system

Abstract

We provide the system. [Solution] A means of receiving project information, A data management system that converts received project information into a unified format and saves it, A means of updating information to collect and update internal service information to the latest state, A recommendation system that analyzes project information and proposes the most suitable internal services, A response generation means for responding to questions regarding the proposed service, An analytical means for collecting feedback and providing analytical results, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] Despite the existence of many services developed within a company, the existence of these services is not fully recognized, and there is a problem that similar services of other companies are selected. In addition, there is a lack of a mechanism for efficiently proposing which in-house services are suitable for actual business cases. Furthermore, user feedback is not fully utilized effectively for service improvement and new feature development. Due to such a situation, the utilization rate of services is low, and there is a waste of development resources.

Means for Solving the Problems

[0005] This invention provides an input means for receiving case information and a data management means for converting and storing the case information in a unified format. It also includes an information update means for collecting and updating internal service information to the latest state. Furthermore, it has a recommendation means for analyzing case information and proposing the most suitable internal service, and includes a response generation means for responding to questions about the proposed service using natural language processing technology. In addition, it includes an analysis provision means for collecting user feedback and providing the analysis results to the development department. This configuration makes it possible to improve awareness of internal services, promote the use of appropriate services, and continuously improve and optimize services based on feedback.

[0006] "Project information" refers to detailed information about a specific project or task, such as its objectives, requirements, budget, and deadline.

[0007] "Input method" refers to the interface or device that users use to input case information into the system.

[0008] A "unified format" refers to a standardized information format designed to maintain data consistency and integrity.

[0009] "Data management means" refers to digital tools and systems used to store, organize, and manage received information.

[0010] "Information update methods" refer to processes and technologies for updating existing data based on the latest information.

[0011] "Recommendation methods" refer to functions within a system that present the optimal options based on specific criteria or algorithms.

[0012] A "question answering system" refers to a mechanism for generating and providing appropriate answers to user inquiries.

[0013] "Natural language processing technology" refers to the technology that enables computers to understand, generate, and analyze human language.

[0014] "Feedback" refers to information such as opinions, evaluations, and improvement requests sent by users of the system.

[0015] "Analysis and provision means" refers to a function for analyzing the collected feedback, organizing the results, and providing them to relevant departments.

Brief Description of Drawings

[0016] [[ID=***]] [[ID=***]] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [[ID=***]] [[ID=***]] [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [[ID=***]] [[ID=***]] [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [[ID=***]] [[ID=***]] [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [[ID=***]] [[ID=***]] [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [[ID=***]] [[ID=***]] [Figure 6] It 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. [[ID=***]] [[ID=***]] [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [[ID=***]] [[ID=***]] [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [[ID=***]] [[ID=***]] [Figure 9] It shows an emotion map to which multiple emotions are mapped. [[ID=***]] [[ID=***]] [Figure 10] It shows an emotion map to which multiple emotions are mapped. [[ID=***]] [[ID=***]] [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [[ID=***]] [[ID=***]] [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [[ID=***]] [[ID=***]] [Figure 13] Note: Some of the tags like - [Figure 1] etc. were marked as *** in the translation as they seem to be part of a series with no specific content change required for translation. If there is more context or specific rules regarding these tags, the translation might need to be adjusted accordingly.It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a processor with a reference number (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.

[0020] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a storage with a reference number 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] The system of this invention aims to optimize overall resources by efficiently utilizing various services developed within a company. Accurate management of project information and effective proposal of internal services are crucial for the successful implementation of this system.

[0038] First, the user provides detailed project or task-related information to the system through an input interface. This clarifies the project's objectives and requirements. The terminal receives this information, converts it into a consistent format, checks for any omissions or inconsistencies, and then sends it to the server.

[0039] Next, the server stores the received case information in a database. Furthermore, it continuously collects and updates internal service information and uses information update mechanisms to maintain the latest status.

[0040] The server utilizes recommendation mechanisms to suggest the most suitable service for each project. Based on AI technology, it analyzes the project requirements, selects the most matching internal service, and presents it to the user via the terminal.

[0041] If a user needs more detailed information about a recommended service, they can ask questions through a chatbot interface. The server uses question-answering mechanisms and natural language processing techniques to generate responses to the user's questions. The generated answers are immediately provided to the user on their device.

[0042] Furthermore, this system collects user feedback on the services used. Based on this, the server performs analysis and provides specific suggestions for service improvement to the development department. Through this process, feedback information can be transformed from mere opinions into valuable data for improving service quality.

[0043] As a concrete example, when a user enters project information such as "I want to plan an online campaign for a new product," the terminal receives this information and organizes the necessary requirements. The server recommends campaign management services, data analysis tools, etc. Based on these suggestions, the user asks further questions to the chatbot and immediately receives information about specific functions and implementation methods. This process enables efficient service adoption and appropriate resource utilization.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user enters project information into the input interface. This includes detailed information such as project name, purpose, requirements, budget, and deadline.

[0047] Step 2:

[0048] The terminal receives input from the user and verifies the completeness and formatting of the information. If there are any deficiencies, it notifies the user and prompts them to correct them.

[0049] Step 3:

[0050] The server converts the received case information into a unified format and stores it in the database. Format conversion rules are applied to maintain data consistency during this process.

[0051] Step 4:

[0052] The server collects the latest internal service information and uses data update mechanisms to keep the database up-to-date. This includes retrieving data from internal systems and updating information via APIs.

[0053] Step 5:

[0054] The server analyzes the project information and uses AI technology to generate a recommended list of the most suitable internal services. Relevant services are selected based on the project requirements and conditions.

[0055] Step 6:

[0056] The terminal displays a recommended list of services provided by the server to the user. The user reviews the details from this list and understands the benefits and features of the services.

[0057] Step 7:

[0058] Users ask additional questions about the recommended service through a chatbot interface. Users input information they want to know about specific features or case studies.

[0059] Step 8:

[0060] The server uses natural language processing technology to analyze user questions and generate appropriate answers. It also references the system's knowledge base to extract the most relevant information.

[0061] Step 9:

[0062] The device displays the generated answer to the user, aiming to resolve their questions. The information is provided in an easy-to-understand format to support the user's decision-making.

[0063] Step 10:

[0064] Users provide feedback after using the system. This includes the service's advantages, areas for improvement, and requests for additional features.

[0065] Step 11:

[0066] The server analyzes the collected feedback and provides the results to the development department. Using feedback analysis tools, it provides insights for improvement suggestions and new feature development.

[0067] (Example 1)

[0068] 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."

[0069] To efficiently utilize diverse services developed within a company and optimize overall resources, effective means are required for accurate management of project information, selection of the most suitable services, and continuous service improvement. In particular, rapid and accurate data collection and analysis are crucial in processing project information and in improvement processes based on feedback.

[0070] 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.

[0071] In this invention, the server includes data receiving means for acquiring case information, data storage means for converting the received case information into an integrated format and storing it on a data storage medium, and information updating means for collecting and maintaining internal system information in an up-to-date state. This makes it possible to accurately manage case information, efficiently select the optimal internal system, and improve services based on feedback.

[0072] "Project information" refers to detailed information about a project or task, including its objectives, requirements, and resources.

[0073] A "data receiving means" is a means that has the function of acquiring case information provided by the user.

[0074] A "data storage means" is a means that has the function of converting received case information into a standardized format and recording it on a data storage medium.

[0075] An "information update mechanism" is a means of periodically collecting information from an internal system and maintaining it in an up-to-date state at all times.

[0076] A "recommendation method" is a means of suggesting the most suitable internal system based on case information, and it has the ability to analyze information using AI technology.

[0077] A "response generation means" is a means that has the function of generating an answer to a user's question using natural language processing technology.

[0078] "Analysis provision means" refers to a means of contributing to service improvement by analyzing collected feedback and providing the results.

[0079] This system aims to streamline the use of various services within a company and optimize resources. It primarily provides functions to simplify project management and consists of corresponding servers, terminals, and users.

[0080] First, the user provides detailed case information related to the project or task through the system's input interface. This information includes the project's objectives and requirements. This allows the system to understand the detailed information needed for the project.

[0081] The terminal receives information entered by the user and converts the data into a unified format. During this process, the accuracy and consistency of the data are verified, and the user is prompted to make corrections as needed. The converted data is then sent to the server.

[0082] The server stores received case information in a database. It also continuously collects and updates internal service information to maintain its current state. This is where the information update mechanism comes in.

[0083] Furthermore, the server utilizes AI technology to analyze case information and recommend the most suitable internal service. The service selected through this recommendation method is then displayed to the user via their terminal.

[0084] If a user wants to learn more about a recommended service, they can ask questions through a chatbot interface. The server uses natural language processing technology (including generative AI models) to instantly generate answers to the user's questions. As a result, the efficiency of training operations is improved.

[0085] For example, when a user enters project information such as "I want to plan an online campaign for a new product," the terminal receives this information and organizes the necessary requirements. The server recommends campaign management services and data analysis tools. If the user wants to know more based on these suggestions, they can contact the chatbot to immediately obtain information on specific functions and implementation methods.

[0086] In this process, an example of a prompt using a generative AI model would be a question such as, "What services are needed for the online campaign of our new product?" This process allows users to efficiently select the most suitable services and effectively utilize the necessary resources.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] Users input project and task-related information into the system through an input interface. This input includes the project's objective, required resources, and specific requirements. This creates a dataset that provides an overview of the project.

[0090] Step 2:

[0091] The terminal retrieves case information entered by the user and converts it into a standardized format. During this format conversion process, the terminal detects any missing or inconsistent data and notifies the user to correct them. This generates a consistent set of information, which is then sent to the server.

[0092] Step 3:

[0093] The server stores case information sent from terminals in a database. This registration of information in the database is crucial for subsequent analysis and service recommendations. The server also collects other relevant service information and updates it regularly to maintain its current state. This ensures that the information is always accurate and up-to-date.

[0094] Step 4:

[0095] The server uses AI technology to analyze case information. Specifically, it uses a generative AI model to select the optimal internal systems and services that meet the case requirements. This generates a list of candidate services, which are then presented to the user via the terminal.

[0096] Step 5:

[0097] Users can ask questions through a chatbot interface if they want to learn more about a recommended service. The server uses natural language processing technology to generate answers to the user's questions. The server uses a generative AI model to create specific answers and sends them to the terminal in real time. This process is automated based on the prompt text.

[0098] Step 6:

[0099] Users provide feedback on the services they use. The server receives this feedback and analyzes the collected data. The analysis results are provided to the development department as suggestions for service improvement. This feedback loop ensures that the quality of the service improves day by day.

[0100] (Application Example 1)

[0101] 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."

[0102] The present invention aims to provide a system that efficiently manages information and proposes optimal resources in production sites such as factories. Conventional systems have problems such as inconsistencies in project information and insufficient proposals for optimal services, making it difficult to optimize production processes.

[0103] 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.

[0104] In this invention, the server includes an information input means for receiving case information, an information management means for converting the received case information into a unified data format and storing it, an information improvement means for collecting service information and updating it to the current status, a recommendation means for analyzing case information and proposing the optimal service resources, a response creation means for responding to inquiries regarding the proposed services, an analysis provision means for collecting feedback and providing analysis results, and a robot operation proposal means for proposing the optimal machine operation pattern to the robot based on production process information. This enables integrated information management and automatic proposal of optimal machine operation.

[0105] "Project information" refers to detailed information related to a project or task, and is data used to clarify its purpose and requirements.

[0106] "Information input means" refers to interfaces or devices for receiving project information from users.

[0107] A "unified data format" is a format designed to maintain data consistency and manage information in an integrated manner.

[0108] "Information management means" refers to means that provide functions and procedures for formatting and storing case information.

[0109] "Information improvement means" refers to means of collecting and updating information in order to keep service information up-to-date.

[0110] "Recommendation methods" refer to functions that analyze project information and propose the most suitable resources and services.

[0111] A "response generation means" is a means for receiving inquiries related to the proposed service and generating appropriate responses.

[0112] The "analysis provision means" refers to a function that performs analysis based on collected feedback and provides the results.

[0113] A "robot operation suggestion means" is a means for analyzing production process information and suggesting the optimal machine operation pattern to the robot.

[0114] This invention is a system for supporting the optimization of production processes within a factory. The system receives case information entered by the user in a consistent format and stores it in an integrated database using information management means. The stored information is kept up-to-date at all times by information improvement means, and based on this, recommendation means propose the optimal service resources and robot operation patterns.

[0115] The terminal operates on smart devices used by workers on the production floor and displays details about selected services to the user. If the user wants additional information or instructions, a response generation mechanism is provided, using natural language processing technology to generate the most appropriate answer to the inquiry and send it back to the user. This allows the user to obtain the necessary information in real time.

[0116] Furthermore, the feedback analysis and provision system collects user feedback, analyzes the results, and provides the information. This information will be used to improve future services. Specifically, when introducing new robot control software to improve the efficiency of production lines, the system will provide the installation procedure, thereby improving work efficiency.

