system

Abstract

Provide a system. 【Solution means】 Means for collecting case data from each department, Means for sorting and storing the collected case data in a database, Means for receiving a search query input by a user, Means for extracting related cases from the database based on the search query, Means for analyzing the synergy between the extracted cases and the own case and generating proposals, Means for displaying the analysis and proposal results to the user, Means for receiving feedback from the user and improving the search algorithm, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] As the enterprise scale grows, the projects being carried out by each department and the adjacent sections become unclear, resulting in missing opportunities to create good synergy through coordination with one's own projects or causing the emergence of similar services. As a result, problems such as a decline in the efficiency and creativity of the entire enterprise occur. In particular, such problems are prominent in large enterprises and improvement is required.

Means for Solving the Problems

[0005] To address this challenge, the system provides means for collecting, organizing, and storing case data from each department in a database; receiving search queries entered by users and extracting relevant cases from the database based on those queries; analyzing the synergy between the extracted cases and the current case and generating proposals; displaying the analysis and proposal results to the user; and receiving user feedback to improve the search algorithm. Furthermore, it calculates a relevance score and sorts the search results in descending order of relevance, enabling users to efficiently find relevant cases. It also includes a means for creating concrete collaboration proposals based on the cases selected by the user and sharing them within the company, thereby strengthening collaboration within the organization. This system facilitates the coordination of measures across the entire company, reducing redundant work and creating efficient synergies.

[0006] "Project data" refers to a collection of information about projects and tasks that are currently underway or planned by each department or division.

[0007] "Means of collection" refers to the methods and systems used to gather case data from each department or division.

[0008] "Means of organizing and storing in a database" refers to methods or systems for organizing collected case data according to certain rules and storing it in a database.

[0009] A "search query" is a keyword or phrase that a user enters to identify information or areas of interest.

[0010] "Means of receiving" refers to the methods and systems used to input search queries entered by users into the system.

[0011] "Extraction method" refers to a method or system for selecting case data from a database that matches or is related to a search query.

[0012] "Synergy" refers to the synergistic effect that arises from the cooperation of different projects or matters.

[0013] "Means for analysis and proposal generation" refers to methods and systems for evaluating synergies and creating specific proposals based on extracted case data.

[0014] "Means of display" refers to methods or systems for visually showing the results of the analysis and proposals to the user.

[0015] "Feedback" refers to users communicating their evaluations and opinions on suggestions and search results to the system.

[0016] "Means of improvement" refer to methods or systems that improve the performance and accuracy of a system or algorithm based on the feedback received.

[0017] The "relevance score" is a numerical criterion used to evaluate the relevance of case data in search results.

[0018] "Sort" means adjusting the order of search results based on their relevance score.

[0019] "Means for creating and sharing collaboration proposals within the company" refers to methods and systems for proposing specific ways of collaboration based on a project selected by the user, and sharing those proposals with other departments and individuals within the company. [Brief explanation of the drawing]

[0020] [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 the data processing device and 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]It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It 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 an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0021] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0022] First, the language used in the following description will be explained.

[0023] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0024] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] 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.

[0026] 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).

[0027] 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."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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".

[0041] The system of this invention collects project data from various departments within a company and analyzes and proposes synergies based on this data, thereby improving the overall efficiency and creativity of the company. The program's processing flow and a specific example are described below.

[0042] Collection and organization of project data

[0043] The server collects project data from each department. Specifically, it exposes APIs and data entry forms on the server, allowing each department to input project names, responsible departments, progress status, and technical / service-related information.

[0044] The server stores the collected case data in a database. The collected data is automatically organized and standardized according to attributes such as project name, responsible department, and progress status.

[0045] Enter your search query

[0046] Users utilize an interface to search for information related to their project. This interface provides keyword input fields and filter options.

[0047] The terminal sends the search query entered by the user to the server. This query may include keywords such as "new product," "market research," and "technology development."

[0048] Extraction of similar cases

[0049] The server searches the database for relevant cases based on the search query. First, it extracts case data that matches or is similar to the project name or technical / service attributes.

[0050] The extracted case data is then given a relevance score. The server sorts the cases in descending order of relevance based on this score.

[0051] Synergy proposal

[0052] The server analyzes the synergy between each search result and the user's existing projects. Using a synergy analysis algorithm, it identifies resources and information that can be shared among the extracted projects and evaluates their synergistic effects.

[0053] The server generates specific synergy proposals based on the analysis results. For example, it might generate proposals such as "utilize market research data in sales strategies" or "share progress in technology development and adjust sales timing."

[0054] Results display and feedback

[0055] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, making it easy for the user to understand.

[0056] Users can select projects of interest based on the displayed results and review detailed information and proposals. Furthermore, they can create specific collaboration plans based on the selected projects and share them with relevant parties within their company.

[0057] Users send feedback on search results and suggestions to the server via their devices. This feedback may include comments such as "The suggestion was helpful" or "It was not very relevant."

[0058] The server uses the received feedback to improve the accuracy of its search algorithms and synergy analysis algorithms.

[0059] Specific example

[0060] For example, suppose data on "market research for new product A" is collected from the marketing department and data on "technical development for new product A" is collected from the development department and stored on a server. This data is organized by attributes such as project name, responsible department, progress status, and technology / service, and stored in a database.

[0061] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to search for information related to their project, "Sales Strategy for New Product A."

[0062] Based on this, the server extracts projects such as "Market research for new product A" and "Technical development for new product A" from the database and determines that they have a high relevance to the user's project.

[0063] The server generates synergy proposals such as "utilizing market research data in sales strategies" and "sharing progress in technology development to adjust sales timing," and displays them to the user via the terminal.

[0064] Users review the suggestions, propose specific ways to collaborate with the marketing and development departments, and share them internally. Finally, they provide feedback to the server indicating that the suggestions were helpful, thereby improving the overall accuracy of the system.

[0065] The following describes the processing flow.

[0066] Step 1:

[0067] The server collects project data from each department. Specifically, each department uses APIs and data entry forms published on the server to input project names, responsible departments, progress status, and technical / service-related information.

[0068] Step 2:

[0069] The server stores the collected case data in a database. The data is automatically organized and standardized by attributes such as project name, responsible department, and progress status. It also checks for duplicate data and optimizes the data format.

[0070] Step 3:

[0071] Users utilize an interface to search for information related to their project. This interface provides keyword input fields and filter options.

[0072] Step 4:

[0073] The terminal sends the search query entered by the user to the server. This query may include keywords such as "new product," "market research," and "technology development."

[0074] Step 5:

[0075] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to the project name or technical / service attributes.

[0076] Step 6:

[0077] The server calculates a relevance score for the extracted case data. Based on the relevance score, the search results are sorted in descending order of relevance.

[0078] Step 7:

[0079] The server analyzes the synergy between each search result and the user's existing projects. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted projects and evaluates their synergistic effects.

[0080] Step 8:

[0081] The server generates specific synergy proposals based on the analysis results. These proposals include concrete examples such as "utilizing market research data in sales strategies" and "sharing progress in technology development to coordinate sales timing."

[0082] Step 9:

[0083] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, presented in a user-friendly format.

[0084] Step 10:

[0085] Based on the displayed results, users select projects of interest and review detailed information and proposals. They also create specific collaboration plans based on the selected projects and share them with relevant parties within their company.

[0086] Step 11:

[0087] Users send feedback on search results and suggestions to the server via their devices. This feedback includes ratings such as "the suggestion was helpful" or "it was not relevant."

[0088] Step 12:

[0089] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This continuously improves the overall performance of the system.

[0090] (Example 1)

[0091] 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."

[0092] There is a need for a system that can effectively collect and organize information on diverse projects underway in each department within a company, extract highly relevant information, and complement it to improve the overall efficiency and creativity of the company. However, current distributed information management systems have the problem of insufficient information sharing between departments, making it difficult to propose effective solutions that generate synergistic effects. This invention aims to solve this problem.

[0093] 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.

[0094] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by the user, means for extracting relevant cases from the database based on the search queries, means for calculating a relevance score for the extracted cases and sorting the cases based on relevance, means for analyzing the synergy between the extracted cases and the current cases and generating specific proposals, means for visually displaying the analysis and proposal results to the user, and means for receiving feedback from the user and improving the search algorithm and synergy analysis algorithm. This enables effective utilization of case data collected from each department, maximizing information sharing and synergy effects across the entire company.

[0095] "Project data" refers to information about projects carried out by each department within a company, and specifically includes data such as project name, responsible department, progress status, and information about technology and services.

[0096] A "server" is a computer system that collects, organizes, stores, retrieves, and analyzes data over a network.

[0097] A "database" is a data management system that efficiently stores collected case data and allows for searching and retrieving it as needed.

[0098] A "search query" refers to the keywords and conditions that a user enters when searching for information, and relevant data is retrieved based on these queries.

[0099] The "relevance score" is a numerical value calculated by the server to evaluate the relevance of the cases extracted from the database based on the search query. The relevance score indicates how well the cases match or are similar to the search query.

[0100] "Synergy" refers to the synergistic effect that arises from the mutual complementarity of multiple projects, and signifies the improvement in value resulting from the sharing of resources and information.

[0101] An "analysis algorithm" is a computational method used by a server to analyze collected data and extract useful information and patterns.

[0102] A "proposal" is a specific action plan or improvement measure generated by an analytical algorithm, aimed at improving the overall efficiency and creativity of the company.

[0103] A "user interface" is a collection of screens and input tools that a user uses to operate a system, providing means for performing searches and providing feedback.

[0104] "Feedback" refers to the act of users sending evaluations and opinions about search results and suggestions to the server. This feedback is used to improve the system's performance.

[0105] Modes for carrying out the invention

[0106] The system of the present invention collects project data from various departments within a company and analyzes and proposes synergies based on this data, thereby improving the overall efficiency and creativity of the company. The following describes specific embodiments of the system.

[0107] Collection and organization of project data

[0108] The server exposes APIs and data entry forms to collect project data from each department. This process is implemented using a RESTful API built, for example, with the Django framework. Each department enters project name, responsible department, progress status, and technical / service information.

[0109] The collected data is stored in a database such as PostgreSQL or MySQL®. The server organizes the received data by attributes such as project name, department in charge, and progress status, and automatically performs a standardization process. This ensures that data collected from each department is stored in a consistent format.

[0110] Enter your search query

[0111] The terminal provides an interface for users to search for information related to a project. This interface, built with React and Vue.js, includes keyword input fields and filter options. Users enter keywords such as "new product," "market research," and "technology development" through the interface.

[0112] The entered search query is sent from the terminal to the server. The server searches the database for relevant cases based on the received query.

[0113] Extraction of similar cases

[0114] The server uses a search engine such as ElasticSearch® to retrieve relevant cases from the database based on the received search query. It extracts case data that matches or is similar to project names or technical / service attributes.

[0115] A relevance score is calculated for the extracted case data. The TF-IDF algorithm is used to assign a score to each case. The server then sorts the cases in descending order of relevance based on this score.

[0116] Synergy proposal

[0117] The server performs synergy analysis on the case data obtained from the search results. It uses machine learning libraries such as Scikit-learn and TENSORFLOW® to perform the analysis, identify shareable resources and information, and evaluate the synergistic effects.

[0118] Based on the analysis results, specific synergy proposals are generated. For example, these may include specific suggestions such as "utilizing market research data in sales strategies" and "sharing the progress of technology development to adjust sales timing."

[0119] Results display and feedback

[0120] The device visually displays search results and synergy suggestions to the user. Using React and D3.js, it generates a dashboard showing relevance scores and synergy details. The user reviews the displayed results and selects projects of interest. They then review the details and suggestions for the selected projects and create specific collaboration plans.

[0121] Users share collaboration proposals with relevant parties within their company and send feedback on search results and proposals to the server via their devices. The server uses this feedback to improve the accuracy of its search algorithms and synergy analysis algorithms.

[0122] Specific example

[0123] For example, suppose the marketing department submits market research data for new product A to the server, and at the same time, the development department provides technical development data for new product A. This data is collected and organized on the server and consistently stored in the database.

[0124] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development," and sends a query on their terminal. The server searches the database for related cases using Elasticsearch, calculates a relevance score, and sorts the cases.

[0125] For example, the search results might include "market research for new product A" and "technological development for new product A," and the server determines that these are highly relevant to the user's project. Subsequently, it generates specific synergy proposals such as "utilize market research data in sales strategy" and "share the progress of technological development to adjust the sales timing," and presents them to the user via the terminal.

[0126] Users review these suggestions, develop specific collaboration plans with the marketing and development departments, and share them internally. By providing feedback to the server about the helpfulness of the suggestions, the system's accuracy can be continuously improved.

[0127] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0128] Step 1:

[0129] The server exposes APIs and data entry forms to collect project data from each department. A RESTful API built with the Django framework is used for this purpose. Each department enters project name, responsible department, progress status, and technical / service-related information. The entered data is sent to the server via the API.

[0130] Input: Project data entered by each department (project name, responsible department, progress status, and technical / service information).

[0131] Output: Case data sent to the server via API.

[0132] Specific operation: The marketing department inputs market research data for new product A and sends it to the server via API.

[0133] Step 2:

[0134] The server stores received project data in a PostgreSQL or MySQL database. The received data is organized by attributes such as project name, department, and progress status, and automatically standardized. For example, if a field contains missing data, it is converted to a consistent format.

[0135] Input: Case data sent to the server via API.

[0136] Output: Case data organized and stored in the database.

[0137] Specific operation: The server saves the received data to the database and automatically aligns fields such as project name and department.

[0138] Step 3:

[0139] The terminal provides an interface for users to search for information related to a project. This interface is implemented using React or Vue.js. Through this interface, users input keywords such as "new product," "market research," and "technology development" to create search queries.

[0140] Input: The search query entered by the user (e.g., "new product", "market research", "technology development").

[0141] Output: Search queries sent from the terminal to the server.

[0142] Specific operation: A user in the sales department enters keywords to obtain information about new product A, and the terminal sends this query to the server.

[0143] Step 4:

[0144] The server searches the database for relevant cases based on the received search query. A search engine such as Elasticsearch is used for this process. Case data that matches or is similar to project names and technical / service attributes is extracted.

[0145] Input: Search query sent from the device.

[0146] Output: A list of case data that matches or is similar to the search query.

[0147] Specific operation: The server uses Elasticsearch to search the database and extract relevant case data.

[0148] Step 5:

[0149] The server calculates a relevance score for the extracted case data. Using the TF-IDF algorithm, scores are assigned based on project name and technical / service attributes. This allows for a relevance assessment for each case.

[0150] Input: A list of case data that matches or is similar to the search query.

[0151] Output: A list of case data assigned a relevance score.

[0152] Specific operation: The server calculates a relevance score for each case data and sorts them in descending order of relevance.

[0153] Step 6:

[0154] The server performs synergy analysis on the case data obtained from the search results. Using Scikit-learn and TensorFlow, it identifies shareable resources and information and evaluates synergistic effects. It generates specific synergy proposals, such as "utilizing market research data in sales strategies."

[0155] Input: A list of case data assigned a relevance score.

[0156] Output: A list of project data to which synergy proposals have been assigned.

[0157] Specific operation: The server uses machine learning algorithms to analyze shared resources and information and generate synergy suggestions.

[0158] Step 7:

[0159] The device visually displays search results and synergy suggestions to the user. Using React and D3.js, it generates a dashboard showing relevance scores and synergy details. The user reviews the displayed results and selects the deals that interest them.

[0160] Input: A list of project data to which synergy proposals have been assigned.

[0161] Output: A visual dashboard displayed on the user's screen.

[0162] Specific operation: The device generates a dashboard, and the user reviews the suggested content.

[0163] Step 8:

[0164] Users select projects of interest based on the displayed results and review detailed information and proposals. They then create specific collaboration plans based on their selections and share them with relevant parties within their company. Furthermore, they send feedback on the search results and proposals to the server via their device.

[0165] Input: User selections and feedback (e.g., "The suggestion was helpful," "It was not relevant").

[0166] Output: Feedback data sent to the server.

[0167] Specific operation: The user creates a collaboration proposal and sends feedback to the server.

[0168] Step 9:

[0169] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This feedback is used as a dataset for the algorithms and is utilized to update the deep learning models.

[0170] Input: User feedback data.

[0171] Output: Improved search algorithm and synergy analysis algorithm.

[0172] Specific operation: The server analyzes the feedback data and improves accuracy by updating the existing algorithm.

[0173] (Application Example 1)

[0174] 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."

[0175] In the current factory, multiple projects are running simultaneously, and each project is managed independently, making resource sharing and progress coordination difficult. This often leads to decreased efficiency and wasted resources. Furthermore, it is difficult for on-site workers and managers to access the latest project information, which can delay immediate responses. The challenge is to solve these problems and improve the overall efficiency and creativity of the factory.

[0176] 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.

[0177] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by users, means for extracting relevant cases from the database based on the search queries, means for analyzing the synergy between the extracted cases and the current case and generating proposals, means for displaying the analysis and proposal results to the user, means for receiving feedback from users and improving the search algorithm, means for collecting and analyzing data for each project within the factory in real time and proposing synergies for resource sharing and progress adjustment between projects, and means for providing an interface that can be easily accessed by on-site workers and managers using smartphones. This enables efficient resource sharing and progress adjustment between projects within the factory, and further promotes immediate response on-site.

[0178] "Project data" refers to project names, responsible departments, progress status, and technical / service-related information collected from each department.

[0179] A "database" is an information system that organizes collected case data and makes it possible to store and search it efficiently.

[0180] A "search query" is a combination of keywords and filter options that a user enters to identify relevant information.

[0181] "Synergy" refers to the synergistic effect that arises from sharing resources and information among multiple projects.

[0182] A "search algorithm" is a computational method used to identify relevant items from a database based on a user's search query.

[0183] "Real-time collection" refers to the process of collecting data the moment it is generated and sending it to the server.

[0184] "Resource sharing" refers to the sharing of resources such as personnel, equipment, and information among multiple projects.

[0185] "Progress adjustment" involves evaluating the progress of a project and making necessary revisions to the plan or changes to resource allocation.

[0186] An "interface" is a visual or mechanical means by which a user accesses and operates a system.

[0187] "Feedback" refers to opinions and data from users, based on their experiences and suggestions, that are used to improve the system's functions and algorithms.

[0188] System program generation

[0189] The system for realizing this invention primarily involves three roles: server, terminal, and user. The following describes each role and function in natural language.

[0190] Server roles and functions

[0191] The server collects project data from each department, organizes it, and stores it in a database. In this case, the server collects project names, responsible departments, progress status, and technical / service information provided by each department using APIs or data entry forms. The collected data is automatically organized and stored in the database.

[0192] When a user enters a search query, the server searches the database for relevant cases based on that query. The retrieved cases are sorted based on their relevance score and displayed to the user. Furthermore, a synergy analysis algorithm is used to generate synergy suggestions for resource sharing and progress coordination between related cases.

[0193] Furthermore, we receive feedback from users and use it to improve our search algorithms and synergy analysis algorithms. This allows us to continuously improve the accuracy and efficiency of the system.

[0194] Terminal roles and functions

[0195] The terminal provides an interface for sending search queries entered by the user to the server. The hardware used here includes smartphones. For example, when a user enters keywords such as "new product," "market research," or "technology development" using the terminal, that query is sent to the server.

[0196] The terminal also plays a role in visually displaying search results and synergy suggestions received from the server to the user. The displayed information includes relevance scores and synergy details, making it easy for the user to understand and interact with.

[0197] User roles and functions

[0198] Users enter search queries through their terminals and evaluate the search results and synergy suggestions from the server. Based on the displayed results, users select projects of interest and review detailed information and proposals. If there are specific proposals, they create and share collaborative plans within the company based on them. Users also send feedback on the search results and synergy suggestions to the server through their terminals, thereby contributing to system improvement.

[0199] Hardware and software to be used

[0200] Hardware: Servers (any brand name), smartphones

[0201] Software: Flask (Python), requests library, database (e.g., PostgreSQL)

[0202] Specific example

[0203] For example, suppose the marketing department collects data on "market research for new product A," and the development department collects data on "technological development for new product A." This data is organized and stored by a server. When a user in the sales department enters a query such as "new product A," "market research," and "technological development," the server extracts relevant cases based on this and sorts them based on their relevance score. The server then generates synergy suggestions such as "utilize market research data in sales strategy" and "share the progress of technological development to adjust the sales timing," and displays them to the user through their terminal.

[0204] Example of a prompt

[0205] "Based on the data from the new technology, please propose how it can have synergistic effects on improvement projects for existing products."

[0206] In this way, efficient resource sharing and progress adjustment within the factory become possible, and immediate responses on-site are facilitated.

