system

Abstract

We provide the system. [Solution] A means of inputting criteria, A means for searching a database based on the aforementioned criteria, Methods for formatting and displaying search results, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] Conventionally, operations such as M&A and selecting business partners have been cumbersome, and it has required a great deal of labor and time to manually select appropriate targets from a vast amount of data. Also, when the selection criteria are ambiguous or lack consistency, it may affect the quality and accuracy of the selection results. Furthermore, validation in the case of deficiencies in the input criteria is often performed manually, and there are issues with efficiency and reliability.

Means for Solving the Problems

[0005] To solve the aforementioned problems, the present invention proposes the following means: a system including means for the user to input criteria, means for automatically searching a database based on the criteria, and means for formatting and displaying the search results. Furthermore, by adding means for validating the criteria, errors in input can be automatically detected, enabling efficient searching. In addition, by providing means for retrieving and displaying detailed information of the company selected by the user from the search results, more detailed information about the selected company can be quickly provided. As a result, users can efficiently and effectively select M&A and sales targets.

[0006] "Criteria" refers to the standards and conditions that users use when selecting M&A targets or business partners.

[0007] "Means for inputting criteria" refers to the interface or tools that allow users to input criteria into the system.

[0008] A "database" refers to a collection of systematically organized and stored data used to perform criteria-based searches.

[0009] "Means of searching a database" refers to functions or programs that automatically search for information within a database based on criteria entered by the user.

[0010] "Search results" refer to the collection of information about companies or subjects obtained through a database search based on the entered criteria.

[0011] "Means of formatting and displaying search results" refers to functions and interfaces that format and display the results obtained from database searches in a way that is easy for users to understand.

[0012] "Validation" refers to the process of checking whether there are any flaws in the content or format of the criteria entered by the user, and verifying their accuracy.

[0013] "Detailed information" refers to more in-depth or additional information about the company or subject selected in the search results.

[0014] "Means for obtaining further detailed information" refers to functions or programs that retrieve more detailed information from a database for selected search results and provide it to the user.

[0015] The term "system" refers to a set of programs and infrastructure that combine the above-mentioned methods to enable users to efficiently select targets based on criteria. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing a process flow of a data processing system according to Example 1. [Figure 12] It is a sequence diagram showing a process flow of a data processing system according to Application Example 1. [Figure 13] It is a sequence diagram showing a process flow of a data processing system according to Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing a process flow of a data processing system according to Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

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

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

[0019] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0021] 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.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

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

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] The system of this invention is for automatically selecting target companies for M&A and sales, and mainly consists of a user, a terminal, and a server. The specific processing is described below in natural language.

[0038] Criteria input

[0039] User: Launch the system and input the criteria necessary for selecting M&A targets and sales opportunities. These criteria include sales volume, industry, region, growth rate, and number of employees. Input is performed using the interface on the terminal.

[0040] Specific example: The user enters criteria such as "sales of 1 billion yen or more, industry is IT, and region is Tokyo."

[0041] Criteria validation

[0042] Terminal: Automatically validates the format and content of criteria entered by the user. If there are any deficiencies, it displays a message prompting the user to correct them.

[0043] Validation example: Check if the sales volume is a numerical value and verify that the industry is present in the selection options.

[0044] Database connection

[0045] Server: After successful validation, establish a database connection and prepare search queries based on the criteria. Securely access the database using authentication credentials.

[0046] Specific example: Connect to an SQL database using a JDBC driver.

[0047] Database Search

[0048] Server: Generates search queries and executes them against the database. Queries are generated based on criteria entered by the user.

[0049] Specific example: Generate and execute the query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0050] Formatting and displaying search results

[0051] Server: Receives search results and formats them into a user-friendly format. Converts them to JSON or XML format and sends them to the terminal.

[0052] Specific example: Format it like this: "[{Company Name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company Name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]".

[0053] Terminal: Analyzes received data and displays it on the user interface in list view or table format. Users then proceed with their selection based on the displayed company information.

[0054] Example of display: Display as follows: "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[0055] Get detailed information

[0056] User: To obtain more detailed information about a selected company, click on that company.

[0057] Server: Retrieves detailed information about the selected company from the database again, formats it similarly, and sends it to the terminal.

[0058] Specific example: If "Company A" is selected, detailed information such as "capital, number of employees, business overview, and recent news" will be retrieved and displayed.

[0059] Device: Receives detailed information and displays it in a pop-up or new screen.

[0060] Example of displaying detailed information: Display detailed information about company A, such as "capital of 200 million yen, 500 employees, business overview, and recent news."

[0061] The above describes a specific embodiment of this system. This system is designed to efficiently execute a series of processes, from inputting criteria to database searching, formatting and displaying results, and obtaining detailed information, enabling users to efficiently select target companies.

[0062] The following describes the processing flow.

[0063] Step 1:

[0064] User: Launch the program and enter criteria for selecting M&A targets or sales opportunities. Criteria include sales volume, industry, region, growth rate, and number of employees.

[0065] Specific example: A user enters "Sales volume: 1 billion yen or more", "Industry: IT", and "Region: Tokyo" into the input field on the screen.

[0066] Step 2:

[0067] Terminal: Receives the entered criteria and validates the content. It verifies that the sales volume is a numerical value, that the industry is present in the selection list, and that the region is in the correct format.

[0068] Specific example: If a value other than a number is entered in the sales volume field, a warning message will be displayed prompting the user to re-enter the value.

[0069] Step 3:

[0070] Terminal: If the criteria are confirmed to be correct, it sends that data to the server. HTTP requests are used for transmission, and the criteria are packaged in JSON format or similar.

[0071] Specific example: The criteria are deemed correct, and the JSON object "{revenue: 'over 1 billion yen', industry: 'IT', location: 'Tokyo'}" is sent to the server.

[0072] Step 4:

[0073] Server: Analyzes the received criteria and establishes a database connection. Securely accesses the database using appropriate authentication credentials.

[0074] Specific example: Connecting to an SQL database using a JDBC driver or ODBC driver.

[0075] Step 5:

[0076] Server: Dynamically generates SQL queries based on criteria. The queries search the database for records that match the conditions.

[0077] Specific example: Generate the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0078] Step 6:

[0079] Server: Executes SQL queries to retrieve matching company information from the database. The retrieved data is temporarily stored and processed on the server.

[0080] Specific example: The query results will retrieve information on companies A, B, and C.

[0081] Step 7:

[0082] Server: Formats search results into a user-friendly format. Converts the formatted data into JSON or XML format and sends it to the terminal.

[0083] Specific example: Convert to JSON format like this: "[{Company Name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company Name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]".

[0084] Step 8:

[0085] Terminal: Parses received JSON data and displays it on the user interface. It displays the data in list view or table format, making it easy for users to browse and compare.

[0086] Specific display example: The screen will display as follows: "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[0087] Step 9:

[0088] User: Select and click on a company of interest from the displayed company information. Request detailed information.

[0089] Example: Click "Company A" to request detailed information.

[0090] Step 10:

[0091] Server: Receives user requests and generates and executes queries to retrieve detailed information about the selected company from the database again.

[0092] Specific example: Generate a query like "SELECT FROM company_details WHERE company_id = 'Company A'".

[0093] Step 11:

[0094] Server: Reformats the retrieved detailed information and sends it to the terminal in JSON format or similar.

[0095] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[0096] Step 12:

[0097] Terminal: Analyzes received details and displays them in a pop-up window or a new screen. Allows users to view and analyze the details.

[0098] Specific display example: A pop-up window will display information such as "Capital: 200 million yen," "Number of employees: 500," "Business overview," and "Recent news."

[0099] The above outlines the specific processing steps of the system. This allows users to select M&A targets and sales opportunities in an efficient and reliable manner.

[0100] (Example 1)

[0101] 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."

[0102] Traditional M&A and target company selection systems suffered from problems such as difficulty in accurately inputting criteria and insufficient validation of entered criteria, resulting in reduced search accuracy. Furthermore, search results were displayed in a format difficult for users to understand, leading to time-consuming target company selection. Additionally, the process of obtaining detailed information on selected companies was cumbersome, resulting in a poor user experience.

[0103] 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.

[0104] In this invention, the server includes means for inputting criteria, means for searching a database based on the criteria, means for formatting and displaying the search results, means for retrieving detailed information of a company selected by the user, means for establishing a database connection to retrieve the search results, and means for validating the criteria. This enables the user to efficiently and accurately select a target company.

[0105] "Criteria" refers to the conditions that users enter to select target companies, and include factors such as sales volume, industry, region, growth rate, and number of employees.

[0106] A "database" is a collection of structured data, a system for managing information that can be searched, stored, updated, and deleted.

[0107] A "search query" is a set of instructions executed to retrieve specific information from a database, and is written in languages ​​such as SQL.

[0108] "Formatting" is the process of converting raw data into a format that is easy for users to understand, and includes data format conversion and layout adjustment.

[0109] A "terminal" is a device used by a user to access a system, and includes personal computers, smartphones, tablets, and other similar devices.

[0110] "Validation" is a checking process to ensure that input data is accurate and in the correct format.

[0111] A "server" is a computer system that provides services to clients over a network, and is responsible for database access and processing business logic.

[0112] "JSON" is an abbreviation for JavaScript (registered trademark) Object Notation, and it is a format for representing data in a lightweight text format.

[0113] XML stands for eXtensible Markup Language, and it is a language for representing data in a structured format using tags.

[0114] A "user interface" is the means by which a user interacts with a system, and includes elements such as buttons, forms, and dialog boxes on the screen.

[0115] The system of this invention is for automatically selecting target companies for M&A and sales, and consists of a user, a terminal, and a server. The operation of each component is described in detail below.

[0116] Criteria input

[0117] User: The user launches the system using a terminal. Through the system interface, they input criteria for selecting target companies. These criteria include sales volume, industry, region, growth rate, and number of employees.

[0118] Specific example: A user uses an input form to enter criteria such as "sales volume of 1 billion yen or more, industry is IT, and region is Tokyo."

[0119] Criteria validation

[0120] Terminal: The terminal automatically validates the format and content of the criteria entered by the user. Validation verifies that the sales volume is a numerical value and that the industry is within the given options. If there are any errors, a message prompting the user to correct them is displayed.

[0121] Specific example: If the sales volume is not a number, display the error message "Please enter the sales volume as a number."

[0122] Database connection

[0123] Server: After successful validation, the server establishes a database connection using a JDBC driver, etc. It then securely accesses the database using authentication credentials (username and password).

[0124] Specific example: The server connects to the SQL database in the format "jdbc:MySQL(registered trademark): / / localhost:3306 / database_name".

[0125] Database Search

[0126] Server: The server generates search queries based on the criteria entered by the user and executes them against the database. A search query might look something like this: "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0127] Specific example: The server sends the aforementioned query to the database and searches for companies that match the criteria.

[0128] Formatting and displaying search results

[0129] Server: Formats the search results retrieved from the database. That is, converts the search results into JSON or XML format to make them easy for the user to understand. Sends the formatted data to the terminal.

[0130] Specific example: Convert the search results to the format "[{Company Name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company Name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]".

[0131] Terminal: The terminal analyzes the received data and displays it on the user interface in list view or table format. The user selects the next action based on the displayed company information.

[0132] Specific example: Company information is displayed in a list format such as "Company A: Sales of 1.2 billion yen, IT, Tokyo" and "Company B: Sales of 1.5 billion yen, IT, Tokyo".

[0133] Get detailed information

[0134] User: If a user wants to get more detailed information about a specific company, they click on that company's entry.

[0135] Specific example: A user clicks on information for "Company A".

[0136] Server: The server retrieves detailed information about the selected company from the database, formats it, and sends it to the terminal.

[0137] Specific example: Retrieve detailed information about company A using an SQL query and convert it into JSON format that includes "capital, number of employees, business overview, and recent news."

[0138] Device: The device receives detailed information and displays it in a pop-up window or a new screen.

[0139] Specific example: Display the following as detailed information: "Company A's capital is 200 million yen, it has 500 employees, and it includes a business overview and recent news."

[0140] Example of a prompt

[0141] 1. Criteria input prompt: "Please search for companies with sales of 1 billion yen or more, in the IT industry, and located in Tokyo."

[0142] 2. Prompt to retrieve detailed information: "Please display detailed information for Company A."

[0143] The above describes a specific embodiment of the target company selection process using the system of the present invention. This system efficiently executes a series of processes, from inputting criteria to database searching, formatting and displaying results, and obtaining detailed information, thereby supporting users in quickly and accurately selecting target companies.

[0144] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0145] Step 1:

[0146] Criteria input

[0147] User: The user starts the system using a terminal. An interface for entering criteria is displayed. The user enters the criteria necessary for selecting target companies.

[0148] Input: Conditions such as sales volume, industry, region, growth rate, and number of employees.

[0149] Output: Input criteria data

[0150] Specific action: The user enters "Sales volume of 1 billion yen or more, industry is IT, region is Tokyo" into the input form and clicks the "Submit" button.

[0151] Step 2:

[0152] Criteria validation

[0153] Terminal: The terminal validates the format and content of the criteria entered by the user. It verifies that the sales volume is a numerical value and that the industry is within the selectable options.

[0154] Input: Entered criteria data

[0155] Output: Validation result (success or failure)

[0156] Specific actions: The terminal verifies that the sales volume is a numerical value and checks that the industry is among the available options. If there are any discrepancies, an error message such as "Please enter the sales volume as a numerical value" will be displayed.

[0157] Step 3:

[0158] Database connection

[0159] Server: If validation is successful, the server establishes a database connection. It then securely accesses the database using a JDBC driver or similar.

[0160] Input: Criteria data that successfully validated

[0161] Output: Database connection established and authentication successful.

[0162] Specific operation: The server connects to the SQL database in the format "jdbc:mysql: / / localhost:3306 / database_name" and verifies the authentication information.

[0163] Step 4:

[0164] Database Search

[0165] Server: The server generates search queries based on the input criteria and executes them against the database.

[0166] Input: Criteria data, database connection information

[0167] Output: Search results (list of company information)

[0168] Specific operation: The server sends a query to the database: "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'", and retrieves company information that matches the conditions.

[0169] Step 5:

[0170] Formatting and displaying search results

[0171] Server: Formats search results retrieved from the database and converts them into a user-friendly format. It then converts them to JSON or XML format and sends them to the terminal.

[0172] Input: Search results retrieved from the database

[0173] Output: Formatted search result data (JSON or XML format)

[0174] Specific operation: The server formats the search results as follows: "[{Company name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]" and sends it to the terminal.

