system

Abstract

We provide the system. [Solution] A means of analyzing past information and automatically generating the optimal document, A means of providing immediate solutions based on past cases of troubleshooting, A means to visualize the technology and knowledge within an organization and automatically identify the appropriate consultant, A means of improving operational efficiency in facility management by analyzing past management cases and proposing the optimal management strategy, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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] There is a need to solve problems such as a decrease in business efficiency within an enterprise, a delay in information sharing, a delay in claim handling, and the difficulty of identifying an appropriate consultation destination. These problems cause delays in business processes and are likely to ultimately affect the performance of the entire enterprise. Therefore, there is a need to provide a system that can quickly and effectively respond to these problems.

Means for Solving the Problems

[0005] This invention aims to streamline proposal work by using a system that automatically generates optimal proposals by analyzing past data. Furthermore, it accelerates complaint handling by providing immediate solutions based on past complaint resolution cases. In addition, it enables efficient consultation by visualizing internal skills and experience and automatically searching for and suggesting appropriate consultants. By combining these methods, it achieves faster operations and effective knowledge sharing, comprehensively solving corporate challenges.

[0006] "Past data" refers to project information, proposals, complaint handling history, employee skill information, and other data accumulated within the company in the past.

[0007] "Analysis" refers to processing collected data and performing calculations and evaluations to identify relationships and patterns within it.

[0008] A "proposal" is a document that outlines the proposed content for a specific project, and includes information such as the project's objectives, methods, and expected outcomes.

[0009] "Automatic generation" refers to the process by which a system autonomously creates necessary documents and data without human intervention.

[0010] "Handling complaints" refers to activities aimed at providing appropriate solutions and promptly addressing customer complaints and problem reports.

[0011] "Countermeasures" refer to a series of actions or plans formulated to address a specific problem or issue.

[0012] "Visualization" means representing data and information visually and providing them in an easy-to-understand format.

[0013] A "consultant" refers to someone who possesses specific skills and experience and can provide appropriate advice and information in response to inquiries from users.

[0014] A "system" refers to a collection of multiple elements (hardware, software, networks, etc.) configured to achieve a specific purpose. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is a technology that uses AI to generate proposals based on historical data, with the aim of improving operational efficiency within companies. This system allows users to input information via a terminal, and the server analyzes collected past project data and proposals to automatically generate appropriate proposals. The proposals can be customized based on templates, enabling rapid and efficient generation.

[0037] For example, if a user needs to create a proposal for a new project, they input the basic project information into their terminal. The server then searches for the most suitable past examples based on that information, automatically generates a proposal, and sends it to the user's terminal. The user can then review the received proposal, add their own project information, and complete it.

[0038] In handling complaints, when a user enters the complaint details on a terminal, the server searches past complaint handling cases for relevant solutions in real time and provides them to the user immediately. Based on this information, the user can process the complaint quickly, and the server plays a role in continuously improving the solutions by collecting subsequent feedback.

[0039] Furthermore, the system of this invention has the function of visualizing the skills and experience of employees within the company and quickly finding the appropriate person to consult. The user enters the consultation details into a terminal, and the server analyzes the information and selects the most suitable consultant from a skills matrix. The server automatically approaches the found consultant, arranges the consultation, and notifies the user's terminal of the details.

[0040] Thus, this invention streamlines internal business processes, accelerates information sharing, and improves complaint handling. Furthermore, by optimally utilizing internal resources, it contributes to improving the overall performance of the company.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user logs into their device and selects one of the following objectives: creating a new proposal, handling a complaint, or seeking advice.

[0044] Step 2:

[0045] The user enters the necessary details on the device. For example, when creating a proposal, they would enter the project's objectives and requirements.

[0046] Step 3:

[0047] The server analyzes the information received from the user and searches the database for relevant historical data.

[0048] Step 4:

[0049] When creating a proposal, the server identifies similar proposal examples and uses a generation AI to automatically generate a proposal based on a template.

[0050] Step 5:

[0051] The server generates a proposal and sends it to the user's terminal, where the user can review and edit it.

[0052] Step 6:

[0053] In the case of a complaint, the server analyzes past complaint cases and immediately provides relevant solutions to the user's terminal.

[0054] Step 7:

[0055] In the case of matching consultants, the server selects the most suitable consultant from the skills matrix and recommends them to the user.

[0056] Step 8:

[0057] The server automatically contacts the selected consultant and sets a consultation schedule.

[0058] Step 9:

[0059] The server sends the final deliverables (proposal, countermeasures, consultation schedule) to the user's terminal, and the user reviews them.

[0060] (Example 1)

[0061] 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."

[0062] Many inefficiencies exist in business processes within companies, and in particular, the accumulation of experience and know-how is not being fully utilized in areas such as proposal writing and complaint handling. Furthermore, the appropriate use of skills and resources is difficult, making it challenging to respond quickly and appropriately. These problems need to be solved to improve and streamline business operations.

[0063] 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.

[0064] In this invention, the server includes means for users to input information via a terminal, analyze past data using a generation AI model, and automatically generate an optimal proposal; means for immediately providing complaint response measures based on past response cases in response to the input complaint content; and means for analyzing internal resources to automatically search for and approach appropriate consultants. This promotes the use of information within the company, enables faster proposal creation and complaint response, and optimizes resource allocation.

[0065] A "user" is an entity that uses the system to input information and receives proposals and complaint resolution strategies.

[0066] A "terminal" is a computer device used by users to input information or to review and edit proposals and countermeasures sent from a server.

[0067] A "server" is a centralized computer system that receives input information from users, performs data analysis and document generation using a generation AI model, and provides the results to the user.

[0068] A "generative AI model" is an artificial intelligence technology that learns from a large amount of historical data and automatically generates optimal proposals and complaint handling strategies based on the input information.

[0069] A "proposal" is a document that summarizes the project's objectives, strategy, and expected results, and is ultimately completed by the user.

[0070] A "complaint handling strategy" refers to a solution or response method that is immediately proposed for a specific complaint based on past cases.

[0071] "Feedback" refers to the process of receiving evaluations and opinions from users, and the information used to improve the generative AI model.

[0072] "Resources" is a general term for assets available within a company, including skills, experience, and human resources.

[0073] A "prompt" is a goal-setting statement automatically generated by a generative AI model based on user input, and it forms the basis for appropriate documents and countermeasures.

[0074] Users input information about new projects and details of claims through their terminals. The terminals are configured to send this input information to a server in real time. Upon receiving the input, the server utilizes a generative AI model to analyze the data. Specific processing includes searching historical data, pattern recognition, and generating optimal proposals and countermeasures. The generative AI model has learned from a large amount of past cases and suggests appropriate actions by evaluating similarities.

[0075] The system on which the server is built includes database management software and machine learning frameworks for running AI models (such as TENSORFLOW® and PyTorch). The generated proposals and complaint resolutions are automatically sent to the user's terminal, where the user can review and edit them.

[0076] For example, if a user wants to develop a sales plan for a new product, they would input the product name, target market, and expected sales volume on their device. The server would then use this information to run an AI model, referencing similar past sales plans to generate a draft proposal.

[0077] An example of a prompt might be, "I want to create a proposal for a new product. Please tell me the product name and sales strategy." Based on such prompts, the server can analyze past data and provide the most suitable document.

[0078] This system aims to improve the speed and accuracy of business operations within a company by streamlining business processes and optimizing resources.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] Users input information into the system using a terminal. Specifically, they enter basic information about a new project and details of the claims in text format. The information entered includes the project name, overview, targets, and key issues. The entered data is sent directly from the terminal to the server.

[0082] Step 2:

[0083] The server receives input information sent from the terminal. Based on the input data, it prepares it as a base dataset for use by the generative AI model. This step also includes error checking to ensure the information has been received correctly. The received information is stored in a database and ready for analysis.

[0084] Step 3:

[0085] The server operates a generative AI model based on the received information. Specifically, it searches past case data in the database and performs calculations to evaluate similarity. The AI ​​model treats the new input information as a prompt, analyzes relevant past cases, and extracts appropriate templates and elements. The output here is a series of proposals or draft solutions.

[0086] Step 4:

[0087] The server generates a draft proposal or claim response plan created by the generative AI model. At this stage, elements derived from past cases are combined and customized according to the template. The completed document is generated in electronic format and ready for transmission to the terminal.

[0088] Step 5:

[0089] The server sends the generated document to the user's terminal. The user can receive the proposal or countermeasure on their terminal and review its contents. The output here is a completed document that the user can directly edit.

[0090] Step 6:

[0091] Users review proposals and action plans on their devices, adding specific information and making minor adjustments as needed. Once the editing is complete and the document aligns with the final objective, they submit or save it. The completed document becomes the output used in actual work.

[0092] Step 7:

[0093] The server receives feedback from users and uses it to improve the accuracy of the generated AI model. This feedback is reflected in subsequent data analysis and document generation processes. As information is accumulated for data processing and learning, the AI ​​model will continuously improve.

[0094] (Application Example 1)

[0095] 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."

[0096] To improve operational efficiency, provide solutions quickly, and optimize equipment management, it is essential to effectively utilize historical data. However, rapidly and accurately analyzing large amounts of historical data within an organization and deriving optimal solutions and management strategies is not easy. Furthermore, visualizing the technology and knowledge within an organization and smoothly identifying the appropriate personnel is also difficult. There is a need for a method that can solve these challenges and dramatically improve operational efficiency.

[0097] 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.

[0098] In this invention, the server includes means for analyzing past information and automatically generating optimal documents, means for providing immediate solutions based on past cases of troubleshooting, means for visualizing the technology and knowledge within the organization and automatically identifying appropriate consultants, and means for analyzing past management cases to present optimal management strategies in order to improve operational efficiency in equipment management. This enables rapid information analysis and efficient operational management.

[0099] "Past information" refers to previously recorded data and case studies, and analyzing this information can provide insights that are useful for current operations.

[0100] "Analysis" is the process of thoroughly examining information and extracting meaningful patterns and insights.

[0101] An "optimal document" is one that is structured in a way that best suits the case or requirements, and comprehensively provides useful information to the user.

[0102] "Past troubleshooting cases" refer to data that records problems that have occurred in the past and their solutions, which can be useful in solving similar problems.

[0103] "Providing immediate solutions" means promptly presenting appropriate solutions when a user enters a problem.

[0104] "Organizational technology and knowledge" refers to the collective term for the specialized skills and expertise possessed by a particular organization.

[0105] "Visualization" refers to displaying abstract data and technologies in an easily understandable format, thereby facilitating judgment and decision-making.

[0106] "Automatically identifying the appropriate consultant" means that, based on the analysis results, the system automatically selects the most suitable person with the necessary skills and knowledge.

[0107] "Facilities management" refers to the maintenance and management necessary to ensure the efficient operation of buildings and facilities.

[0108] "Improving operational efficiency" means optimizing resource usage to reduce costs and increase productivity.

[0109] A "management strategy" is a plan or methodology developed to achieve specific objectives in the operation of an organization or its facilities.

[0110] In this invention, the system primarily uses a server to perform various information analyses and generate appropriate documents. This system utilizes programming languages ​​such as Python and R, and employs libraries like Pandas and NumPy for data analysis and processing. A generative AI model built using TensorFlow or PyTorch generates optimal suggestions and solutions based on historical data.

[0111] The server receives and analyzes information entered by users from their terminals. During this process, the server rapidly searches through a large amount of historical data and extracts relevant information. This allows for the analysis of past management cases and the presentation of efficient operational strategies for equipment management. This information is then delivered to the user's terminal as a web application using Flask or Django.

[0112] As a concrete example, if a data center's cooling system detects an anomaly, the server quickly generates a solution using data from past troubleshooting and immediately sends that information to the user's terminal. The generative AI model plays a central role throughout this process, deriving situation-appropriate countermeasures in real time, as provided by prompt messages.

[0113] An example of a prompt message is, "A rapid temperature increase has been detected in the cooling system. Please generate the optimal countermeasure from past data." This allows the system to extract relevant data and present an optimized solution.

[0114] In this way, this invention enables the rapid generation and response of information, and greatly contributes to improving operational efficiency.

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

[0116] Step 1:

[0117] The user uses a terminal to input specific information into the system. The terminal sends the input to the server as an HTTP request. The input here is, for example, a prompt message such as "A rapid temperature increase has been detected in the cooling system."

[0118] Step 2:

[0119] The server receives an HTTP request and parses the information it contains. Python is used for the parsing, and the Pandas library is used to handle the input data as structured data. At this stage, the server understands the input prompt and generates an appropriate search query.

