system

Abstract

We provide the system. [Solution] It includes a database for aggregating and storing information, and a means for centrally managing business information received from each department within the corporate group, A method for selecting and proposing the appropriate department based on detailed information of a project that requires hypothesis testing using generative AI, A means of presenting information on proposed departments to the user and promoting inter-departmental collaboration according to the user's selection, A means to monitor project progress and update information in real time, 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 performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a corporate group, each department often conducts business independently, and there is a lack of sharing of knowledge and resources between departments, resulting in the problem that it is difficult to solve specific problems or verify hypotheses. In addition, due to overemphasis on product development for external customers, there is a tendency to overlook internal potential problems and improvement points. As a result, efficient hypothesis verification and innovation are hindered, and the competitiveness improvement of the entire enterprise is impeded.

Means for Solving the Problems

[0005] This invention solves the problem by aggregating information from departments within a corporate group and managing it centrally as a database. Furthermore, by using generative AI, it becomes possible to select the appropriate department based on detailed information of the project for which hypothesis testing is desired. The proposed department information is provided to the user, and the system has a mechanism that promotes collaboration between departments based on the user's selection. In addition, by monitoring project progress and updating information in real time, a smooth collaborative system between departments is built, enabling efficient problem solving.

[0006] A "database for aggregating and storing information" is a system that centrally stores business information collected from various departments within a corporate group and manages it in a usable format.

[0007] "Generative AI" is an artificial intelligence technology that analyzes collected data and predicts and proposes the most suitable departments based on detailed project information.

[0008] "Hypothesis testing" is a trial-and-error process used to determine whether a particular business idea or problem-solving solution is feasible.

[0009] "A means of selecting and proposing the appropriate department" refers to a function that identifies the department that best matches the project requirements through data analysis and provides that information to the user.

[0010] "Means to promote interdepartmental collaboration" refers to a system that enables effective communication and collaboration between selected departments and users.

[0011] "A means of monitoring project progress and updating information in real time" refers to a function that constantly monitors the status of ongoing projects and provides stakeholders with the latest information.

[0012] "A means of analyzing the history of past projects to improve the accuracy of proposals" refers to a function that analyzes data from previously conducted projects to enable more accurate department selection.

[0013] "Tagging and linking highly relevant data" refers to a technology that adds metadata to information to improve searchability and easily associate similar data. [Brief explanation of the drawing]

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

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

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

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

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

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

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

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0035] This invention provides a system that supports efficient hypothesis testing within a corporate group. Embodiments include a platform equipped with an information aggregation function, a department selection function using generation AI, and a progress management function.

[0036] The system first aggregates business information from each department within the corporate group and stores it in a database. This information includes the business content, areas of expertise, challenges, and resource information handled by each department. The database is designed to efficiently manage this information and allow for immediate access as needed.

[0037] The generating AI accesses this database and selects the most appropriate department based on the detailed project information entered. This AI improves the accuracy of its departmental selection by analyzing past project history and the expertise of each department. The selected departmental information is presented to the user, who can then choose a department to partner with for hypothesis testing.

[0038] The selected departments and project managers can easily collaborate through this system. The server notifies the selected departments of the project details and acts as a communication bridge between them, thereby supporting the smooth progress of the project.

[0039] As a concrete example, suppose a user in charge of new business development wants to test a hypothesis for a new customer management system. In this case, the user inputs the project objectives, required resources, etc., into the system from their terminal. The server uses this information and its AI generation capabilities to identify departments that have previously carried out similar projects and suggests departments such as the marketing department or the IT solutions department. The user then selects the most suitable department, and the server sends the project information to the selected department to facilitate collaboration.

[0040] This invention aims to improve a company's competitiveness by effectively utilizing internal resources and conducting rapid and effective hypothesis testing.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users input information about their department's operations, areas of expertise, and current challenges from their terminals and send it to the server. This information is provided in a standardized format.

[0044] Step 2:

[0045] The server receives information sent from each department, verifies the consistency of the format, and then registers it in the database. The server segments the information and builds the database to enable efficient management.

[0046] Step 3:

[0047] Users input detailed information about a new project they want to test a hypothesis on, using their terminal, and submit it to the server. This information includes the project's objectives and the necessary skills and resources.

[0048] Step 4:

[0049] The server receives detailed information about the hypothesis testing project and uses generative AI to search for a suitable department for the project. This includes analyzing past project history in the database and the expertise of each department.

[0050] Step 5:

[0051] The server presents the user with a list of suitable department candidates selected by the generating AI. For each candidate department, detailed information, including relevant data and past success stories, is also provided.

[0052] Step 6:

[0053] The user reviews the list of departments presented by the server on their terminal and selects the department best suited for hypothesis testing.

[0054] Step 7:

[0055] The server notifies the department selected by the user of the project details. The server also automatically schedules and sets the date for the initial meeting.

[0056] Step 8:

[0057] The server monitors communications and data updates during project progress and provides real-time progress updates to users and selected departments on a regular basis. The server also assists with coordinating communications and optimizing resources as needed.

[0058] (Example 1)

[0059] 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."

[0060] Traditional hypothesis testing projects within companies have faced problems such as difficulty in selecting the appropriate departments and sharing information, leading to project delays and decreased efficiency. In particular, there was a lack of means to effectively utilize the expertise of each department and to monitor progress in real time.

[0061] 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.

[0062] In this invention, the server includes a data storage device for aggregating and storing information, means for centrally managing business information received from each department within the organization, means for selecting and proposing appropriate departments based on detailed information of a plan to be tested using a generative AI, means for presenting information on the proposed departments to the user and promoting cooperation between departments according to the user's selection, means for monitoring the progress of the plan and updating the information in real time, and means for the generative AI to process and analyze the plan information in prompt message format. This enables the rapid selection of appropriate departments, information sharing, and smooth project progress.

[0063] A "data storage device for aggregating and storing information" is a storage device designed to centrally manage business information received from various departments within an organization, and to enable efficient access and use.

[0064] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze detailed project information and assist in selecting the most suitable department.

[0065] The "prompt format" is a method in which natural language commands are used as input for the generation AI to interpret and process planning information.

[0066] "Means of monitoring the progress of the plan" refers to a system that allows for real-time monitoring of the project's progress and results, and updates the information as needed.

[0067] "Means to promote interdepartmental cooperation" refers to a system that enables information sharing between the proposed department and users, facilitating smooth communication and collaborative work.

[0068] This invention provides a system for efficiently conducting hypothesis testing projects within a company. The system includes a data storage device for aggregating and storing information, a generating AI, a function for processing information in prompt format, and means for facilitating interdepartmental collaboration.

[0069] The server is responsible for aggregating business information from various departments within the organization and storing it in data storage devices. The database software on the server is equipped with indexing and search functions to efficiently manage large amounts of information and facilitate access.

[0070] The generating AI receives project information entered by the user as prompts and selects the appropriate department based on the detailed plan. The AI ​​uses machine learning libraries to analyze past project history and the expertise of each department. In particular, it is designed to effectively interpret prompts by utilizing natural language processing techniques.

[0071] As a concrete example, consider a scenario where a user wants to test a hypothesis for new product development. The user inputs the following prompt into the system from their terminal: "Please select the most suitable department to carry out the new product development project. Please suggest a list of departments that have previously carried out similar projects." The server then has the AI ​​process this information and suggest appropriate departments, such as the IT department or the research and development department.

[0072] This system enables users to quickly select departments, optimize project progress, and effectively utilize resources across the entire company.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] Users input detailed information about their project, such as its objectives and required resources, into the system using their own devices. This input is specifically presented in the form of prompts. The user's input information is sent to the server via a secure protocol.

[0076] Step 2:

[0077] The server stores project information received from users in a data storage device. This allows the server to generate an index to quickly search the stored data. This process organizes the data according to a specific format and performs data processing to enable efficient searching.

[0078] Step 3:

[0079] The server provides the accumulated data to a generating AI model. This model analyzes project information in prompt format and selects the optimal department based on past project history in the database. Here, natural language processing and machine learning algorithms are used to analyze the input data and generate a list of the optimal departments as output.

[0080] Step 4:

[0081] The generated list of departments is presented to the user. The user's device also displays each department's expertise and past project performance, providing data to support their selection. Based on this list, the user selects the departments to conduct hypothesis testing.

[0082] Step 5:

[0083] The server notifies the department selected by the user of the project details. This information is sent via email or a dedicated collaboration tool. Furthermore, the server facilitates communication between the two parties, supporting the smooth progress of the project. The representatives of the selected department receive detailed project information in real time and begin preparations.

[0084] (Application Example 1)

[0085] 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."

[0086] When an organization's project spans multiple departments, appropriate department selection and rapid problem resolution are crucial. However, a lack of smooth information gathering and inter-departmental collaboration delays hypothesis and problem testing, making efficient resolution difficult. Furthermore, rapid analysis of visual information from the field and immediate notification to the most appropriate department remain challenges.

[0087] 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.

[0088] In this invention, the server includes a database for aggregating and storing information, means for centrally managing business information received from each department within the organization, means for selecting and proposing an appropriate department based on detailed information of a project to be tested using generative artificial intelligence, and means for analyzing visual data acquired using visual devices, selecting an appropriate department through generative artificial intelligence, and immediately notifying the user. This enables efficient aggregation of information within the organization, facilitating rapid and accurate department selection and problem solving.

[0089] A "database for aggregating and storing information" is a digital recording device used to centrally manage business information acquired from various departments within an organization.

[0090] "Generative artificial intelligence" is an artificial intelligence system that generates new insights and suggestions using past data and learning algorithms.

[0091] "Visual equipment" refers to devices that use cameras or other means to acquire visual information from a site.

[0092] "Visual data" refers to image and video information acquired through visual devices.

[0093] "Department selection" is the process of determining which department within an organization is best suited for a particular project or task.

[0094] "Immediate notification" refers to the act of promptly informing the relevant department or person in charge upon receiving information.

[0095] "Hypothesis testing" is the process of testing new theories or hypotheses based on actual data.

[0096] In the system implementing this invention, a server plays a central role. The server is equipped with a database that aggregates and stores information, and manages unified information related to the operations of each department collected within the organization. The database stores a large amount of data, and it is possible to quickly retrieve the necessary information through various queries.

[0097] Users record the actual situation on-site using visual devices and acquire visual data. This visual data is sent to a server and analyzed by generative artificial intelligence (generative AI). Based on data such as past project history and departmental expertise, the generative AI selects the most suitable department based on the content of the input data. Once the appropriate department is selected, this information is promptly notified to the relevant parties.

[0098] The server uses the Python programming language and the OpenCV library to process images and videos acquired using Google Glass® and other visual devices. For generative artificial intelligence analysis, it employs a virtual AI library for advanced data processing and pattern recognition. This entire process ensures that information within the organization is handled efficiently, enabling timely action for resolution.

[0099] As a concrete example, consider a scenario where new equipment is introduced to a factory production line, and its installation is checked to ensure it is properly positioned. Workers use smart glasses to capture video footage of the equipment and upload the data to the system. The generated AI analyzes the video data and immediately notifies the appropriate department, such as the equipment management department or the technical support department.

[0100] An example of a prompt statement related to a specific example of this embodiment is, "A new device has been introduced to the production line. Please check the installation status and select the appropriate department." By inputting this prompt statement into the generating AI, a series of processes are initiated, enabling a rapid response.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The terminal uses visual equipment to capture images of the situation on site. The input is visual data (images or videos), and the device used to acquire this data is a smart device with camera functionality. The output is visual data in digital format.

[0104] Step 2:

[0105] The terminal sends visual data to the server. The input is visual data, and the output is visual data stored on the server. This process ensures that the data arrives at the server accurately and quickly using a data transmission protocol.