[0207] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0208] Step 1:

[0209] The server collects project data from each department. Specifically, each department sends project name, responsible department, progress status, and technical / service-related information to the server via APIs or data entry forms. This allows for the input of project data. The entered data is then temporarily stored in a database.

[0210] Step 2:

[0211] The server organizes and standardizes the collected case data. Specifically, it classifies the collected data by attributes such as project name, responsible department, and progress status, and converts it into a standard format. This allows the organized case data to be stored in the database.

[0212] Step 3:

[0213] The terminal receives the search query entered by the user and sends it to the server. The search query entered by the user may include keywords such as "new product," "market research," or "technology development." Based on the input, the terminal sends the search query. The terminal then sends the received query to the server.

[0214] Step 4:

[0215] The server searches the database for relevant cases based on the search query. It matches case data in the database based on keywords in the search query. Case data that matches or is similar to the attributes of the entered query is extracted.

[0216] Step 5:

[0217] The server calculates a relevance score for the extracted case data and sorts the cases in descending order of relevance. Specifically, a similarity calculation algorithm is used to calculate a score for each extracted case data. As a result, the cases are listed in descending order of relevance.

[0218] Step 6:

[0219] The server analyzes the synergies with the user's project and generates synergy proposals. The analysis identifies shareable resources and information, and evaluates their synergistic effects. For example, it generates specific proposals such as "utilize market research data in sales strategies" or "share progress in technology development to adjust sales timing."

[0220] Step 7:

[0221] The device visually displays search results and synergy suggestions received from the server to the user. Relevance scores and synergy details are clearly displayed for easy user understanding. Based on this information, the user selects projects of interest and checks detailed information and suggestions.

[0222] Step 8:

[0223] Users provide feedback on the displayed search results and synergy suggestions. Users input their opinions and impressions of the suggestions using a feedback interface. This feedback is sent to the server via the device.

[0224] Step 9:

[0225] The server improves its search algorithms and synergy analysis algorithms based on user feedback. The feedback is analyzed, and adjustments are made to further improve the accuracy of the search algorithms and synergy suggestions. This continuously improves the overall efficiency and accuracy of the system.

[0226] 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.

[0227] The system of this invention aims to improve the user experience not only by analyzing and proposing synergies based on project data collected from various departments within a company, but also by combining it with an emotion engine that recognizes user emotions. The program's processing flow and a specific example are described below.

[0228] Collection and organization of project data

[0229] The server exposes APIs and data entry forms to collect project data from each department, obtaining information such as project name, responsible department, progress status, and technical / service-related details.

[0230] The server stores the collected case data in a database. The data is automatically organized and standardized by attributes such as project name, responsible department, and progress status. Duplicate data checks and data format optimization are also performed.

[0231] Enter your search query

[0232] Users utilize an interface to search for information related to their project. This interface provides keyword input fields and filter options.

[0233] The terminal sends the search query entered by the user to the server. The query may include keywords such as "new product," "market research," or "technology development."

[0234] Extraction of similar cases

[0235] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to project names and technical / service attributes, and calculates a relevance score. Based on this, it sorts the search results in descending order of relevance.

[0236] Synergy proposal

[0237] The server analyzes the synergy between each case found through the search and the user's existing cases. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted cases and evaluates their synergistic effects.

[0238] The server generates specific synergy proposals based on the analysis results. These proposals may include suggestions such as "utilizing market research data in sales strategies" or "sharing progress in technology development and coordinating sales timings."

[0239] Adjustment by the emotion engine

[0240] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input data and behavioral data to identify the user's emotional state.

[0241] Based on the user's emotional state, the server adjusts search results and synergy suggestions. For example, if a user is stressed, it provides more concise and intuitive suggestions. It also modifies the interface design and presentation methods according to the user's emotions to improve the experience.

[0242] Results display and feedback

[0243] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, presented in a user-friendly format. If adjustments have been made by the sentiment engine, those adjustments are also reflected.

[0244] Users select projects of interest based on the displayed results and review detailed information and proposals. They can also create specific collaboration plans based on their selected projects and share them with relevant parties within their company.

[0245] Users send feedback on search results and suggestions to the server via their devices. This feedback includes ratings such as "the suggestion was helpful" or "it was not relevant."

[0246] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This continuously improves the overall performance of the system.

[0247] Specific example

[0248] For example, suppose data on "market research for new product A" is collected from the marketing department and data on "technical development for new product A" is collected from the development department and stored on a server. This data is organized by attributes such as project name, responsible department, progress status, and technology / service, and stored in a database.

[0249] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to search for information related to the "Sales Strategy for New Product A." The server searches the database for relevant cases based on these keywords, calculates a relevance score, and organizes the results.

[0250] Furthermore, the emotion engine analyzes the user's emotional state, and if it detects that the user is experiencing stress, the server displays more concise and easy-to-understand suggestions. For example, it might display something like, "Check the market research data summary and use it in your sales strategy."

[0251] Users review the suggestions, propose specific ways to collaborate with marketing and development departments, and share them internally. Finally, they can improve the overall accuracy of the system by providing feedback to the server on whether the suggestions were helpful.

[0252] The following describes the processing flow.

[0253] Step 1:

[0254] The server collects project data from each department. Specifically, each department uses APIs and data entry forms published on the server to input project names, responsible departments, progress status, and technical / service-related information.

[0255] Step 2:

[0256] The server stores the collected case data in a database. The data is automatically organized and standardized by attributes such as project name, responsible department, and progress status. It also checks for duplicate data and optimizes the data format.

[0257] Step 3:

[0258] Users utilize an interface to search for information related to their project. This interface provides a keyword input field and filter options.

[0259] Step 4:

[0260] The terminal sends the search query entered by the user to the server. This query may include keywords such as "new product," "market research," and "technology development."

[0261] Step 5:

[0262] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to the project name or technical / service attributes.

[0263] Step 6:

[0264] The server calculates a relevance score for the extracted case data. Based on the relevance score, the search results are sorted in descending order of relevance.

[0265] Step 7:

[0266] The server analyzes the synergy between each case found through the search and the user's existing cases. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted cases and evaluates the synergistic effects.

[0267] Step 8:

[0268] The server generates specific synergy proposals based on the analysis results. These proposals may include suggestions such as "utilizing market research data in sales strategies" or "sharing progress in technology development and coordinating sales timings."

[0269] Step 9:

[0270] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input data and behavioral data to identify the user's emotional state.

[0271] Step 10:

[0272] The server adjusts search results and synergy suggestions based on the user's emotional state. For example, if the user is stressed, it will provide more concise and intuitive suggestions. It also changes the interface design and display method according to the user's emotions to improve the user experience.

[0273] Step 11:

[0274] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, presented in a user-friendly format. If adjustments have been made by the sentiment engine, those adjustments are also reflected.

[0275] Step 12:

[0276] Based on the displayed results, users select projects of interest and review detailed information and proposals. They also create specific collaboration plans based on the selected projects and share them with relevant parties within their company.

[0277] Step 13:

[0278] Users send feedback on search results and suggestions to the server via their devices. This feedback includes ratings such as "the suggestion was helpful" or "it was not relevant."

[0279] Step 14:

[0280] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This continuously improves the overall performance of the system.

[0281] Specific Example

[0282] For example, data on "Market Research of New Product A" from the marketing department and data on "Technical Development of New Product A" from the development department are collected on the server, sorted by project name, responsible department, progress status, and attributes of technology and services, and stored in the database.

[0283] A user in the sales department enters keywords such as "New Product A", "Market Research", and "Technical Development" to search for information related to the "Sales Strategy of New Product A". Based on this, the server searches the database for relevant cases, calculates a relevance score, and organizes the results.

[0284] Furthermore, when the emotion engine analyzes the user's emotional state and detects that the user is feeling stressed, the server displays a more concise and understandable suggestion. For example, it is displayed in the form of "Check the overview of market research data and utilize it in the sales strategy".

[0285] The user confirms the suggestion, specifically proposes a cooperation method with the marketing department and the development department, and shares it within the company. Finally, by providing feedback to the server on whether the suggestion was useful, the accuracy of the entire system can be improved.

[0286] (Example 2)

[0287] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0288] In a conventional system, it is difficult to unify the case data collected from each department and provide appropriate search results for search queries by users. Also, since the content of the proposed synergy is not necessarily optimized for the user's current emotional state, improving the user experience has been an issue.

[0289] 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.

[0290] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by the user, means for extracting relevant cases from the database based on the search queries, means for analyzing the synergy between the extracted cases and the current case and generating proposals, means for displaying the analysis and proposal results to the user, means for recognizing the user's emotions and adjusting the proposal content based on those emotions, and means for receiving feedback from the user and improving the search algorithm and synergy analysis algorithm. This enables the provision of optimal synergy proposals that take the user's emotions into consideration, thereby improving the user experience.

[0291] "Project data" refers to data collected from each department, including project names, responsible departments, progress status, and information regarding technology and services.

[0292] A "database" is a system or storage location that stores collected case data and manages it in a way that allows for access and searching.

[0293] A "search query" is input data used by a user to search for specific information, including keywords and filter options entered into the system.

[0294] "Synergy" refers to creating synergistic effects by utilizing resources and information that can be shared across multiple projects or issues.

[0295] An "emotion engine" is a program or system that analyzes user input data and behavioral data to identify the user's emotional state.

[0296] "Feedback" refers to the evaluations and opinions that users submit to the system regarding search results and suggestions.

[0297] The "relevance score" is a numerical representation of the relationship between a search query and the results in the database; the higher the relevance, the higher the score.

[0298] "Adjusting the suggested content" refers to changing the suggestions and information displayed by the system based on the user's emotional state recognized by the emotion engine.

[0299] An "algorithm" refers to a set of computational procedures or rules for solving a specific problem, and in this context, it includes search algorithms and synergy analysis algorithms.

[0300] The system of this invention not only has the function of analyzing and proposing synergies based on project data collected from various departments within a company, but also aims to improve the user experience by introducing an emotion engine that recognizes user emotions. The program's processing flow and specific examples are described in detail below.

[0301] Collection and organization of project data

[0302] The server exposes API endpoints and data entry forms for collecting project data from each department. Using the API, departments such as marketing and development can automatically retrieve "project name," "responsible department," "progress status," and "technical / service-related information." For example, the marketing department might provide data on "market research for new product A," and the development department might provide data on "technical development for new product A."

[0303] The collected project data is stored in a database (e.g., MySQL, PostgreSQL). The data is organized and standardized by attributes such as project name, responsible department, progress status, and technology / service. This step also includes checking for duplicate data and optimizing the data format. For example, if data with the same project name is sent from different departments, the server detects the duplication and merges the data.

[0304] Input of search query

[0305] The user uses the interface provided through the terminal to input a search query. The interface provides a keyword input field and filter options, and keywords such as "new product", "market research", and "technology development" are input. The terminal sends the input query to the server.

[0306] Extraction of similar cases

[0307] The server receives the input search query and searches the database. For example, market research data and technology development data related to "new product A" are extracted. Based on the keywords of the query, case data that matches or is similar to the related project name and the attributes of technologies and services is extracted, and a relevance score is calculated.

[0308] Synergy proposal

[0309] The server performs synergy analysis based on the extracted case data. Using a synergy analysis algorithm, resources and information that can be shared among cases are identified, and their synergistic effects are evaluated. Specific proposals include "utilize market research data for sales strategies" and "share the progress of technology development and adjust the sales timing".

[0310] Adjustment by emotion engine

[0311] The server uses an emotion engine (such as IBM Watson (registered trademark), Microsoft (registered trademark) Azure (registered trademark) Emotion API, etc.) to recognize the user's emotion. By analyzing the user's input data and behavior data, the user's emotional state is identified. For example, when the user is feeling stressed, the server displays a simpler and more understandable proposal. When the user is relaxed, detailed information is provided.

[0312] Results display and feedback

[0313] The device visually displays search results and synergy suggestions to the user. The results include relevance scores and synergy details, presented in a user-friendly format. The display format is designed, for example, to show highly relevant deals at the top, similar to a dashboard.

[0314] Furthermore, users select projects of interest based on the displayed results and review their details and proposals. They then create concrete collaboration plans based on the selected projects and share them with relevant parties within their company. They also use a feedback form to send evaluations of the search results and proposals to the server. These evaluations may include, for example, "The proposal was helpful" or "It was not very relevant." The server uses this feedback to improve the accuracy of its search algorithm and synergy analysis algorithm.

[0315] Specific example

[0316] For example, suppose market research data for new product A is collected from the marketing department, and technical development data for new product A is collected from the development department and stored on a server. This data is then organized and stored in a database.

[0317] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to obtain information related to the "Sales Strategy for New Product A." The server searches the database for relevant cases based on these keywords, calculates a relevance score, and organizes the results. The emotion engine analyzes the user's emotional state, and if it detects that the user is experiencing stress, the server displays more concise and easy-to-understand suggestions. For example, it might say, "Check the market research data summary and use it in your sales strategy."

[0318] Users review the suggestions, propose specific ways to collaborate with marketing and development departments, and share them internally through the system. Finally, they provide feedback to the server on whether the suggestions were helpful, which helps improve the overall accuracy of the system.

[0319] Example of a prompt

[0320] "We want to develop a sales strategy based on market research data for our new product. Could you tell us what kind of data is relevant?"

[0321] By using this prompt, the generated AI model extracts and analyzes project data from the marketing department and technical information from the development department, providing specific synergy proposals.

[0322] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0323] Step 1: Collecting case data

[0324] The server exposes an API endpoint and a data entry form, collecting the following information from each department: "Project Name," "Responsible Department," "Progress Status," and "Technical / Service Information." Based on this input, the server generates case data and stores it in the database. Specifically, it receives data from the API and converts it to the appropriate format.

[0325] Step 2: Organize and standardize case data

[0326] The server organizes the collected case data and stores it in the database. The input here is the case data collected in Step 1. The server organizes and standardizes the data by attributes such as project name, responsible department, and progress status. It checks for duplicate data and optimizes the data format, and outputs the standardized data. For example, if there are multiple data entries with the same project name, the duplicates are merged.

[0327] Step 3: Enter your search query

[0328] The user enters keywords and filter options into an interface provided through the terminal. These inputs include terms such as "new product," "market research," and "technology development." The terminal then sends this input to the server. Specifically, an HTTP request is generated to send the data from the input form to the server.

[0329] Step 4: Identifying Similar Cases

[0330] The server searches the database based on the search query received in step 3. The input is the user's search query, and the output is the relevant case data. The server extracts cases that match or are similar to the project name and technical / service attributes, and calculates a relevance score. An algorithm is executed to retrieve the data corresponding to the query and calculate the relevance of each.

[0331] Step 5: Propose synergies

[0332] The server performs synergy analysis based on the project data extracted in step 4. The input is the relevant project data, and the output is a synergy proposal. The server uses a synergy analysis algorithm to identify resources and information that can be shared between projects and evaluate the synergistic effects. For example, it generates a specific proposal such as "utilize market research data in sales strategies."

[0333] Step 6: Adjustment by the Emotional Engine

[0334] The server uses an emotion engine to analyze the user's emotional state. Input is user input data and behavioral data, and output is the emotion analysis result. Based on this emotion analysis, the server adjusts the search results and synergy suggestions. The emotion engine's algorithm detects the user's stress and relaxation levels and adjusts the complexity of the suggestions accordingly.

[0335] Step 7: Results display and feedback

[0336] The terminal visually displays search results and synergy suggestions to the user. The input is the adjusted synergy suggestions, and the output is the information presented to the user. Specifically, it has a mechanism that displays highly relevant cases at the top in a dashboard format. Based on the displayed results, the user selects cases of interest and checks their details and suggestions. After that, the user submits an evaluation to the server through a feedback form. The server, upon receiving this feedback, improves the accuracy of its search algorithm and synergy analysis algorithm.

[0337] (Application Example 2)

[0338] 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."

[0339] Traditional systems had problems with effectively utilizing interdepartmental synergies in production management within factories, and with being unable to make flexible suggestions that took into account the emotional state of workers. In particular, the collection and analysis of process data was insufficient, making it difficult to propose optimal resource sharing and cooperation plans to maximize production efficiency. Furthermore, there was a lack of means to provide appropriate support when on-site workers experienced stress, which posed a risk of a decline in overall work efficiency and experience.

[0340] 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.

[0341] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by the user, means for extracting relevant cases from the database based on the search queries, means for analyzing the synergy between the extracted cases and the current case and generating suggestions, means for displaying the analysis and suggestion results to the user, means for receiving feedback from the user and improving the search algorithm, means for recognizing the user's emotions using an emotion engine and adjusting the search results and suggestions based on the user's emotional state, and means for collecting data from each department in the factory and analyzing process data in the production line. This makes it possible to maximize the use of interdepartmental synergies in production management and to make flexible suggestions tailored to the emotional state of workers.

[0342] "Project data" refers to information collected from various departments within a company, including project names, responsible departments, progress status, and technical / service-related information.

[0343] A "database" is an information system that stores, organizes, and allows searching of collected case data.

[0344] A "search query" is a statement of inquiry that includes keywords and conditions entered by a user to obtain specific information.

[0345] "Synergy" refers to the synergistic effect that arises when multiple projects or pieces of information interact with each other.

[0346] An "emotion engine" is a system that recognizes and analyzes a user's emotional state.

[0347] A "production line" is a collection of processes that are carried out sequentially within a factory to produce a product.

[0348] The "relevance score" is a numerical representation of the relationship between a search query and the case data stored in the database.

[0349] "Feedback" is the process of receiving evaluations and opinions from users, and the system is improved based on this feedback.

[0350] "Resources" is a general term encompassing the personnel, equipment, materials, and information necessary to carry out production activities.

[0351] "API" stands for Application Programming Interface, and it is a set of rules for sharing functions and data between software programs.

[0352] The system of this invention collects, organizes, analyzes, and proposes data from each department to streamline production management within a factory. Furthermore, it aims to improve work efficiency and user experience by providing flexible suggestions based on the emotional state of workers using an emotion engine. The embodiments of this system will be described in detail below.

[0353] Data collection and organization

[0354] The server exposes APIs and data entry forms for collecting project data from each department. The collected data includes project name, responsible department, progress status, and technical / service information. The server stores the collected project data in a database (PostgreSQL), where the data is automatically organized and standardized. It also performs checks for duplicate data and optimizes data formats.

[0355] Enter your search query

[0356] Users can enter search queries from their terminal and send them to the server. This interface provides keyword input fields and filter options. For example, users can enter keywords such as "new material X," "processing method," and "manufacturing process." The terminal then sends the search queries entered by the user to the server.

[0357] Extraction of similar cases

[0358] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to project names and technical / service attributes, and calculates a relevance score using natural language processing techniques such as TF-IDF and Word2Vec. Based on this, it sorts the search results in descending order of relevance.

[0359] Synergy proposal

[0360] The server analyzes the synergy between each project found through the search and the user's projects. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted projects and evaluates their synergistic effects. Based on the analysis results, it generates specific synergy proposals. These proposals may include, for example, "sharing process data" and "optimizing resources."

[0361] Adjustment by the emotion engine

[0362] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's camera footage and input data to identify the user's emotional state. This analysis uses TensorFlow.js and a pre-trained emotion recognition model. Based on the emotional state, the server adjusts the search results and synergy suggestions. For example, if the user is stressed, it will provide more concise and intuitive suggestions.

[0363] Results display and feedback

[0364] The device visually displays search results and synergy suggestions to the user. React.js is used for display, providing relevance scores and synergy details in a user-friendly format. Based on the displayed results, the user selects projects of interest and reviews detailed information and suggestions. Users can also create specific collaboration proposals based on their selected projects and share them with internal stakeholders. Furthermore, users can send feedback on the suggestions to the server via the device, improving the overall accuracy of the system.

[0365] Specific example

[0366] Let's consider a scenario where a worker in the production department collects and analyzes data related to "processing methods for new material X." Based on the keywords entered by the worker, a search is performed, and relevant past case data is extracted. If the emotion engine recognizes the worker's stress, a simple procedural guide is displayed, along with suggestions for improving efficiency that incorporate insights from other departments. For example, a suggestion might be made to "check the overview of the processing procedure for new material X and confirm the optimal machine settings."

[0367] Example of a prompt

[0368] We want to investigate processing methods for the new material X. Based on progress data, analyze synergies with other departments and display a simple procedure guide and suggestions for efficiency improvements. If the user is experiencing stress, please use an emotion recognition system to present it in a particularly clear format.

[0369] This invention is expected to improve production efficiency within factories and significantly enhance the worker experience.

[0370] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0371] Step 1:

[0372] The server collects project data from each department. Specifically, it receives project names, responsible departments, progress status, and technical / service information through APIs and data entry forms, standardizes this data, and stores it in a database. The input data is in JSON format, and PostgreSQL is used as the database.

