Specifically, it converts the data into a data frame using the Pandas library and imputes missing values ​​with the mean value. It also normalizes the data and converts it into a format suitable for analysis. It receives the acquired raw data as input and generates preprocessed data as output.

[1462] Step 3: Analyze the data

[1463] The server analyzes the preprocessed data. For the analysis, it uses machine learning algorithms and statistical methods. Specifically, it uses Scikit-learn (registered trademark) to apply anomaly detection algorithms to identify anomalous transactions. It also uses NLTK, a natural language processing library, to analyze query logs and identify frequently occurring issues. It receives the preprocessed data as input and generates analysis results as output.

[1464] Step 4: Automatic detection of repair points

[1465] The server automatically detects points to be improved based on the analysis results. Specifically, if an abnormal transaction is detected, the details of that transaction are identified and improvement measures are proposed. Also, if the analysis of the inquiry log shows an increase in inquiries about a specific topic, the server proposes system improvements related to that topic. The server receives the analysis results as input and identifies points to be improved as output.

[1466] Step 5: Notification of results

[1467] The server notifies the user of the detected modification points. Notifications are made via email or dashboard. Specifically, the analysis results are reported by email. The dashboard is also updated so that the user can check the analysis results in real time. The server receives information about modification points as input, and generates and sends a notification message as output.

[1468] (Application example 1)

[1469] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1470] With conventional systems, it was difficult to comprehensively analyze a wide range of data, including transaction data, operational status, inquiry logs, and legal revision information, and automatically detect areas that needed improvement. Furthermore, there was a lack of means to automatically detect areas that needed optimization in the operation and placement of robots within factories, which hindered efficient operation. This made it difficult to quickly identify and address system problems and areas that could be improved.

[1471] 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.

[1472] In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to the system manager, means for automatically detecting points to be optimized in the operation and placement of robots in the factory, and means for automatically detecting points to be improved by inputting new data. This enables the system to quickly identify problems and areas that can be improved, enabling efficient operation and optimization.

[1473] "Transaction data" refers to data that records transactions and operations that take place within a system.

[1474] "Operation status" is information that indicates the operational status and operating conditions of a system or device.

[1475] An "inquiry log" is data that records the content of inquiries from users and the response history.

[1476] "Legal change information" is information about changes in laws and regulations.

[1477] "Renovation points" refer to areas or parts of systems or equipment that require improvement.

[1478] "Business requirements" refers to the conditions and specifications necessary to carry out a business.

[1479] "System requirements" refers to the technical conditions and specifications that a system must meet.

[1480] "Submitting an opinion" is the act of providing a suggestion or opinion to the system administrator.

[1481] "Factory robots" refer to automated machinery used in factories.

[1482] "Optimization points for movement and placement" refer to areas for improvement to optimize the movement and placement of the robot.

[1483] "New data" refers to the latest information newly entered into the system.

[1484] The system for carrying out the present invention operates in cooperation with three entities: a server, a terminal, and a user. A specific embodiment of the system will be described below.

[1485] Server Processing

[1486] The server retrieves transaction data, operation status, inquiry logs, legal amendment information, etc. from databases and cloud storage. This data is analyzed on the server, and points for modification are automatically detected. Specifically, the server uses the following software and hardware:

[1487] Software: Python (registered trademark), Pandas, Scikit-learn (registered trademark)

[1488] Hardware: High-performance server machine

[1489] The server analyzes data patterns and trends to identify system problems and areas for improvement. It also runs algorithms to automatically detect optimization points for the operation and placement of robots within the factory. When new data is input, the server analyzes it and automatically detects areas for modification.

[1490] Terminal handling

[1491] The device (e.g., smartphone, tablet, PC) receives the analysis results sent from the server and displays them to the user. The device uses the following software and hardware:

[1492] Software: Web browser, mobile application

[1493] Hardware: Smartphones, tablets, PCs

[1494] The terminal provides an interface for users to check system problems and areas for improvement and take necessary actions. For example, optimization points for the operation and placement of robots within a factory are displayed, and users can change the robot settings based on the results.

[1495] User operations

[1496] Users can check the information sent from the server via their terminal and take necessary actions. As system administrators, users can submit opinions and check business and system requirements. They can also change robot settings based on optimization points for robot operation and placement within the factory.