[0117] In implementations utilizing generative AI models, prompt statements play a crucial role. For example, in response to a user's question such as, "I want to know the robot motion patterns that will improve the efficiency of the assembly line," the system can provide the optimal answer. This entire process enables efficient management and automation of the production site.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The user inputs project information through a terminal. This information includes the project's objectives and required conditions. The terminal processes the input information to convert it into a consistent data format and then sends the converted data to the server.

[0121] Step 2:

[0122] The server receives formatted case information sent from the terminal and stores it in the database. Simultaneously, it uses information enhancement tools to collect internal service information and updates it to ensure it is up-to-date. This process involves data calculations necessary for updating the service information.

[0123] Step 3:

[0124] The server analyzes stored case information and utilizes recommendation mechanisms to select the most suitable service resources. This analysis uses AI technology to perform data calculations to identify the internal service best suited to the case requirements. As a result, a list of selected services is generated.

[0125] Step 4:

[0126] The user reviews the details of the recommended service through their device. If the user requires additional information, a question is sent to the server via the device using a response generation mechanism. The server uses a generative AI model to process the data to generate the best possible answer based on the prompt, and then sends the result to the device.

[0127] Step 5:

[0128] The user receives the answers via their device and confirms the necessary information. During this process, the device displays the answers and, if necessary, provides voice guidance and other specific actions.

[0129] Step 6:

[0130] User feedback is entered into the terminal. The terminal sends this feedback information to the server. The server analyzes the feedback and provides the analysis results as information to help determine the next steps for improvement.

[0131] 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.

[0132] The present invention provides an integrated platform for efficiently managing user case information and proposing appropriate internal services. This system incorporates an emotion engine into the selection of optimal services based on user input, thereby improving the user experience and enhancing the effectiveness of service utilization.

[0133] First, the user inputs project information into the system via the interface. This includes the project's objectives and requirements. The terminal receives the information, converts it into a consistent format, and transfers it to the server. The server then stores this input data in a database.

[0134] Furthermore, the server collects and constantly updates the latest internal service information. Through AI-powered analysis, it recommends the most suitable internal service based on the case information. In this process, an emotion engine is used to recognize the user's emotional state and adjust the service recommendation results accordingly. For example, if an emotion indicating urgency is detected, it is possible to prioritize the selection of services that can provide a quick response.

[0135] After the service is proposed, users can use the chatbot to input more detailed information or questions. The server uses a response generation method incorporating natural language processing technology to generate appropriate answers to questions and deliver them through the terminal. An emotion engine detects the user's emotional changes in real time during the conversation and uses this information to adjust the response content.

[0136] Subsequently, user feedback is collected, and the server performs further analysis based on this feedback. The sentiment data collected by the sentiment engine is integrated and used to improve the quality of the service and to formulate new proposals.

[0137] As a concrete example, consider a scenario where a user inputs a project to plan a launch campaign for a new product. If the terminal uses its emotion engine to detect that the user is experiencing urgency or anxiety, the server will prioritize suggesting services that allow for rapid market reach. In this way, the system can flexibly and effectively select services that take into account the user's emotional state, providing better support to the user.

[0138] The following describes the processing flow.

[0139] Step 1:

[0140] The user submits project information to the input interface. This includes the project objectives, required specifications, and other relevant information.

[0141] Step 2:

[0142] The terminal receives the input of case information and converts it into a consistent format. The converted information is then checked to maintain data quality.

[0143] Step 3:

[0144] The server saves the formatted case information to the database. This allows for quick access to the data for future information retrieval and analysis.

[0145] Step 4:

[0146] The server periodically collects the latest internal service information and updates the database using information update mechanisms. This ensures that service information is always up-to-date and reliable.

[0147] Step 5:

[0148] The server uses an emotion engine to analyze the user's emotional state. It evaluates emotions based on available data, such as entered case information and the user's operation history.

[0149] Step 6:

[0150] The server combines case information with user sentiment evaluations from an emotion engine to generate a list of recommended internal services. The recommendations are adjusted according to the user's emotional state.

[0151] Step 7:

[0152] The device displays a list of recommendations to the user. Links to more detailed information are displayed, allowing the user to further explore services that interest them from this list.

[0153] Step 8:

[0154] Users enter questions about the recommended services via the chatbot. Users can request additional information about specific features or service implementation examples.

[0155] Step 9:

[0156] The server uses a question-answering mechanism to analyze the user's question based on natural language processing technology and generate the optimal answer. The answer is generated based on an internal knowledge base.

[0157] Step 10:

[0158] The device displays the generated response to the user. The response is provided quickly and used as information to support the user's decision-making.

[0159] Step 11:

[0160] Users provide feedback after using the service. This feedback may also be submitted along with the results of a sentiment evaluation using a sentiment engine.

[0161] Step 12:

[0162] The server collects and analyzes feedback and sentiment evaluation results. The analysis results are provided to the development department and used to improve the service and develop new features.

[0163] (Example 2)

[0164] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0165] In selecting services within a company, recommendations are often based on standard data processing without considering the user's emotional state. This can result in the provision of services that do not meet user expectations, leading to a degraded user experience and a stagnant adoption rate of proposed services.

[0166] 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.

[0167] In this invention, the server includes a selection means for processing case data and presenting the optimal internal service, a dialogue generation means for generating responses to inquiries related to the presented service, and a means for detecting the user's emotional state using an emotion recognition function and adjusting service suggestions. This makes it possible to select a more appropriate and satisfying service while taking the user's emotional state into consideration in real time.

[0168] An "information input means" is a system device for receiving project data from users.

[0169] "Information management means" refers to a function for converting received case data into a standard format and recording it.

[0170] A "data collection means" is a device for aggregating internal service data and keeping it constantly up-to-date.

[0171] "Selection method" refers to a part of the system that processes case data and presents the most suitable internal service.

[0172] A "dialogue generation means" is a function that generates an appropriate response in response to an inquiry related to the presented service.

[0173] "Analysis provision means" refers to technology for aggregating user feedback and providing analysis results.

[0174] The "emotion recognition function" is a function that detects the user's emotional state and adjusts service suggestions based on that data.

[0175] This invention is a system for efficiently processing case information from users and proposing appropriate internal services. Users input the purpose and requirements of their cases into an interface, providing case data to the system. The terminal then converts this information into a consistent format and transmits it to the server.

[0176] The server stores case data in a database and collects internal service data, keeping it constantly updated. The server uses a generative AI model to analyze case data and propose the most suitable service. It can also detect the user's emotional state in real time using emotion recognition and adjust service recommendations accordingly.

[0177] If a user requests more detailed information about a proposed service, they can submit a question through their device. The server uses natural language processing technology to generate an accurate response to the user's inquiry, and the device returns that information to the user.

[0178] Users provide feedback after using the service, which the server analyzes. Furthermore, the emotional data obtained from this feedback can be used to improve the service. For example, when a user inputs a proposal for a new product launch campaign, the emotion recognition function detects urgency, and the server prioritizes suggesting marketing services that can respond quickly. In this way, the present invention dynamically adjusts service suggestions according to the user's emotional state, enabling the provision of more satisfying support.

[0179] As an example of how this system can be used, a possible prompt message for the generated AI model could be, "Please recommend a service that can respond quickly for an urgent project." This allows for a rapid response to highly urgent needs.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The user inputs case data through the interface.

[0183] The user inputs detailed project objectives and requirements, which the terminal receives and converts into a consistent format. This conversion process edits unstructured data into structured data, preparing it for transfer to the server as input data.

[0184] Step 2:

[0185] The terminal sends the formatted project data to the server.

[0186] The terminal reliably transmits the formatted data to the server. This process involves saving the data to a database via the network using a data communication protocol. As a result, the case data necessary for subsequent analysis and processing is accumulated on the server.

[0187] Step 3:

[0188] The server saves the received case data to the database.

[0189] The server stores case data in appropriate database tables, ensuring data integrity and security while building indexes for queries. This stored data forms the basis for subsequent analysis processes.

[0190] Step 4:

[0191] The server collects and updates the latest internal service data.

[0192] The server periodically retrieves service data from internal information systems and maintains up-to-date information by comparing it with existing data. This information update process is performed using automated scripts and APIs to keep the service database current.

[0193] Step 5:

[0194] The server analyzes the case data and selects the most suitable service.

[0195] The server analyzes case data using a generative AI model. This analysis process uses pattern recognition algorithms and machine learning models to extract case characteristics and select the most suitable service. This selection also takes into account user sentiment recognition results, and prioritization is adjusted accordingly.

[0196] Step 6:

[0197] The server presents the selection results to the user via the terminal.

[0198] The server generates a list of selected services and detailed information, and sends it to the terminal. The terminal visually presents this information to the user through a user interface, allowing the user to select a service.

[0199] Step 7:

[0200] Dialogue when a user requests more information about a service.

[0201] The user enters detailed information and questions about the presented service via the terminal. The terminal sends this information to the server, allowing the user to retrieve additional information.

[0202] Step 8:

[0203] The server uses natural language processing to generate responses to user questions.

[0204] The server uses natural language processing technology to analyze the user's inquiry and generates an appropriate response using a generative AI model. This response is then adjusted as needed based on the user's sentiment data and sent back to the user via the device.

[0205] Step 9:

[0206] Users submit feedback after using the service.

[0207] The user provides feedback on the service execution through the interface. The terminal formats this feedback and sends it to the server as output data.

[0208] Step 10:

[0209] The server analyzes feedback and sentiment data to help improve the service.

[0210] The server integrates and analyzes feedback and sentiment data to derive insights for improving service quality. These analysis results are provided to the service management team in the form of reports generated by a generative AI model.

[0211] (Application Example 2)

[0212] 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".

[0213] In the field of electronic payments, providing timely and personalized rewards is essential to improving the user's purchasing experience. However, conventional systems struggle to offer dynamic offers based on the user's emotional state and payment information, resulting in a limited user experience. Furthermore, there is a lack of mechanisms to generate appropriate responses to user inquiries in real time.

[0214] 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.

[0215] In this invention, the server includes an input device for receiving case information, a data processing device for converting and storing the received case information in a consistent format, an information update device for collecting and keeping internal information up-to-date, a recommendation device for analyzing case information and proposing the optimal internal offer, a response generation device for responding to inquiries regarding the proposed offer, an analysis and provision device for collecting feedback and providing analysis results, a reward proposal device for collecting payment information in real time and proposing rewards based on that information, and an emotion recognition device for recognizing emotional states and adjusting reward proposals. This makes it possible to provide personalized rewards based on the user's real-time payment behavior and emotional state, thereby realizing a better purchasing experience.

[0216] "Project information" refers to information related to projects and transactions entered by users, including their purpose and requirements.

[0217] An "input device" is a terminal that has an interface function for users to input case information into the system.

[0218] A "data processing device" is a device that has the function of converting received case information into a standardized format and storing it.

[0219] An "information update device" is a device that has the function of continuously collecting internally provided information and keeping it constantly up-to-date.

[0220] A "recommendation device" is a device that proposes the most suitable internal offer to the user based on analyzed case information.

[0221] A "response generation device" is a device that uses natural language processing technology to generate appropriate answers to user inquiries.

[0222] An "analysis and provision device" is a device that analyzes collected feedback and provides the results to other parts of the system.

[0223] A "reward suggestion device" is a device that suggests rewards to users based on their payment information.

[0224] An "emotion recognition device" is a device that recognizes the user's emotional state in real time and adjusts the suggested content based on that state.

[0225] This invention is a system for improving the user experience in electronic payments, and its various elements work together to propose the most suitable benefits to the user. The main components of this system include a terminal for receiving transaction information, a data processing device for converting and storing information in a consistent format, an information update device for maintaining real-time internal information, a recommendation device for presenting the most suitable offers, a response generation device for answering user inquiries, and an emotion recognition device that recognizes the user's emotional state and adjusts offers accordingly.

[0226] The terminal provides an interface for users to input case and payment information. The data processing unit stores this input information on the server in a consistent format and prepares it for analysis. The information update unit constantly collects the latest internal information and uses it to maintain the basis for the offers and proposals provided by this system.

[0227] The server analyzes the user's payment history and real-time emotional state using an emotion recognition device. Based on this analysis, it proposes valuable benefits to the user. This process also utilizes a prompt format generated by a generative AI model, enabling personalized offers tailored to individual user needs.

[0228] For example, consider a scenario where a user is trying to purchase concert tickets via their smartphone, and their actions reveal an emotion of urgency. The server analyzes this information using an emotion recognition device and offers a discount coupon for future purchases, thereby improving user satisfaction. This formal prompt might include instructions such as "the user is in a hurry" and "offer a discount you can use now."

[0229] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0230] Step 1:

[0231] The terminal receives project and payment information from the user. When the user interacts with the interface and enters the necessary data, the terminal converts it into a consistent format. This converted data then becomes the input for transmission to the server.

[0232] Step 2:

[0233] The server receives data in a standardized format sent from the terminal. The received data is stored in a database by a data processing unit. This storage process yields output that makes the information available for subsequent analysis and processing.