[0106] Step 3:

[0107] Generative artificial intelligence (generative AI) is used to analyze the visual data received by the server. The input is the visual data stored on the server, and the output is the analysis results (e.g., problem identification and suggestions for improvement). The generative AI performs pattern recognition and data matching to propose the optimal solution.

[0108] Step 4:

[0109] The server identifies the optimal department selected by the generated AI. Inputs include analysis results and reference data such as past project history, while output is information about the optimal department. This clarifies which department should handle the task.

[0110] Step 5:

[0111] The server immediately sends a notification to the identified department. The input is information about the appropriate department, and the output is the notification message sent to that department. Notifications are sent via email or a dedicated application, ensuring that information is quickly disseminated to relevant parties.

[0112] Step 6:

[0113] The user receives a notification and takes additional action or confirmation as needed. The input is the notification message from the server, and the output is the user's action (e.g., approval or feedback). In this step, the user is required to check the situation and take the necessary action.

[0114] 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.

[0115] This invention combines a system for efficiently testing hypotheses within a corporate group with an emotion engine that recognizes user emotions and optimizes proposals based on those emotions. The system includes an information aggregation function, a generation AI, a feedback adjustment function using the emotion engine, and a progress management function.

[0116] In this system, business information collected from each department of a company is first stored in a database. This establishes a system for comprehensively managing the company's resources and areas of expertise. By accessing this information, the generating AI selects and proposes the appropriate department to the user based on the details of the project for which hypothesis testing is desired. During the selection process, the accuracy of the proposal is improved by analyzing the history of past projects.

[0117] Furthermore, this system uses an emotion engine to analyze the user's emotional state through user input and interaction. When a user provides feedback to the system, the server dynamically adjusts the suggestions and interface based on this emotional state data. For example, if a user expresses confusion or dissatisfaction with a suggestion, the server recognizes this using the emotion engine and proposes alternatives or provides additional information through its AI-generated suggestions. The aim of these adjustments is to improve the user experience and ensure the smooth progress of the project.

[0118] During project execution, the server monitors progress in real time and, when necessary, utilizes the emotion engine to propose appropriate communication strategies for users and relevant departments. This integration of the emotion engine, generative AI, and information database maximizes the effectiveness of proposals and streamlines internal communication and hypothesis testing. The implementation of this system is expected to strengthen the competitiveness of the entire corporate group.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] Users input information about their department's operations, areas of expertise, challenges, etc., from their terminals and send it to the server.

[0122] Step 2:

[0123] The server receives information sent from each department, verifies its consistency, and then registers it in the database. This database is designed to systematically organize information and facilitate searching and analysis.

[0124] Step 3:

[0125] Users input detailed information about a new hypothesis testing project from their terminal and submit it to the server.

[0126] Step 4:

[0127] The server receives the details of the hypothesis testing project and uses generative AI to search the database for the most suitable department. This includes analyzing past project history and the expertise of each department.

[0128] Step 5:

[0129] The server lists suitable department candidates selected by the generating AI and presents them to the user along with relevant information.

[0130] Step 6:

[0131] The user reviews the presented list of departments on their device and selects the department best suited for hypothesis testing. During this process, the device monitors the user's reactions using an emotion engine and sends emotion data to the server.

[0132] Step 7:

[0133] The server adjusts feedback based on the emotional data analyzed by the emotion engine, according to the user's choices. If necessary, it uses generative AI to suggest alternatives and make suggestions that are easy for the user to understand.

[0134] Step 8:

[0135] The server notifies the department selected by the user of the project details and automatically schedules the first meeting.

[0136] Step 9:

[0137] The server monitors project progress in real time and provides timely information to relevant departments and users. It leverages an emotion engine to improve communication within the project team and support efficient collaboration.

[0138] (Example 2)

[0139] 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".

[0140] Within companies and organizations, a lack of collaboration between departments and inefficient communication often lead to delays in the hypothesis testing process. Furthermore, proposed solutions may not align with users' emotions and expectations, resulting in a worse user experience. Solving these problems and implementing efficient and flexible hypothesis testing is essential.

[0141] 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.

[0142] In this invention, the server includes information management means for aggregating and storing information, means for selecting and proposing appropriate departments using a generation AI, and means for analyzing the operator's emotions using an emotion analysis engine. This effectively promotes collaboration between departments and enables the dynamic adjustment of proposal content based on the user's emotions.

[0143] "Information management means" refers to a function for centrally aggregating and efficiently storing business information collected from various departments within an organization.

[0144] "Generative AI" is an artificial intelligence technology that selects and proposes the most suitable department based on past data and detailed work information.

[0145] An "emotion analysis engine" is a technology that analyzes the emotional state of the user from their input and dialogue, and uses this information to adjust services and proposals.

[0146] "Operators" refer to users or users of the system, and they are responsible for receiving suggestions from the system and making decisions.

[0147] "Collaboration promotion" is the process of strengthening communication and cooperation between different departments within an organization.

[0148] This invention is a system that utilizes a generative AI model and an emotion analysis engine to streamline the hypothesis testing process within an organization. This system operates through the following configuration and operation.

[0149] The server collects business information in digital format from each department of the company and stores it in a centralized database. This information includes departmental resource status and past project history. General data management software can be used for database management.

[0150] Generative AI models select the most suitable departments for hypothesis testing based on collected information. This process analyzes patterns from past successful projects to improve the accuracy of their recommendations. Generative AI is typically implemented using cloud-based AI computing systems.

[0151] The user receives this suggestion through their device and checks for more details as needed. Furthermore, an emotion analysis engine is activated based on the user's response to analyze their emotional state. This result is fed back to the server, and the suggestions and system interface are dynamically adjusted accordingly.

[0152] For example, if a user inputs the prompt "Please suggest the optimal department for analyzing market reaction to a new product and conducting efficient hypothesis testing" into the generating AI model, the AI ​​will suggest a suitable department by referring to successful examples of past market analysis projects. If the user expresses confusion or dissatisfaction with this suggestion, the sentiment analysis engine will recognize this, and the server will provide alternatives or additional data.

[0153] In this way, servers, terminals, and users collaborate to form an effective communication loop that enables a better hypothesis testing process.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The server collects business information from various departments within the company. Specifically, departmental staff input information via terminals, which the server receives over the network. This input includes resource usage and project progress information. The server stores the received data in a database. This process generates a comprehensive dataset, which is then used for subsequent analysis.

[0157] Step 2:

[0158] The server analyzes accumulated business information and uses a generative AI model to select departments suitable for hypothesis testing. The generative AI analyzes past project history and current resource status. As input, the server retrieves this information from the database and sends prompt messages to the AI ​​model. As output, the AI ​​generates a list of optimal departments and returns it to the server. Specifically, the server calls the AI ​​model to perform inference.

[0159] Step 3:

[0160] The user receives and reviews department information suggested by the generating AI via their device. The user views detailed department information presented on the device and makes a selection as needed. Specifically, the server sends output data from the generating AI to the user's device, and the user views its contents. At this time, the user's selection results are fed back to the server.

[0161] Step 4:

[0162] The server uses an emotion analysis engine to analyze the user's emotions. When the user provides feedback on the suggestions, this is used as input to the emotion analysis engine. Based on the analysis results, the server determines the user's emotional state and, if necessary, adjusts the suggestions. The output of this process is new suggestions to improve the user experience. Specific actions include the server dynamically changing the interface based on the results of the emotion analysis.

[0163] Step 5:

[0164] The server monitors project progress and updates all relevant data in real time. The input is the latest progress-related information retrieved from the database. Based on this data, the server generates progress reports and sends them to stakeholders' terminals. This output provides visibility into the project's current status, allowing for quick adjustments. Specific operations include periodically crawling the database to retrieve the latest information.

[0165] (Application Example 2)

[0166] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0167] Traditional systems often failed to provide personalized recommendations based on individual preferences and emotional states when users selected products or services, leading to decreased satisfaction. Furthermore, there was a lack of effective ways to mitigate the confusion and dissatisfaction users experienced when the suggested options were inappropriate, highlighting the need for improved user experience. Therefore, a new system was needed that could provide more appropriate recommendations based on user emotions and efficiently present alternatives tailored to individual needs.

[0168] 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.

[0169] This invention includes a server equipped with a database for aggregating and storing information, a means for centrally managing work information received from various departments within the organization, a means for selecting and proposing appropriate departments based on detailed information of projects to be tested using generative AI, and a means for analyzing the user's emotions using visual and audio sensors and dynamically adjusting the proposed content using an emotion engine. This makes it possible to provide more appropriate suggestions that are attentive to the user's emotions, thereby improving the user experience.

[0170] A "database" is a structured data storage system that aggregates information and centrally manages work information received from various departments within an organization.

[0171] "Generative AI" refers to an algorithm or system that uses artificial intelligence technology to select and propose appropriate fields based on detailed project information.

[0172] An "emotion engine" is a technology or function that uses data obtained from visual and audio sensors to analyze the user's emotions and dynamically adjust the suggested content.

[0173] A "visual sensor" is a camera or related device used to detect a user's facial expressions and analyze that information.

[0174] A "voice sensor" is a microphone or related device used to analyze the tone and patterns of a user's voice.

[0175] "User experience" is a concept that refers to the overall feeling and satisfaction that users experience when using a system or service.

[0176] The system implementing this invention is comprised of a database for aggregating and storing information, a generative AI, and an emotion engine. The server first aggregates work information received from various departments within the organization into the database. This database plays a central role in managing all the information necessary for a project by structuring and managing the information.

[0177] When a user provides detailed information about a new project, the server uses generative AI to select the appropriate field and propose it to the user. The generative AI analyzes past project history and related data to present the most suitable selection. At this stage, the models used by the generative AI may include, for example, open-source AI models or cloud-based AI platforms.

[0178] Furthermore, the user terminal uses a visual sensor (camera) and an audio sensor (microphone) to analyze the user's emotional state in real time. The emotion engine uses this data to identify the emotions expressed by the user, and based on the results, the server dynamically adjusts the suggested content. Specifically, the generating AI optimizes the suggested product and service information so that it best matches the user's current emotions.

[0179] For example, if a user shows interest in a product, the generating AI will create prompts suggesting related accessories or product sets. A prompt like this might be: "If the user is smiling at their smartphone camera, display related products and suggest information that will make them smile even more."

[0180] This enhances interaction between the server and the user, leading to an improved user experience.

[0181] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0182] Step 1:

[0183] The server aggregates work information collected from various departments within the organization into a database. The input is work information data from each department, and the output is a centrally managed database. The database stores information in a structured, easily accessible, and manageable format. The server efficiently organizes this data and prepares it for queries.

[0184] Step 2:

[0185] The user enters project details. The input is the project details, and the output is the input data for the generative AI. The terminal sends this data to the server and converts it into a formatted format for analysis by the generative AI.

[0186] Step 3:

[0187] The server uses generative AI to select appropriate fields based on the input project details. The input consists of formatted project information and historical project data, and the output is a list of selected fields. The server runs the generative AI model and performs data analysis to identify the most suitable field.

[0188] Step 4:

[0189] The user terminal uses a visual sensor (camera) and an audio sensor (microphone) to analyze the user's emotions. The input is sensor data, and the output is the emotion analysis result. The terminal uses an emotion engine to analyze facial expressions and tone of voice to identify the user's emotional state.

[0190] Step 5:

[0191] The server dynamically adjusts the suggested content based on the analysis results of the emotion engine. The input is the emotion analysis result, and the output is the adjusted suggested content. The server uses a generative AI model to generate suggested content optimized for the user's emotions.

