system

Abstract

We provide the system. [Solution] Means of obtaining business data from users, A means of analyzing acquired business data and identifying problems, A means for generating specific instructions for artificial intelligence based on identified tasks, A means of presenting generated instructions to the user and executing them if selected, A means of monitoring the progress of work and preparing regular reports, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a modern business environment, many users have the problem that they cannot effectively utilize artificial intelligence technology. In particular, users cannot determine whether their business problems can be solved by artificial intelligence, and it is difficult to appropriately generate specific instructions or prompts. As a result, there is a problem that the business proceeds without utilizing artificial intelligence, resulting in a decrease in overall efficiency. Furthermore, there is also a problem that differences in knowledge and experience regarding artificial intelligence technology create gaps among users, and the efficiency improvement and improvement of business have not been sufficiently promoted.

Means for Solving the Problems

[0005] This invention provides a system that automatically identifies specific challenges faced by users by acquiring and analyzing business data from them. Based on the identified challenges, it automatically generates specific instructions for artificial intelligence and presents them to the user, thereby simplifying the problem-solving process using artificial intelligence. Furthermore, by regularly monitoring the progress of work and generating reports, users can easily understand their own work status and take improvement measures. As a result, even users with little knowledge of artificial intelligence technology can effectively utilize artificial intelligence, leading to increased efficiency and improvement of overall business operations.

[0006] A "user" is an entity that uses this system to input business data and receives instructions from artificial intelligence.

[0007] "Business data" refers to information entered by users in relation to their work, including data on task progress and issues.

[0008] "Analysis" is the process of identifying problems from business data and finding their root causes.

[0009] "Challenges" refer to problems or delays that users face when performing their tasks.

[0010] "Artificial intelligence" is a computer program or system that automatically generates analysis and instructions using collected data.

[0011] "Instructions" refer to specific prompts or action plans that artificial intelligence generates to solve a problem.

[0012] "System" refers to the hardware and software used to implement the present invention, and includes functions such as data acquisition, analysis, instruction generation, and report generation.

[0013] A "report" is a report that is generated periodically to visually display the user's work progress. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system designed to help users perform their tasks more efficiently. The system consists of multiple modules, each performing a specific function. The following describes each module and its role.

[0036] 1. Data acquisition module

[0037] User: Inputs information related to work into the system through the interface. This includes task progress and newly arising issues.

[0038] Terminal: Receives user input and saves it to the database.

[0039] 2. Data Analysis Module

[0040] Server: Analyzes collected business data to identify specific challenges faced by users. This analysis utilizes natural language processing and machine learning algorithms.

[0041] 3. Interactive Interface Module

[0042] Terminal: Provides an interface that allows users to confirm the details of the issue in an interactive format. This involves asking questions in the form of a chatbot to delve into the root cause of the problem.

[0043] 4. Instruction generation module

[0044] Server: Automatically generates instructions for artificial intelligence based on identified tasks. These instructions include suggestions for streamlining and improving tasks.

[0045] Terminal: Presents the generated instructions to the user and provides opportunities to modify or approve them as needed.

[0046] 5. AI Execution Module

[0047] Server: The server executes user-approved instructions, and artificial intelligence handles the tasks. This enables efficient task completion.

[0048] 6. Report Generation Module

[0049] Server: Generates reports on the progress of tasks and the results of AI execution, and notifies users. Reports are visually represented using graphs and charts.

[0050] Specific example

[0051] Project Management Cases

[0052] User: The project manager enters data into the system for monthly reporting. This includes task lists, progress rates, reasons for delays, etc.

[0053] Server: Analyzes the input data and identifies problems such as "a specific task is being rescheduled frequently."

[0054] Terminal: Interactively question the project manager about the cause of this delay and gather detailed information.

[0055] Server: Based on the collected information, it automatically generates instructions (e.g., "Adjust resources to increase a specific task") and proposes them to the project manager.

[0056] User: Approve the proposed instructions and decide to proceed.

[0057] Terminal: After execution, it provides the project manager with a report summarizing the project progress and AI execution results.

[0058] In this way, project managers can achieve efficient project management using AI.

[0059] The following describes the processing flow.

[0060] Step 1:

[0061] Users input or upload work-related data into the system interface. This data includes task progress and any issues encountered.

[0062] Step 2:

[0063] The terminal saves the business data entered by the user to a database and prepares it for analysis.

[0064] Step 3:

[0065] The server collects stored data and analyzes it using natural language processing and machine learning algorithms. Here, it detects specific patterns and anomalies, identifying the challenges users are facing.

[0066] Step 4:

[0067] The device presents the user with hypotheses and identified issues in a conversational format, and requests detailed and supplementary information. This conversation takes place via a chatbot.

[0068] Step 5:

[0069] Based on the additional information provided, the server deepens its analysis and generates instructions for the artificial intelligence to take as a solution to the problem. These instructions include specific methods for improving the task.

[0070] Step 6:

[0071] The device presents the generated instructions to the user and provides the user with an opportunity to approve or modify them.

[0072] Step 7:

[0073] The user reviews the instructions provided and approves or modifies them as necessary.

[0074] Step 8:

[0075] The server executes user-approved instructions and begins solving the problem using artificial intelligence. This process is automated, and the AI ​​implements specific task improvements.

[0076] Step 9:

[0077] The server generates a report summarizing the execution results and the progress of the tasks. This report is used by the user to evaluate the effectiveness of the business improvements.

[0078] Step 10:

[0079] The terminal notifies the user of the generated reports, providing insights to streamline operations and determine the next steps.

[0080] (Example 1)

[0081] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0082] To improve operational efficiency, it is essential to provide users with specific and appropriate instructions. Traditional systems have struggled to accurately identify user challenges, acquire necessary information in a timely manner, and then use artificial intelligence to provide efficient operational support based on that information. This has resulted in users' workloads not being reduced and work progress not proceeding smoothly.

[0083] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0084] In this invention, the server includes means for receiving business information from a user, means for processing the stored business information to identify problems, and means for generating instructions for a specific artificial intelligence based on the identified problems. This makes it possible to identify the specific challenges faced by the user and provide solutions efficiently.

[0085] A "user" is someone who uses the system to input business information and receive support for problem-solving.

[0086] "Business information" refers to data necessary for users to perform their tasks, and includes task progress and related files.

[0087] "Device" refers to each component of a system that receives, stores, processes, generates, presents, operates, and monitors business information.

[0088] "Saving" refers to the process of recording received business information in a database in an appropriate format.

[0089] "Processing" means analyzing stored information and applying calculations and algorithms to identify problems.

[0090] "Identifying the problem" means identifying the specific challenges that users face based on business information.

[0091] "Generating instructions" refers to the act of creating instructions that propose solutions using artificial intelligence to an identified problem.

[0092] "To put into action" means to execute the generated instructions and use artificial intelligence or systems to solve problems.

[0093] "Generating a report" means creating a report summarizing the progress of tasks and the results of executing instructions, and providing it to the user.

[0094] "Dialogue format" refers to an interactive method in which the user and the system exchange information through conversation and confirm the details of a problem.

[0095] A "template for automatable tasks" is a template provided to support efficient task execution based on specific task content.

[0096] This system assists users in improving their work efficiency and consists of servers, terminals, and user interactions.

[0097] 1. Use of hardware and software

[0098] The server functions as the core processing device, and for data storage and analysis, it uses scikit-learn and TextBlob as the software environment to execute natural language processing libraries and machine learning algorithms. Furthermore, it uses a general-purpose AI framework (e.g., OpenAI® GPT-3®) as a generative AI model to generate instructions for solving problems.

[0099] The terminal provides an interface for users to input business information. Typically, a web browser or a dedicated application is used as the interface to receive user input and send it to the server.

[0100] 2. Data processing and data calculation

[0101] The server receives business information sent by users, stores it in a database, and performs analysis. During the analysis process, sentiment analysis using natural language processing and pattern recognition using machine learning are performed to identify the specific challenges that users face.

[0102] After identifying the problem, the AI ​​model is used to automatically generate specific solutions and instructions for business improvement for the identified problem. This AI model outputs instructions by processing prompt statements such as "Please suggest ways to optimize resource allocation."

[0103] 3. Specific Examples

[0104] When users manage a project, they input detailed information about the project's progress into the system. This includes task names, progress percentages, and reasons for delays.

[0105] The server processes this information and, if delays are frequent in a particular task, uses a generative AI model to formulate solutions such as "making adjustments to increase resources for that specific task."

[0106] In this way, a system is built that enables concrete and efficient business support through the collaboration of servers, terminals, and users.

[0107] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0108] Step 1:

[0109] Users input work information using a terminal. This input data includes project name, task details, progress rate, reason for delay, and related files. Users enter this information through an interface, and the data is sent from the terminal to the server.

[0110] Step 2:

[0111] When the terminal receives user input data, it checks the data format and corrects it if necessary. During this process, it checks for errors in numerical data and prompts the user to re-enter the data if required. The corrected data is then transferred to the server and recorded in the database.

[0112] Step 3:

[0113] The server collects stored business information and performs data analysis using natural language processing. The input is business information stored in the database, and the output is a report containing specific problems and issues. Data analysis includes sentiment analysis of text data and analysis of the causes of task delays to identify problems.

[0114] Step 4:

[0115] Based on the data analysis results, the server generates instructions for problem solving using a generated AI model. At this stage, the input is the analysis results, and the output is the generated specific instructions. For example, the AI ​​processes a prompt such as "Suggest an optimal method for resource allocation" and generates a scenario for efficient resource allocation.

[0116] Step 5:

[0117] The terminal presents instructions from the server to the user. The user reviews the presented instructions and adds comments or modifies them as needed. At this stage, the user confirms the final execution plan by approving or modifying the instructions.

[0118] Step 6:

[0119] The server executes instructions approved by the user. This includes adjusting schedules using project management software and reallocating resources. AI streamlines the work process in this execution.

[0120] Step 7:

[0121] The server summarizes the results and progress obtained through the above process and generates a report. The report is automatically generated and presented to the user in a visual format using graphs and charts. Based on this report, the user can develop further improvement plans.

[0122] (Application Example 1)

[0123] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0124] For users to perform their tasks efficiently, it is crucial to understand business information, identify problems, propose appropriate solutions, and implement them. However, currently, these processes are time-consuming and laborious, and it is particularly difficult to expedite information gathering and decision-making in physical stores. Under these circumstances, there is a need for a new system that provides optimal business support to users and improves operational efficiency.

[0125] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0126] In this invention, the server includes means for acquiring business information from users, means for analyzing the acquired business information and identifying problems, and means for providing advice to support in-store activities based on the business information and user requests. This enables efficient information gathering, rapid problem identification, and provision of appropriate advice in diverse business environments, thereby enabling efficient resolution of problems faced by users.

[0127] A "user" is someone who uses the system to perform their duties.

[0128] "Business information" refers to data and information regarding the progress of business operations that are necessary for carrying out those operations.

[0129] "Analysis" is the act of analyzing collected information to identify issues and problems.

[0130] "Intelligent processing" is the process of generating instructions for an identified task using artificial intelligence.

[0131] "In-store activities" refer to various activities carried out in a physical store to perform business operations.

[0132] "Advice" refers to guidelines and suggestions provided to users to improve work efficiency and solve problems.

[0133] This invention is a support system for streamlining operations in physical stores. This system allows users to input, analyze, and receive advice on business information using an application that runs on their smartphone.

[0134] The server's role is to collect and analyze business information entered by users using their smartphones. Specifically, a data analysis model using Python runs to analyze business information and identify problems. Based on the analyzed data, the server uses a generative AI model to perform intelligent processing and generate advice for business improvement.

[0135] The smartphone, acting as the terminal, is responsible for acquiring information from the user and presenting generated advice. The user inputs business information and requests through the interface on the terminal, and this information is sent to the server. The analysis results and advice from the server are presented to the user on the terminal, providing them with selectable actions.

[0136] For example, when store operations involve inputting inventory information and customer requests into the app, the server can quickly analyze that information and provide specific advice such as, "You need to replenish the stock of this particular product."

[0137] An example of a prompt is, "Ask the AI ​​assistant for advice on promotions and product placement based on current inventory levels and customer needs." In this way, users can make quick and efficient business decisions based on AI advice.

[0138] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0139] Step 1:

[0140] Users input business information and customer requests using a smartphone application. This input includes inventory status and customer service details, which then serve as the system's input. The entered data is stored in cloud storage.

[0141] Step 2:

[0142] The server accesses and collects data stored in the cloud. Next, it performs data cleaning and standardization on the collected data to prepare it for analysis. The output of this step is an analyzable dataset.

[0143] Step 3:

[0144] The server performs data analysis using a generative AI model based on an analyzable dataset. It utilizes natural language processing (NLP) and machine learning algorithms to identify business challenges and generate specific action items. For example, it might identify a particular product as a best-seller and generate advice for inventory replenishment. The output of this step is the analysis results and advice.

[0145] Step 4:

[0146] The server sends the generated analysis results and advice to the terminal. The terminal, a smartphone, displays this information in a format that is intuitively understandable to the user. The user selects the suggested action and makes a flexible decision on how to respond. In this step, the user is guided through outputs that provide options and support decision-making.

[0147] Step 5:

[0148] The device, having received user selections and feedback, resends that information to the server. This allows the server to learn from the user's decision-making history and use it to improve the model for more appropriate advice in the future. The output of this step is the feedback data necessary for model improvement.