[0175] Terminal: The terminal analyzes the received data and displays it on the user interface in a list view or table format.

[0176] Input: Formatted search result data

[0177] Output: List of companies displayed on the screen

[0178] Specific operation: The terminal displays company information in a list format such as "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[0179] Step 6:

[0180] Get detailed information

[0181] User: Click on the company's entry to get more detailed information about that company.

[0182] Input: User click-through information

[0183] Output: Request for detailed information about the selected companies

[0184] Specific action: The user clicks on the item "Company A".

[0185] Server: The server retrieves detailed information about the selected company from the database, formats it, and sends it to the terminal.

[0186] Input: Request for detailed information, database connection information

[0187] Output: Formatted detailed information data

[0188] Specific operation: The server executes the SQL query again and converts the detailed information of company A, including "capital, number of employees, business overview, and recent news," into JSON format.

[0189] Device: The device receives detailed information and displays it in a pop-up window or a new screen.

[0190] Input: Formatted detailed information data

[0191] Output: Detailed information displayed on the screen

[0192] Specific action: The device displays detailed information about "Company A: Capital of 200 million yen, number of employees of 500, business overview, and recent news" in a new window or pop-up.

[0193] (Application Example 1)

[0194] 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."

[0195] The present invention aims to streamline sales activities within a factory and provide a system that enables sales representatives to immediately select appropriate target companies and present information. In particular, it aims to automate sales support within the factory and expedite on-site information acquisition and proposal activities.

[0196] 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.

[0197] In this invention, the server includes means for inputting criteria, means for searching a database based on the criteria, means for formatting and displaying the search results, means for a factory robot that performs an action based on the displayed search results, and means for providing information to factory staff's smart devices using the displayed results. This enables sales representatives to quickly select target companies on-site and make appropriate sales proposals.

[0198] A "means for inputting criteria" refers to an interface that allows users to input specific conditions or criteria (such as sales volume, industry, or region) into a terminal.

[0199] "Means for searching the database based on the aforementioned criteria" refers to a function that accesses the database based on the input criteria and executes a process to extract suitable data.

[0200] "Methods for formatting and displaying search results" refers to functions that convert the results obtained from database searches into a format that is easy for users to understand and then display them.

[0201] "Factory robot means" refers to a function that includes robots operating within a factory to perform specific actions based on search results.

[0202] "Means of providing information to factory staff's smart devices" refers to a function that transmits formatted search results and detailed information to smart devices (such as smart glasses) within the factory, making them available for use by on-site staff.

[0203] "Means of validation" refers to verification functions that confirm that the input criteria are in the correct format and content.

[0204] "Means for obtaining detailed information again" refers to a function that retrieves and displays more detailed information from the database regarding the search results selected by the user.

[0205] The system of this invention is designed to streamline sales activities within a factory. The system mainly consists of users, terminals, and servers, each of which works in cooperation with the others.

[0206] Criteria input and validation

[0207] The user inputs criteria (e.g., sales volume, industry, region) through a sales support robot using an interface on their smart device. The terminal receives the input and validates it. Specifically, it checks that the sales volume is a numerical value and that the industry and region are in the correct format. If there are any errors, a message prompting the user to correct them is displayed.

[0208] Database connection and search

[0209] After successful validation on the terminal, the server securely establishes a database connection. Using a JDBC driver or similar, it connects to the SQL database and generates a search query based on the criteria entered by the user. The server then executes the query against the database and extracts the relevant company information.

[0210] Formatting and displaying search results

[0211] The server receives the search results and formats them into a user-friendly format (e.g., JSON). The formatted data is sent to the terminal, where the user can view the search results in a list view or table format on a smart device (e.g., smart glasses or a head-mounted display).

[0212] Actions performed by factory robots

[0213] Based on the displayed search results, the sales support robots in the factory perform specific actions. For example, they automatically prepare proposal materials for specific companies or organize data on potential customers.

[0214] Get detailed information

[0215] When a user selects a specific company from the displayed search results, the server accesses the database again to retrieve detailed information about that company. This data is also formatted and displayed on the smart device as a pop-up or in a new screen. This allows the user to proceed with sales activities based on more in-depth information.

[0216] Example prompt statement

[0217] "Please search for companies with sales of 1 billion yen or more, in the manufacturing industry, and located in the Nagoya area."

[0218] By inputting such prompt statements into the AI ​​model, the system selects the appropriate company and executes the function.

[0219] The system of this invention efficiently executes a series of processes, from inputting criteria to database searching, formatting and displaying results, executing actions by factory robots, and obtaining detailed information. This enables users to quickly and effectively select target companies and smoothly advance their sales activities.

[0220] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0221] Step 1: The user accesses the sales support robot using a smart device and enters criteria. For example, sales volume, industry, region, etc., are entered through the smart glasses interface. This input will be used as data in the next step.

[0222] Step 2: The terminal validates the criteria entered by the user. Specifically, it verifies that the sales volume is a number, the industry is a string, and the region is in the correct format. If there are any inappropriate criteria, the terminal displays a message prompting the user to correct them and waits until it receives the correct input.

[0223] Step 3: Upon receiving the criteria that have passed validation, the terminal sends them to the server. At this point, the criteria are formatted in JSON or another format to ensure data consistency.

[0224] Step 4: The server establishes a database connection based on the received criteria. It securely and efficiently connects to the SQL database using tools such as a JDBC driver. Through this connection, query generation and execution become possible.

[0225] Step 5: The server generates an SQL query based on the criteria and executes it against the database. For example, it generates a query such as "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'manufacturing' AND location = 'Nagoya'" and sends it to the database. Executing this query retrieves information about the relevant companies.

[0226] Step 6: The server formats the search results retrieved from the database and converts them into a user-friendly format (e.g., JSON). This makes it easier to display them on terminals and smart devices.

[0227] Step 7: The server sends the formatted data to the terminal. The terminal analyzes the received data and displays it in a list view or table format on the user interface. This allows the user to quickly review the search results in the field.

[0228] Step 8: Once the user selects a company they are interested in, the device requests detailed information about that company from the server. This request is then sent back to the database via the server.

[0229] Step 9: The server retrieves detailed company information from the database, reformats it, and sends it to the terminal. The terminal displays this information to the user in a pop-up or new screen. This allows the user to conduct sales activities based on more in-depth information.

[0230] Step 10: Based on the search results and detailed information, the factory robot performs specific actions. For example, it might prepare proposal materials for a specific company or organize data about potential customers. This makes sales support more efficient and automated.

[0231] 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.

[0232] The system of this invention is for automatically selecting M&A and sales targets, and mainly consists of a user, a terminal, a server, and an emotion engine. The specific processing is described below in natural language.

[0233] Criteria input

[0234] User: Launch the system and input the criteria necessary for selecting M&A targets and sales opportunities. These criteria include sales volume, industry, region, growth rate, and number of employees. Input is performed using the interface on the terminal.

[0235] Specific example: The user enters criteria such as "sales of 1 billion yen or more, industry is IT, and region is Tokyo."

[0236] Criteria validation

[0237] Terminal: Automatically validates the format and content of criteria entered by the user. If there are any deficiencies, it displays a message prompting the user to correct them.

[0238] Validation example: If a value other than a number is entered in the sales volume field, a warning message will be displayed, prompting the user to re-enter the value.

[0239] User emotion recognition

[0240] Emotion Engine: This engine analyzes the user's facial expressions, voice, input speed, and patterns to recognize their emotions. For example, it can capture the user's face with a camera and use facial recognition technology to determine their emotions.

[0241] Specific example: When a user enters criteria, the camera captures the user's facial expression and recognizes emotions such as "excited" or "confused."

[0242] Database connection and search

[0243] Server: Receives criteria and user sentiment data, and establishes a database connection. Securely accesses the database using appropriate authentication credentials.

[0244] Specific example: Connecting to an SQL database using a JDBC driver or ODBC driver.

[0245] Server: Dynamically generates and executes SQL queries based on criteria, and searches the database for records that match the conditions.

[0246] Specific example: Generate and execute the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0247] Formatting and displaying search results

[0248] Server: Receives search results and adjusts the display order of results based on user sentiment data. Converts the formatted data into JSON or XML format and sends it to the terminal.

[0249] Specific example: If the user is in an "excited" state, the display order will be adjusted to prioritize positive information.

[0250] Terminal: Parses received JSON data and displays it on the user interface. It displays the data in list view or table format, making it easy for users to browse and compare.

[0251] Specific display example: The screen will display as follows: "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[0252] Get detailed information

[0253] User: Select and click on a company of interest from the displayed company information. Request detailed information.

[0254] Example: Click "Company A" to request detailed information.

[0255] Server: Receives user requests and generates and executes queries to retrieve detailed information about the selected company from the database again.

[0256] Specific example: Generate a query like "SELECT FROM company_details WHERE company_id = 'Company A'".

[0257] Server: Reformats the retrieved detailed information and sends it to the terminal in JSON format or similar.

[0258] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[0259] Terminal: Analyzes received details and displays them in a pop-up window or a new screen. Allows users to view and analyze the details.

[0260] Specific display example: A pop-up window will display information such as "Capital: 200 million yen," "Number of employees: 500," "Business overview," and "Recent news."

[0261] Storage and use of emotional data

[0262] Server: Stores user sentiment data and uses it to inform future criteria settings. This allows for more personalized search results by considering the user's past emotional states and choices.

[0263] Specific example: Save the companies a user selects based on specific criteria, along with their sentiment data, to use as reference for future searches.

[0264] The above is a specific embodiment of a system that combines an emotion engine. This system efficiently executes a series of processes, from inputting criteria and searching the database to formatting and displaying results, obtaining detailed information, and even utilizing emotion data, thereby providing users with a more personalized service.

[0265] The following describes the processing flow.

[0266] Step 1:

[0267] User: Launch the program and enter criteria for selecting M&A targets or sales opportunities. Criteria include sales volume, industry, region, growth rate, and number of employees.

[0268] Specific example: A user enters "Sales volume: 1 billion yen or more", "Industry: IT", and "Region: Tokyo" into the input field on the screen.

[0269] Step 2:

[0270] Terminal: Receives the entered criteria and validates the content. It verifies that the sales volume is a numerical value, that the industry is present in the selection list, and that the region is in the correct format.

[0271] Specific example: If a value other than a number is entered in the sales volume field, a warning message will be displayed prompting the user to re-enter the value.

[0272] Step 3:

[0273] Terminal: If the criteria are confirmed to be correct, it sends that data to the server. HTTP requests are used for transmission, and the criteria are packaged in JSON format or similar.

[0274] Specific example: The criteria are deemed correct, and the JSON object "{revenue: 'over 1 billion yen', industry: 'IT', location: 'Tokyo'}" is sent to the server.

[0275] Step 4:

[0276] Server: Analyzes the received criteria and establishes a database connection. Securely accesses the database using appropriate authentication credentials.

[0277] Specific example: Connecting to an SQL database using a JDBC driver or ODBC driver.

[0278] Step 5:

[0279] Emotion Engine: Recognizes user emotions by analyzing user facial expressions, voice, input speed and patterns, etc. It uses cameras and microphones to determine emotions.

[0280] Specific example: When a user performs an input task, their face is captured by a camera, and emotions such as "excited" or "confused" are recognized.

[0281] Step 6:

[0282] Server: Dynamically generate an SQL query based on criteria. Use the generated query to search for records that match the conditions in the database.

[0283] Specific example: Generate an SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0284] Step 7:

[0285] Server: Execute the SQL query and retrieve the matching company information from the database. Temporarily save and process the retrieved data on the server.

[0286] Specific example: Information of "Company A, Company B, Company C" is retrieved as the query result.

[0287] Step 8:

[0288] Server: Format the search results and adjust the display order based on the user's sentiment data. Convert the formatted data into JSON or XML format and send it to the terminal.

[0289] Specific example: If the user's sentiment is "excited", display positive information preferentially.

[0290] Step 9:

[0291] Terminal: Analyze the received JSON data and display it on the user interface. Display it in a list view or table format so that the user can easily view and compare it.

[0292] Specific example of display: Display on the screen like "Company A: Revenue 1.2 billion yen, Industry: IT, Location: Tokyo" "Company B: Revenue 1.5 billion yen, Industry: IT, Location: Tokyo".

[0293] Step 10:

[0294] User: Select and click on a company of interest from the displayed company information. Request detailed information.

[0295] Example: Click "Company A" to request detailed information.

[0296] Step 11:

[0297] Server: Receives user requests and generates and executes queries to retrieve detailed information about the selected company from the database again.

[0298] Specific example: Generate a query like "SELECT FROM company_details WHERE company_id = 'Company A'".

[0299] Step 12:

[0300] Server: Formats the retrieved detailed information and sends it to the terminal in JSON format or similar.

[0301] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[0302] Step 13:

[0303] Terminal: Analyzes received details and displays them in a pop-up window or a new screen. Allows users to view and analyze the details.

[0304] Examples of display: Display "Capital of 200 million yen," "Number of employees: 500," "Business overview," "Recent news," etc.

[0305] Step 14:

[0306] Server: Save the user's emotional data and reflect it in future criteria settings. This allows for providing a more personalized service by considering the user's past emotional states and the choices made in those states.

[0307] Specific example: Reflect the corporate data selected by the user in an "excited" state during the previous search in the next search results.

[0308] (Example 2)

[0309] 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".

[0310] In conventional M&A and business prospect selection systems, the database is searched based on the criteria input by the user and the search results are displayed. However, there is a problem that personalized search results cannot be obtained because the user's emotional state cannot be considered. Also, validation of the input criteria and acquisition of detailed information need to be done manually, lacking efficiency.

[0311] The specific processing by the specific processing unit 二十九〇 (290) of the data processing device 12 in Example 2 is realized by the following means.

[0312] In this invention, the server includes means for inputting criteria, means for searching the database based on the criteria, means for recognizing the user's emotions, means for formatting and displaying the search results, and means for adjusting the search results based on the user's emotions. This enables the provision of personalized search results tailored to the user's emotional state.

[0313] "Criteria" refers to the evaluation criteria specified by the user when selecting M&A or business prospects, and includes items such as sales scale, industry type, region, growth rate, number of employees, etc.

[0314] A "database" is a system that efficiently manages, searches, and retrieves a collection of information, and provides information based on specific criteria.

[0315] "User emotions" refers to the user's mental state, analyzed from facial expressions, voice, and other factors, and is used by the system to personalize search results.

[0316] "Means of recognizing emotions" refers to technologies that use input devices such as cameras and microphones to analyze a user's facial expressions and voice to determine their emotions.