[0192] Step 6:

[0193] The user receives the adjusted suggestions and makes an appropriate decision. The input is the adjusted suggestions, and the output is the result of the decision. The terminal displays the suggested information and provides an interface to help the user make an appropriate choice.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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".

[0210] This invention provides a system that supports efficient hypothesis testing within a corporate group. Embodiments include a platform equipped with an information aggregation function, a department selection function using generation AI, and a progress management function.

[0211] The system first aggregates business information from each department within the corporate group and stores it in a database. This information includes the business content, areas of expertise, challenges, and resource information handled by each department. The database is designed to efficiently manage this information and allow for immediate access as needed.

[0212] The generating AI accesses this database and selects the most appropriate department based on the detailed project information entered. This AI improves the accuracy of its departmental selection by analyzing past project history and the expertise of each department. The selected departmental information is presented to the user, who can then choose a department to partner with for hypothesis testing.

[0213] The selected departments and project managers can easily collaborate through this system. The server notifies the selected departments of the project details and acts as a communication bridge between them, thereby supporting the smooth progress of the project.

[0214] As a concrete example, suppose a user in charge of new business development wants to test a hypothesis for a new customer management system. In this case, the user inputs the project objectives, required resources, etc., into the system from their terminal. The server uses this information and its AI generation capabilities to identify departments that have previously carried out similar projects and suggests departments such as the marketing department or the IT solutions department. The user then selects the most suitable department, and the server sends the project information to the selected department to facilitate collaboration.

[0215] This invention aims to improve a company's competitiveness by effectively utilizing internal resources and conducting rapid and effective hypothesis testing.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] Users input information about their department's operations, areas of expertise, and current challenges from their terminals and send it to the server. This information is provided in a standardized format.

[0219] Step 2:

[0220] The server receives information sent from each department, verifies the consistency of the format, and then registers it in the database. The server segments the information and builds the database to enable efficient management.

[0221] Step 3:

[0222] Users input detailed information about a new project they want to test a hypothesis on, using their terminal, and submit it to the server. This information includes the project's objectives and the necessary skills and resources.

[0223] Step 4:

[0224] The server receives detailed information about the hypothesis testing project and uses generative AI to search for a suitable department for the project. This includes analyzing past project history in the database and the expertise of each department.

[0225] Step 5:

[0226] The server presents the user with a list of suitable department candidates selected by the generating AI. For each candidate department, detailed information, including relevant data and past success stories, is also provided.

[0227] Step 6:

[0228] The user reviews the list of departments presented by the server on their terminal and selects the department best suited for hypothesis testing.

[0229] Step 7:

[0230] The server notifies the department selected by the user of the project details. The server also automatically schedules and sets the date for the initial meeting.

[0231] Step 8:

[0232] The server monitors communications and data updates during project progress and provides real-time progress updates to users and selected departments on a regular basis. The server also assists with coordinating communications and optimizing resources as needed.

[0233] (Example 1)

[0234] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0235] Traditional hypothesis testing projects within companies have faced problems such as difficulty in selecting the appropriate departments and sharing information, leading to project delays and decreased efficiency. In particular, there was a lack of means to effectively utilize the expertise of each department and to monitor progress in real time.

[0236] 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.

[0237] In this invention, the server includes a data storage device for aggregating and storing information, means for centrally managing business information received from each department within the organization, means for selecting and proposing appropriate departments based on detailed information of a plan to be tested using a generative AI, means for presenting information on the proposed departments to the user and promoting cooperation between departments according to the user's selection, means for monitoring the progress of the plan and updating the information in real time, and means for the generative AI to process and analyze the plan information in prompt message format. This enables the rapid selection of appropriate departments, information sharing, and smooth project progress.

[0238] A "data storage device for aggregating and storing information" is a storage device designed to centrally manage business information received from various departments within an organization, and to enable efficient access and use.

[0239] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze detailed project information and assist in selecting the most suitable department.

[0240] The "prompt format" is a method in which natural language commands are used as input for the generation AI to interpret and process planning information.

[0241] "Means of monitoring the progress of the plan" refers to a system that allows for real-time monitoring of the project's progress and results, and updates the information as needed.

[0242] "Means to promote interdepartmental cooperation" refers to a system that enables information sharing between the proposed department and users, facilitating smooth communication and collaborative work.

[0243] This invention provides a system for efficiently conducting hypothesis testing projects within a company. The system includes a data storage device for aggregating and storing information, a generating AI, a function for processing information in prompt format, and means for facilitating interdepartmental collaboration.

[0244] The server is responsible for aggregating business information from various departments within the organization and storing it in data storage devices. The database software on the server is equipped with indexing and search functions to efficiently manage large amounts of information and facilitate access.

[0245] The generating AI receives project information entered by the user as prompts and selects the appropriate department based on the detailed plan. The AI ​​uses machine learning libraries to analyze past project history and the expertise of each department. In particular, it is designed to effectively interpret prompts by utilizing natural language processing techniques.

[0246] As a concrete example, consider a scenario where a user wants to test a hypothesis for new product development. The user inputs the following prompt into the system from their terminal: "Please select the most suitable department to carry out the new product development project. Please suggest a list of departments that have previously carried out similar projects." The server then has the AI ​​process this information and suggest appropriate departments, such as the IT department or the research and development department.

[0247] This system enables users to quickly select departments, optimize project progress, and effectively utilize resources across the entire company.

[0248] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0249] Step 1:

[0250] Users input detailed information about their project, such as its objectives and required resources, into the system using their own devices. This input is specifically presented in the form of prompts. The user's input information is sent to the server via a secure protocol.

[0251] Step 2:

[0252] The server stores project information received from users in a data storage device. This allows the server to generate an index to quickly search the stored data. This process organizes the data according to a specific format and performs data processing to enable efficient searching.

[0253] Step 3:

[0254] The server provides the accumulated data to a generating AI model. This model analyzes project information in prompt format and selects the optimal department based on past project history in the database. Here, natural language processing and machine learning algorithms are used to analyze the input data and generate a list of the optimal departments as output.

[0255] Step 4:

[0256] The generated list of departments is presented to the user. The user's device also displays each department's expertise and past project performance, providing data to support their selection. Based on this list, the user selects the departments to conduct hypothesis testing.

[0257] Step 5:

[0258] The server notifies the department selected by the user of the project details. This information is sent via email or a dedicated collaboration tool. Furthermore, the server facilitates communication between the two parties, supporting the smooth progress of the project. The representatives of the selected department receive detailed project information in real time and begin preparations.

[0259] (Application Example 1)

[0260] 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."

[0261] When an organization's project spans multiple departments, appropriate department selection and rapid problem resolution are crucial. However, a lack of smooth information gathering and inter-departmental collaboration delays hypothesis and problem testing, making efficient resolution difficult. Furthermore, rapid analysis of visual information from the field and immediate notification to the most appropriate department remain challenges.

[0262] 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.

[0263] In this invention, the server includes a database for aggregating and storing information, means for centrally managing business information received from each department within the organization, means for selecting and proposing an appropriate department based on detailed information of a project to be tested using generative artificial intelligence, and means for analyzing visual data acquired using visual devices, selecting an appropriate department through generative artificial intelligence, and immediately notifying the user. This enables efficient aggregation of information within the organization, facilitating rapid and accurate department selection and problem solving.

[0264] A "database for aggregating and storing information" is a digital recording device used to centrally manage business information acquired from various departments within an organization.

[0265] "Generative artificial intelligence" is an artificial intelligence system that generates new insights and suggestions using past data and learning algorithms.

[0266] "Visual equipment" refers to devices that use cameras or other means to acquire visual information from a site.

[0267] "Visual data" refers to image and video information acquired through visual devices.

[0268] "Department selection" is the process of determining which department within an organization is best suited for a particular project or task.

[0269] "Immediate notification" refers to the act of promptly informing the relevant department or person in charge upon receiving information.

[0270] "Hypothesis testing" is the process of testing new theories or hypotheses based on actual data.

[0271] In the system implementing this invention, a server plays a central role. The server is equipped with a database that aggregates and stores information, and manages unified information related to the operations of each department collected within the organization. The database stores a large amount of data, and it is possible to quickly retrieve the necessary information through various queries.

[0272] Users record the actual situation on-site using visual devices and acquire visual data. This visual data is sent to a server and analyzed by generative artificial intelligence (generative AI). Based on data such as past project history and departmental expertise, the generative AI selects the most suitable department based on the content of the input data. Once the appropriate department is selected, this information is promptly notified to the relevant parties.

[0273] The server uses the Python programming language and the OpenCV library to process images and videos acquired using Google Glass and other visual devices. For generative artificial intelligence analysis, it employs a virtual AI library for advanced data processing and pattern recognition. This entire process ensures that information within the organization is handled efficiently, enabling timely action for resolution.

[0274] As a concrete example, consider a scenario where new equipment is introduced to a factory production line, and its installation is checked to ensure it is properly positioned. Workers use smart glasses to capture video footage of the equipment and upload the data to the system. The generated AI analyzes the video data and immediately notifies the appropriate department, such as the equipment management department or the technical support department.

[0275] An example of a prompt statement related to a specific example of this embodiment is, "A new device has been introduced to the production line. Please check the installation status and select the appropriate department." By inputting this prompt statement into the generating AI, a series of processes are initiated, enabling a rapid response.

[0276] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0277] Step 1:

[0278] The terminal uses a visual device to photograph the on-site situation. The input is visual data (image or video), and the device for acquiring this is a smart device with a camera function. The output is visual data in digital form.

[0279] Step 2:

[0280] The terminal sends the visual data to the server. The input is visual data, and the output is the visual data stored on the server. In this process, it is confirmed that the data reaches the server accurately and quickly using a data transmission protocol.

[0281] Step 3:

[0282] A generative artificial intelligence (generative AI) is used to analyze the visual data received by the server. The input is the visual data stored on the server, and the output is the analysis result (for example, identification of problems and proposal of improvement points). The generative AI performs pattern recognition and data matching to propose an optimal solution.

[0283] Step 4:

[0284] The server identifies the optimal department selected by the generative AI. The input is the analysis result and reference data such as past project history, and the output is the information of the optimal department. This clarifies which department should be responsible.

[0285] Step 5:

[0286] The server immediately sends a notification to the identified department. The input is the information of the optimal department, and the output is the notification message sent to the department. The notification is carried out through email or a dedicated application, and information is quickly transmitted to the relevant parties.

[0287] Step 6:

[0288] The user receives a notification and takes additional action or confirmation as needed. The input is the notification message from the server, and the output is the user's action (e.g., approval or feedback). In this step, the user is required to check the situation and take the necessary action.

[0289] 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.

[0290] This invention combines a system for efficiently testing hypotheses within a corporate group with an emotion engine that recognizes user emotions and optimizes proposals based on those emotions. The system includes an information aggregation function, a generation AI, a feedback adjustment function using the emotion engine, and a progress management function.

[0291] In this system, business information collected from each department of a company is first stored in a database. This establishes a system for comprehensively managing the company's resources and areas of expertise. By accessing this information, the generating AI selects and proposes the appropriate department to the user based on the details of the project for which hypothesis testing is desired. During the selection process, the accuracy of the proposal is improved by analyzing the history of past projects.

[0292] Furthermore, this system uses an emotion engine to analyze the user's emotional state through user input and interaction. When a user provides feedback to the system, the server dynamically adjusts the suggestions and interface based on this emotional state data. For example, if a user expresses confusion or dissatisfaction with a suggestion, the server recognizes this using the emotion engine and proposes alternatives or provides additional information through its AI-generated suggestions. The aim of these adjustments is to improve the user experience and ensure the smooth progress of the project.