[0120] Step 3:

[0121] The server issues a search query to the database of past cases and extracts relevant data. The extracted data includes troubleshooting cases that the system has collected in the past. The server temporarily holds the search results in memory and prepares them for data analysis.

[0122] Step 4:

[0123] The server inputs the extracted data into an AI model to generate the optimal solution. The AI ​​model is built using TensorFlow and, based on learning from historical datasets, presents the most suitable solution for a given situation. This process evaluates the similarity and effectiveness of past solutions and generates the solution as output data.

[0124] Step 5:

[0125] The generated countermeasures are converted by the server into a data format such as JSON and sent to the terminal. The user receives the countermeasures on the terminal and can view specific action plans. The terminal screen visually represents the generated content and displays detailed information in an organized manner to support the user's decision-making.

[0126] Step 6:

[0127] Users review the provided information and implement countermeasures as needed. Furthermore, by sending feedback on the results of their actions to the server, the system collects data to further improve countermeasures and enhance the accuracy of future troubleshooting.

[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 is a system that incorporates an emotion engine to recognize user emotions, in addition to being a system aimed at improving operational efficiency within a company. This system analyzes past data and automatically generates optimal proposals, while simultaneously suggesting quick countermeasures based on past cases of complaint handling. It also has the ability to visualize internal skills and experience and automatically search for the most suitable consultant. Furthermore, it can use the emotion engine to recognize and evaluate user emotions in real time and adjust the system's operation based on the detected emotions.

[0130] This emotion engine recognizes emotions by analyzing user input information, communication history, and voice tone. For example, if an emotion indicating stress is detected while a user is using their device, the server will present the user with options to simplify their work. This allows the user to continue working without feeling burdened.

[0131] As a concrete example, consider a customer complaint handling scenario. After the user enters the details of the complaint into the terminal, the server provides standard solutions, but at the same time, an emotion engine assesses the user's stress level. If high stress is detected, the server suggests using an AI chatbot to ensure stable communication, or, in urgent cases, the intervention of a specialist assistant.

[0132] Furthermore, throughout the proposal creation process, the emotion engine constantly evaluates the user's emotions, determining whether the user is satisfied or confused. If the system determines that the user is confused, it will offer additional guidance and support to assist the user.

[0133] This invention not only pursues efficiency but also enables flexible support based on user emotions, thereby simultaneously achieving improvements in the quality of business processes within a company and increased employee satisfaction.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user logs into the terminal and selects the purpose of the system. Users can choose to create proposals, handle complaints, or seek advice.

[0137] Step 2:

[0138] The user enters the necessary details into the terminal. For example, when creating a proposal, they enter the project objectives and requirements specifications; when handling a complaint, they enter the details of the problem and customer information.

[0139] Step 3:

[0140] The server receives the input information and searches the database for relevant historical data.

[0141] Step 4:

[0142] When creating a proposal, the server identifies similar proposal examples and automatically generates a proposal based on a template using AI.

[0143] Step 5:

[0144] The server generates a proposal document, which is then sent to the user's terminal, allowing the user to review and edit it.

[0145] Step 6:

[0146] In the case of a complaint, the server quickly searches for relevant solutions based on past cases and provides them to the user's terminal. Furthermore, an emotion engine evaluates the user's emotions to determine if additional support is needed.

[0147] Step 7:

[0148] The emotion engine analyzes the user's speech, input, and voice to determine their emotions. If stress or anxiety is detected, the server will offer options to assist with system operation.

[0149] Step 8:

[0150] In the case of matching a consultant, the server analyzes the consultation content, searches and selects the most suitable consultant from a skills matrix, and automatically adjusts the schedules of the user and the consultant.

[0151] Step 9:

[0152] The server sends the final deliverables and results (e.g., edited proposals, complaint handling strategies, consultation schedules) to the user's terminal, where the user can review them.

[0153] (Example 2)

[0154] 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 will be referred to as the "terminal."

[0155] Improving operational efficiency and providing flexible solutions within an organization requires unprecedented data analysis accuracy, while simultaneously presenting the challenge of providing nuanced support that responds to users' emotions. Furthermore, there is a need to reduce user stress while simultaneously improving the quality and satisfaction of work.

[0156] 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.

[0157] In this invention, the server includes means for analyzing past information and automatically creating optimal documents, means for quickly suggesting countermeasures based on past complaint handling information, and means for analyzing the user's emotions in real time using emotion recognition technology and adjusting the system's operation accordingly. This makes it possible to improve operational efficiency while providing flexible responses and support that respond to the user's emotions.

[0158] "Past information" refers to data and records that were previously collected or generated within an organization.

[0159] "Means for automatically generating documents" refers to a process or technology that automatically generates documents using a computer system based on analyzed data.

[0160] "Past information on complaint handling" refers to data on past problem-solving cases and response histories.

[0161] "Means of providing prompt countermeasures" refers to methods and systems that provide users with quick and appropriate countermeasures based on analysis results.

[0162] "Methods for visualizing skills and knowledge within an organization" refer to methods that make internal resources easier to utilize by making the expertise and experience of individual members visible.

[0163] "Methods for automatically selecting a consultant" refer to technologies in which the system automatically selects the appropriate consultant based on the user's consultation content and circumstances.

[0164] "Emotion recognition technology" is a technology that analyzes a user's emotional state from voice, facial expressions, and behavioral data.

[0165] "Methods for analyzing user emotions in real time" refer to processes and technologies for instantly collecting and analyzing user emotional data.

[0166] "Means for adjusting system operation" refers to functions that dynamically change the system's operation and responses in response to analyzed emotional information.

[0167] "User device usage" refers to the user's behavioral patterns and operation history when operating computers, terminals, etc.

[0168] "Means of offering options to reduce stress" refer to the choices and support methods that the system provides to alleviate the user's mental burden.

[0169] This invention is a system designed to improve operational efficiency within a company, enabling real-time recognition of user emotions and flexible responses accordingly. Specifically, the server collects past business data and complaint handling history from a database and analyzes it using Python or a similar programming language. Based on the analyzed data, it utilizes the machine learning library TensorFlow to automatically generate optimal proposals.

[0170] The generated proposal is provided to the user via the terminal and converted into a format that can be reviewed and customized using the Microsoft® Word API. During user interaction, the terminal analyzes voice input and action logs using IBM Watson® and other emotion recognition algorithms to collect emotion data. Based on this emotion data, the server adjusts the system's operation and provides support measures tailored to the user's stress level. For example, if the system determines that the user is confused, it provides additional guidance.

[0171] As a concrete example, suppose a user encounters a roadblock while creating a proposal for a new project. In this situation, the terminal, based on server instructions, will present support options such as "Would you like to refer to other proposal examples?", allowing the user to receive assistance. Furthermore, by inputting a prompt such as "Please tell me about data analysis methods to streamline the creation of internal company proposals," the system will provide relevant information to support the user's work.

[0172] This system not only improves operational efficiency but also enables emotion-based user support, simultaneously achieving process improvements across the entire organization and increased employee satisfaction.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The server collects past business records and proposal examples from the company's database. It uses SQL queries as input to extract the necessary data. The extracted data is temporarily stored for later analysis. Specifically, the server periodically performs data retrieval tasks to maintain up-to-date information.

[0176] Step 2:

[0177] The server performs analysis based on the collected data to generate the optimal proposal. This analysis uses the Python programming language and a TensorFlow machine learning model. The input is the data obtained in step 1, and through a series of data processing and calculations, the optimal proposal is generated as output. Specific operations include feature extraction from the data and model training.

[0178] Step 3:

[0179] The server sends the generated proposal to the user's terminal using the Microsoft Word API, making it available for review and customization. The input is the proposal data generated in step 2, and the output is a proposal file usable by the user. The specific actions involve formatting the document and sending the file.

[0180] Step 4:

[0181] The terminal collects user operation logs and voice input. A dedicated application on the device is used for this purpose. Input includes user operation data and voice data, while output is data packets for analysis. Specifically, this involves a process where voice is recorded and then transcribed into text.

[0182] Step 5:

[0183] The server performs emotion recognition based on data sent from the terminal. The input is the data from step 4, and an emotion analysis algorithm (e.g., IBM Watson API) is used to determine the user's emotional state. The output is the emotion identification result. Specifically, an emotion score is calculated and determined.

[0184] Step 6:

[0185] The server, based on the emotion recognition results, will suggest appropriate countermeasures as needed. The input is the emotional state information obtained in step 5, and the output is support options and suggestions for the user. The specific action is the process of displaying a notification on the terminal such as "Would you like to call a specialist assistant?"

[0186] Step 7:

[0187] Users can receive support by selecting options presented through their device. Based on the selected option, they will interact with an AI chatbot or be contacted by a support representative. For example, one suggestion might be, "Would you like to see other suggested examples?", which allows the user to receive more detailed information.

[0188] (Application Example 2)

[0189] 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 as a "terminal".

[0190] This invention seeks to improve the quality of work and employee satisfaction by streamlining operations within companies while simultaneously recognizing user emotions in real time and responding flexibly based on those emotions. Furthermore, there is a growing need for personal assistant robots in the home to recognize emotions and reduce family stress and optimize the environment. Currently, there is a lack of systems that can meet these complex needs.

[0191] 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.

[0192] In this invention, the server includes means for analyzing past data and automatically generating optimal proposal documents, means for providing immediate solutions based on past cases of request handling, means for visualizing internal skills and experience and automatically searching for appropriate consultants, and means for recognizing emotions from voice and text and adapting to the environment. This enables both improved work efficiency and flexible responses based on emotions, thereby increasing satisfaction within companies and homes.

[0193] "Analyzing past data" is the act of analyzing information accumulated up to now to gain insights that will be useful for future activities and decisions.

[0194] "Automatically generating optimal proposal documents" refers to the process of automatically creating documents containing efficient and effective recommendations based on predefined criteria.

[0195] "Providing immediate solutions based on past examples of request handling" means referring to similar problem-solving methods used in the past and promptly presenting appropriate solutions.

[0196] "Making internal skills and experience visible" means clearly displaying and making easily understandable the abilities and past achievements of individuals within an organization.

[0197] "Automatically searching for the appropriate consultant" means that the system automatically selects the most suitable person based on the required expertise and experience.

[0198] "Recognizing emotions from voice and text" refers to a technology that analyzes linguistic information to understand and grasp the emotional state of the individual who transmitted that information.

[0199] "Adapting the environment" means adjusting the surrounding circumstances and settings based on the detected emotional state, with the aim of improving the user's comfort and effectiveness.

[0200] The system realizing this invention consists of a network environment including a server, user terminals, and a personal assistant device. The server collects and analyzes historical data to generate optimal proposal documents. It refers to a database of past cases to provide appropriate responses to claims and requests. Furthermore, it has a function to automatically search for appropriate consultants by utilizing an internal database to visualize skills and experience within the company.

[0201] On the user terminal, proposal documents can be received and reviewed, and their content can be customized as needed. Furthermore, data can be sent to the server via the feedback function to aid in continuous system improvement.

[0202] The emotion recognition engine uses Google® Cloud Speech-to-Text for voice input and OpenAI® APIs for emotion analysis to recognize the user's emotional state in real time by analyzing the user's voice and text data. This emotion data is sent to a server, which then adjusts the environment according to the user's emotions. For example, if signs of stress are detected in a family member, the personal assistant device will instruct a smart device to play relaxing music.

[0203] For example, if the server detects that the father is feeling fatigued while relaxing in the living room, the personal assistant will automatically select and play healing music, creating a more relaxing home environment.

[0204] An example of a prompt would be: "Explain how to analyze audio in a home and recognize emotions in real time using the OpenAI API."

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] The server retrieves the information necessary for generating the proposal document from the historical database. Based on this input data, it applies a generation algorithm to automatically generate the optimal proposal document. The output is the proposal document sent to the user's terminal.

[0208] Step 2:

[0209] The user terminal displays the received proposal document, allowing the user to review its contents. The user can customize the document, and once revisions are made, a revised proposal document is generated and sent back to the server as feedback.

[0210] Step 3:

[0211] The server references a database of past cases to search for similar cases in order to handle complaints. The input is detailed complaint information, and the server performs data analysis to select the appropriate course of action. Once a course of action is determined, that information is output to the user's terminal for the user to review.