[0317] "Search results" refer to a list of companies and potential clients retrieved from the database based on the criteria entered by the user.

[0318] "Formatting" refers to the process of converting search results obtained from a database into a format that is easy for users to understand.

[0319] "Means of display" refers to methods for graphically displaying search results on a user interface, and includes display in list view or table format.

[0320] "Methods of adjusting based on emotions" refer to methods of adjusting the display order and content of search results while taking into account the user's emotions.

[0321] The system of this invention automatically selects M&A targets and sales opportunities, and mainly consists of a user, a terminal, a server, and an emotion engine.

[0322] Criteria input

[0323] The user launches the system and inputs the criteria necessary for M&A and selecting sales targets via an interface on the terminal. These criteria include sales volume, industry, region, growth rate, and number of employees. The entered data is used as a basis for processing within the system.

[0324] Specific example: The user enters criteria such as "sales of 1 billion yen or more, industry is IT, and region is Tokyo."

[0325] Criteria validation

[0326] The terminal has a function to automatically validate the format and content of the criteria entered by the user. If there are any deficiencies, a message prompting correction will be displayed. This ensures that the criteria are set correctly.

[0327] Specific example: If a value other than a number is entered in the sales volume field, a warning message will be displayed prompting the user to re-enter the value.

[0328] User emotion recognition

[0329] The emotion engine analyzes the user's facial expressions, voice, input speed, and patterns to recognize their emotions. Specifically, it captures the user's face with a camera and uses facial recognition technology to determine their emotions. It can also perform voice analysis using a microphone.

[0330] Specific example: When a user enters criteria, the camera captures the user's facial expression and recognizes emotions such as "excited" or "confused."

[0331] Database connection and search

[0332] The server receives the criteria and sentiment data entered by the user and uses JDBC or ODBC drivers to establish a database connection. Once a secure connection is established, it dynamically generates SQL queries based on the criteria and searches the database for records that match the conditions.

[0333] Specific example: Generate and execute the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0334] Formatting and displaying search results

[0335] The server receives the search results and adjusts the display order of the results based on the user's sentiment data. The formatted data is converted into JSON or XML format and sent to the terminal. This process results in search results that reflect the user's sentiment.

[0336] Specific example: If the user is in an "excited" state, the display order will be adjusted to prioritize positive information.

[0337] The terminal parses the received JSON data and displays it on the user interface in a list view or table format. This allows users to easily view and compare the results.

[0338] Example display: The screen will show "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[0339] Get detailed information

[0340] Users can request detailed information by selecting a company they are interested in from the displayed company information and clicking on it.

[0341] Example: Click on "Company A" to request more information.

[0342] The server receives the user's request, generates and executes a query to retrieve detailed information about the selected company from the database again. Then, it reformats the retrieved information and sends it to the terminal in JSON format or another appropriate format.

[0343] Specific example: Generate and execute the query "SELECT FROM company_details WHERE company_id = 'Company A'".

[0344] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[0345] The device analyzes the received details and displays them in a pop-up window or a new screen. This allows the user to view and analyze the details in more detail.

[0346] Example display: The pop-up window will show information such as "Capital: 200 million yen," "Number of employees: 500," "Business overview," and "Recent news."

[0347] Storage and use of emotional data

[0348] The server stores user sentiment data and uses it to inform future criteria settings. This allows for more personalized search results by considering the user's past emotional states and choices.

[0349] Specific example: Save the companies a user selects based on specific criteria, along with their sentiment data, to use as reference for future searches.

[0350] Examples of prompt statements:

[0351] "We are looking for IT companies in the Tokyo area with sales of over 1 billion yen."

[0352] "Please enter the following criteria: sales scale of 1 billion yen, industry: IT, region: Tokyo."

[0353] This system takes user emotions into consideration and executes a series of processes, from inputting criteria and searching the database to formatting and displaying results, obtaining detailed information, and even utilizing emotional data. This allows for the provision of more personalized services to users.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1: Enter the criteria

[0356] The user launches the system and uses the interface on the terminal to input the criteria necessary for M&A and selecting potential clients. The input criteria include sales volume, industry, region, growth rate, and number of employees.

[0357] Input: Criteria such as sales volume, industry, region, growth rate, and number of employees.

[0358] Operation: Enter "Sales volume: 1 billion yen or more", "Industry: IT", and "Region: Tokyo" into the form on the terminal.

[0359] Output: Criteria data is input into the system.

[0360] Step 2: Criteria Validation

[0361] The terminal automatically validates the criteria entered by the user. If there are any errors, it displays a warning message about the input error and prompts the user to correct it.

[0362] Input: Criteria entered by the user.

[0363] Action: If anything other than a number is entered in the sales volume field, a warning message "Please enter the sales volume as a number" will be displayed, prompting the user to re-enter the value.

[0364] Output: Accurate criteria data that has passed validation.

[0365] Step 3: User emotion recognition

[0366] The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. Specifically, it captures the user's face with a camera and uses facial recognition technology to determine their emotions.

[0367] Input: User facial expression data, voice data.

[0368] Operation: When criteria are entered, the camera captures the user's facial expression and recognizes emotions such as "excited" or "confused."

[0369] Output: User sentiment data.

[0370] Step 4: Database connection and query generation

[0371] The server receives precise criteria data and user sentiment data, and connects to the database using JDBC or ODBC drivers. It then generates SQL queries based on the criteria and searches the database for records that match the conditions.

[0372] Input: Accurate criteria data, user sentiment data.

[0373] Operation: Connect to "jdbc:mysql: / / localhost:3306 / ma_database" and generate the following query: "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0374] Output: Records from the search results.

[0375] Step 5: Format and reorder search results

[0376] The server receives the search results and formats and adjusts the display order of the results based on the user's sentiment data. The formatted data is converted to JSON format and sent to the terminal.

[0377] Input: Search result records, user sentiment data.

[0378] Function: Ranks search results based on sentiment data, formats them, and converts them to JSON format.

[0379] Output: Formatted JSON data.

[0380] Step 6: Displaying the results

[0381] The terminal parses the received JSON data and displays it on the user interface in a list view or table format, making it easy for users to view and compare the data.

[0382] Input: Formatted JSON data.

[0383] Operation: Displays results such as "Company A: Sales of 1.2 billion yen, IT, Tokyo" and "Company B: Sales of 1.5 billion yen, IT, Tokyo".

[0384] Output: Search results displayed on the user interface.

[0385] Step 7: Obtaining detailed information

[0386] Users select a company they are interested in from the displayed company information and click to request more details.

[0387] Input: The company selected by the user.

[0388] Action: Click "Company A" to request detailed information.

[0389] Output: A request for more information is sent to the server.

[0390] The server receives the request, generates and executes an SQL query to retrieve detailed information about the selected company from the database. It then formats the retrieved information and sends it to the terminal in JSON format.

[0391] Input: Request for detailed information.

[0392] Operation: Generates and executes the query "SELECT FROM company_details WHERE company_id = 'Company A'". Formats it as follows: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500 people', Business Overview: '...', Recent News: '...'}".

[0393] Output: Formatted detailed information in JSON data.

[0394] The device analyzes the received details and displays them in a pop-up window or a new screen, allowing the user to view and analyze the details in more detail.

[0395] Input: Formatted JSON data containing detailed information.

[0396] Function: Displays information such as "Capital of 200 million yen," "Number of employees: 500," and "Business overview" in a pop-up window.

[0397] Output: Detailed information displayed in a pop-up window or new screen.

[0398] Step 8: Storing and using emotional data

[0399] The server stores user sentiment data and uses it to inform future criteria settings. This enables the provision of more personalized search results.

[0400] Input: User sentiment data, criteria data.

[0401] Operation: Saves data on companies selected by the user based on specific criteria, along with their sentiment at the time of selection.

[0402] Output: Sentiment data and criteria data stored on the server.

[0403] The above outlines the specific processing flow of this system, including a detailed explanation of the actions performed at each step.

[0404] (Application Example 2)

[0405] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0406] In conventional monitoring systems, anomaly detection and alert processing were performed using standard methods, without adaptive processing that took into account the emotions and concentration levels of the monitors. This led to challenges such as monitor fatigue and excessive stress, increasing the risk of missing important anomalies. To address these issues, dynamic alert prioritization that takes monitors' emotional states and personalized display methods are necessary.

[0407] 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.

[0408] In this invention, the server includes means for recognizing and analyzing the user's emotions, means for adjusting the priority of display results based on the user's emotion data, and means for storing the user's emotion data and reflecting it in future search results. This makes it possible to analyze the emotional state of monitors in real time and highlight important alerts at the appropriate time. Furthermore, personalized alert displays based on past emotion data can be realized, reducing the burden on monitors and lowering the risk of missing important anomalies.

[0409] "Criteria" refers to the conditions or constraints that a user specifies to a system.

[0410] A "database" is a system for storing structured data and for efficiently searching and retrieving it.

[0411] A "search method" is a function for finding information within a database based on specified criteria.

[0412] "Emotion recognition" is a technology that analyzes a user's facial expressions, voice, and behavior to determine their emotional state.

[0413] "Emotional data" refers to information obtained from the user's emotional perception, and this is what the system uses to determine the user's state.

[0414] A "search result" is a collection of information retrieved from a database based on specific criteria.

[0415] "Display means" refers to a function for presenting formatted search results or alert information on the user interface.

[0416] "Emotional analysis" is the process of analyzing a user's emotional state in detail based on data obtained from emotion recognition.

[0417] "Priority adjustment" is the process of dynamically determining the importance of multiple search results and alert information based on sentiment data.

[0418] A "storage method" is a function that records user emotional data and uses it to improve future system operation.

[0419] "Real-time" means analyzing and processing data instantly and reflecting the results immediately.

[0420] The present invention can be applied to a monitoring system that detects anomalies from user input. This system includes means for inputting criteria, means for recognizing and analyzing emotions, means for searching a database, means for formatting and displaying search results, and means for adjusting the priority of display results based on the user's emotion data. The system also has a function to store the user's emotion data and reflect it in future search results.

[0421] Hardware and software to be used

[0422] The system utilizes wearable devices such as smart glasses and head-mounted displays, as well as surveillance cameras. It employs emotion engines like Affectiva for emotion recognition and AI models such as OpenCV and YOLO for real-time video analysis. MySQL is used as the database management system.

[0423] Program Overview

[0424] The server receives the criteria entered by the user and performs a database search based on them. Next, it analyzes the camera footage to detect anomalies and recognizes the user's emotional state in real time. Based on the analyzed emotional data, it adjusts the importance of alerts and presents the results to the user in an appropriate display method. Furthermore, it saves the user's emotional data and reflects it in future searches and displays.

[0425] Specific example

[0426] Let's say a user wears smart glasses and sets a criterion for monitoring area A: "If three or more people appear between 00:00 and 05:00." The system analyzes the surveillance camera footage in real time within the set time and generates an alert if an anomaly is detected. At this time, the system uses Affectiva to recognize the user's emotions, and if, for example, the system determines that the monitor is "tired," it will highlight only the most important alerts. The user's emotion data is also saved and reflected in the display of future alerts.

[0427] Example of a prompt

[0428] "Monitoring area A: An alert will be triggered if three or more people appear between 00:00 and 05:00."

[0429] "Detecting unusual human movement patterns in surveillance area A during the night and generating an alert."

[0430] "When the monitor is fatigued, only the most important alert will be highlighted."

[0431] This system improves the efficiency and accuracy of monitoring operations, reduces the burden on monitors, and lowers the risk of missing important anomalies.

[0432] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0433] Step 1:

[0434] The user enters the monitoring criteria using smart glasses or a terminal. The user enters the criteria through the terminal's interface, such as "Monitoring area A, between 00:00 and 05:00, if three or more people appear." The entered criteria are then sent to the system.

[0435] Step 2:

[0436] The terminal validates the criteria entered by the user. The terminal checks whether the entered data is in the correct format, and if there are any deficiencies, it displays a warning message and prompts the user to re-enter the data. For example, it checks whether the monitoring time and the number of people are entered correctly.

[0437] Step 3:

[0438] The system uses an emotion engine to recognize the user's emotions in real time. The device uses a camera to capture the user's facial expressions, and emotion recognition software such as Affectiva analyzes them to determine emotions like "stressed" or "tired." The analysis results are then sent to the system.

[0439] Step 4:

[0440] The server receives the criteria and sentiment data and begins searching the database. The server filters the surveillance footage in the database based on the specified criteria. For example, it might search for "when three or more people were detected in Area A between 00:00 and 05:00".

[0441] Step 5:

[0442] The server performs real-time video analysis. Using surveillance camera footage, it detects anomalies using AI models such as OpenCV and YOLO. When an anomaly is detected, it generates information as an alert and formats the analysis results.

[0443] Step 6:

[0444] The server adjusts alert priorities based on sentiment data. Taking user sentiment data into account, for example, if the user is "tired," the server will change priorities to highlight only the most important alerts.

[0445] Step 7:

[0446] The formatted alert information is sent to the terminal. The terminal displays the received information on its user interface. For example, it might display specific information such as "An anomaly was detected in Area A at 02:15 AM."

[0447] Step 8:

[0448] When a user requests more information, they select a specific alert. A request is then sent to the server to retrieve the detailed information for the alert selected by the user from the database.

[0449] Step 9:

[0450] The server retrieves detailed information, formats it, and sends it to the terminal. For example, the server retrieves "detailed information for company A" again, formats it in JSON format, and sends it.

[0451] Step 10:

[0452] The device displays the received details in a pop-up window or a dedicated screen. The user reviews the displayed details and takes the necessary actions.

[0453] Step 11:

[0454] The server stores user sentiment data and uses it to inform future searches and alerts. For example, it stores monitored sentiment fluctuation data and provides personalized responses based on the next alert.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] [Second Embodiment]

[0459] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0460] 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.

[0461] 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).

[0462] 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.

[0463] 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.

[0464] 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).

[0465] 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.

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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".

[0471] The system of this invention is for automatically selecting target companies for M&A and sales, and mainly consists of a user, a terminal, and a server. The specific processing is described below in natural language.

[0472] Criteria input

[0473] User: Launch the system and input the criteria necessary for selecting M&A targets and sales opportunities. These criteria include sales volume, industry, region, growth rate, and number of employees. Input is performed using the interface on the terminal.

[0474] Specific example: The user enters criteria such as "sales of 1 billion yen or more, industry is IT, and region is Tokyo."

[0475] Criteria validation

[0476] Terminal: Automatically validates the format and content of criteria entered by the user. If there are any deficiencies, it displays a message prompting the user to correct them.