[0149] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0150] This invention is an AI navigation system that combines an emotion engine to enable users to perform their tasks efficiently. This system automates the acquisition of work data from users, data analysis, generation of AI instructions for tasks, and provision of feedback to users.

[0151] composition

[0152] 1. Data acquisition module

[0153] User: Enters work-related data into the system. This data includes task progress and current issues.

[0154] Terminal: Saves user input to the database.

[0155] 2. Emotional Engine

[0156] Server: Incorporates an emotion engine to analyze user emotions and stress levels. This enables emotion-based data analysis.

[0157] 3. Data Analysis Module

[0158] Server: Analyzes acquired business data and combines it with user sentiment data to identify business challenges. Sentiment data is acquired through methods such as text analysis and voice analysis.

[0159] 4. AI Instruction Generation Module

[0160] Server: Based on identified issues and emotional data, artificial intelligence generates specific instructions. These instructions are adjusted according to the user's emotional state.

[0161] 5. Interactive Interface Module

[0162] Terminal: Presents generated instructions through interaction with the user and collects user feedback.

[0163] 6. Business template provision module

[0164] Server: Provides automated task templates based on instructions from artificial intelligence.

[0165] 7. Report Generation Module

[0166] Server: Creates and provides reports to users, including their work progress and stress levels. This allows users to visualize the progress of their work improvements.

[0167] Specific example

[0168] Example from a customer support center

[0169] User: A support staff member enters customer inquiry data into the system.

[0170] Server: The emotion engine analyzes the emotional state of the person in charge based on their conversation content and input data, and determines their stress level at that time.

[0171] Server: Analyzes business data and emotional data to identify issues such as "delays in responding to inquiry A and high stress levels among staff."

[0172] Terminal: Provides instructions to the person in charge, such as, "As a countermeasure for inquiry A, use a template to shorten the response time." These instructions are adjusted by adding more detail when the person in charge is under stress, and making them more concise when they are under stress.

[0173] Server: After the countermeasures are implemented, analyze their effectiveness, compile a report detailing the progress and changes in the stress levels of the person in charge, and inform the person in charge.

[0174] Through the above process, users can leverage AI while also considering their own emotions to perform tasks efficiently.

[0175] The following describes the processing flow.

[0176] Step 1:

[0177] Users input work-related information through an interface and send it to the system. This includes data such as task progress, issues, and details of daily work.

[0178] Step 2:

[0179] The terminal receives the data entered by the user and prepares to securely store it in the database. The stored data is also accompanied by an audit log to ensure that no data is missed, for use in later analysis and reporting.

[0180] Step 3:

[0181] The server analyzes stored business data and simultaneously collects related sentiment data. This sentiment data is used to evaluate emotions and stress levels using natural language processing for text analysis and speech data analysis.

[0182] Step 4:

[0183] The server integrates business data and emotional data, and uses machine learning algorithms to identify user issues. This combination reveals complex issues, such as "a specific task is consistently causing stress."

[0184] Step 5:

[0185] The device presents the identified issues to the user through an interactive interface and asks for further details or confirmation of the need for corrections. At this stage, user feedback is incorporated into the system.

[0186] Step 6:

[0187] Based on the feedback it receives, the server generates optimized AI-powered instructions. This includes considering the user's current emotional state and adjusting work instructions or changing priorities to reduce stress.

[0188] Step 7:

[0189] The device presents the generated instructions to the user, prompting them to adapt and make necessary modifications. During this process, the user reviews the instructions and prepares to execute them.

[0190] Step 8:

[0191] The server executes user-approved instructions, and the AI ​​engine initiates automated operations to improve business efficiency. Changes and the results of operations are recorded in real time and saved for future optimization.

[0192] Step 9:

[0193] The server compiles a report summarizing the AI ​​engine's results and a comprehensive evaluation of the business progress, and submits it to the user, including visual elements. The report includes an assessment of the progress of business improvements and changes in emotions, serving as a basis for considering future improvement measures.

[0194] By integrating users' work processes with their emotional states according to these processing steps, effective and reassuring business improvements become possible.

[0195] (Example 2)

[0196] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0197] In today's work environment, users need to process diverse data and solve business problems quickly. However, traditional systems have the problem of not taking into account users' emotional states or stress levels, and therefore failing to adequately improve work efficiency. Furthermore, instructions to users are uniform and cannot be flexibly adapted to the individual needs of each user, which can increase stress and decrease efficiency.

[0198] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0199] In this invention, the server includes means for analyzing the user's emotions and performing data analysis based on that state, means for generating specific instructions for artificial intelligence considering the emotional state, and means for presenting the generated instructions to the user in a way that suits them. This enables efficient work execution that takes the user's emotions into consideration.

[0200] A "user" refers to an entity that utilizes a system to input business data or select and execute instructions.

[0201] "Data" refers to all information that users input into the system as information related to their work, including information about the progress and challenges of their work, as well as information about the user's emotions.

[0202] "Analysis" refers to the process of using acquired data to analyze information and identify problems or the emotional state of users.

[0203] A "problem" refers to an issue or bottleneck that users need to address, identified through the analysis of business data.

[0204] "Instructions" refer to specific responses and solutions for the user that are generated by artificial intelligence based on the analyzed data.

[0205] "User emotions" refers to the user's emotional state and stress level, as analyzed using an emotion engine.

[0206] A "template" refers to a standardized, automatable format or procedure provided by artificial intelligence to assist with tasks.

[0207] A "report" refers to a document that summarizes the progress of work, changes in user sentiment, and other information provided to the user.

[0208] "Artificial intelligence" refers to software technology that generates instructions and suggestions for dealing with problems based on data and analysis results obtained from users.

[0209] This invention is an AI navigation system that incorporates emotion analysis to efficiently carry out the user's work. This system analyzes work data based on the user's input information, and based on the results obtained, generates instructions that take into account the user's emotional state and presents them to the user.

[0210] First, users input work-related information using a terminal. This input includes data indicating work progress, challenges, and even emotions. The terminal stores the data obtained from the user in an internal database. This database is the central part of the system and provides the foundation for data processing.

[0211] Next, the server uses an emotion engine to analyze the user's emotional state from the data they input. The emotion engine incorporates text and voice analysis algorithms to determine stress levels and emotional tendencies from the user's words and actions. This analysis allows for the assessment of potential problems and psychological burdens in the work environment.

[0212] Based on the analyzed data, the server uses a generative AI model to generate specific instructions. These instructions are adjusted according to the user's emotional state. Concise and actionable instructions are provided for high-stress situations, while more detailed instructions are provided for low-stress situations.

[0213] Ultimately, the device presents the user with generated instructions. Through an interactive interface, the user can review and execute these instructions. The device also collects feedback, which is used for subsequent analysis.

[0214] As a concrete example, in customer support operations, a user inputs a customer inquiry, and the server analyzes the representative's emotional state using an emotion engine. Based on the results, the server generates instructions such as "Use a template to respond to inquiry A promptly," which are then presented to the representative via the terminal. The level of detail in these instructions varies depending on the representative's stress level.

[0215] An example of a prompt message is, "Analyze the customer inquiry and generate a response plan that takes into account the support staff member's emotional state and stress level." This forms the basis for the system to appropriately utilize its AI model to provide the user with the most relevant information.

[0216] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0217] Step 1:

[0218] Users input work-related information through a terminal. This input data includes text about task progress, current challenges, and user sentiment. The terminal saves the entered data to a database. During database saving, data integrity is checked, and format conversion is performed as needed.

[0219] Step 2:

[0220] The server retrieves business data stored in the database and analyzes the user's emotions using an emotion engine. The input data is processed by a text analysis algorithm, and the user's emotional state and stress level are output as numerical values. In the emotion analysis, keyword analysis and emotion scoring are performed to evaluate the user's psychological state.

[0221] Step 3:

[0222] The server integrates the sentiment data and business data obtained from the analysis and runs a data mining algorithm. This identifies bottlenecks in business processes and issues that should be prioritized for resolution. The output provides a list of the analyzed issues and their associated sentiment states.

[0223] Step 4:

[0224] The server uses a generative AI model to generate instructions based on identified issues. The input consists of an analyzed list of issues and emotional data, and the output is specific countermeasures. The generated instructions are adjusted according to the user's emotional state. This process involves simplifying or complicating the instructions based on the user's stress level.

[0225] Step 5:

[0226] The terminal presents the generated instructions to the user through an interactive interface. Here, the user is presented with choices regarding the instructions, and can either follow them or provide feedback. The input is the generated instructions, and the output is the user's choices and feedback.

[0227] Step 6:

[0228] The server generates reports on work progress and emotional changes based on feedback and execution status collected from users. The generated reports include visualized graphs and progress statistics. These reports are provided to users to help them identify areas for improvement in their work and track changes in their emotional state.

[0229] (Application Example 2)

[0230] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0231] The present invention aims to support efficient work in business operations and to improve the work environment in accordance with the user's emotional state. In particular, in high-stress environments such as security work, it is necessary to appropriately manage the burden on operators and improve the quality of work.

[0232] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0233] In this invention, the server includes means for acquiring business data from a user, means for analyzing the acquired business data and identifying issues, means for determining the user's emotional state by analyzing emotions, means for generating specific instructions for artificial intelligence based on the identified issues and emotional state, means for presenting the generated instructions to the user and executing them if selected, means for monitoring the progress of the work and creating periodic reports, means for adjusting the content of the instructions based on the user's emotional state, and means for providing an automateable business template. As a result, operators are presented with appropriate instructions that respond to their emotions while reducing their workload, enabling improvements in work efficiency and quality.

[0234] A "user" is a person who uses this system to perform their duties.

[0235] "Means for acquiring business data" refers to a mechanism for collecting business-related information provided by users.

[0236] "Means for analyzing business data and identifying issues" refers to a mechanism that analyzes collected business information and extracts problems and areas for improvement.

[0237] "Means for generating instructions for artificial intelligence" refers to a mechanism that causes AI to generate appropriate countermeasures based on a specific problem.

[0238] "A means of presenting generated instructions to the user and executing them if selected" refers to a mechanism that displays instructions generated by AI to the user and executes the approved instructions.

[0239] "A means of monitoring the progress of work and generating regular reports" refers to a mechanism that tracks the progress of work and provides it to users periodically as a report.

[0240] "A means of determining a user's emotional state by analyzing their emotions" refers to a mechanism that analyzes a user's emotions and evaluates their psychological state.

[0241] "Means for adjusting the content of instructions based on the user's emotional state" refers to a mechanism that appropriately modifies the content and presentation method of instructions according to the user's emotional state.

[0242] "Means of providing automatable business templates" refers to a mechanism that provides users with standardized work procedures to streamline and improve the efficiency of their work.

[0243] The system implementing this invention is designed to support the work of security operators efficiently and while taking into consideration their emotional burden. Its specific form is described below.

[0244] The server acquires voice data and business-related information input from users. This includes real-time data monitored through devices such as sensors and cameras. This acquired data is stored in database software (e.g., MySQL®) for later analysis.

[0245] Emotion analysis uses a cloud-based emotion analysis platform (e.g., Microsoft® Azure® Cognitive Services) to determine a user's emotional state from voice and text data. This analysis allows for the assessment of the user's stress level and psychological state.

[0246] The server combines business data and emotional data and uses an AI instruction generation system (e.g., Google® Cloud AI Platform) to generate instructions that are relevant to a specific task. An example of a prompt might be, "Analyze the operator's voice data and generate suggestions for handing over low-priority tasks when the stress level is high." This automatically generates instructions that are adapted to the user's emotional state.

[0247] The device features an interactive interface (e.g., React Native) that presents generated instructions to the user. The content and amount of information in the instructions are adjusted according to the user's mood, and the instructions are executed once accepted by the user.

[0248] As a concrete example, if a security operator experiences increased stress while monitoring a specific area, the system will suggest that another operator take over monitoring an adjacent area. This encourages the original operator to take a temporary break, thus reducing their workload. Through this process, it becomes possible to improve operational efficiency while avoiding excessive workload.

[0249] This system has the following features:

[0250] By enabling integrated analysis of business data and emotional data, we provide work instructions that are tailored to individual emotions.

[0251] By distributing tasks in a way that takes user stress management into consideration, we aim to achieve both work efficiency and psychological well-being.

[0252] In this way, we achieve highly efficient operational support that includes consideration of emotions in security operations.

[0253] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0254] Step 1:

[0255] Users input work-related information and voice data into the terminal. This input data includes task progress and details of current issues. This data is then stored directly in the database.

[0256] Step 2:

[0257] The server analyzes business data acquired from terminals. Text and speech analysis are used to extract information about the progress of tasks and identify problems. The input is business data, and the output is a list of identified issues.

[0258] Step 3:

[0259] The server uses an emotion analysis platform to analyze the user's voice data and determine their emotional state. The input is the user's voice data, and the output is numerical or categorical data indicating the emotional state.

[0260] Step 4:

[0261] The server combines business data and emotional state data and generates instructions using a generative AI model. Here, prompts are used to get the AI ​​to generate specific instructions. For example, a prompt such as "Analyze the operator's voice data and generate measures to reduce the workload when the stress level is high" might be used. The input is a list of tasks and emotional state data, and the output is the generated instructions.

[0262] Step 5:

[0263] The terminal presents the user with instructions generated by the server. The user selects an instruction, and the automation of the task proceeds based on that selection. The output is the execution of the instruction selected by the user.

