system

Abstract

We provide the system. [Solution] Means for collecting and analyzing business information, A means of identifying and organizing issues through dialogue with users, A means of presenting the generated policies to the user and executing the selected policy, A means of generating and providing reports on progress and problems, Means to support efficiency improvements in the production environment, A means of communicating with robots and executing optimal action plans, A system that includes this.

Description

Technical Field

[0001] The technology of this 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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the field, due to the complexity of operations, it has become difficult to manage tasks and identify problems, and there is a demand for efficient problem-solving by utilizing generative artificial intelligence. However, since the usage method of generative artificial intelligence is difficult for users to understand, it has not been effectively utilized. Therefore, there is a need for a system that can automatically analyze the business problems faced by users, present and execute clear countermeasures, and efficiently solve problems.

Means for Solving the Problems

[0005] This invention provides means for collecting and analyzing business data, and for identifying and organizing business challenges through dialogue with the user. It also includes means for presenting the user with specific countermeasures proposed by generative artificial intelligence for the analyzed challenges, and for automatically executing the countermeasures selected by the user. Furthermore, by including means for generating and providing reports on business progress and problems to the user, the user can improve efficiency in problem solving. Through these means, the user can easily utilize generative artificial intelligence to visualize and efficiently solve problems.

[0006] "Business data" refers to records of information and activities related to a user's business, and includes digital data that is subject to analysis.

[0007] "Analysis" is the act of processing collected data and information using machine learning algorithms and other analytical methods to extract necessary patterns and insights.

[0008] "Dialogue" refers to the communication process between the user and the system, including the exchange of questions and answers to clarify business issues.

[0009] "Generative artificial intelligence" is an artificial intelligence technology that generates predictions and suggestions based on large-scale datasets, and has the ability to present concrete solutions for solving problems.

[0010] A "solution plan" is a proposal for specific methods and procedures designed to solve an identified problem or issue.

[0011] A "report" is a document that summarizes information about the progress, problems, and achievements of a project, and is a collection of information provided to the user. [Brief explanation of the drawing]

[0012] [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]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a labeled 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.

[0016] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a labeled 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.

[0018] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] The system based on this invention efficiently manages the user's work and supports problem-solving. The main components of this system include a server that processes business data, a terminal that acts as an interface with the user, and the user themselves.

[0034] The server's primary role is to collect information from the business database and then analyze that data. Here, machine learning algorithms are used to identify relevant patterns and anomalies within the data. The results of this analysis are then used to identify issues through interaction with users.

[0035] The terminal serves as an interface for communication with users. It sends questions to users in a conversational format, eliciting feedback on their work environment and challenges. This allows users to specifically identify problems and areas for improvement in their work.

[0036] Based on the collected information, the server uses generative artificial intelligence to generate effective countermeasures. This process incorporates predictions based on past cases and data, and these are presented to the user. The proposed countermeasures are concrete and actionable, providing a useful reference for the user's decision-making.

[0037] Once the user selects a specific countermeasure, the server takes on the role of implementing that countermeasure. This is done through process coordination within the system and coordination with external resources, ensuring that the selected countermeasure is implemented quickly and effectively.

[0038] Furthermore, the server continuously monitors the progress of tasks and periodically generates reports on problems and achievements. These reports are provided to users via their terminals, serving as a basis for self-assessment and planning the next steps.

[0039] As a concrete example, suppose a project at a certain company is behind schedule. By utilizing this system, the server analyzes data and identifies the cause of the delay. Subsequently, interaction with the user takes place via a terminal, and the underlying causes are uncovered. Generative artificial intelligence proposes appropriate countermeasures, and once the user selects one, implementation begins immediately. Finally, the server generates a report and provides it to the user in a format that makes it easy to understand the situation.

[0040] Thus, the system of the present invention provides a comprehensive solution for enhancing business efficiency and problem-solving.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server periodically collects necessary business data from the business database. It uses APIs and SQL queries to retrieve relevant task information and business logs and stores them in data storage.

[0044] Step 2:

[0045] The server analyzes the collected data using machine learning algorithms. It identifies patterns and anomalies within the data and generates a task management table based on them. The analysis results can be used to identify problems.

[0046] Step 3:

[0047] The device initiates a conversation with the user. It utilizes chatbot functionality to send questions to the user regarding business challenges and collects the user's responses.

[0048] Step 4:

[0049] The server compares the user's responses obtained through the terminal with pre-analyzed data to identify and organize specific challenges in the user's work.

[0050] Step 5:

[0051] The server uses a generative artificial intelligence model to generate specific solutions to the identified problems. It lists solutions deemed highly effective, referencing past data and successful case studies.

[0052] Step 6:

[0053] The terminal presents the user with proposed solutions sent from the server. The user selects the solution that best suits their work from among the presented options.

[0054] Step 7:

[0055] The server automatically initiates the necessary procedures to implement the measures selected by the user. It manages the execution process by coordinating with external resources and related systems.

[0056] Step 8:

[0057] The server monitors the progress of the implemented measures and evaluates the results. If problems are found, it makes adjustments and corrects as needed.

[0058] Step 9:

[0059] The server compiles detailed reports on work progress and achievement levels. These generated reports are then provided to users via their terminals through data visualization.

[0060] Step 10:

[0061] Based on the presented report, users consider areas for improvement in their operations and next steps, and request additional measures as needed.

[0062] Through these steps, the system provides users with efficient business management and effective problem-solving.

[0063] (Example 1)

[0064] 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."

[0065] In today's business environment, efficient business management and rapid problem-solving are crucial elements. However, previous systems were unable to comprehensively handle everything from data collection and analysis to problem identification, solution proposal, and implementation support. As a result, users had to use multiple tools, leading to complex and inefficient operations.

[0066] 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.

[0067] In this invention, the server includes a device for collecting and analyzing information, a device for identifying and organizing issues through interaction with users, and a device for presenting generated solutions to users and executing the selected solution. This enables comprehensive and efficient management of business data and a rapid and effective problem-solving process.

[0068] "Information" refers to data, knowledge, and other content related to the business.

[0069] "Analysis" refers to the act of identifying useful patterns or anomalies within data by thoroughly investigating and analyzing information.

[0070] "Device" refers to hardware or software components installed to perform a function.

[0071] A "user" refers to a person who operates this system and provides information and instructions for business management and problem-solving.

[0072] "Dialogue" refers to the exchange of information between the user and the system, and usually proceeds in the form of a question-and-answer session.

[0073] "Identifying and analyzing" refers to conducting a detailed investigation to uncover hidden problems or potential issues.

[0074] A "solution" refers to a specific method or means devised to solve a particular problem or issue.

[0075] "Presentation" refers to the act of communicating information or results to users visually or audibly.

[0076] "Selection" refers to the act of deciding on the most appropriate option from among several presented choices.

[0077] A "report" refers to a document that compiles and organizes information regarding progress and problems.

[0078] A "generative AI model" refers to the entire algorithm and system used to realize generative artificial intelligence.

[0079] A "prompt statement" refers to an input statement used to give instructions or questions to a generative AI model.

[0080] This system is designed to enable users to efficiently manage their work and quickly resolve issues. The implementation of the invention is primarily achieved through the interaction of servers, terminals, and users.

[0081] The server is the core of this system, responsible for collecting and analyzing information. Specifically, the server extracts information from business databases and analyzes the data using machine learning algorithms. In this process, clustering techniques and regression analysis are used to identify relevant patterns and anomalies within the data. The server also utilizes generative artificial intelligence models to generate solutions and prepares them for provision to users. In generating solutions, past data and case studies are considered, and highly reliable predictions are incorporated.

[0082] On the other hand, the terminal functions as an interface connecting the user and the server. The terminal is a device that receives input from the user and transmits it to the server. It also plays a role in presenting solutions provided by the server to the user and prompting them to make a choice. The terminal implements a user interface that prioritizes ease of use, allowing for the sending and receiving of information with simple operations.

[0083] Users input business information into the system via a terminal and consider the generated solutions. The interactive interface allows users to organize their issues and select specific solutions. For example, if a user wants to improve project progress, they can input a prompt such as "What are effective ways to improve project progress?" into the system and receive appropriate solutions.

[0084] In this way, servers, terminals, and users work together to achieve effective business management and problem-solving.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] Users use a terminal to input their work information and the challenges they are currently facing. This input includes the progress of their work, specific problems, and areas for improvement. The terminal then sends this information received from the user to the server.

[0088] Step 2:

[0089] The server receives business data sent from the terminal and begins analysis. It accesses the database, verifies the data's up-to-dateness, and then processes it through machine learning algorithms. Here, clustering and regression analysis are used to extract relevant patterns and anomalies from the data. The analysis results are stored on the server for subsequent processing.

[0090] Step 3:

[0091] The server generates solutions using a generative AI model based on the analyzed data. During this process, it considers similar past cases and data as prompt inputs, asking questions such as, "What are effective ways to improve project progress?" The generative AI model responds to these prompts by generating specific solutions and returning them to the server.

[0092] Step 4:

[0093] The generated solutions are sent from the server to the terminal. The terminal displays this information in a user-friendly format and prompts the user to select the most appropriate solution. The user selects a solution based on the displayed information, using clicks or other actions.

[0094] Step 5:

[0095] The solution selected by the user is sent from the terminal to the server. Based on the received selection information, the server adjusts the resources within the system and puts the solution into action. If necessary, it also coordinates with external resources and applications to ensure that the execution process proceeds smoothly.

[0096] Step 6:

[0097] The server continuously monitors the implementation status of the solution and periodically generates reports on progress and results. These reports are provided to the user via their terminal. The user can then use these reports to consider their next course of action.

[0098] (Application Example 1)

[0099] 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."

[0100] Improving productivity and efficiency in factories requires real-time problem analysis and rapid implementation of countermeasures, but this has been difficult with conventional systems. Furthermore, maximizing the use of robots in the production environment requires dynamic and flexible planning and execution, but current methods are insufficient to address this.

[0101] 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.

[0102] In this invention, the server includes means for collecting and analyzing business information, means for identifying and organizing issues through dialogue with the user, and means for presenting generated solutions to the user and executing the selected solution. This enables real-time problem analysis and implementation of countermeasures in the production environment, making it possible to effectively utilize robots and improve productivity.

[0103] "Business information" refers to data and events related to the activities and processes that companies and organizations handle on a daily basis.

[0104] "Machine learning techniques" are technologies that allow computers to recognize patterns from data and automatically learn, predict, and optimize.

[0105] A "user interface" refers to the screens and input methods that allow a user to directly interact with a system.

[0106] A "strategy" is a specific action plan or measure proposed by a system to achieve a particular objective.

[0107] "Production environment" refers to the physical and operational conditions in a factory or manufacturing facility where products are processed and assembled.

[0108] A "robot" is a mechanical device designed and programmed to perform a specific task automatically.

[0109] An "action plan" is a schedule or set of procedures for efficiently and sequentially executing a series of actions that a system or robot will take.

[0110] The system implementing this invention is designed to improve the efficiency of business activities in a factory. The server collects business information from the production floor and performs analysis based on this information. The analysis uses TENSORFLOW®, a machine learning method that runs on cloud services such as AWS® Lambda. Based on the analysis results, the server uses a generative AI model to propose specific strategies to the user. The proposed strategies are processed by a generative AI model such as OpenAI® GPT and presented to the user's terminal.

[0111] The devices used by users are smartphones, tablets, etc., and they interact with the server through a user interface. This interface provides a means for users to select and confirm proposed strategies. This allows users to choose strategies of their own volition and make efficient decisions.

[0112] The selected strategy is immediately executed by the server. During the execution phase, the robot in the production environment and the server communicate, and the robot's action plan is optimized and implemented via the MQTT protocol. This allows for rapid improvement in productivity.

[0113] As a concrete example, if the supply of a specified part is disrupted at a factory, the server identifies the cause through data analysis, and a generated AI model proposes an alternative method. Once the user approves the execution, the robot immediately selects a new path and resumes supply. This allows the production line to quickly return to normal.

[0114] An example of a prompt message is, "Based on the data analysis results from the production line, please suggest the optimal parts supply route and tell me how to improve production efficiency." This allows the system to provide appropriate solutions in response to the user's request.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The server collects operational information from various sensors and input terminals within the factory. This information includes production speed on the production line, parts supply status, and equipment operation data. The collected data is stored in the production management system. The input here is real-time data from sensors and terminals. As output, all information is saved in a database before analysis.

[0118] Step 2:

[0119] The server retrieves accumulated business information from the database using AWS Lambda and analyzes it using machine learning techniques (TensorFlow). The goal of the analysis is to identify patterns indicating performance degradation or anomalies. In this step, business information from the database is used as input, and the location of bottlenecks and anomalies is identified as output.

[0120] Step 3:

[0121] Based on the analysis results, the server generates specific policy proposals using a generative AI model (OpenAI GPT). The generated proposals include measures to improve operational efficiency and address anomalies. In this step, the input is the analysis results from step 2, and the output is the proposed policy statement. The proposals are generated in a language that is easy for the user to understand.

[0122] Step 4:

[0123] The terminal displays a list of proposed strategies in the user interface. The user reviews this list and selects a specific strategy based on their own judgment. The input is the generated strategy statement, and the strategy selected by the user is output. The terminal is responsible for presenting options and accepting user selection input.

[0124] Step 5:

[0125] The server executes the policy selected by the user. It communicates the optimal action plan to the robots in the production environment via the MQTT protocol, causing the robots to perform the configured actions. The input is the policy selected by the user, and the output is an improvement in the ongoing production activity. This process makes the robots' movements more efficient.