[0477] Validation example: Check if the sales volume is a numerical value and verify that the industry is present in the selection options.

[0478] Database connection

[0479] Server: After successful validation, establish a database connection and prepare search queries based on the criteria. Securely access the database using authentication credentials.

[0480] Specific example: Connect to an SQL database using a JDBC driver.

[0481] Database Search

[0482] Server: Generates search queries and executes them against the database. Queries are generated based on criteria entered by the user.

[0483] Specific example: Generate and execute the query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0484] Formatting and displaying search results

[0485] Server: Receives search results and formats them into a user-friendly format. Converts them to JSON or XML format and sends them to the terminal.

[0486] Specific example: Format it like this: "[{Company Name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company Name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]".

[0487] Terminal: Analyzes received data and displays it on the user interface in list view or table format. Users then proceed with their selection based on the displayed company information.

[0488] Example of display: Display as follows: "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[0489] Get detailed information

[0490] User: To obtain more detailed information about a selected company, click on that company.

[0491] Server: Retrieves detailed information about the selected company from the database again, formats it similarly, and sends it to the terminal.

[0492] Specific example: If "Company A" is selected, detailed information such as "capital, number of employees, business overview, and recent news" will be retrieved and displayed.

[0493] Device: Receives detailed information and displays it in a pop-up or new screen.

[0494] Example of displaying detailed information: Display detailed information about company A, such as "capital of 200 million yen, 500 employees, business overview, and recent news."

[0495] The above describes a specific embodiment of this system. This system is designed to efficiently execute a series of processes, from inputting criteria to database searching, formatting and displaying results, and obtaining detailed information, enabling users to efficiently select target companies.

[0496] The following describes the processing flow.

[0497] Step 1:

[0498] User: Launch the program and enter criteria for selecting M&A targets or sales opportunities. Criteria include sales volume, industry, region, growth rate, and number of employees.

[0499] Specific example: A user enters "Sales volume: 1 billion yen or more", "Industry: IT", and "Region: Tokyo" into the input field on the screen.

[0500] Step 2:

[0501] Terminal: Receives the entered criteria and validates the content. It verifies that the sales volume is a numerical value, that the industry is present in the selection list, and that the region is in the correct format.

[0502] Specific example: If a value other than a number is entered in the sales volume field, a warning message will be displayed prompting the user to re-enter the value.

[0503] Step 3:

[0504] Terminal: If the criteria are confirmed to be correct, it sends that data to the server. HTTP requests are used for transmission, and the criteria are packaged in JSON format or similar.

[0505] Specific example: The criteria are deemed correct, and the JSON object "{revenue: 'over 1 billion yen', industry: 'IT', location: 'Tokyo'}" is sent to the server.

[0506] Step 4:

[0507] Server: Analyzes the received criteria and establishes a database connection. Securely accesses the database using appropriate authentication credentials.

[0508] Specific example: Connecting to an SQL database using a JDBC driver or ODBC driver.

[0509] Step 5:

[0510] Server: Dynamically generates SQL queries based on criteria. The queries search the database for records that match the conditions.

[0511] Specific example: Generate the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0512] Step 6:

[0513] Server: Executes SQL queries to retrieve matching company information from the database. The retrieved data is temporarily stored and processed on the server.

[0514] Specific example: The query results will retrieve information on companies A, B, and C.

[0515] Step 7:

[0516] Server: Formats search results into a user-friendly format. Converts the formatted data into JSON or XML format and sends it to the terminal.

[0517] Specific example: Convert to JSON format like this: "[{Company Name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company Name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]".

[0518] Step 8:

[0519] Terminal: Parses received JSON data and displays it on the user interface. It displays the data in list view or table format, making it easy for users to browse and compare.

[0520] Specific display example: The screen will display as follows: "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[0521] Step 9:

[0522] User: Select and click on a company of interest from the displayed company information. Request detailed information.

[0523] Example: Click "Company A" to request detailed information.

[0524] Step 10:

[0525] Server: Receives user requests and generates and executes queries to retrieve detailed information about the selected company from the database again.

[0526] Specific example: Generate a query like "SELECT FROM company_details WHERE company_id = 'Company A'".

[0527] Step 11:

[0528] Server: Reformats the retrieved detailed information and sends it to the terminal in JSON format or similar.

[0529] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[0530] Step 12:

[0531] Terminal: Analyzes received details and displays them in a pop-up window or a new screen. Allows users to view and analyze the details.

[0532] Specific display example: A pop-up window will display information such as "Capital: 200 million yen," "Number of employees: 500," "Business overview," and "Recent news."

[0533] The above outlines the specific processing steps of the system. This allows users to select M&A targets and sales opportunities in an efficient and reliable manner.

[0534] (Example 1)

[0535] 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."

[0536] Traditional M&A and target company selection systems suffered from problems such as difficulty in accurately inputting criteria and insufficient validation of entered criteria, resulting in reduced search accuracy. Furthermore, search results were displayed in a format difficult for users to understand, leading to time-consuming target company selection. Additionally, the process of obtaining detailed information on selected companies was cumbersome, resulting in a poor user experience.

[0537] 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.

[0538] In this invention, the server includes means for inputting criteria, means for searching a database based on the criteria, means for formatting and displaying the search results, means for retrieving detailed information of a company selected by the user, means for establishing a database connection to retrieve the search results, and means for validating the criteria. This enables the user to efficiently and accurately select a target company.

[0539] "Criteria" refers to the conditions that users enter to select target companies, and include factors such as sales volume, industry, region, growth rate, and number of employees.

[0540] A "database" is a collection of structured data, a system for managing information that can be searched, stored, updated, and deleted.

[0541] A "search query" is a set of instructions executed to retrieve specific information from a database, and is written in languages ​​such as SQL.

[0542] "Formatting" is the process of converting raw data into a format that is easy for users to understand, and includes data format conversion and layout adjustment.

[0543] A "terminal" is a device used by a user to access a system, and includes personal computers, smartphones, tablets, and other similar devices.

[0544] "Validation" is a checking process to ensure that input data is accurate and in the correct format.

[0545] A "server" is a computer system that provides services to clients over a network, and is responsible for database access and processing business logic.

[0546] "JSON" stands for JavaScript Object Notation, and it is a format for representing data in a lightweight text format.

[0547] XML stands for eXtensible Markup Language, and it is a language for representing data in a structured format using tags.

[0548] A "user interface" is the means by which a user interacts with a system, and includes elements such as buttons, forms, and dialog boxes on the screen.

[0549] The system of this invention is for automatically selecting target companies for M&A and sales, and consists of a user, a terminal, and a server. The operation of each component is described in detail below.

[0550] Criteria input

[0551] User: The user launches the system using a terminal. Through the system interface, they input criteria for selecting target companies. These criteria include sales volume, industry, region, growth rate, and number of employees.

[0552] Specific example: A user uses an input form to enter criteria such as "sales volume of 1 billion yen or more, industry is IT, and region is Tokyo."

[0553] Criteria validation

[0554] Terminal: The terminal automatically validates the format and content of the criteria entered by the user. Validation verifies that the sales volume is a numerical value and that the industry is within the given options. If there are any errors, a message prompting the user to correct them is displayed.

[0555] Specific example: If the sales volume is not a number, display the error message "Please enter the sales volume as a number."

[0556] Database connection

[0557] Server: After successful validation, the server establishes a database connection using a JDBC driver, etc. It then securely accesses the database using authentication credentials (username and password).

[0558] Specific example: The server connects to the SQL database in the format "jdbc:mysql: / / localhost:3306 / database_name".

[0559] Database Search

[0560] Server: The server generates search queries based on the criteria entered by the user and executes them against the database. A search query might look something like this: "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0561] Specific example: The server sends the aforementioned query to the database and searches for companies that match the criteria.

[0562] Formatting and displaying search results

[0563] Server: Formats the search results retrieved from the database. That is, converts the search results into JSON or XML format to make them easy for the user to understand. Sends the formatted data to the terminal.

[0564] Specific example: Convert the search results to the format "[{Company Name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company Name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]".

[0565] Terminal: The terminal analyzes the received data and displays it on the user interface in list view or table format. The user then selects the next action based on the displayed company information.

[0566] Specific example: Company information is displayed in a list format such as "Company A: Sales of 1.2 billion yen, IT, Tokyo" and "Company B: Sales of 1.5 billion yen, IT, Tokyo".

[0567] Get detailed information

[0568] User: If a user wants to get more detailed information about a specific company, they click on that company's entry.

[0569] Specific example: A user clicks on information for "Company A".

[0570] Server: The server retrieves detailed information about the selected company from the database, formats it, and sends it to the terminal.

[0571] Specific example: Retrieve detailed information about company A using an SQL query and convert it into JSON format that includes "capital, number of employees, business overview, and recent news."

[0572] Device: The device receives detailed information and displays it in a pop-up window or a new screen.

[0573] Specific example: Display the following as detailed information: "Company A's capital is 200 million yen, it has 500 employees, and it includes a business overview and recent news."

[0574] Example of a prompt

[0575] 1. Criteria input prompt: "Please search for companies with sales of 1 billion yen or more, in the IT industry, and located in Tokyo."

[0576] 2. Prompt to retrieve detailed information: "Please display detailed information for Company A."

[0577] The above describes a specific embodiment of the target company selection process using the system of the present invention. This system efficiently executes a series of processes, from inputting criteria to database searching, formatting and displaying results, and obtaining detailed information, thereby supporting users in quickly and accurately selecting target companies.

[0578] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0579] Step 1:

[0580] Criteria input

[0581] User: The user starts the system using a terminal. An interface for entering criteria is displayed. The user enters the criteria necessary for selecting target companies.

[0582] Input: Conditions such as sales volume, industry, region, growth rate, and number of employees.

[0583] Output: Input criteria data

[0584] Specific action: The user enters "Sales volume of 1 billion yen or more, industry is IT, region is Tokyo" into the input form and clicks the "Submit" button.

[0585] Step 2:

[0586] Criteria validation

[0587] Terminal: The terminal validates the format and content of the criteria entered by the user. It verifies that the sales volume is a numerical value and that the industry is within the selectable options.

[0588] Input: Entered criteria data

[0589] Output: Validation result (success or failure)

[0590] Specific actions: The terminal verifies that the sales volume is a numerical value and checks that the industry is among the available options. If there are any discrepancies, an error message such as "Please enter the sales volume as a numerical value" will be displayed.

[0591] Step 3:

[0592] Database connection

[0593] Server: If validation is successful, the server establishes a database connection. It then securely accesses the database using a JDBC driver or similar.

[0594] Input: Criteria data that successfully validated

[0595] Output: Database connection established and authentication successful.

[0596] Specific operation: The server connects to the SQL database in the format "jdbc:mysql: / / localhost:3306 / database_name" and verifies the authentication information.

[0597] Step 4:

[0598] Database Search

[0599] Server: The server generates search queries based on the input criteria and executes them against the database.

[0600] Input: Criteria data, database connection information

[0601] Output: Search results (list of company information)

[0602] Specific operation: The server sends a query to the database: "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'", and retrieves company information that matches the conditions.

[0603] Step 5:

[0604] Formatting and displaying search results

[0605] Server: Formats search results retrieved from the database and converts them into a user-friendly format. It then converts them to JSON or XML format and sends them to the terminal.

[0606] Input: Search results retrieved from the database

[0607] Output: Formatted search result data (JSON or XML format)

[0608] Specific operation: The server formats the search results as follows: "[{Company name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]" and sends it to the terminal.

[0609] Terminal: The terminal analyzes the received data and displays it on the user interface in a list view or table format.

[0610] Input: Formatted search result data

[0611] Output: List of companies displayed on the screen

[0612] Specific operation: The terminal displays company information in a list format such as "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[0613] Step 6:

[0614] Get detailed information

[0615] User: To get detailed information about a specific company, click on that company's entry.

[0616] Input: User click-through information

[0617] Output: Request for detailed information about the selected companies

[0618] Specific action: The user clicks on the item "Company A".

[0619] Server: The server retrieves detailed information about the selected company from the database, formats it, and sends it to the terminal.

[0620] Input: Request for detailed information, database connection information

[0621] Output: Formatted detailed information data

[0622] Specific operation: The server executes the SQL query again and converts the detailed information of company A, including "capital, number of employees, business overview, and recent news," into JSON format.

[0623] Device: The device receives detailed information and displays it in a pop-up window or a new screen.

[0624] Input: Formatted detailed information data

[0625] Output: Detailed information displayed on the screen

[0626] Specific action: The device displays detailed information about "Company A: capital of 200 million yen, number of employees of 500, business overview, and recent news" in a new window or pop-up.

[0627] (Application Example 1)

[0628] 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."

[0629] The present invention aims to streamline sales activities within a factory and provide a system that enables sales representatives to immediately select appropriate target companies and present information. In particular, it aims to automate sales support within the factory and expedite on-site information acquisition and proposal activities.

[0630] 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.

[0631] In this invention, the server includes means for inputting criteria, means for searching a database based on the criteria, means for formatting and displaying the search results, means for a factory robot that performs an action based on the displayed search results, and means for providing information to factory staff's smart devices using the displayed results. This enables sales representatives to quickly select target companies on-site and make appropriate sales proposals.

[0632] A "means for inputting criteria" refers to an interface that allows users to input specific conditions or criteria (such as sales volume, industry, or region) into a terminal.

[0633] "Means for searching the database based on the aforementioned criteria" refers to a function that accesses the database based on the input criteria and executes a process to extract suitable data.

[0634] "Methods for formatting and displaying search results" refers to functions that convert the results obtained from a database search into a format that is easy for the user to understand and then display them.

[0635] "Factory robot means" refers to a function that includes robots operating within a factory to perform specific actions based on search results.

[0636] "Means of providing information to factory staff's smart devices" refers to a function that transmits formatted search results and detailed information to smart devices (such as smart glasses) within the factory, making them available for use by on-site staff.

[0637] "Means of validation" refers to verification functions that confirm that the input criteria are in the correct format and content.

[0638] "Means for obtaining detailed information again" refers to a function that retrieves and displays more detailed information from the database regarding the search results selected by the user.

[0639] The system of this invention is designed to streamline sales activities within a factory. The system mainly consists of users, terminals, and servers, each of which works in cooperation with the others.

[0640] Criteria input and validation

[0641] The user inputs criteria (e.g., sales volume, industry, region) through a sales support robot using an interface on their smart device. The terminal receives the input and validates it. Specifically, it checks that the sales volume is a numerical value and that the industry and region are in the correct format. If there are any errors, a message prompting the user to correct them is displayed.