[0293] During project execution, the server monitors progress in real time and, when necessary, utilizes the emotion engine to propose appropriate communication strategies for users and relevant departments. This integration of the emotion engine, generative AI, and information database maximizes the effectiveness of proposals and streamlines internal communication and hypothesis testing. The implementation of this system is expected to strengthen the competitiveness of the entire corporate group.

[0294] The following describes the processing flow.

[0295] Step 1:

[0296] Users input information about their department's operations, areas of expertise, challenges, etc., from their terminals and send it to the server.

[0297] Step 2:

[0298] The server receives information sent from each department, verifies its consistency, and then registers it in the database. This database is designed to systematically organize information and facilitate searching and analysis.

[0299] Step 3:

[0300] Users input detailed information about a new hypothesis testing project from their terminal and submit it to the server.

[0301] Step 4:

[0302] The server receives the details of the hypothesis testing project and uses generative AI to search the database for the most suitable department. This includes analyzing past project history and the expertise of each department.

[0303] Step 5:

[0304] The server lists suitable department candidates selected by the generating AI and presents them to the user along with relevant information.

[0305] Step 6:

[0306] The user checks the presented department list on the terminal and selects the department most suitable for hypothesis verification. During this operation, the terminal monitors the user's reaction with the emotion engine and transmits the emotion data to the server.

[0307] Step 7:

[0308] Based on the emotion data analyzed by the emotion engine, the server adjusts the feedback according to the user's selection. If necessary, the generative AI presents alternatives and makes proposals that are easy for the user to understand.

[0309] Step 8:

[0310] The server notifies the details of the project to the department selected by the user and automatically adjusts the schedule of the first meeting.

[0311] Step 9:

[0312] The server monitors the progress of the project in real time during the project execution and provides the necessary information to the relevant departments and the user in a timely manner. Utilizing the emotion engine, it improves communication within the project team and supports efficient collaboration.

[0313] (Example 2)

[0314] Next, Example 2 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".

[0315] Within enterprises and organizations, insufficient collaboration between departments and inefficiency in communication often lead to delays in the hypothesis verification process. Furthermore, the proposed content may not match the emotions and expectations of the user, resulting in a deterioration of the user experience. There is a need to solve such problems and enable efficient and flexible hypothesis verification.

[0316] 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.

[0317] In this invention, the server includes information management means for aggregating and storing information, means for selecting and proposing appropriate departments using a generation AI, and means for analyzing the operator's emotions using an emotion analysis engine. This effectively promotes collaboration between departments and enables the dynamic adjustment of proposal content based on the user's emotions.

[0318] "Information management means" refers to a function for centrally aggregating and efficiently storing business information collected from various departments within an organization.

[0319] "Generative AI" is an artificial intelligence technology that selects and proposes the most suitable department based on past data and detailed work information.

[0320] An "emotion analysis engine" is a technology that analyzes the emotional state of the user from their input and dialogue, and uses this information to adjust services and proposals.

[0321] "Operators" refer to users or users of the system, and they are responsible for receiving suggestions from the system and making decisions.

[0322] "Collaboration promotion" is the process of strengthening communication and cooperation between different departments within an organization.

[0323] This invention is a system that utilizes a generative AI model and an emotion analysis engine to streamline the hypothesis testing process within an organization. This system operates through the following configuration and operation.

[0324] The server collects business information in digital format from each department of the company and stores it in a centralized database. This information includes departmental resource status and past project history. General data management software can be used for database management.

[0325] Generative AI models select the most suitable departments for hypothesis testing based on collected information. This process analyzes patterns from past successful projects to improve the accuracy of their recommendations. Generative AI is typically implemented using cloud-based AI computing systems.

[0326] The user receives this suggestion through their device and checks for more details as needed. Furthermore, an emotion analysis engine is activated based on the user's response to analyze their emotional state. This result is fed back to the server, and the suggestions and system interface are dynamically adjusted accordingly.

[0327] For example, if a user inputs the prompt "Please suggest the optimal department for analyzing market reaction to a new product and conducting efficient hypothesis testing" into the generating AI model, the AI ​​will suggest a suitable department by referring to successful examples of past market analysis projects. If the user expresses confusion or dissatisfaction with this suggestion, the sentiment analysis engine will recognize this, and the server will provide alternatives or additional data.

[0328] In this way, servers, terminals, and users collaborate to form an effective communication loop that enables a better hypothesis testing process.

[0329] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0330] Step 1:

[0331] The server collects business information from various departments within the company. Specifically, departmental staff input information via terminals, which the server receives over the network. This input includes resource usage and project progress information. The server stores the received data in a database. This process generates a comprehensive dataset, which is then used for subsequent analysis.

[0332] Step 2:

[0333] The server analyzes accumulated business information and uses a generative AI model to select departments suitable for hypothesis testing. The generative AI analyzes past project history and current resource status. As input, the server retrieves this information from the database and sends prompt messages to the AI ​​model. As output, the AI ​​generates a list of optimal departments and returns it to the server. Specifically, the server calls the AI ​​model to perform inference.

[0334] Step 3:

[0335] The user receives and reviews department information suggested by the generating AI via their device. The user views detailed department information presented on the device and makes a selection as needed. Specifically, the server sends output data from the generating AI to the user's device, and the user views its contents. At this time, the user's selection results are fed back to the server.

[0336] Step 4:

[0337] The server uses an emotion analysis engine to analyze the user's emotions. When the user provides feedback on the suggestions, this is used as input to the emotion analysis engine. Based on the analysis results, the server determines the user's emotional state and, if necessary, adjusts the suggestions. The output of this process is new suggestions to improve the user experience. Specific actions include the server dynamically changing the interface based on the results of the emotion analysis.

[0338] Step 5:

[0339] The server monitors project progress and updates all relevant data in real time. The input is the latest progress-related information retrieved from the database. Based on this data, the server generates progress reports and sends them to stakeholders' terminals. This output provides visibility into the project's current status, allowing for quick adjustments. Specific operations include periodically crawling the database to retrieve the latest information.

[0340] (Application Example 2)

[0341] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0342] Traditional systems often failed to provide personalized recommendations based on individual preferences and emotional states when users selected products or services, leading to decreased satisfaction. Furthermore, there was a lack of effective ways to mitigate the confusion and dissatisfaction users experienced when the suggested options were inappropriate, highlighting the need for improved user experience. Therefore, a new system was needed that could provide more appropriate recommendations based on user emotions and efficiently present alternatives tailored to individual needs.

[0343] 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.

[0344] This invention includes a server equipped with a database for aggregating and storing information, a means for centrally managing work information received from various departments within the organization, a means for selecting and proposing appropriate departments based on detailed information of projects to be tested using generative AI, and a means for analyzing the user's emotions using visual and audio sensors and dynamically adjusting the proposed content using an emotion engine. This makes it possible to provide more appropriate suggestions that are attentive to the user's emotions, thereby improving the user experience.

[0345] A "database" is a structured data storage system that aggregates information and centrally manages work information received from various departments within an organization.

[0346] "Generative AI" refers to an algorithm or system that uses artificial intelligence technology to select and propose appropriate fields based on detailed project information.

[0347] An "emotion engine" is a technology or function that uses data obtained from visual and audio sensors to analyze the user's emotions and dynamically adjust the suggested content.

[0348] A "visual sensor" is a camera or related device used to detect a user's facial expressions and analyze that information.

[0349] A "voice sensor" is a microphone or related device used to analyze the tone and patterns of a user's voice.

[0350] "User experience" is a concept that refers to the overall feeling and satisfaction that users experience when using a system or service.

[0351] The system implementing this invention is comprised of a database for aggregating and storing information, a generative AI, and an emotion engine. The server first aggregates work information received from various departments within the organization into the database. This database plays a central role in managing all the information necessary for a project by structuring and managing the information.

[0352] When a user provides detailed information about a new project, the server uses generative AI to select the appropriate field and propose it to the user. The generative AI analyzes past project history and related data to present the most suitable selection. At this stage, the models used by the generative AI may include, for example, open-source AI models or cloud-based AI platforms.

[0353] Furthermore, the user terminal uses a visual sensor (camera) and an audio sensor (microphone) to analyze the user's emotional state in real time. The emotion engine uses this data to identify the emotions expressed by the user, and based on the results, the server dynamically adjusts the suggested content. Specifically, the generating AI optimizes the suggested product and service information so that it best matches the user's current emotions.

[0354] For example, if a user shows interest in a product, the generating AI will create prompts suggesting related accessories or product sets. A prompt like this might be: "If the user is smiling at their smartphone camera, display related products and suggest information that will make them smile even more."

[0355] This enhances interaction between the server and the user, leading to an improved user experience.

[0356] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0357] Step 1:

[0358] The server aggregates work information collected from various departments within the organization into a database. The input is work information data from each department, and the output is a centrally managed database. The database stores information in a structured, easily accessible, and manageable format. The server efficiently organizes this data and prepares it for queries.

[0359] Step 2:

[0360] The user enters project details. The input is the project details, and the output is the input data for the generative AI. The terminal sends this data to the server and converts it into a formatted format for analysis by the generative AI.

[0361] Step 3:

[0362] The server uses generative AI to select appropriate fields based on the input project details. The input consists of formatted project information and historical project data, and the output is a list of selected fields. The server runs the generative AI model and performs data analysis to identify the most suitable field.

[0363] Step 4:

[0364] The user terminal uses a visual sensor (camera) and an audio sensor (microphone) to analyze the user's emotions. The input is sensor data, and the output is the emotion analysis result. The terminal uses an emotion engine to analyze facial expressions and tone of voice to identify the user's emotional state.

[0365] Step 5:

[0366] The server dynamically adjusts the suggested content based on the analysis results of the emotion engine. The input is the emotion analysis result, and the output is the adjusted suggested content. The server uses a generative AI model to generate suggested content optimized for the user's emotions.

[0367] Step 6:

[0368] The user receives the adjusted suggestions and makes an appropriate decision. The input is the adjusted suggestions, and the output is the result of the decision. The terminal displays the suggested information and provides an interface to help the user make an appropriate choice.

[0369] 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.

[0370] 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.

[0371] 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.

[0372] [Third Embodiment]

[0373] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0374] 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.

[0375] 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).

[0376] 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.

[0377] 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.

[0378] 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).

[0379] 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.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] 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".

[0385] This invention provides a system that supports efficient hypothesis testing within a corporate group. Embodiments include a platform equipped with an information aggregation function, a department selection function using generation AI, and a progress management function.

[0386] The system first aggregates business information from each department within the corporate group and stores it in a database. This information includes the business content, areas of expertise, challenges, and resource information handled by each department. The database is designed to efficiently manage this information and allow for immediate access as needed.

[0387] The generating AI accesses this database and selects the most appropriate department based on the detailed project information entered. This AI improves the accuracy of its departmental selection by analyzing past project history and the expertise of each department. The selected departmental information is presented to the user, who can then choose a department to partner with for hypothesis testing.

[0388] The selected departments and project managers can easily collaborate through this system. The server notifies the selected departments of the project details and acts as a communication bridge between them, thereby supporting the smooth progress of the project.

[0389] As a concrete example, suppose a user in charge of new business development wants to test a hypothesis for a new customer management system. In this case, the user inputs the project objectives, required resources, etc., into the system from their terminal. The server uses this information and its AI generation capabilities to identify departments that have previously carried out similar projects and suggests departments such as the marketing department or the IT solutions department. The user then selects the most suitable department, and the server sends the project information to the selected department to facilitate collaboration.

[0390] This invention aims to improve a company's competitiveness by effectively utilizing internal resources and conducting rapid and effective hypothesis testing.

[0391] The following describes the processing flow.

[0392] Step 1:

[0393] Users input information about their department's operations, areas of expertise, and current challenges from their terminals and send it to the server. This information is provided in a standardized format.