[0234] Step 3:

[0235] The server periodically collects and updates the latest internal information using an information update device. This updated information serves as the basis for the recommendation device and is used to match it with the user's case information.

[0236] Step 4:

[0237] The recommended device analyzes case information and generates the optimal offer using the latest internal data. This process utilizes a generation AI model, and the output obtained in the application example is a proposed benefit to present to the user.

[0238] Step 5:

[0239] The emotion recognition device evaluates the user's emotional state based on user data and real-time input. The evaluated emotional data is used to adjust reward offers. This output results in more personalized reward offers.

[0240] Step 6:

[0241] When a user inquires about a reward offer or promotion, the server uses a response generator to produce an appropriate answer. This process utilizes natural language processing technology to output a precise response tailored to the user's question.

[0242] Step 7:

[0243] Ultimately, the server collects user feedback and provides the analysis results obtained through the analysis provider to other parts of the system. This feedback becomes data used to improve future services and develop new offers.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] [Second Embodiment]

[0248] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0249] 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.

[0250] 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).

[0251] 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.

[0252] 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.

[0253] 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).

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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".

[0260] The system of this invention aims to optimize overall resources by efficiently utilizing various services developed within a company. Accurate management of project information and effective proposal of internal services are crucial for the successful implementation of this system.

[0261] First, the user provides detailed project or task-related information to the system through an input interface. This clarifies the project's objectives and requirements. The terminal receives this information, converts it into a consistent format, checks for any omissions or inconsistencies, and then sends it to the server.

[0262] Next, the server stores the received case information in a database. Furthermore, it continuously collects and updates internal service information and uses information update mechanisms to maintain the latest status.

[0263] The server utilizes recommendation mechanisms to suggest the most suitable service for each project. Based on AI technology, it analyzes the project requirements, selects the most matching internal service, and presents it to the user via the terminal.

[0264] If a user needs more detailed information about a recommended service, they can ask questions through a chatbot interface. The server uses question-answering mechanisms and natural language processing techniques to generate responses to the user's questions. The generated answers are immediately provided to the user on their device.

[0265] Furthermore, this system collects user feedback on the services used. Based on this, the server performs analysis and provides specific suggestions for service improvement to the development department. Through this process, feedback information can be transformed from mere opinions into valuable data for improving service quality.

[0266] As a concrete example, when a user enters project information such as "I want to plan an online campaign for a new product," the terminal receives this information and organizes the necessary requirements. The server recommends campaign management services, data analysis tools, etc. Based on these suggestions, the user asks further questions to the chatbot and immediately receives information about specific functions and implementation methods. This process enables efficient service adoption and appropriate resource utilization.

[0267] The following describes the processing flow.

[0268] Step 1:

[0269] The user enters project information into the input interface. This includes detailed information such as project name, purpose, requirements, budget, and deadline.

[0270] Step 2:

[0271] The terminal receives input from the user and verifies the completeness and formatting of the information. If there are any deficiencies, it notifies the user and prompts them to correct them.

[0272] Step 3:

[0273] The server converts the received case information into a unified format and stores it in the database. Format conversion rules are applied to maintain data consistency during this process.

[0274] Step 4:

[0275] The server collects the latest internal service information and uses data update mechanisms to keep the database up-to-date. This includes retrieving data from internal systems and updating information via APIs.

[0276] Step 5:

[0277] The server analyzes the project information and uses AI technology to generate a recommended list of the most suitable internal services. Relevant services are selected based on the project requirements and conditions.

[0278] Step 6:

[0279] The terminal displays a recommended list of services provided by the server to the user. The user reviews the details from this list and understands the benefits and features of the services.

[0280] Step 7:

[0281] Users ask additional questions about the recommended service through a chatbot interface. Users input information they want to know about specific features or case studies.

[0282] Step 8:

[0283] The server uses natural language processing technology to analyze questions from users and generate appropriate answers. It refers to the knowledge base within the system to extract the most relevant information.

[0284] Step 9:

[0285] The terminal displays the generated answer to the user to resolve doubts. The information is provided in an easy-to-understand format to assist the user's decision-making.

[0286] Step 10:

[0287] The user inputs feedback after using the system. This includes the advantages of the service, areas for improvement, and requests for additional features.

[0288] Step 11:

[0289] The server analyzes the collected feedback and provides the results to the development department. Using feedback analysis means, it provides insights for improvement proposals and new feature development.

[0290] (Example 1)

[0291] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0292] When efficiently utilizing various services developed within an enterprise and aiming for overall resource optimization, effective means are required to achieve accurate management of project information, selection of optimal services, and continuous service improvement. In particular, in the processing of project information and the improvement process based on feedback, the rapid and accurate collection and analysis of data are important.

[0293] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0294] In this invention, the server includes data receiving means for acquiring case information, data storage means for converting the received case information into an integrated format and storing it on a data storage medium, and information updating means for collecting and maintaining internal system information in an up-to-date state. This makes it possible to accurately manage case information, efficiently select the optimal internal system, and improve services based on feedback.

[0295] "Project information" refers to detailed information about a project or task, including its objectives, requirements, and resources.

[0296] A "data receiving means" is a means that has the function of acquiring case information provided by the user.

[0297] A "data storage means" is a means that has the function of converting received case information into a standardized format and recording it on a data storage medium.

[0298] An "information update mechanism" is a means of periodically collecting information from an internal system and maintaining it in an up-to-date state at all times.

[0299] A "recommendation method" is a means of suggesting the most suitable internal system based on case information, and it has the ability to analyze information using AI technology.

[0300] A "response generation means" is a means that has the function of generating an answer to a user's question using natural language processing technology.

[0301] "Analysis provision means" refers to a means of contributing to service improvement by analyzing collected feedback and providing the results.

[0302] This system aims to streamline the use of various services within a company and optimize resources. It primarily provides functions to simplify project management and consists of corresponding servers, terminals, and users.

[0303] First, the user provides detailed case information related to projects or operations through the system's input interface. The information obtained here includes the purpose and requirements of the case. This enables the system to grasp the detailed information necessary for the project.

[0304] The terminal receives the information input by the user and converts the data into a unified format. In this process, it is confirmed whether the data is accurate and consistent, and the user is prompted to make corrections if necessary. The converted data is then sent to the server.

[0305] The server stores the received case information in the database. It also collects and updates the in-house service information at any time to maintain the latest status. The information update means is used for this purpose.

[0306] In addition, the server utilizes AI technology to analyze the case information and recommend the most suitable in-house services. The services selected by this recommendation means are displayed to the user through the terminal.

[0307] When the user wants to know the details about the recommended service, they can ask questions through the chatbot interface. The server uses natural language processing technology (including generative AI models) as this question-and-answer means to immediately generate answers to the user's questions. As a result, the efficiency regarding the actual work of training is improved.

[0308] As a specific example, when the user inputs case information such as "want to plan an online campaign for a new product", the terminal receives this information and organizes the necessary conditions to be provided. The server recommends services such as campaign management services and data analysis tools. If the user wants to know more details based on these proposals, they can inquire with the chatbot and immediately obtain information regarding specific functions and introduction methods.

[0309] In this process, an example of a prompt using a generative AI model would be a question such as, "What services are needed for the online campaign of our new product?" This process allows users to efficiently select the most suitable services and effectively utilize the necessary resources.

[0310] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0311] Step 1:

[0312] Users input project and task-related information into the system through an input interface. This input includes the project's objective, required resources, and specific requirements. This creates a dataset that provides an overview of the project.

[0313] Step 2:

[0314] The terminal retrieves case information entered by the user and converts it into a standardized format. During this format conversion process, the terminal detects any missing or inconsistent data and notifies the user to correct them. This generates a consistent set of information, which is then sent to the server.

[0315] Step 3:

[0316] The server stores case information sent from terminals in a database. This registration of information in the database is crucial for subsequent analysis and service recommendations. The server also collects other relevant service information and updates it regularly to maintain its current state. This ensures that the information is always accurate and up-to-date.

[0317] Step 4:

[0318] The server uses AI technology to analyze case information. Specifically, it uses a generative AI model to select the optimal internal systems and services that meet the case requirements. This generates a list of candidate services, which are then presented to the user via the terminal.

[0319] Step 5:

[0320] Users can ask questions through a chatbot interface if they want to learn more about a recommended service. The server uses natural language processing technology to generate answers to the user's questions. The server uses a generative AI model to create specific answers and sends them to the terminal in real time. This process is automated based on the prompt text.

[0321] Step 6:

[0322] Users provide feedback on the services they use. The server receives this feedback and analyzes the collected data. The analysis results are provided to the development department as suggestions for service improvement. This feedback loop ensures that the quality of the service improves day by day.

[0323] (Application Example 1)

[0324] 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."

[0325] The present invention aims to provide a system that efficiently manages information and proposes optimal resources in production sites such as factories. Conventional systems have problems such as inconsistencies in project information and insufficient proposals for optimal services, making it difficult to optimize production processes.

[0326] 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.

[0327] In this invention, the server includes an information input means for receiving case information, an information management means for converting the received case information into a unified data format and storing it, an information improvement means for collecting service information and updating it to the current status, a recommendation means for analyzing case information and proposing the optimal service resources, a response creation means for responding to inquiries regarding the proposed services, an analysis provision means for collecting feedback and providing analysis results, and a robot operation proposal means for proposing the optimal machine operation pattern to the robot based on production process information. This enables integrated information management and automatic proposal of optimal machine operation.

[0328] "Project information" refers to detailed information related to a project or task, and is data used to clarify its purpose and requirements.

[0329] "Information input means" refers to interfaces or devices for receiving project information from users.

[0330] A "unified data format" is a format designed to maintain data consistency and manage information in an integrated manner.

[0331] "Information management means" refers to means that provide functions and procedures for formatting and storing case information.

[0332] "Information improvement means" refers to means of collecting and updating information in order to keep service information up-to-date.

[0333] "Recommendation methods" refer to functions that analyze project information and propose the most suitable resources and services.

[0334] A "response generation means" is a means for receiving inquiries related to the proposed service and generating appropriate responses.

[0335] The "analysis provision means" refers to a function that performs analysis based on collected feedback and provides the results.

[0336] A "robot operation suggestion means" is a means for analyzing production process information and suggesting the optimal machine operation pattern to the robot.

[0337] This invention is a system for supporting the optimization of production processes within a factory. The system receives case information entered by the user in a consistent format and stores it in an integrated database using information management means. The stored information is kept up-to-date at all times by information improvement means, and based on this, recommendation means propose the optimal service resources and robot operation patterns.

[0338] The terminal operates on smart devices used by workers on the production floor and displays details about selected services to the user. If the user wants additional information or instructions, a response generation mechanism is provided, using natural language processing technology to generate the most appropriate answer to the inquiry and send it back to the user. This allows the user to obtain the necessary information in real time.

[0339] Furthermore, the feedback analysis and provision system collects user feedback, analyzes the results, and provides the information. This information will be used to improve future services. Specifically, when introducing new robot control software to improve the efficiency of production lines, the system will provide the installation procedure, thereby improving work efficiency.

[0340] In implementations utilizing generative AI models, prompt statements play a crucial role. For example, in response to a user's question such as, "I want to know the robot motion patterns that will improve the efficiency of the assembly line," the system can provide the optimal answer. This entire process enables efficient management and automation of the production site.

[0341] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0342] Step 1:

[0343] The user inputs project information through a terminal. This information includes the project's objectives and required conditions. The terminal processes the input information to convert it into a consistent data format and then sends the converted data to the server.

[0344] Step 2:

[0345] The server receives formatted case information sent from the terminal and stores it in the database. Simultaneously, it uses information enhancement tools to collect internal service information and updates it to ensure it is up-to-date. This process involves data calculations necessary for updating the service information.

[0346] Step 3:

[0347] The server analyzes stored case information and utilizes recommendation mechanisms to select the most suitable service resources. This analysis uses AI technology to perform data calculations to identify the internal service best suited to the case requirements. As a result, a list of selected services is generated.

[0348] Step 4:

[0349] The user reviews the details of the recommended service through their device. If the user requires additional information, a question is sent to the server via the device using a response generation mechanism. The server uses a generative AI model to process the data to generate the best possible answer based on the prompt, and then sends the result to the device.

[0350] Step 5:

[0351] The user receives the answers via their device and confirms the necessary information. During this process, the device displays the answers and, if necessary, provides voice guidance and other specific actions.

[0352] Step 6:

[0353] User feedback is entered into the terminal. The terminal sends this feedback information to the server. The server analyzes the feedback and provides the analysis results as information to help determine the next steps for improvement.

[0354] 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.

[0355] The present invention provides an integrated platform for efficiently managing user case information and proposing appropriate internal services. This system incorporates an emotion engine into the selection of optimal services based on user input, thereby improving the user experience and enhancing the effectiveness of service utilization.

[0356] First, the user inputs project information into the system via the interface. This includes the project's objectives and requirements. The terminal receives the information, converts it into a consistent format, and transfers it to the server. The server then stores this input data in a database.