[0642] Database connection and search

[0643] After successful validation on the terminal, the server securely establishes a database connection. Using a JDBC driver or similar, it connects to the SQL database and generates a search query based on the criteria entered by the user. The server then executes the query against the database and extracts the relevant company information.

[0644] Formatting and displaying search results

[0645] The server receives the search results and formats them into a user-friendly format (e.g., JSON). The formatted data is sent to the terminal, where the user can view the search results in a list view or table format on a smart device (e.g., smart glasses or a head-mounted display).

[0646] Actions performed by factory robots

[0647] Based on the displayed search results, the sales support robots in the factory perform specific actions. For example, they automatically prepare proposal materials for specific companies or organize data on potential customers.

[0648] Get detailed information

[0649] When a user selects a specific company from the displayed search results, the server accesses the database again to retrieve detailed information about that company. This data is also formatted and displayed on the smart device as a pop-up or in a new screen. This allows the user to proceed with sales activities based on more in-depth information.

[0650] Example prompt statement

[0651] "Please search for companies with sales of 1 billion yen or more, in the manufacturing industry, and located in the Nagoya area."

[0652] By inputting such prompt statements into the AI ​​model, the system selects the appropriate company and executes the function.

[0653] The system of this invention efficiently executes a series of processes, from inputting criteria to database searching, formatting and displaying results, executing actions by factory robots, and obtaining detailed information. This enables users to quickly and effectively select target companies and smoothly advance their sales activities.

[0654] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0655] Step 1: The user accesses the sales support robot using a smart device and enters criteria. For example, sales volume, industry, region, etc., are entered through the smart glasses interface. This input will be used as data in the next step.

[0656] Step 2: The terminal validates the criteria entered by the user. Specifically, it verifies that the sales volume is a number, the industry is a string, and the region is in the correct format. If there are any inappropriate criteria, the terminal displays a message prompting the user to correct them and waits until it receives the correct input.

[0657] Step 3: Upon receiving the criteria that have passed validation, the terminal sends them to the server. At this point, the criteria are formatted in JSON or another format to ensure data consistency.

[0658] Step 4: The server establishes a database connection based on the received criteria. It securely and efficiently connects to the SQL database using tools such as a JDBC driver. Through this connection, query generation and execution become possible.

[0659] Step 5: The server generates an SQL query based on the criteria and executes it against the database. For example, it generates a query such as "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'manufacturing' AND location = 'Nagoya'" and sends it to the database. Executing this query retrieves information about the relevant companies.

[0660] Step 6: The server formats the search results retrieved from the database and converts them into a user-friendly format (e.g., JSON). This makes it easier to display them on terminals and smart devices.

[0661] Step 7: The server sends the formatted data to the terminal. The terminal analyzes the received data and displays it in a list view or table format on the user interface. This allows the user to quickly review the search results in the field.

[0662] Step 8: Once the user selects a company they are interested in, the device requests detailed information about that company from the server. This request is then sent back to the database via the server.

[0663] Step 9: The server retrieves detailed company information from the database, reformats it, and sends it to the terminal. The terminal displays this information to the user in a pop-up or new screen. This allows the user to conduct sales activities based on more in-depth information.

[0664] Step 10: Based on the search results and detailed information, the factory robot performs specific actions. For example, it might prepare proposal materials for a specific company or organize data about potential customers. This makes sales support more efficient and automated.

[0665] 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.

[0666] The system of this invention is for automatically selecting M&A and sales targets, and mainly consists of a user, a terminal, a server, and an emotion engine. The specific processing is described below in natural language.

[0667] Criteria input

[0668] User: Launch the system and input the criteria necessary for selecting M&A targets and sales opportunities. These criteria include sales volume, industry, region, growth rate, and number of employees. Input is performed using the interface on the terminal.

[0669] Specific example: The user enters criteria such as "sales of 1 billion yen or more, industry is IT, and region is Tokyo."

[0670] Criteria validation

[0671] Terminal: Automatically validates the format and content of criteria entered by the user. If there are any deficiencies, it displays a message prompting the user to correct them.

[0672] Validation example: If a value other than a number is entered in the sales volume field, a warning message will be displayed, prompting the user to re-enter the value.

[0673] User emotion recognition

[0674] Emotion Engine: This engine analyzes the user's facial expressions, voice, input speed, and patterns to recognize their emotions. For example, it can capture the user's face with a camera and use facial recognition technology to determine their emotions.

[0675] Specific example: When a user enters criteria, the camera captures the user's facial expression and recognizes emotions such as "excited" or "confused."

[0676] Database connection and search

[0677] Server: Receives criteria and user sentiment data, and establishes a database connection. Securely accesses the database using appropriate authentication credentials.

[0678] Specific example: Connecting to an SQL database using a JDBC driver or ODBC driver.

[0679] Server: Dynamically generates and executes SQL queries based on criteria, and searches the database for records that match the conditions.

[0680] Specific example: Generate and execute the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0681] Formatting and displaying search results

[0682] Server: Receives search results and adjusts the display order of results based on user sentiment data. Converts the formatted data into JSON or XML format and sends it to the terminal.

[0683] Specific example: If the user is in an "excited" state, the display order will be adjusted to prioritize positive information.

[0684] Terminal: Parses received JSON data and displays it on the user interface. It displays the data in list view or table format, making it easy for users to browse and compare.

[0685] Specific display example: The screen will display as follows: "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[0686] Get detailed information

[0687] User: Select and click on a company of interest from the displayed company information. Request detailed information.

[0688] Example: Click "Company A" to request detailed information.

[0689] Server: Receives user requests and generates and executes queries to retrieve detailed information about the selected company from the database again.

[0690] Specific example: Generate a query like "SELECT FROM company_details WHERE company_id = 'Company A'".

[0691] Server: Reformats the retrieved detailed information and sends it to the terminal in JSON format or similar.

[0692] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[0693] Terminal: Analyzes received details and displays them in a pop-up window or a new screen. Allows users to view and analyze the details.

[0694] Specific display example: A pop-up window will display information such as "Capital: 200 million yen," "Number of employees: 500," "Business overview," and "Recent news."

[0695] Storage and use of emotional data

[0696] Server: Stores user sentiment data and uses it to inform future criteria settings. This allows for more personalized search results by considering the user's past emotional states and choices.

[0697] Specific example: Save the companies a user selects based on specific criteria, along with their sentiment data, to use as reference for future searches.

[0698] The above is a specific embodiment of a system that combines an emotion engine. This system efficiently executes a series of processes, from inputting criteria and searching the database to formatting and displaying results, obtaining detailed information, and even utilizing emotion data, thereby providing users with a more personalized service.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] User: Launch the program and enter criteria for selecting M&A targets or sales opportunities. Criteria include sales volume, industry, region, growth rate, and number of employees.

[0702] Specific example: A user enters "Sales volume: 1 billion yen or more", "Industry: IT", and "Region: Tokyo" into the input field on the screen.

[0703] Step 2:

[0704] Terminal: Receives the entered criteria and validates the content. It verifies that the sales volume is a numerical value, that the industry is present in the selection list, and that the region is in the correct format.

[0705] Specific example: If a value other than a number is entered in the sales volume field, a warning message will be displayed prompting the user to re-enter the value.

[0706] Step 3:

[0707] Terminal: If the criteria are confirmed to be correct, it sends that data to the server. HTTP requests are used for transmission, and the criteria are packaged in JSON format or similar.

[0708] Specific example: The criteria are deemed correct, and the JSON object "{revenue: 'over 1 billion yen', industry: 'IT', location: 'Tokyo'}" is sent to the server.

[0709] Step 4:

[0710] Server: Analyzes the received criteria and establishes a database connection. Securely accesses the database using appropriate authentication credentials.

[0711] Specific example: Connecting to an SQL database using a JDBC driver or ODBC driver.

[0712] Step 5:

[0713] Emotion Engine: Recognizes user emotions by analyzing user facial expressions, voice, input speed and patterns, etc. It uses cameras and microphones to determine emotions.

[0714] Specific example: When a user performs an input task, their face is captured by a camera, and emotions such as "excited" or "confused" are recognized.

[0715] Step 6:

[0716] Server: Dynamically generates SQL queries based on criteria. Uses the generated queries to search the database for records that match the conditions.

[0717] Specific example: Generate the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0718] Step 7:

[0719] Server: Executes SQL queries to retrieve matching company information from the database. The retrieved data is temporarily stored and processed on the server.

[0720] Specific example: The query results will retrieve information on "Company A, Company B, Company C".

[0721] Step 8:

[0722] Server: Formats search results and adjusts the display order based on user sentiment data. Converts the formatted data to JSON or XML format and sends it to the terminal.

[0723] Specific example: If the user's emotional state is "excited," prioritize displaying positive information.

[0724] Step 9:

[0725] Terminal: Parses received JSON data and displays it in the user interface. It displays the data in list view or table format, making it easy for users to browse and compare.

[0726] Specific display examples: The screen will display as follows: "Company A: Sales 1.2 billion yen, Industry: IT, Location: Tokyo" and "Company B: Sales 1.5 billion yen, Industry: IT, Location: Tokyo".

[0727] Step 10:

[0728] User: Select and click on a company of interest from the displayed company information. Request detailed information.

[0729] Example: Click "Company A" to request detailed information.

[0730] Step 11:

[0731] Server: Receives user requests and generates and executes queries to retrieve detailed information about the selected company from the database again.

[0732] Specific example: Generate a query like "SELECT FROM company_details WHERE company_id = 'Company A'".

[0733] Step 12:

[0734] Server: Formats the retrieved detailed information and sends it to the terminal in JSON format or similar.

[0735] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[0736] Step 13:

[0737] Terminal: Analyzes received details and displays them in a pop-up window or a new screen. Allows users to view and analyze the details.

[0738] Examples of display: Display "Capital of 200 million yen," "Number of employees: 500," "Business overview," "Recent news," etc.

[0739] Step 14:

[0740] Server: Stores user emotional data and uses it to inform future criteria settings. This allows for more personalized service by considering the user's past emotional states and choices.

[0741] Specific example: In the next search, reflect the company data selected by the user while they were "excited" during their previous search.

[0742] (Example 2)

[0743] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0744] Traditional M&A and sales target selection systems search a database based on user-inputted criteria and display search results. However, they lack the ability to consider the user's emotional state, resulting in a failure to provide personalized search results. Furthermore, validation of the input criteria and retrieval of detailed information had to be done manually, which was inefficient.

[0745] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0746] In this invention, the server includes means for inputting criteria, means for searching a database based on the criteria, means for recognizing the user's emotions, means for formatting and displaying the search results, and means for adjusting the search results based on the user's emotions. This makes it possible to provide personalized search results that match the user's emotional state.

[0747] "Criteria" refers to the evaluation criteria specified by the user when selecting M&A or sales targets, and includes items such as sales volume, industry, region, growth rate, and number of employees.

[0748] A "database" is a system that efficiently manages, searches, and retrieves a collection of information, and provides information based on specific criteria.

[0749] "User emotions" refers to the user's mental state, analyzed from facial expressions, voice, and other factors, and is used by the system to personalize search results.

[0750] "Means of recognizing emotions" refers to technologies that use input devices such as cameras and microphones to analyze a user's facial expressions and voice to determine their emotions.

[0751] "Search results" refer to a list of companies and potential clients retrieved from the database based on the criteria entered by the user.

[0752] "Formatting" refers to the process of converting search results obtained from a database into a format that is easy for users to understand.

[0753] "Means of display" refers to methods for graphically displaying search results on a user interface, and includes display in list view or table format.

[0754] "Methods of adjusting based on emotions" refer to methods of adjusting the display order and content of search results while taking into account the user's emotions.

[0755] The system of this invention automatically selects M&A targets and sales opportunities, and mainly consists of a user, a terminal, a server, and an emotion engine.

[0756] Criteria input

[0757] The user launches the system and inputs the criteria necessary for M&A and selecting sales targets via an interface on the terminal. These criteria include sales volume, industry, region, growth rate, and number of employees. The entered data is used as a basis for processing within the system.

[0758] Specific example: The user enters criteria such as "sales of 1 billion yen or more, industry is IT, and region is Tokyo."

[0759] Criteria validation

[0760] The terminal has a function to automatically validate the format and content of the criteria entered by the user. If there are any deficiencies, a message prompting correction will be displayed. This ensures that the criteria are set correctly.

[0761] Specific example: If a value other than a number is entered in the sales volume field, a warning message will be displayed prompting the user to re-enter the value.

[0762] User emotion recognition

[0763] The emotion engine analyzes the user's facial expressions, voice, input speed, and patterns to recognize their emotions. Specifically, it captures the user's face with a camera and uses facial recognition technology to determine their emotions. It can also perform voice analysis using a microphone.

[0764] Specific example: When a user enters criteria, the camera captures the user's facial expression and recognizes emotions such as "excited" or "confused."

[0765] Database connection and search

[0766] The server receives the criteria and sentiment data entered by the user and uses JDBC or ODBC drivers to establish a database connection. Once a secure connection is established, it dynamically generates SQL queries based on the criteria and searches the database for records that match the conditions.

[0767] Specific example: Generate and execute the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0768] Formatting and displaying search results

[0769] The server receives the search results and adjusts the display order of the results based on the user's sentiment data. The formatted data is converted into JSON or XML format and sent to the terminal. This process results in search results that reflect the user's sentiment.

[0770] Specific example: If the user is in an "excited" state, the display order will be adjusted to prioritize positive information.

[0771] The terminal parses the received JSON data and displays it on the user interface in a list view or table format. This allows users to easily view and compare the results.

[0772] Example display: The screen will show "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[0773] Get detailed information

[0774] Users can request detailed information by selecting a company they are interested in from the displayed company information and clicking on it.

[0775] Example: Click on "Company A" to request more information.

[0776] The server receives the user's request, generates and executes a query to retrieve detailed information about the selected company from the database again. Then, it reformats the retrieved information and sends it to the terminal in JSON format or another appropriate format.

[0777] Specific example: Generate and execute the query "SELECT FROM company_details WHERE company_id = 'Company A'".

[0778] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[0779] The device analyzes the received details and displays them in a pop-up window or a new screen. This allows the user to view and analyze the details in more detail.

[0780] Example display: The pop-up window will show information such as "Capital: 200 million yen," "Number of employees: 500," "Business overview," and "Recent news."

[0781] Storage and use of emotional data

[0782] The server stores user sentiment data and uses it to inform future criteria settings. This allows for more personalized search results by considering the user's past emotional states and choices.

[0783] Specific example: Save the companies a user selects based on specific criteria, along with their sentiment data, to use as reference for future searches.