[0264] Step 6:

[0265] The server continuously monitors the progress of tasks and the emotional state of users, and generates reports periodically. Inputs are task data and emotional state history, and output is a report document. This report provides users with information that visualizes the progress of task improvements and changes in their emotional state.

[0266] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0267] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0268] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0269] [Second Embodiment]

[0270] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0271] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0272] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0273] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0274] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0275] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0276] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0277] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0278] The specific processing program 56 is an example of the "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 operating as the specific processing unit 290 according to the specific processing program 56 executed by the processor 28 on the RAM 30.

[0279] 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 specific processing unit 290.

[0280] In the smart glasses 214, the processor 46 performs reception / output processing. The storage 50 stores a reception / output program 60. 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 processing is realized by operating as the control unit 46A according to the reception / output program 60 executed by the processor 46 on the RAM 48.

[0281] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0282] This invention is a system for assisting a user to more efficiently perform a task. The system is composed of a plurality of modules, and each module realizes a specific function. The following will explain each module and its role.

[0283] 1. Data acquisition module

[0284] User: Inputs information related to the task into the system through an interface. This includes the progress of the task and newly occurring issues, etc.

[0285] Terminal: Receive user input and save it to the database.

[0286] 2. Data Analysis Module

[0287] Server: Analyze the collected business data to identify the specific issues faced by the user. Natural language processing and machine learning algorithms are used in this analysis.

[0288] 3. Interactive Interface Module

[0289] Terminal: Provide an interface to confirm the details of the issue with the user in an interactive format. This asks in-depth questions in the form of a chatbot to dig into the root problem.

[0290] 4. Instruction Generation Module

[0291] Server: Automatically generate instructions for the artificial intelligence based on the identified issues. These instructions include task efficiency improvements and improvement measures.

[0292] Terminal: Present the generated instructions to the user and provide an opportunity to modify or approve them as necessary.

[0293] 5. AI Execution Module

[0294] Server: Execute the instructions approved by the user and have the artificial intelligence address the issues. This enables the efficient execution of tasks.

[0295] 6. Report Generation Module

[0296] Server: Create a report on the progress of the business and the results of AI execution and notify the user. The report is visually presented using graphs and charts.

[0297] Specific Example

[0298] Case of Project Management

[0299] User: The project manager inputs data into the system for the monthly report. This includes the task list, progress rate, reasons for delays, etc.

[0300] Server: Analyze the input data to identify problems such as "a specific task is frequently rescheduled".

[0301] Terminal: Ask the project manager interactive questions about the causes of this delay and collect detailed information.

[0302] Server: Propose to the project manager automatically generated instructions (e.g., "Make adjustments to increase the resources for a specific task") based on the collected information.

[0303] User: Approve the proposed instructions and decide on the execution.

[0304] Terminal: After execution, provide the project manager with a report summarizing the project progress and AI execution results.

[0305] In this way, the project manager can achieve efficient project management using AI.

[0306] The following describes the processing flow.

[0307] Step 1:

[0308] The user inputs or uploads business-related data to the system interface. The data input at this point includes the progress status and problems of tasks.

[0309] Step 2:

[0310] The terminal saves the business data input by the user to the database and prepares for analysis.

[0311] Step 3:

[0312] The server collects stored data and analyzes it using natural language processing and machine learning algorithms. Here, it detects specific patterns and anomalies, identifying the challenges users are facing.

[0313] Step 4:

[0314] The device presents the user with hypotheses and identified issues in a conversational format, and requests detailed and supplementary information. This conversation takes place via a chatbot.

[0315] Step 5:

[0316] Based on the additional information provided, the server deepens its analysis and generates instructions for the artificial intelligence to take as a solution to the problem. These instructions include specific methods for improving the task.

[0317] Step 6:

[0318] The device presents the generated instructions to the user and provides the user with an opportunity to approve or modify them.

[0319] Step 7:

[0320] The user reviews the instructions provided and approves or modifies them as necessary.

[0321] Step 8:

[0322] The server executes user-approved instructions and begins solving the problem using artificial intelligence. This process is automated, and the AI ​​implements specific task improvements.

[0323] Step 9:

[0324] The server generates a report summarizing the execution results and the progress of the tasks. This report is used by the user to evaluate the effectiveness of the business improvements.

[0325] Step 10:

[0326] The terminal notifies the user of the generated reports, providing insights to streamline operations and determine the next steps.

[0327] (Example 1)

[0328] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0329] To improve operational efficiency, it is essential to provide users with specific and appropriate instructions. Traditional systems have struggled to accurately identify user challenges, acquire necessary information in a timely manner, and then use artificial intelligence to provide efficient operational support based on that information. This has resulted in users' workloads not being reduced and work progress not proceeding smoothly.

[0330] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0331] In this invention, the server includes means for receiving business information from a user, means for processing the stored business information to identify problems, and means for generating instructions for a specific artificial intelligence based on the identified problems. This makes it possible to identify the specific challenges faced by the user and provide solutions efficiently.

[0332] A "user" is someone who uses the system to input business information and receive support for problem-solving.

[0333] "Business information" refers to data necessary for users to perform their tasks, and includes task progress and related files.

[0334] "Device" refers to each component of a system that receives, stores, processes, generates, presents, operates, and monitors business information.

[0335] "Saving" refers to the process of recording received business information in a database in an appropriate format.

[0336] "Processing" means analyzing stored information and applying calculations and algorithms to identify problems.

[0337] "Identifying the problem" means identifying the specific challenges that users face based on business information.

[0338] "Generating instructions" refers to the act of creating instructions that propose solutions using artificial intelligence to an identified problem.

[0339] "To put into action" means to execute the generated instructions and use artificial intelligence or systems to solve problems.

[0340] "Generating a report" means creating a report summarizing the progress of tasks and the results of executing instructions, and providing it to the user.

[0341] "Dialogue format" refers to an interactive method in which the user and the system exchange information through conversation and confirm the details of a problem.

[0342] A "template for automatable tasks" is a template provided to support efficient task execution based on specific task content.

[0343] This system assists users in improving their work efficiency and consists of servers, terminals, and user interactions.

[0344] 1. Use of hardware and software

[0345] The server functions as the core processing device, and for data storage and analysis, it uses scikit-learn and TextBlob as software environments to execute natural language processing libraries and machine learning algorithms. Furthermore, it generates instructions for problem solving using a general-purpose AI framework (e.g., OpenAI GPT-3) as a generative AI model.

[0346] The terminal provides an interface for users to input business information. Typically, a web browser or a dedicated application is used as the interface to receive user input and send it to the server.

[0347] 2. Data processing and data calculation

[0348] The server receives business information sent by users, stores it in a database, and performs analysis. During the analysis process, sentiment analysis using natural language processing and pattern recognition using machine learning are performed to identify the specific challenges that users face.

[0349] After identifying the problem, the AI ​​model is used to automatically generate specific solutions and instructions for business improvement for the identified problem. This AI model outputs instructions by processing prompt statements such as "Please suggest ways to optimize resource allocation."

[0350] 3. Specific Examples

[0351] When users manage a project, they input detailed information about the project's progress into the system. This includes task names, progress percentages, and reasons for delays.

[0352] The server processes this information and, if delays are frequent in a particular task, uses a generative AI model to formulate solutions such as "making adjustments to increase resources for that specific task."

[0353] In this way, a system is built that enables concrete and efficient business support through the collaboration of servers, terminals, and users.

[0354] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0355] Step 1:

[0356] Users input work information using a terminal. This input data includes project name, task details, progress rate, reason for delay, and related files. Users enter this information through an interface, and the data is sent from the terminal to the server.

[0357] Step 2:

[0358] When the terminal receives user input data, it checks the data format and corrects it if necessary. During this process, it checks for errors in numerical data and prompts the user to re-enter the data if required. The corrected data is then transferred to the server and recorded in the database.

[0359] Step 3:

[0360] The server collects stored business information and performs data analysis using natural language processing. The input is business information stored in the database, and the output is a report containing specific problems and issues. Data analysis includes sentiment analysis of text data and analysis of the causes of task delays to identify problems.

[0361] Step 4:

[0362] Based on the data analysis results, the server generates instructions for problem solving using a generated AI model. At this stage, the input is the analysis results, and the output is the generated specific instructions. For example, the AI ​​processes a prompt such as "Suggest an optimal method for resource allocation" and generates a scenario for efficient resource allocation.

[0363] Step 5:

[0364] The terminal presents instructions from the server to the user. The user reviews the presented instructions and adds comments or modifies them as needed. At this stage, the user confirms the final execution plan by approving or modifying the instructions.

[0365] Step 6:

[0366] The server executes instructions approved by the user. This includes adjusting schedules using project management software and reallocating resources. AI streamlines the work process in this execution.

[0367] Step 7:

[0368] The server summarizes the results and progress obtained through the above process and generates a report. The report is automatically generated and presented to the user in a visual format using graphs and charts. Based on this report, the user can develop further improvement plans.

[0369] (Application Example 1)

[0370] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0371] For users to perform their tasks efficiently, it is crucial to understand business information, identify problems, propose appropriate solutions, and implement them. However, currently, these processes are time-consuming and laborious, and it is particularly difficult to expedite information gathering and decision-making in physical stores. Under these circumstances, there is a need for a new system that provides optimal business support to users and improves operational efficiency.

[0372] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0373] In this invention, the server includes means for acquiring business information from users, means for analyzing the acquired business information and identifying problems, and means for providing advice to support in-store activities based on the business information and user requests. This enables efficient information gathering, rapid problem identification, and provision of appropriate advice in diverse business environments, thereby enabling efficient resolution of problems faced by users.

[0374] A "user" is someone who uses the system to perform their duties.

[0375] "Business information" refers to data and information regarding the progress of business operations that are necessary for carrying out those operations.

[0376] "Analysis" is the act of analyzing collected information to identify issues and problems.

[0377] "Intelligent processing" is the process of generating instructions for an identified task using artificial intelligence.

[0378] "In-store activities" refer to various activities carried out in a physical store to perform business operations.

[0379] "Advice" refers to guidelines and suggestions provided to users to improve work efficiency and solve problems.

[0380] This invention is a support system for streamlining operations in physical stores. This system allows users to input, analyze, and receive advice on business information using an application that runs on their smartphone.

[0381] The server's role is to collect and analyze business information entered by users using their smartphones. Specifically, a data analysis model using Python runs to analyze business information and identify problems. Based on the analyzed data, the server uses a generative AI model to perform intelligent processing and generate advice for business improvement.

[0382] The smartphone, acting as the terminal, is responsible for acquiring information from the user and presenting generated advice. The user inputs business information and requests through the interface on the terminal, and this information is sent to the server. The analysis results and advice from the server are presented to the user on the terminal, providing them with selectable actions.

[0383] For example, when store operations involve inputting inventory information and customer requests into the app, the server can quickly analyze that information and provide specific advice such as, "You need to replenish the stock of this particular product."

[0384] An example of a prompt is, "Ask the AI ​​assistant for advice on promotions and product placement based on current inventory levels and customer needs." In this way, users can make quick and efficient business decisions based on AI advice.

[0385] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0386] Step 1:

[0387] Users input business information and customer requests using a smartphone application. This input includes inventory status and customer service details, which then serve as the system's input. The entered data is stored in cloud storage.

[0388] Step 2:

[0389] The server accesses and collects data stored in the cloud. Next, it performs data cleaning and standardization on the collected data to prepare it for analysis. The output of this step is an analyzable dataset.

[0390] Step 3:

[0391] The server performs data analysis using a generative AI model based on an analyzable dataset. It utilizes natural language processing (NLP) and machine learning algorithms to identify business challenges and generate specific action items. For example, it might identify a particular product as a best-seller and generate advice for inventory replenishment. The output of this step is the analysis results and advice.

[0392] Step 4:

[0393] The server sends the generated analysis results and advice to the terminal. The terminal, a smartphone, displays this information in a format that is intuitively understandable to the user. The user selects the suggested action and makes a flexible decision on how to respond. In this step, the user is guided through outputs that provide options and support decision-making.

[0394] Step 5:

[0395] The device, having received user selections and feedback, resends that information to the server. This allows the server to learn from the user's decision-making history and use it to improve the model for more appropriate advice in the future. The output of this step is the feedback data necessary for model improvement.

[0396] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0397] This invention is an AI navigation system that combines an emotion engine to enable users to perform their tasks efficiently. This system automates the acquisition of work data from users, data analysis, generation of AI instructions for tasks, and provision of feedback to users.

[0398] composition

[0399] 1. Data acquisition module

[0400] User: Enters work-related data into the system. This data includes task progress and current issues.

[0401] Terminal: Saves user input to the database.

[0402] 2. Emotional Engine

[0403] Server: Incorporates an emotion engine to analyze user emotions and stress levels. This enables emotion-based data analysis.

[0404] 3. Data Analysis Module

[0405] Server: Analyzes acquired business data and combines it with user sentiment data to identify business challenges. Sentiment data is acquired through methods such as text analysis and voice analysis.

[0406] 4. AI Instruction Generation Module

[0407] Server: Based on identified issues and emotional data, artificial intelligence generates specific instructions. These instructions are adjusted according to the user's emotional state.

[0408] 5. Interactive Interface Module

[0409] Terminal: Presents generated instructions through interaction with the user and collects user feedback.

[0410] 6. Business template provision module

[0411] Server: Provides automated task templates based on instructions from artificial intelligence.