[0373] Step 2:

[0374] The server organizes the collected project data. It classifies the data in the database by attributes such as project name, responsible department, and progress status, checks for duplicate data, and optimizes the data format. It uses the Python pandas library to process and organize the data.

[0375] Step 3:

[0376] The user enters a search query through an interface on their device. The interface includes a keyword input field and filter options, allowing the user to enter keywords such as "new material X" or "processing method." The entered search query is then sent from the device to the server.

[0377] Step 4:

[0378] The server extracts relevant cases from the database based on the search query. It searches for highly relevant data using natural language processing techniques such as TF-IDF and Word2Vec, and calculates a relevance score. Based on the calculation results, the search results are sorted in descending order of relevance.

[0379] Step 5:

[0380] The server analyzes the synergies between extracted cases and its own cases and generates proposals. It uses a synergy analysis algorithm to evaluate resource sharing and the synergistic effects of information. The generated proposals include specific details regarding resource optimization and process sharing.

[0381] Step 6:

[0382] The server uses an emotion engine to recognize the user's emotions. It analyzes the user's video data and input data collected in real time by the device and identifies the emotional state using TensorFlow.js. A pre-trained emotion recognition model is used for the model.

[0383] Step 7:

[0384] The server adjusts search results and suggestions based on the user's emotional state. For example, if a user is stressed, it generates more concise and intuitive suggestions and adjusts the content accordingly. Specifically, it provides simple step-by-step guides and concrete measures to improve efficiency.

[0385] Step 8:

[0386] The device visually displays search results and synergy suggestions to the user. Using React.js, it provides an interface that allows users to see relevance scores and synergy details at a glance. Based on the displayed results, the user selects projects of interest and views their detailed information.

[0387] Step 9:

[0388] Users provide feedback on the suggested results. Evaluations and opinions from users are sent from their terminals to the server, which uses this information to improve the search algorithm and synergy analysis algorithm. This improves the overall system performance.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] [Second Embodiment]

[0393] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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".

[0405] The system of this invention collects project data from various departments within a company and analyzes and proposes synergies based on this data, thereby improving the overall efficiency and creativity of the company. The program's processing flow and a specific example are described below.

[0406] Collection and organization of project data

[0407] The server collects project data from each department. Specifically, it exposes APIs and data entry forms on the server, allowing each department to input project names, responsible departments, progress status, and technical / service-related information.

[0408] The server stores the collected case data in a database. The collected data is automatically organized and standardized according to attributes such as project name, responsible department, and progress status.

[0409] Enter your search query

[0410] Users utilize an interface to search for information related to their project. This interface provides keyword input fields and filter options.

[0411] The terminal sends the search query entered by the user to the server. This query may include keywords such as "new product," "market research," and "technology development."

[0412] Extraction of similar cases

[0413] The server searches the database for relevant cases based on the search query. First, it extracts case data that matches or is similar to the project name or technical / service attributes.

[0414] The extracted case data is then given a relevance score. The server sorts the cases in descending order of relevance based on this score.

[0415] Synergy proposal

[0416] The server analyzes the synergy between each search result and the user's existing projects. Using a synergy analysis algorithm, it identifies resources and information that can be shared among the extracted projects and evaluates their synergistic effects.

[0417] The server generates specific synergy proposals based on the analysis results. For example, it might generate proposals such as "utilize market research data in sales strategies" or "share progress in technology development and adjust sales timing."

[0418] Results display and feedback

[0419] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, making it easy for the user to understand.

[0420] Users can select projects of interest based on the displayed results and review detailed information and proposals. Furthermore, they can create specific collaboration plans based on the selected projects and share them with relevant parties within their company.

[0421] Users send feedback on search results and suggestions to the server via their devices. This feedback may include comments such as "The suggestion was helpful" or "It was not very relevant."

[0422] The server improves the accuracy of its search algorithms and synergy analysis algorithms based on the feedback it receives.

[0423] Specific example

[0424] For example, suppose data on "market research for new product A" is collected from the marketing department and data on "technical development for new product A" is collected from the development department and stored on a server. This data is organized by attributes such as project name, responsible department, progress status, and technology / service, and stored in a database.

[0425] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to search for information related to their project, "Sales Strategy for New Product A."

[0426] Based on this, the server extracts projects such as "Market research for new product A" and "Technical development for new product A" from the database and determines that they have a high relevance to the user's project.

[0427] The server generates synergy proposals such as "utilizing market research data in sales strategies" and "sharing progress in technology development to adjust sales timing," and displays them to the user via the terminal.

[0428] Users review the suggestions, propose specific ways to collaborate with the marketing and development departments, and share them internally. Finally, they provide feedback to the server indicating that the suggestions were helpful, thereby improving the overall accuracy of the system.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] The server collects project data from each department. Specifically, each department uses APIs and data entry forms published on the server to input project names, responsible departments, progress status, and technical / service-related information.

[0432] Step 2:

[0433] The server stores the collected case data in a database. The data is automatically organized and standardized by attributes such as project name, responsible department, and progress status. It also checks for duplicate data and optimizes the data format.

[0434] Step 3:

[0435] Users utilize an interface to search for information related to their project. This interface provides keyword input fields and filter options.

[0436] Step 4:

[0437] The terminal sends the search query entered by the user to the server. This query may include keywords such as "new product," "market research," and "technology development."

[0438] Step 5:

[0439] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to the project name or technical / service attributes.

[0440] Step 6:

[0441] The server calculates a relevance score for the extracted case data. Based on the relevance score, the search results are sorted in descending order of relevance.

[0442] Step 7:

[0443] The server analyzes the synergy between each search result and the user's existing projects. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted projects and evaluates their synergistic effects.

[0444] Step 8:

[0445] The server generates specific synergy proposals based on the analysis results. These proposals include concrete examples such as "utilizing market research data in sales strategies" and "sharing progress in technology development to coordinate sales timing."

[0446] Step 9:

[0447] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, presented in a user-friendly format.

[0448] Step 10:

[0449] Based on the displayed results, users select projects of interest and review detailed information and proposals. They also create specific collaboration plans based on the selected projects and share them with relevant parties within their company.

[0450] Step 11:

[0451] Users send feedback on search results and suggestions to the server via their devices. This feedback includes ratings such as "the suggestion was helpful" or "it was not relevant."

[0452] Step 12:

[0453] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This continuously improves the overall performance of the system.

[0454] (Example 1)

[0455] 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."

[0456] There is a need for a system that can effectively collect and organize information on diverse projects underway in each department within a company, extract highly relevant information, and complement it to improve the overall efficiency and creativity of the company. However, current distributed information management systems have the problem of insufficient information sharing between departments, making it difficult to propose effective solutions that generate synergistic effects. This invention aims to solve this problem.

[0457] 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.

[0458] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by the user, means for extracting relevant cases from the database based on the search queries, means for calculating a relevance score for the extracted cases and sorting the cases based on relevance, means for analyzing the synergy between the extracted cases and the current cases and generating specific proposals, means for visually displaying the analysis and proposal results to the user, and means for receiving feedback from the user and improving the search algorithm and synergy analysis algorithm. This enables effective utilization of case data collected from each department, maximizing information sharing and synergy effects across the entire company.

[0459] "Project data" refers to information about projects carried out by each department within a company, and specifically includes data such as project name, responsible department, progress status, and information about technology and services.

[0460] A "server" is a computer system that collects, organizes, stores, retrieves, and analyzes data over a network.

[0461] A "database" is a data management system that efficiently stores collected case data and allows for searching and retrieving it as needed.

[0462] A "search query" refers to the keywords and conditions that a user enters when searching for information, and relevant data is retrieved based on these queries.

[0463] The "relevance score" is a numerical value calculated by the server to evaluate the relevance of the cases it extracts from the database based on the search query. The relevance score indicates how well the cases match or are similar to the search query.

[0464] "Synergy" refers to the synergistic effect that arises from the mutual complementarity of multiple projects, and signifies the improvement in value resulting from the sharing of resources and information.

[0465] An "analysis algorithm" is a computational method used by a server to analyze collected data and extract useful information and patterns.

[0466] A "proposal" is a specific action plan or improvement measure generated by an analytical algorithm, aimed at improving the overall efficiency and creativity of the company.

[0467] A "user interface" is a collection of screens and input tools that a user uses to operate a system, providing means for performing searches and providing feedback.

[0468] "Feedback" refers to the act of users sending evaluations and opinions about search results and suggestions to the server. This feedback is used to improve the system's performance.

[0469] Modes for carrying out the invention

[0470] The system of the present invention collects project data from various departments within a company and analyzes and proposes synergies based on this data, thereby improving the overall efficiency and creativity of the company. The following describes specific embodiments of the system.

[0471] Collection and organization of project data

[0472] The server exposes APIs and data entry forms to collect project data from each department. This process is implemented using a RESTful API built, for example, with the Django framework. Each department enters the project name, responsible department, progress status, and technical / service-related information.

[0473] The collected data is stored in a database such as PostgreSQL or MySQL. The server organizes the received data by attributes such as project name, department, and progress, and automatically performs a standardization process. This ensures that data collected from each department is stored in a consistent format.

[0474] Enter your search query

[0475] The terminal provides an interface for users to search for information related to a project. This interface, built with React and Vue.js, includes keyword input fields and filter options. Users enter keywords such as "new product," "market research," and "technology development" through the interface.

[0476] The entered search query is sent from the terminal to the server. The server searches the database for relevant cases based on the received query.

[0477] Extraction of similar cases

[0478] The server uses a search engine like Elasticsearch to retrieve relevant cases from the database based on the received search query. It extracts case data that matches or is similar to project names or technical / service attributes.

[0479] A relevance score is calculated for the extracted case data. The TF-IDF algorithm is used to assign a score to each case. The server then sorts the cases in descending order of relevance based on this score.

[0480] Synergy proposal

[0481] The server performs synergy analysis on the case data obtained from the search results. It uses machine learning libraries such as Scikit-learn and TensorFlow to perform the analysis, identify shareable resources and information, and evaluate the synergistic effects.

[0482] Based on the analysis results, specific synergy proposals are generated. For example, these may include specific suggestions such as "utilizing market research data in sales strategies" and "sharing the progress of technology development to adjust sales timing."

[0483] Results display and feedback

[0484] The device visually displays search results and synergy suggestions to the user. Using React and D3.js, it generates a dashboard showing relevance scores and synergy details. The user reviews the displayed results and selects projects of interest. They then review the details and suggestions for the selected projects and create specific collaboration plans.

[0485] Users share collaboration proposals with relevant parties within their company and send feedback on search results and proposals to the server via their devices. The server uses this feedback to improve the accuracy of its search algorithms and synergy analysis algorithms.

[0486] Specific example

[0487] For example, suppose the marketing department submits market research data for new product A to the server, and at the same time, the development department provides technical development data for new product A. This data is collected and organized on the server and consistently stored in the database.

[0488] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development," and sends a query on their terminal. The server searches the database for related cases using Elasticsearch, calculates a relevance score, and sorts the cases.

[0489] For example, the search results might include "market research for new product A" and "technological development for new product A," and the server determines that these are highly relevant to the user's project. Subsequently, it generates specific synergy proposals such as "utilize market research data in sales strategy" and "share the progress of technological development to adjust the sales timing," and presents them to the user via the terminal.

[0490] Users review these suggestions, develop specific collaboration plans with the marketing and development departments, and share them internally. By providing feedback to the server about the helpfulness of the suggestions, the system's accuracy can be continuously improved.

[0491] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0492] Step 1:

[0493] The server exposes APIs and data entry forms to collect project data from each department. A RESTful API built with the Django framework is used for this purpose. Each department enters project name, responsible department, progress status, and technical / service-related information. The entered data is sent to the server via the API.

[0494] Input: Project data entered by each department (project name, responsible department, progress status, and technical / service information).

[0495] Output: Case data sent to the server via API.

[0496] Specific operation: The marketing department inputs market research data for new product A and sends it to the server via API.

[0497] Step 2:

[0498] The server stores received project data in a PostgreSQL or MySQL database. The received data is organized by attributes such as project name, department, and progress status, and automatically standardized. For example, if a field contains missing data, it is converted to a consistent format.

[0499] Input: Case data sent to the server via API.

[0500] Output: Case data organized and stored in the database.

[0501] Specific operation: The server saves the received data to the database and automatically aligns fields such as project name and department.

[0502] Step 3:

[0503] The terminal provides an interface for users to search for information related to a project. This interface is implemented using React or Vue.js. Through this interface, users enter keywords such as "new product," "market research," and "technology development" to create search queries.

[0504] Input: The search query entered by the user (e.g., "new product", "market research", "technology development").

[0505] Output: Search queries sent from the terminal to the server.

[0506] Specific operation: A user in the sales department enters keywords to obtain information about new product A, and the terminal sends this query to the server.

[0507] Step 4:

[0508] The server searches the database for relevant cases based on the received search query. A search engine such as Elasticsearch is used for this process. Case data that matches or is similar to project names and technical / service attributes is extracted.

[0509] Input: Search query sent from the device.

[0510] Output: A list of case data that matches or is similar to the search query.

[0511] Specific operation: The server uses Elasticsearch to search the database and extract relevant case data.

[0512] Step 5:

[0513] The server calculates a relevance score for the extracted case data. Using the TF-IDF algorithm, scores are assigned based on project name and technical / service attributes. This allows for a relevance assessment for each case.

[0514] Input: A list of case data that matches or is similar to the search query.

[0515] Output: A list of case data assigned a relevance score.

[0516] Specific operation: The server calculates a relevance score for each case data and sorts them in descending order of relevance.

[0517] Step 6:

[0518] The server performs synergy analysis on the case data obtained from the search results. Using Scikit-learn and TensorFlow, it identifies shareable resources and information and evaluates synergistic effects. It generates specific synergy proposals, such as "utilizing market research data in sales strategies."

[0519] Input: A list of case data assigned a relevance score.

[0520] Output: A list of project data to which synergy proposals have been assigned.

[0521] Specific operation: The server uses machine learning algorithms to analyze shared resources and information and generate synergy suggestions.

[0522] Step 7:

[0523] The device visually displays search results and synergy suggestions to the user. Using React and D3.js, it generates a dashboard showing relevance scores and synergy details. The user reviews the displayed results and selects deals that interest them.

[0524] Input: A list of project data to which synergy proposals have been assigned.

[0525] Output: A visual dashboard displayed on the user's screen.

[0526] Specific operation: The device generates a dashboard, and the user reviews the suggested content.

[0527] Step 8:

[0528] Users select projects of interest based on the displayed results and review detailed information and proposals. They then create specific collaboration plans based on their selections and share them with relevant parties within their company. Furthermore, they send feedback on the search results and proposals to the server via their device.

[0529] Input: User selections and feedback (e.g., "The suggestion was helpful," "It was not relevant").

[0530] Output: Feedback data sent to the server.

[0531] Specific operation: The user creates a collaboration proposal and sends feedback to the server.

[0532] Step 9:

[0533] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This feedback is used as a dataset for the algorithms and is utilized to update the deep learning models.

[0534] Input: User feedback data.

[0535] Output: Improved search algorithm and synergy analysis algorithm.

[0536] Specific operation: The server analyzes the feedback data and improves accuracy by updating the existing algorithm.

[0537] (Application Example 1)

[0538] 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."

[0539] In the current factory, multiple projects are running simultaneously, and each project is managed independently, making resource sharing and progress coordination difficult. This often leads to decreased efficiency and wasted resources. Furthermore, it is difficult for on-site workers and managers to access the latest project information, which can delay immediate responses. The challenge is to solve these problems and improve the overall efficiency and creativity of the factory.

[0540] 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.

[0541] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by users, means for extracting relevant cases from the database based on the search queries, means for analyzing the synergy between the extracted cases and the current case and generating proposals, means for displaying the analysis and proposal results to the user, means for receiving feedback from users and improving the search algorithm, means for collecting and analyzing data for each project within the factory in real time and proposing synergies for resource sharing and progress adjustment between projects, and means for providing an interface that can be easily accessed by on-site workers and managers using smartphones. This enables efficient resource sharing and progress adjustment between projects within the factory, and further promotes immediate response on-site.

[0542] "Project data" refers to project names, responsible departments, progress status, and technical / service-related information collected from each department.

[0543] A "database" is an information system that organizes collected case data and makes it possible to store and search it efficiently.

[0544] A "search query" is a combination of keywords and filter options that a user enters to identify relevant information.

[0545] "Synergy" refers to the synergistic effect that arises from sharing resources and information among multiple projects.

[0546] A "search algorithm" is a computational method used to identify relevant items from a database based on a user's search query.

[0547] "Real-time collection" refers to the process of collecting data the moment it is generated and sending it to the server.

[0548] "Resource sharing" refers to the sharing of resources such as personnel, equipment, and information among multiple projects.

[0549] "Progress adjustment" involves evaluating the progress of a project and making necessary revisions to the plan or changes to resource allocation.

[0550] An "interface" is a visual or mechanical means by which a user accesses and operates a system.

[0551] "Feedback" refers to opinions and data from users, based on their experiences and suggestions, that are used to improve the system's functions and algorithms.

[0552] System program generation

[0553] The system for realizing this invention primarily involves three roles: server, terminal, and user. The following describes each role and function in natural language.

[0554] Server roles and functions

[0555] The server collects project data from each department, organizes it, and stores it in a database. In this case, the server collects project names, responsible departments, progress status, and technical / service information provided by each department using APIs or data entry forms. The collected data is automatically organized and stored in the database.

[0556] When a user enters a search query, the server searches the database for relevant cases based on that query. The retrieved cases are sorted based on their relevance score and displayed to the user. Furthermore, a synergy analysis algorithm is used to generate synergy suggestions for resource sharing and progress coordination between related cases.

[0557] Furthermore, we receive feedback from users and use it to improve our search algorithms and synergy analysis algorithms. This allows us to continuously improve the accuracy and efficiency of the system.

[0558] Terminal roles and functions

[0559] The terminal provides an interface for sending search queries entered by the user to the server. The hardware used here includes smartphones. For example, when a user enters keywords such as "new product," "market research," or "technology development" using the terminal, that query is sent to the server.

[0560] The terminal also plays a role in visually displaying search results and synergy suggestions received from the server to the user. The displayed information includes relevance scores and synergy details, making it easy for the user to understand and interact with.

[0561] User roles and functions

[0562] Users enter search queries through their terminals and evaluate the search results and synergy suggestions from the server. Based on the displayed results, users select projects of interest and review detailed information and proposals. If there are specific proposals, they create and share collaborative plans within the company based on them. Users also send feedback on the search results and synergy suggestions to the server through their terminals, thereby contributing to system improvement.

[0563] Hardware and software to be used

[0564] Hardware: Servers (any brand name), smartphones

[0565] Software: Flask (Python), requests library, database (e.g., PostgreSQL)

[0566] Specific example

[0567] For example, suppose the marketing department collects data on "market research for new product A," and the development department collects data on "technological development for new product A." This data is organized and stored by a server. When a user in the sales department enters a query such as "new product A," "market research," and "technological development," the server extracts relevant cases based on this and sorts them based on their relevance score. The server then generates synergy suggestions such as "utilize market research data in sales strategy" and "share the progress of technological development to adjust the sales timing," and displays them to the user through their terminal.

[0568] Example of a prompt

[0569] "Based on the data from the new technology, please propose how it can have synergistic effects on improvement projects for existing products."

[0570] In this way, efficient resource sharing and progress adjustment within the factory become possible, and immediate responses on-site are facilitated.

[0571] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0572] Step 1:

[0573] The server collects project data from each department. Specifically, each department sends project name, responsible department, progress status, and technical / service-related information to the server via APIs or data entry forms. This allows for the input of project data. The entered data is then temporarily stored in a database.

[0574] Step 2:

[0575] The server organizes and standardizes the collected case data. Specifically, it classifies the collected data by attributes such as project name, responsible department, and progress status, and converts it into a standard format. This allows the organized case data to be stored in the database.

[0576] Step 3:

[0577] The terminal receives the search query entered by the user and sends it to the server. The search query entered by the user may include keywords such as "new product," "market research," or "technology development." Based on the input, the terminal sends the search query. The terminal then sends the received query to the server.

[0578] Step 4:

[0579] The server searches the database for relevant cases based on the search query. It matches case data in the database based on keywords in the search query. Case data that matches or is similar to the attributes of the entered query is extracted.

[0580] Step 5:

[0581] The server calculates a relevance score for the extracted case data and sorts the cases in descending order of relevance. Specifically, a similarity calculation algorithm is used to calculate a score for each extracted case data. As a result, the cases are listed in descending order of relevance.