[0784] Examples of prompt statements:

[0785] "We are looking for IT companies in the Tokyo area with sales of over 1 billion yen."

[0786] "Please enter the following criteria: sales scale of 1 billion yen, industry: IT, region: Tokyo."

[0787] This system takes user emotions into consideration and executes a series of processes, from inputting criteria and searching the database to formatting and displaying results, obtaining detailed information, and even utilizing emotional data. This allows for the provision of more personalized services to users.

[0788] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0789] Step 1: Enter the criteria

[0790] The user launches the system and uses the interface on the terminal to input the criteria necessary for M&A and selecting potential clients. The input criteria include sales volume, industry, region, growth rate, and number of employees.

[0791] Input: Criteria such as sales volume, industry, region, growth rate, and number of employees.

[0792] Operation: Enter "Sales volume: 1 billion yen or more", "Industry: IT", and "Region: Tokyo" into the form on the terminal.

[0793] Output: Criteria data is input into the system.

[0794] Step 2: Criteria Validation

[0795] The terminal automatically validates the criteria entered by the user. If there are any errors, it displays a warning message about the input error and prompts the user to correct it.

[0796] Input: Criteria entered by the user.

[0797] Action: If anything other than a number is entered in the sales volume field, a warning message "Please enter the sales volume as a number" will be displayed, prompting the user to re-enter the value.

[0798] Output: Accurate criteria data that has passed validation.

[0799] Step 3: User emotion recognition

[0800] The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. Specifically, it captures the user's face with a camera and uses facial recognition technology to determine their emotions.

[0801] Input: User facial expression data, voice data.

[0802] Operation: When criteria are entered, the camera captures the user's facial expression and recognizes emotions such as "excited" or "confused."

[0803] Output: User sentiment data.

[0804] Step 4: Database connection and query generation

[0805] The server receives precise criteria data and user sentiment data, and connects to the database using JDBC or ODBC drivers. It then generates SQL queries based on the criteria and searches the database for records that match the conditions.

[0806] Input: Accurate criteria data, user sentiment data.

[0807] Operation: Connect to "jdbc:mysql: / / localhost:3306 / ma_database" and generate the following query: "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0808] Output: Records from the search results.

[0809] Step 5: Format and reorder search results

[0810] The server receives the search results and formats and adjusts the display order of the results based on the user's sentiment data. The formatted data is converted to JSON format and sent to the terminal.

[0811] Input: Search result records, user sentiment data.

[0812] Function: Ranks search results based on sentiment data, formats them, and converts them to JSON format.

[0813] Output: Formatted JSON data.

[0814] Step 6: Displaying the results

[0815] The terminal parses the received JSON data and displays it on the user interface in a list view or table format, making it easy for users to view and compare the data.

[0816] Input: Formatted JSON data.

[0817] Operation: Displays results such as "Company A: Sales of 1.2 billion yen, IT, Tokyo" and "Company B: Sales of 1.5 billion yen, IT, Tokyo".

[0818] Output: Search results displayed on the user interface.

[0819] Step 7: Obtaining detailed information

[0820] Users select a company they are interested in from the displayed company information and click to request more details.

[0821] Input: The company selected by the user.

[0822] Action: Click "Company A" to request detailed information.

[0823] Output: A request for more information is sent to the server.

[0824] The server receives the request, generates and executes an SQL query to retrieve detailed information about the selected company from the database. It then formats the retrieved information and sends it to the terminal in JSON format.

[0825] Input: Request for detailed information.

[0826] Operation: Generates and executes the query "SELECT FROM company_details WHERE company_id = 'Company A'". Formats it as follows: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500 people', Business Overview: '...', Recent News: '...'}".

[0827] Output: Formatted detailed information in JSON data.

[0828] The device analyzes the received details and displays them in a pop-up window or a new screen, allowing the user to view and analyze the details in more detail.

[0829] Input: Formatted JSON data containing detailed information.

[0830] Function: Displays information such as "Capital of 200 million yen," "Number of employees: 500," and "Business overview" in a pop-up window.

[0831] Output: Detailed information displayed in a pop-up window or new screen.

[0832] Step 8: Storing and using emotional data

[0833] The server stores user sentiment data and uses it to inform future criteria settings. This enables the provision of more personalized search results.

[0834] Input: User sentiment data, criteria data.

[0835] Operation: Saves data on companies selected by the user based on specific criteria, along with their sentiment at the time of selection.

[0836] Output: Sentiment data and criteria data stored on the server.

[0837] The above outlines the specific processing flow of this system, including a detailed explanation of the actions performed at each step.

[0838] (Application Example 2)

[0839] 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."

[0840] In conventional monitoring systems, anomaly detection and alert processing were performed using standard methods, without adaptive processing that took into account the emotions and concentration levels of the monitors. This led to challenges such as monitor fatigue and excessive stress, increasing the risk of missing important anomalies. To address these issues, dynamic alert prioritization that takes monitors' emotional states and personalized display methods are necessary.

[0841] 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.

[0842] In this invention, the server includes means for recognizing and analyzing the user's emotions, means for adjusting the priority of display results based on the user's emotion data, and means for storing the user's emotion data and reflecting it in future search results. This makes it possible to analyze the emotional state of monitors in real time and highlight important alerts at the appropriate time. Furthermore, it enables personalized alert display based on past emotion data, reducing the burden on monitors and lowering the risk of missing important anomalies.

[0843] "Criteria" refers to the conditions or constraints that a user specifies to a system.

[0844] A "database" is a system for storing structured data and for efficiently searching and retrieving it.

[0845] A "search method" is a function for finding information within a database based on specified criteria.

[0846] "Emotion recognition" is a technology that analyzes a user's facial expressions, voice, and behavior to determine their emotional state.

[0847] "Emotional data" refers to information obtained from the user's emotional perception, and this is what the system uses to determine the user's state.

[0848] A "search result" is a collection of information retrieved from a database based on specific criteria.

[0849] "Display means" refers to a function for presenting formatted search results or alert information on the user interface.

[0850] "Emotional analysis" is the process of analyzing a user's emotional state in detail based on data obtained from emotion recognition.

[0851] "Priority adjustment" is the process of dynamically determining the importance of multiple search results and alert information based on sentiment data.

[0852] A "storage method" is a function that records user emotional data and uses it to improve future system operation.

[0853] "Real-time" means analyzing and processing data instantly and reflecting the results immediately.

[0854] The present invention can be applied to a monitoring system that detects anomalies from user input. This system includes means for inputting criteria, means for recognizing and analyzing emotions, means for searching a database, means for formatting and displaying search results, and means for adjusting the priority of display results based on the user's emotion data. The system also has a function to store the user's emotion data and reflect it in future search results.

[0855] Hardware and software to be used

[0856] The system utilizes wearable devices such as smart glasses and head-mounted displays, as well as surveillance cameras. It employs emotion engines like Affectiva for emotion recognition and AI models such as OpenCV and YOLO for real-time video analysis. MySQL is used as the database management system.

[0857] Program Overview

[0858] The server receives the criteria entered by the user and performs a database search based on them. Next, it analyzes the camera footage to detect anomalies and recognizes the user's emotional state in real time. Based on the analyzed emotional data, it adjusts the importance of alerts and presents the results to the user in an appropriate display method. Furthermore, it saves the user's emotional data and reflects it in future searches and displays.

[0859] Specific example

[0860] Let's say a user wears smart glasses and sets a criterion for monitoring area A: "If three or more people appear between 00:00 and 05:00." The system analyzes the surveillance camera footage in real time within the set time and generates an alert if an anomaly is detected. At this time, the system uses Affectiva to recognize the user's emotions, and if, for example, the system determines that the monitor is "tired," it will highlight only the most important alerts. The user's emotion data is also saved and reflected in the display of future alerts.

[0861] Example of a prompt

[0862] "Monitoring area A: An alert will be triggered if three or more people appear between 00:00 and 05:00."

[0863] "Detecting unusual human movement patterns in surveillance area A during the night and generating an alert."

[0864] "When the monitor is fatigued, only the most important alert will be highlighted."

[0865] This system improves the efficiency and accuracy of monitoring operations, reduces the burden on monitors, and lowers the risk of missing important anomalies.

[0866] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0867] Step 1:

[0868] The user enters the monitoring criteria using smart glasses or a terminal. The user enters the criteria through the terminal's interface, such as "Monitoring area A, between 00:00 and 05:00, if three or more people appear." The entered criteria are then sent to the system.

[0869] Step 2:

[0870] The terminal validates the criteria entered by the user. The terminal checks whether the entered data is in the correct format, and if there are any deficiencies, it displays a warning message and prompts the user to re-enter the data. For example, it checks whether the monitoring time and the number of people are entered correctly.

[0871] Step 3:

[0872] The system uses an emotion engine to recognize the user's emotions in real time. The device uses a camera to capture the user's facial expressions, and emotion recognition software such as Affectiva analyzes them to determine emotions like "stressed" or "tired." The analysis results are then sent to the system.

[0873] Step 4:

[0874] The server receives the criteria and sentiment data and begins searching the database. The server filters the surveillance footage in the database based on the specified criteria. For example, it might search for "when three or more people were detected in Area A between 00:00 and 05:00".

[0875] Step 5:

[0876] The server performs real-time video analysis. Using surveillance camera footage, it detects anomalies using AI models such as OpenCV and YOLO. When an anomaly is detected, it generates information as an alert and formats the analysis results.

[0877] Step 6:

[0878] The server adjusts alert priorities based on sentiment data. Taking user sentiment data into account, for example, if the user is "tired," the server will change priorities to highlight only the most important alerts.

[0879] Step 7:

[0880] The formatted alert information is sent to the terminal. The terminal displays the received information on its user interface. For example, it might display specific information such as "An anomaly was detected in Area A at 02:15 AM."

[0881] Step 8:

[0882] When a user requests more information, they select a specific alert. A request is then sent to the server to retrieve the details of the alert selected by the user from the database.

[0883] Step 9:

[0884] The server retrieves detailed information, formats it, and sends it to the terminal. For example, the server retrieves "detailed information for company A" again, formats it in JSON format, and sends it.

[0885] Step 10:

[0886] The device displays the received details in a pop-up window or a dedicated screen. The user reviews the displayed details and takes the necessary actions.

[0887] Step 11:

[0888] The server stores user sentiment data and uses it to inform future searches and alerts. For example, it stores monitored sentiment fluctuation data and provides personalized responses based on the next alert.

[0889] 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.

[0890] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0891] 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.

[0892] [Third Embodiment]

[0893] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0894] 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.

[0895] 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).

[0896] 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.

[0897] 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.

[0898] 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).

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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".

[0905] The system of this invention is for automatically selecting target companies for M&A and sales, and mainly consists of a user, a terminal, and a server. The specific processing is described below in natural language.

[0906] Criteria input

[0907] User: Launch the system and input the criteria necessary for selecting M&A targets and sales opportunities. These criteria include sales volume, industry, region, growth rate, and number of employees. Input is performed using the interface on the terminal.

[0908] Specific example: The user enters criteria such as "sales of 1 billion yen or more, industry is IT, and region is Tokyo."

[0909] Criteria validation

[0910] Terminal: Automatically validates the format and content of criteria entered by the user. If there are any deficiencies, it displays a message prompting the user to correct them.

[0911] Validation example: Check if the sales volume is a numerical value and verify that the industry is present in the selection options.

[0912] Database connection

[0913] Server: After successful validation, establish a database connection and prepare search queries based on the criteria. Securely access the database using authentication credentials.

[0914] Specific example: Connect to an SQL database using a JDBC driver.

[0915] Database Search

[0916] Server: Generates search queries and executes them against the database. Queries are generated based on criteria entered by the user.

[0917] Specific example: Generate and execute the query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0918] Formatting and displaying search results

[0919] Server: Receives search results and formats them into a user-friendly format. Converts them to JSON or XML format and sends them to the terminal.

[0920] Specific example: Format it like this: "[{Company Name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company Name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]".

[0921] Terminal: Analyzes received data and displays it on the user interface in list view or table format. Users then proceed with their selection based on the displayed company information.

[0922] Example of display: Display as follows: "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[0923] Get detailed information

[0924] User: To obtain more detailed information about a selected company, click on that company.

[0925] Server: Retrieves detailed information about the selected company from the database again, formats it similarly, and sends it to the terminal.

[0926] Specific example: If "Company A" is selected, detailed information such as "capital, number of employees, business overview, and recent news" will be retrieved and displayed.

[0927] Device: Receives detailed information and displays it in a pop-up or new screen.

[0928] Example of displaying detailed information: Display detailed information about company A, such as "capital of 200 million yen, 500 employees, business overview, and recent news."

[0929] The above describes a specific embodiment of this system. This system is designed to efficiently execute a series of processes, from inputting criteria to database searching, formatting and displaying results, and obtaining detailed information, enabling users to efficiently select target companies.

[0930] The following describes the processing flow.

[0931] Step 1:

[0932] User: Launch the program and enter criteria for selecting M&A targets or sales opportunities. Criteria include sales volume, industry, region, growth rate, and number of employees.

[0933] Specific example: A user enters "Sales volume: 1 billion yen or more", "Industry: IT", and "Region: Tokyo" into the input field on the screen.

[0934] Step 2:

[0935] Terminal: Receives the entered criteria and validates the content. It verifies that the sales volume is a numerical value, that the industry is present in the selection list, and that the region is in the correct format.

[0936] Specific example: If a value other than a number is entered in the sales volume field, a warning message will be displayed prompting the user to re-enter the value.

[0937] Step 3:

[0938] Terminal: If the criteria are confirmed to be correct, it sends that data to the server. HTTP requests are used for transmission, and the criteria are packaged in JSON format or similar.

[0939] Specific example: The criteria are deemed correct, and the JSON object "{revenue: 'over 1 billion yen', industry: 'IT', location: 'Tokyo'}" is sent to the server.

[0940] Step 4:

[0941] Server: Analyzes the received criteria and establishes a database connection. Securely accesses the database using appropriate authentication credentials.

[0942] Specific example: Connecting to an SQL database using a JDBC driver or ODBC driver.

[0943] Step 5:

[0944] Server: Dynamically generates SQL queries based on criteria. The queries search the database for records that match the conditions.

[0945] Specific example: Generate the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0946] Step 6:

[0947] Server: Executes SQL queries to retrieve matching company information from the database. The retrieved data is temporarily stored and processed on the server.

[0948] Specific example: The query results will retrieve information on companies A, B, and C.

[0949] Step 7:

[0950] Server: Formats search results into a user-friendly format. Converts the formatted data into JSON or XML format and sends it to the terminal.