[0412] 7. Report Generation Module

[0413] Server: Creates and provides reports to users, including their work progress and stress levels. This allows users to visualize the progress of their work improvements.

[0414] Specific example

[0415] Example from a customer support center

[0416] User: A support staff member enters customer inquiry data into the system.

[0417] Server: The emotion engine analyzes the emotional state of the person in charge based on their conversation content and input data, and determines their stress level at that time.

[0418] Server: Analyzes business data and emotional data to identify issues such as "delays in responding to inquiry A and high stress levels among staff."

[0419] Terminal: Provides instructions to the person in charge, such as, "As a countermeasure for inquiry A, use a template to shorten the response time." These instructions are adjusted by adding more detail when the person in charge is under stress, and making them more concise when they are under stress.

[0420] Server: After the countermeasures are implemented, analyze their effectiveness, compile a report detailing the progress and changes in the stress levels of the person in charge, and inform the person in charge.

[0421] Through the above process, users can leverage AI while also considering their own emotions to perform tasks efficiently.

[0422] The following describes the processing flow.

[0423] Step 1:

[0424] Users input work-related information through an interface and send it to the system. This includes data such as task progress, issues, and details of daily work.

[0425] Step 2:

[0426] The terminal receives the data entered by the user and prepares to securely store it in the database. The stored data is also accompanied by an audit log to ensure that no data is missed, for use in later analysis and reporting.

[0427] Step 3:

[0428] The server analyzes stored business data and simultaneously collects related sentiment data. This sentiment data is used to evaluate emotions and stress levels using natural language processing for text analysis and speech data analysis.

[0429] Step 4:

[0430] The server integrates business data and emotional data, and uses machine learning algorithms to identify user issues. This combination reveals complex issues, such as "a specific task is consistently causing stress."

[0431] Step 5:

[0432] The device presents the identified issues to the user through an interactive interface and asks for further details or confirmation of the need for corrections. At this stage, user feedback is incorporated into the system.

[0433] Step 6:

[0434] Based on the feedback it receives, the server generates optimized AI-powered instructions. This includes considering the user's current emotional state and adjusting work instructions or changing priorities to reduce stress.

[0435] Step 7:

[0436] The device presents the generated instructions to the user, prompting them to adapt and make necessary modifications. During this process, the user reviews the instructions and prepares to execute them.

[0437] Step 8:

[0438] The server executes user-approved instructions, and the AI ​​engine initiates automated operations to improve business efficiency. Changes and the results of operations are recorded in real time and saved for future optimization.

[0439] Step 9:

[0440] The server compiles a report summarizing the AI ​​engine's results and a comprehensive evaluation of the business progress, and submits it to the user, including visual elements. The report includes an assessment of the progress of business improvements and changes in emotions, serving as a basis for considering future improvement measures.

[0441] By integrating users' work processes with their emotional states according to these processing steps, effective and reassuring business improvements become possible.

[0442] (Example 2)

[0443] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0444] In today's work environment, users need to process diverse data and solve business problems quickly. However, traditional systems have the problem of not taking into account users' emotional states or stress levels, and therefore failing to adequately improve work efficiency. Furthermore, instructions to users are uniform and cannot be flexibly adapted to the individual needs of each user, which can increase stress and decrease efficiency.

[0445] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0446] In this invention, the server includes means for analyzing the user's emotions and performing data analysis based on that state, means for generating specific instructions for artificial intelligence considering the emotional state, and means for presenting the generated instructions to the user in a way that suits them. This enables efficient work execution that takes the user's emotions into consideration.

[0447] A "user" refers to an entity that utilizes a system to input business data or select and execute instructions.

[0448] "Data" refers to all information that users input into the system as information related to their work, including information about the progress and challenges of their work, as well as information about the user's emotions.

[0449] "Analysis" refers to the process of using acquired data to analyze information and identify problems or the emotional state of users.

[0450] A "problem" refers to an issue or bottleneck that users need to address, identified through the analysis of business data.

[0451] "Instructions" refer to specific responses and solutions for the user that are generated by artificial intelligence based on the analyzed data.

[0452] "User emotions" refers to the user's emotional state and stress level, as analyzed using an emotion engine.

[0453] A "template" refers to a standardized, automatable format or procedure provided by artificial intelligence to assist with tasks.

[0454] A "report" refers to a document that summarizes the progress of work, changes in user sentiment, and other information provided to the user.

[0455] "Artificial intelligence" refers to software technology that generates instructions and suggestions for dealing with problems based on data and analysis results obtained from users.

[0456] This invention is an AI navigation system that incorporates emotion analysis to efficiently carry out the user's work. This system analyzes work data based on the user's input information, and based on the results obtained, generates instructions that take into account the user's emotional state and presents them to the user.

[0457] First, users input work-related information using a terminal. This input includes data indicating work progress, challenges, and even emotions. The terminal stores the data obtained from the user in an internal database. This database is the central part of the system and provides the foundation for data processing.

[0458] Next, the server uses an emotion engine to analyze the user's emotional state from the data they input. The emotion engine incorporates text and voice analysis algorithms to determine stress levels and emotional tendencies from the user's words and actions. This analysis allows for the assessment of potential problems and psychological burdens in the work environment.

[0459] Based on the analyzed data, the server uses a generative AI model to generate specific instructions. These instructions are adjusted according to the user's emotional state. Concise and actionable instructions are provided for high-stress situations, while more detailed instructions are provided for low-stress situations.

[0460] Ultimately, the device presents the user with generated instructions. Through an interactive interface, the user can review and execute these instructions. The device also collects feedback, which is used for subsequent analysis.

[0461] As a concrete example, in customer support operations, a user inputs a customer inquiry, and the server analyzes the representative's emotional state using an emotion engine. Based on the results, the server generates instructions such as "Use a template to respond to inquiry A promptly," which are then presented to the representative via the terminal. The level of detail in these instructions varies depending on the representative's stress level.

[0462] An example of a prompt message is, "Analyze the customer inquiry and generate a response plan that takes into account the support staff member's emotional state and stress level." This forms the basis for the system to appropriately utilize its AI model to provide the user with the most relevant information.

[0463] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0464] Step 1:

[0465] Users input work-related information through a terminal. This input data includes text about task progress, current challenges, and user sentiment. The terminal saves the entered data to a database. During database saving, data integrity is checked, and format conversion is performed as needed.

[0466] Step 2:

[0467] The server retrieves business data stored in the database and analyzes the user's emotions using an emotion engine. The input data is processed by a text analysis algorithm, and the user's emotional state and stress level are output as numerical values. In the emotion analysis, keyword analysis and emotion scoring are performed to evaluate the user's psychological state.

[0468] Step 3:

[0469] The server integrates the sentiment data and business data obtained from the analysis and runs a data mining algorithm. This identifies bottlenecks in business processes and issues that should be prioritized for resolution. The output provides a list of the analyzed issues and their associated sentiment states.

[0470] Step 4:

[0471] The server uses a generative AI model to generate instructions based on identified issues. The input consists of an analyzed list of issues and emotional data, and the output is specific countermeasures. The generated instructions are adjusted according to the user's emotional state. This process involves simplifying or complicating the instructions based on the user's stress level.

[0472] Step 5:

[0473] The terminal presents the generated instructions to the user through an interactive interface. Here, the user is presented with choices regarding the instructions, and can either follow them or provide feedback. The input is the generated instructions, and the output is the user's choices and feedback.

[0474] Step 6:

[0475] The server generates reports on work progress and emotional changes based on feedback and execution status collected from users. The generated reports include visualized graphs and progress statistics. These reports are provided to users to help them identify areas for improvement in their work and track changes in their emotional state.

[0476] (Application Example 2)

[0477] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0478] The present invention aims to support efficient work in business operations and to improve the work environment in accordance with the user's emotional state. In particular, in high-stress environments such as security work, it is necessary to appropriately manage the burden on operators and improve the quality of work.

[0479] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0480] In this invention, the server includes means for acquiring business data from a user, means for analyzing the acquired business data and identifying issues, means for determining the user's emotional state by analyzing emotions, means for generating specific instructions for artificial intelligence based on the identified issues and emotional state, means for presenting the generated instructions to the user and executing them if selected, means for monitoring the progress of the work and creating periodic reports, means for adjusting the content of the instructions based on the user's emotional state, and means for providing an automateable business template. As a result, operators are presented with appropriate instructions that respond to their emotions while reducing their workload, enabling improvements in work efficiency and quality.

[0481] A "user" is a person who uses this system to perform their duties.

[0482] "Means for acquiring business data" refers to a mechanism for collecting business-related information provided by users.

[0483] "Means for analyzing business data and identifying issues" refers to a mechanism that analyzes collected business information and extracts problems and areas for improvement.

[0484] "Means for generating instructions for artificial intelligence" refers to a mechanism that causes AI to generate appropriate countermeasures based on a specific problem.

[0485] "A means of presenting generated instructions to the user and executing them if selected" refers to a mechanism that displays instructions generated by AI to the user and executes the approved instructions.

[0486] "A means of monitoring the progress of work and generating regular reports" refers to a mechanism that tracks the progress of work and provides it to users periodically as a report.

[0487] "A means of determining a user's emotional state by analyzing their emotions" refers to a mechanism that analyzes a user's emotions and evaluates their psychological state.

[0488] "Means for adjusting the content of instructions based on the user's emotional state" refers to a mechanism that appropriately modifies the content and presentation method of instructions according to the user's emotional state.

[0489] "Means of providing automatable business templates" refers to a mechanism that provides users with standardized work procedures to streamline and improve the efficiency of their work.

[0490] The system implementing this invention is designed to support the work of security operators efficiently and while taking into consideration their emotional burden. Its specific form is described below.

[0491] The server acquires voice data and business-related information input from users. This includes real-time data monitored through devices such as sensors and cameras. This acquired data is stored in database software (e.g., MySQL) for later analysis.

[0492] Sentiment analysis uses a cloud-based sentiment analysis platform (e.g., Microsoft Azure Cognitive Services) to determine a user's emotional state from voice and text data. This analysis can be used to assess the user's stress level and psychological state.

[0493] The server combines business data and emotional data, and uses an AI instruction generation system (e.g., Google Cloud AI Platform) to generate instructions that are relevant to a specific task. An example of a prompt might be, "Analyze the operator's voice data and generate suggestions for handing over low-priority tasks when the stress level is high." This automatically generates instructions that are adapted to the user's emotional state.

[0494] The device features an interactive interface (e.g., React Native) that presents generated instructions to the user. The content and amount of information in the instructions are adjusted according to the user's mood, and the instructions are executed once accepted by the user.

[0495] As a concrete example, if a security operator experiences increased stress while monitoring a specific area, the system will suggest that another operator take over monitoring an adjacent area. This encourages the original operator to take a temporary break, thus reducing their workload. Through this process, it becomes possible to improve operational efficiency while avoiding excessive workload.

[0496] This system has the following features:

[0497] By enabling integrated analysis of business data and emotional data, we provide work instructions that are tailored to individual emotions.

[0498] By distributing tasks in a way that takes user stress management into consideration, we aim to achieve both work efficiency and psychological well-being.

[0499] In this way, we achieve highly efficient operational support that includes consideration of emotions in security operations.

[0500] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0501] Step 1:

[0502] Users input work-related information and voice data into the terminal. This input data includes task progress and details of current issues. This data is then stored directly in the database.

[0503] Step 2:

[0504] The server analyzes business data acquired from terminals. Text and speech analysis are used to extract information about the progress of tasks and identify problems. The input is business data, and the output is a list of identified issues.

[0505] Step 3:

[0506] The server uses an emotion analysis platform to analyze the user's voice data and determine their emotional state. The input is the user's voice data, and the output is numerical or categorical data indicating the emotional state.

[0507] Step 4:

[0508] The server combines business data and emotional state data and generates instructions using a generative AI model. Here, prompts are used to get the AI ​​to generate specific instructions. For example, a prompt such as "Analyze the operator's voice data and generate measures to reduce the workload when the stress level is high" might be used. The input is a list of tasks and emotional state data, and the output is the generated instructions.

[0509] Step 5:

[0510] The terminal presents the user with instructions generated by the server. The user selects an instruction, and the automation of the task proceeds based on that selection. The output is the execution of the instruction selected by the user.

[0511] Step 6:

[0512] The server continuously monitors the progress of tasks and the emotional state of users, and generates reports periodically. Inputs are task data and emotional state history, and output is a report document. This report provides users with information that visualizes the progress of task improvements and changes in their emotional state.

[0513] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0514] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0515] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0516] [Third Embodiment]

[0517] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0518] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0519] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0520] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0521] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0522] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0523] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0524] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0525] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0526] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0527] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0528] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0529] This invention is a system designed to help users perform their tasks more efficiently. The system consists of multiple modules, each performing a specific function. The following describes each module and its role.

[0530] 1. Data acquisition module

[0531] User: Inputs information related to work into the system through the interface. This includes task progress and newly arising issues.

[0532] Terminal: Receives user input and saves it to the database.

[0533] 2. Data Analysis Module

[0534] Server: Analyzes collected business data to identify specific challenges faced by users. This analysis utilizes natural language processing and machine learning algorithms.

[0535] 3. Interactive Interface Module

[0536] Terminal: Provides an interface that allows users to confirm the details of the issue in an interactive format. This involves asking questions in the form of a chatbot to delve into the root cause of the problem.

[0537] 4. Instruction generation module

[0538] Server: Automatically generates instructions for artificial intelligence based on identified tasks. These instructions include suggestions for streamlining and improving tasks.