[0582] Step 6:

[0583] The server analyzes the synergies with the user's project and generates synergy proposals. The analysis identifies shareable resources and information, and evaluates their synergistic effects. For example, it generates specific proposals such as "utilize market research data in sales strategies" or "share progress in technology development to adjust sales timing."

[0584] Step 7:

[0585] The device visually displays search results and synergy suggestions received from the server to the user. Relevance scores and synergy details are clearly displayed for easy user understanding. Based on this information, the user selects projects of interest and checks detailed information and suggestions.

[0586] Step 8:

[0587] Users provide feedback on the displayed search results and synergy suggestions. Users input their opinions and impressions of the suggestions using a feedback interface. This feedback is sent to the server via the device.

[0588] Step 9:

[0589] The server improves its search algorithms and synergy analysis algorithms based on user feedback. The feedback is analyzed, and adjustments are made to further improve the accuracy of the search algorithms and synergy suggestions. This continuously improves the overall efficiency and accuracy of the system.

[0590] 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.

[0591] The system of this invention aims to improve the user experience not only by analyzing and proposing synergies based on project data collected from various departments within a company, but also by combining it with an emotion engine that recognizes user emotions. The program's processing flow and a specific example are described below.

[0592] Collection and organization of project data

[0593] The server exposes APIs and data entry forms to collect project data from each department, obtaining information such as project name, responsible department, progress status, and technical / service-related details.

[0594] The server stores the collected case data in a database. The data is automatically organized and standardized by attributes such as project name, responsible department, and progress status. Duplicate data checks and data format optimization are also performed.

[0595] Enter your search query

[0596] Users utilize an interface to search for information related to their project. This interface provides keyword input fields and filter options.

[0597] The terminal sends the search query entered by the user to the server. The query may include keywords such as "new product," "market research," or "technology development."

[0598] Extraction of similar cases

[0599] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to the project name or technical / service attributes, and calculates a relevance score. Based on this, it sorts the search results in descending order of relevance.

[0600] Synergy proposal

[0601] The server analyzes the synergy between each case found through the search and the user's existing cases. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted cases and evaluates their synergistic effects.

[0602] The server generates specific synergy proposals based on the analysis results. These proposals may include suggestions such as "utilizing market research data in sales strategies" or "sharing progress in technology development and coordinating sales timings."

[0603] Adjustment by the emotion engine

[0604] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input data and behavioral data to identify the user's emotional state.

[0605] Based on the user's emotional state, the server adjusts search results and synergy suggestions. For example, if a user is stressed, it provides more concise and intuitive suggestions. It also modifies the interface design and presentation methods according to the user's emotions to improve the experience.

[0606] Results display and feedback

[0607] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, presented in a user-friendly format. If adjustments have been made by the sentiment engine, those adjustments are also reflected.

[0608] Users select projects of interest based on the displayed results and review detailed information and proposals. They can also create specific collaboration plans based on their selected projects and share them with relevant parties within their company.

[0609] Users send feedback on search results and suggestions to the server via their devices. This feedback includes ratings such as "the suggestion was helpful" or "it was not relevant."

[0610] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This continuously improves the overall performance of the system.

[0611] Specific example

[0612] For example, suppose data on "market research for new product A" is collected from the marketing department and data on "technical development for new product A" is collected from the development department and stored on a server. This data is organized by attributes such as project name, responsible department, progress status, and technology / service, and stored in a database.

[0613] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to search for information related to the "Sales Strategy for New Product A." The server searches the database for relevant cases based on these keywords, calculates a relevance score, and organizes the results.

[0614] Furthermore, the emotion engine analyzes the user's emotional state, and if it detects that the user is experiencing stress, the server displays more concise and easy-to-understand suggestions. For example, it might display something like, "Check the market research data summary and use it in your sales strategy."

[0615] Users review the suggestions, propose specific ways to collaborate with the marketing and development departments, and share them internally. Finally, they can improve the overall accuracy of the system by providing feedback to the server on whether the suggestions were helpful.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] The server collects project data from each department. Specifically, each department uses APIs and data entry forms published on the server to input project names, responsible departments, progress status, and technical / service-related information.

[0619] Step 2:

[0620] The server stores the collected case data in a database. The data is automatically organized and standardized by attributes such as project name, responsible department, and progress status. It also checks for duplicate data and optimizes the data format.

[0621] Step 3:

[0622] Users utilize an interface to search for information related to their project. This interface provides a keyword input field and filter options.

[0623] Step 4:

[0624] The terminal sends the search query entered by the user to the server. This query may include keywords such as "new product," "market research," and "technology development."

[0625] Step 5:

[0626] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to the project name or technical / service attributes.

[0627] Step 6:

[0628] The server calculates a relevance score for the extracted case data. Based on the relevance score, the search results are sorted in descending order of relevance.

[0629] Step 7:

[0630] The server analyzes the synergy between each case found through the search and the user's existing cases. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted cases and evaluates the synergistic effects.

[0631] Step 8:

[0632] The server generates specific synergy proposals based on the analysis results. These proposals may include suggestions such as "utilizing market research data in sales strategies" or "sharing progress in technology development and coordinating sales timings."

[0633] Step 9:

[0634] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input data and behavioral data to identify the user's emotional state.

[0635] Step 10:

[0636] The server adjusts search results and synergy suggestions based on the user's emotional state. For example, if the user is stressed, it will provide more concise and intuitive suggestions. It also changes the interface design and display method according to the user's emotions to improve the user experience.

[0637] Step 11:

[0638] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, presented in a user-friendly format. If adjustments have been made by the sentiment engine, those adjustments are also reflected.

[0639] Step 12:

[0640] Based on the displayed results, users select projects of interest and review detailed information and proposals. They also create specific collaboration plans based on the selected projects and share them with relevant parties within their company.

[0641] Step 13:

[0642] Users send feedback on search results and suggestions to the server via their devices. This feedback includes ratings such as "the suggestion was helpful" or "it was not relevant."

[0643] Step 14:

[0644] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This continuously improves the overall performance of the system.

[0645] Specific example

[0646] For example, data on "market research for new product A" from the marketing department and data on "technical development for new product A" from the development department are collected on a server, organized by project name, responsible department, progress status, and technical / service attributes, and stored in a database.

[0647] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to search for information related to the "Sales Strategy for New Product A." The server searches the database for relevant cases based on these keywords, calculates a relevance score, and organizes the results.

[0648] Furthermore, the emotion engine analyzes the user's emotional state, and if it detects that the user is experiencing stress, the server displays more concise and easy-to-understand suggestions. For example, it might display something like, "Check the market research data summary and use it in your sales strategy."

[0649] Users review the suggestions, propose specific ways to collaborate with marketing and development departments, and share them internally. Finally, they provide feedback to the server on whether the suggestions were helpful, thereby improving the overall accuracy of the system.

[0650] (Example 2)

[0651] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0652] Traditional systems struggle to centralize project data collected from various departments and to provide appropriate search results for user queries. Furthermore, the proposed synergies are not always optimized for the user's current emotional state, making user experience improvement a challenge.

[0653] 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.

[0654] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by the user, means for extracting relevant cases from the database based on the search queries, means for analyzing the synergy between the extracted cases and the current case and generating proposals, means for displaying the analysis and proposal results to the user, means for recognizing the user's emotions and adjusting the proposal content based on those emotions, and means for receiving feedback from the user and improving the search algorithm and synergy analysis algorithm. This enables the provision of optimal synergy proposals that take the user's emotions into consideration, thereby improving the user experience.

[0655] "Project data" refers to data collected from each department, including project names, responsible departments, progress status, and information regarding technology and services.

[0656] A "database" is a system or storage location that stores collected case data and manages it in a way that allows for access and searching.

[0657] A "search query" is input data used by a user to search for specific information, including keywords and filter options entered into the system.

[0658] "Synergy" refers to creating synergistic effects by utilizing resources and information that can be shared across multiple projects or issues.

[0659] An "emotion engine" is a program or system that analyzes user input data and behavioral data to identify the user's emotional state.

[0660] "Feedback" refers to the evaluations and opinions that users submit to the system regarding search results and suggestions.

[0661] The "relevance score" is a numerical representation of the relationship between a search query and the results in the database; the higher the relevance, the higher the score.

[0662] "Adjusting the suggested content" refers to changing the suggestions and information displayed by the system based on the user's emotional state recognized by the emotion engine.

[0663] An "algorithm" refers to a set of computational procedures or rules for solving a specific problem, and in this context, it includes search algorithms and synergy analysis algorithms.

[0664] The system of this invention not only has the function of analyzing and proposing synergies based on project data collected from various departments within a company, but also aims to improve the user experience by introducing an emotion engine that recognizes user emotions. The program's processing flow and specific examples are described in detail below.

[0665] Collection and organization of project data

[0666] The server exposes API endpoints and data entry forms for collecting project data from each department. Using the API, departments such as marketing and development can automatically retrieve "project name," "responsible department," "progress status," and "technical / service-related information." For example, the marketing department might provide data on "market research for new product A," and the development department might provide data on "technical development for new product A."

[0667] The collected project data is stored in a database (e.g., MySQL, PostgreSQL). The data is organized and standardized by attributes such as project name, responsible department, progress status, and technology / service. This step also includes checking for duplicate data and optimizing the data format. For example, if data with the same project name is sent from different departments, the server detects the duplication and merges the data.

[0668] Enter your search query

[0669] The user enters a search query using an interface provided through the terminal. The interface includes keyword input fields and filter options, allowing the user to enter keywords such as "new product," "market research," or "technology development." The terminal then sends the entered query to the server.

[0670] Extraction of similar cases

[0671] The server receives the entered search query and searches the database. For example, it extracts market research data and technology development data related to "New Product A". Based on the keywords in the query, it extracts case data that matches or is similar to the relevant project name or technology / service attributes and calculates a relevance score.

[0672] Synergy proposal

[0673] The server performs synergy analysis based on the extracted project data. Using a synergy analysis algorithm, it identifies resources and information that can be shared between projects and evaluates their synergistic effects. Specific proposals include "utilizing market research data in sales strategies" and "sharing the progress of technology development and coordinating sales timings."

[0674] Adjustment by the emotion engine

[0675] The server uses an emotion engine (such as IBM Watson or Microsoft Azure Emotion API) to recognize the user's emotions. It analyzes user input and behavioral data to determine the user's emotional state. For example, if the user is stressed, the server displays simpler and easier-to-understand suggestions. If the user is relaxed, it provides more detailed information.

[0676] Results display and feedback

[0677] The device visually displays search results and synergy suggestions to the user. The results include relevance scores and synergy details, presented in a user-friendly format. The display format is designed, for example, to show highly relevant deals at the top, similar to a dashboard.

[0678] Furthermore, users select projects of interest based on the displayed results and review their details and proposals. They then create concrete collaboration plans based on the selected projects and share them with relevant parties within their company. They also use a feedback form to send evaluations of the search results and proposals to the server. These evaluations may include, for example, "The proposal was helpful" or "It was not very relevant." The server uses this feedback to improve the accuracy of its search algorithm and synergy analysis algorithm.

[0679] Specific example

[0680] For example, suppose market research data for new product A is collected from the marketing department, and technical development data for new product A is collected from the development department and stored on a server. This data is then organized and stored in a database.

[0681] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to obtain information related to the "Sales Strategy for New Product A." The server searches the database for relevant cases based on these keywords, calculates a relevance score, and organizes the results. The emotion engine analyzes the user's emotional state, and if it detects that the user is experiencing stress, the server displays more concise and easy-to-understand suggestions. For example, it might say, "Check the market research data summary and use it in your sales strategy."

[0682] Users review the suggestions, propose specific ways to collaborate with marketing and development departments, and share them internally through the system. Finally, they provide feedback to the server on whether the suggestions were helpful, which helps improve the overall accuracy of the system.

[0683] Example of a prompt

[0684] "We want to develop a sales strategy based on market research data for our new product. Could you tell us what kind of data is relevant?"

[0685] By using this prompt, the generated AI model extracts and analyzes project data from the marketing department and technical information from the development department, providing specific synergy proposals.

[0686] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0687] Step 1: Collecting case data

[0688] The server exposes an API endpoint and a data entry form, collecting the following information from each department: "Project Name," "Responsible Department," "Progress Status," and "Technical / Service Information." Based on this input, the server generates case data and stores it in the database. Specifically, it receives data from the API and converts it to the appropriate format.

[0689] Step 2: Organize and standardize case data

[0690] The server organizes the collected case data and stores it in the database. The input here is the case data collected in Step 1. The server organizes and standardizes the data by attributes such as project name, responsible department, and progress status. It checks for duplicate data and optimizes the data format, and outputs the standardized data. For example, if there are multiple data entries with the same project name, the duplicates are merged.

[0691] Step 3: Enter your search query

[0692] The user enters keywords and filter options into an interface provided through the terminal. These inputs include terms such as "new product," "market research," and "technology development." The terminal then sends this input to the server. Specifically, an HTTP request is generated to send the data from the input form to the server.

[0693] Step 4: Identifying Similar Cases

[0694] The server searches the database based on the search query received in step 3. The input is the user's search query, and the output is the relevant case data. The server extracts cases that match or are similar to the project name and technical / service attributes, and calculates a relevance score. An algorithm is executed to retrieve the data corresponding to the query and calculate the relevance of each.

[0695] Step 5: Propose synergies

[0696] The server performs synergy analysis based on the project data extracted in step 4. The input is the relevant project data, and the output is a synergy proposal. The server uses a synergy analysis algorithm to identify resources and information that can be shared between projects and evaluate the synergistic effects. For example, it generates a specific proposal such as "utilize market research data in sales strategies."

[0697] Step 6: Adjustment by the Emotional Engine

[0698] The server uses an emotion engine to analyze the user's emotional state. Input is user input data and behavioral data, and output is the emotion analysis result. Based on this emotion analysis, the server adjusts the search results and synergy suggestions. The emotion engine's algorithm detects the user's stress and relaxation levels and adjusts the complexity of the suggestions accordingly.

[0699] Step 7: Results display and feedback

[0700] The terminal visually displays search results and synergy suggestions to the user. The input is the adjusted synergy suggestions, and the output is the information presented to the user. Specifically, it has a mechanism that displays highly relevant cases at the top in a dashboard format. Based on the displayed results, the user selects cases of interest and checks their details and suggestions. After that, the user submits an evaluation to the server through a feedback form. The server, upon receiving this feedback, improves the accuracy of its search algorithm and synergy analysis algorithm.

[0701] (Application Example 2)

[0702] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0703] Traditional systems had problems with effectively utilizing interdepartmental synergies in production management within factories, and with being unable to make flexible suggestions that took into account the emotional state of workers. In particular, the collection and analysis of process data was insufficient, making it difficult to propose optimal resource sharing and cooperation plans to maximize production efficiency. Furthermore, there was a lack of means to provide appropriate support when on-site workers experienced stress, which posed a risk of a decline in overall work efficiency and experience.

[0704] 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.

[0705] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by the user, means for extracting relevant cases from the database based on the search queries, means for analyzing the synergy between the extracted cases and the current case and generating suggestions, means for displaying the analysis and suggestion results to the user, means for receiving feedback from the user and improving the search algorithm, means for recognizing the user's emotions using an emotion engine and adjusting the search results and suggestions based on the user's emotional state, and means for collecting data from each department in the factory and analyzing process data in the production line. This makes it possible to maximize the use of interdepartmental synergies in production management and to make flexible suggestions tailored to the emotional state of workers.

[0706] "Project data" refers to information collected from various departments within a company, including project names, responsible departments, progress status, and technical / service-related information.

[0707] A "database" is an information system that stores, organizes, and allows searching of collected case data.

[0708] A "search query" is a statement of inquiry that includes keywords and conditions entered by a user to obtain specific information.

[0709] "Synergy" refers to the synergistic effect that arises when multiple projects or pieces of information interact with each other.

[0710] An "emotion engine" is a system that recognizes and analyzes a user's emotional state.

[0711] A "production line" is a collection of processes that are carried out sequentially within a factory to produce a product.

[0712] The "relevance score" is a numerical representation of the relationship between a search query and the case data stored in the database.

[0713] "Feedback" is the process of receiving evaluations and opinions from users, and the system is improved based on this feedback.

[0714] "Resources" is a general term encompassing the personnel, equipment, materials, and information necessary to carry out production activities.

[0715] "API" stands for Application Programming Interface, and it is a set of rules for sharing functions and data between software programs.

[0716] The system of this invention collects, organizes, analyzes, and proposes data from each department to streamline production management within a factory. Furthermore, it aims to improve work efficiency and user experience by providing flexible suggestions based on the emotional state of workers using an emotion engine. The embodiments of this system will be described in detail below.

[0717] Data collection and organization

[0718] The server exposes APIs and data entry forms for collecting project data from each department. The collected data includes project name, responsible department, progress status, and technical / service information. The server stores the collected project data in a database (PostgreSQL), where the data is automatically organized and standardized. It also performs checks for duplicate data and optimizes data formats.

[0719] Enter your search query

[0720] Users can enter search queries from their terminal and send them to the server. This interface provides keyword input fields and filter options. For example, users can enter keywords such as "new material X," "processing method," and "manufacturing process." The terminal then sends the search queries entered by the user to the server.

[0721] Extraction of similar cases

[0722] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to project names and technical / service attributes, and calculates a relevance score using natural language processing techniques such as TF-IDF and Word2Vec. Based on this, it sorts the search results in descending order of relevance.

[0723] Synergy proposal

[0724] The server analyzes the synergy between each project found through the search and the user's projects. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted projects and evaluates their synergistic effects. Based on the analysis results, it generates specific synergy proposals. These proposals may include, for example, "sharing process data" and "optimizing resources."

[0725] Adjustment by the emotion engine

[0726] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's camera footage and input data to identify the user's emotional state. This analysis uses TensorFlow.js and a pre-trained emotion recognition model. Based on the emotional state, the server adjusts the search results and synergy suggestions. For example, if the user is stressed, it will provide more concise and intuitive suggestions.

[0727] Results display and feedback

[0728] The device visually displays search results and synergy suggestions to the user. React.js is used for display, providing relevance scores and synergy details in a user-friendly format. Based on the displayed results, the user selects projects of interest and reviews detailed information and suggestions. Users can also create specific collaboration proposals based on their selected projects and share them with internal stakeholders. Furthermore, users can send feedback on the suggestions to the server via the device, improving the overall accuracy of the system.

[0729] Specific example

[0730] Let's consider a scenario where a worker in the production department collects and analyzes data related to "processing methods for new material X." Based on the keywords entered by the worker, a search is performed, and relevant past case data is extracted. If the emotion engine recognizes the worker's stress, a simple procedural guide is displayed, along with suggestions for improving efficiency that incorporate insights from other departments. For example, a suggestion might be made to "check the overview of the processing procedure for new material X and confirm the optimal machine settings."

[0731] Example of a prompt

[0732] We want to investigate processing methods for the new material X. Based on progress data, analyze synergies with other departments and display a simple procedure guide and suggestions for efficiency improvements. If the user is experiencing stress, please use an emotion recognition system to present it in a particularly clear and understandable format.

[0733] This invention is expected to improve production efficiency within factories and significantly enhance the worker experience.

[0734] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0735] Step 1:

[0736] The server collects project data from each department. Specifically, it receives project names, responsible departments, progress status, and technical / service information through APIs and data entry forms, standardizes this data, and stores it in a database. The input data is in JSON format, and PostgreSQL is used as the database.

[0737] Step 2:

[0738] The server organizes the collected project data. It classifies the data in the database by attributes such as project name, department in charge, and progress status, checks for duplicate data, and optimizes the data format. It uses the Python pandas library to process and organize the data.

[0739] Step 3:

[0740] The user enters a search query through an interface on their device. The interface includes a keyword input field and filter options, allowing the user to enter keywords such as "new material X" or "processing method." The entered search query is then sent from the device to the server.

[0741] Step 4:

[0742] The server extracts relevant cases from the database based on the search query. It searches for highly relevant data using natural language processing techniques such as TF-IDF and Word2Vec, and calculates a relevance score. Based on the calculation results, the search results are sorted in descending order of relevance.

[0743] Step 5:

[0744] The server analyzes the synergies between extracted projects and its own projects and generates proposals. It uses a synergy analysis algorithm to evaluate resource sharing and the synergistic effects of information. The generated proposals include specific details regarding resource optimization and process sharing.

[0745] Step 6:

[0746] The server uses an emotion engine to recognize the user's emotions. It analyzes the user's video data and input data collected in real time by the device and identifies the emotional state using TensorFlow.js. A pre-trained emotion recognition model is used for this purpose.