[0126] Step 6:

[0127] The server monitors the progress and achievement of the implemented measures and generates reports periodically. These reports are sent to terminals, allowing users to accurately understand the improvements being made to the production environment. Inputs are feedback data from robots and production activities, and outputs are status reports for the user.

[0128] 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.

[0129] This invention improves the quality of dialogue by combining a system that streamlines business management and supports problem-solving with an emotion engine that recognizes user emotions. The system's main components are a server that processes business data, a terminal that functions as an interface with the user, and the user themselves.

[0130] The server collects and analyzes necessary data from the business database. Machine learning algorithms are used to identify patterns and anomalies in the business data, extracting meaningful information. The obtained information is then used to organize the issues identified through interaction with users and to formulate solutions.

[0131] The terminal serves as an interface that supports communication with the user, obtaining user feedback through dialogue. In this process, an emotion engine is utilized to analyze the user's emotional state. The terminal infers emotions from voice tone and input data, and sends this information to the server.

[0132] The server analyzes the user's emotional state and generates optimal solutions. These solutions are adjusted according to the user's emotions and current work situation, making them more effective and acceptable. The terminal then presents these solutions to the user and prompts them to make a choice.

[0133] For example, if a user is experiencing excessive stress, the emotion engine will detect this and prioritize suggesting measures to reduce stress. These measures may include task redistribution and scheduling, and the server will automatically arrange the necessary resources for their execution.

[0134] Furthermore, the server evaluates work progress and the quality of deliverables, including from an emotional perspective, and generates detailed reports. This allows users to understand the impact of their emotional state on their work and use that information to implement improvement measures.

[0135] Thus, the present invention provides a system that improves operational efficiency and user satisfaction by incorporating an emotion engine and enabling individualized responses according to the user's emotional state.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The server collects business data, such as tasks and logs, from the business database. This includes a data collection process based on a regular schedule.

[0139] Step 2:

[0140] The server analyzes the collected business data using machine learning algorithms. Based on the analysis results, it identifies patterns and anomalies in the data and understands the user's work situation.

[0141] Step 3:

[0142] The device initiates interaction with the user. It presents questions through the interface and collects information about the user's work-related challenges.

[0143] Step 4:

[0144] The device activates an emotion engine to analyze the user's emotions from their voice tone and text input. The emotion engine determines the emotional state and sends the result to the server.

[0145] Step 5:

[0146] The server integrates emotional information with analyzed business data to identify user issues. It then generates specific solutions to these issues and adjusts them optimally according to the user's emotional state.

[0147] Step 6:

[0148] The device presents the generated countermeasures to the user. The user can select the most suitable countermeasure from multiple options.

[0149] Step 7:

[0150] The server initiates the specific procedures for implementing the selected countermeasures. It arranges the necessary resources and coordinates with external systems, aiming for immediate execution.

[0151] Step 8:

[0152] The server monitors the progress of implemented countermeasures and performs evaluations that include sentiment information. It fine-tunes the countermeasures as needed to optimize the results.

[0153] Step 9:

[0154] The server generates detailed reports on the progress of tasks and the emotional state of users. These reports are provided to users via their terminals.

[0155] Step 10:

[0156] Based on the provided report, users will decide on the next steps to improve their work and emotional state.

[0157] This system supports business efficiency and effective problem-solving by taking into account the user's emotional state through an emotion engine.

[0158] (Example 2)

[0159] 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".

[0160] The present invention aims to achieve efficient problem-solving in business management while considering the emotional aspects of users. Conventional business management systems have the problem of not being able to reflect the emotional state of users, and thus failing to contribute to improving business efficiency and user satisfaction. Therefore, there is a need for a system that can detect the emotional state of users and reflect it in business decision-making.

[0161] 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.

[0162] In this invention, the server includes means for collecting and analyzing business resources, means for identifying and organizing issues through interaction with the user, means for presenting generated solutions to the user and implementing the selected solution, means for evaluating progress and problems and creating reports, means for recognizing and analyzing the user's emotional state, and means for generating optimal countermeasures that take into account the user's emotional information using a generative AI model. This enables individualized responses according to the user's emotional state, as well as improvements in business efficiency and user satisfaction.

[0163] "Business resources" refer to the information and data necessary for an organization or individual to carry out its work.

[0164] "Collection" is the process of gathering data and information based on a specific purpose.

[0165] "Analysis" is the act of analyzing collected data and information to understand and clarify its meaning and trends.

[0166] A "user" is an individual or organization that uses a system or service.

[0167] "Interaction" refers to the exchange of information and instructions between a user and a system.

[0168] A "challenge" refers to a problem or challenge that needs to be solved.

[0169] "Identification" means clearly defining the issue or subject and identifying it.

[0170] "Organization" refers to the process of arranging and compiling information and data in an orderly manner.

[0171] A "solution" refers to a method or means of solving an identified problem or issue.

[0172] "Execution" means actually putting plans and measures into practice.

[0173] "Progress status" refers to the progress toward the completion of a task or project.

[0174] A "problem" refers to a failure or malfunction that occurs within a business or system.

[0175] "Evaluation" is the act of judging the value and significance of an object and determining the outcome.

[0176] A "report" is a document that compiles information on a specific topic or issue.

[0177] "Emotional state" refers to an individual's emotional and psychological state at a given point in time.

[0178] "Recognition" means grasping and understanding emotions and situations.

[0179] A "generative AI model" refers to a collection of algorithms designed based on artificial intelligence to create new information and data.

[0180] "Taking into account" means adjusting the whole by taking certain factors or information into consideration.

[0181] "Optimal" means being the most appropriate and effective.

[0182] A "proposed solution" refers to specific policies or plans to be implemented in response to anticipated problems or existing challenges.

[0183] "Individualized support" means implementing specific countermeasures tailored to each user and their specific problem.

[0184] "Business efficiency" refers to the efficiency and productivity of tasks performed in carrying out business operations.

[0185] "User satisfaction" refers to an evaluation of the degree of satisfaction users feel when using a service or system.

[0186] This invention relates to a system for improving the efficiency of business management and providing individualized support that takes into account the emotional state of users. This system analyzes business data and combines it with user emotions to provide the optimal solution.

[0187] The server's role is to collect and analyze business resources. It retrieves necessary information from the business database and analyzes the data using machine learning algorithms. Specifically, it can utilize anomaly detection algorithms and clustering algorithms. From the analyzed data, it identifies important business trends and anomalies. Then, it uses a generative AI model to generate optimal countermeasures that take into account user sentiment. For example, when considering measures to reduce the workload, it uses the prompt message "Consider the current sentiment state and generate recommended measures to reduce the user's workload" to create countermeasures.

[0188] The terminal functions as an interface with the user. It receives voice and text input from the user and processes the input using speech recognition software. The terminal converts voice data into text and analyzes the user's emotional state using an emotion engine. The analyzed emotional information is sent to a server and used to generate countermeasures.

[0189] Users receive proposed solutions via their terminals and select the one best suited to their work. The user's selection is fed back to the server via the terminal, and the selected solution is implemented by the system. The overall effect of this system is that users receive personalized support tailored to their emotional state, leading to improved work efficiency and user satisfaction.

[0190] Thus, the present invention improves business management processes and provides an efficient and emotionally sensitive interactive system through the analysis of business data and the generation of countermeasures that take into account user emotional information.

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The server retrieves business resources from the business database. Input is a data retrieval request, such as an SQL query. Output is the retrieved business data. After retrieving the data, the server analyzes it using machine learning algorithms (e.g., anomaly detection algorithms) to identify useful patterns and anomalies. This analysis process involves processing large amounts of data and performing statistical analysis to obtain the analysis results as output.

[0194] Step 2:

[0195] The device accepts voice input to collect user feedback. The input is the user's voice data. The device converts this voice data into text data using speech recognition software. Furthermore, it uses an emotion engine to infer and analyze the user's emotional state from this text data and voice tone. The output is the analyzed emotion information. This emotion information is crucial for understanding the context of the conversation.

[0196] Step 3:

[0197] The server receives emotional information sent from the terminal. The input consists of the user's emotional information and the results of the work analysis. Based on this information, the server uses a generative AI model to generate optimal countermeasures that take the user's emotional information into account. The prompt used is "Consider the current emotional state and generate recommended measures to reduce the user's workload." The output is the generated countermeasures.

[0198] Step 4:

[0199] The terminal receives proposed solutions sent from the server. The input is the generated solutions. The terminal presents these solutions to the user and prompts them to make a choice through an intuitively understandable interface. Output feedback is obtained by receiving user selection feedback.

[0200] Step 5:

[0201] The server prepares to implement the action plan selected by the user. The input is the user's selection. Based on this, the server adjusts the relevant business resources and performs scheduling and task reallocation. The output is the business tasks that have been put into action and their progress information.

[0202] Step 6:

[0203] The server monitors the progress of tasks and generates reports that include emotional perspectives. Inputs include progress data and emotional information. The server integrates these to evaluate the overall work situation and outputs a report. This report is provided to the user via a terminal, allowing them to understand the impact of their emotions on their work and use it as a reference for improvement measures.

[0204] (Application Example 2)

[0205] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as a "server," and the smart device 14 will be referred to as a "terminal." Conventional business management systems have a problem in that they proceed with work without adequately considering the emotional state of the user, leading to the accumulation of user stress and dissatisfaction, and a decrease in work efficiency and user satisfaction. To solve this problem, it is necessary to accurately analyze the emotional state of the user and provide appropriate countermeasures based on that analysis.

[0206] (Means for solving the problem)

[0207] In this invention, the server includes means for collecting and analyzing business data, means for identifying and organizing issues through interaction with the user, and means for analyzing the user's emotional state and proposing an appropriate care plan based on those emotions. This enables flexible care that responds to the user's emotions.

[0208] 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.

[0209] Conventional business management systems often fail to adequately consider users' emotional states, leading to the accumulation of user stress and dissatisfaction, and ultimately reducing work efficiency and user satisfaction. To solve this problem, it is necessary to accurately analyze users' emotional states and provide appropriate countermeasures based on that analysis.

[0210] (Means for solving the problem)

[0211] In this invention, the server includes means for collecting and analyzing business data, means for identifying and organizing issues through interaction with the user, and means for analyzing the user's emotional state and proposing an appropriate care plan based on those emotions. This enables flexible care that responds to the user's emotions.

[0212] "Business data" refers to a collection of information generated from the daily activities of an organization or company, and is used to improve and streamline operations.

[0213] "Means of analysis" refer to methods and processes for analyzing collected data in detail using various technologies and extracting meaningful information.

[0214] A "user" refers to an individual or organization that interacts with or operates through this system, and is primarily the recipient of the services and information provided by the system.

[0215] "Dialogue" is the process by which a user and a system exchange information, and it is carried out through voice or text.

[0216] "Methods for identifying and organizing issues" refer to methods and processes for finding problems and areas for improvement through dialogue with users, and then effectively structuring them.

[0217] "Emotional state" refers to the psychological and physiological state a user is experiencing at a given point in time, and is composed of elements such as stress and a sense of security.

[0218] A "care plan" is a set of plans and ideas that propose the most appropriate actions and treatments according to the user's emotions and circumstances.

[0219] "Means of suggestion" refers to methods and functions that, based on analyzed information, present users with the most suitable solutions or action plans.

[0220] The system for implementing this invention streamlines business management by recognizing the user's emotions and proposing an appropriate care plan. The system mainly consists of three elements: a server, a terminal, and the user.

[0221] The server is the primary component for analyzing business data and user emotional states. The server utilizes machine learning algorithms such as Google® Cloud Speech-to-Text API and TensorFlow for data analysis. The server collects speech data and performs calculations for emotion recognition. Based on this information, it generates and presents the most suitable care plan for the user.

[0222] The terminal functions as an interface with the user and transmits data such as voice to the server. Based on the voice data collected from the user, it uses an emotion engine to infer the user's emotional state in real time and transmits it to the server. The terminal provides the user with intuitive and convenient operation.

[0223] Users interact with the server using their devices and receive feedback tailored to their emotional state and work situation. The care plans proposed to the users are tailored to their emotional state, such as suggestions for activities to reduce stress.

[0224] As a concrete example, consider a situation where a user feels anxious after lunch. In this case, the system analyzes the user's voice tone and feedback and determines that they are feeling anxious. The server then generates a care plan recommending a walk and proposes it to the user via the terminal.

[0225] An example of a prompt for a generative AI model could be: "Please suggest appropriate activities when the user feels anxious. List options for activities that reduce stress and briefly explain the effect of each."

[0226] This invention provides a system that utilizes an emotion engine to efficiently improve user satisfaction.

[0227] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0228] Step 1:

[0229] The device collects the user's voice data through the microphone. The input data is captured as an audio file. This data is temporarily stored locally in preparation for transmission to the server in real time.

[0230] Step 2:

[0231] The server receives audio data sent from the terminal. It uses the Google Cloud Speech-to-Text API to convert the received audio data into text. The input is an audio file, and the output is parsed text data. The API generates highly accurate text data from audio.

[0232] Step 3:

[0233] The server performs sentiment analysis using the obtained text data. This process applies a sentiment analysis model utilizing TensorFlow. The input is text data, and the output is the user's emotional state (e.g., "anxiety," "stress"). The analysis model extracts emotion-related keywords from the text and identifies the emotional state.

[0234] Step 4:

[0235] The server generates an optimal care plan for the user based on the results of an analysis of their emotional state. The care plan generation uses a generative AI model that incorporates past work data and emotional analysis results. The input is the user's emotional state and work data, and the output is a specific care plan (e.g., "Suggestion for a walk").