[0357] Furthermore, the server collects and constantly updates the latest internal service information. Through AI-powered analysis, it recommends the most suitable internal service based on the case information. In this process, an emotion engine is used to recognize the user's emotional state and adjust the service recommendation results accordingly. For example, if an emotion indicating urgency is detected, it is possible to prioritize the selection of services that can provide a quick response.

[0358] After the service is proposed, users can use the chatbot to input more detailed information or questions. The server uses a response generation method incorporating natural language processing technology to generate appropriate answers to questions and deliver them through the terminal. An emotion engine detects the user's emotional changes in real time during the conversation and uses this information to adjust the response content.

[0359] Subsequently, user feedback is collected, and the server performs further analysis based on this feedback. The sentiment data collected by the sentiment engine is integrated and used to improve the quality of the service and to formulate new proposals.

[0360] As a concrete example, consider a scenario where a user inputs a project to plan a launch campaign for a new product. If the terminal uses its emotion engine to detect that the user is experiencing urgency or anxiety, the server will prioritize suggesting services that allow for rapid market reach. In this way, the system can flexibly and effectively select services that take into account the user's emotional state, providing better support to the user.

[0361] The following describes the processing flow.

[0362] Step 1:

[0363] The user submits project information to the input interface. This includes the project objectives, required specifications, and other relevant information.

[0364] Step 2:

[0365] The terminal receives the input of case information and converts it into a consistent format. The converted information is then checked to maintain data quality.

[0366] Step 3:

[0367] The server saves the formatted case information to the database. This allows for quick access to the data for future information retrieval and analysis.

[0368] Step 4:

[0369] The server periodically collects the latest internal service information and updates the database using information update mechanisms. This ensures that service information is always up-to-date and reliable.

[0370] Step 5:

[0371] The server uses an emotion engine to analyze the user's emotional state. It evaluates emotions based on available data, such as entered case information and the user's operation history.

[0372] Step 6:

[0373] The server combines case information with user sentiment evaluations from an emotion engine to generate a list of recommended internal services. The recommendations are adjusted according to the user's emotional state.

[0374] Step 7:

[0375] The device displays a list of recommendations to the user. Links to more detailed information are displayed, allowing the user to further explore services that interest them from this list.

[0376] Step 8:

[0377] Users enter questions about the recommended services via the chatbot. Users can request additional information about specific features or service implementation examples.

[0378] Step 9:

[0379] The server uses a question-answering mechanism to analyze the user's question based on natural language processing technology and generate the optimal answer. The answer is generated based on an internal knowledge base.

[0380] Step 10:

[0381] The device displays the generated response to the user. The response is provided quickly and used as information to support the user's decision-making.

[0382] Step 11:

[0383] Users provide feedback after using the service. This feedback may also be submitted along with the results of a sentiment evaluation using a sentiment engine.

[0384] Step 12:

[0385] The server collects and analyzes feedback and sentiment evaluation results. The analysis results are provided to the development department and used to improve the service and develop new features.

[0386] (Example 2)

[0387] 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".

[0388] In selecting services within a company, recommendations are often based on standard data processing without considering the user's emotional state. This can result in the provision of services that do not meet user expectations, leading to a degraded user experience and a stagnant adoption rate of proposed services.

[0389] 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.

[0390] In this invention, the server includes a selection means for processing case data and presenting the optimal internal service, a dialogue generation means for generating responses to inquiries related to the presented service, and a means for detecting the user's emotional state using an emotion recognition function and adjusting service suggestions. This makes it possible to select a more appropriate and satisfying service while taking the user's emotional state into consideration in real time.

[0391] An "information input means" is a system device for receiving project data from users.

[0392] "Information management means" refers to a function for converting received case data into a standard format and recording it.

[0393] A "data collection means" is a device for aggregating internal service data and keeping it constantly up-to-date.

[0394] "Selection method" refers to a part of the system that processes case data and presents the most suitable internal service.

[0395] A "dialogue generation means" is a function that generates an appropriate response in response to an inquiry related to the presented service.

[0396] "Analysis provision means" refers to technology for aggregating user feedback and providing analysis results.

[0397] The "emotion recognition function" is a function that detects the user's emotional state and adjusts service suggestions based on that data.

[0398] This invention is a system for efficiently processing case information from users and proposing appropriate internal services. Users input the purpose and requirements of their cases into an interface, providing case data to the system. The terminal then converts this information into a consistent format and transmits it to the server.

[0399] The server stores case data in a database and collects internal service data, keeping it constantly updated. The server uses a generative AI model to analyze case data and propose the most suitable service. It can also detect the user's emotional state in real time using emotion recognition and adjust service recommendations accordingly.

[0400] If a user requests more detailed information about a proposed service, they can submit a question through their device. The server uses natural language processing technology to generate an accurate response to the user's inquiry, and the device returns that information to the user.

[0401] Users provide feedback after using the service, which the server analyzes. Furthermore, the emotional data obtained from this feedback can be used to improve the service. For example, when a user inputs a proposal for a new product launch campaign, the emotion recognition function detects urgency, and the server prioritizes suggesting marketing services that can respond quickly. In this way, the present invention dynamically adjusts service suggestions according to the user's emotional state, enabling the provision of more satisfying support.

[0402] As an example of how this system can be used, a possible prompt message for the generated AI model could be, "Please recommend a service that can respond quickly for an urgent project." This allows for a rapid response to highly urgent needs.

[0403] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0404] Step 1:

[0405] The user inputs case data through the interface.

[0406] The user inputs detailed project objectives and requirements, which the terminal receives and converts into a consistent format. This conversion process edits unstructured data into structured data, preparing it for transfer to the server as input data.

[0407] Step 2:

[0408] The terminal sends the formatted project data to the server.

[0409] The terminal reliably transmits the formatted data to the server. This process involves saving the data to a database via the network using a data communication protocol. As a result, the case data necessary for subsequent analysis and processing is accumulated on the server.

[0410] Step 3:

[0411] The server saves the received case data to the database.

[0412] The server stores case data in appropriate database tables, ensuring data integrity and security while building indexes for queries. This stored data forms the basis for subsequent analysis processes.

[0413] Step 4:

[0414] The server collects and updates the latest internal service data.

[0415] The server periodically retrieves service data from internal information systems and maintains up-to-date information by comparing it with existing data. This information update process is performed using automated scripts and APIs to keep the service database current.

[0416] Step 5:

[0417] The server analyzes the case data and selects the most suitable service.

[0418] The server analyzes case data using a generative AI model. This analysis process uses pattern recognition algorithms and machine learning models to extract case characteristics and select the most suitable service. This selection also takes into account user sentiment recognition results, and prioritization is adjusted accordingly.

[0419] Step 6:

[0420] The server presents the selection results to the user via the terminal.

[0421] The server generates a list of selected services and detailed information, and sends it to the terminal. The terminal visually presents this information to the user through a user interface, allowing the user to select a service.

[0422] Step 7:

[0423] Dialogue when a user requests more information about a service.

[0424] The user enters detailed information and questions about the presented service via the terminal. The terminal sends this information to the server, allowing the user to retrieve additional information.

[0425] Step 8:

[0426] The server uses natural language processing to generate responses to user questions.

[0427] The server uses natural language processing technology to analyze the user's inquiry and generates an appropriate response using a generative AI model. This response is then adjusted as needed based on the user's sentiment data and sent back to the user via the device.

[0428] Step 9:

[0429] Users submit feedback after using the service.

[0430] The user provides feedback on the service execution through the interface. The terminal formats this feedback and sends it to the server as output data.

[0431] Step 10:

[0432] The server analyzes feedback and sentiment data to help improve the service.

[0433] The server integrates and analyzes feedback and sentiment data to derive insights for improving service quality. These analysis results are provided to the service management team in the form of reports generated by a generative AI model.

[0434] (Application Example 2)

[0435] 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."

[0436] In the field of electronic payments, providing timely and personalized rewards is essential to improving the user's purchasing experience. However, conventional systems struggle to offer dynamic offers based on the user's emotional state and payment information, resulting in a limited user experience. Furthermore, there is a lack of mechanisms to generate appropriate responses to user inquiries in real time.

[0437] 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.

[0438] In this invention, the server includes an input device for receiving case information, a data processing device for converting and storing the received case information in a consistent format, an information update device for collecting and keeping internal information up-to-date, a recommendation device for analyzing case information and proposing the optimal internal offer, a response generation device for responding to inquiries regarding the proposed offer, an analysis and provision device for collecting feedback and providing analysis results, a reward proposal device for collecting payment information in real time and proposing rewards based on that information, and an emotion recognition device for recognizing emotional states and adjusting reward proposals. This makes it possible to provide personalized rewards based on the user's real-time payment behavior and emotional state, thereby realizing a better purchasing experience.

[0439] "Project information" refers to information related to projects and transactions entered by users, including their purpose and requirements.

[0440] An "input device" is a terminal that has an interface function for users to input case information into the system.

[0441] A "data processing device" is a device that has the function of converting received case information into a standardized format and storing it.

[0442] An "information update device" is a device that has the function of continuously collecting internally provided information and keeping it constantly up-to-date.

[0443] A "recommendation device" is a device that proposes the most suitable internal offer to the user based on analyzed case information.

[0444] A "response generation device" is a device that uses natural language processing technology to generate appropriate answers to user inquiries.

[0445] An "analysis and provision device" is a device that analyzes collected feedback and provides the results to other parts of the system.

[0446] A "reward suggestion device" is a device that suggests rewards to users based on their payment information.

[0447] An "emotion recognition device" is a device that recognizes the user's emotional state in real time and adjusts the suggested content based on that state.

[0448] This invention is a system for improving the user experience in electronic payments, and its various elements work together to propose the most suitable benefits to the user. The main components of this system include a terminal for receiving transaction information, a data processing device for converting and storing information in a consistent format, an information update device for maintaining real-time internal information, a recommendation device for presenting the most suitable offers, a response generation device for answering user inquiries, and an emotion recognition device that recognizes the user's emotional state and adjusts offers accordingly.

[0449] The terminal provides an interface for users to input case and payment information. The data processing unit stores this input information on the server in a consistent format and prepares it for analysis. The information update unit constantly collects the latest internal information and uses it to maintain the basis for the offers and proposals provided by this system.

[0450] The server analyzes the user's payment history and real-time emotional state using an emotion recognition device. Based on this analysis, it proposes valuable benefits to the user. This process also utilizes a prompt format generated by a generative AI model, enabling personalized offers tailored to individual user needs.

[0451] For example, consider a scenario where a user is trying to purchase concert tickets via their smartphone, and their actions reveal an emotion of urgency. The server analyzes this information using an emotion recognition device and offers a discount coupon for future purchases, thereby improving user satisfaction. This formal prompt might include instructions such as "the user is in a hurry" and "offer a discount you can use now."

[0452] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0453] Step 1:

[0454] The terminal receives project and payment information from the user. When the user interacts with the interface and enters the necessary data, the terminal converts it into a consistent format. This converted data then becomes the input for transmission to the server.

[0455] Step 2:

[0456] The server receives data in a standardized format sent from the terminal. The received data is stored in a database by a data processing unit. This storage process yields output that makes the information available for subsequent analysis and processing.

[0457] Step 3:

[0458] The server periodically collects and updates the latest internal information using an information update device. This updated information serves as the basis for the recommendation device and is used to match it with the user's case information.

[0459] Step 4:

[0460] The recommended device analyzes case information and generates the optimal offer using the latest internal data. This process utilizes a generation AI model, and the output obtained in the application example is a proposed benefit to present to the user.

[0461] Step 5:

[0462] The emotion recognition device evaluates the user's emotional state based on user data and real-time input. The evaluated emotional data is used to adjust reward offers. This output results in more personalized reward offers.

[0463] Step 6:

[0464] When a user inquires about a reward offer or promotion, the server uses a response generator to produce an appropriate answer. This process utilizes natural language processing technology to output a precise response tailored to the user's question.

[0465] Step 7:

[0466] Ultimately, the server collects user feedback and provides the analysis results obtained through the analysis provider to other parts of the system. This feedback becomes data used to improve future services and develop new offers.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] [Third Embodiment]

[0471] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0472] 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.

[0473] 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).

[0474] 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.

[0475] 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.

[0476] 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).

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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.

[0482] 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".

[0483] The system of this invention aims to optimize overall resources by efficiently utilizing various services developed within a company. Accurate management of project information and effective proposal of internal services are crucial for the successful implementation of this system.

[0484] First, the user provides detailed project or task-related information to the system through an input interface. This clarifies the project's objectives and requirements. The terminal receives this information, converts it into a consistent format, checks for any omissions or inconsistencies, and then sends it to the server.

[0485] Next, the server stores the received case information in a database. Furthermore, it continuously collects and updates internal service information and uses information update mechanisms to maintain the latest status.

[0486] The server utilizes recommendation mechanisms to suggest the most suitable service for each project. Based on AI technology, it analyzes the project requirements, selects the most matching internal service, and presents it to the user via the terminal.

[0487] If a user needs more detailed information about a recommended service, they can ask questions through a chatbot interface. The server uses question-answering mechanisms and natural language processing techniques to generate responses to the user's questions. The generated answers are immediately provided to the user on their device.