[0394] Step 2:

[0395] The server receives information sent from each department, verifies the consistency of the format, and then registers it in the database. The server segments the information and builds the database to enable efficient management.

[0396] Step 3:

[0397] Users input detailed information about a new project they want to test a hypothesis on, using their terminal, and submit it to the server. This information includes the project's objectives and the necessary skills and resources.

[0398] Step 4:

[0399] The server receives detailed information about the hypothesis testing project and uses generative AI to search for a suitable department for the project. This includes analyzing past project history in the database and the expertise of each department.

[0400] Step 5:

[0401] The server presents the user with a list of suitable department candidates selected by the generating AI. For each candidate department, detailed information, including relevant data and past success stories, is also provided.

[0402] Step 6:

[0403] The user reviews the list of departments presented by the server on their terminal and selects the department best suited for hypothesis testing.

[0404] Step 7:

[0405] The server notifies the department selected by the user of the project details. The server also automatically schedules and sets the date for the initial meeting.

[0406] Step 8:

[0407] The server monitors communications and data updates during project progress and provides real-time progress updates to users and selected departments on a regular basis. The server also assists with coordinating communications and optimizing resources as needed.

[0408] (Example 1)

[0409] 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."

[0410] Traditional hypothesis testing projects within companies have faced problems such as difficulty in selecting the appropriate departments and sharing information, leading to project delays and decreased efficiency. In particular, there was a lack of means to effectively utilize the expertise of each department and to monitor progress in real time.

[0411] 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.

[0412] In this invention, the server includes a data storage device for aggregating and storing information, means for centrally managing business information received from each department within the organization, means for selecting and proposing appropriate departments based on detailed information of a plan to be tested using a generative AI, means for presenting information on the proposed departments to the user and promoting cooperation between departments according to the user's selection, means for monitoring the progress of the plan and updating the information in real time, and means for the generative AI to process and analyze the plan information in prompt message format. This enables the rapid selection of appropriate departments, information sharing, and smooth project progress.

[0413] A "data storage device for aggregating and storing information" is a storage device designed to centrally manage business information received from various departments within an organization, and to enable efficient access and use.

[0414] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze detailed project information and assist in selecting the most suitable department.

[0415] The "prompt format" is a method in which natural language commands are used as input for the generation AI to interpret and process planning information.

[0416] "Means of monitoring the progress of the plan" refers to a system that allows for real-time monitoring of the project's progress and results, and updates the information as needed.

[0417] "Means to promote interdepartmental cooperation" refers to a system that enables information sharing between the proposed department and users, facilitating smooth communication and collaborative work.

[0418] This invention provides a system for efficiently conducting hypothesis testing projects within a company. The system includes a data storage device for aggregating and storing information, a generating AI, a function for processing information in prompt format, and means for facilitating interdepartmental collaboration.

[0419] The server is responsible for aggregating business information from various departments within the organization and storing it in data storage devices. The database software on the server is equipped with indexing and search functions to efficiently manage large amounts of information and facilitate access.

[0420] The generating AI receives project information entered by the user as prompts and selects the appropriate department based on the detailed plan. The AI ​​uses machine learning libraries to analyze past project history and the expertise of each department. In particular, it is designed to effectively interpret prompts by utilizing natural language processing techniques.

[0421] As a concrete example, consider a scenario where a user wants to test a hypothesis for new product development. The user inputs the following prompt into the system from their terminal: "Please select the most suitable department to carry out the new product development project. Please suggest a list of departments that have previously carried out similar projects." The server then has the AI ​​process this information and suggest appropriate departments, such as the IT department or the research and development department.

[0422] This system enables users to quickly select departments, optimize project progress, and effectively utilize resources across the entire company.

[0423] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0424] Step 1:

[0425] Users input detailed information about their project, such as its objectives and required resources, into the system using their own devices. This input is specifically presented in the form of prompts. The user's input information is sent to the server via a secure protocol.

[0426] Step 2:

[0427] The server stores project information received from users in a data storage device. This allows the server to generate an index to quickly search the stored data. This process organizes the data according to a specific format and performs data processing to enable efficient searching.

[0428] Step 3:

[0429] The server provides the accumulated data to a generating AI model. This model analyzes project information in prompt format and selects the optimal department based on past project history in the database. Here, natural language processing and machine learning algorithms are used to analyze the input data and generate a list of the optimal departments as output.

[0430] Step 4:

[0431] The generated list of departments is presented to the user. The user's device also displays each department's expertise and past project performance, providing data to support their selection. Based on this list, the user selects the departments to conduct hypothesis testing.

[0432] Step 5:

[0433] The server notifies the department selected by the user of the project details. This information is sent via email or a dedicated collaboration tool. Furthermore, the server facilitates communication between the two parties, supporting the smooth progress of the project. The representatives of the selected department receive detailed project information in real time and begin preparations.

[0434] (Application Example 1)

[0435] 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."

[0436] When an organization's project spans multiple departments, appropriate department selection and rapid problem resolution are crucial. However, a lack of smooth information gathering and inter-departmental collaboration delays hypothesis and problem testing, making efficient resolution difficult. Furthermore, rapid analysis of visual information from the field and immediate notification to the most appropriate department remain challenges.

[0437] 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.

[0438] In this invention, the server includes a database for aggregating and storing information, means for centrally managing business information received from each department within the organization, means for selecting and proposing an appropriate department based on detailed information of a project to be tested using generative artificial intelligence, and means for analyzing visual data acquired using visual devices, selecting an appropriate department through generative artificial intelligence, and immediately notifying the user. This enables efficient aggregation of information within the organization, facilitating rapid and accurate department selection and problem solving.

[0439] A "database for aggregating and storing information" is a digital recording device used to centrally manage business information acquired from various departments within an organization.

[0440] "Generative artificial intelligence" is an artificial intelligence system that generates new insights and suggestions using past data and learning algorithms.

[0441] "Visual equipment" refers to devices that use cameras or other means to acquire visual information from a site.

[0442] "Visual data" refers to image and video information acquired through visual devices.

[0443] "Department selection" is the process of determining which department within an organization is best suited for a particular project or task.

[0444] "Immediate notification" refers to the act of promptly informing the relevant department or person in charge upon receiving information.

[0445] "Hypothesis testing" is the process of testing new theories or hypotheses based on actual data.

[0446] In the system implementing this invention, a server plays a central role. The server is equipped with a database that aggregates and stores information, and manages unified information related to the operations of each department collected within the organization. The database stores a large amount of data, and it is possible to quickly retrieve the necessary information through various queries.

[0447] Users record the actual situation on-site using visual devices and acquire visual data. This visual data is sent to a server and analyzed by generative artificial intelligence (generative AI). Based on data such as past project history and departmental expertise, the generative AI selects the most suitable department based on the content of the input data. Once the appropriate department is selected, this information is promptly notified to the relevant parties.

[0448] The server uses the Python programming language and the OpenCV library to process images and videos acquired using Google Glass and other visual devices. For generative artificial intelligence analysis, it employs a virtual AI library for advanced data processing and pattern recognition. This entire process ensures that information within the organization is handled efficiently, enabling timely action for resolution.

[0449] As a concrete example, consider a scenario where new equipment is introduced to a factory production line, and its installation is checked to ensure it is properly positioned. Workers use smart glasses to capture video footage of the equipment and upload the data to the system. The generated AI analyzes the video data and immediately notifies the appropriate department, such as the equipment management department or the technical support department.

[0450] An example of a prompt statement related to a specific example of this embodiment is, "A new device has been introduced to the production line. Please check the installation status and select the appropriate department." By inputting this prompt statement into the generating AI, a series of processes are initiated, enabling a rapid response.

[0451] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0452] Step 1:

[0453] The terminal uses visual equipment to capture images of the situation on site. The input is visual data (images or videos), and the device used to acquire this data is a smart device with camera functionality. The output is visual data in digital format.

[0454] Step 2:

[0455] The terminal sends visual data to the server. The input is visual data, and the output is visual data stored on the server. This process ensures that the data arrives at the server accurately and quickly using a data transmission protocol.

[0456] Step 3:

[0457] Generative artificial intelligence (generative AI) is used to analyze the visual data received by the server. The input is the visual data stored on the server, and the output is the analysis results (e.g., problem identification and suggestions for improvement). The generative AI performs pattern recognition and data matching to propose the optimal solution.

[0458] Step 4:

[0459] The server identifies the optimal department selected by the generated AI. Inputs include analysis results and reference data such as past project history, while output is information about the optimal department. This clarifies which department should handle the task.

[0460] Step 5:

[0461] The server immediately sends a notification to the identified department. The input is information about the appropriate department, and the output is the notification message sent to that department. Notifications are sent via email or a dedicated application, ensuring that information is quickly disseminated to relevant parties.

[0462] Step 6:

[0463] The user receives a notification and takes additional action or confirmation as needed. The input is the notification message from the server, and the output is the user's action (e.g., approval or feedback). In this step, the user is required to check the situation and take the necessary action.

[0464] 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.

[0465] This invention combines a system for efficiently testing hypotheses within a corporate group with an emotion engine that recognizes user emotions and optimizes proposals based on those emotions. The system includes an information aggregation function, a generation AI, a feedback adjustment function using the emotion engine, and a progress management function.

[0466] In this system, business information collected from each department of a company is first stored in a database. This establishes a system for comprehensively managing the company's resources and areas of expertise. By accessing this information, the generating AI selects and proposes the appropriate department to the user based on the details of the project for which hypothesis testing is desired. During the selection process, the accuracy of the proposal is improved by analyzing the history of past projects.

[0467] Furthermore, this system uses an emotion engine to analyze the user's emotional state through user input and interaction. When a user provides feedback to the system, the server dynamically adjusts the suggestions and interface based on this emotional state data. For example, if a user expresses confusion or dissatisfaction with a suggestion, the server recognizes this using the emotion engine and proposes alternatives or provides additional information through its AI-generated suggestions. The aim of these adjustments is to improve the user experience and ensure the smooth progress of the project.

[0468] During project execution, the server monitors progress in real time and, when necessary, utilizes the emotion engine to propose appropriate communication strategies for users and relevant departments. This integration of the emotion engine, generative AI, and information database maximizes the effectiveness of proposals and streamlines internal communication and hypothesis testing. The implementation of this system is expected to strengthen the competitiveness of the entire corporate group.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] Users input information about their department's operations, areas of expertise, challenges, etc., from their terminals and send it to the server.

[0472] Step 2:

[0473] The server receives information sent from each department, verifies its consistency, and then registers it in the database. This database is designed to systematically organize information and facilitate searching and analysis.

[0474] Step 3:

[0475] Users input detailed information about a new hypothesis testing project from their terminal and submit it to the server.

[0476] Step 4:

[0477] The server receives the details of the hypothesis testing project and uses generative AI to search the database for the most suitable department. This includes analyzing past project history and the expertise of each department.

[0478] Step 5:

[0479] The server lists suitable department candidates selected by the generating AI and presents them to the user along with relevant information.

[0480] Step 6:

[0481] The user reviews the presented list of departments on their device and selects the department best suited for hypothesis testing. During this process, the device monitors the user's reactions using an emotion engine and sends emotion data to the server.

[0482] Step 7:

[0483] The server adjusts feedback based on the emotional data analyzed by the emotion engine, according to the user's choices. If necessary, it uses generative AI to suggest alternatives and make suggestions that are easy for the user to understand.

[0484] Step 8:

[0485] The server notifies the department selected by the user of the project details and automatically schedules the first meeting.

[0486] Step 9:

[0487] The server monitors project progress in real time and provides timely information to relevant departments and users. It leverages an emotion engine to improve communication within the project team and support efficient collaboration.