[0236] Step 5:

[0237] The terminal presents the user with a care plan sent from the server. The proposal is displayed visually on the terminal's screen and also provides auditory guidance using the voice output function. The user can review the presented care plan and, if necessary, select or begin implementation.

[0238] 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.

[0239] Data generation model 58 is a type of 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.

[0240] 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.

[0241] [Second Embodiment]

[0242] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0243] 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.

[0244] 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).

[0245] 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.

[0246] 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.

[0247] 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).

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] In the smart glasses 214, 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.

[0253] Next, the identification processing performed by the identification 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 glasses 214 will be referred to as the "terminal".

[0254] The system based on this invention efficiently manages the user's work and supports problem-solving. The main components of this system include a server that processes business data, a terminal that acts as an interface with the user, and the user themselves.

[0255] The server's primary role is to collect information from the business database and then analyze that data. Here, machine learning algorithms are used to identify relevant patterns and anomalies within the data. The results of this analysis are then used to identify issues through interaction with users.

[0256] The terminal serves as an interface for communication with users. It sends questions to users in a conversational format, eliciting feedback on their work environment and challenges. This allows users to specifically identify problems and areas for improvement in their work.

[0257] Based on the collected information, the server uses generative artificial intelligence to generate effective countermeasures. This process incorporates predictions based on past cases and data, and these are presented to the user. The proposed countermeasures are concrete and actionable, providing a useful reference for the user's decision-making.

[0258] Once the user selects a specific countermeasure, the server takes on the role of implementing that countermeasure. This is done through process coordination within the system and coordination with external resources, ensuring that the selected countermeasure is implemented quickly and effectively.

[0259] Furthermore, the server continuously monitors the progress of tasks and periodically generates reports on problems and achievements. These reports are provided to users via their terminals, serving as a basis for self-assessment and planning the next steps.

[0260] As a concrete example, suppose a project at a certain company is behind schedule. By utilizing this system, the server analyzes data and identifies the cause of the delay. Subsequently, interaction with the user takes place via a terminal, and the underlying causes are uncovered. Generative artificial intelligence proposes appropriate countermeasures, and once the user selects one, implementation begins immediately. Finally, the server generates a report and provides it to the user in a format that makes it easy to understand the situation.

[0261] Thus, the system of the present invention provides a comprehensive solution for enhancing business efficiency and problem-solving.

[0262] The following describes the processing flow.

[0263] Step 1:

[0264] The server periodically collects necessary business data from the business database. It uses APIs and SQL queries to retrieve relevant task information and business logs and stores them in data storage.

[0265] Step 2:

[0266] The server analyzes the collected data using machine learning algorithms. It identifies patterns and anomalies within the data and generates a task management table based on them. The analysis results can be used to identify problems.

[0267] Step 3:

[0268] The device initiates a conversation with the user. It utilizes chatbot functionality to send questions to the user regarding business challenges and collects the user's responses.

[0269] Step 4:

[0270] The server compares the user's responses obtained through the terminal with pre-analyzed data to identify and organize specific challenges in the user's work.

[0271] Step 5:

[0272] The server uses a generative artificial intelligence model to generate specific solutions to the identified problems. It lists solutions deemed highly effective, referencing past data and successful case studies.

[0273] Step 6:

[0274] The terminal presents the user with proposed solutions sent from the server. The user selects the solution that best suits their work from among the presented options.

[0275] Step 7:

[0276] The server automatically initiates the procedures necessary to execute the countermeasures selected by the user. It coordinates with external resources and related systems and manages the execution process.

[0277] Step 8:

[0278] The server monitors the progress of the executed countermeasures and evaluates the execution results. If problems are found, appropriate adjustments are made and necessary corrections are added.

[0279] Step 9:

[0280] The server summarizes the business progress and achievement level in detail as a report. The generated report is provided to the user via the terminal through data visualization.

[0281] Step 10:

[0282] Based on the presented report, the user considers the improvement points and next actions of the business and requests new countermeasures if necessary.

[0283] Through these steps, the system provides the user with efficient management of the business and effective problem-solving.

[0284] (Example 1)

[0285] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0286] In the modern business environment, efficient management of business and rapid problem-solving are important elements. However, conventional systems have not been able to comprehensively perform from data collection and analysis to issue identification, solution presentation, and execution support. For this reason, users have had to use multiple tools, and there has been a problem that the operation is complex and inefficient.

[0287] 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.

[0288] In this invention, the server includes a device for collecting and analyzing information, a device for identifying and organizing issues through interaction with users, and a device for presenting generated solutions to users and executing the selected solution. This enables comprehensive and efficient management of business data and a rapid and effective problem-solving process.

[0289] "Information" refers to data, knowledge, and other content related to the business.

[0290] "Analysis" refers to the act of identifying useful patterns or anomalies within data by thoroughly investigating and analyzing information.

[0291] "Device" refers to hardware or software components installed to perform a function.

[0292] A "user" refers to a person who operates this system and provides information and instructions for business management and problem-solving.

[0293] "Dialogue" refers to the exchange of information between the user and the system, and usually proceeds in the form of a question-and-answer session.

[0294] "Identifying and analyzing" refers to conducting a detailed investigation to uncover hidden problems or potential issues.

[0295] A "solution" refers to a specific method or means devised to solve a particular problem or issue.

[0296] "Presentation" refers to the act of communicating information or results to users visually or audibly.

[0297] "Selection" refers to the act of deciding on the most appropriate option from among several presented choices.

[0298] A "report" refers to a document that compiles and organizes information regarding progress and problems.

[0299] A "generative AI model" refers to the entire algorithm and system used to realize generative artificial intelligence.

[0300] A "prompt statement" refers to an input statement used to give instructions or questions to a generative AI model.

[0301] This system is designed to enable users to efficiently manage their work and quickly resolve issues. The implementation of the invention is primarily achieved through the interaction of servers, terminals, and users.

[0302] The server is the core of this system, responsible for collecting and analyzing information. Specifically, the server extracts information from business databases and analyzes the data using machine learning algorithms. In this process, clustering techniques and regression analysis are used to identify relevant patterns and anomalies within the data. The server also utilizes generative artificial intelligence models to generate solutions and prepares them for provision to users. In generating solutions, past data and case studies are considered, and highly reliable predictions are incorporated.

[0303] On the other hand, the terminal functions as an interface connecting the user and the server. The terminal is a device that receives input from the user and transmits it to the server. It also plays a role in presenting solutions provided by the server to the user and prompting them to make a choice. The terminal implements a user interface that prioritizes ease of use, allowing for the sending and receiving of information with simple operations.

[0304] The user inputs business information into the system via the terminal and considers the generated solutions. Through the interactive interface, the user can organize their problems and select specific solutions. For example, if the user wants to improve the progress of a project, they can input a prompt sentence such as "What are the effective ways to improve the progress of the project?" into the system and receive an appropriate solution.

[0305] In this way, the server, the terminal, and the user work together to achieve effective business management and problem-solving.

[0306] The flow of specific processing in Example 1 will be described using FIG. 11.

[0307] Step 1:

[0308] The user uses the terminal to input their business information and the problems they are currently facing. This input includes the progress of the business, specific problems, and points to be improved. The terminal sends this information received from the user to the server.

[0309] Step 2:

[0310] The server receives the business data sent from the terminal and starts analyzing it. It accesses the database, checks the currency of the data, and then passes it through a machine learning algorithm. Here, clustering and regression analysis are used to extract relevant patterns and anomalies from the data. The analysis results are stored in the server for subsequent processing.

[0311] Step 3:

[0312] The server generates solutions using a generative AI model based on the analyzed data. During this process, it considers similar past cases and data as prompt inputs, asking questions such as, "What are effective ways to improve project progress?" The generative AI model responds to these prompts by generating specific solutions and returning them to the server.

[0313] Step 4:

[0314] The generated solutions are sent from the server to the terminal. The terminal displays this information in a user-friendly format and prompts the user to select the most appropriate solution. The user selects a solution based on the displayed information, using clicks or other actions.

[0315] Step 5:

[0316] The solution selected by the user is sent from the terminal to the server. Based on the received selection information, the server adjusts the resources within the system and puts the solution into action. If necessary, it also coordinates with external resources and applications to ensure that the execution process proceeds smoothly.

[0317] Step 6:

[0318] The server continuously monitors the implementation status of the solution and periodically generates reports on progress and results. These reports are provided to the user via their terminal. The user can then use these reports to consider their next course of action.

[0319] (Application Example 1)

[0320] 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."

[0321] Improving productivity and efficiency in factories requires real-time problem analysis and rapid implementation of countermeasures, but this has been difficult with conventional systems. Furthermore, maximizing the use of robots in the production environment requires dynamic and flexible planning and execution, but current methods are insufficient to address this.

[0322] 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.

[0323] In this invention, the server includes means for collecting and analyzing business information, means for identifying and organizing issues through dialogue with the user, and means for presenting generated solutions to the user and executing the selected solution. This enables real-time problem analysis and implementation of countermeasures in the production environment, making it possible to effectively utilize robots and improve productivity.

[0324] "Business information" refers to data and events related to the activities and processes that companies and organizations handle on a daily basis.

[0325] "Machine learning techniques" are technologies that allow computers to recognize patterns from data and automatically learn, predict, and optimize.

[0326] A "user interface" refers to the screens and input methods that allow a user to directly interact with a system.

[0327] A "strategy" is a specific action plan or measure proposed by a system to achieve a particular objective.

[0328] "Production environment" refers to the physical and operational conditions in a factory or manufacturing facility where products are processed and assembled.

[0329] A "robot" is a mechanical device designed and programmed to perform a specific task automatically.

[0330] An "action plan" is a schedule or set of procedures for efficiently and sequentially executing a series of actions that a system or robot will take.

[0331] The system implementing this invention is designed to improve the efficiency of business activities in a factory. The server collects business information from the production floor and performs analysis based on this information. TensorFlow, a machine learning method that runs on cloud services such as AWS Lambda, is used for the analysis. Based on the analysis results, the server uses a generative AI model to propose specific strategies to the user. The proposed strategies are processed by a generative AI model such as OpenAI GPT and presented to the user's terminal.

[0332] The devices used by users are smartphones, tablets, etc., and they interact with the server through a user interface. This interface provides a means for users to select and confirm proposed strategies. This allows users to choose strategies of their own volition and make efficient decisions.

[0333] The selected strategy is immediately executed by the server. During the execution phase, the robot in the production environment and the server communicate, and the robot's action plan is optimized and implemented via the MQTT protocol. This allows for rapid improvement in productivity.

[0334] As a concrete example, if the supply of a specified part is disrupted at a factory, the server identifies the cause through data analysis, and a generated AI model proposes an alternative method. Once the user approves the execution, the robot immediately selects a new path and resumes supply. This allows the production line to quickly return to normal.

[0335] An example of a prompt message is, "Based on the data analysis results from the production line, please suggest the optimal parts supply route and tell me how to improve production efficiency." This allows the system to provide appropriate solutions in response to the user's request.

[0336] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0337] Step 1:

[0338] The server collects operational information from various sensors and input terminals within the factory. This information includes production speed on the production line, parts supply status, and equipment operation data. The collected data is stored in the production management system. The input here is real-time data from sensors and terminals. As output, all information is saved in a database before analysis.

[0339] Step 2:

[0340] The server retrieves accumulated business information from the database using AWS Lambda and analyzes it using machine learning techniques (TensorFlow). The goal of the analysis is to identify patterns indicating performance degradation or anomalies. In this step, business information from the database is used as input, and the location of bottlenecks and anomalies is identified as output.

[0341] Step 3:

[0342] Based on the analysis results, the server generates specific policy proposals using a generative AI model (OpenAI GPT). The generated proposals include measures to improve operational efficiency and address anomalies. In this step, the input is the analysis results from step 2, and the output is the proposed policy statement. The proposals are generated in a language that is easy for the user to understand.

[0343] Step 4:

[0344] The terminal displays a list of proposed strategies in the user interface. The user reviews this list and selects a specific strategy based on their own judgment. The input is the generated strategy statement, and the strategy selected by the user is output. The terminal is responsible for presenting options and accepting user selection input.

[0345] Step 5:

[0346] The server executes the policy selected by the user. It communicates the optimal action plan to the robots in the production environment via the MQTT protocol, causing the robots to perform the configured actions. The input is the policy selected by the user, and the output is an improvement in the ongoing production activity. This process makes the robots' movements more efficient.

[0347] Step 6:

[0348] The server monitors the progress and achievement of the implemented measures and generates reports periodically. These reports are sent to terminals, allowing users to accurately understand the improvements being made to the production environment. Inputs are feedback data from robots and production activities, and outputs are status reports for the user.

[0349] 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.

[0350] This invention improves the quality of dialogue by combining a system that streamlines business management and supports problem-solving with an emotion engine that recognizes user emotions. The system's main components are a server that processes business data, a terminal that functions as an interface with the user, and the user themselves.

[0351] The server collects and analyzes necessary data from the business database. Machine learning algorithms are used to identify patterns and anomalies in the business data, extracting meaningful information. The obtained information is then used to organize the issues identified through interaction with users and to formulate solutions.

[0352] The terminal serves as an interface that supports communication with the user, obtaining user feedback through dialogue. In this process, an emotion engine is utilized to analyze the user's emotional state. The terminal infers emotions from voice tone and input data, and sends this information to the server.

[0353] The server analyzes the user's emotional state and generates optimal solutions. These solutions are adjusted according to the user's emotions and current work situation, making them more effective and acceptable. The terminal then presents these solutions to the user and prompts them to make a choice.

[0354] For example, if a user is experiencing excessive stress, the emotion engine will detect this and prioritize suggesting measures to reduce stress. These measures may include task redistribution and scheduling, and the server will automatically arrange the necessary resources for their execution.