[0212] Step 4:

[0213] The personal assistant device receives the user's voice input and converts it into text data using Google Cloud Speech-to-Text. Using this data as input, it analyzes the emotional data using the OpenAI API to recognize the user's emotional state. The resulting emotional information is then sent to the server.

[0214] Step 5:

[0215] The server evaluates the need for environmental adjustments based on the emotional information it receives. If it determines that environmental adjustments are necessary, it outputs appropriate instructions to the smart device and takes specific actions, such as playing relaxing music.

[0216] 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.

[0217] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] 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.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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".

[0232] This invention is a technology that uses AI to generate proposals based on historical data, with the aim of improving operational efficiency within companies. This system allows users to input information via a terminal, and the server analyzes collected past project data and proposals to automatically generate appropriate proposals. The proposals can be customized based on templates, enabling rapid and efficient generation.

[0233] For example, if a user needs to create a proposal for a new project, they input the basic project information into their terminal. The server then searches for the most suitable past examples based on that information, automatically generates a proposal, and sends it to the user's terminal. The user can then review the received proposal, add their own project information, and complete it.

[0234] In handling complaints, when a user enters the complaint details on a terminal, the server searches past complaint handling cases for relevant solutions in real time and provides them to the user immediately. Based on this information, the user can process the complaint quickly, and the server plays a role in continuously improving the solutions by collecting subsequent feedback.

[0235] Furthermore, the system of this invention has the function of visualizing the skills and experience of employees within the company and quickly finding the appropriate person to consult. The user enters the consultation details into a terminal, and the server analyzes the information and selects the most suitable consultant from a skills matrix. The server automatically approaches the found consultant, arranges the consultation, and notifies the user's terminal of the details.

[0236] Thus, this invention streamlines internal business processes, accelerates information sharing, and improves complaint handling. Furthermore, by optimally utilizing internal resources, it contributes to improving the overall performance of the company.

[0237] The following describes the processing flow.

[0238] Step 1:

[0239] The user logs into their device and selects one of the following objectives: creating a new proposal, handling a complaint, or seeking advice.

[0240] Step 2:

[0241] The user enters the necessary details on the device. For example, when creating a proposal, they would enter the project's objectives and requirements.

[0242] Step 3:

[0243] The server analyzes the information received from the user and searches the database for relevant historical data.

[0244] Step 4:

[0245] When creating a proposal, the server identifies similar proposal examples and uses a generation AI to automatically generate a proposal based on a template.

[0246] Step 5:

[0247] The server generates a proposal and sends it to the user's terminal, where the user can review and edit it.

[0248] Step 6:

[0249] In the case of a complaint, the server analyzes past complaint cases and immediately provides relevant solutions to the user's terminal.

[0250] Step 7:

[0251] In the case of matching consultants, the server selects the most suitable consultant from the skills matrix and recommends them to the user.

[0252] Step 8:

[0253] The server automatically contacts the selected consultant and sets a consultation schedule.

[0254] Step 9:

[0255] The server sends the final deliverables (proposal, countermeasures, consultation schedule) to the user's terminal, and the user reviews them.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] Many inefficiencies exist in business processes within companies, and in particular, the accumulation of experience and know-how is not being fully utilized in areas such as proposal writing and complaint handling. Furthermore, the appropriate use of skills and resources is difficult, making it challenging to respond quickly and appropriately. These problems need to be solved to improve and streamline business operations.

[0259] 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.

[0260] In this invention, the server includes means for users to input information via a terminal, analyze past data using a generation AI model, and automatically generate an optimal proposal; means for immediately providing complaint response measures based on past response cases in response to the input complaint content; and means for analyzing internal resources to automatically search for and approach appropriate consultants. This promotes the use of information within the company, enables faster proposal creation and complaint response, and optimizes resource allocation.

[0261] A "user" is an entity that uses the system to input information and receives proposals and complaint resolution strategies.

[0262] A "terminal" is a computer device used by users to input information or to review and edit proposals and countermeasures sent from a server.

[0263] A "server" is a centralized computer system that receives input information from users, performs data analysis and document generation using a generation AI model, and provides the results to the user.

[0264] A "generative AI model" is an artificial intelligence technology that learns from a large amount of historical data and automatically generates optimal proposals and complaint handling strategies based on the input information.

[0265] A "proposal" is a document that summarizes the project's objectives, strategy, and expected results, and is ultimately completed by the user.

[0266] A "complaint handling strategy" refers to a solution or response method that is immediately proposed for a specific complaint based on past cases.

[0267] "Feedback" refers to the process of receiving evaluations and opinions from users, and the information used to improve the generative AI model.

[0268] "Resources" is a general term for assets available within a company, including skills, experience, and human resources.

[0269] A "prompt" is a goal-setting statement automatically generated by a generative AI model based on user input, and it forms the basis for appropriate documents and countermeasures.

[0270] Users input information about new projects and details of claims through their terminals. The terminals are configured to send this input information to a server in real time. Upon receiving the input, the server utilizes a generative AI model to analyze the data. Specific processing includes searching historical data, pattern recognition, and generating optimal proposals and countermeasures. The generative AI model has learned from a large amount of past cases and suggests appropriate actions by evaluating similarities.

[0271] The system on which the server is built includes database management software and machine learning frameworks for running AI models (such as TensorFlow and PyTorch). The generated proposals and complaint resolutions are automatically sent to the user's terminal, where the user can review and edit them.

[0272] For example, if a user wants to develop a sales plan for a new product, they would input the product name, target market, and expected sales volume on their device. The server would then use this information to run an AI model, referencing similar past sales plans to generate a draft proposal.

[0273] An example of a prompt might be, "I want to create a proposal for a new product. Please tell me the product name and sales strategy." Based on such prompts, the server can analyze past data and provide the most suitable document.

[0274] This system aims to improve the speed and accuracy of business operations within a company by streamlining business processes and optimizing resources.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] Users input information into the system using a terminal. Specifically, they enter basic information about a new project and details of the claims in text format. The information entered includes the project name, overview, targets, and key issues. The entered data is sent directly from the terminal to the server.

[0278] Step 2:

[0279] The server receives input information sent from the terminal. Based on the input data, it prepares it as a base dataset for use by the generative AI model. This step also includes error checking to ensure the information has been received correctly. The received information is stored in a database and ready for analysis.

[0280] Step 3:

[0281] The server operates the generated AI model based on the received information. Specifically, it searches for past case data in the database and performs calculations for similarity evaluation. The AI model treats the input new information as a prompt, analyzes relevant past cases, and extracts appropriate templates and elements. The output here is a series of proposal documents or draft countermeasures.

[0282] Step 4:

[0283] The server generates a draft proposal or claim countermeasure created by the generated AI model. At this stage, elements obtained from past cases are combined and customized according to the template. The completed document is generated in electronic form and is ready to be sent to the terminal.

[0284] Step 5:

[0285] The server sends the generated document to the user's terminal. The user can receive the sent proposal and countermeasure on the terminal and view the content. The output here is a completed document that can be directly edited by the user.

[0286] Step 6:

[0287] The user checks the proposal and countermeasure documents on the terminal, adds specific information or fine-tunes the content as needed. When the editing is completed according to the final goal, the document is submitted or saved. The completed document becomes the output for actual business use.

[0288] Step 7:

[0289] The server receives feedback from the user and uses it to improve the accuracy of the generated AI model. This feedback is reflected in the next data analysis and document generation processes. Since information is accumulated for data processing and learning, the AI model will be continuously improved.

[0290] (Application Example 1)

[0291] 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."

[0292] To improve operational efficiency, provide solutions quickly, and optimize equipment management, it is essential to effectively utilize historical data. However, rapidly and accurately analyzing large amounts of historical data within an organization and deriving optimal solutions and management strategies is not easy. Furthermore, visualizing the technology and knowledge within an organization and smoothly identifying the appropriate personnel is also difficult. There is a need for a method that can solve these challenges and dramatically improve operational efficiency.

[0293] 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.

[0294] In this invention, the server includes means for analyzing past information and automatically generating optimal documents, means for providing immediate solutions based on past cases of troubleshooting, means for visualizing the technology and knowledge within the organization and automatically identifying appropriate consultants, and means for analyzing past management cases to present optimal management strategies in order to improve operational efficiency in equipment management. This enables rapid information analysis and efficient operational management.

[0295] "Past information" refers to previously recorded data and case studies, and analyzing this information can provide insights that are useful for current operations.

[0296] "Analysis" is the process of thoroughly examining information and extracting meaningful patterns and insights.

[0297] An "optimal document" is one that is structured in a way that best suits the case or requirements, and comprehensively provides useful information to the user.

[0298] "Past troubleshooting cases" refer to data that records problems that have occurred in the past and their solutions, which can be useful in solving similar problems.

[0299] "Providing immediate solutions" means promptly presenting appropriate solutions when a user enters a problem.

[0300] "Organizational technology and knowledge" refers to the collective term for the specialized skills and expertise possessed by a particular organization.

[0301] "Visualization" refers to displaying abstract data and technologies in an easily understandable format, thereby facilitating judgment and decision-making.

[0302] "Automatically identifying the appropriate consultant" means that, based on the analysis results, the system automatically selects the most suitable person with the necessary skills and knowledge.

[0303] "Facilities management" refers to the maintenance and management necessary to ensure the efficient operation of buildings and facilities.

[0304] "Improving operational efficiency" means optimizing resource usage to reduce costs and increase productivity.

[0305] A "management strategy" is a plan or methodology developed to achieve specific objectives in the operation of an organization or its facilities.

[0306] In this invention, the system primarily uses a server to perform various information analyses and generate appropriate documents. This system utilizes programming languages ​​such as Python and R, and employs libraries like Pandas and NumPy for data analysis and processing. A generative AI model built using TensorFlow or PyTorch generates optimal suggestions and solutions based on historical data.

[0307] The server receives the information input by the user from the terminal and performs analysis. At this time, the server quickly searches a large amount of past information and extracts relevant data. As a result, it becomes possible to analyze past management cases and present an efficient operation strategy in facility management. This information is distributed to the user's terminal as a web application using Flask or Django.

[0308] As a specific example, when the cooling system of the data center detects an abnormality, the server quickly generates a solution using past trouble - shooting data and immediately sends that information to the user's terminal. The generated AI model plays a central role throughout this process, deriving real - time countermeasures tailored to the situation provided by the prompt text.

[0309] An example of the prompt text is "A rapid temperature increase has been detected in the cooling system. Please generate an optimal countermeasure from past data." As a result, the system can extract relevant data and present an optimized solution.

[0310] In this way, this invention enables the rapid generation and response of information, greatly contributing to the improvement of business efficiency.

[0311] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0312] Step 1:

[0313] The user uses the terminal to input specific information to the system. The terminal sends the input content to the server as an HTTP request. The input here is, for example, the prompt text "A rapid temperature increase has been detected in the cooling system."

[0314] Step 2:

[0315] The server receives an HTTP request and parses the information it contains. Python is used for the parsing, and the Pandas library is used to handle the input data as structured data. At this stage, the server understands the input prompt and generates an appropriate search query.

[0316] Step 3:

[0317] The server issues a search query to the database of past cases and extracts relevant data. The extracted data includes troubleshooting cases that the system has collected in the past. The server temporarily holds the search results in memory and prepares them for data analysis.

[0318] Step 4:

[0319] The server inputs the extracted data into an AI model to generate the optimal solution. The AI ​​model is built using TensorFlow and, based on learning from historical datasets, presents the most suitable solution for a given situation. This process evaluates the similarity and effectiveness of past solutions and generates the solution as output data.

[0320] Step 5:

[0321] The generated countermeasures are converted by the server into a data format such as JSON and sent to the terminal. The user receives the countermeasures on the terminal and can view specific action plans. The terminal screen visually represents the generated content and displays detailed information in an organized manner to support the user's decision-making.

[0322] Step 6:

[0323] Users review the provided information and implement countermeasures as needed. Furthermore, by sending feedback on the results of their actions to the server, the system collects data to further improve countermeasures and enhance the accuracy of future troubleshooting.

[0324] 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.

[0325] This invention is a system that incorporates an emotion engine to recognize user emotions, in addition to being a system aimed at improving operational efficiency within a company. This system analyzes past data and automatically generates optimal proposals, while simultaneously suggesting quick countermeasures based on past cases of complaint handling. It also has the ability to visualize internal skills and experience and automatically search for the most suitable consultant. Furthermore, it can use the emotion engine to recognize and evaluate user emotions in real time and adjust the system's operation based on the detected emotions.