[0488] (Example 2)

[0489] 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."

[0490] Within companies and organizations, a lack of collaboration between departments and inefficient communication often lead to delays in the hypothesis testing process. Furthermore, proposed solutions may not align with users' emotions and expectations, resulting in a worse user experience. Solving these problems and implementing efficient and flexible hypothesis testing is essential.

[0491] 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.

[0492] In this invention, the server includes information management means for aggregating and storing information, means for selecting and proposing appropriate departments using a generation AI, and means for analyzing the operator's emotions using an emotion analysis engine. This effectively promotes collaboration between departments and enables the dynamic adjustment of proposal content based on the user's emotions.

[0493] "Information management means" refers to a function for centrally aggregating and efficiently storing business information collected from various departments within an organization.

[0494] "Generative AI" is an artificial intelligence technology that selects and proposes the most suitable department based on past data and detailed work information.

[0495] An "emotion analysis engine" is a technology that analyzes the emotional state of the user from their input and dialogue, and uses this information to adjust services and proposals.

[0496] "Operators" refer to users or users of the system, and they are responsible for receiving suggestions from the system and making decisions.

[0497] "Collaboration promotion" is the process of strengthening communication and cooperation between different departments within an organization.

[0498] This invention is a system that utilizes a generative AI model and an emotion analysis engine to streamline the hypothesis testing process within an organization. This system operates through the following configuration and operation.

[0499] The server collects business information in digital format from each department of the company and stores it in a centralized database. This information includes departmental resource status and past project history. General data management software can be used for database management.

[0500] Generative AI models select the most suitable departments for hypothesis testing based on collected information. This process analyzes patterns from past successful projects to improve the accuracy of their recommendations. Generative AI is typically implemented using cloud-based AI computing systems.

[0501] The user receives this suggestion through their device and checks for more details as needed. Furthermore, an emotion analysis engine is activated based on the user's response to analyze their emotional state. This result is fed back to the server, and the suggestions and system interface are dynamically adjusted accordingly.

[0502] For example, if a user inputs the prompt "Please suggest the optimal department for analyzing market reaction to a new product and conducting efficient hypothesis testing" into the generating AI model, the AI ​​will suggest a suitable department by referring to successful examples of past market analysis projects. If the user expresses confusion or dissatisfaction with this suggestion, the sentiment analysis engine will recognize this, and the server will provide alternatives or additional data.

[0503] In this way, servers, terminals, and users collaborate to form an effective communication loop that enables a better hypothesis testing process.

[0504] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0505] Step 1:

[0506] The server collects business information from various departments within the company. Specifically, departmental staff input information via terminals, which the server receives over the network. This input includes resource usage and project progress information. The server stores the received data in a database. This process generates a comprehensive dataset, which is then used for subsequent analysis.

[0507] Step 2:

[0508] The server analyzes accumulated business information and uses a generative AI model to select departments suitable for hypothesis testing. The generative AI analyzes past project history and current resource status. As input, the server retrieves this information from the database and sends prompt messages to the AI ​​model. As output, the AI ​​generates a list of optimal departments and returns it to the server. Specifically, the server calls the AI ​​model to perform inference.

[0509] Step 3:

[0510] The user receives and reviews department information suggested by the generating AI via their device. The user views detailed department information presented on the device and makes a selection as needed. Specifically, the server sends output data from the generating AI to the user's device, and the user views its contents. At this time, the user's selection results are fed back to the server.

[0511] Step 4:

[0512] The server uses an emotion analysis engine to analyze the user's emotions. When the user provides feedback on the suggestions, this is used as input to the emotion analysis engine. Based on the analysis results, the server determines the user's emotional state and, if necessary, adjusts the suggestions. The output of this process is new suggestions to improve the user experience. Specific actions include the server dynamically changing the interface based on the results of the emotion analysis.

[0513] Step 5:

[0514] The server monitors project progress and updates all relevant data in real time. The input is the latest progress-related information retrieved from the database. Based on this data, the server generates progress reports and sends them to stakeholders' terminals. This output provides visibility into the project's current status, allowing for quick adjustments. Specific operations include periodically crawling the database to retrieve the latest information.

[0515] (Application Example 2)

[0516] 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."

[0517] Traditional systems often failed to provide personalized recommendations based on individual preferences and emotional states when users selected products or services, leading to decreased satisfaction. Furthermore, there was a lack of effective ways to mitigate the confusion and dissatisfaction users experienced when the suggested options were inappropriate, highlighting the need for improved user experience. Therefore, a new system was needed that could provide more appropriate recommendations based on user emotions and efficiently present alternatives tailored to individual needs.

[0518] 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.

[0519] This invention includes a server equipped with a database for aggregating and storing information, a means for centrally managing work information received from various departments within the organization, a means for selecting and proposing appropriate departments based on detailed information of projects to be tested using generative AI, and a means for analyzing the user's emotions using visual and audio sensors and dynamically adjusting the proposed content using an emotion engine. This makes it possible to provide more appropriate suggestions that are attentive to the user's emotions, thereby improving the user experience.

[0520] A "database" is a structured data storage system that aggregates information and centrally manages work information received from various departments within an organization.

[0521] "Generative AI" refers to an algorithm or system that uses artificial intelligence technology to select and propose appropriate fields based on detailed project information.

[0522] An "emotion engine" is a technology or function that uses data obtained from visual and audio sensors to analyze the user's emotions and dynamically adjust the suggested content.

[0523] A "visual sensor" is a camera or related device used to detect a user's facial expressions and analyze that information.

[0524] A "voice sensor" is a microphone or related device used to analyze the tone and patterns of a user's voice.

[0525] "User experience" is a concept that refers to the overall feeling and satisfaction that users experience when using a system or service.

[0526] The system implementing this invention is comprised of a database for aggregating and storing information, a generative AI, and an emotion engine. The server first aggregates work information received from various departments within the organization into the database. This database plays a central role in managing all the information necessary for a project by structuring and managing the information.

[0527] When a user provides detailed information about a new project, the server uses generative AI to select the appropriate field and propose it to the user. The generative AI analyzes past project history and related data to present the most suitable selection. At this stage, the models used by the generative AI may include, for example, open-source AI models or cloud-based AI platforms.

[0528] Furthermore, the user terminal uses a visual sensor (camera) and an audio sensor (microphone) to analyze the user's emotional state in real time. The emotion engine uses this data to identify the emotions expressed by the user, and based on the results, the server dynamically adjusts the suggested content. Specifically, the generating AI optimizes the suggested product and service information so that it best matches the user's current emotions.

[0529] For example, if a user shows interest in a product, the generating AI will create prompts suggesting related accessories or product sets. A prompt like this might be: "If the user is smiling at their smartphone camera, display related products and suggest information that will make them smile even more."

[0530] This enhances interaction between the server and the user, leading to an improved user experience.

[0531] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0532] Step 1:

[0533] The server aggregates work information collected from various departments within the organization into a database. The input is work information data from each department, and the output is a centrally managed database. The database stores information in a structured, easily accessible, and manageable format. The server efficiently organizes this data and prepares it for queries.

[0534] Step 2:

[0535] The user enters project details. The input is the project details, and the output is the input data for the generative AI. The terminal sends this data to the server and converts it into a formatted format for analysis by the generative AI.

[0536] Step 3:

[0537] The server uses generative AI to select appropriate fields based on the input project details. The input consists of formatted project information and historical project data, and the output is a list of selected fields. The server runs the generative AI model and performs data analysis to identify the most suitable field.

[0538] Step 4:

[0539] The user terminal uses a visual sensor (camera) and an audio sensor (microphone) to analyze the user's emotions. The input is sensor data, and the output is the emotion analysis result. The terminal uses an emotion engine to analyze facial expressions and tone of voice to identify the user's emotional state.

[0540] Step 5:

[0541] The server dynamically adjusts the suggested content based on the analysis results of the emotion engine. The input is the emotion analysis result, and the output is the adjusted suggested content. The server uses a generative AI model to generate suggested content optimized for the user's emotions.

[0542] Step 6:

[0543] The user receives the adjusted suggestions and makes an appropriate decision. The input is the adjusted suggestions, and the output is the result of the decision. The terminal displays the suggested information and provides an interface to help the user make an appropriate choice.

[0544] 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.

[0545] 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.

[0546] 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.

[0547] [Fourth Embodiment]

[0548] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0549] 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.

[0550] 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).

[0551] 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.

[0552] 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.

[0553] 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).

[0554] 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.

[0555] 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.

[0556] 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.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] 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".

[0561] This invention provides a system that supports efficient hypothesis testing within a corporate group. Embodiments include a platform equipped with an information aggregation function, a department selection function using generation AI, and a progress management function.

[0562] The system first aggregates business information from each department within the corporate group and stores it in a database. This information includes the business content, areas of expertise, challenges, and resource information handled by each department. The database is designed to efficiently manage this information and allow for immediate access as needed.

[0563] The generating AI accesses this database and selects the most appropriate department based on the detailed project information entered. This AI improves the accuracy of its departmental selection by analyzing past project history and the expertise of each department. The selected departmental information is presented to the user, who can then choose a department to partner with for hypothesis testing.

[0564] The selected departments and project managers can easily collaborate through this system. The server notifies the selected departments of the project details and acts as a communication bridge between them, thereby supporting the smooth progress of the project.

[0565] As a concrete example, suppose a user in charge of new business development wants to test a hypothesis for a new customer management system. In this case, the user inputs the project objectives, required resources, etc., into the system from their terminal. The server uses this information and its AI generation capabilities to identify departments that have previously carried out similar projects and suggests departments such as the marketing department or the IT solutions department. The user then selects the most suitable department, and the server sends the project information to the selected department to facilitate collaboration.

[0566] This invention aims to improve a company's competitiveness by effectively utilizing internal resources and conducting rapid and effective hypothesis testing.

[0567] The following describes the processing flow.

[0568] Step 1:

[0569] Users input information about their department's operations, areas of expertise, and current challenges from their terminals and send it to the server. This information is provided in a standardized format.

[0570] Step 2:

[0571] The server receives information sent from each department, verifies the consistency of the format, and then registers it in the database. The server segments the information and builds the database to enable efficient management.

[0572] Step 3:

[0573] Users input detailed information about a new project they want to test a hypothesis on, using their terminal, and submit it to the server. This information includes the project's objectives and the necessary skills and resources.

[0574] Step 4:

[0575] The server receives detailed information about the hypothesis testing project and uses generative AI to search for a suitable department for the project. This includes analyzing past project history in the database and the expertise of each department.

[0576] Step 5:

[0577] The server presents the user with a list of suitable department candidates selected by the generating AI. For each candidate department, detailed information, including relevant data and past success stories, is also provided.

[0578] Step 6:

[0579] The user reviews the list of departments presented by the server on their terminal and selects the department best suited for hypothesis testing.

[0580] Step 7:

[0581] The server notifies the department selected by the user of the project details. The server also automatically schedules and sets the date for the initial meeting.

[0582] Step 8:

[0583] The server monitors communications and data updates during project progress and provides real-time progress updates to users and selected departments on a regular basis. The server also assists with coordinating communications and optimizing resources as needed.

[0584] (Example 1)

[0585] 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".

[0586] Traditional hypothesis testing projects within companies have faced problems such as difficulty in selecting the appropriate departments and sharing information, leading to project delays and decreased efficiency. In particular, there was a lack of means to effectively utilize the expertise of each department and to monitor progress in real time.

[0587] 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.