[0355] Furthermore, the server evaluates work progress and the quality of deliverables, including from an emotional perspective, and generates detailed reports. This allows users to understand the impact of their emotional state on their work and use that information to implement improvement measures.

[0356] Thus, the present invention provides a system that improves operational efficiency and user satisfaction by incorporating an emotion engine and enabling individualized responses according to the user's emotional state.

[0357] The following describes the processing flow.

[0358] Step 1:

[0359] The server collects business data, such as tasks and logs, from the business database. This includes a data collection process based on a regular schedule.

[0360] Step 2:

[0361] The server analyzes the collected business data using machine learning algorithms. Based on the analysis results, it identifies patterns and anomalies in the data and understands the user's work situation.

[0362] Step 3:

[0363] The device initiates interaction with the user. It presents questions through the interface and collects information about the user's work-related challenges.

[0364] Step 4:

[0365] The device activates an emotion engine to analyze the user's emotions from their voice tone and text input. The emotion engine determines the emotional state and sends the result to the server.

[0366] Step 5:

[0367] The server integrates emotional information with analyzed business data to identify user issues. It then generates specific solutions to these issues and adjusts them optimally according to the user's emotional state.

[0368] Step 6:

[0369] The device presents the generated countermeasures to the user. The user can select the most suitable countermeasure from multiple options.

[0370] Step 7:

[0371] The server initiates the specific procedures for implementing the selected countermeasures. It arranges the necessary resources and coordinates with external systems, aiming for immediate execution.

[0372] Step 8:

[0373] The server monitors the progress of implemented countermeasures and performs evaluations that include sentiment information. It fine-tunes the countermeasures as needed to optimize the results.

[0374] Step 9:

[0375] The server generates detailed reports on the progress of tasks and the emotional state of users. These reports are provided to users via their terminals.

[0376] Step 10:

[0377] Based on the provided report, users will decide on the next steps to improve their work and emotional state.

[0378] This system supports business efficiency and effective problem-solving by taking into account the user's emotional state through an emotion engine.

[0379] (Example 2)

[0380] 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".

[0381] The present invention aims to achieve efficient problem-solving in business management while considering the emotional aspects of users. Conventional business management systems have the problem of not being able to reflect the emotional state of users, and thus failing to contribute to improving business efficiency and user satisfaction. Therefore, there is a need for a system that can detect the emotional state of users and reflect it in business decision-making.

[0382] 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.

[0383] In this invention, the server includes means for collecting and analyzing business resources, means for identifying and organizing issues through interaction with the user, means for presenting generated solutions to the user and implementing the selected solution, means for evaluating progress and problems and creating reports, means for recognizing and analyzing the user's emotional state, and means for generating optimal countermeasures that take into account the user's emotional information using a generative AI model. This enables individualized responses according to the user's emotional state, as well as improvements in business efficiency and user satisfaction.

[0384] "Business resources" refer to the information and data necessary for an organization or individual to carry out its work.

[0385] "Collection" is the process of gathering data and information based on a specific purpose.

[0386] "Analysis" is the act of analyzing collected data and information to understand and clarify its meaning and trends.

[0387] A "user" is an individual or organization that uses a system or service.

[0388] "Interaction" refers to the exchange of information and instructions between a user and a system.

[0389] A "challenge" refers to a problem or challenge that needs to be solved.

[0390] "Identification" means clearly defining the issue or subject and identifying it.

[0391] "Organization" refers to the process of arranging and compiling information and data in an orderly manner.

[0392] A "solution" refers to a method or means of solving an identified problem or issue.

[0393] "Execution" means actually putting plans and measures into practice.

[0394] "Progress status" refers to the progress toward the completion of a task or project.

[0395] A "problem" refers to a failure or malfunction that occurs within a business or system.

[0396] "Evaluation" is the act of judging the value and significance of an object and determining the outcome.

[0397] A "report" is a document that compiles information on a specific topic or issue.

[0398] "Emotional state" refers to an individual's emotional and psychological state at a given point in time.

[0399] "Recognition" means grasping and understanding emotions and situations.

[0400] A "generative AI model" refers to a collection of algorithms designed based on artificial intelligence to create new information and data.

[0401] "Taking into account" means adjusting the whole by taking certain factors or information into consideration.

[0402] "Optimal" means being the most appropriate and effective.

[0403] A "proposed solution" refers to specific policies or plans to be implemented in response to anticipated problems or existing challenges.

[0404] "Individualized support" means implementing specific countermeasures tailored to each user and their specific problem.

[0405] "Business efficiency" refers to the efficiency and productivity of tasks performed in carrying out business operations.

[0406] "User satisfaction" refers to an evaluation of the degree of satisfaction users feel when using a service or system.

[0407] This invention relates to a system for improving the efficiency of business management and providing individualized support that takes into account the emotional state of users. This system analyzes business data and combines it with user emotions to provide the optimal solution.

[0408] The server's role is to collect and analyze business resources. It retrieves necessary information from the business database and analyzes the data using machine learning algorithms. Specifically, it can utilize anomaly detection algorithms and clustering algorithms. From the analyzed data, it identifies important business trends and anomalies. Then, it uses a generative AI model to generate optimal countermeasures that take into account user sentiment. For example, when considering measures to reduce the workload, it uses the prompt message "Consider the current sentiment state and generate recommended measures to reduce the user's workload" to create countermeasures.

[0409] The terminal functions as an interface with the user. It receives voice and text input from the user and processes the input using speech recognition software. The terminal converts voice data into text and analyzes the user's emotional state using an emotion engine. The analyzed emotional information is sent to a server and used to generate countermeasures.

[0410] Users receive proposed solutions via their terminals and select the one best suited to their work. The user's selection is fed back to the server via the terminal, and the selected solution is implemented by the system. The overall effect of this system is that users receive personalized support tailored to their emotional state, leading to improved work efficiency and user satisfaction.

[0411] Thus, the present invention improves business management processes and provides an efficient and emotionally sensitive interactive system through the analysis of business data and the generation of countermeasures that take into account user emotional information.

[0412] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0413] Step 1:

[0414] The server retrieves business resources from the business database. Input is a data retrieval request, such as an SQL query. Output is the retrieved business data. After retrieving the data, the server analyzes it using machine learning algorithms (e.g., anomaly detection algorithms) to identify useful patterns and anomalies. This analysis process involves processing large amounts of data and performing statistical analysis to obtain the analysis results as output.

[0415] Step 2:

[0416] The device accepts voice input to collect user feedback. The input is the user's voice data. The device converts this voice data into text data using speech recognition software. Furthermore, it uses an emotion engine to infer and analyze the user's emotional state from this text data and voice tone. The output is the analyzed emotion information. This emotion information is crucial for understanding the context of the conversation.

[0417] Step 3:

[0418] The server receives emotional information sent from the terminal. The input consists of the user's emotional information and the results of the work analysis. Based on this information, the server uses a generative AI model to generate optimal countermeasures that take the user's emotional information into account. The prompt used is "Consider the current emotional state and generate recommended measures to reduce the user's workload." The output is the generated countermeasures.

[0419] Step 4:

[0420] The terminal receives proposed solutions sent from the server. The input is the generated solutions. The terminal presents these solutions to the user and prompts them to make a choice through an intuitively understandable interface. Output feedback is obtained by receiving user selection feedback.

[0421] Step 5:

[0422] The server prepares to implement the action plan selected by the user. The input is the user's selection. Based on this, the server adjusts the relevant business resources and performs scheduling and task reallocation. The output is the business tasks that have been put into action and their progress information.

[0423] Step 6:

[0424] The server monitors the progress of tasks and generates reports that include emotional perspectives. Inputs include progress data and emotional information. The server integrates these to evaluate the overall work situation and outputs a report. This report is provided to the user via a terminal, allowing them to understand the impact of their emotions on their work and use it as a reference for improvement measures.

[0425] (Application Example 2)

[0426] 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 will be referred to as the "terminal." Conventional business management systems have a problem in that they proceed with work without adequately considering the emotional state of the user, leading to the accumulation of user stress and dissatisfaction, and a decrease in work efficiency and user satisfaction. To solve this problem, it is necessary to accurately analyze the emotional state of the user and provide appropriate countermeasures based on that analysis.

[0427] (Means for solving the problem)

[0428] In this invention, the server includes means for collecting and analyzing business data, means for identifying and organizing issues through interaction with the user, and means for analyzing the user's emotional state and proposing an appropriate care plan based on those emotions. This enables flexible care that responds to the user's emotions.

[0429] 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.

[0430] Conventional business management systems often fail to adequately consider users' emotional states, leading to the accumulation of user stress and dissatisfaction, and ultimately reducing work efficiency and user satisfaction. To solve this problem, it is necessary to accurately analyze users' emotional states and provide appropriate countermeasures based on that analysis.

[0431] (Means for solving the problem)

[0432] In this invention, the server includes means for collecting and analyzing business data, means for identifying and organizing issues through interaction with the user, and means for analyzing the user's emotional state and proposing an appropriate care plan based on those emotions. This enables flexible care that responds to the user's emotions.

[0433] "Business data" refers to a collection of information generated from the daily activities of an organization or company, and is used to improve and streamline operations.

[0434] "Means of analysis" refer to methods and processes for analyzing collected data in detail using various technologies and extracting meaningful information.

[0435] A "user" refers to an individual or organization that interacts with or operates through this system, and is primarily the recipient of the services and information provided by the system.

[0436] "Dialogue" is the process by which a user and a system exchange information, and it is carried out through voice or text.

[0437] "Methods for identifying and organizing issues" refer to methods and processes for finding problems and areas for improvement through dialogue with users, and then effectively structuring them.

[0438] "Emotional state" refers to the psychological and physiological state a user is experiencing at a given point in time, and is composed of elements such as stress and a sense of security.

[0439] A "care plan" is a set of plans and ideas that propose the most appropriate actions and treatments according to the user's emotions and circumstances.

[0440] "Means of suggestion" refers to methods and functions that, based on analyzed information, present users with the most suitable solutions or action plans.

[0441] The system for implementing this invention streamlines business management by recognizing the user's emotions and proposing an appropriate care plan. The system mainly consists of three elements: a server, a terminal, and the user.

[0442] The server is the primary component for analyzing business data and user emotional states. The server utilizes machine learning algorithms such as the Google Cloud Speech-to-Text API and TensorFlow for data analysis. The server collects speech data and performs calculations for emotion recognition. Based on this information, it generates and presents the most suitable care plan for the user.

[0443] The terminal functions as an interface with the user and transmits data such as voice to the server. Based on the voice data collected from the user, it uses an emotion engine to infer the user's emotional state in real time and transmits it to the server. The terminal provides the user with intuitive and convenient operation.

[0444] Users interact with the server using their devices and receive feedback tailored to their emotional state and work situation. The care plans proposed to the users are tailored to their emotional state, such as suggestions for activities to reduce stress.

[0445] As a concrete example, consider a situation where a user feels anxious after lunch. In this case, the system analyzes the user's voice tone and feedback and determines that they are feeling anxious. The server then generates a care plan recommending a walk and proposes it to the user via the terminal.

[0446] An example of a prompt for a generative AI model could be: "Please suggest appropriate activities when the user feels anxious. List options for activities that reduce stress and briefly explain the effect of each."

[0447] This invention provides a system that utilizes an emotion engine to efficiently improve user satisfaction.

[0448] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0449] Step 1:

[0450] The device collects the user's voice data through the microphone. The input data is captured as an audio file. This data is temporarily stored locally in preparation for transmission to the server in real time.

[0451] Step 2:

[0452] The server receives audio data sent from the terminal. It uses the Google Cloud Speech-to-Text API to convert the received audio data into text. The input is an audio file, and the output is parsed text data. The API generates highly accurate text data from audio.

[0453] Step 3:

[0454] The server performs sentiment analysis using the obtained text data. This process applies a sentiment analysis model utilizing TensorFlow. The input is text data, and the output is the user's emotional state (e.g., "anxiety," "stress"). The analysis model extracts emotion-related keywords from the text and identifies the emotional state.

[0455] Step 4:

[0456] The server generates an optimal care plan for the user based on the results of an analysis of their emotional state. The care plan generation uses a generative AI model that incorporates past work data and emotional analysis results. The input is the user's emotional state and work data, and the output is a specific care plan (e.g., "Suggestion for a walk").

[0457] Step 5:

[0458] The terminal presents the user with a care plan sent from the server. The proposal is displayed visually on the terminal's screen and also provides auditory guidance using the voice output function. The user can review the presented care plan and, if necessary, select or begin implementation.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] [Third Embodiment]

[0463] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0464] 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.

[0465] 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).

[0466] 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.

[0467] 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.

[0468] 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).

[0469] 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.

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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.

[0474] 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".

[0475] The system based on this invention efficiently manages the user's work and supports problem-solving. The main components of this system include a server that processes business data, a terminal that acts as an interface with the user, and the user themselves.

[0476] The server's primary role is to collect information from the business database and then analyze that data. Here, machine learning algorithms are used to identify relevant patterns and anomalies within the data. The results of this analysis are then used to identify issues through interaction with users.

[0477] The terminal serves as an interface for communication with users. It sends questions to users in a conversational format, eliciting feedback on their work environment and challenges. This allows users to specifically identify problems and areas for improvement in their work.

[0478] Based on the collected information, the server uses generative artificial intelligence to generate effective countermeasures. This process incorporates predictions based on past cases and data, and these are presented to the user. The proposed countermeasures are concrete and actionable, providing a useful reference for the user's decision-making.