[1497] Specific examples

[1498] For example, if a robot in a factory frequently stops working, the server can identify the cause and suggest an optimal maintenance schedule. Also, if new safety standards are introduced due to legal changes, the server can adjust the robot's operation based on those standards.

[1499] Prompt Sentence Examples

[1500] "You will be asked to develop an application that analyzes transaction data, operational status, maintenance logs, and legal revision information from robots in factories, and automatically detects optimization points for robot operation and placement. Specifically, the application will have the ability to analyze data patterns and trends, and identify system problems and areas that can be improved."

[1501] In this way, the server, terminal, and user work together to quickly identify problems and areas that can be improved in the system, enabling efficient operation and optimization.

[1502] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1503] Step 1:

[1504] The server retrieves transaction data, operation status, inquiry logs, legal amendment information, etc. from databases and cloud storage. The input is various data sources, and the output is an integrated dataset. Specifically, the server retrieves data using SQL queries and API requests, and integrates the data using the Pandas library.

[1505] Step 2:

[1506] The server preprocesses the integrated dataset. The input is the integrated dataset, and the output is the preprocessed data. Specifically, the server performs missing value imputation, data normalization, and categorical data encoding.

[1507] Step 3:

[1508] The server automatically detects modification points using the preprocessed data. The input is the preprocessed data, and the output is a list of modification points. Specifically, the server trains a machine learning model using the Scikit-learn (registered trademark) library to analyze patterns and trends in the data.

[1509] Step 4:

[1510] The server automatically detects optimization points for the robot's operation and placement within the factory. The input is pre-processed data, and the output is a list of optimization points. Specifically, the server executes an algorithm to identify optimization points for the robot's operation and placement.

[1511] Step 5:

[1512] The server automatically detects modification points by inputting new data. The input is new data, and the output is a list of modification points. Specifically, the server inputs new data into an existing model and predicts modification points.

[1513] Step 6:

[1514] The terminal receives the analysis results sent from the server and displays them to the user. The input is the analysis results from the server, and the output is the display on the user interface. Specifically, the terminal displays the analysis results using a web browser or mobile application.

[1515] Step 7:

[1516] The user checks the information sent from the server through the terminal and takes the necessary action. The input is the analysis results on the terminal, and the output is the user's action. Specifically, the user checks the system's problems and areas that can be improved, and takes action such as changing the robot's settings.

[1517] Example 2

[1518] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1519] In conventional systems, the process of detecting points for improvement from transaction data, operational status, inquiry logs, legal amendment information, etc., and automatically creating business and system requirements was often done manually, resulting in inefficiency. In addition, generating improvement proposals and submitting opinions to system personnel was also done manually, which was time-consuming and labor-intensive, and prone to errors.

[1520] 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 a means for automatically detecting modification points from transaction data, operation status, inquiry logs, legal amendment information, etc., a means for sending a prompt message to the generative AI model based on the modification points and generating a specific modification proposal, a means for submitting the generated modification proposal as a proposal to the system manager, and a means for automatically creating business requirements and system requirements. This makes it possible to automate the process from detecting modification points to generating a modification proposal and submitting a proposal efficiently and accurately.

[1521] "Transaction Data" means data relating to transactions and operations conducted within the system.

[1522] "Operation status" refers to information relating to the operational status and running status of the system.

[1523] An "inquiry log" is a record of inquiries and support requests from users.

[1524] "Legal Change Information" means information about changes to relevant laws and regulations.

[1525] "Modification points" are areas or elements of the system that require improvement or correction.

[1526] A "generative AI model" is a model that has been trained using artificial intelligence to perform a specific task.

[1527] A "prompt" is an instruction entered into a generative AI model to make it perform a specific task.

[1528] "Modification Proposal" means a specific plan or method proposed for improving or modifying a system.

[1529] "Submitting an opinion" is the act of formally submitting a proposal or opinion to the system administrator.

[1530] "Business requirements" are the business requirements and functions that the system must meet.

[1531] "System requirements" are the technical conditions and specifications that a system must meet.

[1532] This invention is a system that automatically detects points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., and automatically creates business requirements and system requirements. Furthermore, it can generate specific improvement proposals using a generative AI model and submit them as suggestions to the system manager.