[0488] Furthermore, this system collects user feedback on the services used. Based on this, the server performs analysis and provides specific suggestions for service improvement to the development department. Through this process, feedback information can be transformed from mere opinions into valuable data for improving service quality.

[0489] As a concrete example, when a user enters project information such as "I want to plan an online campaign for a new product," the terminal receives this information and organizes the necessary requirements. The server recommends campaign management services, data analysis tools, etc. Based on these suggestions, the user asks further questions to the chatbot and immediately receives information about specific functions and implementation methods. This process enables efficient service adoption and appropriate resource utilization.

[0490] The following describes the processing flow.

[0491] Step 1:

[0492] The user enters project information into the input interface. This includes detailed information such as project name, purpose, requirements, budget, and deadline.

[0493] Step 2:

[0494] The terminal receives input from the user and verifies the completeness and formatting of the information. If there are any deficiencies, it notifies the user and prompts them to correct them.

[0495] Step 3:

[0496] The server converts the received case information into a unified format and stores it in the database. Format conversion rules are applied to maintain data consistency during this process.

[0497] Step 4:

[0498] The server collects the latest internal service information and uses data update mechanisms to keep the database up-to-date. This includes retrieving data from internal systems and updating information via APIs.

[0499] Step 5:

[0500] The server analyzes the project information and uses AI technology to generate a recommended list of the most suitable internal services. Relevant services are selected based on the project requirements and conditions.

[0501] Step 6:

[0502] The terminal displays a recommended list of services provided by the server to the user. The user reviews the details from this list and understands the benefits and features of the services.

[0503] Step 7:

[0504] Users ask additional questions about the recommended service through a chatbot interface. Users input information they want to know about specific features or case studies.

[0505] Step 8:

[0506] The server uses natural language processing technology to analyze user questions and generate appropriate answers. It also references the system's knowledge base to extract the most relevant information.

[0507] Step 9:

[0508] The device displays the generated answer to the user, aiming to resolve their questions. The information is provided in an easy-to-understand format to support the user's decision-making.

[0509] Step 10:

[0510] Users provide feedback after using the system. This includes the service's advantages, areas for improvement, and requests for additional features.

[0511] Step 11:

[0512] The server analyzes the collected feedback and provides the results to the development department. Using feedback analysis tools, it provides insights for improvement suggestions and new feature development.

[0513] (Example 1)

[0514] 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."

[0515] To efficiently utilize diverse services developed within a company and optimize overall resources, effective means are required for accurate management of project information, selection of the most suitable services, and continuous service improvement. In particular, rapid and accurate data collection and analysis are crucial in processing project information and in improvement processes based on feedback.

[0516] 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.

[0517] In this invention, the server includes data receiving means for acquiring case information, data storage means for converting the received case information into an integrated format and storing it on a data storage medium, and information updating means for collecting and maintaining internal system information in an up-to-date state. This makes it possible to accurately manage case information, efficiently select the optimal internal system, and improve services based on feedback.

[0518] "Project information" refers to detailed information about a project or task, including its objectives, requirements, and resources.

[0519] A "data receiving means" is a means that has the function of acquiring case information provided by the user.

[0520] A "data storage means" is a means that has the function of converting received case information into a standardized format and recording it on a data storage medium.

[0521] An "information update mechanism" is a means of periodically collecting information from an internal system and maintaining it in an up-to-date state at all times.

[0522] A "recommendation method" is a means of suggesting the most suitable internal system based on case information, and it has the ability to analyze information using AI technology.

[0523] A "response generation means" is a means that has the function of generating an answer to a user's question using natural language processing technology.

[0524] "Analysis provision means" refers to a means of contributing to service improvement by analyzing collected feedback and providing the results.

[0525] This system aims to streamline the use of various services within a company and optimize resources. It primarily provides functions to simplify project management and consists of corresponding servers, terminals, and users.

[0526] First, the user provides detailed case information related to the project or task through the system's input interface. This information includes the project's objectives and requirements. This allows the system to understand the detailed information needed for the project.

[0527] The terminal receives information entered by the user and converts the data into a unified format. During this process, the accuracy and consistency of the data are verified, and the user is prompted to make corrections as needed. The converted data is then sent to the server.

[0528] The server stores received case information in a database. It also continuously collects and updates internal service information to maintain its current state. This is where the information update mechanism comes in.

[0529] Furthermore, the server utilizes AI technology to analyze case information and recommend the most suitable internal service. The service selected through this recommendation method is then displayed to the user via their terminal.

[0530] If a user wants to learn more about a recommended service, they can ask questions through a chatbot interface. The server uses natural language processing technology (including generative AI models) to instantly generate answers to the user's questions. As a result, the efficiency of training operations is improved.

[0531] For example, when a user enters project information such as "I want to plan an online campaign for a new product," the terminal receives this information and organizes the necessary requirements. The server recommends campaign management services and data analysis tools. If the user wants to know more based on these suggestions, they can contact the chatbot to immediately obtain information on specific functions and implementation methods.

[0532] In this process, an example of a prompt using a generative AI model would be a question such as, "What services are needed for the online campaign of our new product?" This process allows users to efficiently select the most suitable services and effectively utilize the necessary resources.

[0533] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0534] Step 1:

[0535] Users input project and task-related information into the system through an input interface. This input includes the project's objective, required resources, and specific requirements. This creates a dataset that provides an overview of the project.

[0536] Step 2:

[0537] The terminal retrieves case information entered by the user and converts it into a standardized format. During this format conversion process, the terminal detects any missing or inconsistent data and notifies the user to correct them. This generates a consistent set of information, which is then sent to the server.

[0538] Step 3:

[0539] The server stores case information sent from terminals in a database. This registration of information in the database is crucial for subsequent analysis and service recommendations. The server also collects other relevant service information and updates it regularly to maintain its current state. This ensures that the information is always accurate and up-to-date.

[0540] Step 4:

[0541] The server uses AI technology to analyze case information. Specifically, it uses a generative AI model to select the optimal internal systems and services that meet the case requirements. This generates a list of candidate services, which are then presented to the user via the terminal.

[0542] Step 5:

[0543] Users can ask questions through a chatbot interface if they want to learn more about a recommended service. The server uses natural language processing technology to generate answers to the user's questions. The server uses a generative AI model to create specific answers and sends them to the terminal in real time. This process is automated based on the prompt text.

[0544] Step 6:

[0545] Users provide feedback on the services they use. The server receives this feedback and analyzes the collected data. The analysis results are provided to the development department as suggestions for service improvement. This feedback loop ensures that the quality of the service improves day by day.

[0546] (Application Example 1)

[0547] 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."

[0548] The present invention aims to provide a system that efficiently manages information and proposes optimal resources in production sites such as factories. Conventional systems have problems such as inconsistencies in project information and insufficient proposals for optimal services, making it difficult to optimize production processes.

[0549] 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.

[0550] In this invention, the server includes an information input means for receiving case information, an information management means for converting the received case information into a unified data format and storing it, an information improvement means for collecting service information and updating it to the current status, a recommendation means for analyzing case information and proposing the optimal service resources, a response creation means for responding to inquiries regarding the proposed services, an analysis provision means for collecting feedback and providing analysis results, and a robot operation proposal means for proposing the optimal machine operation pattern to the robot based on production process information. This enables integrated information management and automatic proposal of optimal machine operation.

[0551] "Project information" refers to detailed information related to a project or task, and is data used to clarify its purpose and requirements.

[0552] "Information input means" refers to interfaces or devices for receiving project information from users.

[0553] A "unified data format" is a format designed to maintain data consistency and manage information in an integrated manner.

[0554] "Information management means" refers to means that provide functions and procedures for formatting and storing case information.

[0555] "Information improvement means" refers to means of collecting and updating information in order to keep service information up-to-date.

[0556] "Recommendation methods" refer to functions that analyze project information and propose the most suitable resources and services.

[0557] A "response generation means" is a means for receiving inquiries related to the proposed service and generating appropriate responses.

[0558] The "analysis provision means" refers to a function that performs analysis based on collected feedback and provides the results.

[0559] A "robot operation suggestion means" is a means for analyzing production process information and suggesting the optimal machine operation pattern to the robot.

[0560] This invention is a system for supporting the optimization of production processes within a factory. The system receives case information entered by the user in a consistent format and stores it in an integrated database using information management means. The stored information is kept up-to-date at all times by information improvement means, and based on this, recommendation means propose the optimal service resources and robot operation patterns.

[0561] The terminal operates on smart devices used by workers on the production floor and displays details about selected services to the user. If the user wants additional information or instructions, a response generation mechanism is provided, using natural language processing technology to generate the most appropriate answer to the inquiry and send it back to the user. This allows the user to obtain the necessary information in real time.

[0562] Furthermore, the feedback analysis and provision system collects user feedback, analyzes the results, and provides the information. This information will be used to improve future services. Specifically, when introducing new robot control software to improve the efficiency of production lines, the system will provide the installation procedure, thereby improving work efficiency.

[0563] In implementations utilizing generative AI models, prompt statements play a crucial role. For example, in response to a user's question such as, "I want to know the robot motion patterns that will improve the efficiency of the assembly line," the system can provide the optimal answer. This entire process enables efficient management and automation of the production site.

[0564] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0565] Step 1:

[0566] The user inputs project information through a terminal. This information includes the project's objectives and required conditions. The terminal processes the input information to convert it into a consistent data format and then sends the converted data to the server.

[0567] Step 2:

[0568] The server receives formatted case information sent from the terminal and stores it in the database. Simultaneously, it uses information enhancement tools to collect internal service information and updates it to ensure it is up-to-date. This process involves data calculations necessary for updating the service information.

[0569] Step 3:

[0570] The server analyzes stored case information and utilizes recommendation mechanisms to select the most suitable service resources. This analysis uses AI technology to perform data calculations to identify the internal service best suited to the case requirements. As a result, a list of selected services is generated.

[0571] Step 4:

[0572] The user reviews the details of the recommended service through their device. If the user requires additional information, a question is sent to the server via the device using a response generation mechanism. The server uses a generative AI model to process the data to generate the best possible answer based on the prompt, and then sends the result to the device.

[0573] Step 5:

[0574] The user receives the answers via their device and confirms the necessary information. During this process, the device displays the answers and, if necessary, provides voice guidance and other specific actions.

[0575] Step 6:

[0576] User feedback is entered into the terminal. The terminal sends this feedback information to the server. The server analyzes the feedback and provides the analysis results as information to help determine the next steps for improvement.

[0577] 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.

[0578] The present invention provides an integrated platform for efficiently managing user case information and proposing appropriate internal services. This system incorporates an emotion engine into the selection of optimal services based on user input, thereby improving the user experience and enhancing the effectiveness of service utilization.

[0579] First, the user inputs project information into the system via the interface. This includes the project's objectives and requirements. The terminal receives the information, converts it into a consistent format, and transfers it to the server. The server then stores this input data in a database.

[0580] Furthermore, the server collects and constantly updates the latest internal service information. Through AI-powered analysis, it recommends the most suitable internal service based on the case information. In this process, an emotion engine is used to recognize the user's emotional state and adjust the service recommendation results accordingly. For example, if an emotion indicating urgency is detected, it is possible to prioritize the selection of services that can provide a quick response.

[0581] After the service is proposed, users can use the chatbot to input more detailed information or questions. The server uses a response generation method incorporating natural language processing technology to generate appropriate answers to questions and deliver them through the terminal. An emotion engine detects the user's emotional changes in real time during the conversation and uses this information to adjust the response content.

[0582] Subsequently, user feedback is collected, and the server performs further analysis based on this feedback. The sentiment data collected by the sentiment engine is integrated and used to improve the quality of the service and to formulate new proposals.

[0583] As a concrete example, consider a scenario where a user inputs a project to plan a launch campaign for a new product. If the terminal uses its emotion engine to detect that the user is experiencing urgency or anxiety, the server will prioritize suggesting services that allow for rapid market reach. In this way, the system can flexibly and effectively select services that take into account the user's emotional state, providing better support to the user.

[0584] The following describes the processing flow.

[0585] Step 1:

[0586] The user submits project information to the input interface. This includes the project objectives, required specifications, and other relevant information.

[0587] Step 2:

[0588] The terminal receives the input of case information and converts it into a consistent format. The converted information is then checked to maintain data quality.

[0589] Step 3:

[0590] The server saves the formatted case information to the database. This allows for quick access to the data for future information retrieval and analysis.

[0591] Step 4:

[0592] The server periodically collects the latest internal service information and updates the database using information update mechanisms. This ensures that service information is always up-to-date and reliable.

[0593] Step 5:

[0594] The server uses an emotion engine to analyze the user's emotional state. It evaluates emotions based on available data, such as entered case information and the user's operation history.

[0595] Step 6:

[0596] The server combines case information with user sentiment evaluations from an emotion engine to generate a list of recommended internal services. The recommendations are adjusted according to the user's emotional state.