[0951] Specific example: Convert to JSON format like this: "[{Company Name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company Name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]".

[0952] Step 8:

[0953] Terminal: Parses received JSON data and displays it on the user interface. It displays the data in list view or table format, making it easy for users to browse and compare.

[0954] Specific display example: The screen will display as follows: "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[0955] Step 9:

[0956] User: Select and click on a company of interest from the displayed company information. Request detailed information.

[0957] Example: Click "Company A" to request detailed information.

[0958] Step 10:

[0959] Server: Receives user requests and generates and executes queries to retrieve detailed information about the selected company from the database again.

[0960] Specific example: Generate a query like "SELECT FROM company_details WHERE company_id = 'Company A'".

[0961] Step 11:

[0962] Server: Reformats the retrieved detailed information and sends it to the terminal in JSON format or similar.

[0963] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[0964] Step 12:

[0965] Terminal: Analyzes received details and displays them in a pop-up window or a new screen. Allows users to view and analyze the details.

[0966] Specific display example: A pop-up window will display information such as "Capital: 200 million yen," "Number of employees: 500," "Business overview," and "Recent news."

[0967] The above outlines the specific processing steps of the system. This allows users to select M&A targets and sales opportunities in an efficient and reliable manner.

[0968] (Example 1)

[0969] 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."

[0970] Traditional M&A and target company selection systems suffered from problems such as difficulty in accurately inputting criteria and insufficient validation of entered criteria, resulting in reduced search accuracy. Furthermore, search results were displayed in a format difficult for users to understand, leading to time-consuming target company selection. Additionally, the process of obtaining detailed information on selected companies was cumbersome, resulting in a poor user experience.

[0971] 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.

[0972] In this invention, the server includes means for inputting criteria, means for searching a database based on the criteria, means for formatting and displaying the search results, means for retrieving detailed information of a company selected by the user, means for establishing a database connection to retrieve the search results, and means for validating the criteria. This enables the user to efficiently and accurately select a target company.

[0973] "Criteria" refers to the conditions that users enter to select target companies, and include factors such as sales volume, industry, region, growth rate, and number of employees.

[0974] A "database" is a collection of structured data, a system for managing information that can be searched, stored, updated, and deleted.

[0975] A "search query" is a set of instructions executed to retrieve specific information from a database, and is written in languages ​​such as SQL.

[0976] "Formatting" is the process of converting raw data into a format that is easy for users to understand, and includes data format conversion and layout adjustment.

[0977] A "terminal" is a device used by a user to access a system, and includes personal computers, smartphones, tablets, and other similar devices.

[0978] "Validation" is a checking process to ensure that input data is accurate and in the correct format.

[0979] A "server" is a computer system that provides services to clients over a network, and is responsible for database access and processing business logic.

[0980] "JSON" stands for JavaScript Object Notation, and it is a format for representing data in a lightweight text format.

[0981] XML stands for eXtensible Markup Language, and it is a language for representing data in a structured format using tags.

[0982] A "user interface" is the means by which a user interacts with a system, and includes elements such as buttons, forms, and dialog boxes on the screen.

[0983] The system of this invention is for automatically selecting target companies for M&A and sales, and consists of a user, a terminal, and a server. The operation of each component is described in detail below.

[0984] Criteria input

[0985] User: The user launches the system using a terminal. Through the system interface, they input criteria for selecting target companies. These criteria include sales volume, industry, region, growth rate, and number of employees.

[0986] Specific example: A user uses an input form to enter criteria such as "sales volume of 1 billion yen or more, industry is IT, and region is Tokyo."

[0987] Criteria validation

[0988] Terminal: The terminal automatically validates the format and content of the criteria entered by the user. Validation verifies that the sales volume is a numerical value and that the industry is within the given options. If there are any errors, a message prompting the user to correct them is displayed.

[0989] Specific example: If the sales volume is not a number, display the error message "Please enter the sales volume as a number."

[0990] Database connection

[0991] Server: After successful validation, the server establishes a database connection using a JDBC driver, etc. It then securely accesses the database using authentication credentials (username and password).

[0992] Specific example: The server connects to the SQL database in the format "jdbc:mysql: / / localhost:3306 / database_name".

[0993] Database Search

[0994] Server: The server generates search queries based on the criteria entered by the user and executes them against the database. A search query might look something like this: "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[0995] Specific example: The server sends the aforementioned query to the database and searches for companies that match the criteria.

[0996] Formatting and displaying search results

[0997] Server: Formats the search results retrieved from the database. That is, converts the search results into JSON or XML format to make them easy for the user to understand. Sends the formatted data to the terminal.

[0998] Specific example: Convert the search results to the format "[{Company Name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company Name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]".

[0999] Terminal: The terminal analyzes the received data and displays it on the user interface in list view or table format. The user then selects the next action based on the displayed company information.

[1000] Specific example: Company information is displayed in a list format such as "Company A: Sales of 1.2 billion yen, IT, Tokyo" and "Company B: Sales of 1.5 billion yen, IT, Tokyo".

[1001] Get detailed information

[1002] User: If a user wants to get more detailed information about a specific company, they click on that company's entry.

[1003] Specific example: A user clicks on information for "Company A".

[1004] Server: The server retrieves detailed information about the selected company from the database, formats it, and sends it to the terminal.

[1005] Specific example: Retrieve detailed information about company A using an SQL query and convert it into JSON format that includes "capital, number of employees, business overview, and recent news."

[1006] Device: The device receives detailed information and displays it in a pop-up window or a new screen.

[1007] Specific example: Display the following as detailed information: "Company A's capital is 200 million yen, it has 500 employees, and it includes a business overview and recent news."

[1008] Example of a prompt

[1009] 1. Criteria input prompt: "Please search for companies with sales of 1 billion yen or more, in the IT industry, and located in Tokyo."

[1010] 2. Prompt to retrieve detailed information: "Please display detailed information for Company A."

[1011] The above describes a specific embodiment of the target company selection process using the system of the present invention. This system efficiently executes a series of processes, from inputting criteria to database searching, formatting and displaying results, and obtaining detailed information, thereby supporting users in quickly and accurately selecting target companies.

[1012] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1013] Step 1:

[1014] Criteria input

[1015] User: The user starts the system using a terminal. An interface for entering criteria is displayed. The user enters the criteria necessary for selecting target companies.

[1016] Input: Conditions such as sales volume, industry, region, growth rate, and number of employees.

[1017] Output: Input criteria data

[1018] Specific action: The user enters "Sales volume of 1 billion yen or more, industry is IT, region is Tokyo" into the input form and clicks the "Submit" button.

[1019] Step 2:

[1020] Criteria validation

[1021] Terminal: The terminal validates the format and content of the criteria entered by the user. It verifies that the sales volume is a numerical value and that the industry is within the selectable options.

[1022] Input: Entered criteria data

[1023] Output: Validation result (success or failure)

[1024] Specific actions: The terminal verifies that the sales volume is a numerical value and checks that the industry is among the available options. If there are any discrepancies, an error message such as "Please enter the sales volume as a numerical value" will be displayed.

[1025] Step 3:

[1026] Database connection

[1027] Server: If validation is successful, the server establishes a database connection. It then securely accesses the database using a JDBC driver or similar.

[1028] Input: Criteria data that successfully validated

[1029] Output: Database connection established and authentication successful.

[1030] Specific operation: The server connects to the SQL database in the format "jdbc:mysql: / / localhost:3306 / database_name" and verifies the authentication information.

[1031] Step 4:

[1032] Database Search

[1033] Server: The server generates search queries based on the input criteria and executes them against the database.

[1034] Input: Criteria data, database connection information

[1035] Output: Search results (list of company information)

[1036] Specific operation: The server sends a query to the database: "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'", and retrieves company information that matches the conditions.

[1037] Step 5:

[1038] Formatting and displaying search results

[1039] Server: Formats search results retrieved from the database and converts them into a user-friendly format. It then converts them to JSON or XML format and sends them to the terminal.

[1040] Input: Search results retrieved from the database

[1041] Output: Formatted search result data (JSON or XML format)

[1042] Specific operation: The server formats the search results as follows: "[{Company name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]" and sends it to the terminal.

[1043] Terminal: The terminal analyzes the received data and displays it on the user interface in a list view or table format.

[1044] Input: Formatted search result data

[1045] Output: List of companies displayed on the screen

[1046] Specific operation: The terminal displays company information in a list format such as "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[1047] Step 6:

[1048] Get detailed information

[1049] User: To get detailed information about a specific company, click on that company's entry.

[1050] Input: User click-through information

[1051] Output: Request for detailed information about the selected companies

[1052] Specific action: The user clicks on the item "Company A".

[1053] Server: The server retrieves detailed information about the selected company from the database, formats it, and sends it to the terminal.

[1054] Input: Request for detailed information, database connection information

[1055] Output: Formatted detailed information data

[1056] Specific operation: The server executes the SQL query again and converts the detailed information of company A, including "capital, number of employees, business overview, and recent news," into JSON format.

[1057] Device: The device receives detailed information and displays it in a pop-up window or a new screen.

[1058] Input: Formatted detailed information data

[1059] Output: Detailed information displayed on the screen

[1060] Specific action: The device displays detailed information about "Company A: capital of 200 million yen, number of employees of 500, business overview, and recent news" in a new window or pop-up.

[1061] (Application Example 1)

[1062] 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."

[1063] The present invention aims to streamline sales activities within a factory and provide a system that enables sales representatives to immediately select appropriate target companies and present information. In particular, it aims to automate sales support within the factory and expedite on-site information acquisition and proposal activities.

[1064] 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.

[1065] In this invention, the server includes means for inputting criteria, means for searching a database based on the criteria, means for formatting and displaying the search results, means for a factory robot that performs an action based on the displayed search results, and means for providing information to factory staff's smart devices using the displayed results. This enables sales representatives to quickly select target companies on-site and make appropriate sales proposals.

[1066] A "means for inputting criteria" refers to an interface that allows users to input specific conditions or criteria (such as sales volume, industry, or region) into a terminal.

[1067] "Means for searching the database based on the aforementioned criteria" refers to a function that accesses the database based on the input criteria and executes a process to extract suitable data.

[1068] "Methods for formatting and displaying search results" refers to functions that convert the results obtained from a database search into a format that is easy for the user to understand and then display them.

[1069] "Factory robot means" refers to a function that includes robots operating within a factory to perform specific actions based on search results.

[1070] "Means of providing information to factory staff's smart devices" refers to a function that transmits formatted search results and detailed information to smart devices (such as smart glasses) within the factory, making them available for use by on-site staff.

[1071] "Means of validation" refers to verification functions that confirm that the input criteria are in the correct format and content.

[1072] "Means for obtaining detailed information again" refers to a function that retrieves and displays more detailed information from the database regarding the search results selected by the user.

[1073] The system of this invention is designed to streamline sales activities within a factory. The system mainly consists of users, terminals, and servers, each of which works in cooperation with the others.

[1074] Criteria input and validation

[1075] The user inputs criteria (e.g., sales volume, industry, region) through a sales support robot using an interface on their smart device. The terminal receives the input and validates it. Specifically, it checks that the sales volume is a numerical value and that the industry and region are in the correct format. If there are any errors, a message prompting the user to correct them is displayed.

[1076] Database connection and search

[1077] After successful validation on the terminal, the server securely establishes a database connection. Using a JDBC driver or similar, it connects to the SQL database and generates a search query based on the criteria entered by the user. The server then executes the query against the database and extracts the relevant company information.

[1078] Formatting and displaying search results

[1079] The server receives the search results and formats them into a user-friendly format (e.g., JSON). The formatted data is sent to the terminal, where the user can view the search results in a list view or table format on a smart device (e.g., smart glasses or a head-mounted display).

[1080] Actions performed by factory robots

[1081] Based on the displayed search results, the sales support robots in the factory perform specific actions. For example, they automatically prepare proposal materials for specific companies or organize data on potential customers.

[1082] Get detailed information

[1083] When a user selects a specific company from the displayed search results, the server accesses the database again to retrieve detailed information about that company. This data is also formatted and displayed on the smart device as a pop-up or in a new screen. This allows the user to proceed with sales activities based on more in-depth information.

[1084] Example prompt statement

[1085] "Please search for companies with sales of 1 billion yen or more, in the manufacturing industry, and located in the Nagoya area."

[1086] By inputting such prompt statements into the AI ​​model, the system selects the appropriate company and executes the function.

[1087] The system of this invention efficiently executes a series of processes, from inputting criteria to database searching, formatting and displaying results, executing actions by factory robots, and obtaining detailed information. This enables users to quickly and effectively select target companies and smoothly advance their sales activities.

[1088] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1089] Step 1: The user accesses the sales support robot using a smart device and enters criteria. For example, sales volume, industry, region, etc., are entered through the smart glasses interface. This input will be used as data in the next step.

[1090] Step 2: The terminal validates the criteria entered by the user. Specifically, it verifies that the sales volume is a number, the industry is a string, and the region is in the correct format. If there are any inappropriate criteria, the terminal displays a message prompting the user to correct them and waits until it receives the correct input.

[1091] Step 3: Upon receiving the criteria that have passed validation, the terminal sends them to the server. At this point, the criteria are formatted in JSON or another format to ensure data consistency.

[1092] Step 4: The server establishes a database connection based on the received criteria. It securely and efficiently connects to the SQL database using tools such as a JDBC driver. Through this connection, query generation and execution become possible.

[1093] Step 5: The server generates an SQL query based on the criteria and executes it against the database. For example, it generates a query such as "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'manufacturing' AND location = 'Nagoya'" and sends it to the database. Executing this query retrieves information about the relevant companies.

[1094] Step 6: The server formats the search results retrieved from the database and converts them into a user-friendly format (e.g., JSON). This makes it easier to display them on terminals and smart devices.

[1095] Step 7: The server sends the formatted data to the terminal. The terminal analyzes the received data and displays it in a list view or table format on the user interface. This allows the user to quickly review the search results in the field.

[1096] Step 8: Once the user selects a company they are interested in, the device requests detailed information about that company from the server. This request is then sent back to the database via the server.

[1097] Step 9: The server retrieves detailed company information from the database, reformats it, and sends it to the terminal. The terminal displays this information to the user in a pop-up or new screen. This allows the user to conduct sales activities based on more in-depth information.

[1098] Step 10: Based on the search results and detailed information, the factory robot performs specific actions. For example, it might prepare proposal materials for a specific company or organize data about potential customers. This makes sales support more efficient and automated.

[1099] 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.