[0479] Once the user selects a specific countermeasure, the server takes on the role of implementing that countermeasure. This is done through process coordination within the system and coordination with external resources, ensuring that the selected countermeasure is implemented quickly and effectively.

[0480] Furthermore, the server continuously monitors the progress of tasks and periodically generates reports on problems and achievements. These reports are provided to users via their terminals, serving as a basis for self-assessment and planning the next steps.

[0481] As a concrete example, suppose a project at a certain company is behind schedule. By utilizing this system, the server analyzes data and identifies the cause of the delay. Subsequently, interaction with the user takes place via a terminal, and the underlying causes are uncovered. Generative artificial intelligence proposes appropriate countermeasures, and once the user selects one, implementation begins immediately. Finally, the server generates a report and provides it to the user in a format that makes it easy to understand the situation.

[0482] Thus, the system of the present invention provides a comprehensive solution for enhancing business efficiency and problem-solving.

[0483] The following describes the processing flow.

[0484] Step 1:

[0485] The server periodically collects necessary business data from the business database. It uses APIs and SQL queries to retrieve relevant task information and business logs and stores them in data storage.

[0486] Step 2:

[0487] The server analyzes the collected data using machine learning algorithms. It identifies patterns and anomalies within the data and generates a task management table based on them. The analysis results can be used to identify problems.

[0488] Step 3:

[0489] The device initiates a conversation with the user. It utilizes chatbot functionality to send questions to the user regarding business challenges and collects the user's responses.

[0490] Step 4:

[0491] The server compares the user's responses obtained through the terminal with pre-analyzed data to identify and organize specific challenges in the user's work.

[0492] Step 5:

[0493] The server uses a generative artificial intelligence model to generate specific solutions to the identified problems. It lists solutions deemed highly effective, referencing past data and successful case studies.

[0494] Step 6:

[0495] The terminal presents the user with proposed solutions sent from the server. The user selects the solution that best suits their work from among the presented options.

[0496] Step 7:

[0497] The server automatically initiates the necessary procedures to implement the measures selected by the user. It manages the execution process by coordinating with external resources and related systems.

[0498] Step 8:

[0499] The server monitors the progress of the implemented measures and evaluates the results. If problems are found, it makes adjustments and corrects as needed.

[0500] Step 9:

[0501] The server compiles detailed reports on work progress and achievement levels. These generated reports are then provided to users via their terminals through data visualization.

[0502] Step 10:

[0503] Based on the presented report, users consider areas for improvement in their operations and next steps, and request additional measures as needed.

[0504] Through these steps, the system provides users with efficient business management and effective problem-solving.

[0505] (Example 1)

[0506] 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."

[0507] In today's business environment, efficient business management and rapid problem-solving are crucial elements. However, previous systems were unable to comprehensively handle everything from data collection and analysis to problem identification, solution proposal, and implementation support. As a result, users had to use multiple tools, leading to complex and inefficient operations.

[0508] 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.

[0509] In this invention, the server includes a device for collecting and analyzing information, a device for identifying and organizing issues through interaction with users, and a device for presenting generated solutions to users and executing the selected solution. This enables comprehensive and efficient management of business data and a rapid and effective problem-solving process.

[0510] "Information" refers to data, knowledge, and other content related to the business.

[0511] "Analysis" refers to the act of identifying useful patterns or anomalies within data by thoroughly investigating and analyzing information.

[0512] "Device" refers to hardware or software components installed to perform a function.

[0513] A "user" refers to a person who operates this system and provides information and instructions for business management and problem-solving.

[0514] "Dialogue" refers to the exchange of information between the user and the system, and usually proceeds in the form of a question-and-answer session.

[0515] "Identifying and analyzing" refers to conducting a detailed investigation to uncover hidden problems or potential issues.

[0516] A "solution" refers to a specific method or means devised to solve a particular problem or issue.

[0517] "Presentation" refers to the act of communicating information or results to users visually or audibly.

[0518] "Selection" refers to the act of deciding on the most appropriate option from among several presented choices.

[0519] A "report" refers to a document that compiles and organizes information regarding progress and problems.

[0520] A "generative AI model" refers to the entire algorithm and system used to realize generative artificial intelligence.

[0521] A "prompt statement" refers to an input statement used to give instructions or questions to a generative AI model.

[0522] This system is designed to enable users to efficiently manage their work and quickly resolve issues. The implementation of the invention is primarily achieved through the interaction of servers, terminals, and users.

[0523] The server is the core of this system, responsible for collecting and analyzing information. Specifically, the server extracts information from business databases and analyzes the data using machine learning algorithms. In this process, clustering techniques and regression analysis are used to identify relevant patterns and anomalies within the data. The server also utilizes generative artificial intelligence models to generate solutions and prepares them for provision to users. In generating solutions, past data and case studies are considered, and highly reliable predictions are incorporated.

[0524] On the other hand, the terminal functions as an interface connecting the user and the server. The terminal is a device that receives input from the user and transmits it to the server. It also plays a role in presenting solutions provided by the server to the user and prompting them to make a choice. The terminal implements a user interface that prioritizes ease of use, allowing for the sending and receiving of information with simple operations.

[0525] Users input business information into the system via a terminal and consider the generated solutions. The interactive interface allows users to organize their issues and select specific solutions. For example, if a user wants to improve project progress, they can input a prompt such as "What are effective ways to improve project progress?" into the system and receive appropriate solutions.

[0526] In this way, servers, terminals, and users work together to achieve effective business management and problem-solving.

[0527] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0528] Step 1:

[0529] Users use a terminal to input their work information and the challenges they are currently facing. This input includes the progress of their work, specific problems, and areas for improvement. The terminal then sends this information received from the user to the server.

[0530] Step 2:

[0531] The server receives business data sent from the terminal and begins analysis. It accesses the database, verifies the data's up-to-dateness, and then processes it through machine learning algorithms. Here, clustering and regression analysis are used to extract relevant patterns and anomalies from the data. The analysis results are stored on the server for subsequent processing.

[0532] Step 3:

[0533] The server generates solutions using a generative AI model based on the analyzed data. During this process, it considers similar past cases and data as prompt inputs, asking questions such as, "What are effective ways to improve project progress?" The generative AI model responds to these prompts by generating specific solutions and returning them to the server.

[0534] Step 4:

[0535] The generated solutions are sent from the server to the terminal. The terminal displays this information in a user-friendly format and prompts the user to select the most appropriate solution. The user selects a solution based on the displayed information, using clicks or other actions.

[0536] Step 5:

[0537] The solution selected by the user is sent from the terminal to the server. Based on the received selection information, the server adjusts the resources within the system and puts the solution into action. If necessary, it also coordinates with external resources and applications to ensure that the execution process proceeds smoothly.

[0538] Step 6:

[0539] The server continuously monitors the implementation status of the solution and periodically generates reports on progress and results. These reports are provided to the user via their terminal. The user can then use these reports to consider their next course of action.

[0540] (Application Example 1)

[0541] 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."

[0542] Improving productivity and efficiency in factories requires real-time problem analysis and rapid implementation of countermeasures, but this has been difficult with conventional systems. Furthermore, maximizing the use of robots in the production environment requires dynamic and flexible planning and execution, but current methods are insufficient to address this.

[0543] 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.

[0544] In this invention, the server includes means for collecting and analyzing business information, means for identifying and organizing issues through dialogue with the user, and means for presenting generated solutions to the user and executing the selected solution. This enables real-time problem analysis and implementation of countermeasures in the production environment, making it possible to effectively utilize robots and improve productivity.

[0545] "Business information" refers to data and events related to the activities and processes that companies and organizations handle on a daily basis.

[0546] "Machine learning techniques" are technologies that allow computers to recognize patterns from data and automatically learn, predict, and optimize.

[0547] A "user interface" refers to the screens and input methods that allow a user to directly interact with a system.

[0548] A "strategy" is a specific action plan or measure proposed by a system to achieve a particular objective.

[0549] "Production environment" refers to the physical and operational conditions in a factory or manufacturing facility where products are processed and assembled.

[0550] A "robot" is a mechanical device designed and programmed to perform a specific task automatically.

[0551] An "action plan" is a schedule or set of procedures for efficiently and sequentially executing a series of actions that a system or robot will take.

[0552] The system implementing this invention is designed to improve the efficiency of business activities in a factory. The server collects business information from the production floor and performs analysis based on this information. TensorFlow, a machine learning method that runs on cloud services such as AWS Lambda, is used for the analysis. Based on the analysis results, the server uses a generative AI model to propose specific strategies to the user. The proposed strategies are processed by a generative AI model such as OpenAI GPT and presented to the user's terminal.

[0553] The devices used by users are smartphones, tablets, etc., and they interact with the server through a user interface. This interface provides a means for users to select and confirm proposed strategies. This allows users to choose strategies of their own volition and make efficient decisions.

[0554] The selected strategy is immediately executed by the server. During the execution phase, the robot in the production environment and the server communicate, and the robot's action plan is optimized and implemented via the MQTT protocol. This allows for rapid improvement in productivity.

[0555] As a concrete example, if the supply of a specified part is disrupted at a factory, the server identifies the cause through data analysis, and a generated AI model proposes an alternative method. Once the user approves the execution, the robot immediately selects a new path and resumes supply. This allows the production line to quickly return to normal.

[0556] An example of a prompt message is, "Based on the data analysis results from the production line, please suggest the optimal parts supply route and tell me how to improve production efficiency." This allows the system to provide appropriate solutions in response to the user's request.

[0557] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0558] Step 1:

[0559] The server collects operational information from various sensors and input terminals within the factory. This information includes production speed on the production line, parts supply status, and equipment operation data. The collected data is stored in the production management system. The input here is real-time data from sensors and terminals. As output, all information is saved in a database before analysis.

[0560] Step 2:

[0561] The server retrieves accumulated business information from the database using AWS Lambda and analyzes it using machine learning techniques (TensorFlow). The goal of the analysis is to identify patterns indicating performance degradation or anomalies. In this step, business information from the database is used as input, and the location of bottlenecks and anomalies is identified as output.

[0562] Step 3:

[0563] Based on the analysis results, the server generates specific policy proposals using a generative AI model (OpenAI GPT). The generated proposals include measures to improve operational efficiency and address anomalies. In this step, the input is the analysis results from step 2, and the output is the proposed policy statement. The proposals are generated in a language that is easy for the user to understand.

[0564] Step 4:

[0565] The terminal displays a list of proposed strategies in the user interface. The user reviews this list and selects a specific strategy based on their own judgment. The input is the generated strategy statement, and the strategy selected by the user is output. The terminal is responsible for presenting options and accepting user selection input.

[0566] Step 5:

[0567] The server executes the policy selected by the user. It communicates the optimal action plan to the robots in the production environment via the MQTT protocol, causing the robots to perform the configured actions. The input is the policy selected by the user, and the output is an improvement in the ongoing production activity. This process makes the robots' movements more efficient.

[0568] Step 6:

[0569] The server monitors the progress and achievement of the implemented measures and generates reports periodically. These reports are sent to terminals, allowing users to accurately understand the improvements being made to the production environment. Inputs are feedback data from robots and production activities, and outputs are status reports for the user.

[0570] 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.

[0571] This invention improves the quality of dialogue by combining a system that streamlines business management and supports problem-solving with an emotion engine that recognizes user emotions. The system's main components are a server that processes business data, a terminal that functions as an interface with the user, and the user themselves.

[0572] The server collects and analyzes necessary data from the business database. Machine learning algorithms are used to identify patterns and anomalies in the business data, extracting meaningful information. The obtained information is then used to organize the issues identified through interaction with users and to formulate solutions.

[0573] The terminal serves as an interface that supports communication with the user, obtaining user feedback through dialogue. In this process, an emotion engine is utilized to analyze the user's emotional state. The terminal infers emotions from voice tone and input data, and sends this information to the server.

[0574] The server analyzes the user's emotional state and generates optimal solutions. These solutions are adjusted according to the user's emotions and current work situation, making them more effective and acceptable. The terminal then presents these solutions to the user and prompts them to make a choice.

[0575] For example, if a user is experiencing excessive stress, the emotion engine will detect this and prioritize suggesting measures to reduce stress. These measures may include task redistribution and scheduling, and the server will automatically arrange the necessary resources for their execution.

[0576] Furthermore, the server evaluates work progress and the quality of deliverables, including from an emotional perspective, and generates detailed reports. This allows users to understand the impact of their emotional state on their work and use that information to implement improvement measures.

[0577] Thus, the present invention provides a system that improves operational efficiency and user satisfaction by incorporating an emotion engine and enabling individualized responses according to the user's emotional state.

[0578] The following describes the processing flow.

[0579] Step 1:

[0580] The server collects business data, such as tasks and logs, from the business database. This includes a data collection process based on a regular schedule.

[0581] Step 2:

[0582] The server analyzes the collected business data using machine learning algorithms. Based on the analysis results, it identifies patterns and anomalies in the data and understands the user's work situation.

[0583] Step 3:

[0584] The device initiates interaction with the user. It presents questions through the interface and collects information about the user's work-related challenges.

[0585] Step 4:

[0586] The device activates an emotion engine to analyze the user's emotions from their voice tone and text input. The emotion engine determines the emotional state and sends the result to the server.

[0587] Step 5:

[0588] The server integrates emotional information with analyzed business data to identify user issues. It then generates specific solutions to these issues and adjusts them optimally according to the user's emotional state.

[0589] Step 6:

[0590] The device presents the generated countermeasures to the user. The user can select the most suitable countermeasure from multiple options.

[0591] Step 7:

[0592] The server initiates the specific procedures for implementing the selected countermeasures. It arranges the necessary resources and coordinates with external systems, aiming for immediate execution.