[0597] Step 7:

[0598] The device displays a list of recommendations to the user. Links to more detailed information are displayed, allowing the user to further explore services that interest them from this list.

[0599] Step 8:

[0600] Users enter questions about the recommended services via the chatbot. Users can request additional information about specific features or service implementation examples.

[0601] Step 9:

[0602] The server uses a question-answering mechanism to analyze the user's question based on natural language processing technology and generate the optimal answer. The answer is generated based on an internal knowledge base.

[0603] Step 10:

[0604] The device displays the generated response to the user. The response is provided quickly and used as information to support the user's decision-making.

[0605] Step 11:

[0606] Users provide feedback after using the service. This feedback may also be submitted along with the results of a sentiment evaluation using a sentiment engine.

[0607] Step 12:

[0608] The server collects and analyzes feedback and sentiment evaluation results. The analysis results are provided to the development department and used to improve the service and develop new features.

[0609] (Example 2)

[0610] 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."

[0611] In selecting services within a company, recommendations are often based on standard data processing without considering the user's emotional state. This can result in the provision of services that do not meet user expectations, leading to a degraded user experience and a stagnant adoption rate of proposed services.

[0612] 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.

[0613] In this invention, the server includes a selection means for processing case data and presenting the optimal internal service, a dialogue generation means for generating responses to inquiries related to the presented service, and a means for detecting the user's emotional state using an emotion recognition function and adjusting service suggestions. This makes it possible to select a more appropriate and satisfying service while taking the user's emotional state into consideration in real time.

[0614] An "information input means" is a system device for receiving project data from users.

[0615] "Information management means" refers to a function for converting received case data into a standard format and recording it.

[0616] A "data collection means" is a device for aggregating internal service data and keeping it constantly up-to-date.

[0617] "Selection method" refers to a part of the system that processes case data and presents the most suitable internal service.

[0618] A "dialogue generation means" is a function that generates an appropriate response in response to an inquiry related to the presented service.

[0619] "Analysis provision means" refers to technology for aggregating user feedback and providing analysis results.

[0620] The "emotion recognition function" is a function that detects the user's emotional state and adjusts service suggestions based on that data.

[0621] This invention is a system for efficiently processing case information from users and proposing appropriate internal services. Users input the purpose and requirements of their cases into an interface, providing case data to the system. The terminal then converts this information into a consistent format and transmits it to the server.

[0622] The server stores case data in a database and collects internal service data, keeping it constantly updated. The server uses a generative AI model to analyze case data and propose the most suitable service. It can also detect the user's emotional state in real time using emotion recognition and adjust service recommendations accordingly.

[0623] If a user requests more detailed information about a proposed service, they can submit a question through their device. The server uses natural language processing technology to generate an accurate response to the user's inquiry, and the device returns that information to the user.

[0624] Users provide feedback after using the service, which the server analyzes. Furthermore, the emotional data obtained from this feedback can be used to improve the service. For example, when a user inputs a proposal for a new product launch campaign, the emotion recognition function detects urgency, and the server prioritizes suggesting marketing services that can respond quickly. In this way, the present invention dynamically adjusts service suggestions according to the user's emotional state, enabling the provision of more satisfying support.

[0625] As an example of how this system can be used, a possible prompt message for the generated AI model could be, "Please recommend a service that can respond quickly for an urgent project." This allows for a rapid response to highly urgent needs.

[0626] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0627] Step 1:

[0628] The user inputs case data through the interface.

[0629] The user inputs detailed project objectives and requirements, which the terminal receives and converts into a consistent format. This conversion process edits unstructured data into structured data, preparing it for transfer to the server as input data.

[0630] Step 2:

[0631] The terminal sends the formatted project data to the server.

[0632] The terminal reliably transmits the formatted data to the server. This process involves saving the data to a database via the network using a data communication protocol. As a result, the case data necessary for subsequent analysis and processing is accumulated on the server.

[0633] Step 3:

[0634] The server saves the received case data to the database.

[0635] The server stores case data in appropriate database tables, ensuring data integrity and security while building indexes for queries. This stored data forms the basis for subsequent analysis processes.

[0636] Step 4:

[0637] The server collects and updates the latest internal service data.

[0638] The server periodically retrieves service data from internal information systems and maintains up-to-date information by comparing it with existing data. This information update process is performed using automated scripts and APIs to keep the service database current.

[0639] Step 5:

[0640] The server analyzes the case data and selects the most suitable service.

[0641] The server analyzes case data using a generative AI model. This analysis process uses pattern recognition algorithms and machine learning models to extract case characteristics and select the most suitable service. This selection also takes into account user sentiment recognition results, and prioritization is adjusted accordingly.

[0642] Step 6:

[0643] The server presents the selection results to the user via the terminal.

[0644] The server generates a list of selected services and detailed information, and sends it to the terminal. The terminal visually presents this information to the user through a user interface, allowing the user to select a service.

[0645] Step 7:

[0646] Dialogue when a user requests more information about a service.

[0647] The user enters detailed information and questions about the presented service via the terminal. The terminal sends this information to the server, allowing the user to retrieve additional information.

[0648] Step 8:

[0649] The server uses natural language processing to generate responses to user questions.

[0650] The server uses natural language processing technology to analyze the user's inquiry and generates an appropriate response using a generative AI model. This response is then adjusted as needed based on the user's sentiment data and sent back to the user via the device.

[0651] Step 9:

[0652] Users submit feedback after using the service.

[0653] The user provides feedback on the service execution through the interface. The terminal formats this feedback and sends it to the server as output data.

[0654] Step 10:

[0655] The server analyzes feedback and sentiment data to help improve the service.

[0656] The server integrates and analyzes feedback and sentiment data to derive insights for improving service quality. These analysis results are provided to the service management team in the form of reports generated by a generative AI model.

[0657] (Application Example 2)

[0658] 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."

[0659] In the field of electronic payments, providing timely and personalized rewards is essential to improving the user's purchasing experience. However, conventional systems struggle to offer dynamic offers based on the user's emotional state and payment information, resulting in a limited user experience. Furthermore, there is a lack of mechanisms to generate appropriate responses to user inquiries in real time.

[0660] 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.

[0661] In this invention, the server includes an input device for receiving case information, a data processing device for converting and storing the received case information in a consistent format, an information update device for collecting and keeping internal information up-to-date, a recommendation device for analyzing case information and proposing the optimal internal offer, a response generation device for responding to inquiries regarding the proposed offer, an analysis and provision device for collecting feedback and providing analysis results, a reward proposal device for collecting payment information in real time and proposing rewards based on that information, and an emotion recognition device for recognizing emotional states and adjusting reward proposals. This makes it possible to provide personalized rewards based on the user's real-time payment behavior and emotional state, thereby realizing a better purchasing experience.

[0662] "Project information" refers to information related to projects and transactions entered by users, including their purpose and requirements.

[0663] An "input device" is a terminal that has an interface function for users to input case information into the system.

[0664] A "data processing device" is a device that has the function of converting received case information into a standardized format and storing it.

[0665] An "information update device" is a device that has the function of continuously collecting internally provided information and keeping it constantly up-to-date.

[0666] A "recommendation device" is a device that proposes the most suitable internal offer to the user based on analyzed case information.

[0667] A "response generation device" is a device that uses natural language processing technology to generate appropriate answers to user inquiries.

[0668] An "analysis and provision device" is a device that analyzes collected feedback and provides the results to other parts of the system.

[0669] A "reward suggestion device" is a device that suggests rewards to users based on their payment information.

[0670] An "emotion recognition device" is a device that recognizes the user's emotional state in real time and adjusts the suggested content based on that state.

[0671] This invention is a system for improving the user experience in electronic payments, and its various elements work together to propose the most suitable benefits to the user. The main components of this system include a terminal for receiving transaction information, a data processing device for converting and storing information in a consistent format, an information update device for maintaining real-time internal information, a recommendation device for presenting the most suitable offers, a response generation device for answering user inquiries, and an emotion recognition device that recognizes the user's emotional state and adjusts offers accordingly.

[0672] The terminal provides an interface for users to input case and payment information. The data processing unit stores this input information on the server in a consistent format and prepares it for analysis. The information update unit constantly collects the latest internal information and uses it to maintain the basis for the offers and proposals provided by this system.

[0673] The server analyzes the user's payment history and real-time emotional state using an emotion recognition device. Based on this analysis, it proposes valuable benefits to the user. This process also utilizes a prompt format generated by a generative AI model, enabling personalized offers tailored to individual user needs.

[0674] For example, consider a scenario where a user is trying to purchase concert tickets via their smartphone, and their actions reveal an emotion of urgency. The server analyzes this information using an emotion recognition device and offers a discount coupon for future purchases, thereby improving user satisfaction. This formal prompt might include instructions such as "the user is in a hurry" and "offer a discount you can use now."

[0675] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0676] Step 1:

[0677] The terminal receives project and payment information from the user. When the user interacts with the interface and enters the necessary data, the terminal converts it into a consistent format. This converted data then becomes the input for transmission to the server.

[0678] Step 2:

[0679] The server receives data in a standardized format sent from the terminal. The received data is stored in a database by a data processing unit. This storage process yields output that makes the information available for subsequent analysis and processing.

[0680] Step 3:

[0681] The server periodically collects and updates the latest internal information using an information update device. This updated information serves as the basis for the recommendation device and is used to match it with the user's case information.

[0682] Step 4:

[0683] The recommended device analyzes case information and generates the optimal offer using the latest internal data. This process utilizes a generation AI model, and the output obtained in the application example is a proposed benefit to present to the user.

[0684] Step 5:

[0685] The emotion recognition device evaluates the user's emotional state based on user data and real-time input. The evaluated emotional data is used to adjust reward offers. This output results in more personalized reward offers.

[0686] Step 6:

[0687] When a user inquires about a reward offer or promotion, the server uses a response generator to produce an appropriate answer. This process utilizes natural language processing technology to output a precise response tailored to the user's question.

[0688] Step 7:

[0689] Ultimately, the server collects user feedback and provides the analysis results obtained through the analysis provider to other parts of the system. This feedback becomes data used to improve future services and develop new offers.

[0690] 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.

[0691] 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.

[0692] 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.

[0693] [Fourth Embodiment]

[0694] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0695] 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.

[0696] 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).

[0697] 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.

[0698] 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.

[0699] 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).

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] 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".

[0707] The system of this invention aims to optimize overall resources by efficiently utilizing various services developed within a company. Accurate management of project information and effective proposal of internal services are crucial for the successful implementation of this system.

[0708] First, the user provides detailed project or task-related information to the system through an input interface. This clarifies the project's objectives and requirements. The terminal receives this information, converts it into a consistent format, checks for any omissions or inconsistencies, and then sends it to the server.

[0709] Next, the server stores the received case information in a database. Furthermore, it continuously collects and updates internal service information and uses information update mechanisms to maintain the latest status.

[0710] The server utilizes recommendation mechanisms to suggest the most suitable service for each project. Based on AI technology, it analyzes the project requirements, selects the most matching internal service, and presents it to the user via the terminal.

[0711] If a user needs more detailed information about a recommended service, they can ask questions through a chatbot interface. The server uses question-answering mechanisms and natural language processing techniques to generate responses to the user's questions. The generated answers are immediately provided to the user on their device.

[0712] Furthermore, this system collects user feedback on the services used. Based on this, the server performs analysis and provides specific suggestions for service improvement to the development department. Through this process, feedback information can be transformed from mere opinions into valuable data for improving service quality.

[0713] As a concrete example, when a user enters project information such as "I want to plan an online campaign for a new product," the terminal receives this information and organizes the necessary requirements. The server recommends campaign management services, data analysis tools, etc. Based on these suggestions, the user asks further questions to the chatbot and immediately receives information about specific functions and implementation methods. This process enables efficient service adoption and appropriate resource utilization.

[0714] The following describes the processing flow.

[0715] Step 1:

[0716] The user enters project information into the input interface. This includes detailed information such as project name, purpose, requirements, budget, and deadline.

[0717] Step 2:

[0718] The terminal receives input from the user and verifies the completeness and formatting of the information. If there are any deficiencies, it notifies the user and prompts them to correct them.

[0719] Step 3:

[0720] The server converts the received case information into a unified format and stores it in the database. Format conversion rules are applied to maintain data consistency during this process.

[0721] Step 4:

[0722] The server collects the latest internal service information and uses data update mechanisms to keep the database up-to-date. This includes retrieving data from internal systems and updating information via APIs.

[0723] Step 5:

[0724] The server analyzes the project information and uses AI technology to generate a recommended list of the most suitable internal services. Relevant services are selected based on the project requirements and conditions.

[0725] Step 6:

[0726] The terminal displays a recommended list of services provided by the server to the user. The user reviews the details from this list and understands the benefits and features of the services.

[0727] Step 7:

[0728] Users ask additional questions about the recommended service through a chatbot interface. Users input information they want to know about specific features or case studies.

[0729] Step 8:

[0730] The server uses natural language processing technology to analyze user questions and generate appropriate answers. It also references the system's knowledge base to extract the most relevant information.