[0539] Terminal: Presents the generated instructions to the user and provides opportunities to modify or approve them as needed.

[0540] 5. AI Execution Module

[0541] Server: The server executes user-approved instructions, and artificial intelligence handles the tasks. This enables efficient task completion.

[0542] 6. Report Generation Module

[0543] Server: Generates reports on the progress of tasks and the results of AI execution, and notifies users. Reports are visually represented using graphs and charts.

[0544] Specific example

[0545] Project Management Cases

[0546] User: The project manager enters data into the system for monthly reporting. This includes task lists, progress rates, reasons for delays, etc.

[0547] Server: Analyzes the input data and identifies problems such as "a specific task is being rescheduled frequently."

[0548] Terminal: Interactively question the project manager about the cause of this delay and gather detailed information.

[0549] Server: Based on the collected information, it automatically generates instructions (e.g., "Adjust resources to increase a specific task") and proposes them to the project manager.

[0550] User: Approve the proposed instructions and decide to proceed.

[0551] Terminal: After execution, it provides the project manager with a report summarizing the project progress and AI execution results.

[0552] In this way, project managers can achieve efficient project management using AI.

[0553] The following describes the processing flow.

[0554] Step 1:

[0555] Users input or upload work-related data into the system interface. This data includes task progress and any issues encountered.

[0556] Step 2:

[0557] The terminal saves the business data entered by the user to a database and prepares it for analysis.

[0558] Step 3:

[0559] The server collects stored data and analyzes it using natural language processing and machine learning algorithms. Here, it detects specific patterns and anomalies, identifying the challenges users are facing.

[0560] Step 4:

[0561] The device presents the user with hypotheses and identified issues in a conversational format, and requests detailed and supplementary information. This conversation takes place via a chatbot.

[0562] Step 5:

[0563] Based on the additional information provided, the server deepens its analysis and generates instructions for the artificial intelligence to take as a solution to the problem. These instructions include specific methods for improving the task.

[0564] Step 6:

[0565] The device presents the generated instructions to the user and provides the user with an opportunity to approve or modify them.

[0566] Step 7:

[0567] The user reviews the instructions provided and approves or modifies them as necessary.

[0568] Step 8:

[0569] The server executes user-approved instructions and begins solving the problem using artificial intelligence. This process is automated, and the AI ​​implements specific task improvements.

[0570] Step 9:

[0571] The server generates a report summarizing the execution results and the progress of the tasks. This report is used by the user to evaluate the effectiveness of the business improvements.

[0572] Step 10:

[0573] The terminal notifies the user of the generated reports, providing insights to streamline operations and determine the next steps.

[0574] (Example 1)

[0575] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0576] To improve operational efficiency, it is essential to provide users with specific and appropriate instructions. Traditional systems have struggled to accurately identify user challenges, acquire necessary information in a timely manner, and then use artificial intelligence to provide efficient operational support based on that information. This has resulted in users' workloads not being reduced and work progress not proceeding smoothly.

[0577] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0578] In this invention, the server includes means for receiving business information from a user, means for processing the stored business information to identify problems, and means for generating instructions for a specific artificial intelligence based on the identified problems. This makes it possible to identify the specific challenges faced by the user and provide solutions efficiently.

[0579] A "user" is someone who uses the system to input business information and receive support for problem-solving.

[0580] "Business information" refers to data necessary for users to perform their tasks, and includes task progress and related files.

[0581] "Device" refers to each component of a system that receives, stores, processes, generates, presents, operates, and monitors business information.

[0582] "Saving" refers to the process of recording received business information in a database in an appropriate format.

[0583] "Processing" means analyzing stored information and applying calculations and algorithms to identify problems.

[0584] "Identifying the problem" means identifying the specific challenges that users face based on business information.

[0585] "Generating instructions" refers to the act of creating instructions that propose solutions using artificial intelligence to an identified problem.

[0586] "To put into action" means to execute the generated instructions and use artificial intelligence or systems to solve problems.

[0587] "Generating a report" means creating a report summarizing the progress of tasks and the results of executing instructions, and providing it to the user.

[0588] "Dialogue format" refers to an interactive method in which the user and the system exchange information through conversation and confirm the details of a problem.

[0589] A "template for automatable tasks" is a template provided to support efficient task execution based on specific task content.

[0590] This system assists users in improving their work efficiency and consists of servers, terminals, and user interactions.

[0591] 1. Use of hardware and software

[0592] The server functions as the core processing device, and for data storage and analysis, it uses scikit-learn and TextBlob as software environments to execute natural language processing libraries and machine learning algorithms. Furthermore, it generates instructions for problem solving using a general-purpose AI framework (e.g., OpenAI GPT-3) as a generative AI model.

[0593] The terminal provides an interface for users to input business information. Typically, a web browser or a dedicated application is used as the interface to receive user input and send it to the server.

[0594] 2. Data processing and data calculation

[0595] The server receives business information sent by users, stores it in a database, and performs analysis. During the analysis process, sentiment analysis using natural language processing and pattern recognition using machine learning are performed to identify the specific challenges that users face.

[0596] After identifying the problem, the AI ​​model is used to automatically generate specific solutions and instructions for business improvement for the identified problem. This AI model outputs instructions by processing prompt statements such as "Please suggest ways to optimize resource allocation."

[0597] 3. Specific Examples

[0598] When users manage a project, they input detailed information about the project's progress into the system. This includes task names, progress percentages, and reasons for delays.

[0599] The server processes this information and, if delays are frequent in a particular task, uses a generative AI model to formulate solutions such as "making adjustments to increase resources for that specific task."

[0600] In this way, a system is built that enables concrete and efficient business support through the collaboration of servers, terminals, and users.

[0601] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0602] Step 1:

[0603] Users input work information using a terminal. This input data includes project name, task details, progress rate, reason for delay, and related files. Users enter this information through an interface, and the data is sent from the terminal to the server.

[0604] Step 2:

[0605] When the terminal receives user input data, it checks the data format and corrects it if necessary. During this process, it checks for errors in numerical data and prompts the user to re-enter the data if required. The corrected data is then transferred to the server and recorded in the database.

[0606] Step 3:

[0607] The server collects stored business information and performs data analysis using natural language processing. The input is business information stored in the database, and the output is a report containing specific problems and issues. Data analysis includes sentiment analysis of text data and analysis of the causes of task delays to identify problems.

[0608] Step 4:

[0609] Based on the data analysis results, the server generates instructions for problem solving using a generated AI model. At this stage, the input is the analysis results, and the output is the generated specific instructions. For example, the AI ​​processes a prompt such as "Suggest an optimal method for resource allocation" and generates a scenario for efficient resource allocation.

[0610] Step 5:

[0611] The terminal presents instructions from the server to the user. The user reviews the presented instructions and adds comments or modifies them as needed. At this stage, the user confirms the final execution plan by approving or modifying the instructions.

[0612] Step 6:

[0613] The server executes instructions approved by the user. This includes adjusting schedules using project management software and reallocating resources. AI streamlines the work process in this execution.

[0614] Step 7:

[0615] The server summarizes the results and progress obtained through the above process and generates a report. The report is automatically generated and presented to the user in a visual format using graphs and charts. Based on this report, the user can develop further improvement plans.

[0616] (Application Example 1)

[0617] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0618] For users to perform their tasks efficiently, it is crucial to understand business information, identify problems, propose appropriate solutions, and implement them. However, currently, these processes are time-consuming and laborious, and it is particularly difficult to expedite information gathering and decision-making in physical stores. Under these circumstances, there is a need for a new system that provides optimal business support to users and improves operational efficiency.

[0619] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0620] In this invention, the server includes means for acquiring business information from users, means for analyzing the acquired business information and identifying problems, and means for providing advice to support in-store activities based on the business information and user requests. This enables efficient information gathering, rapid problem identification, and provision of appropriate advice in diverse business environments, thereby enabling efficient resolution of problems faced by users.

[0621] A "user" is someone who uses the system to perform their duties.

[0622] "Business information" refers to data and information regarding the progress of business operations that are necessary for carrying out those operations.

[0623] "Analysis" is the act of analyzing collected information to identify issues and problems.

[0624] "Intelligent processing" is the process of generating instructions for an identified task using artificial intelligence.

[0625] "In-store activities" refer to various activities carried out in a physical store to perform business operations.

[0626] "Advice" refers to guidelines and suggestions provided to users to improve work efficiency and solve problems.

[0627] This invention is a support system for streamlining operations in physical stores. This system allows users to input, analyze, and receive advice on business information using an application that runs on their smartphone.

[0628] The server's role is to collect and analyze business information entered by users using their smartphones. Specifically, a data analysis model using Python runs to analyze business information and identify problems. Based on the analyzed data, the server uses a generative AI model to perform intelligent processing and generate advice for business improvement.

[0629] The smartphone, acting as the terminal, is responsible for acquiring information from the user and presenting generated advice. The user inputs business information and requests through the interface on the terminal, and this information is sent to the server. The analysis results and advice from the server are presented to the user on the terminal, providing them with selectable actions.

[0630] For example, when store operations involve inputting inventory information and customer requests into the app, the server can quickly analyze that information and provide specific advice such as, "You need to replenish the stock of this particular product."

[0631] An example of a prompt is, "Ask the AI ​​assistant for advice on promotions and product placement based on current inventory levels and customer needs." In this way, users can make quick and efficient business decisions based on AI advice.

[0632] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0633] Step 1:

[0634] Users input business information and customer requests using a smartphone application. This input includes inventory status and customer service details, which then serve as the system's input. The entered data is stored in cloud storage.

[0635] Step 2:

[0636] The server accesses and collects data stored in the cloud. Next, it performs data cleaning and standardization on the collected data to prepare it for analysis. The output of this step is an analyzable dataset.

[0637] Step 3:

[0638] The server performs data analysis using a generative AI model based on an analyzable dataset. It utilizes natural language processing (NLP) and machine learning algorithms to identify business challenges and generate specific action items. For example, it might identify a particular product as a best-seller and generate advice for inventory replenishment. The output of this step is the analysis results and advice.

[0639] Step 4:

[0640] The server sends the generated analysis results and advice to the terminal. The terminal, a smartphone, displays this information in a format that is intuitively understandable to the user. The user selects the suggested action and makes a flexible decision on how to respond. In this step, the user is guided through outputs that provide options and support decision-making.

[0641] Step 5:

[0642] The device, having received user selections and feedback, resends that information to the server. This allows the server to learn from the user's decision-making history and use it to improve the model for more appropriate advice in the future. The output of this step is the feedback data necessary for model improvement.

[0643] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0644] This invention is an AI navigation system that combines an emotion engine to enable users to perform their tasks efficiently. This system automates the acquisition of work data from users, data analysis, generation of AI instructions for tasks, and provision of feedback to users.

[0645] composition

[0646] 1. Data acquisition module

[0647] User: Enters work-related data into the system. This data includes task progress and current issues.

[0648] Terminal: Saves user input to the database.

[0649] 2. Emotional Engine

[0650] Server: Incorporates an emotion engine to analyze user emotions and stress levels. This enables emotion-based data analysis.

[0651] 3. Data Analysis Module

[0652] Server: Analyzes acquired business data and combines it with user sentiment data to identify business challenges. Sentiment data is acquired through methods such as text analysis and voice analysis.

[0653] 4. AI Instruction Generation Module

[0654] Server: Based on identified issues and emotional data, artificial intelligence generates specific instructions. These instructions are adjusted according to the user's emotional state.

[0655] 5. Interactive Interface Module

[0656] Terminal: Presents generated instructions through interaction with the user and collects user feedback.

[0657] 6. Business template provision module

[0658] Server: Provides automated task templates based on instructions from artificial intelligence.

[0659] 7. Report Generation Module

[0660] Server: Creates and provides reports to users, including their work progress and stress levels. This allows users to visualize the progress of their work improvements.

[0661] Specific example

[0662] Example from a customer support center

[0663] User: A support staff member enters customer inquiry data into the system.

[0664] Server: The emotion engine analyzes the emotional state of the person in charge based on their conversation content and input data, and determines their stress level at that time.

[0665] Server: Analyzes business data and emotional data to identify issues such as "delays in responding to inquiry A and high stress levels among staff."

[0666] Terminal: Provides instructions to the person in charge, such as, "As a countermeasure for inquiry A, use a template to shorten the response time." These instructions are adjusted by adding more detail when the person in charge is under stress, and making them more concise when they are under stress.

[0667] Server: After the countermeasures are implemented, analyze their effectiveness, compile a report detailing the progress and changes in the stress levels of the person in charge, and inform the person in charge.

[0668] Through the above process, users can leverage AI while also considering their own emotions to perform tasks efficiently.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] Users input work-related information through an interface and send it to the system. This includes data such as task progress, issues, and details of daily work.

[0672] Step 2:

[0673] The terminal receives the data entered by the user and prepares to securely store it in the database. The stored data is also accompanied by an audit log to ensure that no data is missed, for use in later analysis and reporting.

[0674] Step 3:

[0675] The server analyzes stored business data and simultaneously collects related sentiment data. This sentiment data is used to evaluate emotions and stress levels using natural language processing for text analysis and speech data analysis.

[0676] Step 4:

[0677] The server integrates business data and emotional data, and uses machine learning algorithms to identify user issues. This combination reveals complex issues, such as "a specific task is consistently causing stress."