[0593] Step 8:

[0594] The server monitors the progress of implemented countermeasures and performs evaluations that include sentiment information. It fine-tunes the countermeasures as needed to optimize the results.

[0595] Step 9:

[0596] The server generates detailed reports on the progress of tasks and the emotional state of users. These reports are provided to users via their terminals.

[0597] Step 10:

[0598] Based on the provided report, users will decide on the next steps to improve their work and emotional state.

[0599] This system supports business efficiency and effective problem-solving by taking into account the user's emotional state through an emotion engine.

[0600] (Example 2)

[0601] 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."

[0602] The present invention aims to achieve efficient problem-solving in business management while considering the emotional aspects of users. Conventional business management systems have the problem of not being able to reflect the emotional state of users, and thus failing to contribute to improving business efficiency and user satisfaction. Therefore, there is a need for a system that can detect the emotional state of users and reflect it in business decision-making.

[0603] 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.

[0604] In this invention, the server includes means for collecting and analyzing business resources, means for identifying and organizing issues through interaction with the user, means for presenting generated solutions to the user and implementing the selected solution, means for evaluating progress and problems and creating reports, means for recognizing and analyzing the user's emotional state, and means for generating optimal countermeasures that take into account the user's emotional information using a generative AI model. This enables individualized responses according to the user's emotional state, as well as improvements in business efficiency and user satisfaction.

[0605] "Business resources" refer to the information and data necessary for an organization or individual to carry out its work.

[0606] "Collection" is the process of gathering data and information based on a specific purpose.

[0607] "Analysis" is the act of analyzing collected data and information to understand and clarify its meaning and trends.

[0608] A "user" is an individual or organization that uses a system or service.

[0609] "Interaction" refers to the exchange of information and instructions between a user and a system.

[0610] A "challenge" refers to a problem or challenge that needs to be solved.

[0611] "Identification" means clearly defining the issue or subject and identifying it.

[0612] "Organization" refers to the process of arranging and compiling information and data in an orderly manner.

[0613] A "solution" refers to a method or means of solving an identified problem or issue.

[0614] "Execution" means actually putting plans and measures into practice.

[0615] "Progress status" refers to the progress toward the completion of a task or project.

[0616] A "problem" refers to a failure or malfunction that occurs within a business or system.

[0617] "Evaluation" is the act of judging the value and significance of an object and determining the outcome.

[0618] A "report" is a document that compiles information on a specific topic or issue.

[0619] "Emotional state" refers to an individual's emotional and psychological state at a given point in time.

[0620] "Recognition" means grasping and understanding emotions and situations.

[0621] A "generative AI model" refers to a collection of algorithms designed based on artificial intelligence to create new information and data.

[0622] "Taking into account" means adjusting the whole by taking certain factors or information into consideration.

[0623] "Optimal" means being the most appropriate and effective.

[0624] A "proposed solution" refers to specific policies or plans to be implemented in response to anticipated problems or existing challenges.

[0625] "Individualized support" means implementing specific countermeasures tailored to each user and their specific problem.

[0626] "Business efficiency" refers to the efficiency and productivity of tasks performed in carrying out business operations.

[0627] "User satisfaction" refers to an evaluation of the degree of satisfaction users feel when using a service or system.

[0628] This invention relates to a system for improving the efficiency of business management and providing individualized support that takes into account the emotional state of users. This system analyzes business data and combines it with user emotions to provide the optimal solution.

[0629] The server's role is to collect and analyze business resources. It retrieves necessary information from the business database and analyzes the data using machine learning algorithms. Specifically, it can utilize anomaly detection algorithms and clustering algorithms. From the analyzed data, it identifies important business trends and anomalies. Then, it uses a generative AI model to generate optimal countermeasures that take into account user sentiment. For example, when considering measures to reduce the workload, it uses the prompt message "Consider the current sentiment state and generate recommended measures to reduce the user's workload" to create countermeasures.

[0630] The terminal functions as an interface with the user. It receives voice and text input from the user and processes the input using speech recognition software. The terminal converts voice data into text and analyzes the user's emotional state using an emotion engine. The analyzed emotional information is sent to a server and used to generate countermeasures.

[0631] Users receive proposed solutions via their terminals and select the one best suited to their work. The user's selection is fed back to the server via the terminal, and the selected solution is implemented by the system. The overall effect of this system is that users receive personalized support tailored to their emotional state, leading to improved work efficiency and user satisfaction.

[0632] Thus, the present invention improves business management processes and provides an efficient and emotionally sensitive interactive system through the analysis of business data and the generation of countermeasures that take into account user emotional information.

[0633] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0634] Step 1:

[0635] The server retrieves business resources from the business database. Input is a data retrieval request, such as an SQL query. Output is the retrieved business data. After retrieving the data, the server analyzes it using machine learning algorithms (e.g., anomaly detection algorithms) to identify useful patterns and anomalies. This analysis process involves processing large amounts of data and performing statistical analysis to obtain the analysis results as output.

[0636] Step 2:

[0637] The device accepts voice input to collect user feedback. The input is the user's voice data. The device converts this voice data into text data using speech recognition software. Furthermore, it uses an emotion engine to infer and analyze the user's emotional state from this text data and voice tone. The output is the analyzed emotion information. This emotion information is crucial for understanding the context of the conversation.

[0638] Step 3:

[0639] The server receives emotional information sent from the terminal. The input consists of the user's emotional information and the results of the work analysis. Based on this information, the server uses a generative AI model to generate optimal countermeasures that take the user's emotional information into account. The prompt used is "Consider the current emotional state and generate recommended measures to reduce the user's workload." The output is the generated countermeasures.

[0640] Step 4:

[0641] The terminal receives proposed solutions sent from the server. The input is the generated solutions. The terminal presents these solutions to the user and prompts them to make a choice through an intuitively understandable interface. Output feedback is obtained by receiving user selection feedback.

[0642] Step 5:

[0643] The server prepares to implement the action plan selected by the user. The input is the user's selection. Based on this, the server adjusts the relevant business resources and performs scheduling and task reallocation. The output is the business tasks that have been put into action and their progress information.

[0644] Step 6:

[0645] The server monitors the progress of tasks and generates reports that include emotional perspectives. Inputs include progress data and emotional information. The server integrates these to evaluate the overall work situation and outputs a report. This report is provided to the user via a terminal, allowing them to understand the impact of their emotions on their work and use it as a reference for improvement measures.

[0646] (Application Example 2)

[0647] 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 terminal 314 will be referred to as the "terminal." Conventional business management systems have a problem in that they proceed with work without adequately considering the emotional state of the user, leading to the accumulation of user stress and dissatisfaction, and a decrease in work efficiency and user satisfaction. To solve this problem, it is necessary to accurately analyze the emotional state of the user and provide appropriate countermeasures based on that analysis.

[0648] (Means for solving the problem)

[0649] In this invention, the server includes means for collecting and analyzing business data, means for identifying and organizing issues through interaction with the user, and means for analyzing the user's emotional state and proposing an appropriate care plan based on those emotions. This enables flexible care that responds to the user's emotions.

[0650] 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.

[0651] Conventional business management systems often fail to adequately consider users' emotional states, leading to the accumulation of user stress and dissatisfaction, and ultimately reducing work efficiency and user satisfaction. To solve this problem, it is necessary to accurately analyze users' emotional states and provide appropriate countermeasures based on that analysis.

[0652] (Means for solving the problem)

[0653] In this invention, the server includes means for collecting and analyzing business data, means for identifying and organizing issues through interaction with the user, and means for analyzing the user's emotional state and proposing an appropriate care plan based on those emotions. This enables flexible care that responds to the user's emotions.

[0654] "Business data" refers to a collection of information generated from the daily activities of an organization or company, and is used to improve and streamline operations.

[0655] "Means of analysis" refer to methods and processes for analyzing collected data in detail using various technologies and extracting meaningful information.

[0656] A "user" refers to an individual or organization that interacts with or operates through this system, and is primarily the recipient of the services and information provided by the system.

[0657] "Dialogue" is the process by which a user and a system exchange information, and it is carried out through voice or text.

[0658] "Methods for identifying and organizing issues" refer to methods and processes for finding problems and areas for improvement through dialogue with users, and then effectively structuring them.

[0659] "Emotional state" refers to the psychological and physiological state a user is experiencing at a given point in time, and is composed of elements such as stress and a sense of security.

[0660] A "care plan" is a set of plans and ideas that propose the most appropriate actions and treatments according to the user's emotions and circumstances.

[0661] "Means of suggestion" refers to methods and functions that, based on analyzed information, present users with the most suitable solutions or action plans.

[0662] The system for implementing this invention streamlines business management by recognizing the user's emotions and proposing an appropriate care plan. The system mainly consists of three elements: a server, a terminal, and the user.

[0663] The server is the primary component for analyzing business data and user emotional states. The server utilizes machine learning algorithms such as the Google Cloud Speech-to-Text API and TensorFlow for data analysis. The server collects speech data and performs calculations for emotion recognition. Based on this information, it generates and presents the most suitable care plan for the user.

[0664] The terminal functions as an interface with the user and transmits data such as voice to the server. Based on the voice data collected from the user, it uses an emotion engine to infer the user's emotional state in real time and transmits it to the server. The terminal provides the user with intuitive and convenient operation.

[0665] Users interact with the server using their devices and receive feedback tailored to their emotional state and work situation. The care plans proposed to the users are tailored to their emotional state, such as suggestions for activities to reduce stress.

[0666] As a concrete example, consider a situation where a user feels anxious after lunch. In this case, the system analyzes the user's voice tone and feedback and determines that they are feeling anxious. The server then generates a care plan recommending a walk and proposes it to the user via the terminal.

[0667] An example of a prompt for a generative AI model could be: "Please suggest appropriate activities when the user feels anxious. List options for activities that reduce stress and briefly explain the effect of each."

[0668] This invention provides a system that utilizes an emotion engine to efficiently improve user satisfaction.

[0669] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0670] Step 1:

[0671] The device collects the user's voice data through the microphone. The input data is captured as an audio file. This data is temporarily stored locally in preparation for transmission to the server in real time.

[0672] Step 2:

[0673] The server receives audio data sent from the terminal. It uses the Google Cloud Speech-to-Text API to convert the received audio data into text. The input is an audio file, and the output is parsed text data. The API generates highly accurate text data from audio.

[0674] Step 3:

[0675] The server performs sentiment analysis using the obtained text data. This process applies a sentiment analysis model utilizing TensorFlow. The input is text data, and the output is the user's emotional state (e.g., "anxiety," "stress"). The analysis model extracts emotion-related keywords from the text and identifies the emotional state.

[0676] Step 4:

[0677] The server generates an optimal care plan for the user based on the results of an analysis of their emotional state. The care plan generation uses a generative AI model that incorporates past work data and emotional analysis results. The input is the user's emotional state and work data, and the output is a specific care plan (e.g., "Suggestion for a walk").

[0678] Step 5:

[0679] The terminal presents the user with a care plan sent from the server. The proposal is displayed visually on the terminal's screen and also provides auditory guidance using the voice output function. The user can review the presented care plan and, if necessary, select or begin implementation.

[0680] 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.

[0681] 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.

[0682] 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.

[0683] [Fourth Embodiment]

[0684] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0685] 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.

[0686] 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).

[0687] 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.

[0688] 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.

[0689] 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).

[0690] 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.

[0691] 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.

[0692] 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.

[0693] 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.

[0694] 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.

[0695] 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.

[0696] 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".

[0697] The system based on this invention efficiently manages the user's work and supports problem-solving. The main components of this system include a server that processes business data, a terminal that acts as an interface with the user, and the user themselves.

[0698] The server's primary role is to collect information from the business database and then analyze that data. Here, machine learning algorithms are used to identify relevant patterns and anomalies within the data. The results of this analysis are then used to identify issues through interaction with users.

[0699] The terminal serves as an interface for communication with users. It sends questions to users in a conversational format, eliciting feedback on their work environment and challenges. This allows users to specifically identify problems and areas for improvement in their work.

[0700] Based on the collected information, the server uses generative artificial intelligence to generate effective countermeasures. This process incorporates predictions based on past cases and data, and these are presented to the user. The proposed countermeasures are concrete and actionable, providing a useful reference for the user's decision-making.

[0701] Once the user selects a specific countermeasure, the server takes on the role of implementing that countermeasure. This is done through process coordination within the system and coordination with external resources, ensuring that the selected countermeasure is implemented quickly and effectively.

[0702] Furthermore, the server continuously monitors the progress of tasks and periodically generates reports on problems and achievements. These reports are provided to users via their terminals, serving as a basis for self-assessment and planning the next steps.

[0703] As a concrete example, suppose a project at a certain company is behind schedule. By utilizing this system, the server analyzes data and identifies the cause of the delay. Subsequently, interaction with the user takes place via a terminal, and the underlying causes are uncovered. Generative artificial intelligence proposes appropriate countermeasures, and once the user selects one, implementation begins immediately. Finally, the server generates a report and provides it to the user in a format that makes it easy to understand the situation.

[0704] Thus, the system of the present invention provides a comprehensive solution for enhancing business efficiency and problem-solving.

[0705] The following describes the processing flow.

[0706] Step 1:

[0707] The server periodically collects necessary business data from the business database. It uses APIs and SQL queries to retrieve relevant task information and business logs and stores them in data storage.

[0708] Step 2:

[0709] The server analyzes the collected data using machine learning algorithms. It identifies patterns and anomalies within the data and generates a task management table based on them. The analysis results can be used to identify problems.

[0710] Step 3:

[0711] The device initiates a conversation with the user. It utilizes chatbot functionality to send questions to the user regarding business challenges and collects the user's responses.