[0326] This emotion engine recognizes emotions by analyzing user input information, communication history, and voice tone. For example, if an emotion indicating stress is detected while a user is using their device, the server will present the user with options to simplify their work. This allows the user to continue working without feeling burdened.

[0327] As a concrete example, consider a customer complaint handling scenario. After the user enters the details of the complaint into the terminal, the server provides standard solutions, but at the same time, an emotion engine assesses the user's stress level. If high stress is detected, the server suggests using an AI chatbot to ensure stable communication, or, in urgent cases, the intervention of a specialist assistant.

[0328] Furthermore, throughout the proposal creation process, the emotion engine constantly evaluates the user's emotions, determining whether the user is satisfied or confused. If the system determines that the user is confused, it will offer additional guidance and support to assist the user.

[0329] This invention not only pursues efficiency but also enables flexible support based on user emotions, thereby simultaneously achieving improvements in the quality of business processes within a company and increased employee satisfaction.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The user logs into the terminal and selects the purpose of the system. Users can choose to create proposals, handle complaints, or seek advice.

[0333] Step 2:

[0334] The user enters the necessary details into the terminal. For example, when creating a proposal, they enter the project objectives and requirements specifications; when handling a complaint, they enter the details of the problem and customer information.

[0335] Step 3:

[0336] The server receives the input information and searches the database for relevant historical data.

[0337] Step 4:

[0338] When creating a proposal, the server identifies similar proposal examples and automatically generates a proposal based on a template using AI.

[0339] Step 5:

[0340] The server generates a proposal document, which is then sent to the user's terminal, allowing the user to review and edit it.

[0341] Step 6:

[0342] In the case of a complaint, the server quickly searches for relevant solutions based on past cases and provides them to the user's terminal. Furthermore, an emotion engine evaluates the user's emotions to determine if additional support is needed.

[0343] Step 7:

[0344] The emotion engine analyzes the user's speech, input, and voice to determine their emotions. If stress or anxiety is detected, the server will offer options to assist with system operation.

[0345] Step 8:

[0346] In the case of matching a consultant, the server analyzes the consultation content, searches and selects the most suitable consultant from a skills matrix, and automatically adjusts the schedules of the user and the consultant.

[0347] Step 9:

[0348] The server sends the final deliverables and results (e.g., edited proposals, complaint handling strategies, consultation schedules) to the user's terminal, where the user can review them.

[0349] (Example 2)

[0350] 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".

[0351] Improving operational efficiency and providing flexible solutions within an organization requires unprecedented data analysis accuracy, while simultaneously presenting the challenge of providing nuanced support that responds to users' emotions. Furthermore, there is a need to reduce user stress while simultaneously improving the quality and satisfaction of work.

[0352] 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.

[0353] In this invention, the server includes means for analyzing past information and automatically creating optimal documents, means for quickly suggesting countermeasures based on past complaint handling information, and means for analyzing the user's emotions in real time using emotion recognition technology and adjusting the system's operation accordingly. This makes it possible to improve operational efficiency while providing flexible responses and support that respond to the user's emotions.

[0354] "Past information" refers to data and records that were previously collected or generated within an organization.

[0355] "Means for automatically generating documents" refers to a process or technology that automatically generates documents using a computer system based on analyzed data.

[0356] "Past information on complaint handling" refers to data on past problem-solving cases and response histories.

[0357] "Means of providing prompt countermeasures" refers to methods and systems that provide users with quick and appropriate countermeasures based on analysis results.

[0358] "Methods for visualizing skills and knowledge within an organization" refer to methods that make internal resources easier to utilize by making the expertise and experience of individual members visible.

[0359] "Methods for automatically selecting a consultant" refer to technologies in which the system automatically selects the appropriate consultant based on the user's consultation content and circumstances.

[0360] "Emotion recognition technology" is a technology that analyzes a user's emotional state from voice, facial expressions, and behavioral data.

[0361] "Methods for analyzing user emotions in real time" refer to processes and technologies for instantly collecting and analyzing user emotional data.

[0362] "Means for adjusting system operation" refers to functions that dynamically change the system's operation and responses in response to analyzed emotional information.

[0363] "User device usage" refers to the user's behavioral patterns and operation history when operating computers, terminals, etc.

[0364] "Means of offering options to reduce stress" refer to the choices and support methods that the system provides to alleviate the user's mental burden.

[0365] This invention is a system designed to improve operational efficiency within a company, enabling real-time recognition of user emotions and flexible responses accordingly. Specifically, the server collects past business data and complaint handling history from a database and analyzes it using Python or a similar programming language. Based on the analyzed data, it utilizes the machine learning library TensorFlow to automatically generate optimal proposals.

[0366] The generated proposal is provided to the user via the terminal and converted into a format that can be reviewed and customized using the Microsoft Word API. While the user is using the terminal, it analyzes voice input and action logs using IBM Watson and other emotion recognition algorithms to collect emotion data. Based on this emotion data, the server adjusts the system's operation and provides support measures tailored to the user's stress level. For example, if the system determines that the user is confused, it provides additional guidance.

[0367] As a concrete example, suppose a user encounters a roadblock while creating a proposal for a new project. In this situation, the terminal, based on server instructions, will present support options such as "Would you like to refer to other proposal examples?", allowing the user to receive assistance. Furthermore, by inputting a prompt such as "Please tell me about data analysis methods to streamline the creation of internal company proposals," the system will provide relevant information to support the user's work.

[0368] This system not only improves operational efficiency but also enables emotion-based user support, simultaneously achieving process improvements across the entire organization and increased employee satisfaction.

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The server collects past business records and proposal examples from the company's database. It uses SQL queries as input to extract the necessary data. The extracted data is temporarily stored for later analysis. Specifically, the server periodically performs data retrieval tasks to maintain up-to-date information.

[0372] Step 2:

[0373] The server performs analysis based on the collected data to generate the optimal proposal. This analysis uses the Python programming language and a TensorFlow machine learning model. The input is the data obtained in step 1, and through a series of data processing and calculations, the optimal proposal is generated as output. Specific operations include feature extraction from the data and model training.

[0374] Step 3:

[0375] The server sends the generated proposal to the user's terminal using the Microsoft Word API, making it available for review and customization. The input is the proposal data generated in step 2, and the output is a proposal file usable by the user. The specific actions involve formatting the document and sending the file.

[0376] Step 4:

[0377] The terminal collects user operation logs and voice input. A dedicated application on the device is used for this purpose. Input includes user operation data and voice data, while output is data packets for analysis. Specifically, this involves a process where voice is recorded and then transcribed into text.

[0378] Step 5:

[0379] The server performs emotion recognition based on data sent from the terminal. The input is the data from step 4, and an emotion analysis algorithm (e.g., IBM Watson API) is used to determine the user's emotional state. The output is the emotion identification result. Specifically, an emotion score is calculated and determined.

[0380] Step 6:

[0381] The server, based on the emotion recognition results, will suggest appropriate countermeasures as needed. The input is the emotional state information obtained in step 5, and the output is support options and suggestions for the user. The specific action is the process of displaying a notification on the terminal such as "Would you like to call a specialist assistant?"

[0382] Step 7:

[0383] Users can receive support by selecting options presented through their device. Based on the selected option, they will interact with an AI chatbot or be contacted by a support representative. For example, one suggestion might be, "Would you like to see other suggested examples?", which allows the user to receive more detailed information.

[0384] (Application Example 2)

[0385] 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."

[0386] This invention seeks to improve the quality of work and employee satisfaction by streamlining operations within companies while simultaneously recognizing user emotions in real time and responding flexibly based on those emotions. Furthermore, there is a growing need for personal assistant robots in the home to recognize emotions and reduce family stress and optimize the environment. Currently, there is a lack of systems that can meet these complex needs.

[0387] 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.

[0388] In this invention, the server includes means for analyzing past data and automatically generating optimal proposal documents, means for providing immediate solutions based on past cases of request handling, means for visualizing internal skills and experience and automatically searching for appropriate consultants, and means for recognizing emotions from voice and text and adapting to the environment. This enables both improved work efficiency and flexible responses based on emotions, thereby increasing satisfaction within companies and homes.

[0389] "Analyzing past data" is the act of analyzing information accumulated up to now to gain insights that will be useful for future activities and decisions.

[0390] "Automatically generating optimal proposal documents" refers to the process of automatically creating documents containing efficient and effective recommendations based on predefined criteria.

[0391] "Providing immediate solutions based on past examples of request handling" means referring to similar problem-solving methods used in the past and promptly presenting appropriate solutions.

[0392] "Making internal skills and experience visible" means clearly displaying and making easily understandable the abilities and past achievements of individuals within an organization.

[0393] "Automatically searching for the appropriate consultant" means that the system automatically selects the most suitable person based on the required expertise and experience.

[0394] "Recognizing emotions from voice and text" refers to a technology that analyzes linguistic information to understand and grasp the emotional state of the individual who transmitted that information.

[0395] "Adapting the environment" means adjusting the surrounding circumstances and settings based on the detected emotional state, with the aim of improving the user's comfort and effectiveness.

[0396] The system realizing this invention consists of a network environment including a server, user terminals, and a personal assistant device. The server collects and analyzes historical data to generate optimal proposal documents. It refers to a database of past cases to provide appropriate responses to claims and requests. Furthermore, it has a function to automatically search for appropriate consultants by utilizing an internal database to visualize skills and experience within the company.

[0397] On the user terminal, proposal documents can be received and reviewed, and their content can be customized as needed. Furthermore, data can be sent to the server via the feedback function to aid in continuous system improvement.

[0398] The emotion recognition engine uses Google Cloud Speech-to-Text for voice input and OpenAI APIs for emotion analysis, recognizing the user's emotional state in real time by analyzing the user's voice and text data. This emotion data is sent to a server, which then adjusts the environment according to the user's emotions. For example, if signs of stress are detected in a family member, the personal assistant device will instruct a smart device to play relaxing music.

[0399] For example, if the server detects that the father is feeling fatigued while relaxing in the living room, the personal assistant will automatically select and play healing music, creating a more relaxing home environment.

[0400] An example of a prompt would be: "Explain how to analyze audio in a home and recognize emotions in real time using the OpenAI API."

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The server retrieves the information necessary for generating the proposal document from the historical database. Based on this input data, it applies a generation algorithm to automatically generate the optimal proposal document. The output is the proposal document sent to the user's terminal.

[0404] Step 2:

[0405] The user terminal displays the received proposal document, allowing the user to review its contents. The user can customize the document, and once revisions are made, a revised proposal document is generated and sent back to the server as feedback.

[0406] Step 3:

[0407] The server references a database of past cases to search for similar cases in order to handle complaints. The input is detailed complaint information, and the server performs data analysis to select the appropriate course of action. Once a course of action is determined, that information is output to the user's terminal for the user to review.

[0408] Step 4:

[0409] The personal assistant device receives the user's voice input and converts it into text data using Google Cloud Speech-to-Text. Using this data as input, it analyzes the emotional data using the OpenAI API to recognize the user's emotional state. The resulting emotional information is then sent to the server.

[0410] Step 5:

[0411] The server evaluates the need for environmental adjustments based on the emotional information it receives. If it determines that environmental adjustments are necessary, it outputs appropriate instructions to the smart device and takes specific actions, such as playing relaxing music.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] [Third Embodiment]

[0416] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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".

[0428] This invention is a technology that uses AI to generate proposals based on historical data, with the aim of improving operational efficiency within companies. This system allows users to input information via a terminal, and the server analyzes collected past project data and proposals to automatically generate appropriate proposals. The proposals can be customized based on templates, enabling rapid and efficient generation.

[0429] For example, if a user needs to create a proposal for a new project, they input the basic project information into their terminal. The server then searches for the most suitable past examples based on that information, automatically generates a proposal, and sends it to the user's terminal. The user can then review the received proposal, add their own project information, and complete it.

[0430] In handling complaints, when a user enters the complaint details on a terminal, the server searches past complaint handling cases for relevant solutions in real time and provides them to the user immediately. Based on this information, the user can process the complaint quickly, and the server plays a role in continuously improving the solutions by collecting subsequent feedback.

[0431] Furthermore, the system of this invention has the function of visualizing the skills and experience of employees within the company and quickly finding the appropriate person to consult. The user enters the consultation details into a terminal, and the server analyzes the information and selects the most suitable consultant from a skills matrix. The server automatically approaches the found consultant, arranges the consultation, and notifies the user's terminal of the details.