[0678] Step 5:

[0679] The device presents the identified issues to the user through an interactive interface and asks for further details or confirmation of the need for corrections. At this stage, user feedback is incorporated into the system.

[0680] Step 6:

[0681] Based on the feedback it receives, the server generates optimized AI-powered instructions. This includes considering the user's current emotional state and adjusting work instructions or changing priorities to reduce stress.

[0682] Step 7:

[0683] The device presents the generated instructions to the user, prompting them to adapt and make necessary modifications. During this process, the user reviews the instructions and prepares to execute them.

[0684] Step 8:

[0685] The server executes user-approved instructions, and the AI ​​engine initiates automated operations to improve business efficiency. Changes and the results of operations are recorded in real time and saved for future optimization.

[0686] Step 9:

[0687] The server compiles a report summarizing the AI ​​engine's results and a comprehensive evaluation of the business progress, and submits it to the user, including visual elements. The report includes an assessment of the progress of business improvements and changes in emotions, serving as a basis for considering future improvement measures.

[0688] By integrating users' work processes with their emotional states according to these processing steps, effective and reassuring business improvements become possible.

[0689] (Example 2)

[0690] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0691] In today's work environment, users need to process diverse data and solve business problems quickly. However, traditional systems have the problem of not taking into account users' emotional states or stress levels, and therefore failing to adequately improve work efficiency. Furthermore, instructions to users are uniform and cannot be flexibly adapted to the individual needs of each user, which can increase stress and decrease efficiency.

[0692] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0693] In this invention, the server includes means for analyzing the user's emotions and performing data analysis based on that state, means for generating specific instructions for artificial intelligence considering the emotional state, and means for presenting the generated instructions to the user in a way that suits them. This enables efficient work execution that takes the user's emotions into consideration.

[0694] A "user" refers to an entity that utilizes a system to input business data or select and execute instructions.

[0695] "Data" refers to all information that users input into the system as information related to their work, including information about the progress and challenges of their work, as well as information about the user's emotions.

[0696] "Analysis" refers to the process of using acquired data to analyze information and identify problems or the emotional state of users.

[0697] A "problem" refers to an issue or bottleneck that users need to address, identified through the analysis of business data.

[0698] "Instructions" refer to specific responses and solutions for the user that are generated by artificial intelligence based on the analyzed data.

[0699] "User emotions" refers to the user's emotional state and stress level, as analyzed using an emotion engine.

[0700] A "template" refers to a standardized, automatable format or procedure provided by artificial intelligence to assist with tasks.

[0701] A "report" refers to a document that summarizes the progress of work, changes in user sentiment, and other information provided to the user.

[0702] "Artificial intelligence" refers to software technology that generates instructions and suggestions for dealing with problems based on data and analysis results obtained from users.

[0703] This invention is an AI navigation system that incorporates emotion analysis to efficiently carry out the user's work. This system analyzes work data based on the user's input information, and based on the results obtained, generates instructions that take into account the user's emotional state and presents them to the user.

[0704] First, users input work-related information using a terminal. This input includes data indicating work progress, challenges, and even emotions. The terminal stores the data obtained from the user in an internal database. This database is the central part of the system and provides the foundation for data processing.

[0705] Next, the server uses an emotion engine to analyze the user's emotional state from the data they input. The emotion engine incorporates text and voice analysis algorithms to determine stress levels and emotional tendencies from the user's words and actions. This analysis allows for the assessment of potential problems and psychological burdens in the work environment.

[0706] Based on the analyzed data, the server uses a generative AI model to generate specific instructions. These instructions are adjusted according to the user's emotional state. Concise and actionable instructions are provided for high-stress situations, while more detailed instructions are provided for low-stress situations.

[0707] Ultimately, the device presents the user with generated instructions. Through an interactive interface, the user can review and execute these instructions. The device also collects feedback, which is used for subsequent analysis.

[0708] As a concrete example, in customer support operations, a user inputs a customer inquiry, and the server analyzes the representative's emotional state using an emotion engine. Based on the results, the server generates instructions such as "Use a template to respond to inquiry A promptly," which are then presented to the representative via the terminal. The level of detail in these instructions varies depending on the representative's stress level.

[0709] An example of a prompt message is, "Analyze the customer inquiry and generate a response plan that takes into account the support staff member's emotional state and stress level." This forms the basis for the system to appropriately utilize its AI model to provide the user with the most relevant information.

[0710] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0711] Step 1:

[0712] Users input work-related information through a terminal. This input data includes text about task progress, current challenges, and user sentiment. The terminal saves the entered data to a database. During database saving, data integrity is checked, and format conversion is performed as needed.

[0713] Step 2:

[0714] The server retrieves business data stored in the database and analyzes the user's emotions using an emotion engine. The input data is processed by a text analysis algorithm, and the user's emotional state and stress level are output as numerical values. In the emotion analysis, keyword analysis and emotion scoring are performed to evaluate the user's psychological state.

[0715] Step 3:

[0716] The server integrates the sentiment data and business data obtained from the analysis and runs a data mining algorithm. This identifies bottlenecks in business processes and issues that should be prioritized for resolution. The output provides a list of the analyzed issues and their associated sentiment states.

[0717] Step 4:

[0718] The server uses a generative AI model to generate instructions based on identified issues. The input consists of an analyzed list of issues and emotional data, and the output is specific countermeasures. The generated instructions are adjusted according to the user's emotional state. This process involves simplifying or complicating the instructions based on the user's stress level.

[0719] Step 5:

[0720] The terminal presents the generated instructions to the user through an interactive interface. Here, the user is presented with choices regarding the instructions, and can either follow them or provide feedback. The input is the generated instructions, and the output is the user's choices and feedback.

[0721] Step 6:

[0722] The server generates reports on work progress and emotional changes based on feedback and execution status collected from users. The generated reports include visualized graphs and progress statistics. These reports are provided to users to help them identify areas for improvement in their work and track changes in their emotional state.

[0723] (Application Example 2)

[0724] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0725] The present invention aims to support efficient work in business operations and to improve the work environment in accordance with the user's emotional state. In particular, in high-stress environments such as security work, it is necessary to appropriately manage the burden on operators and improve the quality of work.

[0726] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0727] In this invention, the server includes means for acquiring business data from a user, means for analyzing the acquired business data and identifying issues, means for determining the user's emotional state by analyzing emotions, means for generating specific instructions for artificial intelligence based on the identified issues and emotional state, means for presenting the generated instructions to the user and executing them if selected, means for monitoring the progress of the work and creating periodic reports, means for adjusting the content of the instructions based on the user's emotional state, and means for providing an automateable business template. As a result, operators are presented with appropriate instructions that respond to their emotions while reducing their workload, enabling improvements in work efficiency and quality.

[0728] A "user" is a person who uses this system to perform their duties.

[0729] "Means for acquiring business data" refers to a mechanism for collecting business-related information provided by users.

[0730] "Means for analyzing business data and identifying issues" refers to a mechanism that analyzes collected business information and extracts problems and areas for improvement.

[0731] "Means for generating instructions for artificial intelligence" refers to a mechanism that causes AI to generate appropriate countermeasures based on a specific problem.

[0732] "A means of presenting generated instructions to the user and executing them if selected" refers to a mechanism that displays instructions generated by AI to the user and executes the approved instructions.

[0733] "A means of monitoring the progress of work and generating regular reports" refers to a mechanism that tracks the progress of work and provides it to users periodically as a report.

[0734] "A means of determining a user's emotional state by analyzing their emotions" refers to a mechanism that analyzes a user's emotions and evaluates their psychological state.

[0735] "Means for adjusting the content of instructions based on the user's emotional state" refers to a mechanism that appropriately modifies the content and presentation method of instructions according to the user's emotional state.

[0736] "Means of providing automatable business templates" refers to a mechanism that provides users with standardized work procedures to streamline and improve the efficiency of their work.

[0737] The system implementing this invention is designed to support the work of security operators efficiently and while taking into consideration their emotional burden. Its specific form is described below.

[0738] The server acquires voice data and business-related information input from users. This includes real-time data monitored through devices such as sensors and cameras. This acquired data is stored in database software (e.g., MySQL) for later analysis.

[0739] Sentiment analysis uses a cloud-based sentiment analysis platform (e.g., Microsoft Azure Cognitive Services) to determine a user's emotional state from voice and text data. This analysis can be used to assess the user's stress level and psychological state.

[0740] The server combines business data and emotional data, and uses an AI instruction generation system (e.g., Google Cloud AI Platform) to generate instructions that are relevant to a specific task. An example of a prompt might be, "Analyze the operator's voice data and generate suggestions for handing over low-priority tasks when the stress level is high." This automatically generates instructions that are adapted to the user's emotional state.

[0741] The device features an interactive interface (e.g., React Native) that presents generated instructions to the user. The content and amount of information in the instructions are adjusted according to the user's mood, and the instructions are executed once accepted by the user.

[0742] As a concrete example, if a security operator experiences increased stress while monitoring a specific area, the system will suggest that another operator take over monitoring an adjacent area. This encourages the original operator to take a temporary break, thus reducing their workload. Through this process, it becomes possible to improve operational efficiency while avoiding excessive workload.

[0743] This system has the following features:

[0744] By enabling integrated analysis of business data and emotional data, we provide work instructions that are tailored to individual emotions.

[0745] By distributing tasks in a way that takes user stress management into consideration, we aim to achieve both work efficiency and psychological well-being.

[0746] In this way, we achieve highly efficient operational support that includes consideration of emotions in security operations.

[0747] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0748] Step 1:

[0749] Users input work-related information and voice data into the terminal. This input data includes task progress and details of current issues. This data is then stored directly in the database.

[0750] Step 2:

[0751] The server analyzes business data acquired from terminals. Text and speech analysis are used to extract information about the progress of tasks and identify problems. The input is business data, and the output is a list of identified issues.

[0752] Step 3:

[0753] The server uses an emotion analysis platform to analyze the user's voice data and determine their emotional state. The input is the user's voice data, and the output is numerical or categorical data indicating the emotional state.

[0754] Step 4:

[0755] The server combines business data and emotional state data and generates instructions using a generative AI model. Here, prompts are used to get the AI ​​to generate specific instructions. For example, a prompt such as "Analyze the operator's voice data and generate measures to reduce the workload when the stress level is high" might be used. The input is a list of tasks and emotional state data, and the output is the generated instructions.

[0756] Step 5:

[0757] The terminal presents the user with instructions generated by the server. The user selects an instruction, and the automation of the task proceeds based on that selection. The output is the execution of the instruction selected by the user.

[0758] Step 6:

[0759] The server continuously monitors the progress of tasks and the emotional state of users, and generates reports periodically. Inputs are task data and emotional state history, and output is a report document. This report provides users with information that visualizes the progress of task improvements and changes in their emotional state.

[0760] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0761] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0762] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0763] [Fourth Embodiment]

[0764] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0765] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0766] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0767] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0768] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0769] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0770] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0771] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0772] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0773] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0774] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0775] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0776] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0777] This invention is a system designed to help users perform their tasks more efficiently. The system consists of multiple modules, each performing a specific function. The following describes each module and its role.

[0778] 1. Data acquisition module

[0779] User: Inputs information related to work into the system through the interface. This includes task progress and newly arising issues.

[0780] Terminal: Receives user input and saves it to the database.

[0781] 2. Data Analysis Module

[0782] Server: Analyzes collected business data to identify specific challenges faced by users. This analysis utilizes natural language processing and machine learning algorithms.

[0783] 3. Interactive Interface Module

[0784] Terminal: Provides an interface that allows users to confirm the details of the issue in an interactive format. This involves asking questions in the form of a chatbot to delve into the root cause of the problem.

[0785] 4. Instruction generation module

[0786] Server: Automatically generates instructions for artificial intelligence based on identified tasks. These instructions include suggestions for streamlining and improving tasks.

[0787] Terminal: Presents the generated instructions to the user and provides opportunities to modify or approve them as needed.

[0788] 5. AI Execution Module

[0789] Server: The server executes user-approved instructions, and artificial intelligence handles the tasks. This enables efficient task completion.

[0790] 6. Report Generation Module

[0791] Server: Generates reports on the progress of tasks and the results of AI execution, and notifies users. Reports are visually represented using graphs and charts.

[0792] Specific example

[0793] Project Management Cases

[0794] User: The project manager enters data into the system for monthly reporting. This includes task lists, progress rates, reasons for delays, etc.

[0795] Server: Analyzes the input data and identifies problems such as "a specific task is being rescheduled frequently."

[0796] Terminal: Interactively question the project manager about the cause of this delay and gather detailed information.

[0797] Server: Based on the collected information, it automatically generates instructions (e.g., "Adjust resources to increase a specific task") and proposes them to the project manager.

[0798] User: Approve the proposed instructions and decide to proceed.

[0799] Terminal: After execution, it provides the project manager with a report summarizing the project progress and AI execution results.

[0800] In this way, project managers can achieve efficient project management using AI.

[0801] The following describes the processing flow.

[0802] Step 1:

[0803] Users input or upload work-related data into the system interface. This data includes task progress and any issues encountered.

[0804] Step 2:

[0805] The terminal saves the business data entered by the user to a database and prepares it for analysis.

[0806] Step 3:

[0807] The server collects stored data and analyzes it using natural language processing and machine learning algorithms. Here, it detects specific patterns and anomalies, identifying the challenges users are facing.

[0808] Step 4:

[0809] The device presents the user with hypotheses and identified issues in a conversational format, and requests detailed and supplementary information. This conversation takes place via a chatbot.