[0588] In this invention, the server includes a data storage device for aggregating and storing information, means for centrally managing business information received from each department within the organization, means for selecting and proposing appropriate departments based on detailed information of a plan to be tested using a generative AI, means for presenting information on the proposed departments to the user and promoting cooperation between departments according to the user's selection, means for monitoring the progress of the plan and updating the information in real time, and means for the generative AI to process and analyze the plan information in prompt message format. This enables the rapid selection of appropriate departments, information sharing, and smooth project progress.

[0589] A "data storage device for aggregating and storing information" is a storage device designed to centrally manage business information received from various departments within an organization, and to enable efficient access and use.

[0590] "Generative AI" is an artificial intelligence technology that uses machine learning algorithms to analyze detailed project information and assist in selecting the most suitable department.

[0591] The "prompt format" is a method in which natural language commands are used as input for the generation AI to interpret and process planning information.

[0592] "Means of monitoring the progress of the plan" refers to a system that allows for real-time monitoring of the project's progress and results, and updates the information as needed.

[0593] "Means to promote interdepartmental cooperation" refers to a system that enables information sharing between the proposed department and users, facilitating smooth communication and collaborative work.

[0594] This invention provides a system for efficiently conducting hypothesis testing projects within a company. The system includes a data storage device for aggregating and storing information, a generating AI, a function for processing information in prompt format, and means for facilitating interdepartmental collaboration.

[0595] The server is responsible for aggregating business information from various departments within the organization and storing it in data storage devices. The database software on the server is equipped with indexing and search functions to efficiently manage large amounts of information and facilitate access.

[0596] The generating AI receives project information entered by the user as prompts and selects the appropriate department based on the detailed plan. The AI ​​uses machine learning libraries to analyze past project history and the expertise of each department. In particular, it is designed to effectively interpret prompts by utilizing natural language processing techniques.

[0597] As a concrete example, consider a scenario where a user wants to test a hypothesis for new product development. The user inputs the following prompt into the system from their terminal: "Please select the most suitable department to carry out the new product development project. Please suggest a list of departments that have previously carried out similar projects." The server then has the AI ​​process this information and suggest appropriate departments, such as the IT department or the research and development department.

[0598] This system enables users to quickly select departments, optimize project progress, and effectively utilize resources across the entire company.

[0599] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0600] Step 1:

[0601] Users input detailed information about their project, such as its objectives and required resources, into the system using their own devices. This input is specifically presented in the form of prompts. The user's input information is sent to the server via a secure protocol.

[0602] Step 2:

[0603] The server stores project information received from users in a data storage device. This allows the server to generate an index to quickly search the stored data. This process organizes the data according to a specific format and performs data processing to enable efficient searching.

[0604] Step 3:

[0605] The server provides the accumulated data to a generating AI model. This model analyzes project information in prompt format and selects the optimal department based on past project history in the database. Here, natural language processing and machine learning algorithms are used to analyze the input data and generate a list of the optimal departments as output.

[0606] Step 4:

[0607] The generated list of departments is presented to the user. The user's device also displays each department's expertise and past project performance, providing data to support their selection. Based on this list, the user selects the departments to conduct hypothesis testing.

[0608] Step 5:

[0609] The server notifies the department selected by the user of the project details. This information is sent via email or a dedicated collaboration tool. Furthermore, the server facilitates communication between the two parties, supporting the smooth progress of the project. The representatives of the selected department receive detailed project information in real time and begin preparations.

[0610] (Application Example 1)

[0611] 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".

[0612] When an organization's project spans multiple departments, appropriate department selection and rapid problem resolution are crucial. However, a lack of smooth information gathering and inter-departmental collaboration delays hypothesis and problem testing, making efficient resolution difficult. Furthermore, rapid analysis of visual information from the field and immediate notification to the most appropriate department remain challenges.

[0613] 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.

[0614] In this invention, the server includes a database for aggregating and storing information, means for centrally managing business information received from each department within the organization, means for selecting and proposing an appropriate department based on detailed information of a project to be tested using generative artificial intelligence, and means for analyzing visual data acquired using visual devices, selecting an appropriate department through generative artificial intelligence, and immediately notifying the user. This enables efficient aggregation of information within the organization, facilitating rapid and accurate department selection and problem solving.

[0615] A "database for aggregating and storing information" is a digital recording device used to centrally manage business information acquired from various departments within an organization.

[0616] "Generative artificial intelligence" is an artificial intelligence system that generates new insights and suggestions using past data and learning algorithms.

[0617] "Visual equipment" refers to devices that use cameras or other means to acquire visual information from a site.

[0618] "Visual data" refers to image and video information acquired through visual devices.

[0619] "Department selection" is the process of determining which department within an organization is best suited for a particular project or task.

[0620] "Immediate notification" refers to the act of promptly informing the relevant department or person in charge upon receiving information.

[0621] "Hypothesis testing" is the process of testing new theories or hypotheses based on actual data.

[0622] In the system implementing this invention, a server plays a central role. The server is equipped with a database that aggregates and stores information, and manages unified information related to the operations of each department collected within the organization. The database stores a large amount of data, and it is possible to quickly retrieve the necessary information through various queries.

[0623] Users record the actual situation on-site using visual devices and acquire visual data. This visual data is sent to a server and analyzed by generative artificial intelligence (generative AI). Based on data such as past project history and departmental expertise, the generative AI selects the most suitable department based on the content of the input data. Once the appropriate department is selected, this information is promptly notified to the relevant parties.

[0624] The server uses the Python programming language and the OpenCV library to process images and videos acquired using Google Glass and other visual devices. For generative artificial intelligence analysis, it employs a virtual AI library for advanced data processing and pattern recognition. This entire process ensures that information within the organization is handled efficiently, enabling timely action for resolution.

[0625] As a concrete example, consider a scenario where new equipment is introduced to a factory production line, and its installation is checked to ensure it is properly positioned. Workers use smart glasses to capture video footage of the equipment and upload the data to the system. The generated AI analyzes the video data and immediately notifies the appropriate department, such as the equipment management department or the technical support department.

[0626] An example of a prompt statement related to a specific example of this embodiment is, "A new device has been introduced to the production line. Please check the installation status and select the appropriate department." By inputting this prompt statement into the generating AI, a series of processes are initiated, enabling a rapid response.

[0627] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0628] Step 1:

[0629] The terminal uses visual equipment to capture images of the situation on site. The input is visual data (images or videos), and the device used to acquire this data is a smart device with camera functionality. The output is visual data in digital format.

[0630] Step 2:

[0631] The terminal sends visual data to the server. The input is visual data, and the output is visual data stored on the server. This process ensures that the data arrives at the server accurately and quickly using a data transmission protocol.

[0632] Step 3:

[0633] Generative artificial intelligence (generative AI) is used to analyze the visual data received by the server. The input is the visual data stored on the server, and the output is the analysis results (e.g., problem identification and suggestions for improvement). The generative AI performs pattern recognition and data matching to propose the optimal solution.

[0634] Step 4:

[0635] The server identifies the optimal department selected by the generated AI. Inputs include analysis results and reference data such as past project history, while output is information about the optimal department. This clarifies which department should handle the task.

[0636] Step 5:

[0637] The server immediately sends a notification to the identified department. The input is information about the appropriate department, and the output is the notification message sent to that department. Notifications are sent via email or a dedicated application, ensuring that information is quickly disseminated to relevant parties.

[0638] Step 6:

[0639] The user receives a notification and takes additional action or confirmation as needed. The input is the notification message from the server, and the output is the user's action (e.g., approval or feedback). In this step, the user is required to check the situation and take the necessary action.

[0640] 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.

[0641] This invention combines a system for efficiently testing hypotheses within a corporate group with an emotion engine that recognizes user emotions and optimizes proposals based on those emotions. The system includes an information aggregation function, a generation AI, a feedback adjustment function using the emotion engine, and a progress management function.

[0642] In this system, business information collected from each department of a company is first stored in a database. This establishes a system for comprehensively managing the company's resources and areas of expertise. By accessing this information, the generating AI selects and proposes the appropriate department to the user based on the details of the project for which hypothesis testing is desired. During the selection process, the accuracy of the proposal is improved by analyzing the history of past projects.

[0643] Furthermore, this system uses an emotion engine to analyze the user's emotional state through user input and interaction. When a user provides feedback to the system, the server dynamically adjusts the suggestions and interface based on this emotional state data. For example, if a user expresses confusion or dissatisfaction with a suggestion, the server recognizes this using the emotion engine and proposes alternatives or provides additional information through its AI-generated suggestions. The aim of these adjustments is to improve the user experience and ensure the smooth progress of the project.

[0644] During project execution, the server monitors progress in real time and, when necessary, utilizes the emotion engine to propose appropriate communication strategies for users and relevant departments. This integration of the emotion engine, generative AI, and information database maximizes the effectiveness of proposals and streamlines internal communication and hypothesis testing. The implementation of this system is expected to strengthen the competitiveness of the entire corporate group.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] Users input information about their department's operations, areas of expertise, challenges, etc., from their terminals and send it to the server.

[0648] Step 2:

[0649] The server receives information sent from each department, verifies its consistency, and then registers it in the database. This database is designed to systematically organize information and facilitate searching and analysis.

[0650] Step 3:

[0651] Users input detailed information about a new hypothesis testing project from their terminal and submit it to the server.

[0652] Step 4:

[0653] The server receives the details of the hypothesis testing project and uses generative AI to search the database for the most suitable department. This includes analyzing past project history and the expertise of each department.

[0654] Step 5:

[0655] The server lists suitable department candidates selected by the generating AI and presents them to the user along with relevant information.

[0656] Step 6:

[0657] The user reviews the presented list of departments on their device and selects the department best suited for hypothesis testing. During this process, the device monitors the user's reactions using an emotion engine and sends emotion data to the server.

[0658] Step 7:

[0659] The server adjusts feedback based on the emotional data analyzed by the emotion engine, according to the user's choices. If necessary, it uses generative AI to suggest alternatives and make suggestions that are easy for the user to understand.

[0660] Step 8:

[0661] The server notifies the department selected by the user of the project details and automatically schedules the first meeting.

[0662] Step 9:

[0663] The server monitors project progress in real time and provides timely information to relevant departments and users. It leverages an emotion engine to improve communication within the project team and support efficient collaboration.

[0664] (Example 2)

[0665] 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".

[0666] Within companies and organizations, a lack of collaboration between departments and inefficient communication often lead to delays in the hypothesis testing process. Furthermore, proposed solutions may not align with users' emotions and expectations, resulting in a worse user experience. Solving these problems and implementing efficient and flexible hypothesis testing is essential.

[0667] 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.

[0668] In this invention, the server includes information management means for aggregating and storing information, means for selecting and proposing appropriate departments using a generation AI, and means for analyzing the operator's emotions using an emotion analysis engine. This effectively promotes collaboration between departments and enables the dynamic adjustment of proposal content based on the user's emotions.

[0669] "Information management means" refers to a function for centrally aggregating and efficiently storing business information collected from various departments within an organization.

[0670] "Generative AI" is an artificial intelligence technology that selects and proposes the most suitable department based on past data and detailed work information.

[0671] An "emotion analysis engine" is a technology that analyzes the emotional state of the user from their input and dialogue, and uses this information to adjust services and proposals.

[0672] "Operators" refer to users or users of the system, and they are responsible for receiving suggestions from the system and making decisions.

[0673] "Collaboration promotion" is the process of strengthening communication and cooperation between different departments within an organization.

[0674] This invention is a system that utilizes a generative AI model and an emotion analysis engine to streamline the hypothesis testing process within an organization. This system operates through the following configuration and operation.

[0675] The server collects business information in digital format from each department of the company and stores it in a centralized database. This information includes departmental resource status and past project history. General data management software can be used for database management.