[0712] Step 4:

[0713] The server compares the user's responses obtained through the terminal with pre-analyzed data to identify and organize specific challenges in the user's work.

[0714] Step 5:

[0715] The server uses a generative artificial intelligence model to generate specific solutions to the identified problems. It lists solutions deemed highly effective, referencing past data and successful case studies.

[0716] Step 6:

[0717] The terminal presents the user with proposed solutions sent from the server. The user selects the solution that best suits their work from among the presented options.

[0718] Step 7:

[0719] The server automatically initiates the necessary procedures to implement the measures selected by the user. It manages the execution process by coordinating with external resources and related systems.

[0720] Step 8:

[0721] The server monitors the progress of the implemented measures and evaluates the results. If problems are found, it makes adjustments and corrects as needed.

[0722] Step 9:

[0723] The server compiles detailed reports on work progress and achievement levels. These generated reports are then provided to users via their terminals through data visualization.

[0724] Step 10:

[0725] Based on the presented report, users consider areas for improvement in their operations and next steps, and request additional measures as needed.

[0726] Through these steps, the system provides users with efficient business management and effective problem-solving.

[0727] (Example 1)

[0728] 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".

[0729] In today's business environment, efficient business management and rapid problem-solving are crucial elements. However, previous systems were unable to comprehensively handle everything from data collection and analysis to problem identification, solution proposal, and implementation support. As a result, users had to use multiple tools, leading to complex and inefficient operations.

[0730] 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.

[0731] In this invention, the server includes a device for collecting and analyzing information, a device for identifying and organizing issues through interaction with users, and a device for presenting generated solutions to users and executing the selected solution. This enables comprehensive and efficient management of business data and a rapid and effective problem-solving process.

[0732] "Information" refers to data, knowledge, and other content related to the business.

[0733] "Analysis" refers to the act of identifying useful patterns or anomalies within data by thoroughly investigating and analyzing information.

[0734] "Device" refers to hardware or software components installed to perform a function.

[0735] A "user" refers to a person who operates this system and provides information and instructions for business management and problem-solving.

[0736] "Dialogue" refers to the exchange of information between the user and the system, and usually proceeds in the form of a question-and-answer session.

[0737] "Identifying and analyzing" refers to conducting a detailed investigation to uncover hidden problems or potential issues.

[0738] A "solution" refers to a specific method or means devised to solve a particular problem or issue.

[0739] "Presentation" refers to the act of communicating information or results to users visually or audibly.

[0740] "Selection" refers to the act of deciding on the most appropriate option from among several presented choices.

[0741] A "report" refers to a document that compiles and organizes information regarding progress and problems.

[0742] A "generative AI model" refers to the entire algorithm and system used to realize generative artificial intelligence.

[0743] A "prompt statement" refers to an input statement used to give instructions or questions to a generative AI model.

[0744] This system is designed to enable users to efficiently manage their work and quickly resolve issues. The implementation of the invention is primarily achieved through the interaction of servers, terminals, and users.

[0745] The server is the core of this system, responsible for collecting and analyzing information. Specifically, the server extracts information from business databases and analyzes the data using machine learning algorithms. In this process, clustering techniques and regression analysis are used to identify relevant patterns and anomalies within the data. The server also utilizes generative artificial intelligence models to generate solutions and prepares them for provision to users. In generating solutions, past data and case studies are considered, and highly reliable predictions are incorporated.

[0746] On the other hand, the terminal functions as an interface connecting the user and the server. The terminal is a device that receives input from the user and transmits it to the server. It also plays a role in presenting solutions provided by the server to the user and prompting them to make a choice. The terminal implements a user interface that prioritizes ease of use, allowing for the sending and receiving of information with simple operations.

[0747] Users input business information into the system via a terminal and consider the generated solutions. The interactive interface allows users to organize their issues and select specific solutions. For example, if a user wants to improve project progress, they can input a prompt such as "What are effective ways to improve project progress?" into the system and receive appropriate solutions.

[0748] In this way, servers, terminals, and users work together to achieve effective business management and problem-solving.

[0749] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0750] Step 1:

[0751] Users use a terminal to input their work information and the challenges they are currently facing. This input includes the progress of their work, specific problems, and areas for improvement. The terminal then sends this information received from the user to the server.

[0752] Step 2:

[0753] The server receives business data sent from the terminal and begins analysis. It accesses the database, verifies the data's up-to-dateness, and then processes it through machine learning algorithms. Here, clustering and regression analysis are used to extract relevant patterns and anomalies from the data. The analysis results are stored on the server for subsequent processing.

[0754] Step 3:

[0755] The server generates solutions using a generative AI model based on the analyzed data. During this process, it considers similar past cases and data as prompt inputs, asking questions such as, "What are effective ways to improve project progress?" The generative AI model responds to these prompts by generating specific solutions and returning them to the server.

[0756] Step 4:

[0757] The generated solutions are sent from the server to the terminal. The terminal displays this information in a user-friendly format and prompts the user to select the most appropriate solution. The user selects a solution based on the displayed information, using clicks or other actions.

[0758] Step 5:

[0759] The solution selected by the user is sent from the terminal to the server. Based on the received selection information, the server adjusts the resources within the system and puts the solution into action. If necessary, it also coordinates with external resources and applications to ensure that the execution process proceeds smoothly.

[0760] Step 6:

[0761] The server continuously monitors the implementation status of the solution and periodically generates reports on progress and results. These reports are provided to the user via their terminal. The user can then use these reports to consider their next course of action.

[0762] (Application Example 1)

[0763] 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".

[0764] Improving productivity and efficiency in factories requires real-time problem analysis and rapid implementation of countermeasures, but this has been difficult with conventional systems. Furthermore, maximizing the use of robots in the production environment requires dynamic and flexible planning and execution, but current methods are insufficient to address this.

[0765] 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.

[0766] In this invention, the server includes means for collecting and analyzing business information, means for identifying and organizing issues through dialogue with the user, and means for presenting generated solutions to the user and executing the selected solution. This enables real-time problem analysis and implementation of countermeasures in the production environment, making it possible to effectively utilize robots and improve productivity.

[0767] "Business information" refers to data and events related to the activities and processes that companies and organizations handle on a daily basis.

[0768] "Machine learning techniques" are technologies that allow computers to recognize patterns from data and automatically learn, predict, and optimize.

[0769] A "user interface" refers to the screens and input methods that allow a user to directly interact with a system.

[0770] A "strategy" is a specific action plan or measure proposed by a system to achieve a particular objective.

[0771] "Production environment" refers to the physical and operational conditions in a factory or manufacturing facility where products are processed and assembled.

[0772] A "robot" is a mechanical device designed and programmed to perform a specific task automatically.

[0773] An "action plan" is a schedule or set of procedures for efficiently and sequentially executing a series of actions that a system or robot will take.

[0774] The system implementing this invention is designed to improve the efficiency of business activities in a factory. The server collects business information from the production floor and performs analysis based on this information. TensorFlow, a machine learning method that runs on cloud services such as AWS Lambda, is used for the analysis. Based on the analysis results, the server uses a generative AI model to propose specific strategies to the user. The proposed strategies are processed by a generative AI model such as OpenAI GPT and presented to the user's terminal.

[0775] The devices used by users are smartphones, tablets, etc., and they interact with the server through a user interface. This interface provides a means for users to select and confirm proposed strategies. This allows users to choose strategies of their own volition and make efficient decisions.

[0776] The selected strategy is immediately executed by the server. During the execution phase, the robot in the production environment and the server communicate, and the robot's action plan is optimized and implemented via the MQTT protocol. This allows for rapid improvement in productivity.

[0777] As a concrete example, if the supply of a specified part is disrupted at a factory, the server identifies the cause through data analysis, and a generated AI model proposes an alternative method. Once the user approves the execution, the robot immediately selects a new path and resumes supply. This allows the production line to quickly return to normal.

[0778] An example of a prompt message is, "Based on the data analysis results from the production line, please suggest the optimal parts supply route and tell me how to improve production efficiency." This allows the system to provide appropriate solutions in response to the user's request.

[0779] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0780] Step 1:

[0781] The server collects operational information from various sensors and input terminals within the factory. This information includes production speed on the production line, parts supply status, and equipment operation data. The collected data is stored in the production management system. The input here is real-time data from sensors and terminals. As output, all information is saved in a database before analysis.

[0782] Step 2:

[0783] The server retrieves accumulated business information from the database using AWS Lambda and analyzes it using machine learning techniques (TensorFlow). The goal of the analysis is to identify patterns indicating performance degradation or anomalies. In this step, business information from the database is used as input, and the location of bottlenecks and anomalies is identified as output.

[0784] Step 3:

[0785] Based on the analysis results, the server generates specific policy proposals using a generative AI model (OpenAI GPT). The generated proposals include measures to improve operational efficiency and address anomalies. In this step, the input is the analysis results from step 2, and the output is the proposed policy statement. The proposals are generated in a language that is easy for the user to understand.

[0786] Step 4:

[0787] The terminal displays a list of proposed strategies in the user interface. The user reviews this list and selects a specific strategy based on their own judgment. The input is the generated strategy statement, and the strategy selected by the user is output. The terminal is responsible for presenting options and accepting user selection input.

[0788] Step 5:

[0789] The server executes the policy selected by the user. It communicates the optimal action plan to the robots in the production environment via the MQTT protocol, causing the robots to perform the configured actions. The input is the policy selected by the user, and the output is an improvement in the ongoing production activity. This process makes the robots' movements more efficient.

[0790] Step 6:

[0791] The server monitors the progress and achievement of the implemented measures and generates reports periodically. These reports are sent to terminals, allowing users to accurately understand the improvements being made to the production environment. Inputs are feedback data from robots and production activities, and outputs are status reports for the user.

[0792] 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.

[0793] This invention improves the quality of dialogue by combining a system that streamlines business management and supports problem-solving with an emotion engine that recognizes user emotions. The system's main components are a server that processes business data, a terminal that functions as an interface with the user, and the user themselves.

[0794] The server collects and analyzes necessary data from the business database. Machine learning algorithms are used to identify patterns and anomalies in the business data, extracting meaningful information. The obtained information is then used to organize the issues identified through interaction with users and to formulate solutions.

[0795] The terminal serves as an interface that supports communication with the user, obtaining user feedback through dialogue. In this process, an emotion engine is utilized to analyze the user's emotional state. The terminal infers emotions from voice tone and input data, and sends this information to the server.

[0796] The server analyzes the user's emotional state and generates optimal solutions. These solutions are adjusted according to the user's emotions and current work situation, making them more effective and acceptable. The terminal then presents these solutions to the user and prompts them to make a choice.

[0797] For example, if a user is experiencing excessive stress, the emotion engine will detect this and prioritize suggesting measures to reduce stress. These measures may include task redistribution and scheduling, and the server will automatically arrange the necessary resources for their execution.

[0798] Furthermore, the server evaluates work progress and the quality of deliverables, including from an emotional perspective, and generates detailed reports. This allows users to understand the impact of their emotional state on their work and use that information to implement improvement measures.

[0799] Thus, the present invention provides a system that improves operational efficiency and user satisfaction by incorporating an emotion engine and enabling individualized responses according to the user's emotional state.

[0800] The following describes the processing flow.

[0801] Step 1:

[0802] The server collects business data, such as tasks and logs, from the business database. This includes a data collection process based on a regular schedule.

[0803] Step 2:

[0804] The server analyzes the collected business data using machine learning algorithms. Based on the analysis results, it identifies patterns and anomalies in the data and understands the user's work situation.

[0805] Step 3:

[0806] The device initiates interaction with the user. It presents questions through the interface and collects information about the user's work-related challenges.

[0807] Step 4:

[0808] The device activates an emotion engine to analyze the user's emotions from their voice tone and text input. The emotion engine determines the emotional state and sends the result to the server.

[0809] Step 5:

[0810] The server integrates emotional information with analyzed business data to identify user issues. It then generates specific solutions to these issues and adjusts them optimally according to the user's emotional state.

[0811] Step 6:

[0812] The device presents the generated countermeasures to the user. The user can select the most suitable countermeasure from multiple options.

[0813] Step 7:

[0814] The server initiates the specific procedures for implementing the selected countermeasures. It arranges the necessary resources and coordinates with external systems, aiming for immediate execution.

[0815] Step 8:

[0816] The server monitors the progress of implemented countermeasures and performs evaluations that include sentiment information. It fine-tunes the countermeasures as needed to optimize the results.

[0817] Step 9:

[0818] The server generates detailed reports on the progress of tasks and the emotional state of users. These reports are provided to users via their terminals.

[0819] Step 10:

[0820] Based on the provided report, users will decide on the next steps to improve their work and emotional state.

[0821] This system supports business efficiency and effective problem-solving by taking into account the user's emotional state through an emotion engine.

[0822] (Example 2)

[0823] 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".

[0824] The present invention aims to achieve efficient problem-solving in business management while considering the emotional aspects of users. Conventional business management systems have the problem of not being able to reflect the emotional state of users, and thus failing to contribute to improving business efficiency and user satisfaction. Therefore, there is a need for a system that can detect the emotional state of users and reflect it in business decision-making.

[0825] 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.

[0826] In this invention, the server includes means for collecting and analyzing business resources, means for identifying and organizing issues through interaction with the user, means for presenting generated solutions to the user and implementing the selected solution, means for evaluating progress and problems and creating reports, means for recognizing and analyzing the user's emotional state, and means for generating optimal countermeasures that take into account the user's emotional information using a generative AI model. This enables individualized responses according to the user's emotional state, as well as improvements in business efficiency and user satisfaction.