[0747] Step 7:

[0748] The server adjusts search results and suggestions based on the user's emotional state. For example, if a user is stressed, it generates more concise and intuitive suggestions and adjusts the content accordingly. Specifically, it provides simple step-by-step guides and concrete measures to improve efficiency.

[0749] Step 8:

[0750] The device visually displays search results and synergy suggestions to the user. Using React.js, it provides an interface that allows users to see relevance scores and synergy details at a glance. Based on the displayed results, the user selects projects of interest and views their detailed information.

[0751] Step 9:

[0752] Users provide feedback on the suggested results. Evaluations and opinions from users are sent from their terminals to the server, which uses this information to improve the search algorithm and synergy analysis algorithm. This improves the overall system performance.

[0753] 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.

[0754] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0755] 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.

[0756] [Third Embodiment]

[0757] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0758] 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.

[0759] 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).

[0760] 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.

[0761] 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.

[0762] 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).

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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".

[0769] The system of this invention collects project data from various departments within a company and analyzes and proposes synergies based on this data, thereby improving the overall efficiency and creativity of the company. The program's processing flow and a specific example are described below.

[0770] Collection and organization of project data

[0771] The server collects project data from each department. Specifically, it exposes APIs and data entry forms on the server, allowing each department to input project names, responsible departments, progress status, and technical / service-related information.

[0772] The server stores the collected case data in a database. The collected data is automatically organized and standardized according to attributes such as project name, responsible department, and progress status.

[0773] Enter your search query

[0774] Users utilize an interface to search for information related to their project. This interface provides keyword input fields and filter options.

[0775] The terminal sends the search query entered by the user to the server. This query may include keywords such as "new product," "market research," and "technology development."

[0776] Extraction of similar cases

[0777] The server searches the database for relevant cases based on the search query. First, it extracts case data that matches or is similar to the project name or technical / service attributes.

[0778] The extracted case data is then given a relevance score. The server sorts the cases in descending order of relevance based on this score.

[0779] Synergy proposal

[0780] The server analyzes the synergy between each search result and the user's existing projects. Using a synergy analysis algorithm, it identifies resources and information that can be shared among the extracted projects and evaluates their synergistic effects.

[0781] The server generates specific synergy proposals based on the analysis results. For example, it might generate proposals such as "utilize market research data in sales strategies" or "share progress in technology development and adjust sales timing."

[0782] Results display and feedback

[0783] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, making it easy for the user to understand.

[0784] Users can select projects of interest based on the displayed results and review detailed information and proposals. Furthermore, they can create specific collaboration plans based on the selected projects and share them with relevant parties within their company.

[0785] Users send feedback on search results and suggestions to the server via their devices. This feedback may include comments such as "The suggestion was helpful" or "It was not very relevant."

[0786] The server improves the accuracy of its search algorithms and synergy analysis algorithms based on the feedback it receives.

[0787] Specific example

[0788] For example, suppose data on "market research for new product A" is collected from the marketing department and data on "technical development for new product A" is collected from the development department and stored on a server. This data is organized by attributes such as project name, responsible department, progress status, and technology / service, and stored in a database.

[0789] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to search for information related to their project, "Sales Strategy for New Product A."

[0790] Based on this, the server extracts projects such as "Market research for new product A" and "Technical development for new product A" from the database and determines that they have a high relevance to the user's project.

[0791] The server generates synergy proposals such as "utilizing market research data in sales strategies" and "sharing progress in technology development to adjust sales timing," and displays them to the user via the terminal.

[0792] Users review the suggestions, propose specific ways to collaborate with the marketing and development departments, and share them internally. Finally, they provide feedback to the server indicating that the suggestions were helpful, thereby improving the overall accuracy of the system.

[0793] The following describes the processing flow.

[0794] Step 1:

[0795] The server collects project data from each department. Specifically, each department uses APIs and data entry forms published on the server to input project names, responsible departments, progress status, and technical / service-related information.

[0796] Step 2:

[0797] The server stores the collected case data in a database. The data is automatically organized and standardized by attributes such as project name, responsible department, and progress status. It also checks for duplicate data and optimizes the data format.

[0798] Step 3:

[0799] Users utilize an interface to search for information related to their project. This interface provides keyword input fields and filter options.

[0800] Step 4:

[0801] The terminal sends the search query entered by the user to the server. This query may include keywords such as "new product," "market research," and "technology development."

[0802] Step 5:

[0803] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to the project name or technical / service attributes.

[0804] Step 6:

[0805] The server calculates a relevance score for the extracted case data. Based on the relevance score, the search results are sorted in descending order of relevance.

[0806] Step 7:

[0807] The server analyzes the synergy between each search result and the user's existing projects. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted projects and evaluates their synergistic effects.

[0808] Step 8:

[0809] The server generates specific synergy proposals based on the analysis results. These proposals include concrete examples such as "utilizing market research data in sales strategies" and "sharing progress in technology development to coordinate sales timing."

[0810] Step 9:

[0811] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, presented in a user-friendly format.

[0812] Step 10:

[0813] Based on the displayed results, users select projects of interest and review detailed information and proposals. They also create specific collaboration plans based on the selected projects and share them with relevant parties within their company.

[0814] Step 11:

[0815] Users send feedback on search results and suggestions to the server via their devices. This feedback includes ratings such as "the suggestion was helpful" or "it was not relevant."

[0816] Step 12:

[0817] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This continuously improves the overall performance of the system.

[0818] (Example 1)

[0819] 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."

[0820] There is a need for a system that can effectively collect and organize information on diverse projects underway in each department within a company, extract highly relevant information, and complement it to improve the overall efficiency and creativity of the company. However, current distributed information management systems have the problem of insufficient information sharing between departments, making it difficult to propose effective solutions that generate synergistic effects. This invention aims to solve this problem.

[0821] 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.

[0822] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by the user, means for extracting relevant cases from the database based on the search queries, means for calculating a relevance score for the extracted cases and sorting the cases based on relevance, means for analyzing the synergy between the extracted cases and the current cases and generating specific proposals, means for visually displaying the analysis and proposal results to the user, and means for receiving feedback from the user and improving the search algorithm and synergy analysis algorithm. This enables effective utilization of case data collected from each department, maximizing information sharing and synergy effects across the entire company.

[0823] "Project data" refers to information about projects carried out by each department within a company, and specifically includes data such as project name, responsible department, progress status, and information about technology and services.

[0824] A "server" is a computer system that collects, organizes, stores, retrieves, and analyzes data over a network.

[0825] A "database" is a data management system that efficiently stores collected case data and allows for searching and retrieving it as needed.

[0826] A "search query" refers to the keywords and conditions that a user enters when searching for information, and relevant data is retrieved based on these queries.

[0827] The "relevance score" is a numerical value calculated by the server to evaluate the relevance of the cases it extracts from the database based on the search query. The relevance score indicates how well the cases match or are similar to the search query.

[0828] "Synergy" refers to the synergistic effect that arises from the mutual complementarity of multiple projects, and signifies the improvement in value resulting from the sharing of resources and information.

[0829] An "analysis algorithm" is a computational method used by a server to analyze collected data and extract useful information and patterns.

[0830] A "proposal" is a specific action plan or improvement measure generated by an analytical algorithm, aimed at improving the overall efficiency and creativity of the company.

[0831] A "user interface" is a collection of screens and input tools that a user uses to operate a system, providing means for performing searches and providing feedback.

[0832] "Feedback" refers to the act of users sending evaluations and opinions about search results and suggestions to the server. This feedback is used to improve the system's performance.

[0833] Modes for carrying out the invention

[0834] The system of the present invention collects project data from various departments within a company and analyzes and proposes synergies based on this data, thereby improving the overall efficiency and creativity of the company. The following describes specific embodiments of the system.

[0835] Collection and organization of project data

[0836] The server exposes APIs and data entry forms to collect project data from each department. This process is implemented using a RESTful API built, for example, with the Django framework. Each department enters the project name, responsible department, progress status, and technical / service-related information.

[0837] The collected data is stored in a database such as PostgreSQL or MySQL. The server organizes the received data by attributes such as project name, department, and progress, and automatically performs a standardization process. This ensures that data collected from each department is stored in a consistent format.

[0838] Enter your search query

[0839] The terminal provides an interface for users to search for information related to a project. This interface, built with React and Vue.js, includes keyword input fields and filter options. Users enter keywords such as "new product," "market research," and "technology development" through the interface.

[0840] The entered search query is sent from the terminal to the server. The server searches the database for relevant cases based on the received query.

[0841] Extraction of similar cases

[0842] The server uses a search engine like Elasticsearch to retrieve relevant cases from the database based on the received search query. It extracts case data that matches or is similar to project names or technical / service attributes.

[0843] A relevance score is calculated for the extracted case data. The TF-IDF algorithm is used to assign a score to each case. The server then sorts the cases in descending order of relevance based on this score.

[0844] Synergy proposal

[0845] The server performs synergy analysis on the case data obtained from the search results. It uses machine learning libraries such as Scikit-learn and TensorFlow to perform the analysis, identify shareable resources and information, and evaluate the synergistic effects.

[0846] Based on the analysis results, specific synergy proposals are generated. For example, these may include specific suggestions such as "utilizing market research data in sales strategies" and "sharing the progress of technology development to adjust sales timing."

[0847] Results display and feedback

[0848] The device visually displays search results and synergy suggestions to the user. Using React and D3.js, it generates a dashboard showing relevance scores and synergy details. The user reviews the displayed results and selects projects of interest. They then review the details and suggestions for the selected projects and create specific collaboration plans.

[0849] Users share collaboration proposals with relevant parties within their company and send feedback on search results and proposals to the server via their devices. The server uses this feedback to improve the accuracy of its search algorithms and synergy analysis algorithms.

[0850] Specific example

[0851] For example, suppose the marketing department submits market research data for new product A to the server, and at the same time, the development department provides technical development data for new product A. This data is collected and organized on the server and consistently stored in the database.

[0852] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development," and sends a query on their terminal. The server searches the database for related cases using Elasticsearch, calculates a relevance score, and sorts the cases.

[0853] For example, the search results might include "market research for new product A" and "technological development for new product A," and the server determines that these are highly relevant to the user's project. Subsequently, it generates specific synergy proposals such as "utilize market research data in sales strategy" and "share the progress of technological development to adjust the sales timing," and presents them to the user via the terminal.

[0854] Users review these suggestions, develop specific collaboration plans with the marketing and development departments, and share them internally. By providing feedback to the server about the helpfulness of the suggestions, the system's accuracy can be continuously improved.

[0855] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0856] Step 1:

[0857] The server exposes APIs and data entry forms to collect project data from each department. A RESTful API built with the Django framework is used for this purpose. Each department enters project name, responsible department, progress status, and technical / service-related information. The entered data is sent to the server via the API.

[0858] Input: Project data entered by each department (project name, responsible department, progress status, and technical / service information).

[0859] Output: Case data sent to the server via API.

[0860] Specific operation: The marketing department inputs market research data for new product A and sends it to the server via API.

[0861] Step 2:

[0862] The server stores received project data in a PostgreSQL or MySQL database. The received data is organized by attributes such as project name, department, and progress status, and automatically standardized. For example, if a field contains missing data, it is converted to a consistent format.

[0863] Input: Case data sent to the server via API.

[0864] Output: Case data organized and stored in the database.

[0865] Specific operation: The server saves the received data to the database and automatically aligns fields such as project name and department.

[0866] Step 3:

[0867] The terminal provides an interface for users to search for information related to a project. This interface is implemented using React or Vue.js. Through this interface, users enter keywords such as "new product," "market research," and "technology development" to create search queries.

[0868] Input: The search query entered by the user (e.g., "new product", "market research", "technology development").

[0869] Output: Search queries sent from the terminal to the server.

[0870] Specific operation: A user in the sales department enters keywords to obtain information about new product A, and the terminal sends this query to the server.

[0871] Step 4:

[0872] The server searches the database for relevant cases based on the received search query. A search engine such as Elasticsearch is used for this process. Case data that matches or is similar to project names and technical / service attributes is extracted.

[0873] Input: Search query sent from the device.

[0874] Output: A list of case data that matches or is similar to the search query.

[0875] Specific operation: The server uses Elasticsearch to search the database and extract relevant case data.

[0876] Step 5:

[0877] The server calculates a relevance score for the extracted case data. Using the TF-IDF algorithm, scores are assigned based on project name and technical / service attributes. This allows for a relevance assessment for each case.

[0878] Input: A list of case data that matches or is similar to the search query.

[0879] Output: A list of case data assigned a relevance score.

[0880] Specific operation: The server calculates a relevance score for each case data and sorts them in descending order of relevance.

[0881] Step 6:

[0882] The server performs synergy analysis on the case data obtained from the search results. Using Scikit-learn and TensorFlow, it identifies shareable resources and information and evaluates synergistic effects. It generates specific synergy proposals, such as "utilizing market research data in sales strategies."

[0883] Input: A list of case data assigned a relevance score.

[0884] Output: A list of project data to which synergy proposals have been assigned.

[0885] Specific operation: The server uses machine learning algorithms to analyze shared resources and information and generate synergy suggestions.

[0886] Step 7:

[0887] The device visually displays search results and synergy suggestions to the user. Using React and D3.js, it generates a dashboard showing relevance scores and synergy details. The user reviews the displayed results and selects deals that interest them.

[0888] Input: A list of project data to which synergy proposals have been assigned.

[0889] Output: A visual dashboard displayed on the user's screen.

[0890] Specific operation: The device generates a dashboard, and the user reviews the suggested content.

[0891] Step 8:

[0892] Users select projects of interest based on the displayed results and review detailed information and proposals. They then create specific collaboration plans based on their selections and share them with relevant parties within their company. Furthermore, they send feedback on the search results and proposals to the server via their device.

[0893] Input: User selections and feedback (e.g., "The suggestion was helpful," "It was not relevant").

[0894] Output: Feedback data sent to the server.

[0895] Specific operation: The user creates a collaboration proposal and sends feedback to the server.

[0896] Step 9:

[0897] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This feedback is used as a dataset for the algorithms and is utilized to update the deep learning models.

[0898] Input: User feedback data.

[0899] Output: Improved search algorithm and synergy analysis algorithm.

[0900] Specific operation: The server analyzes the feedback data and improves accuracy by updating the existing algorithm.

[0901] (Application Example 1)

[0902] 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."

[0903] In the current factory, multiple projects are running simultaneously, and each project is managed independently, making resource sharing and progress coordination difficult. This often leads to decreased efficiency and wasted resources. Furthermore, it is difficult for on-site workers and managers to access the latest project information, which can delay immediate responses. The challenge is to solve these problems and improve the overall efficiency and creativity of the factory.

[0904] 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.

[0905] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by users, means for extracting relevant cases from the database based on the search queries, means for analyzing the synergy between the extracted cases and the current case and generating proposals, means for displaying the analysis and proposal results to the user, means for receiving feedback from users and improving the search algorithm, means for collecting and analyzing data for each project within the factory in real time and proposing synergies for resource sharing and progress adjustment between projects, and means for providing an interface that can be easily accessed by on-site workers and managers using smartphones. This enables efficient resource sharing and progress adjustment between projects within the factory, and further promotes immediate response on-site.

[0906] "Project data" refers to project names, responsible departments, progress status, and technical / service-related information collected from each department.

[0907] A "database" is an information system that organizes collected case data and makes it possible to store and search it efficiently.

[0908] A "search query" is a combination of keywords and filter options that a user enters to identify relevant information.

[0909] "Synergy" refers to the synergistic effect that arises from sharing resources and information among multiple projects.

[0910] A "search algorithm" is a computational method used to identify relevant items from a database based on a user's search query.

[0911] "Real-time collection" refers to the process of collecting data the moment it is generated and sending it to the server.

[0912] "Resource sharing" refers to the sharing of resources such as personnel, equipment, and information among multiple projects.

[0913] "Progress adjustment" involves evaluating the progress of a project and making necessary revisions to the plan or changes to resource allocation.

[0914] An "interface" is a visual or mechanical means by which a user accesses and operates a system.

[0915] "Feedback" refers to opinions and data from users, based on their experiences and suggestions, that are used to improve the system's functions and algorithms.

[0916] System program generation

[0917] The system for realizing this invention primarily involves three roles: server, terminal, and user. The following describes each role and function in natural language.

[0918] Server roles and functions

[0919] The server collects project data from each department, organizes it, and stores it in a database. In this case, the server collects project names, responsible departments, progress status, and technical / service information provided by each department using APIs or data entry forms. The collected data is automatically organized and stored in the database.

[0920] When a user enters a search query, the server searches the database for relevant cases based on that query. The retrieved cases are sorted based on their relevance score and displayed to the user. Furthermore, a synergy analysis algorithm is used to generate synergy suggestions for resource sharing and progress coordination between related cases.

[0921] Furthermore, we receive feedback from users and use it to improve our search algorithms and synergy analysis algorithms. This allows us to continuously improve the accuracy and efficiency of the system.

[0922] Terminal roles and functions

[0923] The terminal provides an interface for sending search queries entered by the user to the server. The hardware used here includes smartphones. For example, when a user enters keywords such as "new product," "market research," or "technology development" using the terminal, that query is sent to the server.

[0924] The terminal also plays a role in visually displaying search results and synergy suggestions received from the server to the user. The displayed information includes relevance scores and synergy details, making it easy for the user to understand and interact with.

[0925] User roles and functions

[0926] Users enter search queries through their terminals and evaluate the search results and synergy suggestions from the server. Based on the displayed results, users select projects of interest and review detailed information and proposals. If there are specific proposals, they create and share collaborative plans within the company based on them. Users also send feedback on the search results and synergy suggestions to the server through their terminals, thereby contributing to system improvement.

[0927] Hardware and software to be used

[0928] Hardware: Servers (any brand name), smartphones

[0929] Software: Flask (Python), requests library, database (e.g., PostgreSQL)

[0930] Specific example

[0931] For example, suppose the marketing department collects data on "market research for new product A," and the development department collects data on "technological development for new product A." This data is organized and stored by a server. When a user in the sales department enters a query such as "new product A," "market research," and "technological development," the server extracts relevant cases based on this and sorts them based on their relevance score. The server then generates synergy suggestions such as "utilize market research data in sales strategy" and "share the progress of technological development to adjust the sales timing," and displays them to the user through their terminal.

[0932] Example of a prompt

[0933] "Based on the data from the new technology, please propose how it can have synergistic effects on improvement projects for existing products."

[0934] In this way, efficient resource sharing and progress adjustment within the factory become possible, and immediate responses on-site are facilitated.

[0935] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0936] Step 1:

[0937] The server collects project data from each department. Specifically, each department sends project name, responsible department, progress status, and technical / service-related information to the server via APIs or data entry forms. This allows for the input of project data. The entered data is then temporarily stored in a database.

[0938] Step 2:

[0939] The server organizes and standardizes the collected case data. Specifically, it classifies the collected data by attributes such as project name, responsible department, and progress status, and converts it into a standard format. This allows the organized case data to be stored in the database.

[0940] Step 3:

[0941] The terminal receives the search query entered by the user and sends it to the server. The search query entered by the user may include keywords such as "new product," "market research," or "technology development." Based on the input, the terminal sends the search query. The terminal then sends the received query to the server.

[0942] Step 4:

[0943] The server searches the database for relevant cases based on the search query. It matches case data in the database based on keywords in the search query. Case data that matches or is similar to the attributes of the entered query is extracted.

[0944] Step 5:

[0945] The server calculates a relevance score for the extracted case data and sorts the cases in descending order of relevance. Specifically, a similarity calculation algorithm is used to calculate a score for each extracted case data. As a result, the cases are listed in descending order of relevance.

[0946] Step 6:

[0947] The server analyzes the synergies with the user's project and generates synergy proposals. The analysis identifies shareable resources and information, and evaluates their synergistic effects. For example, it generates specific proposals such as "utilize market research data in sales strategies" or "share progress in technology development to adjust sales timing."

[0948] Step 7:

[0949] The device visually displays search results and synergy suggestions received from the server to the user. Relevance scores and synergy details are clearly displayed for easy user understanding. Based on this information, the user selects projects of interest and checks detailed information and suggestions.

[0950] Step 8:

[0951] Users provide feedback on the displayed search results and synergy suggestions. Users input their opinions and impressions of the suggestions using a feedback interface. This feedback is sent to the server via the device.

[0952] Step 9:

[0953] The server improves its search algorithms and synergy analysis algorithms based on user feedback. The feedback is analyzed, and adjustments are made to further improve the accuracy of the search algorithms and synergy suggestions. This continuously improves the overall efficiency and accuracy of the system.

[0954] 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.

[0955] The system of this invention aims to improve the user experience not only by analyzing and proposing synergies based on project data collected from various departments within a company, but also by combining it with an emotion engine that recognizes user emotions. The program's processing flow and a specific example are described below.