[0432] Thus, this invention streamlines internal business processes, accelerates information sharing, and improves complaint handling. Furthermore, by optimally utilizing internal resources, it contributes to improving the overall performance of the company.

[0433] The following describes the processing flow.

[0434] Step 1:

[0435] The user logs into their device and selects one of the following objectives: creating a new proposal, handling a complaint, or seeking advice.

[0436] Step 2:

[0437] The user enters the necessary details on the device. For example, when creating a proposal, they would enter the project's objectives and requirements.

[0438] Step 3:

[0439] The server analyzes the information received from the user and searches the database for relevant historical data.

[0440] Step 4:

[0441] When creating a proposal, the server identifies similar proposal examples and uses a generation AI to automatically generate a proposal based on a template.

[0442] Step 5:

[0443] The server generates a proposal and sends it to the user's terminal, where the user can review and edit it.

[0444] Step 6:

[0445] In the case of a complaint, the server analyzes past complaint cases and immediately provides relevant solutions to the user's terminal.

[0446] Step 7:

[0447] In the case of matching consultants, the server selects the most suitable consultant from the skills matrix and recommends them to the user.

[0448] Step 8:

[0449] The server automatically contacts the selected consultant and sets a consultation schedule.

[0450] Step 9:

[0451] The server sends the final deliverables (proposal, countermeasures, consultation schedule) to the user's terminal, and the user reviews them.

[0452] (Example 1)

[0453] 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."

[0454] Many inefficiencies exist in business processes within companies, and in particular, the accumulation of experience and know-how is not being fully utilized in areas such as proposal writing and complaint handling. Furthermore, the appropriate use of skills and resources is difficult, making it challenging to respond quickly and appropriately. These problems need to be solved to improve and streamline business operations.

[0455] 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.

[0456] In this invention, the server includes means for users to input information via a terminal, analyze past data using a generation AI model, and automatically generate an optimal proposal; means for immediately providing complaint response measures based on past response cases in response to the input complaint content; and means for analyzing internal resources to automatically search for and approach appropriate consultants. This promotes the use of information within the company, enables faster proposal creation and complaint response, and optimizes resource allocation.

[0457] A "user" is an entity that uses the system to input information and receives proposals and complaint resolution strategies.

[0458] A "terminal" is a computer device used by users to input information or to review and edit proposals and countermeasures sent from a server.

[0459] A "server" is a centralized computer system that receives input information from users, performs data analysis and document generation using a generation AI model, and provides the results to the user.

[0460] A "generative AI model" is an artificial intelligence technology that learns from a large amount of historical data and automatically generates optimal proposals and complaint handling strategies based on the input information.

[0461] A "proposal" is a document that summarizes the project's objectives, strategy, and expected results, and is ultimately completed by the user.

[0462] A "complaint handling strategy" refers to a solution or response method that is immediately proposed for a specific complaint based on past cases.

[0463] "Feedback" refers to the process of receiving evaluations and opinions from users, and the information used to improve the generative AI model.

[0464] "Resources" is a general term for assets available within a company, including skills, experience, and human resources.

[0465] A "prompt" is a goal-setting statement automatically generated by a generative AI model based on user input, and it forms the basis for appropriate documents and countermeasures.

[0466] Users input information about new projects and details of claims through their terminals. The terminals are configured to send this input information to a server in real time. Upon receiving the input, the server utilizes a generative AI model to analyze the data. Specific processing includes searching historical data, pattern recognition, and generating optimal proposals and countermeasures. The generative AI model has learned from a large amount of past cases and suggests appropriate actions by evaluating similarities.

[0467] The system on which the server is built includes database management software and machine learning frameworks for running AI models (such as TensorFlow and PyTorch). The generated proposals and complaint resolutions are automatically sent to the user's terminal, where the user can review and edit them.

[0468] For example, if a user wants to develop a sales plan for a new product, they would input the product name, target market, and expected sales volume on their device. The server would then use this information to run an AI model, referencing similar past sales plans to generate a draft proposal.

[0469] An example of a prompt might be, "I want to create a proposal for a new product. Please tell me the product name and sales strategy." Based on such prompts, the server can analyze past data and provide the most suitable document.

[0470] This system aims to improve the speed and accuracy of business operations within a company by streamlining business processes and optimizing resources.

[0471] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0472] Step 1:

[0473] Users input information into the system using a terminal. Specifically, they enter basic information about a new project and details of the claims in text format. The information entered includes the project name, overview, targets, and key issues. The entered data is sent directly from the terminal to the server.

[0474] Step 2:

[0475] The server receives input information sent from the terminal. Based on the input data, it prepares it as a base dataset for use by the generative AI model. This step also includes error checking to ensure the information has been received correctly. The received information is stored in a database and ready for analysis.

[0476] Step 3:

[0477] The server operates a generative AI model based on the received information. Specifically, it searches past case data in the database and performs calculations to evaluate similarity. The AI ​​model treats the new input information as a prompt, analyzes relevant past cases, and extracts appropriate templates and elements. The output here is a series of proposals or draft solutions.

[0478] Step 4:

[0479] The server generates a draft proposal or claim response plan created by the generative AI model. At this stage, elements derived from past cases are combined and customized according to the template. The completed document is generated in electronic format and ready for transmission to the terminal.

[0480] Step 5:

[0481] The server sends the generated document to the user's terminal. The user can receive the proposal or countermeasure on their terminal and review its contents. The output here is a completed document that the user can directly edit.

[0482] Step 6:

[0483] Users review proposals and action plans on their devices, adding specific information and making minor adjustments as needed. Once the editing is complete and the document aligns with the final objective, they submit or save it. The completed document becomes the output used in actual work.

[0484] Step 7:

[0485] The server receives feedback from users and uses it to improve the accuracy of the generated AI model. This feedback is reflected in subsequent data analysis and document generation processes. As information is accumulated for data processing and learning, the AI ​​model will continuously improve.

[0486] (Application Example 1)

[0487] 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."

[0488] To improve operational efficiency, provide solutions quickly, and optimize equipment management, it is essential to effectively utilize historical data. However, rapidly and accurately analyzing large amounts of historical data within an organization and deriving optimal solutions and management strategies is not easy. Furthermore, visualizing the technology and knowledge within an organization and smoothly identifying the appropriate personnel is also difficult. There is a need for a method that can solve these challenges and dramatically improve operational efficiency.

[0489] 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.

[0490] In this invention, the server includes means for analyzing past information and automatically generating optimal documents, means for providing immediate solutions based on past cases of troubleshooting, means for visualizing the technology and knowledge within the organization and automatically identifying appropriate consultants, and means for analyzing past management cases to present optimal management strategies in order to improve operational efficiency in equipment management. This enables rapid information analysis and efficient operational management.

[0491] "Past information" refers to previously recorded data and case studies, and analyzing this information can provide insights that are useful for current operations.

[0492] "Analysis" is the process of thoroughly examining information and extracting meaningful patterns and insights.

[0493] An "optimal document" is one that is structured in a way that best suits the case or requirements, and comprehensively provides useful information to the user.

[0494] "Past troubleshooting cases" refer to data that records problems that have occurred in the past and their solutions, which can be useful in solving similar problems.

[0495] "Providing immediate solutions" means promptly presenting appropriate solutions when a user enters a problem.

[0496] "Organizational technology and knowledge" refers to the collective term for the specialized skills and expertise possessed by a particular organization.

[0497] "Visualization" refers to displaying abstract data and technologies in an easily understandable format, thereby facilitating judgment and decision-making.

[0498] "Automatically identifying the appropriate consultant" means that, based on the analysis results, the system automatically selects the most suitable person with the necessary skills and knowledge.

[0499] "Facilities management" refers to the maintenance and management necessary to ensure the efficient operation of buildings and facilities.

[0500] "Improving operational efficiency" means optimizing resource usage to reduce costs and increase productivity.

[0501] A "management strategy" is a plan or methodology developed to achieve specific objectives in the operation of an organization or its facilities.

[0502] In this invention, the system primarily uses a server to perform various information analyses and generate appropriate documents. This system utilizes programming languages ​​such as Python and R, and employs libraries like Pandas and NumPy for data analysis and processing. A generative AI model built using TensorFlow or PyTorch generates optimal suggestions and solutions based on historical data.

[0503] The server receives and analyzes information entered by users from their terminals. During this process, the server rapidly searches through a large amount of historical data and extracts relevant information. This allows for the analysis of past management cases and the presentation of efficient operational strategies for equipment management. This information is then delivered to the user's terminal as a web application using Flask or Django.

[0504] As a concrete example, if a data center's cooling system detects an anomaly, the server quickly generates a solution using data from past troubleshooting and immediately sends that information to the user's terminal. The generative AI model plays a central role throughout this process, deriving situation-appropriate countermeasures in real time, as provided by prompt messages.

[0505] An example of a prompt message is, "A rapid temperature increase has been detected in the cooling system. Please generate the optimal countermeasure from past data." This allows the system to extract relevant data and present an optimized solution.

[0506] In this way, this invention enables the rapid generation and response of information, and greatly contributes to improving operational efficiency.

[0507] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0508] Step 1:

[0509] The user uses a terminal to input specific information into the system. The terminal sends the input to the server as an HTTP request. The input here is, for example, a prompt message such as "A rapid temperature increase has been detected in the cooling system."

[0510] Step 2:

[0511] The server receives an HTTP request and parses the information it contains. Python is used for the parsing, and the Pandas library is used to handle the input data as structured data. At this stage, the server understands the input prompt and generates an appropriate search query.

[0512] Step 3:

[0513] The server issues a search query to the database of past cases and extracts relevant data. The extracted data includes troubleshooting cases that the system has collected in the past. The server temporarily holds the search results in memory and prepares them for data analysis.

[0514] Step 4:

[0515] The server inputs the extracted data into an AI model to generate the optimal solution. The AI ​​model is built using TensorFlow and, based on learning from historical datasets, presents the most suitable solution for a given situation. This process evaluates the similarity and effectiveness of past solutions and generates the solution as output data.

[0516] Step 5:

[0517] The generated countermeasures are converted by the server into a data format such as JSON and sent to the terminal. The user receives the countermeasures on the terminal and can view specific action plans. The terminal screen visually represents the generated content and displays detailed information in an organized manner to support the user's decision-making.

[0518] Step 6:

[0519] Users review the provided information and implement countermeasures as needed. Furthermore, by sending feedback on the results of their actions to the server, the system collects data to further improve countermeasures and enhance the accuracy of future troubleshooting.

[0520] 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.

[0521] This invention is a system that incorporates an emotion engine to recognize user emotions, in addition to being a system aimed at improving operational efficiency within a company. This system analyzes past data and automatically generates optimal proposals, while simultaneously suggesting quick countermeasures based on past cases of complaint handling. It also has the ability to visualize internal skills and experience and automatically search for the most suitable consultant. Furthermore, it can use the emotion engine to recognize and evaluate user emotions in real time and adjust the system's operation based on the detected emotions.

[0522] This emotion engine recognizes emotions by analyzing user input information, communication history, and voice tone. For example, if an emotion indicating stress is detected while a user is using their device, the server will present the user with options to simplify their work. This allows the user to continue working without feeling burdened.

[0523] As a concrete example, consider a customer complaint handling scenario. After the user enters the details of the complaint into the terminal, the server provides standard solutions, but at the same time, an emotion engine assesses the user's stress level. If high stress is detected, the server suggests using an AI chatbot to ensure stable communication, or, in urgent cases, the intervention of a specialist assistant.

[0524] Furthermore, throughout the proposal creation process, the emotion engine constantly evaluates the user's emotions, determining whether the user is satisfied or confused. If the system determines that the user is confused, it will offer additional guidance and support to assist the user.

[0525] This invention not only pursues efficiency but also enables flexible support based on user emotions, thereby simultaneously achieving improvements in the quality of business processes within a company and increased employee satisfaction.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The user logs into the terminal and selects the purpose of the system. Users can choose to create proposals, handle complaints, or seek advice.

[0529] Step 2:

[0530] The user enters the necessary details into the terminal. For example, when creating a proposal, they enter the project objectives and requirements specifications; when handling a complaint, they enter the details of the problem and customer information.

[0531] Step 3:

[0532] The server receives the input information and searches the database for relevant historical data.

[0533] Step 4:

[0534] When creating a proposal, the server identifies similar proposal examples and automatically generates a proposal based on a template using AI.

[0535] Step 5:

[0536] The server generates a proposal document, which is then sent to the user's terminal, allowing the user to review and edit it.