[0810] Step 5:

[0811] Based on the additional information provided, the server deepens its analysis and generates instructions for the artificial intelligence to take as a solution to the problem. These instructions include specific methods for improving the task.

[0812] Step 6:

[0813] The device presents the generated instructions to the user and provides the user with an opportunity to approve or modify them.

[0814] Step 7:

[0815] The user reviews the instructions provided and approves or modifies them as necessary.

[0816] Step 8:

[0817] The server executes user-approved instructions and begins solving the problem using artificial intelligence. This process is automated, and the AI ​​implements specific task improvements.

[0818] Step 9:

[0819] The server generates a report summarizing the execution results and the progress of the tasks. This report is used by the user to evaluate the effectiveness of the business improvements.

[0820] Step 10:

[0821] The terminal notifies the user of the generated reports, providing insights to streamline operations and determine the next steps.

[0822] (Example 1)

[0823] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0824] To improve operational efficiency, it is essential to provide users with specific and appropriate instructions. Traditional systems have struggled to accurately identify user challenges, acquire necessary information in a timely manner, and then use artificial intelligence to provide efficient operational support based on that information. This has resulted in users' workloads not being reduced and work progress not proceeding smoothly.

[0825] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0826] In this invention, the server includes means for receiving business information from a user, means for processing the stored business information to identify problems, and means for generating instructions for a specific artificial intelligence based on the identified problems. This makes it possible to identify the specific challenges faced by the user and provide solutions efficiently.

[0827] A "user" is someone who uses the system to input business information and receive support for problem-solving.

[0828] "Business information" refers to data necessary for users to perform their tasks, and includes task progress and related files.

[0829] "Device" refers to each component of a system that receives, stores, processes, generates, presents, operates, and monitors business information.

[0830] "Saving" refers to the process of recording received business information in a database in an appropriate format.

[0831] "Processing" means analyzing stored information and applying calculations and algorithms to identify problems.

[0832] "Identifying the problem" means identifying the specific challenges that users face based on business information.

[0833] "Generating instructions" refers to the act of creating instructions that propose solutions using artificial intelligence to an identified problem.

[0834] "To put into action" means to execute the generated instructions and use artificial intelligence or systems to solve problems.

[0835] "Generating a report" means creating a report summarizing the progress of tasks and the results of executing instructions, and providing it to the user.

[0836] "Dialogue format" refers to an interactive method in which the user and the system exchange information through conversation and confirm the details of a problem.

[0837] A "template for automatable tasks" is a template provided to support efficient task execution based on specific task content.

[0838] This system assists users in improving their work efficiency and consists of servers, terminals, and user interactions.

[0839] 1. Use of hardware and software

[0840] The server functions as the core processing device, and for data storage and analysis, it uses scikit-learn and TextBlob as software environments to execute natural language processing libraries and machine learning algorithms. Furthermore, it generates instructions for problem solving using a general-purpose AI framework (e.g., OpenAI GPT-3) as a generative AI model.

[0841] The terminal provides an interface for users to input business information. Typically, a web browser or a dedicated application is used as the interface to receive user input and send it to the server.

[0842] 2. Data processing and data calculation

[0843] The server receives business information sent by users, stores it in a database, and performs analysis. During the analysis process, sentiment analysis using natural language processing and pattern recognition using machine learning are performed to identify the specific challenges that users face.

[0844] After identifying the problem, the AI ​​model is used to automatically generate specific solutions and instructions for business improvement for the identified problem. This AI model outputs instructions by processing prompt statements such as "Please suggest ways to optimize resource allocation."

[0845] 3. Specific Examples

[0846] When users manage a project, they input detailed information about the project's progress into the system. This includes task names, progress percentages, and reasons for delays.

[0847] The server processes this information and, if delays are frequent in a particular task, uses a generative AI model to formulate solutions such as "making adjustments to increase resources for that specific task."

[0848] In this way, a system is built that enables concrete and efficient business support through the collaboration of servers, terminals, and users.

[0849] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0850] Step 1:

[0851] Users input work information using a terminal. This input data includes project name, task details, progress rate, reason for delay, and related files. Users enter this information through an interface, and the data is sent from the terminal to the server.

[0852] Step 2:

[0853] When the terminal receives user input data, it checks the data format and corrects it if necessary. During this process, it checks for errors in numerical data and prompts the user to re-enter the data if required. The corrected data is then transferred to the server and recorded in the database.

[0854] Step 3:

[0855] The server collects stored business information and performs data analysis using natural language processing. The input is business information stored in the database, and the output is a report containing specific problems and issues. Data analysis includes sentiment analysis of text data and analysis of the causes of task delays to identify problems.

[0856] Step 4:

[0857] Based on the data analysis results, the server generates instructions for problem solving using a generated AI model. At this stage, the input is the analysis results, and the output is the generated specific instructions. For example, the AI ​​processes a prompt such as "Suggest an optimal method for resource allocation" and generates a scenario for efficient resource allocation.

[0858] Step 5:

[0859] The terminal presents instructions from the server to the user. The user reviews the presented instructions and adds comments or modifies them as needed. At this stage, the user confirms the final execution plan by approving or modifying the instructions.

[0860] Step 6:

[0861] The server executes instructions approved by the user. This includes adjusting schedules using project management software and reallocating resources. AI streamlines the work process in this execution.

[0862] Step 7:

[0863] The server summarizes the results and progress obtained through the above process and generates a report. The report is automatically generated and presented to the user in a visual format using graphs and charts. Based on this report, the user can develop further improvement plans.

[0864] (Application Example 1)

[0865] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0866] For users to perform their tasks efficiently, it is crucial to understand business information, identify problems, propose appropriate solutions, and implement them. However, currently, these processes are time-consuming and laborious, and it is particularly difficult to expedite information gathering and decision-making in physical stores. Under these circumstances, there is a need for a new system that provides optimal business support to users and improves operational efficiency.

[0867] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0868] In this invention, the server includes means for acquiring business information from users, means for analyzing the acquired business information and identifying problems, and means for providing advice to support in-store activities based on the business information and user requests. This enables efficient information gathering, rapid problem identification, and provision of appropriate advice in diverse business environments, thereby enabling efficient resolution of problems faced by users.

[0869] A "user" is someone who uses the system to perform their duties.

[0870] "Business information" refers to data and information regarding the progress of business operations that are necessary for carrying out those operations.

[0871] "Analysis" is the act of analyzing collected information to identify issues and problems.

[0872] "Intelligent processing" is the process of generating instructions for an identified task using artificial intelligence.

[0873] "In-store activities" refer to various activities carried out in a physical store to perform business operations.

[0874] "Advice" refers to guidelines and suggestions provided to users to improve work efficiency and solve problems.

[0875] This invention is a support system for streamlining operations in physical stores. This system allows users to input, analyze, and receive advice on business information using an application that runs on their smartphone.

[0876] The server's role is to collect and analyze business information entered by users using their smartphones. Specifically, a data analysis model using Python runs to analyze business information and identify problems. Based on the analyzed data, the server uses a generative AI model to perform intelligent processing and generate advice for business improvement.

[0877] The smartphone, acting as the terminal, is responsible for acquiring information from the user and presenting generated advice. The user inputs business information and requests through the interface on the terminal, and this information is sent to the server. The analysis results and advice from the server are presented to the user on the terminal, providing them with selectable actions.

[0878] For example, when store operations involve inputting inventory information and customer requests into the app, the server can quickly analyze that information and provide specific advice such as, "You need to replenish the stock of this particular product."

[0879] An example of a prompt is, "Ask the AI ​​assistant for advice on promotions and product placement based on current inventory levels and customer needs." In this way, users can make quick and efficient business decisions based on AI advice.

[0880] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0881] Step 1:

[0882] Users input business information and customer requests using a smartphone application. This input includes inventory status and customer service details, which then serve as the system's input. The entered data is stored in cloud storage.

[0883] Step 2:

[0884] The server accesses and collects data stored in the cloud. Next, it performs data cleaning and standardization on the collected data to prepare it for analysis. The output of this step is an analyzable dataset.

[0885] Step 3:

[0886] The server performs data analysis using a generative AI model based on an analyzable dataset. It utilizes natural language processing (NLP) and machine learning algorithms to identify business challenges and generate specific action items. For example, it might identify a particular product as a best-seller and generate advice for inventory replenishment. The output of this step is the analysis results and advice.

[0887] Step 4:

[0888] The server sends the generated analysis results and advice to the terminal. The terminal, a smartphone, displays this information in a format that is intuitively understandable to the user. The user selects the suggested action and makes a flexible decision on how to respond. In this step, the user is guided through outputs that provide options and support decision-making.

[0889] Step 5:

[0890] The device, having received user selections and feedback, resends that information to the server. This allows the server to learn from the user's decision-making history and use it to improve the model for more appropriate advice in the future. The output of this step is the feedback data necessary for model improvement.

[0891] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0892] This invention is an AI navigation system that combines an emotion engine to enable users to perform their tasks efficiently. This system automates the acquisition of work data from users, data analysis, generation of AI instructions for tasks, and provision of feedback to users.

[0893] composition

[0894] 1. Data acquisition module

[0895] User: Enters work-related data into the system. This data includes task progress and current issues.

[0896] Terminal: Saves user input to the database.

[0897] 2. Emotional Engine

[0898] Server: Incorporates an emotion engine to analyze user emotions and stress levels. This enables emotion-based data analysis.

[0899] 3. Data Analysis Module

[0900] Server: Analyzes acquired business data and combines it with user sentiment data to identify business challenges. Sentiment data is acquired through methods such as text analysis and voice analysis.

[0901] 4. AI Instruction Generation Module

[0902] Server: Based on identified issues and emotional data, artificial intelligence generates specific instructions. These instructions are adjusted according to the user's emotional state.

[0903] 5. Interactive Interface Module

[0904] Terminal: Presents generated instructions through interaction with the user and collects user feedback.

[0905] 6. Business template provision module

[0906] Server: Provides automated task templates based on instructions from artificial intelligence.

[0907] 7. Report Generation Module

[0908] Server: Creates and provides reports to users, including their work progress and stress levels. This allows users to visualize the progress of their work improvements.

[0909] Specific example

[0910] Example from a customer support center

[0911] User: A support staff member enters customer inquiry data into the system.

[0912] Server: The emotion engine analyzes the emotional state of the person in charge based on their conversation content and input data, and determines their stress level at that time.

[0913] Server: Analyzes business data and emotional data to identify issues such as "delays in responding to inquiry A and high stress levels among staff."

[0914] Terminal: Provides instructions to the person in charge, such as, "As a countermeasure for inquiry A, use a template to shorten the response time." These instructions are adjusted by adding more detail when the person in charge is under stress, and making them more concise when they are under stress.

[0915] Server: After the countermeasures are implemented, analyze their effectiveness, compile a report detailing the progress and changes in the stress levels of the person in charge, and inform the person in charge.

[0916] Through the above process, users can leverage AI while also considering their own emotions to perform tasks efficiently.

[0917] The following describes the processing flow.

[0918] Step 1:

[0919] Users input work-related information through an interface and send it to the system. This includes data such as task progress, issues, and details of daily work.

[0920] Step 2:

[0921] The terminal receives the data entered by the user and prepares to securely store it in the database. The stored data is also accompanied by an audit log to ensure that no data is missed, for use in later analysis and reporting.

[0922] Step 3:

[0923] The server analyzes stored business data and simultaneously collects related sentiment data. This sentiment data is used to evaluate emotions and stress levels using natural language processing for text analysis and speech data analysis.

[0924] Step 4:

[0925] The server integrates business data and emotional data, and uses machine learning algorithms to identify user issues. This combination reveals complex issues, such as "a specific task is consistently causing stress."

[0926] Step 5:

[0927] The device presents the identified issues to the user through an interactive interface and asks for further details or confirmation of the need for corrections. At this stage, user feedback is incorporated into the system.

[0928] Step 6:

[0929] Based on the feedback it receives, the server generates optimized AI-powered instructions. This includes considering the user's current emotional state and adjusting work instructions or changing priorities to reduce stress.

[0930] Step 7:

[0931] The device presents the generated instructions to the user, prompting them to adapt and make necessary modifications. During this process, the user reviews the instructions and prepares to execute them.

[0932] Step 8:

[0933] The server executes user-approved instructions, and the AI ​​engine initiates automated operations to improve business efficiency. Changes and the results of operations are recorded in real time and saved for future optimization.

[0934] Step 9:

[0935] The server compiles a report summarizing the AI ​​engine's results and a comprehensive evaluation of the business progress, and submits it to the user, including visual elements. The report includes an assessment of the progress of business improvements and changes in emotions, serving as a basis for considering future improvement measures.

[0936] By integrating users' work processes with their emotional states according to these processing steps, effective and reassuring business improvements become possible.

[0937] (Example 2)

[0938] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0939] In today's work environment, users need to process diverse data and solve business problems quickly. However, traditional systems have the problem of not taking into account users' emotional states or stress levels, and therefore failing to adequately improve work efficiency. Furthermore, instructions to users are uniform and cannot be flexibly adapted to the individual needs of each user, which can increase stress and decrease efficiency.

[0940] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0941] In this invention, the server includes means for analyzing the user's emotions and performing data analysis based on that state, means for generating specific instructions for artificial intelligence considering the emotional state, and means for presenting the generated instructions to the user in a way that suits them. This enables efficient work execution that takes the user's emotions into consideration.