[0956] Collection and organization of project data

[0957] The server exposes APIs and data entry forms to collect project data from each department, obtaining information such as project name, responsible department, progress status, and technical / service-related details.

[0958] The server stores the collected case data in a database. The data is automatically organized and standardized by attributes such as project name, responsible department, and progress status. Duplicate data checks and data format optimization are also performed.

[0959] Enter your search query

[0960] Users utilize an interface to search for information related to their project. This interface provides keyword input fields and filter options.

[0961] The terminal sends the search query entered by the user to the server. The query may include keywords such as "new product," "market research," or "technology development."

[0962] Extraction of similar cases

[0963] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to the project name or technical / service attributes, and calculates a relevance score. Based on this, it sorts the search results in descending order of relevance.

[0964] Synergy proposal

[0965] The server analyzes the synergy between each case found through the search and the user's existing cases. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted cases and evaluates their synergistic effects.

[0966] The server generates specific synergy proposals based on the analysis results. These proposals may include suggestions such as "utilizing market research data in sales strategies" or "sharing progress in technology development and coordinating sales timings."

[0967] Adjustment by the emotion engine

[0968] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input data and behavioral data to identify the user's emotional state.

[0969] Based on the user's emotional state, the server adjusts search results and synergy suggestions. For example, if a user is stressed, it provides more concise and intuitive suggestions. It also modifies the interface design and presentation methods according to the user's emotions to improve the experience.

[0970] Results display and feedback

[0971] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, presented in a user-friendly format. If adjustments have been made by the sentiment engine, those adjustments are also reflected.

[0972] Users select projects of interest based on the displayed results and review detailed information and proposals. They can also create specific collaboration plans based on their selected projects and share them with relevant parties within their company.

[0973] Users send feedback on search results and suggestions to the server via their devices. This feedback includes ratings such as "the suggestion was helpful" or "it was not relevant."

[0974] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This continuously improves the overall performance of the system.

[0975] Specific example

[0976] For example, suppose data on "market research for new product A" is collected from the marketing department and data on "technical development for new product A" is collected from the development department and stored on a server. This data is organized by attributes such as project name, responsible department, progress status, and technology / service, and stored in a database.

[0977] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to search for information related to the "Sales Strategy for New Product A." The server searches the database for relevant cases based on these keywords, calculates a relevance score, and organizes the results.

[0978] Furthermore, the emotion engine analyzes the user's emotional state, and if it detects that the user is experiencing stress, the server displays more concise and easy-to-understand suggestions. For example, it might display something like, "Check the market research data summary and use it in your sales strategy."

[0979] Users review the suggestions, propose specific ways to collaborate with the marketing and development departments, and share them internally. Finally, they can improve the overall accuracy of the system by providing feedback to the server on whether the suggestions were helpful.

[0980] The following describes the processing flow.

[0981] Step 1:

[0982] The server collects project data from each department. Specifically, each department uses APIs and data entry forms published on the server to input project names, responsible departments, progress status, and technical / service-related information.

[0983] Step 2:

[0984] The server stores the collected case data in a database. The data is automatically organized and standardized by attributes such as project name, responsible department, and progress status. It also checks for duplicate data and optimizes the data format.

[0985] Step 3:

[0986] Users utilize an interface to search for information related to their project. This interface provides a keyword input field and filter options.

[0987] Step 4:

[0988] The terminal sends the search query entered by the user to the server. This query may include keywords such as "new product," "market research," and "technology development."

[0989] Step 5:

[0990] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to the project name or technical / service attributes.

[0991] Step 6:

[0992] The server calculates a relevance score for the extracted case data. Based on the relevance score, the search results are sorted in descending order of relevance.

[0993] Step 7:

[0994] The server analyzes the synergy between each case found through the search and the user's existing cases. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted cases and evaluates the synergistic effects.

[0995] Step 8:

[0996] The server generates specific synergy proposals based on the analysis results. These proposals may include suggestions such as "utilizing market research data in sales strategies" or "sharing progress in technology development and coordinating sales timings."

[0997] Step 9:

[0998] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input data and behavioral data to identify the user's emotional state.

[0999] Step 10:

[1000] The server adjusts search results and synergy suggestions based on the user's emotional state. For example, if the user is stressed, it will provide more concise and intuitive suggestions. It also changes the interface design and display method according to the user's emotions to improve the user experience.

[1001] Step 11:

[1002] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, presented in a user-friendly format. If adjustments have been made by the sentiment engine, those adjustments are also reflected.

[1003] Step 12:

[1004] Based on the displayed results, users select projects of interest and review detailed information and proposals. They also create specific collaboration plans based on the selected projects and share them with relevant parties within their company.

[1005] Step 13:

[1006] Users send feedback on search results and suggestions to the server via their devices. This feedback includes ratings such as "the suggestion was helpful" or "it was not relevant."

[1007] Step 14:

[1008] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This continuously improves the overall performance of the system.

[1009] Specific example

[1010] For example, data on "market research for new product A" from the marketing department and data on "technical development for new product A" from the development department are collected on a server, organized by project name, responsible department, progress status, and technical / service attributes, and stored in a database.

[1011] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to search for information related to the "Sales Strategy for New Product A." The server searches the database for relevant cases based on these keywords, calculates a relevance score, and organizes the results.

[1012] Furthermore, the emotion engine analyzes the user's emotional state, and if it detects that the user is experiencing stress, the server displays more concise and easy-to-understand suggestions. For example, it might display something like, "Check the market research data summary and use it in your sales strategy."

[1013] Users review the suggestions, propose specific ways to collaborate with marketing and development departments, and share them internally. Finally, they provide feedback to the server on whether the suggestions were helpful, thereby improving the overall accuracy of the system.

[1014] (Example 2)

[1015] 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."

[1016] Traditional systems struggle to centralize project data collected from various departments and to provide appropriate search results for user queries. Furthermore, the proposed synergies are not always optimized for the user's current emotional state, making user experience improvement a challenge.

[1017] 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.

[1018] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by the user, means for extracting relevant cases from the database based on the search queries, means for analyzing the synergy between the extracted cases and the current case and generating proposals, means for displaying the analysis and proposal results to the user, means for recognizing the user's emotions and adjusting the proposal content based on those emotions, and means for receiving feedback from the user and improving the search algorithm and synergy analysis algorithm. This enables the provision of optimal synergy proposals that take the user's emotions into consideration, thereby improving the user experience.

[1019] "Project data" refers to data collected from each department, including project names, responsible departments, progress status, and information regarding technology and services.

[1020] A "database" is a system or storage location that stores collected case data and manages it in a way that allows for access and searching.

[1021] A "search query" is input data used by a user to search for specific information, including keywords and filter options entered into the system.

[1022] "Synergy" refers to creating synergistic effects by utilizing resources and information that can be shared across multiple projects or issues.

[1023] An "emotion engine" is a program or system that analyzes user input data and behavioral data to identify the user's emotional state.

[1024] "Feedback" refers to the evaluations and opinions that users submit to the system regarding search results and suggestions.

[1025] The "relevance score" is a numerical representation of the relationship between a search query and the results in the database; the higher the relevance, the higher the score.

[1026] "Adjusting the suggested content" refers to changing the suggestions and information displayed by the system based on the user's emotional state recognized by the emotion engine.

[1027] An "algorithm" refers to a set of computational procedures or rules for solving a specific problem, and in this context, it includes search algorithms and synergy analysis algorithms.

[1028] The system of this invention not only has the function of analyzing and proposing synergies based on project data collected from various departments within a company, but also aims to improve the user experience by introducing an emotion engine that recognizes user emotions. The program's processing flow and specific examples are described in detail below.

[1029] Collection and organization of project data

[1030] The server exposes API endpoints and data entry forms for collecting project data from each department. Using the API, departments such as marketing and development can automatically retrieve "project name," "responsible department," "progress status," and "technical / service-related information." For example, the marketing department might provide data on "market research for new product A," and the development department might provide data on "technical development for new product A."

[1031] The collected project data is stored in a database (e.g., MySQL, PostgreSQL). The data is organized and standardized by attributes such as project name, responsible department, progress status, and technology / service. This step also includes checking for duplicate data and optimizing the data format. For example, if data with the same project name is sent from different departments, the server detects the duplication and merges the data.

[1032] Enter your search query

[1033] The user enters a search query using an interface provided through the terminal. The interface includes keyword input fields and filter options, allowing the user to enter keywords such as "new product," "market research," or "technology development." The terminal then sends the entered query to the server.

[1034] Extraction of similar cases

[1035] The server receives the entered search query and searches the database. For example, it extracts market research data and technology development data related to "New Product A". Based on the keywords in the query, it extracts case data that matches or is similar to the relevant project name or technology / service attributes and calculates a relevance score.

[1036] Synergy proposal

[1037] The server performs synergy analysis based on the extracted project data. Using a synergy analysis algorithm, it identifies resources and information that can be shared between projects and evaluates their synergistic effects. Specific proposals include "utilizing market research data in sales strategies" and "sharing the progress of technology development and coordinating sales timings."

[1038] Adjustment by the emotion engine

[1039] The server uses an emotion engine (such as IBM Watson or Microsoft Azure Emotion API) to recognize the user's emotions. It analyzes user input and behavioral data to determine the user's emotional state. For example, if the user is stressed, the server displays simpler and easier-to-understand suggestions. If the user is relaxed, it provides more detailed information.

[1040] Results display and feedback

[1041] The device visually displays search results and synergy suggestions to the user. The results include relevance scores and synergy details, presented in a user-friendly format. The display format is designed, for example, to show highly relevant deals at the top, similar to a dashboard.

[1042] Furthermore, users select projects of interest based on the displayed results and review their details and proposals. They then create concrete collaboration plans based on the selected projects and share them with relevant parties within their company. They also use a feedback form to send evaluations of the search results and proposals to the server. These evaluations may include, for example, "The proposal was helpful" or "It was not very relevant." The server uses this feedback to improve the accuracy of its search algorithm and synergy analysis algorithm.

[1043] Specific example

[1044] For example, suppose market research data for new product A is collected from the marketing department, and technical development data for new product A is collected from the development department and stored on a server. This data is then organized and stored in a database.

[1045] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to obtain information related to the "Sales Strategy for New Product A." The server searches the database for relevant cases based on these keywords, calculates a relevance score, and organizes the results. The emotion engine analyzes the user's emotional state, and if it detects that the user is experiencing stress, the server displays more concise and easy-to-understand suggestions. For example, it might say, "Check the market research data summary and use it in your sales strategy."

[1046] Users review the suggestions, propose specific ways to collaborate with marketing and development departments, and share them internally through the system. Finally, they provide feedback to the server on whether the suggestions were helpful, which helps improve the overall accuracy of the system.

[1047] Example of a prompt

[1048] "We want to develop a sales strategy based on market research data for our new product. Could you tell us what kind of data is relevant?"

[1049] By using this prompt, the generated AI model extracts and analyzes project data from the marketing department and technical information from the development department, providing specific synergy proposals.

[1050] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1051] Step 1: Collecting case data

[1052] The server exposes an API endpoint and a data entry form, collecting the following information from each department: "Project Name," "Responsible Department," "Progress Status," and "Technical / Service Information." Based on this input, the server generates case data and stores it in the database. Specifically, it receives data from the API and converts it to the appropriate format.

[1053] Step 2: Organize and standardize case data

[1054] The server organizes the collected case data and stores it in the database. The input here is the case data collected in Step 1. The server organizes and standardizes the data by attributes such as project name, responsible department, and progress status. It checks for duplicate data and optimizes the data format, and outputs the standardized data. For example, if there are multiple data entries with the same project name, the duplicates are merged.

[1055] Step 3: Enter your search query

[1056] The user enters keywords and filter options into an interface provided through the terminal. These inputs include terms such as "new product," "market research," and "technology development." The terminal then sends this input to the server. Specifically, an HTTP request is generated to send the data from the input form to the server.

[1057] Step 4: Identifying Similar Cases

[1058] The server searches the database based on the search query received in step 3. The input is the user's search query, and the output is the relevant case data. The server extracts cases that match or are similar to the project name and technical / service attributes, and calculates a relevance score. An algorithm is executed to retrieve the data corresponding to the query and calculate the relevance of each.

[1059] Step 5: Propose synergies

[1060] The server performs synergy analysis based on the project data extracted in step 4. The input is the relevant project data, and the output is a synergy proposal. The server uses a synergy analysis algorithm to identify resources and information that can be shared between projects and evaluate the synergistic effects. For example, it generates a specific proposal such as "utilize market research data in sales strategies."

[1061] Step 6: Adjustment by the Emotional Engine

[1062] The server uses an emotion engine to analyze the user's emotional state. Input is user input data and behavioral data, and output is the emotion analysis result. Based on this emotion analysis, the server adjusts the search results and synergy suggestions. The emotion engine's algorithm detects the user's stress and relaxation levels and adjusts the complexity of the suggestions accordingly.

[1063] Step 7: Results display and feedback

[1064] The terminal visually displays search results and synergy suggestions to the user. The input is the adjusted synergy suggestions, and the output is the information presented to the user. Specifically, it has a mechanism that displays highly relevant cases at the top in a dashboard format. Based on the displayed results, the user selects cases of interest and checks their details and suggestions. After that, the user submits an evaluation to the server through a feedback form. The server, upon receiving this feedback, improves the accuracy of its search algorithm and synergy analysis algorithm.

[1065] (Application Example 2)

[1066] 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."

[1067] Traditional systems had problems with effectively utilizing interdepartmental synergies in production management within factories, and with being unable to make flexible suggestions that took into account the emotional state of workers. In particular, the collection and analysis of process data was insufficient, making it difficult to propose optimal resource sharing and cooperation plans to maximize production efficiency. Furthermore, there was a lack of means to provide appropriate support when on-site workers experienced stress, which posed a risk of a decline in overall work efficiency and experience.

[1068] 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.

[1069] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by the user, means for extracting relevant cases from the database based on the search queries, means for analyzing the synergy between the extracted cases and the current case and generating suggestions, means for displaying the analysis and suggestion results to the user, means for receiving feedback from the user and improving the search algorithm, means for recognizing the user's emotions using an emotion engine and adjusting the search results and suggestions based on the user's emotional state, and means for collecting data from each department in the factory and analyzing process data in the production line. This makes it possible to maximize the use of interdepartmental synergies in production management and to make flexible suggestions tailored to the emotional state of workers.

[1070] "Project data" refers to information collected from various departments within a company, including project names, responsible departments, progress status, and technical / service-related information.

[1071] A "database" is an information system that stores, organizes, and allows searching of collected case data.

[1072] A "search query" is a statement of inquiry that includes keywords and conditions entered by a user to obtain specific information.

[1073] "Synergy" refers to the synergistic effect that arises when multiple projects or pieces of information interact with each other.

[1074] An "emotion engine" is a system that recognizes and analyzes a user's emotional state.

[1075] A "production line" is a collection of processes that are carried out sequentially within a factory to produce a product.

[1076] The "relevance score" is a numerical representation of the relationship between a search query and the case data stored in the database.

[1077] "Feedback" is the process of receiving evaluations and opinions from users, and the system is improved based on this feedback.

[1078] "Resources" is a general term encompassing the personnel, equipment, materials, and information necessary to carry out production activities.

[1079] "API" stands for Application Programming Interface, and it is a set of rules for sharing functions and data between software programs.

[1080] The system of this invention collects, organizes, analyzes, and proposes data from each department to streamline production management within a factory. Furthermore, it aims to improve work efficiency and user experience by providing flexible suggestions based on the emotional state of workers using an emotion engine. The embodiments of this system will be described in detail below.

[1081] Data collection and organization

[1082] The server exposes APIs and data entry forms for collecting project data from each department. The collected data includes project name, responsible department, progress status, and technical / service information. The server stores the collected project data in a database (PostgreSQL), where the data is automatically organized and standardized. It also performs checks for duplicate data and optimizes data formats.

[1083] Enter your search query

[1084] Users can enter search queries from their terminal and send them to the server. This interface provides keyword input fields and filter options. For example, users can enter keywords such as "new material X," "processing method," and "manufacturing process." The terminal then sends the search queries entered by the user to the server.

[1085] Extraction of similar cases

[1086] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to project names and technical / service attributes, and calculates a relevance score using natural language processing techniques such as TF-IDF and Word2Vec. Based on this, it sorts the search results in descending order of relevance.

[1087] Synergy proposal

[1088] The server analyzes the synergy between each project found through the search and the user's projects. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted projects and evaluates their synergistic effects. Based on the analysis results, it generates specific synergy proposals. These proposals may include, for example, "sharing process data" and "optimizing resources."

[1089] Adjustment by the emotion engine

[1090] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's camera footage and input data to identify the user's emotional state. This analysis uses TensorFlow.js and a pre-trained emotion recognition model. Based on the emotional state, the server adjusts the search results and synergy suggestions. For example, if the user is stressed, it will provide more concise and intuitive suggestions.

[1091] Results display and feedback

[1092] The device visually displays search results and synergy suggestions to the user. React.js is used for display, providing relevance scores and synergy details in a user-friendly format. Based on the displayed results, the user selects projects of interest and reviews detailed information and suggestions. Users can also create specific collaboration proposals based on their selected projects and share them with internal stakeholders. Furthermore, users can send feedback on the suggestions to the server via the device, improving the overall accuracy of the system.

[1093] Specific example

[1094] Let's consider a scenario where a worker in the production department collects and analyzes data related to "processing methods for new material X." Based on the keywords entered by the worker, a search is performed, and relevant past case data is extracted. If the emotion engine recognizes the worker's stress, a simple procedural guide is displayed, along with suggestions for improving efficiency that incorporate insights from other departments. For example, a suggestion might be made to "check the overview of the processing procedure for new material X and confirm the optimal machine settings."

[1095] Example of a prompt

[1096] We want to investigate processing methods for the new material X. Based on progress data, analyze synergies with other departments and display a simple procedure guide and suggestions for efficiency improvements. If the user is experiencing stress, please use an emotion recognition system to present it in a particularly clear and understandable format.

[1097] This invention is expected to improve production efficiency within factories and significantly enhance the worker experience.

[1098] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1099] Step 1:

[1100] The server collects project data from each department. Specifically, it receives project names, responsible departments, progress status, and technical / service information through APIs and data entry forms, standardizes this data, and stores it in a database. The input data is in JSON format, and PostgreSQL is used as the database.

[1101] Step 2:

[1102] The server organizes the collected project data. It classifies the data in the database by attributes such as project name, department in charge, and progress status, checks for duplicate data, and optimizes the data format. It uses the Python pandas library to process and organize the data.

[1103] Step 3:

[1104] The user enters a search query through an interface on their device. The interface includes a keyword input field and filter options, allowing the user to enter keywords such as "new material X" or "processing method." The entered search query is then sent from the device to the server.

[1105] Step 4:

[1106] The server extracts relevant cases from the database based on the search query. It searches for highly relevant data using natural language processing techniques such as TF-IDF and Word2Vec, and calculates a relevance score. Based on the calculation results, the search results are sorted in descending order of relevance.

[1107] Step 5:

[1108] The server analyzes the synergies between extracted projects and its own projects and generates proposals. It uses a synergy analysis algorithm to evaluate resource sharing and the synergistic effects of information. The generated proposals include specific details regarding resource optimization and process sharing.

[1109] Step 6:

[1110] The server uses an emotion engine to recognize the user's emotions. It analyzes the user's video data and input data collected in real time by the device and identifies the emotional state using TensorFlow.js. A pre-trained emotion recognition model is used for this purpose.

[1111] Step 7:

[1112] The server adjusts search results and suggestions based on the user's emotional state. For example, if a user is stressed, it generates more concise and intuitive suggestions and adjusts the content accordingly. Specifically, it provides simple step-by-step guides and concrete measures to improve efficiency.

[1113] Step 8:

[1114] The device visually displays search results and synergy suggestions to the user. Using React.js, it provides an interface that allows users to see relevance scores and synergy details at a glance. Based on the displayed results, the user selects projects of interest and views their detailed information.

[1115] Step 9:

[1116] Users provide feedback on the suggested results. Evaluations and opinions from users are sent from their terminals to the server, which uses this information to improve the search algorithm and synergy analysis algorithm. This improves the overall system performance.

[1117] 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.

[1118] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[1119] 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.

[1120] [Fourth Embodiment]

[1121] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1122] 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.

[1123] 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).

[1124] 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.

[1125] 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.

[1126] 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).

[1127] 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.

[1128] 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.

[1129] 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.

[1130] 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.

[1131] 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.

[1132] 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.

[1133] 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".