[0537] Step 6:

[0538] In the case of a complaint, the server quickly searches for relevant solutions based on past cases and provides them to the user's terminal. Furthermore, an emotion engine evaluates the user's emotions to determine if additional support is needed.

[0539] Step 7:

[0540] The emotion engine analyzes the user's speech, input, and voice to determine their emotions. If stress or anxiety is detected, the server will offer options to assist with system operation.

[0541] Step 8:

[0542] In the case of matching a consultant, the server analyzes the consultation content, searches and selects the most suitable consultant from a skills matrix, and automatically adjusts the schedules of the user and the consultant.

[0543] Step 9:

[0544] The server sends the final deliverables and results (e.g., edited proposals, complaint handling strategies, consultation schedules) to the user's terminal, where the user can review them.

[0545] (Example 2)

[0546] 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."

[0547] Improving operational efficiency and providing flexible solutions within an organization requires unprecedented data analysis accuracy, while simultaneously presenting the challenge of providing nuanced support that responds to users' emotions. Furthermore, there is a need to reduce user stress while simultaneously improving the quality and satisfaction of work.

[0548] 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.

[0549] In this invention, the server includes means for analyzing past information and automatically creating optimal documents, means for quickly suggesting countermeasures based on past complaint handling information, and means for analyzing the user's emotions in real time using emotion recognition technology and adjusting the system's operation accordingly. This makes it possible to improve operational efficiency while providing flexible responses and support that respond to the user's emotions.

[0550] "Past information" refers to data and records that were previously collected or generated within an organization.

[0551] "Means for automatically generating documents" refers to a process or technology that automatically generates documents using a computer system based on analyzed data.

[0552] "Past information on complaint handling" refers to data on past problem-solving cases and response histories.

[0553] "Means of providing prompt countermeasures" refers to methods and systems that provide users with quick and appropriate countermeasures based on analysis results.

[0554] "Methods for visualizing skills and knowledge within an organization" refer to methods that make internal resources easier to utilize by making the expertise and experience of individual members visible.

[0555] "Methods for automatically selecting a consultant" refer to technologies in which the system automatically selects the appropriate consultant based on the user's consultation content and circumstances.

[0556] "Emotion recognition technology" is a technology that analyzes a user's emotional state from voice, facial expressions, and behavioral data.

[0557] "Methods for analyzing user emotions in real time" refer to processes and technologies for instantly collecting and analyzing user emotional data.

[0558] "Means for adjusting system operation" refers to functions that dynamically change the system's operation and responses in response to analyzed emotional information.

[0559] "User device usage" refers to the user's behavioral patterns and operation history when operating computers, terminals, etc.

[0560] "Means of offering options to reduce stress" refer to the choices and support methods that the system provides to alleviate the user's mental burden.

[0561] This invention is a system designed to improve operational efficiency within a company, enabling real-time recognition of user emotions and flexible responses accordingly. Specifically, the server collects past business data and complaint handling history from a database and analyzes it using Python or a similar programming language. Based on the analyzed data, it utilizes the machine learning library TensorFlow to automatically generate optimal proposals.

[0562] The generated proposal is provided to the user via the terminal and converted into a format that can be reviewed and customized using the Microsoft Word API. While the user is using the terminal, it analyzes voice input and action logs using IBM Watson and other emotion recognition algorithms to collect emotion data. Based on this emotion data, the server adjusts the system's operation and provides support measures tailored to the user's stress level. For example, if the system determines that the user is confused, it provides additional guidance.

[0563] As a concrete example, suppose a user encounters a roadblock while creating a proposal for a new project. In this situation, the terminal, based on server instructions, will present support options such as "Would you like to refer to other proposal examples?", allowing the user to receive assistance. Furthermore, by inputting a prompt such as "Please tell me about data analysis methods to streamline the creation of internal company proposals," the system will provide relevant information to support the user's work.

[0564] This system not only improves operational efficiency but also enables emotion-based user support, simultaneously achieving process improvements across the entire organization and increased employee satisfaction.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The server collects past business records and proposal examples from the company's database. It uses SQL queries as input to extract the necessary data. The extracted data is temporarily stored for later analysis. Specifically, the server periodically performs data retrieval tasks to maintain up-to-date information.

[0568] Step 2:

[0569] The server performs analysis based on the collected data to generate the optimal proposal. This analysis uses the Python programming language and a TensorFlow machine learning model. The input is the data obtained in step 1, and through a series of data processing and calculations, the optimal proposal is generated as output. Specific operations include feature extraction from the data and model training.

[0570] Step 3:

[0571] The server sends the generated proposal to the user's terminal using the Microsoft Word API, making it available for review and customization. The input is the proposal data generated in step 2, and the output is a proposal file usable by the user. The specific actions involve formatting the document and sending the file.

[0572] Step 4:

[0573] The terminal collects user operation logs and voice input. A dedicated application on the device is used for this purpose. Input includes user operation data and voice data, while output is data packets for analysis. Specifically, this involves a process where voice is recorded and then transcribed into text.

[0574] Step 5:

[0575] The server performs emotion recognition based on data sent from the terminal. The input is the data from step 4, and an emotion analysis algorithm (e.g., IBM Watson API) is used to determine the user's emotional state. The output is the emotion identification result. Specifically, an emotion score is calculated and determined.

[0576] Step 6:

[0577] The server, based on the emotion recognition results, will suggest appropriate countermeasures as needed. The input is the emotional state information obtained in step 5, and the output is support options and suggestions for the user. The specific action is the process of displaying a notification on the terminal such as "Would you like to call a specialist assistant?"

[0578] Step 7:

[0579] Users can receive support by selecting options presented through their device. Based on the selected option, they will interact with an AI chatbot or be contacted by a support representative. For example, one suggestion might be, "Would you like to see other suggested examples?", which allows the user to receive more detailed information.

[0580] (Application Example 2)

[0581] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0582] This invention seeks to improve the quality of work and employee satisfaction by streamlining operations within companies while simultaneously recognizing user emotions in real time and responding flexibly based on those emotions. Furthermore, there is a growing need for personal assistant robots in the home to recognize emotions and reduce family stress and optimize the environment. Currently, there is a lack of systems that can meet these complex needs.

[0583] 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.

[0584] In this invention, the server includes means for analyzing past data and automatically generating optimal proposal documents, means for providing immediate solutions based on past cases of request handling, means for visualizing internal skills and experience and automatically searching for appropriate consultants, and means for recognizing emotions from voice and text and adapting to the environment. This enables both improved work efficiency and flexible responses based on emotions, thereby increasing satisfaction within companies and homes.

[0585] "Analyzing past data" is the act of analyzing information accumulated up to now to gain insights that will be useful for future activities and decisions.

[0586] "Automatically generating optimal proposal documents" refers to the process of automatically creating documents containing efficient and effective recommendations based on predefined criteria.

[0587] "Providing immediate solutions based on past examples of request handling" means referring to similar problem-solving methods used in the past and promptly presenting appropriate solutions.

[0588] "Making internal skills and experience visible" means clearly displaying and making easily understandable the abilities and past achievements of individuals within an organization.

[0589] "Automatically searching for the appropriate consultant" means that the system automatically selects the most suitable person based on the required expertise and experience.

[0590] "Recognizing emotions from voice and text" refers to a technology that analyzes linguistic information to understand and grasp the emotional state of the individual who transmitted that information.

[0591] "Adapting the environment" means adjusting the surrounding circumstances and settings based on the detected emotional state, with the aim of improving the user's comfort and effectiveness.

[0592] The system realizing this invention consists of a network environment including a server, user terminals, and a personal assistant device. The server collects and analyzes historical data to generate optimal proposal documents. It refers to a database of past cases to provide appropriate responses to claims and requests. Furthermore, it has a function to automatically search for appropriate consultants by utilizing an internal database to visualize skills and experience within the company.

[0593] On the user terminal, proposal documents can be received and reviewed, and their content can be customized as needed. Furthermore, data can be sent to the server via the feedback function to aid in continuous system improvement.

[0594] The emotion recognition engine uses Google Cloud Speech-to-Text for voice input and OpenAI APIs for emotion analysis, recognizing the user's emotional state in real time by analyzing the user's voice and text data. This emotion data is sent to a server, which then adjusts the environment according to the user's emotions. For example, if signs of stress are detected in a family member, the personal assistant device will instruct a smart device to play relaxing music.

[0595] For example, if the server detects that the father is feeling fatigued while relaxing in the living room, the personal assistant will automatically select and play healing music, creating a more relaxing home environment.

[0596] An example of a prompt would be: "Explain how to analyze audio in a home and recognize emotions in real time using the OpenAI API."

[0597] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0598] Step 1:

[0599] The server retrieves the information necessary for generating the proposal document from the historical database. Based on this input data, it applies a generation algorithm to automatically generate the optimal proposal document. The output is the proposal document sent to the user's terminal.

[0600] Step 2:

[0601] The user terminal displays the received proposal document, allowing the user to review its contents. The user can customize the document, and once revisions are made, a revised proposal document is generated and sent back to the server as feedback.

[0602] Step 3:

[0603] The server references a database of past cases to search for similar cases in order to handle complaints. The input is detailed complaint information, and the server performs data analysis to select the appropriate course of action. Once a course of action is determined, that information is output to the user's terminal for the user to review.

[0604] Step 4:

[0605] The personal assistant device receives the user's voice input and converts it into text data using Google Cloud Speech-to-Text. Using this data as input, it analyzes the emotional data using the OpenAI API to recognize the user's emotional state. The resulting emotional information is then sent to the server.

[0606] Step 5:

[0607] The server evaluates the need for environmental adjustments based on the emotional information it receives. If it determines that environmental adjustments are necessary, it outputs appropriate instructions to the smart device and takes specific actions, such as playing relaxing music.

[0608] 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.

[0609] 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.

[0610] 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.

[0611] [Fourth Embodiment]

[0612] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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).

[0618] 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.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] 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.

[0623] 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.

[0624] 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".

[0625] This invention is a technology that uses AI to generate proposals based on historical data, with the aim of improving operational efficiency within companies. This system allows users to input information via a terminal, and the server analyzes collected past project data and proposals to automatically generate appropriate proposals. The proposals can be customized based on templates, enabling rapid and efficient generation.

[0626] For example, if a user needs to create a proposal for a new project, they input the basic project information into their terminal. The server then searches for the most suitable past examples based on that information, automatically generates a proposal, and sends it to the user's terminal. The user can then review the received proposal, add their own project information, and complete it.

[0627] In handling complaints, when a user enters the complaint details on a terminal, the server searches past complaint handling cases for relevant solutions in real time and provides them to the user immediately. Based on this information, the user can process the complaint quickly, and the server plays a role in continuously improving the solutions by collecting subsequent feedback.

[0628] Furthermore, the system of this invention has the function of visualizing the skills and experience of employees within the company and quickly finding the appropriate person to consult. The user enters the consultation details into a terminal, and the server analyzes the information and selects the most suitable consultant from a skills matrix. The server automatically approaches the found consultant, arranges the consultation, and notifies the user's terminal of the details.

[0629] Thus, this invention streamlines internal business processes, accelerates information sharing, and improves complaint handling. Furthermore, by optimally utilizing internal resources, it contributes to improving the overall performance of the company.

[0630] The following describes the processing flow.

[0631] Step 1:

[0632] The user logs into their device and selects one of the following objectives: creating a new proposal, handling a complaint, or seeking advice.

[0633] Step 2:

[0634] The user enters the necessary details on the device. For example, when creating a proposal, they would enter the project's objectives and requirements.

[0635] Step 3:

[0636] The server analyzes the information received from the user and searches the database for relevant historical data.

[0637] Step 4:

[0638] When creating a proposal, the server identifies similar proposal examples and uses a generation AI to automatically generate a proposal based on a template.

[0639] Step 5:

[0640] The server generates a proposal and sends it to the user's terminal, where the user can review and edit it.

[0641] Step 6:

[0642] In the case of a complaint, the server analyzes past complaint cases and immediately provides relevant solutions to the user's terminal.

[0643] Step 7:

[0644] In the case of matching consultants, the server selects the most suitable consultant from the skills matrix and recommends them to the user.

[0645] Step 8:

[0646] The server automatically contacts the selected consultant and sets a consultation schedule.

[0647] Step 9:

[0648] The server sends the final deliverables (proposal, countermeasures, consultation schedule) to the user's terminal, and the user reviews them.

[0649] (Example 1)

[0650] 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".