[0731] Step 9:

[0732] The device displays the generated answer to the user, aiming to resolve their questions. The information is provided in an easy-to-understand format to support the user's decision-making.

[0733] Step 10:

[0734] Users provide feedback after using the system. This includes the service's advantages, areas for improvement, and requests for additional features.

[0735] Step 11:

[0736] The server analyzes the collected feedback and provides the results to the development department. Using feedback analysis tools, it provides insights for improvement suggestions and new feature development.

[0737] (Example 1)

[0738] 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".

[0739] To efficiently utilize diverse services developed within a company and optimize overall resources, effective means are required for accurate management of project information, selection of the most suitable services, and continuous service improvement. In particular, rapid and accurate data collection and analysis are crucial in processing project information and in improvement processes based on feedback.

[0740] 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.

[0741] In this invention, the server includes data receiving means for acquiring case information, data storage means for converting the received case information into an integrated format and storing it on a data storage medium, and information updating means for collecting and maintaining internal system information in an up-to-date state. This makes it possible to accurately manage case information, efficiently select the optimal internal system, and improve services based on feedback.

[0742] "Project information" refers to detailed information about a project or task, including its objectives, requirements, and resources.

[0743] A "data receiving means" is a means that has the function of acquiring case information provided by the user.

[0744] A "data storage means" is a means that has the function of converting received case information into a standardized format and recording it on a data storage medium.

[0745] An "information update mechanism" is a means of periodically collecting information from an internal system and maintaining it in an up-to-date state at all times.

[0746] A "recommendation method" is a means of suggesting the most suitable internal system based on case information, and it has the ability to analyze information using AI technology.

[0747] A "response generation means" is a means that has the function of generating an answer to a user's question using natural language processing technology.

[0748] "Analysis provision means" refers to a means of contributing to service improvement by analyzing collected feedback and providing the results.

[0749] This system aims to streamline the use of various services within a company and optimize resources. It primarily provides functions to simplify project management and consists of corresponding servers, terminals, and users.

[0750] First, the user provides detailed case information related to the project or task through the system's input interface. This information includes the project's objectives and requirements. This allows the system to understand the detailed information needed for the project.

[0751] The terminal receives information entered by the user and converts the data into a unified format. During this process, the accuracy and consistency of the data are verified, and the user is prompted to make corrections as needed. The converted data is then sent to the server.

[0752] The server stores received case information in a database. It also continuously collects and updates internal service information to maintain its current state. This is where the information update mechanism comes in.

[0753] Furthermore, the server utilizes AI technology to analyze case information and recommend the most suitable internal service. The service selected through this recommendation method is then displayed to the user via their terminal.

[0754] If a user wants to learn more about a recommended service, they can ask questions through a chatbot interface. The server uses natural language processing technology (including generative AI models) to instantly generate answers to the user's questions. As a result, the efficiency of training operations is improved.

[0755] For example, when a user enters project information such as "I want to plan an online campaign for a new product," the terminal receives this information and organizes the necessary requirements. The server recommends campaign management services and data analysis tools. If the user wants to know more based on these suggestions, they can contact the chatbot to immediately obtain information on specific functions and implementation methods.

[0756] In this process, an example of a prompt using a generative AI model would be a question such as, "What services are needed for the online campaign of our new product?" This process allows users to efficiently select the most suitable services and effectively utilize the necessary resources.

[0757] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0758] Step 1:

[0759] Users input project and task-related information into the system through an input interface. This input includes the project's objective, required resources, and specific requirements. This creates a dataset that provides an overview of the project.

[0760] Step 2:

[0761] The terminal retrieves case information entered by the user and converts it into a standardized format. During this format conversion process, the terminal detects any missing or inconsistent data and notifies the user to correct them. This generates a consistent set of information, which is then sent to the server.

[0762] Step 3:

[0763] The server stores case information sent from terminals in a database. This registration of information in the database is crucial for subsequent analysis and service recommendations. The server also collects other relevant service information and updates it regularly to maintain its current state. This ensures that the information is always accurate and up-to-date.

[0764] Step 4:

[0765] The server uses AI technology to analyze case information. Specifically, it uses a generative AI model to select the optimal internal systems and services that meet the case requirements. This generates a list of candidate services, which are then presented to the user via the terminal.

[0766] Step 5:

[0767] Users can ask questions through a chatbot interface if they want to learn more about a recommended service. The server uses natural language processing technology to generate answers to the user's questions. The server uses a generative AI model to create specific answers and sends them to the terminal in real time. This process is automated based on the prompt text.

[0768] Step 6:

[0769] Users provide feedback on the services they use. The server receives this feedback and analyzes the collected data. The analysis results are provided to the development department as suggestions for service improvement. This feedback loop ensures that the quality of the service improves day by day.

[0770] (Application Example 1)

[0771] 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".

[0772] The present invention aims to provide a system that efficiently manages information and proposes optimal resources in production sites such as factories. Conventional systems have problems such as inconsistencies in project information and insufficient proposals for optimal services, making it difficult to optimize production processes.

[0773] 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.

[0774] In this invention, the server includes an information input means for receiving case information, an information management means for converting the received case information into a unified data format and storing it, an information improvement means for collecting service information and updating it to the current status, a recommendation means for analyzing case information and proposing the optimal service resources, a response creation means for responding to inquiries regarding the proposed services, an analysis provision means for collecting feedback and providing analysis results, and a robot operation proposal means for proposing the optimal machine operation pattern to the robot based on production process information. This enables integrated information management and automatic proposal of optimal machine operation.

[0775] "Project information" refers to detailed information related to a project or task, and is data used to clarify its purpose and requirements.

[0776] "Information input means" refers to interfaces or devices for receiving project information from users.

[0777] A "unified data format" is a format designed to maintain data consistency and manage information in an integrated manner.

[0778] "Information management means" refers to means that provide functions and procedures for formatting and storing case information.

[0779] "Information improvement means" refers to means of collecting and updating information in order to keep service information up-to-date.

[0780] "Recommendation methods" refer to functions that analyze project information and propose the most suitable resources and services.

[0781] A "response generation means" is a means for receiving inquiries related to the proposed service and generating appropriate responses.

[0782] The "analysis provision means" refers to a function that performs analysis based on collected feedback and provides the results.

[0783] A "robot operation suggestion means" is a means for analyzing production process information and suggesting the optimal machine operation pattern to the robot.

[0784] This invention is a system for supporting the optimization of production processes within a factory. The system receives case information entered by the user in a consistent format and stores it in an integrated database using information management means. The stored information is kept up-to-date at all times by information improvement means, and based on this, recommendation means propose the optimal service resources and robot operation patterns.

[0785] The terminal operates on smart devices used by workers on the production floor and displays details about selected services to the user. If the user wants additional information or instructions, a response generation mechanism is provided, using natural language processing technology to generate the most appropriate answer to the inquiry and send it back to the user. This allows the user to obtain the necessary information in real time.

[0786] Furthermore, the feedback analysis and provision system collects user feedback, analyzes the results, and provides the information. This information will be used to improve future services. Specifically, when introducing new robot control software to improve the efficiency of production lines, the system will provide the installation procedure, thereby improving work efficiency.

[0787] In implementations utilizing generative AI models, prompt statements play a crucial role. For example, in response to a user's question such as, "I want to know the robot motion patterns that will improve the efficiency of the assembly line," the system can provide the optimal answer. This entire process enables efficient management and automation of the production site.

[0788] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0789] Step 1:

[0790] The user inputs project information through a terminal. This information includes the project's objectives and required conditions. The terminal processes the input information to convert it into a consistent data format and then sends the converted data to the server.

[0791] Step 2:

[0792] The server receives formatted case information sent from the terminal and stores it in the database. Simultaneously, it uses information enhancement tools to collect internal service information and updates it to ensure it is up-to-date. This process involves data calculations necessary for updating the service information.

[0793] Step 3:

[0794] The server analyzes stored case information and utilizes recommendation mechanisms to select the most suitable service resources. This analysis uses AI technology to perform data calculations to identify the internal service best suited to the case requirements. As a result, a list of selected services is generated.

[0795] Step 4:

[0796] The user reviews the details of the recommended service through their device. If the user requires additional information, a question is sent to the server via the device using a response generation mechanism. The server uses a generative AI model to process the data to generate the best possible answer based on the prompt, and then sends the result to the device.

[0797] Step 5:

[0798] The user receives the answers via their device and confirms the necessary information. During this process, the device displays the answers and, if necessary, provides voice guidance and other specific actions.

[0799] Step 6:

[0800] User feedback is entered into the terminal. The terminal sends this feedback information to the server. The server analyzes the feedback and provides the analysis results as information to help determine the next steps for improvement.

[0801] 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.

[0802] The present invention provides an integrated platform for efficiently managing user case information and proposing appropriate internal services. This system incorporates an emotion engine into the selection of optimal services based on user input, thereby improving the user experience and enhancing the effectiveness of service utilization.

[0803] First, the user inputs project information into the system via the interface. This includes the project's objectives and requirements. The terminal receives the information, converts it into a consistent format, and transfers it to the server. The server then stores this input data in a database.

[0804] Furthermore, the server collects and constantly updates the latest internal service information. Through AI-powered analysis, it recommends the most suitable internal service based on the case information. In this process, an emotion engine is used to recognize the user's emotional state and adjust the service recommendation results accordingly. For example, if an emotion indicating urgency is detected, it is possible to prioritize the selection of services that can provide a quick response.

[0805] After the service is proposed, users can use the chatbot to input more detailed information or questions. The server uses a response generation method incorporating natural language processing technology to generate appropriate answers to questions and deliver them through the terminal. An emotion engine detects the user's emotional changes in real time during the conversation and uses this information to adjust the response content.

[0806] Subsequently, user feedback is collected, and the server performs further analysis based on this feedback. The sentiment data collected by the sentiment engine is integrated and used to improve the quality of the service and to formulate new proposals.

[0807] As a concrete example, consider a scenario where a user inputs a project to plan a launch campaign for a new product. If the terminal uses its emotion engine to detect that the user is experiencing urgency or anxiety, the server will prioritize suggesting services that allow for rapid market reach. In this way, the system can flexibly and effectively select services that take into account the user's emotional state, providing better support to the user.

[0808] The following describes the processing flow.

[0809] Step 1:

[0810] The user submits project information to the input interface. This includes the project objectives, required specifications, and other relevant information.

[0811] Step 2:

[0812] The terminal receives the input of case information and converts it into a consistent format. The converted information is then checked to maintain data quality.

[0813] Step 3:

[0814] The server saves the formatted case information to the database. This allows for quick access to the data for future information retrieval and analysis.

[0815] Step 4:

[0816] The server periodically collects the latest internal service information and updates the database using information update mechanisms. This ensures that service information is always up-to-date and reliable.

[0817] Step 5:

[0818] The server uses an emotion engine to analyze the user's emotional state. It evaluates emotions based on available data, such as entered case information and the user's operation history.

[0819] Step 6:

[0820] The server combines case information with user sentiment evaluations from an emotion engine to generate a list of recommended internal services. The recommendations are adjusted according to the user's emotional state.

[0821] Step 7:

[0822] The device displays a list of recommendations to the user. Links to more detailed information are displayed, allowing the user to further explore services that interest them from this list.

[0823] Step 8:

[0824] Users enter questions about the recommended services via the chatbot. Users can request additional information about specific features or service implementation examples.

[0825] Step 9:

[0826] The server uses a question-answering mechanism to analyze the user's question based on natural language processing technology and generate the optimal answer. The answer is generated based on an internal knowledge base.

[0827] Step 10:

[0828] The device displays the generated response to the user. The response is provided quickly and used as information to support the user's decision-making.

[0829] Step 11:

[0830] Users provide feedback after using the service. This feedback may also be submitted along with the results of a sentiment evaluation using a sentiment engine.

[0831] Step 12:

[0832] The server collects and analyzes feedback and sentiment evaluation results. The analysis results are provided to the development department and used to improve the service and develop new features.

[0833] (Example 2)

[0834] 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".

[0835] In selecting services within a company, recommendations are often based on standard data processing without considering the user's emotional state. This can result in the provision of services that do not meet user expectations, leading to a degraded user experience and a stagnant adoption rate of proposed services.

[0836] 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.

[0837] In this invention, the server includes a selection means for processing case data and presenting the optimal internal service, a dialogue generation means for generating responses to inquiries related to the presented service, and a means for detecting the user's emotional state using an emotion recognition function and adjusting service suggestions. This makes it possible to select a more appropriate and satisfying service while taking the user's emotional state into consideration in real time.

[0838] An "information input means" is a system device for receiving project data from users.

[0839] "Information management means" refers to a function for converting received case data into a standard format and recording it.

[0840] A "data collection means" is a device for aggregating internal service data and keeping it constantly up-to-date.

[0841] "Selection method" refers to a part of the system that processes case data and presents the most suitable internal service.

[0842] A "dialogue generation means" is a function that generates an appropriate response in response to an inquiry related to the presented service.

[0843] "Analysis provision means" refers to technology for aggregating user feedback and providing analysis results.