[1134] The system of this invention collects project data from various departments within a company and analyzes and proposes synergies based on this data, thereby improving the overall efficiency and creativity of the company. The program's processing flow and a specific example are described below.

[1135] Collection and organization of project data

[1136] The server collects project data from each department. Specifically, it exposes APIs and data entry forms on the server, allowing each department to input project names, responsible departments, progress status, and technical / service-related information.

[1137] The server stores the collected case data in a database. The collected data is automatically organized and standardized according to attributes such as project name, responsible department, and progress status.

[1138] Enter your search query

[1139] Users utilize an interface to search for information related to their project. This interface provides keyword input fields and filter options.

[1140] The terminal sends the search query entered by the user to the server. This query may include keywords such as "new product," "market research," and "technology development."

[1141] Extraction of similar cases

[1142] The server searches the database for relevant cases based on the search query. First, it extracts case data that matches or is similar to the project name or technical / service attributes.

[1143] The extracted case data is then given a relevance score. The server sorts the cases in descending order of relevance based on this score.

[1144] Synergy proposal

[1145] The server analyzes the synergy between each search result and the user's existing projects. Using a synergy analysis algorithm, it identifies resources and information that can be shared among the extracted projects and evaluates their synergistic effects.

[1146] The server generates specific synergy proposals based on the analysis results. For example, it might generate proposals such as "utilize market research data in sales strategies" or "share progress in technology development and adjust sales timing."

[1147] Results display and feedback

[1148] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, making it easy for the user to understand.

[1149] Users can select projects of interest based on the displayed results and review detailed information and proposals. Furthermore, they can create specific collaboration plans based on the selected projects and share them with relevant parties within their company.

[1150] Users send feedback on search results and suggestions to the server via their devices. This feedback may include comments such as "The suggestion was helpful" or "It was not very relevant."

[1151] The server improves the accuracy of its search algorithms and synergy analysis algorithms based on the feedback it receives.

[1152] Specific example

[1153] For example, suppose data on "market research for new product A" is collected from the marketing department and data on "technical development for new product A" is collected from the development department and stored on a server. This data is organized by attributes such as project name, responsible department, progress status, and technology / service, and stored in a database.

[1154] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to search for information related to their project, "Sales Strategy for New Product A."

[1155] Based on this, the server extracts projects such as "Market research for new product A" and "Technical development for new product A" from the database and determines that they have a high relevance to the user's project.

[1156] The server generates synergy proposals such as "utilizing market research data in sales strategies" and "sharing progress in technology development to adjust sales timing," and displays them to the user via the terminal.

[1157] Users review the suggestions, propose specific ways to collaborate with the marketing and development departments, and share them internally. Finally, they provide feedback to the server indicating that the suggestions were helpful, thereby improving the overall accuracy of the system.

[1158] The following describes the processing flow.

[1159] Step 1:

[1160] The server collects project data from each department. Specifically, each department uses APIs and data entry forms published on the server to input project names, responsible departments, progress status, and technical / service-related information.

[1161] Step 2:

[1162] The server stores the collected case data in a database. The data is automatically organized and standardized by attributes such as project name, responsible department, and progress status. It also checks for duplicate data and optimizes the data format.

[1163] Step 3:

[1164] Users utilize an interface to search for information related to their project. This interface provides keyword input fields and filter options.

[1165] Step 4:

[1166] The terminal sends the search query entered by the user to the server. This query may include keywords such as "new product," "market research," and "technology development."

[1167] Step 5:

[1168] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to the project name or technical / service attributes.

[1169] Step 6:

[1170] The server calculates a relevance score for the extracted case data. Based on the relevance score, the search results are sorted in descending order of relevance.

[1171] Step 7:

[1172] The server analyzes the synergy between each search result and the user's existing projects. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted projects and evaluates their synergistic effects.

[1173] Step 8:

[1174] The server generates specific synergy proposals based on the analysis results. These proposals include concrete examples such as "utilizing market research data in sales strategies" and "sharing progress in technology development to coordinate sales timing."

[1175] Step 9:

[1176] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, presented in a user-friendly format.

[1177] Step 10:

[1178] Based on the displayed results, users select projects of interest and review detailed information and proposals. They also create specific collaboration plans based on the selected projects and share them with relevant parties within their company.

[1179] Step 11:

[1180] Users send feedback on search results and suggestions to the server via their devices. This feedback includes ratings such as "the suggestion was helpful" or "it was not relevant."

[1181] Step 12:

[1182] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This continuously improves the overall performance of the system.

[1183] (Example 1)

[1184] 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".

[1185] There is a need for a system that can effectively collect and organize information on diverse projects underway in each department within a company, extract highly relevant information, and complement it to improve the overall efficiency and creativity of the company. However, current distributed information management systems have the problem of insufficient information sharing between departments, making it difficult to propose effective solutions that generate synergistic effects. This invention aims to solve this problem.

[1186] 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.

[1187] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by the user, means for extracting relevant cases from the database based on the search queries, means for calculating a relevance score for the extracted cases and sorting the cases based on relevance, means for analyzing the synergy between the extracted cases and the current cases and generating specific proposals, means for visually displaying the analysis and proposal results to the user, and means for receiving feedback from the user and improving the search algorithm and synergy analysis algorithm. This enables effective utilization of case data collected from each department, maximizing information sharing and synergy effects across the entire company.

[1188] "Project data" refers to information about projects carried out by each department within a company, and specifically includes data such as project name, responsible department, progress status, and information about technology and services.

[1189] A "server" is a computer system that collects, organizes, stores, retrieves, and analyzes data over a network.

[1190] A "database" is a data management system that efficiently stores collected case data and allows for searching and retrieving it as needed.

[1191] A "search query" refers to the keywords and conditions that a user enters when searching for information, and relevant data is retrieved based on these queries.

[1192] The "relevance score" is a numerical value calculated by the server to evaluate the relevance of the cases it extracts from the database based on the search query. The relevance score indicates how well the cases match or are similar to the search query.

[1193] "Synergy" refers to the synergistic effect that arises from the mutual complementarity of multiple projects, and signifies the improvement in value resulting from the sharing of resources and information.

[1194] An "analysis algorithm" is a computational method used by a server to analyze collected data and extract useful information and patterns.

[1195] A "proposal" is a specific action plan or improvement measure generated by an analytical algorithm, aimed at improving the overall efficiency and creativity of the company.

[1196] A "user interface" is a collection of screens and input tools that a user uses to operate a system, providing means for performing searches and providing feedback.

[1197] "Feedback" refers to the act of users sending evaluations and opinions about search results and suggestions to the server. This feedback is used to improve the system's performance.

[1198] Modes for carrying out the invention

[1199] The system of the present invention collects project data from various departments within a company and analyzes and proposes synergies based on this data, thereby improving the overall efficiency and creativity of the company. The following describes specific embodiments of the system.

[1200] Collection and organization of project data

[1201] The server exposes APIs and data entry forms to collect project data from each department. This process is implemented using a RESTful API built, for example, with the Django framework. Each department enters the project name, responsible department, progress status, and technical / service-related information.

[1202] The collected data is stored in a database such as PostgreSQL or MySQL. The server organizes the received data by attributes such as project name, department, and progress, and automatically performs a standardization process. This ensures that data collected from each department is stored in a consistent format.

[1203] Enter your search query

[1204] The terminal provides an interface for users to search for information related to a project. This interface, built with React and Vue.js, includes keyword input fields and filter options. Users enter keywords such as "new product," "market research," and "technology development" through the interface.

[1205] The entered search query is sent from the terminal to the server. The server searches the database for relevant cases based on the received query.

[1206] Extraction of similar cases

[1207] The server uses a search engine like Elasticsearch to retrieve relevant cases from the database based on the received search query. It extracts case data that matches or is similar to project names or technical / service attributes.

[1208] A relevance score is calculated for the extracted case data. The TF-IDF algorithm is used to assign a score to each case. The server then sorts the cases in descending order of relevance based on this score.

[1209] Synergy proposal

[1210] The server performs synergy analysis on the case data obtained from the search results. It uses machine learning libraries such as Scikit-learn and TensorFlow to perform the analysis, identify shareable resources and information, and evaluate the synergistic effects.

[1211] Based on the analysis results, specific synergy proposals are generated. For example, these may include specific suggestions such as "utilizing market research data in sales strategies" and "sharing the progress of technology development to adjust sales timing."

[1212] Results display and feedback

[1213] The device visually displays search results and synergy suggestions to the user. Using React and D3.js, it generates a dashboard showing relevance scores and synergy details. The user reviews the displayed results and selects projects of interest. They then review the details and suggestions for the selected projects and create specific collaboration plans.

[1214] Users share collaboration proposals with relevant parties within their company and send feedback on search results and proposals to the server via their devices. The server uses this feedback to improve the accuracy of its search algorithms and synergy analysis algorithms.

[1215] Specific example

[1216] For example, suppose the marketing department submits market research data for new product A to the server, and at the same time, the development department provides technical development data for new product A. This data is collected and organized on the server and consistently stored in the database.

[1217] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development," and sends a query on their terminal. The server searches the database for related cases using Elasticsearch, calculates a relevance score, and sorts the cases.

[1218] For example, the search results might include "market research for new product A" and "technological development for new product A," and the server determines that these are highly relevant to the user's project. Subsequently, it generates specific synergy proposals such as "utilize market research data in sales strategy" and "share the progress of technological development to adjust the sales timing," and presents them to the user via the terminal.

[1219] Users review these suggestions, develop specific collaboration plans with the marketing and development departments, and share them internally. By providing feedback to the server about the helpfulness of the suggestions, the system's accuracy can be continuously improved.

[1220] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1221] Step 1:

[1222] The server exposes APIs and data entry forms to collect project data from each department. A RESTful API built with the Django framework is used for this purpose. Each department enters project name, responsible department, progress status, and technical / service-related information. The entered data is sent to the server via the API.

[1223] Input: Project data entered by each department (project name, responsible department, progress status, and technical / service information).

[1224] Output: Case data sent to the server via API.

[1225] Specific operation: The marketing department inputs market research data for new product A and sends it to the server via API.

[1226] Step 2:

[1227] The server stores received project data in a PostgreSQL or MySQL database. The received data is organized by attributes such as project name, department, and progress status, and automatically standardized. For example, if a field contains missing data, it is converted to a consistent format.

[1228] Input: Case data sent to the server via API.

[1229] Output: Case data organized and stored in the database.

[1230] Specific operation: The server saves the received data to the database and automatically aligns fields such as project name and department.

[1231] Step 3:

[1232] The terminal provides an interface for users to search for information related to a project. This interface is implemented using React or Vue.js. Through this interface, users enter keywords such as "new product," "market research," and "technology development" to create search queries.

[1233] Input: The search query entered by the user (e.g., "new product", "market research", "technology development").

[1234] Output: Search queries sent from the terminal to the server.

[1235] Specific operation: A user in the sales department enters keywords to obtain information about new product A, and the terminal sends this query to the server.

[1236] Step 4:

[1237] The server searches the database for relevant cases based on the received search query. A search engine such as Elasticsearch is used for this process. Case data that matches or is similar to project names and technical / service attributes is extracted.

[1238] Input: Search query sent from the device.

[1239] Output: A list of case data that matches or is similar to the search query.

[1240] Specific operation: The server uses Elasticsearch to search the database and extract relevant case data.

[1241] Step 5:

[1242] The server calculates a relevance score for the extracted case data. Using the TF-IDF algorithm, scores are assigned based on project name and technical / service attributes. This allows for a relevance assessment for each case.

[1243] Input: A list of case data that matches or is similar to the search query.

[1244] Output: A list of case data assigned a relevance score.

[1245] Specific operation: The server calculates a relevance score for each case data and sorts them in descending order of relevance.

[1246] Step 6:

[1247] The server performs synergy analysis on the case data obtained from the search results. Using Scikit-learn and TensorFlow, it identifies shareable resources and information and evaluates synergistic effects. It generates specific synergy proposals, such as "utilizing market research data in sales strategies."

[1248] Input: A list of case data assigned a relevance score.

[1249] Output: A list of project data to which synergy proposals have been assigned.

[1250] Specific operation: The server uses machine learning algorithms to analyze shared resources and information and generate synergy suggestions.

[1251] Step 7:

[1252] The device visually displays search results and synergy suggestions to the user. Using React and D3.js, it generates a dashboard showing relevance scores and synergy details. The user reviews the displayed results and selects deals that interest them.

[1253] Input: A list of project data to which synergy proposals have been assigned.

[1254] Output: A visual dashboard displayed on the user's screen.

[1255] Specific operation: The device generates a dashboard, and the user reviews the suggested content.

[1256] Step 8:

[1257] Users select projects of interest based on the displayed results and review detailed information and proposals. They then create specific collaboration plans based on their selections and share them with relevant parties within their company. Furthermore, they send feedback on the search results and proposals to the server via their device.

[1258] Input: User selections and feedback (e.g., "The suggestion was helpful," "It was not relevant").

[1259] Output: Feedback data sent to the server.

[1260] Specific operation: The user creates a collaboration proposal and sends feedback to the server.

[1261] Step 9:

[1262] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This feedback is used as a dataset for the algorithms and is utilized to update the deep learning models.

[1263] Input: User feedback data.

[1264] Output: Improved search algorithm and synergy analysis algorithm.

[1265] Specific operation: The server analyzes the feedback data and improves accuracy by updating the existing algorithm.

[1266] (Application Example 1)

[1267] 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".

[1268] In the current factory, multiple projects are running simultaneously, and each project is managed independently, making resource sharing and progress coordination difficult. This often leads to decreased efficiency and wasted resources. Furthermore, it is difficult for on-site workers and managers to access the latest project information, which can delay immediate responses. The challenge is to solve these problems and improve the overall efficiency and creativity of the factory.

[1269] 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.

[1270] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by users, means for extracting relevant cases from the database based on the search queries, means for analyzing the synergy between the extracted cases and the current case and generating proposals, means for displaying the analysis and proposal results to the user, means for receiving feedback from users and improving the search algorithm, means for collecting and analyzing data for each project within the factory in real time and proposing synergies for resource sharing and progress adjustment between projects, and means for providing an interface that can be easily accessed by on-site workers and managers using smartphones. This enables efficient resource sharing and progress adjustment between projects within the factory, and further promotes immediate response on-site.

[1271] "Project data" refers to project names, responsible departments, progress status, and technical / service-related information collected from each department.

[1272] A "database" is an information system that organizes collected case data and makes it possible to store and search it efficiently.

[1273] A "search query" is a combination of keywords and filter options that a user enters to identify relevant information.

[1274] "Synergy" refers to the synergistic effect that arises from sharing resources and information among multiple projects.

[1275] A "search algorithm" is a computational method used to identify relevant items from a database based on a user's search query.

[1276] "Real-time collection" refers to the process of collecting data the moment it is generated and sending it to the server.

[1277] "Resource sharing" refers to the sharing of resources such as personnel, equipment, and information among multiple projects.

[1278] "Progress adjustment" involves evaluating the progress of a project and making necessary revisions to the plan or changes to resource allocation.

[1279] An "interface" is a visual or mechanical means by which a user accesses and operates a system.

[1280] "Feedback" refers to opinions and data from users, based on their experiences and suggestions, that are used to improve the system's functions and algorithms.

[1281] System program generation

[1282] The system for realizing this invention primarily involves three roles: server, terminal, and user. The following describes each role and function in natural language.

[1283] Server roles and functions

[1284] The server collects project data from each department, organizes it, and stores it in a database. In this case, the server collects project names, responsible departments, progress status, and technical / service information provided by each department using APIs or data entry forms. The collected data is automatically organized and stored in the database.

[1285] When a user enters a search query, the server searches the database for relevant cases based on that query. The retrieved cases are sorted based on their relevance score and displayed to the user. Furthermore, a synergy analysis algorithm is used to generate synergy suggestions for resource sharing and progress coordination between related cases.

[1286] Furthermore, we receive feedback from users and use it to improve our search algorithms and synergy analysis algorithms. This allows us to continuously improve the accuracy and efficiency of the system.

[1287] Terminal roles and functions

[1288] The terminal provides an interface for sending search queries entered by the user to the server. The hardware used here includes smartphones. For example, when a user enters keywords such as "new product," "market research," or "technology development" using the terminal, that query is sent to the server.

[1289] The terminal also plays a role in visually displaying search results and synergy suggestions received from the server to the user. The displayed information includes relevance scores and synergy details, making it easy for the user to understand and interact with.

[1290] User roles and functions

[1291] Users enter search queries through their terminals and evaluate the search results and synergy suggestions from the server. Based on the displayed results, users select projects of interest and review detailed information and proposals. If there are specific proposals, they create and share collaborative plans within the company based on them. Users also send feedback on the search results and synergy suggestions to the server through their terminals, thereby contributing to system improvement.

[1292] Hardware and software to be used

[1293] Hardware: Servers (any brand name), smartphones

[1294] Software: Flask (Python), requests library, database (e.g., PostgreSQL)

[1295] Specific example

[1296] For example, suppose the marketing department collects data on "market research for new product A," and the development department collects data on "technological development for new product A." This data is organized and stored by a server. When a user in the sales department enters a query such as "new product A," "market research," and "technological development," the server extracts relevant cases based on this and sorts them based on their relevance score. The server then generates synergy suggestions such as "utilize market research data in sales strategy" and "share the progress of technological development to adjust the sales timing," and displays them to the user through their terminal.

[1297] Example of a prompt

[1298] "Based on the data from the new technology, please propose how it can have synergistic effects on improvement projects for existing products."

[1299] In this way, efficient resource sharing and progress adjustment within the factory become possible, and immediate responses on-site are facilitated.

[1300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1301] Step 1:

[1302] The server collects project data from each department. Specifically, each department sends project name, responsible department, progress status, and technical / service-related information to the server via APIs or data entry forms. This allows for the input of project data. The entered data is then temporarily stored in a database.

[1303] Step 2:

[1304] The server organizes and standardizes the collected case data. Specifically, it classifies the collected data by attributes such as project name, responsible department, and progress status, and converts it into a standard format. This allows the organized case data to be stored in the database.

[1305] Step 3:

[1306] The terminal receives the search query entered by the user and sends it to the server. The search query entered by the user may include keywords such as "new product," "market research," or "technology development." Based on the input, the terminal sends the search query. The terminal then sends the received query to the server.

[1307] Step 4:

[1308] The server searches the database for relevant cases based on the search query. It matches case data in the database based on keywords in the search query. Case data that matches or is similar to the attributes of the entered query is extracted.

[1309] Step 5:

[1310] The server calculates a relevance score for the extracted case data and sorts the cases in descending order of relevance. Specifically, a similarity calculation algorithm is used to calculate a score for each extracted case data. As a result, the cases are listed in descending order of relevance.

[1311] Step 6:

[1312] The server analyzes the synergies with the user's project and generates synergy proposals. The analysis identifies shareable resources and information, and evaluates their synergistic effects. For example, it generates specific proposals such as "utilize market research data in sales strategies" or "share progress in technology development to adjust sales timing."

[1313] Step 7:

[1314] The device visually displays search results and synergy suggestions received from the server to the user. Relevance scores and synergy details are clearly displayed for easy user understanding. Based on this information, the user selects projects of interest and checks detailed information and suggestions.

[1315] Step 8:

[1316] Users provide feedback on the displayed search results and synergy suggestions. Users input their opinions and impressions of the suggestions using a feedback interface. This feedback is sent to the server via the device.

[1317] Step 9:

[1318] The server improves its search algorithms and synergy analysis algorithms based on user feedback. The feedback is analyzed, and adjustments are made to further improve the accuracy of the search algorithms and synergy suggestions. This continuously improves the overall efficiency and accuracy of the system.

[1319] 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.

[1320] The system of this invention aims to improve the user experience not only by analyzing and proposing synergies based on project data collected from various departments within a company, but also by combining it with an emotion engine that recognizes user emotions. The program's processing flow and a specific example are described below.

[1321] Collection and organization of project data

[1322] The server exposes APIs and data entry forms to collect project data from each department, obtaining information such as project name, responsible department, progress status, and technical / service-related details.

[1323] The server stores the collected case data in a database. The data is automatically organized and standardized by attributes such as project name, responsible department, and progress status. Duplicate data checks and data format optimization are also performed.

[1324] Enter your search query

[1325] Users utilize an interface to search for information related to their project. This interface provides keyword input fields and filter options.

[1326] The terminal sends the search query entered by the user to the server. The query may include keywords such as "new product," "market research," or "technology development."

[1327] Extraction of similar cases

[1328] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to the project name or technical / service attributes, and calculates a relevance score. Based on this, it sorts the search results in descending order of relevance.

[1329] Synergy proposal

[1330] The server analyzes the synergy between each case found through the search and the user's existing cases. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted cases and evaluates their synergistic effects.