[0651] Many inefficiencies exist in business processes within companies, and in particular, the accumulation of experience and know-how is not being fully utilized in areas such as proposal writing and complaint handling. Furthermore, the appropriate use of skills and resources is difficult, making it challenging to respond quickly and appropriately. These problems need to be solved to improve and streamline business operations.

[0652] 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.

[0653] In this invention, the server includes means for users to input information via a terminal, analyze past data using a generation AI model, and automatically generate an optimal proposal; means for immediately providing complaint response measures based on past response cases in response to the input complaint content; and means for analyzing internal resources to automatically search for and approach appropriate consultants. This promotes the use of information within the company, enables faster proposal creation and complaint response, and optimizes resource allocation.

[0654] A "user" is an entity that uses the system to input information and receives proposals and complaint resolution strategies.

[0655] A "terminal" is a computer device used by users to input information or to review and edit proposals and countermeasures sent from a server.

[0656] A "server" is a centralized computer system that receives input information from users, performs data analysis and document generation using a generation AI model, and provides the results to the user.

[0657] A "generative AI model" is an artificial intelligence technology that learns from a large amount of historical data and automatically generates optimal proposals and complaint handling strategies based on the input information.

[0658] A "proposal" is a document that summarizes the project's objectives, strategy, and expected results, and is ultimately completed by the user.

[0659] A "complaint handling strategy" refers to a solution or response method that is immediately proposed for a specific complaint based on past cases.

[0660] "Feedback" refers to the process of receiving evaluations and opinions from users, and the information used to improve the generative AI model.

[0661] "Resources" is a general term for assets available within a company, including skills, experience, and human resources.

[0662] A "prompt" is a goal-setting statement automatically generated by a generative AI model based on user input, and it forms the basis for appropriate documents and countermeasures.

[0663] Users input information about new projects and details of claims through their terminals. The terminals are configured to send this input information to a server in real time. Upon receiving the input, the server utilizes a generative AI model to analyze the data. Specific processing includes searching historical data, pattern recognition, and generating optimal proposals and countermeasures. The generative AI model has learned from a large amount of past cases and suggests appropriate actions by evaluating similarities.

[0664] The system on which the server is built includes database management software and machine learning frameworks for running AI models (such as TensorFlow and PyTorch). The generated proposals and complaint resolutions are automatically sent to the user's terminal, where the user can review and edit them.

[0665] For example, if a user wants to develop a sales plan for a new product, they would input the product name, target market, and expected sales volume on their device. The server would then use this information to run an AI model, referencing similar past sales plans to generate a draft proposal.

[0666] An example of a prompt might be, "I want to create a proposal for a new product. Please tell me the product name and sales strategy." Based on such prompts, the server can analyze past data and provide the most suitable document.

[0667] This system aims to improve the speed and accuracy of business operations within a company by streamlining business processes and optimizing resources.

[0668] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0669] Step 1:

[0670] Users input information into the system using a terminal. Specifically, they enter basic information about a new project and details of the claims in text format. The information entered includes the project name, overview, targets, and key issues. The entered data is sent directly from the terminal to the server.

[0671] Step 2:

[0672] The server receives input information sent from the terminal. Based on the input data, it prepares it as a base dataset for use by the generative AI model. This step also includes error checking to ensure the information has been received correctly. The received information is stored in a database and ready for analysis.

[0673] Step 3:

[0674] The server operates a generative AI model based on the received information. Specifically, it searches past case data in the database and performs calculations to evaluate similarity. The AI ​​model treats the new input information as a prompt, analyzes relevant past cases, and extracts appropriate templates and elements. The output here is a series of proposals or draft solutions.

[0675] Step 4:

[0676] The server generates a draft proposal or claim response plan created by the generative AI model. At this stage, elements derived from past cases are combined and customized according to the template. The completed document is generated in electronic format and ready for transmission to the terminal.

[0677] Step 5:

[0678] The server sends the generated document to the user's terminal. The user can receive the proposal or countermeasure on their terminal and review its contents. The output here is a completed document that the user can directly edit.

[0679] Step 6:

[0680] Users review proposals and action plans on their devices, adding specific information and making minor adjustments as needed. Once the editing is complete and the document aligns with the final objective, they submit or save it. The completed document becomes the output used in actual work.

[0681] Step 7:

[0682] The server receives feedback from users and uses it to improve the accuracy of the generated AI model. This feedback is reflected in subsequent data analysis and document generation processes. As information is accumulated for data processing and learning, the AI ​​model will continuously improve.

[0683] (Application Example 1)

[0684] 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".

[0685] To improve operational efficiency, provide solutions quickly, and optimize equipment management, it is essential to effectively utilize historical data. However, rapidly and accurately analyzing large amounts of historical data within an organization and deriving optimal solutions and management strategies is not easy. Furthermore, visualizing the technology and knowledge within an organization and smoothly identifying the appropriate personnel is also difficult. There is a need for a method that can solve these challenges and dramatically improve operational efficiency.

[0686] 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.

[0687] In this invention, the server includes means for analyzing past information and automatically generating optimal documents, means for providing immediate solutions based on past cases of troubleshooting, means for visualizing the technology and knowledge within the organization and automatically identifying appropriate consultants, and means for analyzing past management cases to present optimal management strategies in order to improve operational efficiency in equipment management. This enables rapid information analysis and efficient operational management.

[0688] "Past information" refers to previously recorded data and case studies, and analyzing this information can provide insights that are useful for current operations.

[0689] "Analysis" is the process of thoroughly examining information and extracting meaningful patterns and insights.

[0690] An "optimal document" is one that is structured in a way that best suits the case or requirements, and comprehensively provides useful information to the user.

[0691] "Past troubleshooting cases" refer to data that records problems that have occurred in the past and their solutions, which can be useful in solving similar problems.

[0692] "Providing immediate solutions" means promptly presenting appropriate solutions when a user enters a problem.

[0693] "Organizational technology and knowledge" refers to the collective term for the specialized skills and expertise possessed by a particular organization.

[0694] "Visualization" refers to displaying abstract data and technologies in an easily understandable format, thereby facilitating judgment and decision-making.

[0695] "Automatically identifying the appropriate consultant" means that, based on the analysis results, the system automatically selects the most suitable person with the necessary skills and knowledge.

[0696] "Facilities management" refers to the maintenance and management necessary to ensure the efficient operation of buildings and facilities.

[0697] "Improving operational efficiency" means optimizing resource usage to reduce costs and increase productivity.

[0698] A "management strategy" is a plan or methodology developed to achieve specific objectives in the operation of an organization or its facilities.

[0699] In this invention, the system primarily uses a server to perform various information analyses and generate appropriate documents. This system utilizes programming languages ​​such as Python and R, and employs libraries like Pandas and NumPy for data analysis and processing. A generative AI model built using TensorFlow or PyTorch generates optimal suggestions and solutions based on historical data.

[0700] The server receives and analyzes information entered by users from their terminals. During this process, the server rapidly searches through a large amount of historical data and extracts relevant information. This allows for the analysis of past management cases and the presentation of efficient operational strategies for equipment management. This information is then delivered to the user's terminal as a web application using Flask or Django.

[0701] As a concrete example, if a data center's cooling system detects an anomaly, the server quickly generates a solution using data from past troubleshooting and immediately sends that information to the user's terminal. The generative AI model plays a central role throughout this process, deriving situation-appropriate countermeasures in real time, as provided by prompt messages.

[0702] An example of a prompt message is, "A rapid temperature increase has been detected in the cooling system. Please generate the optimal countermeasure from past data." This allows the system to extract relevant data and present an optimized solution.

[0703] In this way, this invention enables the rapid generation and response of information, and greatly contributes to improving operational efficiency.

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The user uses a terminal to input specific information into the system. The terminal sends the input to the server as an HTTP request. The input here is, for example, a prompt message such as "A rapid temperature increase has been detected in the cooling system."

[0707] Step 2:

[0708] The server receives an HTTP request and parses the information it contains. Python is used for the parsing, and the Pandas library is used to handle the input data as structured data. At this stage, the server understands the input prompt and generates an appropriate search query.

[0709] Step 3:

[0710] The server issues a search query to the database of past cases and extracts relevant data. The extracted data includes troubleshooting cases that the system has collected in the past. The server temporarily holds the search results in memory and prepares them for data analysis.

[0711] Step 4:

[0712] The server inputs the extracted data into an AI model to generate the optimal solution. The AI ​​model is built using TensorFlow and, based on learning from historical datasets, presents the most suitable solution for a given situation. This process evaluates the similarity and effectiveness of past solutions and generates the solution as output data.

[0713] Step 5:

[0714] The generated countermeasures are converted by the server into a data format such as JSON and sent to the terminal. The user receives the countermeasures on the terminal and can view specific action plans. The terminal screen visually represents the generated content and displays detailed information in an organized manner to support the user's decision-making.

[0715] Step 6:

[0716] Users review the provided information and implement countermeasures as needed. Furthermore, by sending feedback on the results of their actions to the server, the system collects data to further improve countermeasures and enhance the accuracy of future troubleshooting.

[0717] 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.

[0718] This invention is a system that incorporates an emotion engine to recognize user emotions, in addition to being a system aimed at improving operational efficiency within a company. This system analyzes past data and automatically generates optimal proposals, while simultaneously suggesting quick countermeasures based on past cases of complaint handling. It also has the ability to visualize internal skills and experience and automatically search for the most suitable consultant. Furthermore, it can use the emotion engine to recognize and evaluate user emotions in real time and adjust the system's operation based on the detected emotions.

[0719] This emotion engine recognizes emotions by analyzing user input information, communication history, and voice tone. For example, if an emotion indicating stress is detected while a user is using their device, the server will present the user with options to simplify their work. This allows the user to continue working without feeling burdened.

[0720] As a concrete example, consider a customer complaint handling scenario. After the user enters the details of the complaint into the terminal, the server provides standard solutions, but at the same time, an emotion engine assesses the user's stress level. If high stress is detected, the server suggests using an AI chatbot to ensure stable communication, or, in urgent cases, the intervention of a specialist assistant.

[0721] Furthermore, throughout the proposal creation process, the emotion engine constantly evaluates the user's emotions, determining whether the user is satisfied or confused. If the system determines that the user is confused, it will offer additional guidance and support to assist the user.

[0722] This invention not only pursues efficiency but also enables flexible support based on user emotions, thereby simultaneously achieving improvements in the quality of business processes within a company and increased employee satisfaction.

[0723] The following describes the processing flow.

[0724] Step 1:

[0725] The user logs into the terminal and selects the purpose of the system. Users can choose to create proposals, handle complaints, or seek advice.

[0726] Step 2:

[0727] The user enters the necessary details into the terminal. For example, when creating a proposal, they enter the project objectives and requirements specifications; when handling a complaint, they enter the details of the problem and customer information.

[0728] Step 3:

[0729] The server receives the input information and searches the database for relevant historical data.

[0730] Step 4:

[0731] When creating a proposal, the server identifies similar proposal examples and automatically generates a proposal based on a template using AI.

[0732] Step 5:

[0733] The server generates a proposal document, which is then sent to the user's terminal, allowing the user to review and edit it.

[0734] Step 6:

[0735] In the case of a complaint, the server quickly searches for relevant solutions based on past cases and provides them to the user's terminal. Furthermore, an emotion engine evaluates the user's emotions to determine if additional support is needed.

[0736] Step 7:

[0737] The emotion engine analyzes the user's speech, input, and voice to determine their emotions. If stress or anxiety is detected, the server will offer options to assist with system operation.

[0738] Step 8:

[0739] In the case of matching a consultant, the server analyzes the consultation content, searches and selects the most suitable consultant from a skills matrix, and automatically adjusts the schedules of the user and the consultant.

[0740] Step 9:

[0741] The server sends the final deliverables and results (e.g., edited proposals, complaint handling strategies, consultation schedules) to the user's terminal, where the user can review them.

[0742] (Example 2)

[0743] 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".

[0744] Improving operational efficiency and providing flexible solutions within an organization requires unprecedented data analysis accuracy, while simultaneously presenting the challenge of providing nuanced support that responds to users' emotions. Furthermore, there is a need to reduce user stress while simultaneously improving the quality and satisfaction of work.

[0745] 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.

[0746] In this invention, the server includes means for analyzing past information and automatically creating optimal documents, means for quickly suggesting countermeasures based on past complaint handling information, and means for analyzing the user's emotions in real time using emotion recognition technology and adjusting the system's operation accordingly. This makes it possible to improve operational efficiency while providing flexible responses and support that respond to the user's emotions.