[0942] A "user" refers to an entity that utilizes a system to input business data or select and execute instructions.

[0943] "Data" refers to all information that users input into the system as information related to their work, including information about the progress and challenges of their work, as well as information about the user's emotions.

[0944] "Analysis" refers to the process of using acquired data to analyze information and identify problems or the emotional state of users.

[0945] A "problem" refers to an issue or bottleneck that users need to address, identified through the analysis of business data.

[0946] "Instructions" refer to specific responses and solutions for the user that are generated by artificial intelligence based on the analyzed data.

[0947] "User emotions" refers to the user's emotional state and stress level, as analyzed using an emotion engine.

[0948] A "template" refers to a standardized, automatable format or procedure provided by artificial intelligence to assist with tasks.

[0949] A "report" refers to a document that summarizes the progress of work, changes in user sentiment, and other information provided to the user.

[0950] "Artificial intelligence" refers to software technology that generates instructions and suggestions for dealing with problems based on data and analysis results obtained from users.

[0951] This invention is an AI navigation system that incorporates emotion analysis to efficiently carry out the user's work. This system analyzes work data based on the user's input information, and based on the results obtained, generates instructions that take into account the user's emotional state and presents them to the user.

[0952] First, users input work-related information using a terminal. This input includes data indicating work progress, challenges, and even emotions. The terminal stores the data obtained from the user in an internal database. This database is the central part of the system and provides the foundation for data processing.

[0953] Next, the server uses an emotion engine to analyze the user's emotional state from the data they input. The emotion engine incorporates text and voice analysis algorithms to determine stress levels and emotional tendencies from the user's words and actions. This analysis allows for the assessment of potential problems and psychological burdens in the work environment.

[0954] Based on the analyzed data, the server uses a generative AI model to generate specific instructions. These instructions are adjusted according to the user's emotional state. Concise and actionable instructions are provided for high-stress situations, while more detailed instructions are provided for low-stress situations.

[0955] Ultimately, the device presents the user with generated instructions. Through an interactive interface, the user can review and execute these instructions. The device also collects feedback, which is used for subsequent analysis.

[0956] As a concrete example, in customer support operations, a user inputs a customer inquiry, and the server analyzes the representative's emotional state using an emotion engine. Based on the results, the server generates instructions such as "Use a template to respond to inquiry A promptly," which are then presented to the representative via the terminal. The level of detail in these instructions varies depending on the representative's stress level.

[0957] An example of a prompt message is, "Analyze the customer inquiry and generate a response plan that takes into account the support staff member's emotional state and stress level." This forms the basis for the system to appropriately utilize its AI model to provide the user with the most relevant information.

[0958] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0959] Step 1:

[0960] Users input work-related information through a terminal. This input data includes text about task progress, current challenges, and user sentiment. The terminal saves the entered data to a database. During database saving, data integrity is checked, and format conversion is performed as needed.

[0961] Step 2:

[0962] The server retrieves business data stored in the database and analyzes the user's emotions using an emotion engine. The input data is processed by a text analysis algorithm, and the user's emotional state and stress level are output as numerical values. In the emotion analysis, keyword analysis and emotion scoring are performed to evaluate the user's psychological state.

[0963] Step 3:

[0964] The server integrates the sentiment data and business data obtained from the analysis and runs a data mining algorithm. This identifies bottlenecks in business processes and issues that should be prioritized for resolution. The output provides a list of the analyzed issues and their associated sentiment states.

[0965] Step 4:

[0966] The server uses a generative AI model to generate instructions based on identified issues. The input consists of an analyzed list of issues and emotional data, and the output is specific countermeasures. The generated instructions are adjusted according to the user's emotional state. This process involves simplifying or complicating the instructions based on the user's stress level.

[0967] Step 5:

[0968] The terminal presents the generated instructions to the user through an interactive interface. Here, the user is presented with choices regarding the instructions, and can either follow them or provide feedback. The input is the generated instructions, and the output is the user's choices and feedback.

[0969] Step 6:

[0970] The server generates reports on work progress and emotional changes based on feedback and execution status collected from users. The generated reports include visualized graphs and progress statistics. These reports are provided to users to help them identify areas for improvement in their work and track changes in their emotional state.

[0971] (Application Example 2)

[0972] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0973] The present invention aims to support efficient work in business operations and to improve the work environment in accordance with the user's emotional state. In particular, in high-stress environments such as security work, it is necessary to appropriately manage the burden on operators and improve the quality of work.

[0974] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0975] In this invention, the server includes means for acquiring business data from a user, means for analyzing the acquired business data and identifying issues, means for determining the user's emotional state by analyzing emotions, means for generating specific instructions for artificial intelligence based on the identified issues and emotional state, means for presenting the generated instructions to the user and executing them if selected, means for monitoring the progress of the work and creating periodic reports, means for adjusting the content of the instructions based on the user's emotional state, and means for providing an automateable business template. As a result, operators are presented with appropriate instructions that respond to their emotions while reducing their workload, enabling improvements in work efficiency and quality.

[0976] A "user" is a person who uses this system to perform their duties.

[0977] "Means for acquiring business data" refers to a mechanism for collecting business-related information provided by users.

[0978] "Means for analyzing business data and identifying issues" refers to a mechanism that analyzes collected business information and extracts problems and areas for improvement.

[0979] "Means for generating instructions for artificial intelligence" refers to a mechanism that causes AI to generate appropriate countermeasures based on a specific problem.

[0980] "A means of presenting generated instructions to the user and executing them if selected" refers to a mechanism that displays instructions generated by AI to the user and executes the approved instructions.

[0981] "A means of monitoring the progress of work and generating regular reports" refers to a mechanism that tracks the progress of work and provides it to users periodically as a report.

[0982] "A means of determining a user's emotional state by analyzing their emotions" refers to a mechanism that analyzes a user's emotions and evaluates their psychological state.

[0983] "Means for adjusting the content of instructions based on the user's emotional state" refers to a mechanism that appropriately modifies the content and presentation method of instructions according to the user's emotional state.

[0984] "Means of providing automatable business templates" refers to a mechanism that provides users with standardized work procedures to streamline and improve the efficiency of their work.

[0985] The system implementing this invention is designed to support the work of security operators efficiently and while taking into consideration their emotional burden. Its specific form is described below.

[0986] The server acquires voice data and business-related information input from users. This includes real-time data monitored through devices such as sensors and cameras. This acquired data is stored in database software (e.g., MySQL) for later analysis.

[0987] Sentiment analysis uses a cloud-based sentiment analysis platform (e.g., Microsoft Azure Cognitive Services) to determine a user's emotional state from voice and text data. This analysis can be used to assess the user's stress level and psychological state.

[0988] The server combines business data and emotional data, and uses an AI instruction generation system (e.g., Google Cloud AI Platform) to generate instructions that are relevant to a specific task. An example of a prompt might be, "Analyze the operator's voice data and generate suggestions for handing over low-priority tasks when the stress level is high." This automatically generates instructions that are adapted to the user's emotional state.

[0989] The device features an interactive interface (e.g., React Native) that presents generated instructions to the user. The content and amount of information in the instructions are adjusted according to the user's mood, and the instructions are executed once accepted by the user.

[0990] As a concrete example, if a security operator experiences increased stress while monitoring a specific area, the system will suggest that another operator take over monitoring an adjacent area. This encourages the original operator to take a temporary break, thus reducing their workload. Through this process, it becomes possible to improve operational efficiency while avoiding excessive workload.

[0991] This system has the following features:

[0992] By enabling integrated analysis of business data and emotional data, we provide work instructions that are tailored to individual emotions.

[0993] By distributing tasks in a way that takes user stress management into consideration, we aim to achieve both work efficiency and psychological well-being.

[0994] In this way, we achieve highly efficient operational support that includes consideration of emotions in security operations.

[0995] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0996] Step 1:

[0997] Users input work-related information and voice data into the terminal. This input data includes task progress and details of current issues. This data is then stored directly in the database.

[0998] Step 2:

[0999] The server analyzes business data acquired from terminals. Text and speech analysis are used to extract information about the progress of tasks and identify problems. The input is business data, and the output is a list of identified issues.

[1000] Step 3:

[1001] The server uses an emotion analysis platform to analyze the user's voice data and determine their emotional state. The input is the user's voice data, and the output is numerical or categorical data indicating the emotional state.

[1002] Step 4:

[1003] The server combines business data and emotional state data and generates instructions using a generative AI model. Here, prompts are used to get the AI ​​to generate specific instructions. For example, a prompt such as "Analyze the operator's voice data and generate measures to reduce the workload when the stress level is high" might be used. The input is a list of tasks and emotional state data, and the output is the generated instructions.

[1004] Step 5:

[1005] The terminal presents the user with instructions generated by the server. The user selects an instruction, and the automation of the task proceeds based on that selection. The output is the execution of the instruction selected by the user.

[1006] Step 6:

[1007] The server continuously monitors the progress of tasks and the emotional state of users, and generates reports periodically. Inputs are task data and emotional state history, and output is a report document. This report provides users with information that visualizes the progress of task improvements and changes in their emotional state.

[1008] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1009] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1010] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1011] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1012] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1013] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1014] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1015] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1016] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1017] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1018] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1019] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1020] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1021] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1022] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1023] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1024] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1025] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1026] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1027] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1028] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1029] The following is further disclosed regarding the embodiments described above.

[1030] (Claim 1)

[1031] Means of obtaining business data from users,

[1032] A means of analyzing acquired business data and identifying problems,

[1033] A means for generating specific instructions for artificial intelligence based on identified tasks,

[1034] A means of presenting generated instructions to the user and executing them if selected,

[1035] A means of monitoring the progress of work and preparing regular reports,

[1036] A system that includes this.

[1037] (Claim 2)

[1038] The system according to claim 1, further comprising means for confirming the details of the issue with the user in an interactive manner.

[1039] (Claim 3)

[1040] The system according to claim 1, further comprising means for providing an automateable work template to assist in problem-solving using artificial intelligence.

[1041] "Example 1"

[1042] (Claim 1)

[1043] A device that receives business information from users,

[1044] A device for storing received business information,

[1045] A device that processes stored business information to identify problems,

[1046] A device that generates instructions for a specific artificial intelligence based on identified problems,

[1047] A device that presents generated instructions to the user and operates them if selected,

[1048] A device that monitors the progress of work and generates reports periodically,

[1049] A system that includes this.

[1050] (Claim 2)

[1051] The system according to claim 1, further comprising a device that allows the user to confirm the details of a problem in an interactive manner.

[1052] (Claim 3)

[1053] The system according to claim 1, further comprising a device that provides a template for tasks that can be automated in order to assist artificial intelligence in problem solving.

[1054] "Application Example 1"

[1055] (Claim 1)

[1056] A means of obtaining business information from users,

[1057] A means of analyzing acquired business information and identifying problems,

[1058] A means for generating instructions for specific intellectual processing based on an identified task,

[1059] A means of presenting generated instructions to the user and executing them if selected,

[1060] A means of monitoring the progress of work and preparing regular reports,

[1061] A means of providing advice to support in-store activities based on business information and user requests,

[1062] A system that includes this.

[1063] (Claim 2)

[1064] The system according to claim 1, further comprising means for confirming the details of the issue with the user in an interactive manner.

[1065] (Claim 3)

[1066] The system according to claim 1, further comprising means for providing an automatable work template to assist in solving problems through intelligent processing.

[1067] "Example 2 of combining an emotion engine"

[1068] (Claim 1)

[1069] Means of obtaining data from users,

[1070] A means of analyzing the acquired data and identifying the problem,

[1071] A means for generating specific instructions for artificial intelligence based on identified problems,

[1072] A means of presenting generated instructions to the user and executing them if selected,

[1073] A means of analyzing emotions and performing data analysis based on the user's state,

[1074] A means of monitoring the progress of work and preparing regular reports,

[1075] A system that includes this.

[1076] (Claim 2)

[1077] The system according to claim 1, further comprising means for confirming details with the user in an interactive manner.

[1078] (Claim 3)

[1079] The system according to claim 1, further comprising means for providing an automateable template to assist in solving problems using artificial intelligence.

[1080] "Application example 2 of combining emotional engines"

[1081] (Claim 1)

[1082] Means of obtaining business data from users,

[1083] A means of analyzing acquired business data and identifying problems,

[1084] A means for generating specific instructions for artificial intelligence based on identified tasks,

[1085] A means of presenting generated instructions to the user and executing them if selected,

[1086] A means of monitoring the progress of work and preparing regular reports,

[1087] A means of determining a user's emotional state by analyzing their emotions,

[1088] A means of adjusting the content of instructions based on the user's emotional state,

[1089] A system that includes this.

[1090] (Claim 2)

[1091] The system according to claim 1, further comprising means for confirming the details of the issue with the user in an interactive manner.

[1092] (Claim 3)

[1093] The system according to claim 1, further comprising means for providing an automateable work template to assist in problem-solving using artificial intelligence. [Explanation of Symbols]

[1094] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Means of obtaining business data from users, A means of analyzing acquired business data and identifying problems, A means for generating specific instructions for artificial intelligence based on identified tasks, A means of presenting generated instructions to the user and executing them if selected, A means of monitoring the progress of work and preparing regular reports, A system that includes this.

2. The system according to claim 1, further comprising means for confirming the details of the issue with the user in an interactive manner.

3. The system according to claim 1, further comprising means for providing an automateable work template to assist in problem-solving using artificial intelligence.

Images