[0827] "Business resources" refer to the information and data necessary for an organization or individual to carry out its work.

[0828] "Collection" is the process of gathering data and information based on a specific purpose.

[0829] "Analysis" is the act of analyzing collected data and information to understand and clarify its meaning and trends.

[0830] A "user" is an individual or organization that uses a system or service.

[0831] "Interaction" refers to the exchange of information and instructions between a user and a system.

[0832] A "challenge" refers to a problem or challenge that needs to be solved.

[0833] "Identification" means clearly defining the issue or subject and identifying it.

[0834] "Organization" refers to the process of arranging and compiling information and data in an orderly manner.

[0835] A "solution" refers to a method or means of solving an identified problem or issue.

[0836] "Execution" means actually putting plans and measures into practice.

[0837] "Progress status" refers to the progress toward the completion of a task or project.

[0838] A "problem" refers to a failure or malfunction that occurs within a business or system.

[0839] "Evaluation" is the act of judging the value and significance of an object and determining the outcome.

[0840] A "report" is a document that compiles information on a specific topic or issue.

[0841] "Emotional state" refers to an individual's emotional and psychological state at a given point in time.

[0842] "Recognition" means grasping and understanding emotions and situations.

[0843] A "generative AI model" refers to a collection of algorithms designed based on artificial intelligence to create new information and data.

[0844] "Taking into account" means adjusting the whole by taking certain factors or information into consideration.

[0845] "Optimal" means being the most appropriate and effective.

[0846] A "proposed solution" refers to specific policies or plans to be implemented in response to anticipated problems or existing challenges.

[0847] "Individualized support" means implementing specific countermeasures tailored to each user and their specific problem.

[0848] "Business efficiency" refers to the efficiency and productivity of tasks performed in carrying out business operations.

[0849] "User satisfaction" refers to an evaluation of the degree of satisfaction users feel when using a service or system.

[0850] This invention relates to a system for improving the efficiency of business management and providing individualized support that takes into account the emotional state of users. This system analyzes business data and combines it with user emotions to provide the optimal solution.

[0851] The server's role is to collect and analyze business resources. It retrieves necessary information from the business database and analyzes the data using machine learning algorithms. Specifically, it can utilize anomaly detection algorithms and clustering algorithms. From the analyzed data, it identifies important business trends and anomalies. Then, it uses a generative AI model to generate optimal countermeasures that take into account user sentiment. For example, when considering measures to reduce the workload, it uses the prompt message "Consider the current sentiment state and generate recommended measures to reduce the user's workload" to create countermeasures.

[0852] The terminal functions as an interface with the user. It receives voice and text input from the user and processes the input using speech recognition software. The terminal converts voice data into text and analyzes the user's emotional state using an emotion engine. The analyzed emotional information is sent to a server and used to generate countermeasures.

[0853] Users receive proposed solutions via their terminals and select the one best suited to their work. The user's selection is fed back to the server via the terminal, and the selected solution is implemented by the system. The overall effect of this system is that users receive personalized support tailored to their emotional state, leading to improved work efficiency and user satisfaction.

[0854] Thus, the present invention improves business management processes and provides an efficient and emotionally sensitive interactive system through the analysis of business data and the generation of countermeasures that take into account user emotional information.

[0855] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0856] Step 1:

[0857] The server retrieves business resources from the business database. Input is a data retrieval request, such as an SQL query. Output is the retrieved business data. After retrieving the data, the server analyzes it using machine learning algorithms (e.g., anomaly detection algorithms) to identify useful patterns and anomalies. This analysis process involves processing large amounts of data and performing statistical analysis to obtain the analysis results as output.

[0858] Step 2:

[0859] The device accepts voice input to collect user feedback. The input is the user's voice data. The device converts this voice data into text data using speech recognition software. Furthermore, it uses an emotion engine to infer and analyze the user's emotional state from this text data and voice tone. The output is the analyzed emotion information. This emotion information is crucial for understanding the context of the conversation.

[0860] Step 3:

[0861] The server receives emotional information sent from the terminal. The input consists of the user's emotional information and the results of the work analysis. Based on this information, the server uses a generative AI model to generate optimal countermeasures that take the user's emotional information into account. The prompt used is "Consider the current emotional state and generate recommended measures to reduce the user's workload." The output is the generated countermeasures.

[0862] Step 4:

[0863] The terminal receives proposed solutions sent from the server. The input is the generated solutions. The terminal presents these solutions to the user and prompts them to make a choice through an intuitively understandable interface. Output feedback is obtained by receiving user selection feedback.

[0864] Step 5:

[0865] The server prepares to implement the action plan selected by the user. The input is the user's selection. Based on this, the server adjusts the relevant business resources and performs scheduling and task reallocation. The output is the business tasks that have been put into action and their progress information.

[0866] Step 6:

[0867] The server monitors the progress of tasks and generates reports that include emotional perspectives. Inputs include progress data and emotional information. The server integrates these to evaluate the overall work situation and outputs a report. This report is provided to the user via a terminal, allowing them to understand the impact of their emotions on their work and use it as a reference for improvement measures.

[0868] (Application Example 2)

[0869] 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." Conventional business management systems have a problem in that they proceed with work without adequately considering the emotional state of the user, leading to the accumulation of user stress and dissatisfaction, and a decrease in work efficiency and user satisfaction. To solve this problem, it is necessary to accurately analyze the emotional state of the user and provide appropriate countermeasures based on that analysis.

[0870] (Means for solving the problem)

[0871] In this invention, the server includes means for collecting and analyzing business data, means for identifying and organizing issues through interaction with the user, and means for analyzing the user's emotional state and proposing an appropriate care plan based on those emotions. This enables flexible care that responds to the user's emotions.

[0872] 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.

[0873] Conventional business management systems often fail to adequately consider users' emotional states, leading to the accumulation of user stress and dissatisfaction, and ultimately reducing work efficiency and user satisfaction. To solve this problem, it is necessary to accurately analyze users' emotional states and provide appropriate countermeasures based on that analysis.

[0874] (Means for solving the problem)

[0875] In this invention, the server includes means for collecting and analyzing business data, means for identifying and organizing issues through interaction with the user, and means for analyzing the user's emotional state and proposing an appropriate care plan based on those emotions. This enables flexible care that responds to the user's emotions.

[0876] "Business data" refers to a collection of information generated from the daily activities of an organization or company, and is used to improve and streamline operations.

[0877] "Means of analysis" refer to methods and processes for analyzing collected data in detail using various technologies and extracting meaningful information.

[0878] A "user" refers to an individual or organization that interacts with or operates through this system, and is primarily the recipient of the services and information provided by the system.

[0879] "Dialogue" is the process by which a user and a system exchange information, and it is carried out through voice or text.

[0880] "Methods for identifying and organizing issues" refer to methods and processes for finding problems and areas for improvement through dialogue with users, and then effectively structuring them.

[0881] "Emotional state" refers to the psychological and physiological state a user is experiencing at a given point in time, and is composed of elements such as stress and a sense of security.

[0882] A "care plan" is a set of plans and ideas that propose the most appropriate actions and treatments according to the user's emotions and circumstances.

[0883] "Means of suggestion" refers to methods and functions that, based on analyzed information, present users with the most suitable solutions or action plans.

[0884] The system for implementing this invention streamlines business management by recognizing the user's emotions and proposing an appropriate care plan. The system mainly consists of three elements: a server, a terminal, and the user.

[0885] The server is the primary component for analyzing business data and user emotional states. The server utilizes machine learning algorithms such as the Google Cloud Speech-to-Text API and TensorFlow for data analysis. The server collects speech data and performs calculations for emotion recognition. Based on this information, it generates and presents the most suitable care plan for the user.

[0886] The terminal functions as an interface with the user and transmits data such as voice to the server. Based on the voice data collected from the user, it uses an emotion engine to infer the user's emotional state in real time and transmits it to the server. The terminal provides the user with intuitive and convenient operation.

[0887] Users interact with the server using their devices and receive feedback tailored to their emotional state and work situation. The care plans proposed to the users are tailored to their emotional state, such as suggestions for activities to reduce stress.

[0888] As a concrete example, consider a situation where a user feels anxious after lunch. In this case, the system analyzes the user's voice tone and feedback and determines that they are feeling anxious. The server then generates a care plan recommending a walk and proposes it to the user via the terminal.

[0889] An example of a prompt for a generative AI model could be: "Please suggest appropriate activities when the user feels anxious. List options for activities that reduce stress and briefly explain the effect of each."

[0890] This invention provides a system that utilizes an emotion engine to efficiently improve user satisfaction.

[0891] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0892] Step 1:

[0893] The device collects the user's voice data through the microphone. The input data is captured as an audio file. This data is temporarily stored locally in preparation for transmission to the server in real time.

[0894] Step 2:

[0895] The server receives audio data sent from the terminal. It uses the Google Cloud Speech-to-Text API to convert the received audio data into text. The input is an audio file, and the output is parsed text data. The API generates highly accurate text data from audio.

[0896] Step 3:

[0897] The server performs sentiment analysis using the obtained text data. This process applies a sentiment analysis model utilizing TensorFlow. The input is text data, and the output is the user's emotional state (e.g., "anxiety," "stress"). The analysis model extracts emotion-related keywords from the text and identifies the emotional state.

[0898] Step 4:

[0899] The server generates an optimal care plan for the user based on the results of an analysis of their emotional state. The care plan generation uses a generative AI model that incorporates past work data and emotional analysis results. The input is the user's emotional state and work data, and the output is a specific care plan (e.g., "Suggestion for a walk").

[0900] Step 5:

[0901] The terminal presents the user with a care plan sent from the server. The proposal is displayed visually on the terminal's screen and also provides auditory guidance using the voice output function. The user can review the presented care plan and, if necessary, select or begin implementation.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] 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.

[0909] 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.

[0910] 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."

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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 to be incorporated by reference.

[0923] The following is further disclosed regarding the embodiments described above.

[0924] (Claim 1)

[0925] Means for collecting and analyzing business data,

[0926] A means of identifying and organizing issues through dialogue with users,

[0927] The generated countermeasures are presented to the user, and the means of implementing the selected countermeasures are provided.

[0928] A means of generating and providing reports on progress and problems,

[0929] A system that includes this.

[0930] (Claim 2)

[0931] The system according to claim 1, comprising means for analyzing business data using a machine learning algorithm.

[0932] (Claim 3)

[0933] The system according to claim 1, comprising means for prompting the user to select a countermeasure through a user interface.

[0934] "Example 1"

[0935] (Claim 1)

[0936] A device for collecting and analyzing information,

[0937] A device that identifies and organizes issues through dialogue with users,

[0938] A device that presents the generated solutions to the user and executes the selected solution,

[0939] A device that generates and provides reports on processes and problems,

[0940] A system that includes this.

[0941] (Claim 2)

[0942] The system according to claim 1, comprising a device for analyzing information using a machine learning algorithm.

[0943] (Claim 3)

[0944] The system according to claim 1, comprising a device that prompts the user to select a solution through a user interface.

[0945] "Application Example 1"

[0946] (Claim 1)

[0947] Means for collecting and analyzing business information,

[0948] A means of identifying and organizing issues through dialogue with users,

[0949] A means of presenting the generated policies to the user and executing the selected policy,

[0950] A means of generating and providing reports on progress and problems,

[0951] Means to support efficiency improvements in the production environment,

[0952] A means of communicating with robots and executing optimal action plans,

[0953] A system that includes this.

[0954] (Claim 2)

[0955] The system according to claim 1, comprising means for analyzing business information using machine learning techniques.

[0956] (Claim 3)

[0957] The system according to claim 1, comprising means for prompting the selection of a strategy through a user interface.

[0958] "Example 2 of combining an emotion engine"

[0959] (Claim 1)

[0960] Means for collecting and analyzing business resources,

[0961] A means of identifying and organizing issues through user interaction,

[0962] The generated solutions are presented to the user, and the means to implement the selected solution are provided.

[0963] A means of evaluating progress and problems and creating a report,

[0964] A means of recognizing and analyzing the emotional state of users,

[0965] A method for generating optimal countermeasures that take into account user emotional information using a generative AI model,

[0966] A system that includes this.

[0967] (Claim 2)

[0968] The system according to claim 1, comprising means for analyzing business resources using a machine learning algorithm.

[0969] (Claim 3)

[0970] The system according to claim 1, comprising means for prompting the user to select a solution via a user interface.

[0971] "Application example 2 when combining with an emotional engine"

[0972] (Claim 1)

[0973] Means for collecting and analyzing business data,

[0974] A means of identifying and organizing issues through dialogue with users,

[0975] A means of analyzing the user's emotional state and proposing an appropriate care plan based on those emotions,

[0976] The generated countermeasures are presented to the user, and the means of implementing the selected countermeasures are provided.

[0977] A means of generating and providing reports on progress and problems,

[0978] A system that includes this.

[0979] (Claim 2)

[0980] The system according to claim 1, comprising means for analyzing business data using a machine learning algorithm and integrating the results with emotion recognition to analyze the data.

[0981] (Claim 3)

[0982] The system according to claim 1, comprising means for prompting the user to select a countermeasure that takes into account their emotional state through a user interface. [Explanation of Symbols]

[0983] 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 for collecting and analyzing business information, A means of identifying and organizing issues through dialogue with users, A means of presenting the generated policies to the user and executing the selected policy, A means of generating and providing reports on progress and problems, Means to support efficiency improvements in the production environment, A means of communicating with robots and executing optimal action plans, A system that includes this.

2. The system according to claim 1, comprising means for analyzing business information using machine learning techniques.

3. The system according to claim 1, comprising means for prompting the user to select a strategy through a user interface.

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