[0676] Generative AI models select the most suitable departments for hypothesis testing based on collected information. This process analyzes patterns from past successful projects to improve the accuracy of their recommendations. Generative AI is typically implemented using cloud-based AI computing systems.

[0677] The user receives this suggestion through their device and checks for more details as needed. Furthermore, an emotion analysis engine is activated based on the user's response to analyze their emotional state. This result is fed back to the server, and the suggestions and system interface are dynamically adjusted accordingly.

[0678] For example, if a user inputs the prompt "Please suggest the optimal department for analyzing market reaction to a new product and conducting efficient hypothesis testing" into the generating AI model, the AI ​​will suggest a suitable department by referring to successful examples of past market analysis projects. If the user expresses confusion or dissatisfaction with this suggestion, the sentiment analysis engine will recognize this, and the server will provide alternatives or additional data.

[0679] In this way, servers, terminals, and users collaborate to form an effective communication loop that enables a better hypothesis testing process.

[0680] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0681] Step 1:

[0682] The server collects business information from various departments within the company. Specifically, departmental staff input information via terminals, which the server receives over the network. This input includes resource usage and project progress information. The server stores the received data in a database. This process generates a comprehensive dataset, which is then used for subsequent analysis.

[0683] Step 2:

[0684] The server analyzes accumulated business information and uses a generative AI model to select departments suitable for hypothesis testing. The generative AI analyzes past project history and current resource status. As input, the server retrieves this information from the database and sends prompt messages to the AI ​​model. As output, the AI ​​generates a list of optimal departments and returns it to the server. Specifically, the server calls the AI ​​model to perform inference.

[0685] Step 3:

[0686] The user receives and reviews department information suggested by the generating AI via their device. The user views detailed department information presented on the device and makes a selection as needed. Specifically, the server sends output data from the generating AI to the user's device, and the user views its contents. At this time, the user's selection results are fed back to the server.

[0687] Step 4:

[0688] The server uses an emotion analysis engine to analyze the user's emotions. When the user provides feedback on the suggestions, this is used as input to the emotion analysis engine. Based on the analysis results, the server determines the user's emotional state and, if necessary, adjusts the suggestions. The output of this process is new suggestions to improve the user experience. Specific actions include the server dynamically changing the interface based on the results of the emotion analysis.

[0689] Step 5:

[0690] The server monitors project progress and updates all relevant data in real time. The input is the latest progress-related information retrieved from the database. Based on this data, the server generates progress reports and sends them to stakeholders' terminals. This output provides visibility into the project's current status, allowing for quick adjustments. Specific operations include periodically crawling the database to retrieve the latest information.

[0691] (Application Example 2)

[0692] 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".

[0693] Traditional systems often failed to provide personalized recommendations based on individual preferences and emotional states when users selected products or services, leading to decreased satisfaction. Furthermore, there was a lack of effective ways to mitigate the confusion and dissatisfaction users experienced when the suggested options were inappropriate, highlighting the need for improved user experience. Therefore, a new system was needed that could provide more appropriate recommendations based on user emotions and efficiently present alternatives tailored to individual needs.

[0694] 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.

[0695] This invention includes a server equipped with a database for aggregating and storing information, a means for centrally managing work information received from various departments within the organization, a means for selecting and proposing appropriate departments based on detailed information of projects to be tested using generative AI, and a means for analyzing the user's emotions using visual and audio sensors and dynamically adjusting the proposed content using an emotion engine. This makes it possible to provide more appropriate suggestions that are attentive to the user's emotions, thereby improving the user experience.

[0696] A "database" is a structured data storage system that aggregates information and centrally manages work information received from various departments within an organization.

[0697] "Generative AI" refers to an algorithm or system that uses artificial intelligence technology to select and propose appropriate fields based on detailed project information.

[0698] An "emotion engine" is a technology or function that uses data obtained from visual and audio sensors to analyze the user's emotions and dynamically adjust the suggested content.

[0699] A "visual sensor" is a camera or related device used to detect a user's facial expressions and analyze that information.

[0700] A "voice sensor" is a microphone or related device used to analyze the tone and patterns of a user's voice.

[0701] "User experience" is a concept that refers to the overall feeling and satisfaction that users experience when using a system or service.

[0702] The system implementing this invention is comprised of a database for aggregating and storing information, a generative AI, and an emotion engine. The server first aggregates work information received from various departments within the organization into the database. This database plays a central role in managing all the information necessary for a project by structuring and managing the information.

[0703] When a user provides detailed information about a new project, the server uses generative AI to select the appropriate field and propose it to the user. The generative AI analyzes past project history and related data to present the most suitable selection. At this stage, the models used by the generative AI may include, for example, open-source AI models or cloud-based AI platforms.

[0704] Furthermore, the user terminal uses a visual sensor (camera) and an audio sensor (microphone) to analyze the user's emotional state in real time. The emotion engine uses this data to identify the emotions expressed by the user, and based on the results, the server dynamically adjusts the suggested content. Specifically, the generating AI optimizes the suggested product and service information so that it best matches the user's current emotions.

[0705] For example, if a user shows interest in a product, the generating AI will create prompts suggesting related accessories or product sets. A prompt like this might be: "If the user is smiling at their smartphone camera, display related products and suggest information that will make them smile even more."

[0706] This enhances interaction between the server and the user, leading to an improved user experience.

[0707] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0708] Step 1:

[0709] The server aggregates work information collected from various departments within the organization into a database. The input is work information data from each department, and the output is a centrally managed database. The database stores information in a structured, easily accessible, and manageable format. The server efficiently organizes this data and prepares it for queries.

[0710] Step 2:

[0711] The user enters project details. The input is the project details, and the output is the input data for the generative AI. The terminal sends this data to the server and converts it into a formatted format for analysis by the generative AI.

[0712] Step 3:

[0713] The server uses generative AI to select appropriate fields based on the input project details. The input consists of formatted project information and historical project data, and the output is a list of selected fields. The server runs the generative AI model and performs data analysis to identify the most suitable field.

[0714] Step 4:

[0715] The user terminal uses a visual sensor (camera) and an audio sensor (microphone) to analyze the user's emotions. The input is sensor data, and the output is the emotion analysis result. The terminal uses an emotion engine to analyze facial expressions and tone of voice to identify the user's emotional state.

[0716] Step 5:

[0717] The server dynamically adjusts the suggested content based on the analysis results of the emotion engine. The input is the emotion analysis result, and the output is the adjusted suggested content. The server uses a generative AI model to generate suggested content optimized for the user's emotions.

[0718] Step 6:

[0719] The user receives the adjusted suggestions and makes an appropriate decision. The input is the adjusted suggestions, and the output is the result of the decision. The terminal displays the suggested information and provides an interface to help the user make an appropriate choice.

[0720] 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.

[0721] 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.

[0722] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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."

[0729] 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.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0741] The following is further disclosed regarding the embodiments described above.

[0742] (Claim 1)

[0743] It includes a database for aggregating and storing information, and a means for centrally managing business information received from each department within the corporate group,

[0744] A method for selecting and proposing the appropriate department based on detailed information of a project that requires hypothesis testing using generative AI,

[0745] A means of presenting information on proposed departments to the user and promoting inter-departmental collaboration according to the user's selection,

[0746] A means to monitor project progress and update information in real time,

[0747] A system that includes this.

[0748] (Claim 2)

[0749] The system according to claim 1, comprising a generating AI that analyzes the history of past projects and provides means for improving the accuracy of proposals.

[0750] (Claim 3)

[0751] The system according to claim 1, comprising means for tagging and linking highly relevant data based on input information from each department.

[0752] "Example 1"

[0753] (Claim 1)

[0754] It includes a data storage device that aggregates and stores information, and a means of centrally managing business information received from each department within the organization,

[0755] A method for selecting and proposing appropriate departments based on detailed information of a plan to be tested using generative AI,

[0756] A means of presenting information on proposed departments to users and promoting inter-departmental cooperation according to user selection,

[0757] A means to monitor the progress of the plan and update information in real time,

[0758] A means by which a generating AI processes and analyzes planning information in the form of prompt sentences,

[0759] A system that includes this.

[0760] (Claim 2)

[0761] The system according to claim 1, comprising a generating AI that analyzes the history of past plans and means for improving the accuracy of proposals.

[0762] (Claim 3)

[0763] The system according to claim 1, comprising means for tagging and linking highly relevant data based on input information from each department.

[0764] "Application Example 1"

[0765] (Claim 1)

[0766] It includes a database for aggregating and storing information, and a means for centrally managing business information received from each department within the organization.

[0767] A method for selecting and proposing the appropriate department based on detailed information of a project that wants to test hypotheses using generative artificial intelligence,

[0768] A means of presenting information on proposed departments to users and promoting inter-departmental collaboration according to user selection,

[0769] A means to monitor project progress and update information immediately,

[0770] A means of analyzing visual data acquired using visual devices, selecting the appropriate department through generated artificial intelligence, and immediately notifying the relevant department,

[0771] A system that includes this.

[0772] (Claim 2)

[0773] The system according to claim 1, wherein the generating artificial intelligence includes means for analyzing the history of past projects and improving the accuracy of the proposals.

[0774] (Claim 3)

[0775] The system according to claim 1, comprising means for tagging and linking highly relevant data based on input information from each department.

[0776] "Example 2 of combining an emotion engine"

[0777] (Claim 1)

[0778] It includes an information management system for aggregating and storing information, and a means for centrally managing business information received from each department within the organization,

[0779] A method for selecting and proposing the appropriate department based on detailed information of the task to be tested using generative AI,

[0780] A means of presenting information on proposed departments to the operator and promoting inter-departmental collaboration according to the operator's selection,

[0781] A means to monitor the progress of the work and update information in real time,

[0782] A means of analyzing the operator's emotions using an emotion analysis engine and dynamically adjusting the suggested content and user interface based on the results,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, wherein the generating AI has means for analyzing the history of past work to improve the accuracy of suggestions, and further the generating AI takes into account the results of an emotion analysis engine.

[0786] (Claim 3)

[0787] The system according to claim 1, which, in addition to tagging and linking highly relevant data based on input information from each department, also includes means for deriving collaboration promotion measures based on sentiment analysis results.

[0788] "Application example 2 of combining emotional engines"

[0789] (Claim 1)

[0790] It includes a database for aggregating and storing information, and a means for centrally managing work information received from various departments within the organization,

[0791] A method for selecting and proposing appropriate fields based on detailed information of a project that wants to test hypotheses using generative AI,

[0792] A means of presenting information on proposed fields to users and promoting inter-field collaboration according to user selection,

[0793] A means to monitor project progress and update information in real time,

[0794] A means of analyzing the user's emotions using visual and audio sensors, and dynamically adjusting the suggested content using an emotion engine,

[0795] A means of presenting alternative solutions through a generative AI based on the user's emotional state,

[0796] A system that includes this.

[0797] (Claim 2)

[0798] The system according to claim 1, comprising a generating AI that analyzes the history of past projects and means for improving the accuracy of proposals.

[0799] (Claim 3)

[0800] The system according to claim 1, comprising means for tagging and linking highly relevant information based on input information from each field. [Explanation of Symbols]

[0801] 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. It includes a database for aggregating and storing information, and a means for centrally managing business information received from each department within the corporate group, A method for selecting and proposing the appropriate department based on detailed information of a project that requires hypothesis testing using generative AI, A means of presenting information on proposed departments to the user and promoting inter-departmental collaboration according to the user's selection, A means to monitor project progress and update information in real time, A system that includes this.

2. The system according to claim 1, comprising a means for the generating AI to analyze the history of past projects and improve the accuracy of the proposals.

3. The system according to claim 1, comprising means for tagging and linking highly relevant data based on input information from each department.

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