[0844] The "emotion recognition function" is a function that detects the user's emotional state and adjusts service suggestions based on that data.

[0845] This invention is a system for efficiently processing case information from users and proposing appropriate internal services. Users input the purpose and requirements of their cases into an interface, providing case data to the system. The terminal then converts this information into a consistent format and transmits it to the server.

[0846] The server stores case data in a database and collects internal service data, keeping it constantly updated. The server uses a generative AI model to analyze case data and propose the most suitable service. It can also detect the user's emotional state in real time using emotion recognition and adjust service recommendations accordingly.

[0847] If a user requests more detailed information about a proposed service, they can submit a question through their device. The server uses natural language processing technology to generate an accurate response to the user's inquiry, and the device returns that information to the user.

[0848] Users provide feedback after using the service, which the server analyzes. Furthermore, the emotional data obtained from this feedback can be used to improve the service. For example, when a user inputs a proposal for a new product launch campaign, the emotion recognition function detects urgency, and the server prioritizes suggesting marketing services that can respond quickly. In this way, the present invention dynamically adjusts service suggestions according to the user's emotional state, enabling the provision of more satisfying support.

[0849] As an example of how this system can be used, a possible prompt message for the generated AI model could be, "Please recommend a service that can respond quickly for an urgent project." This allows for a rapid response to highly urgent needs.

[0850] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0851] Step 1:

[0852] The user inputs case data through the interface.

[0853] The user inputs detailed project objectives and requirements, which the terminal receives and converts into a consistent format. This conversion process edits unstructured data into structured data, preparing it for transfer to the server as input data.

[0854] Step 2:

[0855] The terminal sends the formatted project data to the server.

[0856] The terminal reliably transmits the formatted data to the server. This process involves saving the data to a database via the network using a data communication protocol. As a result, the case data necessary for subsequent analysis and processing is accumulated on the server.

[0857] Step 3:

[0858] The server saves the received case data to the database.

[0859] The server stores case data in appropriate database tables, ensuring data integrity and security while building indexes for queries. This stored data forms the basis for subsequent analysis processes.

[0860] Step 4:

[0861] The server collects and updates the latest internal service data.

[0862] The server periodically retrieves service data from internal information systems and maintains up-to-date information by comparing it with existing data. This information update process is performed using automated scripts and APIs to keep the service database current.

[0863] Step 5:

[0864] The server analyzes the case data and selects the most suitable service.

[0865] The server analyzes case data using a generative AI model. This analysis process uses pattern recognition algorithms and machine learning models to extract case characteristics and select the most suitable service. This selection also takes into account user sentiment recognition results, and prioritization is adjusted accordingly.

[0866] Step 6:

[0867] The server presents the selection results to the user via the terminal.

[0868] The server generates a list of selected services and detailed information, and sends it to the terminal. The terminal visually presents this information to the user through a user interface, allowing the user to select a service.

[0869] Step 7:

[0870] Dialogue when a user requests more information about a service.

[0871] The user enters detailed information and questions about the presented service via the terminal. The terminal sends this information to the server, allowing the user to retrieve additional information.

[0872] Step 8:

[0873] The server uses natural language processing to generate responses to user questions.

[0874] The server uses natural language processing technology to analyze the user's inquiry and generates an appropriate response using a generative AI model. This response is then adjusted as needed based on the user's sentiment data and sent back to the user via the device.

[0875] Step 9:

[0876] Users submit feedback after using the service.

[0877] The user provides feedback on the service execution through the interface. The terminal formats this feedback and sends it to the server as output data.

[0878] Step 10:

[0879] The server analyzes feedback and sentiment data to help improve the service.

[0880] The server integrates and analyzes feedback and sentiment data to derive insights for improving service quality. These analysis results are provided to the service management team in the form of reports generated by a generative AI model.

[0881] (Application Example 2)

[0882] 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".

[0883] In the field of electronic payments, providing timely and personalized rewards is essential to improving the user's purchasing experience. However, conventional systems struggle to offer dynamic offers based on the user's emotional state and payment information, resulting in a limited user experience. Furthermore, there is a lack of mechanisms to generate appropriate responses to user inquiries in real time.

[0884] 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.

[0885] In this invention, the server includes an input device for receiving case information, a data processing device for converting and storing the received case information in a consistent format, an information update device for collecting and keeping internal information up-to-date, a recommendation device for analyzing case information and proposing the optimal internal offer, a response generation device for responding to inquiries regarding the proposed offer, an analysis and provision device for collecting feedback and providing analysis results, a reward proposal device for collecting payment information in real time and proposing rewards based on that information, and an emotion recognition device for recognizing emotional states and adjusting reward proposals. This makes it possible to provide personalized rewards based on the user's real-time payment behavior and emotional state, thereby realizing a better purchasing experience.

[0886] "Project information" refers to information related to projects and transactions entered by users, including their purpose and requirements.

[0887] An "input device" is a terminal that has an interface function for users to input case information into the system.

[0888] A "data processing device" is a device that has the function of converting received case information into a standardized format and storing it.

[0889] An "information update device" is a device that has the function of continuously collecting internally provided information and keeping it constantly up-to-date.

[0890] A "recommendation device" is a device that proposes the most suitable internal offer to the user based on analyzed case information.

[0891] A "response generation device" is a device that uses natural language processing technology to generate appropriate answers to user inquiries.

[0892] An "analysis and provision device" is a device that analyzes collected feedback and provides the results to other parts of the system.

[0893] A "reward suggestion device" is a device that suggests rewards to users based on their payment information.

[0894] An "emotion recognition device" is a device that recognizes the user's emotional state in real time and adjusts the suggested content based on that state.

[0895] This invention is a system for improving the user experience in electronic payments, and its various elements work together to propose the most suitable benefits to the user. The main components of this system include a terminal for receiving transaction information, a data processing device for converting and storing information in a consistent format, an information update device for maintaining real-time internal information, a recommendation device for presenting the most suitable offers, a response generation device for answering user inquiries, and an emotion recognition device that recognizes the user's emotional state and adjusts offers accordingly.

[0896] The terminal provides an interface for users to input case and payment information. The data processing unit stores this input information on the server in a consistent format and prepares it for analysis. The information update unit constantly collects the latest internal information and uses it to maintain the basis for the offers and proposals provided by this system.

[0897] The server analyzes the user's payment history and real-time emotional state using an emotion recognition device. Based on this analysis, it proposes valuable benefits to the user. This process also utilizes a prompt format generated by a generative AI model, enabling personalized offers tailored to individual user needs.

[0898] For example, consider a scenario where a user is trying to purchase concert tickets via their smartphone, and their actions reveal an emotion of urgency. The server analyzes this information using an emotion recognition device and offers a discount coupon for future purchases, thereby improving user satisfaction. This formal prompt might include instructions such as "the user is in a hurry" and "offer a discount you can use now."

[0899] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0900] Step 1:

[0901] The terminal receives project and payment information from the user. When the user interacts with the interface and enters the necessary data, the terminal converts it into a consistent format. This converted data then becomes the input for transmission to the server.

[0902] Step 2:

[0903] The server receives data in a standardized format sent from the terminal. The received data is stored in a database by a data processing unit. This storage process yields output that makes the information available for subsequent analysis and processing.

[0904] Step 3:

[0905] The server periodically collects and updates the latest internal information using an information update device. This updated information serves as the basis for the recommendation device and is used to match it with the user's case information.

[0906] Step 4:

[0907] The recommended device analyzes case information and generates the optimal offer using the latest internal data. This process utilizes a generation AI model, and the output obtained in the application example is a proposed benefit to present to the user.

[0908] Step 5:

[0909] The emotion recognition device evaluates the user's emotional state based on user data and real-time input. The evaluated emotional data is used to adjust reward offers. This output results in more personalized reward offers.

[0910] Step 6:

[0911] When a user inquires about a reward offer or promotion, the server uses a response generator to produce an appropriate answer. This process utilizes natural language processing technology to output a precise response tailored to the user's question.

[0912] Step 7:

[0913] Ultimately, the server collects user feedback and provides the analysis results obtained through the analysis provider to other parts of the system. This feedback becomes data used to improve future services and develop new offers.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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.

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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."

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] 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.

[0935] The following is further disclosed regarding the embodiments described above.

[0936] (Claim 1)

[0937] A means of receiving project information,

[0938] A data management system that converts received project information into a unified format and saves it,

[0939] A means of updating information to collect and update internal service information to the latest state,

[0940] A recommendation system that analyzes project information and proposes the most suitable internal services,

[0941] A response generation means for responding to questions regarding the proposed service,

[0942] An analytical means for collecting feedback and providing analytical results,

[0943] A system that includes this.

[0944] (Claim 2)

[0945] The system according to claim 1, which receives feedback and generates improvement suggestions based on the recommended service selection.

[0946] (Claim 3)

[0947] The system according to claim 1, wherein the question answering means uses natural language processing technology to generate the optimal answer to the user's inquiry.

[0948] "Example 1"

[0949] (Claim 1)

[0950] A data receiving method for acquiring case information,

[0951] A data storage means that converts received case information into an integrated format and saves it to a data storage medium,

[0952] Information update means for collecting and maintaining internal system information in an up-to-date state,

[0953] A recommendation system that processes case information and suggests the most suitable internal system,

[0954] A response generation means that generates answers to questions about the internal system presented,

[0955] An analytical means for collecting feedback, analyzing that data, and providing analytical results,

[0956] A system that includes this.

[0957] (Claim 2)

[0958] The system according to claim 1, which receives feedback and creates improvement suggestions based on the selection of recommended internal systems.

[0959] (Claim 3)

[0960] The system according to claim 1, wherein the response generation means uses natural language processing technology to generate the optimal response to a user's inquiry.

[0961] "Application Example 1"

[0962] (Claim 1)

[0963] A means of receiving project information and inputting information,

[0964] An information management system that converts received case information into a unified data format and stores it,

[0965] A means of improving information by collecting service information and updating it to the current status,

[0966] A recommendation system that analyzes project information and proposes the most suitable service resources,

[0967] A means for creating responses to inquiries regarding the proposed service,

[0968] An analysis provision means for collecting feedback and providing analysis results,

[0969] A robot operation suggestion means that proposes the optimal machine operation pattern to the robot based on production process information,

[0970] A system that includes this.

[0971] (Claim 2)

[0972] The system according to claim 1, which receives feedback and generates improvement suggestions based on recommended service selections and production processes.

[0973] (Claim 3)

[0974] The system according to claim 1, wherein the response generation means generates an optimal response to a user's inquiry using natural language processing technology and provides the information through an information presentation device.

[0975] "Example 2 of combining an emotion engine"

[0976] (Claim 1)

[0977] A means of receiving project data and inputting information,

[0978] An information management system that converts received project data into a standard format and records it,

[0979] A data collection method that aggregates internal service data and maintains it in an up-to-date state,

[0980] A selection method that processes case data and presents the most suitable internal service,

[0981] A dialogue generation means that generates a response to an inquiry related to the presented service,

[0982] An analysis provision means that aggregates feedback and provides analysis results,

[0983] A means of detecting the user's emotional state using emotion recognition functionality and adjusting service suggestions accordingly,

[0984] A system that includes this.

[0985] (Claim 2)

[0986] The system according to claim 1, which collects feedback and generates improvement proposals based on selected service proposals.

[0987] (Claim 3)

[0988] The system according to claim 1, wherein the dialogue generation means uses natural language processing technology to generate an appropriate response to a user inquiry.

[0989] "Application example 2 when combining with an emotional engine"

[0990] (Claim 1)

[0991] An input device for receiving project information,

[0992] A data processing device that converts received case information into a consistent format and stores it,

[0993] An information update device that collects and maintains internal information,

[0994] A recommendation system that analyzes project information and proposes the most suitable internal offer,

[0995] A response generation device that responds to inquiries regarding the proposed offer,

[0996] An analysis device that collects feedback and provides analysis results,

[0997] A rewards suggestion device that collects payment information in real time and suggests rewards based on that information,

[0998] An emotion recognition device that recognizes emotional states and adjusts reward suggestions,

[0999] A system that includes this.

[1000] (Claim 2)

[1001] The system according to claim 1, which receives feedback based on the recommended offer selection, generates improvement suggestions, and adjusts benefits according to the user's status.

[1002] (Claim 3)

[1003] The system according to claim 1, wherein a response generation device generates an optimal response to a user's inquiry using natural language processing technology, and an emotion recognition device evaluates the emotional state in real time and reflects the result. [Explanation of Symbols]

[1004] 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 receiving project information, A data management system that converts received project information into a unified format and saves it, A means of updating information to collect and update internal service information to the latest state, A recommendation system that analyzes project information and proposes the most suitable internal services, A response generation means for responding to questions regarding the proposed service, An analytical means for collecting feedback and providing analytical results, A system that includes this.

2. The system according to claim 1, which receives feedback and generates improvement suggestions based on the recommended service selection.

3. The system according to claim 1, wherein the question answering means uses natural language processing technology to generate the optimal answer to the user's inquiry.

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