[1331] The server generates specific synergy proposals based on the analysis results. These proposals may include suggestions such as "utilizing market research data in sales strategies" or "sharing progress in technology development and coordinating sales timings."

[1332] Adjustment by the emotion engine

[1333] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input data and behavioral data to identify the user's emotional state.

[1334] Based on the user's emotional state, the server adjusts search results and synergy suggestions. For example, if a user is stressed, it provides more concise and intuitive suggestions. It also modifies the interface design and presentation methods according to the user's emotions to improve the experience.

[1335] Results display and feedback

[1336] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, presented in a user-friendly format. If adjustments have been made by the sentiment engine, those adjustments are also reflected.

[1337] Users select projects of interest based on the displayed results and review detailed information and proposals. They can also create specific collaboration plans based on their selected projects and share them with relevant parties within their company.

[1338] Users send feedback on search results and suggestions to the server via their devices. This feedback includes ratings such as "the suggestion was helpful" or "it was not relevant."

[1339] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This continuously improves the overall performance of the system.

[1340] Specific example

[1341] For example, suppose data on "market research for new product A" is collected from the marketing department and data on "technical development for new product A" is collected from the development department and stored on a server. This data is organized by attributes such as project name, responsible department, progress status, and technology / service, and stored in a database.

[1342] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to search for information related to the "Sales Strategy for New Product A." The server searches the database for relevant cases based on these keywords, calculates a relevance score, and organizes the results.

[1343] Furthermore, the emotion engine analyzes the user's emotional state, and if it detects that the user is experiencing stress, the server displays more concise and easy-to-understand suggestions. For example, it might display something like, "Check the market research data summary and use it in your sales strategy."

[1344] Users review the suggestions, propose specific ways to collaborate with the marketing and development departments, and share them internally. Finally, they can improve the overall accuracy of the system by providing feedback to the server on whether the suggestions were helpful.

[1345] The following describes the processing flow.

[1346] Step 1:

[1347] The server collects project data from each department. Specifically, each department uses APIs and data entry forms published on the server to input project names, responsible departments, progress status, and technical / service-related information.

[1348] Step 2:

[1349] The server stores the collected case data in a database. The data is automatically organized and standardized by attributes such as project name, responsible department, and progress status. It also checks for duplicate data and optimizes the data format.

[1350] Step 3:

[1351] Users utilize an interface to search for information related to their project. This interface provides a keyword input field and filter options.

[1352] Step 4:

[1353] The terminal sends the search query entered by the user to the server. This query may include keywords such as "new product," "market research," and "technology development."

[1354] Step 5:

[1355] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to the project name or technical / service attributes.

[1356] Step 6:

[1357] The server calculates a relevance score for the extracted case data. Based on the relevance score, the search results are sorted in descending order of relevance.

[1358] Step 7:

[1359] The server analyzes the synergy between each case found through the search and the user's existing cases. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted cases and evaluates the synergistic effects.

[1360] Step 8:

[1361] The server generates specific synergy proposals based on the analysis results. These proposals may include suggestions such as "utilizing market research data in sales strategies" or "sharing progress in technology development and coordinating sales timings."

[1362] Step 9:

[1363] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's input data and behavioral data to identify the user's emotional state.

[1364] Step 10:

[1365] The server adjusts search results and synergy suggestions based on the user's emotional state. For example, if the user is stressed, it will provide more concise and intuitive suggestions. It also changes the interface design and display method according to the user's emotions to improve the user experience.

[1366] Step 11:

[1367] The device visually displays search results and synergy suggestions to the user. The display includes relevance scores and synergy details, presented in a user-friendly format. If adjustments have been made by the sentiment engine, those adjustments are also reflected.

[1368] Step 12:

[1369] Based on the displayed results, users select projects of interest and review detailed information and proposals. They also create specific collaboration plans based on the selected projects and share them with relevant parties within their company.

[1370] Step 13:

[1371] Users send feedback on search results and suggestions to the server via their devices. This feedback includes ratings such as "the suggestion was helpful" or "it was not relevant."

[1372] Step 14:

[1373] The server improves the accuracy of its search and synergy analysis algorithms based on the feedback it receives. This continuously improves the overall performance of the system.

[1374] Specific example

[1375] For example, data on "market research for new product A" from the marketing department and data on "technical development for new product A" from the development department are collected on a server, organized by project name, responsible department, progress status, and technical / service attributes, and stored in a database.

[1376] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to search for information related to the "Sales Strategy for New Product A." The server searches the database for relevant cases based on these keywords, calculates a relevance score, and organizes the results.

[1377] Furthermore, the emotion engine analyzes the user's emotional state, and if it detects that the user is experiencing stress, the server displays more concise and easy-to-understand suggestions. For example, it might display something like, "Check the market research data summary and use it in your sales strategy."

[1378] Users review the suggestions, propose specific ways to collaborate with marketing and development departments, and share them internally. Finally, they provide feedback to the server on whether the suggestions were helpful, thereby improving the overall accuracy of the system.

[1379] (Example 2)

[1380] 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".

[1381] Traditional systems struggle to centralize project data collected from various departments and to provide appropriate search results for user queries. Furthermore, the proposed synergies are not always optimized for the user's current emotional state, making user experience improvement a challenge.

[1382] 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.

[1383] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by the user, means for extracting relevant cases from the database based on the search queries, means for analyzing the synergy between the extracted cases and the current case and generating proposals, means for displaying the analysis and proposal results to the user, means for recognizing the user's emotions and adjusting the proposal content based on those emotions, and means for receiving feedback from the user and improving the search algorithm and synergy analysis algorithm. This enables the provision of optimal synergy proposals that take the user's emotions into consideration, thereby improving the user experience.

[1384] "Project data" refers to data collected from each department, including project names, responsible departments, progress status, and information regarding technology and services.

[1385] A "database" is a system or storage location that stores collected case data and manages it in a way that allows for access and searching.

[1386] A "search query" is input data used by a user to search for specific information, including keywords and filter options entered into the system.

[1387] "Synergy" refers to creating synergistic effects by utilizing resources and information that can be shared across multiple projects or issues.

[1388] An "emotion engine" is a program or system that analyzes user input data and behavioral data to identify the user's emotional state.

[1389] "Feedback" refers to the evaluations and opinions that users submit to the system regarding search results and suggestions.

[1390] The "relevance score" is a numerical representation of the relationship between a search query and the results in the database; the higher the relevance, the higher the score.

[1391] "Adjusting the suggested content" refers to changing the suggestions and information displayed by the system based on the user's emotional state recognized by the emotion engine.

[1392] An "algorithm" refers to a set of computational procedures or rules for solving a specific problem, and in this context, it includes search algorithms and synergy analysis algorithms.

[1393] The system of this invention not only has the function of analyzing and proposing synergies based on project data collected from various departments within a company, but also aims to improve the user experience by introducing an emotion engine that recognizes user emotions. The program's processing flow and specific examples are described in detail below.

[1394] Collection and organization of project data

[1395] The server exposes API endpoints and data entry forms for collecting project data from each department. Using the API, departments such as marketing and development can automatically retrieve "project name," "responsible department," "progress status," and "technical / service-related information." For example, the marketing department might provide data on "market research for new product A," and the development department might provide data on "technical development for new product A."

[1396] The collected project data is stored in a database (e.g., MySQL, PostgreSQL). The data is organized and standardized by attributes such as project name, responsible department, progress status, and technology / service. This step also includes checking for duplicate data and optimizing the data format. For example, if data with the same project name is sent from different departments, the server detects the duplication and merges the data.

[1397] Enter your search query

[1398] The user enters a search query using an interface provided through the terminal. The interface includes keyword input fields and filter options, allowing the user to enter keywords such as "new product," "market research," or "technology development." The terminal then sends the entered query to the server.

[1399] Extraction of similar cases

[1400] The server receives the entered search query and searches the database. For example, it extracts market research data and technology development data related to "New Product A". Based on the keywords in the query, it extracts case data that matches or is similar to the relevant project name or technology / service attributes and calculates a relevance score.

[1401] Synergy proposal

[1402] The server performs synergy analysis based on the extracted project data. Using a synergy analysis algorithm, it identifies resources and information that can be shared between projects and evaluates their synergistic effects. Specific proposals include "utilizing market research data in sales strategies" and "sharing the progress of technology development and coordinating sales timings."

[1403] Adjustment by the emotion engine

[1404] The server uses an emotion engine (such as IBM Watson or Microsoft Azure Emotion API) to recognize the user's emotions. It analyzes user input and behavioral data to determine the user's emotional state. For example, if the user is stressed, the server displays simpler and easier-to-understand suggestions. If the user is relaxed, it provides more detailed information.

[1405] Results display and feedback

[1406] The device visually displays search results and synergy suggestions to the user. The results include relevance scores and synergy details, presented in a user-friendly format. The display format is designed, for example, to show highly relevant deals at the top, similar to a dashboard.

[1407] Furthermore, users select projects of interest based on the displayed results and review their details and proposals. They then create concrete collaboration plans based on the selected projects and share them with relevant parties within their company. They also use a feedback form to send evaluations of the search results and proposals to the server. These evaluations may include, for example, "The proposal was helpful" or "It was not very relevant." The server uses this feedback to improve the accuracy of its search algorithm and synergy analysis algorithm.

[1408] Specific example

[1409] For example, suppose market research data for new product A is collected from the marketing department, and technical development data for new product A is collected from the development department and stored on a server. This data is then organized and stored in a database.

[1410] A user in the sales department enters keywords such as "New Product A," "Market Research," and "Technology Development" to obtain information related to the "Sales Strategy for New Product A." The server searches the database for relevant cases based on these keywords, calculates a relevance score, and organizes the results. The emotion engine analyzes the user's emotional state, and if it detects that the user is experiencing stress, the server displays more concise and easy-to-understand suggestions. For example, it might say, "Check the market research data summary and use it in your sales strategy."

[1411] Users review the suggestions, propose specific ways to collaborate with marketing and development departments, and share them internally through the system. Finally, they provide feedback to the server on whether the suggestions were helpful, which helps improve the overall accuracy of the system.

[1412] Example of a prompt

[1413] "We want to develop a sales strategy based on market research data for our new product. Could you tell us what kind of data is relevant?"

[1414] By using this prompt, the generated AI model extracts and analyzes project data from the marketing department and technical information from the development department, providing specific synergy proposals.

[1415] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1416] Step 1: Collecting case data

[1417] The server exposes an API endpoint and a data entry form, collecting the following information from each department: "Project Name," "Responsible Department," "Progress Status," and "Technical / Service Information." Based on this input, the server generates case data and stores it in the database. Specifically, it receives data from the API and converts it to the appropriate format.

[1418] Step 2: Organize and standardize case data

[1419] The server organizes the collected case data and stores it in the database. The input here is the case data collected in Step 1. The server organizes and standardizes the data by attributes such as project name, responsible department, and progress status. It checks for duplicate data and optimizes the data format, and outputs the standardized data. For example, if there are multiple data entries with the same project name, the duplicates are merged.

[1420] Step 3: Enter your search query

[1421] The user enters keywords and filter options into an interface provided through the terminal. These inputs include terms such as "new product," "market research," and "technology development." The terminal then sends this input to the server. Specifically, an HTTP request is generated to send the data from the input form to the server.

[1422] Step 4: Identifying Similar Cases

[1423] The server searches the database based on the search query received in step 3. The input is the user's search query, and the output is the relevant case data. The server extracts cases that match or are similar to the project name and technical / service attributes, and calculates a relevance score. An algorithm is executed to retrieve the data corresponding to the query and calculate the relevance of each.

[1424] Step 5: Propose synergies

[1425] The server performs synergy analysis based on the project data extracted in step 4. The input is the relevant project data, and the output is a synergy proposal. The server uses a synergy analysis algorithm to identify resources and information that can be shared between projects and evaluate the synergistic effects. For example, it generates a specific proposal such as "utilize market research data in sales strategies."

[1426] Step 6: Adjustment by the Emotional Engine

[1427] The server uses an emotion engine to analyze the user's emotional state. Input is user input data and behavioral data, and output is the emotion analysis result. Based on this emotion analysis, the server adjusts the search results and synergy suggestions. The emotion engine's algorithm detects the user's stress and relaxation levels and adjusts the complexity of the suggestions accordingly.

[1428] Step 7: Results display and feedback

[1429] The terminal visually displays search results and synergy suggestions to the user. The input is the adjusted synergy suggestions, and the output is the information presented to the user. Specifically, it has a mechanism that displays highly relevant cases at the top in a dashboard format. Based on the displayed results, the user selects cases of interest and checks their details and suggestions. After that, the user submits an evaluation to the server through a feedback form. The server, upon receiving this feedback, improves the accuracy of its search algorithm and synergy analysis algorithm.

[1430] (Application Example 2)

[1431] 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".

[1432] Traditional systems had problems with effectively utilizing interdepartmental synergies in production management within factories, and with being unable to make flexible suggestions that took into account the emotional state of workers. In particular, the collection and analysis of process data was insufficient, making it difficult to propose optimal resource sharing and cooperation plans to maximize production efficiency. Furthermore, there was a lack of means to provide appropriate support when on-site workers experienced stress, which posed a risk of a decline in overall work efficiency and experience.

[1433] 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.

[1434] In this invention, the server includes means for collecting case data from each department, means for organizing the collected case data and storing it in a database, means for receiving search queries entered by the user, means for extracting relevant cases from the database based on the search queries, means for analyzing the synergy between the extracted cases and the current case and generating suggestions, means for displaying the analysis and suggestion results to the user, means for receiving feedback from the user and improving the search algorithm, means for recognizing the user's emotions using an emotion engine and adjusting the search results and suggestions based on the user's emotional state, and means for collecting data from each department in the factory and analyzing process data in the production line. This makes it possible to maximize the use of interdepartmental synergies in production management and to make flexible suggestions tailored to the emotional state of workers.

[1435] "Project data" refers to information collected from various departments within a company, including project names, responsible departments, progress status, and technical / service-related information.

[1436] A "database" is an information system that stores, organizes, and allows searching of collected case data.

[1437] A "search query" is a statement of inquiry that includes keywords and conditions entered by a user to obtain specific information.

[1438] "Synergy" refers to the synergistic effect that arises when multiple projects or pieces of information interact with each other.

[1439] An "emotion engine" is a system that recognizes and analyzes a user's emotional state.

[1440] A "production line" is a collection of processes that are carried out sequentially within a factory to produce a product.

[1441] The "relevance score" is a numerical representation of the relationship between a search query and the case data stored in the database.

[1442] "Feedback" is the process of receiving evaluations and opinions from users, and the system is improved based on this feedback.

[1443] "Resources" is a general term encompassing the personnel, equipment, materials, and information necessary to carry out production activities.

[1444] "API" stands for Application Programming Interface, and it is a set of rules for sharing functions and data between software programs.

[1445] The system of this invention collects, organizes, analyzes, and proposes data from each department to streamline production management within a factory. Furthermore, it aims to improve work efficiency and user experience by providing flexible suggestions based on the emotional state of workers using an emotion engine. The embodiments of this system will be described in detail below.

[1446] Data collection and organization

[1447] The server exposes APIs and data entry forms for collecting project data from each department. The collected data includes project name, responsible department, progress status, and technical / service information. The server stores the collected project data in a database (PostgreSQL), where the data is automatically organized and standardized. It also performs checks for duplicate data and optimizes data formats.

[1448] Enter your search query

[1449] Users can enter search queries from their terminal and send them to the server. This interface provides keyword input fields and filter options. For example, users can enter keywords such as "new material X," "processing method," and "manufacturing process." The terminal then sends the search queries entered by the user to the server.

[1450] Extraction of similar cases

[1451] The server searches the database for relevant cases based on the search query. It extracts case data that matches or is similar to project names and technical / service attributes, and calculates a relevance score using natural language processing techniques such as TF-IDF and Word2Vec. Based on this, it sorts the search results in descending order of relevance.

[1452] Synergy proposal

[1453] The server analyzes the synergy between each project found through the search and the user's projects. Using a synergy analysis algorithm, it identifies resources and information that can be shared between the extracted projects and evaluates their synergistic effects. Based on the analysis results, it generates specific synergy proposals. These proposals may include, for example, "sharing process data" and "optimizing resources."

[1454] Adjustment by the emotion engine

[1455] The server uses an emotion engine to recognize the user's emotions. The emotion engine analyzes the user's camera footage and input data to identify the user's emotional state. This analysis uses TensorFlow.js and a pre-trained emotion recognition model. Based on the emotional state, the server adjusts the search results and synergy suggestions. For example, if the user is stressed, it will provide more concise and intuitive suggestions.

[1456] Results display and feedback

[1457] The device visually displays search results and synergy suggestions to the user. React.js is used for display, providing relevance scores and synergy details in a user-friendly format. Based on the displayed results, the user selects projects of interest and reviews detailed information and suggestions. Users can also create specific collaboration proposals based on their selected projects and share them with internal stakeholders. Furthermore, users can send feedback on the suggestions to the server via the device, improving the overall accuracy of the system.

[1458] Specific example

[1459] Let's consider a scenario where a worker in the production department collects and analyzes data related to "processing methods for new material X." Based on the keywords entered by the worker, a search is performed, and relevant past case data is extracted. If the emotion engine recognizes the worker's stress, a simple procedural guide is displayed, along with suggestions for improving efficiency that incorporate insights from other departments. For example, a suggestion might be made to "check the overview of the processing procedure for new material X and confirm the optimal machine settings."

[1460] Example of a prompt

[1461] We want to investigate processing methods for the new material X. Based on progress data, analyze synergies with other departments and display a simple procedure guide and suggestions for efficiency improvements. If the user is experiencing stress, please use an emotion recognition system to present it in a particularly clear and understandable format.

[1462] This invention is expected to improve production efficiency within factories and significantly enhance the worker experience.

[1463] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1464] Step 1:

[1465] The server collects project data from each department. Specifically, it receives project names, responsible departments, progress status, and technical / service information through APIs and data entry forms, standardizes this data, and stores it in a database. The input data is in JSON format, and PostgreSQL is used as the database.

[1466] Step 2:

[1467] The server organizes the collected project data. It classifies the data in the database by attributes such as project name, department in charge, and progress status, checks for duplicate data, and optimizes the data format. It uses the Python pandas library to process and organize the data.

[1468] Step 3:

[1469] The user enters a search query through an interface on their device. The interface includes a keyword input field and filter options, allowing the user to enter keywords such as "new material X" or "processing method." The entered search query is then sent from the device to the server.

[1470] Step 4:

[1471] The server extracts relevant cases from the database based on the search query. It searches for highly relevant data using natural language processing techniques such as TF-IDF and Word2Vec, and calculates a relevance score. Based on the calculation results, the search results are sorted in descending order of relevance.

[1472] Step 5:

[1473] The server analyzes the synergies between extracted projects and its own projects and generates proposals. It uses a synergy analysis algorithm to evaluate resource sharing and the synergistic effects of information. The generated proposals include specific details regarding resource optimization and process sharing.

[1474] Step 6:

[1475] The server uses an emotion engine to recognize the user's emotions. It analyzes the user's video data and input data collected in real time by the device and identifies the emotional state using TensorFlow.js. A pre-trained emotion recognition model is used for this purpose.

[1476] Step 7:

[1477] The server adjusts search results and suggestions based on the user's emotional state. For example, if a user is stressed, it generates more concise and intuitive suggestions and adjusts the content accordingly. Specifically, it provides simple step-by-step guides and concrete measures to improve efficiency.

[1478] Step 8:

[1479] The device visually displays search results and synergy suggestions to the user. Using React.js, it provides an interface that allows users to see relevance scores and synergy details at a glance. Based on the displayed results, the user selects projects of interest and views their detailed information.

[1480] Step 9:

[1481] Users provide feedback on the suggested results. Evaluations and opinions from users are sent from their terminals to the server, which uses this information to improve the search algorithm and synergy analysis algorithm. This improves the overall system performance.

[1482] 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.

[1483] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[1484] 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.

[1485] 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.

[1486] 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.

[1487] 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.

[1488] 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.

[1489] 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.

[1490] 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 e...

Claims

1. A means of collecting project data from each department, A means for organizing and storing the collected case data in a database, A means of receiving search queries entered by the user, A means of extracting relevant cases from a database based on a search query, A means for analyzing the synergies between extracted projects and our own projects and generating proposals, Means for displaying the analysis and proposed results to the user, A means of receiving user feedback and improving the search algorithm, A system that includes this.

2. The system according to claim 1, further comprising means for calculating a relevance score and sorting the search results in descending order of relevance.

3. The system according to claim 1, further comprising means for creating specific cooperation proposals based on projects selected by the user and sharing them within the company.

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