[1533] Hardware and software used

[1534] server

[1535] The server includes a database, analytical engine, generative AI model, and communication module. The database stores transaction data, operation status, inquiry logs, legal amendment information, and other data. The analytical engine analyzes this data to detect areas that need improvement. OpenAI's GPT-4 is used as the generative AI model, for example. The communication module sends suggestions to the system administrator via email or a web-based dashboard.

[1536] Terminal

[1537] A terminal is a device through which a user accesses the system. A user uses a web browser to access the system's web application and initiates the automatic creation of business and system requirements.

[1538] User

[1539] The user logs in to the system and starts the automatic creation of business and system requirements. The information entered by the user is sent to the server and analyzed.

[1540] Data processing and calculation

[1541] Detection of repair points

[1542] The server detects points to be modified based on information entered by the user and data automatically collected by the system. For example, if a user enters "I want to add a new customer management function," the server analyzes this information and detects points to be modified related to the customer management function.

[1543] Generate renovation proposals

[1544] The server sends prompts to the generative AI model based on the detected repair points, and the generative AI model generates specific repair proposals based on the prompts and returns them to the server.

[1545] Example prompt sentence:

[1546] Generate business and system requirements for adding new customer management features. Include specific proposed modifications.

[1547] The generative AI model generates the following modifications:

[1548] Business requirements:

[1549] 1. Add the ability to register, update, and delete customer information.

[1550] 2. Add a search function for customer information.

[1551] System requirements:

[1552] 1. Add a new customer table to the database.

[1553] 2. Create an API endpoint to manage customer information.

[1554] 3. Add a customer management screen to the front end.

[1555] Specific renovation proposals:

[1556] 1. Create a SQL script to add a customer table to the PostgreSQL database.

[1557] 2. Use Python® and Flask to create an API endpoint to manage customer information.

[1558] 3. Create a customer management screen using React.

[1559] Submitting opinions

[1560] The server then submits the generated proposed fixes to the system administrator as a proposal via email or a web-based dashboard. For example, if the proposal is sent via email, the server uses the SMTP protocol to send the proposed fixes to the system administrator.

[1561] Specific behavior:

[1562] The server converts the generated revision plan into an email format.

[1563] The server uses the SMTP protocol to send an email to the system administrator.

[1564] The system administrator receives an email and confirms the proposed modifications.

[1565] In this way, the system can automatically create business and system requirements, generate specific modification proposals, and submit them to the system administrator. This automates the process from detecting modification points to generating modification proposals and submitting opinions, making it possible to carry out the process efficiently and accurately.

[1566] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1567] Step 1:

[1568] A user accesses the system and begins automatically creating business and system requirements.

[1569] Input: User authentication information (username, password)

[1570] Output: User is logged in

[1571] Specific behavior:

[1572] The user enters the system URL in a web browser and accesses the login screen.

[1573] The user enters their username and password and clicks the "Login" button.

[1574] The server validates the credentials and logs the user in.

[1575] The user clicks the "Automatically create business and system requirements" button on the dashboard screen.

[1576] Step 2:

[1577] The server receives the user's input and detects the modification points.

[1578] Input: User request (e.g. "I want to add a new customer management function")

[1579] Output: Detected modification points

[1580] Specific behavior:

[1581] The user enters a request into the system and clicks the submit button.

[1582] The server receives the user's input data and stores it in a database.

[1583] The server analyzes the stored data and runs an algorithm to detect points of modification.

[1584] Step 3:

[1585] The server sends a prompt to the generative AI model, which generates specific repair proposals.

[1586] Input: Detected modification point

[1587] Output: Generated modification proposals

[1588] Specific behavior:

[1589] The server connects to the API of the generative AI model and sends a prompt.

[1590] The generative AI model analyzes the prompt text and generates specific repair suggestions.

[1591] The generative AI model returns the generated improvement proposals to the server.

[1592] Example prompt sentence:

[1593] Generate business and system requirements for adding new customer management features. Include specific proposed modifications.

[1594] Step 4:

[1595] The server submits the generated modification plan to the system administrator as a proposal.