[0747] "Past information" refers to data and records that were previously collected or generated within an organization.

[0748] "Means for automatically generating documents" refers to a process or technology that automatically generates documents using a computer system based on analyzed data.

[0749] "Past information on complaint handling" refers to data on past problem-solving cases and response histories.

[0750] "Means of providing prompt countermeasures" refers to methods and systems that provide users with quick and appropriate countermeasures based on analysis results.

[0751] "Methods for visualizing skills and knowledge within an organization" refer to methods that make internal resources easier to utilize by making the expertise and experience of individual members visible.

[0752] "Methods for automatically selecting a consultant" refer to technologies in which the system automatically selects the appropriate consultant based on the user's consultation content and circumstances.

[0753] "Emotion recognition technology" is a technology that analyzes a user's emotional state from voice, facial expressions, and behavioral data.

[0754] "Methods for analyzing user emotions in real time" refer to processes and technologies for instantly collecting and analyzing user emotional data.

[0755] "Means for adjusting system operation" refers to functions that dynamically change the system's operation and responses in response to analyzed emotional information.

[0756] "User device usage" refers to the user's behavioral patterns and operation history when operating computers, terminals, etc.

[0757] "Means of offering options to reduce stress" refer to the choices and support methods that the system provides to alleviate the user's mental burden.

[0758] This invention is a system designed to improve operational efficiency within a company, enabling real-time recognition of user emotions and flexible responses accordingly. Specifically, the server collects past business data and complaint handling history from a database and analyzes it using Python or a similar programming language. Based on the analyzed data, it utilizes the machine learning library TensorFlow to automatically generate optimal proposals.

[0759] The generated proposal is provided to the user via the terminal and converted into a format that can be reviewed and customized using the Microsoft Word API. While the user is using the terminal, it analyzes voice input and action logs using IBM Watson and other emotion recognition algorithms to collect emotion data. Based on this emotion data, the server adjusts the system's operation and provides support measures tailored to the user's stress level. For example, if the system determines that the user is confused, it provides additional guidance.

[0760] As a concrete example, suppose a user encounters a roadblock while creating a proposal for a new project. In this situation, the terminal, based on server instructions, will present support options such as "Would you like to refer to other proposal examples?", allowing the user to receive assistance. Furthermore, by inputting a prompt such as "Please tell me about data analysis methods to streamline the creation of internal company proposals," the system will provide relevant information to support the user's work.

[0761] This system not only improves operational efficiency but also enables emotion-based user support, simultaneously achieving process improvements across the entire organization and increased employee satisfaction.

[0762] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0763] Step 1:

[0764] The server collects past business records and proposal examples from the company's database. It uses SQL queries as input to extract the necessary data. The extracted data is temporarily stored for later analysis. Specifically, the server periodically performs data retrieval tasks to maintain up-to-date information.

[0765] Step 2:

[0766] The server performs analysis based on the collected data to generate the optimal proposal. This analysis uses the Python programming language and a TensorFlow machine learning model. The input is the data obtained in step 1, and through a series of data processing and calculations, the optimal proposal is generated as output. Specific operations include feature extraction from the data and model training.

[0767] Step 3:

[0768] The server sends the generated proposal to the user's terminal using the Microsoft Word API, making it available for review and customization. The input is the proposal data generated in step 2, and the output is a proposal file usable by the user. The specific actions involve formatting the document and sending the file.

[0769] Step 4:

[0770] The terminal collects user operation logs and voice input. A dedicated application on the device is used for this purpose. Input includes user operation data and voice data, while output is data packets for analysis. Specifically, this involves a process where voice is recorded and then transcribed into text.

[0771] Step 5:

[0772] The server performs emotion recognition based on data sent from the terminal. The input is the data from step 4, and an emotion analysis algorithm (e.g., IBM Watson API) is used to determine the user's emotional state. The output is the emotion identification result. Specifically, an emotion score is calculated and determined.

[0773] Step 6:

[0774] The server, based on the emotion recognition results, will suggest appropriate countermeasures as needed. The input is the emotional state information obtained in step 5, and the output is support options and suggestions for the user. The specific action is the process of displaying a notification on the terminal such as "Would you like to call a specialist assistant?"

[0775] Step 7:

[0776] Users can receive support by selecting options presented through their device. Based on the selected option, they will interact with an AI chatbot or be contacted by a support representative. For example, one suggestion might be, "Would you like to see other suggested examples?", which allows the user to receive more detailed information.

[0777] (Application Example 2)

[0778] 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".

[0779] This invention seeks to improve the quality of work and employee satisfaction by streamlining operations within companies while simultaneously recognizing user emotions in real time and responding flexibly based on those emotions. Furthermore, there is a growing need for personal assistant robots in the home to recognize emotions and reduce family stress and optimize the environment. Currently, there is a lack of systems that can meet these complex needs.

[0780] 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.

[0781] In this invention, the server includes means for analyzing past data and automatically generating optimal proposal documents, means for providing immediate solutions based on past cases of request handling, means for visualizing internal skills and experience and automatically searching for appropriate consultants, and means for recognizing emotions from voice and text and adapting to the environment. This enables both improved work efficiency and flexible responses based on emotions, thereby increasing satisfaction within companies and homes.

[0782] "Analyzing past data" is the act of analyzing information accumulated up to now to gain insights that will be useful for future activities and decisions.

[0783] "Automatically generating optimal proposal documents" refers to the process of automatically creating documents containing efficient and effective recommendations based on predefined criteria.

[0784] "Providing immediate solutions based on past examples of request handling" means referring to similar problem-solving methods used in the past and promptly presenting appropriate solutions.

[0785] "Making internal skills and experience visible" means clearly displaying and making easily understandable the abilities and past achievements of individuals within an organization.

[0786] "Automatically searching for the appropriate consultant" means that the system automatically selects the most suitable person based on the required expertise and experience.

[0787] "Recognizing emotions from voice and text" refers to a technology that analyzes linguistic information to understand and grasp the emotional state of the individual who transmitted that information.

[0788] "Adapting the environment" means adjusting the surrounding circumstances and settings based on the detected emotional state, with the aim of improving the user's comfort and effectiveness.

[0789] The system realizing this invention consists of a network environment including a server, user terminals, and a personal assistant device. The server collects and analyzes historical data to generate optimal proposal documents. It refers to a database of past cases to provide appropriate responses to claims and requests. Furthermore, it has a function to automatically search for appropriate consultants by utilizing an internal database to visualize skills and experience within the company.

[0790] On the user terminal, proposal documents can be received and reviewed, and their content can be customized as needed. Furthermore, data can be sent to the server via the feedback function to aid in continuous system improvement.

[0791] The emotion recognition engine uses Google Cloud Speech-to-Text for voice input and OpenAI APIs for emotion analysis, recognizing the user's emotional state in real time by analyzing the user's voice and text data. This emotion data is sent to a server, which then adjusts the environment according to the user's emotions. For example, if signs of stress are detected in a family member, the personal assistant device will instruct a smart device to play relaxing music.

[0792] For example, if the server detects that the father is feeling fatigued while relaxing in the living room, the personal assistant will automatically select and play healing music, creating a more relaxing home environment.

[0793] An example of a prompt would be: "Explain how to analyze audio in a home and recognize emotions in real time using the OpenAI API."

[0794] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0795] Step 1:

[0796] The server retrieves the information necessary for generating the proposal document from the historical database. Based on this input data, it applies a generation algorithm to automatically generate the optimal proposal document. The output is the proposal document sent to the user's terminal.

[0797] Step 2:

[0798] The user terminal displays the received proposal document, allowing the user to review its contents. The user can customize the document, and once revisions are made, a revised proposal document is generated and sent back to the server as feedback.

[0799] Step 3:

[0800] The server references a database of past cases to search for similar cases in order to handle complaints. The input is detailed complaint information, and the server performs data analysis to select the appropriate course of action. Once a course of action is determined, that information is output to the user's terminal for the user to review.

[0801] Step 4:

[0802] The personal assistant device receives the user's voice input and converts it into text data using Google Cloud Speech-to-Text. Using this data as input, it analyzes the emotional data using the OpenAI API to recognize the user's emotional state. The resulting emotional information is then sent to the server.

[0803] Step 5:

[0804] The server evaluates the need for environmental adjustments based on the emotional information it receives. If it determines that environmental adjustments are necessary, it outputs appropriate instructions to the smart device and takes specific actions, such as playing relaxing music.

[0805] 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.

[0806] 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.

[0807] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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."

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] The following is further disclosed regarding the embodiments described above.

[0827] (Claim 1)

[0828] A method for analyzing past data and automatically generating the optimal proposal,

[0829] A means of providing immediate countermeasures based on past cases of handling complaints,

[0830] A method to visualize internal skills and experience and automatically search for the appropriate consultant,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1, further comprising means for sending the generated proposal to the user's terminal and allowing it to be reviewed and customized.

[0834] (Claim 3)

[0835] The system according to claim 1, further comprising means for collecting feedback and enabling continuous improvement of claims handling measures.

[0836] "Example 1"

[0837] (Claim 1)

[0838] A method by which a user inputs information via a terminal, a server analyzes past data using an AI model, and automatically generates an optimal proposal.

[0839] A system in which the server immediately provides a complaint resolution solution based on past handling cases, in response to the entered complaint details.

[0840] By analyzing internal resources, we can automatically search for and approach the appropriate consultant.

[0841] A means of sending the generated proposals and countermeasures to the user's terminal, allowing the user to review and edit them,

[0842] A means of collecting feedback and training the generative AI model to continuously improve it,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, characterized by continuously improving the quality of proposals and complaint handling measures based on feedback.

[0846] (Claim 3)

[0847] The system according to claim 1, characterized in that it uses a generative AI model to create a prompt from information input by the user and provides information based on that prompt.

[0848] "Application Example 1"

[0849] (Claim 1)

[0850] A means of analyzing past information and automatically generating the optimal document,

[0851] A means of providing immediate solutions based on past cases of troubleshooting,

[0852] A means to visualize the technology and knowledge within an organization and automatically identify the appropriate consultant,

[0853] A means of improving operational efficiency in facility management by analyzing past management cases and proposing the optimal management strategy,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, further comprising means for sending the generated document to the user's terminal and making it available for review and editing.

[0857] (Claim 3)

[0858] The system according to claim 1, further comprising means for collecting evaluation information and for achieving continuous improvement of troubleshooting measures.

[0859] "Example 2 of combining an emotion engine"

[0860] (Claim 1)

[0861] A means of automatically creating the optimal document by analyzing past information,

[0862] A means of quickly proposing countermeasures based on past information regarding complaint handling,

[0863] A means to visualize skills and knowledge within an organization and automatically select the appropriate person to consult with,

[0864] A means for analyzing a user's emotions in real time using emotion recognition technology and adjusting the system's operation based on that analysis,

[0865] A means of evaluating how users use their devices and presenting options to reduce stress,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, further comprising means for transferring the generated document to a user's device and making it available for review and modification.

[0869] (Claim 3)

[0870] The system according to claim 1, further comprising means for collecting feedback and enabling continuous improvement of complaint handling measures.

[0871] "Application example 2 when combining with an emotional engine"

[0872] (Claim 1)

[0873] A means to analyze past data and automatically generate the optimal proposal document,

[0874] A means of providing immediate countermeasures based on past cases of responding to requests,

[0875] A means to visualize internal skills and experience and automatically search for the appropriate consultant,

[0876] A means of recognizing emotions from voice and text and adapting to the environment,

[0877] A system that includes this.

[0878] (Claim 2)

[0879] The system according to claim 1, further comprising means for sending the generated proposal document to the user's terminal and allowing it to be reviewed and customized.

[0880] (Claim 3)

[0881] The system according to claim 1, further comprising means for collecting feedback and enabling continuous improvement of the response to requests. [Explanation of Symbols]

[0882] 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. A means of analyzing past information and automatically generating the optimal document, A means of providing immediate solutions based on past cases of troubleshooting, A means to visualize the technology and knowledge within an organization and automatically identify the appropriate consultant, A means of improving operational efficiency in facility management by analyzing past management cases and proposing the optimal management strategy, A system that includes this.

2. The system according to claim 1, further comprising means for sending the generated document to the user's terminal and making it available for review and editing.

3. The system according to claim 1, further comprising means for collecting evaluation information and realizing continuous improvement of troubleshooting measures.

Images