[1596] Input: Generated modification proposal

[1597] Output: Submitted feedback to the system administrator

[1598] Specific behavior:

[1599] The server converts the generated revision plan into an email format.

[1600] The server uses the SMTP protocol to send an email to the system administrator.

[1601] The system administrator receives an email and confirms the proposed modifications.

[1602] In this way, the system can automatically create business and system requirements, generate specific modification proposals, and submit them to the system administrator. This automates the process from detecting modification points to generating modification proposals and submitting opinions, making it possible to carry out the process efficiently and accurately.

[1603] (Application example 2)

[1604] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1605] While conventional systems can detect points that need to be improved based on transaction data and operational status, they are unable to monitor robot operation logs in real time, automatically generate business and system requirements when an abnormality is detected, and quickly notify system personnel of specific improvement plans. This has resulted in slow responses when an abnormality occurs, and has led to issues such as insufficient improvement of work efficiency and reduction of personnel required.

[1606] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1607] In this invention, the server includes means for automatically detecting points to be improved from transaction data, operation status, inquiry logs, legal amendment information, etc., means for automatically creating business requirements and system requirements, means for submitting opinions to a system manager, means for monitoring the robot's operation log in real time, means for creating business requirements and system requirements when an abnormality is detected, and means for submitting the created improvement plan to the system manager via email or a web-based dashboard. This makes it possible to quickly and automatically create a countermeasure when a robot abnormality occurs and notify the system manager.

[1608] "Transaction Data" refers to information relating to transactions and operations conducted within the System.

[1609] "Operation status" refers to information that indicates the operational status and operating conditions of systems and equipment.

[1610] "Inquiry log" refers to data that records the history of inquiries and requests from users and systems.

[1611] "Legal Change Information" refers to information regarding changes in relevant laws and regulations.

[1612] "Modification points" refer to areas of systems or equipment that require improvement or correction.

[1613] "Business requirements" refers to the conditions and specifications necessary to carry out a business.

[1614] "System requirements" refers to the technical conditions and specifications that a system must meet.

[1615] "Submitting opinions" refers to the act of submitting suggestions or opinions to the system administrator.

[1616] "Robot operation log" refers to data that records the robot's operations and operating conditions.

[1617] "Real-time monitoring" refers to monitoring the ongoing situation immediately.

[1618] "When an abnormality is detected" refers to when a state that deviates from normal operation is detected.

[1619] "Email" means electronic messages sent and received over the Internet.

[1620] "Web-based Dashboard" means an information display screen accessible through a web browser.

[1621] The system for implementing this invention is configured as follows: The server has a means for automatically detecting points that need to be modified from transaction data, operation status, inquiry logs, legal amendment information, etc. This allows the system's operational status to be constantly monitored and necessary modifications to be quickly identified.

[1622] Furthermore, the server has a means for automatically creating business and system requirements. This allows specific business and system requirements to be automatically generated based on the detected modification points. The generated requirements are submitted as a suggestion to the system manager. The suggestion can be submitted via email or a web-based dashboard.

[1623] The server also has a means of monitoring the robot's operation log in real time. This allows the robot's operating status to be constantly monitored, and any abnormalities detected can be dealt with immediately. If an abnormality is detected, the server generates business and system requirements and creates a specific modification plan. The generated modification plan is submitted to the system administrator via email or a web-based dashboard.

[1624] This system is implemented using a program written in Python (registered trademark). Specifically, the smtplib library is used to implement a function for sending emails. The server acquires the robot's operation logs and runs an algorithm to detect abnormalities. If an abnormality is detected, business and system requirements are generated and a repair plan is created. This information is notified to the system administrator via email or a web-based dashboard.

[1625] For example, if a "Motor malfunction" is detected in the robot's operation log, the server will suggest "replacement of the motor" and notify the system administrator by email. In this way, it is possible to quickly and automatically generate countermeasures when a robot malfunction occurs and notify the system administrator.

[1626] Examples of prompt sentences include the following:

[1627] Create a Python (registered trademark) program that will automatically generate business requirements and system requirements, create specific repair plans, and notify the system administrator when an abnormality is detected in the robot's operation log. The condition for detecting an abnormality is assumed to be when the log status is "error."

[1628] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1629] Step 1:

[1630] The server collects transaction data, operation status, inquiry logs, legal amendment information, etc. These data are necessary to understand the operational status of the system. The input includes various log data and legal amendment information, and the output is stored in the server.

[1631] Step 2:

[1632] The server analyzes the acquired data and automatically detects points to be fixed. Specifically, it uses an algorithm to detect outliers and patterns to identify which parts of the system need to be fixed. The input includes the data acquired in step 1, and the output generates a list of points to be fixed.

[1633] Step 3:

[1634] The server automatically creates business and system requirements based on the detected modification points. To do this, it uses a generative AI model and prompts to generate specific requirements as input. The output is a document of business and system requirements.

[1635] Step 4:

[1636] The server submits the generated business and system requirements to the system administrator as a proposal via email or a web-based dashboard. The input includes the requirements document generated in step 3, and the output includes a notification sent to the system administrator.

[1637] Step 5:

[1638] The server monitors the robot's operation logs in real time. This involves a process of continuously receiving and analyzing the log data sent from the robot. The input includes the robot's operation logs, and the output includes the analysis results.

[1639] Step 6:

[1640] If the server detects an anomaly in the robot's operation log, it generates business requirements and system requirements. Specifically, it uses an anomaly detection algorithm to identify the location where the anomaly occurred and generates requirements based on that. The input includes the log data analyzed in step 5, and the output is a requirements document that corresponds to the anomaly.

[1641] Step 7:

[1642] The server then submits the generated remediation plan to the system administrator via email or a web-based dashboard, allowing the administrator to quickly review and implement the plan. The input includes the remediation plan generated in step 6, and the output includes the notification sent to the system administrator.

[1643] Example 3

[1644] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1645] In modern system operations, it is extremely important to quickly detect operational problems and propose appropriate remedial measures. However, in conventional systems, problem detection, cause identification, and remedial measures are often done manually, which not only takes time to respond but also consumes a lot of human resources. In addition, because proposals for modifications to development vendors and the planning of acceptance test items are also done manually, it is inefficient and prone to errors. This makes stable system operation difficult and poses the problem of hindering business efficiency.

[1646] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for monitoring the operation status of the system in real time, a means for detecting problems and identifying their causes, and a means for proposing improvements based on the identified causes. This makes it possible to quickly detect operational problems and automatically propose appropriate improvements. In addition, the system also presents improvement proposals to development vendors and automatically plans acceptance test items, thereby reducing the number of personnel required and improving work efficiency.

[1647] "Transaction data" is data related to a series of operations or transactions that take place within a system.

[1648] "Operational status" refers to information about the operating status and performance of a system.

[1649] An "inquiry log" is a record of inquiries and support requests from users.

[1650] "Legal Change Information" means information about changes to relevant laws and regulations.

[1651] "Modification points" are areas of the system that require improvement or correction.

[1652] "Business requirements" are the business conditions and needs that the system must meet.

[1653] "System requirements" are the technical conditions and specifications that a system must meet.

[1654] "Submitting an opinion" is the act of providing a suggestion or opinion to the system administrator.

[1655] "Real-time monitoring" means monitoring the operating status of a system immediately.

[1656] "Detecting problems" means discovering abnormalities or errors that occur within a system.

[1657] "Identifying the cause" means identifying the root cause of a detected problem.

[1658] "Proposing improvements" means presenting ways to improve the system based on the identified causes....

Claims

[Claim 1] A means for automatically detecting problems in the system or areas requiring improvement by acquiring transaction data, operation status, and inquiry logs related to the system to be repaired from a database or cloud storage, performing preprocessing on the acquired data including missing value completion, data normalization, and categorical data encoding, detecting outliers using a machine learning algorithm, and analyzing the inquiry logs using a natural language processing library, and analyzing the results of the outlier detection and the analysis of the inquiry logs; A means for generating a prompt statement based on the modification point, which describes the content of the modification point and instructs the generation of a modification plan, and sending the prompt statement to a generation AI model to generate the modification plan including the business requirements and system requirements corresponding to the modification point; A means for submitting the generated modification proposal to the system manager as a suggestion; A means for automatically generating test cases for verifying that the modified system operates normally using the generated AI model based on the generated modification plan, and automatically formulating a list of acceptance test items; A system including:

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