[1100] The system of this invention is for automatically selecting M&A and sales targets, and mainly consists of a user, a terminal, a server, and an emotion engine. The specific processing is described below in natural language.

[1101] Criteria input

[1102] User: Launch the system and input the criteria necessary for selecting M&A targets and sales opportunities. These criteria include sales volume, industry, region, growth rate, and number of employees. Input is performed using the interface on the terminal.

[1103] Specific example: The user enters criteria such as "sales of 1 billion yen or more, industry is IT, and region is Tokyo."

[1104] Criteria validation

[1105] Terminal: Automatically validates the format and content of criteria entered by the user. If there are any deficiencies, it displays a message prompting the user to correct them.

[1106] Validation example: If a value other than a number is entered in the sales volume field, a warning message will be displayed, prompting the user to re-enter the value.

[1107] User emotion recognition

[1108] Emotion Engine: This engine analyzes the user's facial expressions, voice, input speed, and patterns to recognize their emotions. For example, it can capture the user's face with a camera and use facial recognition technology to determine their emotions.

[1109] Specific example: When a user enters criteria, the camera captures the user's facial expression and recognizes emotions such as "excited" or "confused."

[1110] Database connection and search

[1111] Server: Receives criteria and user sentiment data, and establishes a database connection. Securely accesses the database using appropriate authentication credentials.

[1112] Specific example: Connecting to an SQL database using a JDBC driver or ODBC driver.

[1113] Server: Dynamically generates and executes SQL queries based on criteria, and searches the database for records that match the conditions.

[1114] Specific example: Generate and execute the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[1115] Formatting and displaying search results

[1116] Server: Receives search results and adjusts the display order of results based on user sentiment data. Converts the formatted data into JSON or XML format and sends it to the terminal.

[1117] Specific example: If the user is in an "excited" state, the display order will be adjusted to prioritize positive information.

[1118] Terminal: Parses received JSON data and displays it on the user interface. It displays the data in list view or table format, making it easy for users to browse and compare.

[1119] Specific display example: The screen will display as follows: "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[1120] Get detailed information

[1121] User: Select and click on a company of interest from the displayed company information. Request detailed information.

[1122] Example: Click "Company A" to request detailed information.

[1123] Server: Receives user requests and generates and executes queries to retrieve detailed information about the selected company from the database again.

[1124] Specific example: Generate a query like "SELECT FROM company_details WHERE company_id = 'Company A'".

[1125] Server: Reformats the retrieved detailed information and sends it to the terminal in JSON format or similar.

[1126] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[1127] Terminal: Analyzes received details and displays them in a pop-up window or a new screen. Allows users to view and analyze the details.

[1128] Specific display example: A pop-up window will display information such as "Capital: 200 million yen," "Number of employees: 500," "Business overview," and "Recent news."

[1129] Storage and use of emotional data

[1130] Server: Stores user sentiment data and uses it to inform future criteria settings. This allows for more personalized search results by considering the user's past emotional states and choices.

[1131] Specific example: Save the companies a user selects based on specific criteria, along with their sentiment data, to use as reference for future searches.

[1132] The above is a specific embodiment of a system that combines an emotion engine. This system efficiently executes a series of processes, from inputting criteria and searching the database to formatting and displaying results, obtaining detailed information, and even utilizing emotion data, thereby providing users with a more personalized service.

[1133] The following describes the processing flow.

[1134] Step 1:

[1135] User: Launch the program and enter criteria for selecting M&A targets or sales opportunities. Criteria include sales volume, industry, region, growth rate, and number of employees.

[1136] Specific example: A user enters "Sales volume: 1 billion yen or more", "Industry: IT", and "Region: Tokyo" into the input field on the screen.

[1137] Step 2:

[1138] Terminal: Receives the entered criteria and validates the content. It verifies that the sales volume is a numerical value, that the industry is present in the selection list, and that the region is in the correct format.

[1139] Specific example: If a value other than a number is entered in the sales volume field, a warning message will be displayed prompting the user to re-enter the value.

[1140] Step 3:

[1141] Terminal: If the criteria are confirmed to be correct, it sends that data to the server. HTTP requests are used for transmission, and the criteria are packaged in JSON format or similar.

[1142] Specific example: The criteria are deemed correct, and the JSON object "{revenue: 'over 1 billion yen', industry: 'IT', location: 'Tokyo'}" is sent to the server.

[1143] Step 4:

[1144] Server: Analyzes the received criteria and establishes a database connection. Securely accesses the database using appropriate authentication credentials.

[1145] Specific example: Connecting to an SQL database using a JDBC driver or ODBC driver.

[1146] Step 5:

[1147] Emotion Engine: Recognizes user emotions by analyzing user facial expressions, voice, input speed and patterns, etc. It uses cameras and microphones to determine emotions.

[1148] Specific example: When a user performs an input task, their face is captured by a camera, and emotions such as "excited" or "confused" are recognized.

[1149] Step 6:

[1150] Server: Dynamically generates SQL queries based on criteria. Uses the generated queries to search the database for records that match the conditions.

[1151] Specific example: Generate the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[1152] Step 7:

[1153] Server: Executes SQL queries to retrieve matching company information from the database. The retrieved data is temporarily stored and processed on the server.

[1154] Specific example: The query results will retrieve information on "Company A, Company B, Company C".

[1155] Step 8:

[1156] Server: Formats search results and adjusts the display order based on user sentiment data. Converts the formatted data to JSON or XML format and sends it to the terminal.

[1157] Specific example: If the user's emotional state is "excited," prioritize displaying positive information.

[1158] Step 9:

[1159] Terminal: Parses received JSON data and displays it in the user interface. It displays the data in list view or table format, making it easy for users to browse and compare.

[1160] Specific display examples: The screen will display as follows: "Company A: Sales 1.2 billion yen, Industry: IT, Location: Tokyo" and "Company B: Sales 1.5 billion yen, Industry: IT, Location: Tokyo".

[1161] Step 10:

[1162] User: Select and click on a company of interest from the displayed company information. Request detailed information.

[1163] Example: Click "Company A" to request detailed information.

[1164] Step 11:

[1165] Server: Receives user requests and generates and executes queries to retrieve detailed information about the selected company from the database again.

[1166] Specific example: Generate a query like "SELECT FROM company_details WHERE company_id = 'Company A'".

[1167] Step 12:

[1168] Server: Formats the retrieved detailed information and sends it to the terminal in JSON format or similar.

[1169] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[1170] Step 13:

[1171] Terminal: Analyzes received details and displays them in a pop-up window or a new screen. Allows users to view and analyze the details.

[1172] Examples of display: Display "Capital of 200 million yen," "Number of employees: 500," "Business overview," "Recent news," etc.

[1173] Step 14:

[1174] Server: Stores user emotional data and uses it to inform future criteria settings. This allows for more personalized service by considering the user's past emotional states and choices.

[1175] Specific example: In the next search, reflect the company data selected by the user while they were "excited" during their previous search.

[1176] (Example 2)

[1177] 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."

[1178] Traditional M&A and sales target selection systems search a database based on user-inputted criteria and display search results. However, they lack the ability to consider the user's emotional state, resulting in a failure to provide personalized search results. Furthermore, validation of the input criteria and retrieval of detailed information had to be done manually, which was inefficient.

[1179] 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.

[1180] In this invention, the server includes means for inputting criteria, means for searching a database based on the criteria, means for recognizing the user's emotions, means for formatting and displaying the search results, and means for adjusting the search results based on the user's emotions. This makes it possible to provide personalized search results that match the user's emotional state.

[1181] "Criteria" refers to the evaluation criteria specified by the user when selecting M&A or sales targets, and includes items such as sales volume, industry, region, growth rate, and number of employees.

[1182] A "database" is a system that efficiently manages, searches, and retrieves a collection of information, and provides information based on specific criteria.

[1183] "User emotions" refers to the user's mental state, analyzed from facial expressions, voice, and other factors, and is used by the system to personalize search results.

[1184] "Means of recognizing emotions" refers to technologies that use input devices such as cameras and microphones to analyze a user's facial expressions and voice to determine their emotions.

[1185] "Search results" refer to a list of companies and potential clients retrieved from the database based on the criteria entered by the user.

[1186] "Formatting" refers to the process of converting search results obtained from a database into a format that is easy for users to understand.

[1187] "Means of display" refers to methods for graphically displaying search results on a user interface, and includes display in list view or table format.

[1188] "Methods of adjusting based on emotions" refer to methods of adjusting the display order and content of search results while taking into account the user's emotions.

[1189] The system of this invention automatically selects M&A targets and sales opportunities, and mainly consists of a user, a terminal, a server, and an emotion engine.

[1190] Criteria input

[1191] The user launches the system and inputs the criteria necessary for M&A and selecting sales targets via an interface on the terminal. These criteria include sales volume, industry, region, growth rate, and number of employees. The entered data is used as a basis for processing within the system.

[1192] Specific example: The user enters criteria such as "sales of 1 billion yen or more, industry is IT, and region is Tokyo."

[1193] Criteria validation

[1194] The terminal has a function to automatically validate the format and content of the criteria entered by the user. If there are any deficiencies, a message prompting correction will be displayed. This ensures that the criteria are set correctly.

[1195] Specific example: If a value other than a number is entered in the sales volume field, a warning message will be displayed prompting the user to re-enter the value.

[1196] User emotion recognition

[1197] The emotion engine analyzes the user's facial expressions, voice, input speed, and patterns to recognize their emotions. Specifically, it captures the user's face with a camera and uses facial recognition technology to determine their emotions. It can also perform voice analysis using a microphone.

[1198] Specific example: When a user enters criteria, the camera captures the user's facial expression and recognizes emotions such as "excited" or "confused."

[1199] Database connection and search

[1200] The server receives the criteria and sentiment data entered by the user and uses JDBC or ODBC drivers to establish a database connection. Once a secure connection is established, it dynamically generates SQL queries based on the criteria and searches the database for records that match the conditions.

[1201] Specific example: Generate and execute the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[1202] Formatting and displaying search results

[1203] The server receives the search results and adjusts the display order of the results based on the user's sentiment data. The formatted data is converted into JSON or XML format and sent to the terminal. This process results in search results that reflect the user's sentiment.

[1204] Specific example: If the user is in an "excited" state, the display order will be adjusted to prioritize positive information.

[1205] The terminal parses the received JSON data and displays it on the user interface in a list view or table format. This allows users to easily view and compare the results.

[1206] Example display: The screen will show "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[1207] Get detailed information

[1208] Users can request detailed information by selecting a company they are interested in from the displayed company information and clicking on it.

[1209] Example: Click on "Company A" to request more information.

[1210] The server receives the user's request, generates and executes a query to retrieve detailed information about the selected company from the database again. Then, it reformats the retrieved information and sends it to the terminal in JSON format or another appropriate format.

[1211] Specific example: Generate and execute the query "SELECT FROM company_details WHERE company_id = 'Company A'".

[1212] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[1213] The device analyzes the received details and displays them in a pop-up window or a new screen. This allows the user to view and analyze the details in more detail.

[1214] Example display: The pop-up window will show information such as "Capital: 200 million yen," "Number of employees: 500," "Business overview," and "Recent news."

[1215] Storage and use of emotional data

[1216] The server stores user sentiment data and uses it to inform future criteria settings. This allows for more personalized search results by considering the user's past emotional states and choices.

[1217] Specific example: Save the companies a user selects based on specific criteria, along with their sentiment data, to use as reference for future searches.

[1218] Examples of prompt statements:

[1219] "We are looking for IT companies in the Tokyo area with sales of over 1 billion yen."

[1220] "Please enter the following criteria: sales scale of 1 billion yen, industry: IT, region: Tokyo."

[1221] This system takes user emotions into consideration and executes a series of processes, from inputting criteria and searching the database to formatting and displaying results, obtaining detailed information, and even utilizing emotional data. This allows for the provision of more personalized services to users.

[1222] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1223] Step 1: Enter the criteria

[1224] The user launches the system and uses the interface on the terminal to input the criteria necessary for M&A and selecting potential clients. The input criteria include sales volume, industry, region, growth rate, and number of employees.

[1225] Input: Criteria such as sales volume, industry, region, growth rate, and number of employees.

[1226] Operation: Enter "Sales volume: 1 billion yen or more", "Industry: IT", and "Region: Tokyo" into the form on the terminal.

[1227] Output: Criteria data is input into the system.

[1228] Step 2: Criteria Validation

[1229] The terminal automatically validates the criteria entered by the user. If there are any errors, it displays a warning message about the input error and prompts the user to correct it.

[1230] Input: Criteria entered by the user.

[1231] Action: If anything other than a number is entered in the sales volume field, a warning message "Please enter the sales volume as a number" will be displayed, prompting the user to re-enter the value.

[1232] Output: Accurate criteria data that has passed validation.

[1233] Step 3: User emotion recognition

[1234] The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. Specifically, it captures the user's face with a camera and uses facial recognition technology to determine their emotions.

[1235] Input: User facial expression data, voice data.

[1236] Operation: When criteria are entered, the camera captures the user's facial expression and recognizes emotions such as "excited" or "confused."

[1237] Output: User sentiment data.

[1238] Step 4: Database connection and query generation

[1239] The server receives precise criteria data and user sentiment data, and connects to the database using JDBC or ODBC drivers. It then generates SQL queries based on the criteria and searches the database for records that match the conditions.

[1240] Input: Accurate criteria data, user sentiment data.

[1241] Operation: Connect to "jdbc:mysql: / / localhost:3306 / ma_database" and generate the following query: "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[1242] Output: Records from the search results.

[1243] Step 5: Format and reorder search results

[1244] The server receives the search results and formats and adjusts the display order of the results based on the user's sentiment data. The formatted data is converted to JSON format and sent to the terminal.

[1245] Input: Search result records, user sentiment data.

[1246] Function: Ranks search results based on sentiment data, formats them, and converts them to JSON format.

[1247] Output: Formatted JSON data.

[1248] Step 6: Displaying the results

[1249] The terminal parses the received JSON data and displays it on the user interface in a list view or table format, making it easy for users to view and compare the data.

[1250] Input: Formatted JSON data.

[1251] Operation: Displays results such as "Company A: Sales of 1.2 billion yen, IT, Tokyo" and "Company B: Sales of 1.5 billion yen, IT, Tokyo".

[1252] Output: Search results displayed on the user interface.

[1253] Step 7: Obtaining detailed information

[1254] Users select a company they are interested in from the displayed company information and click to request more details.

[1255] Input: The company selected by the user.

[1256] Action: Click "Company A" to request detailed information.

[1257] Output: A request for more information is sent to the server.

[1258] The server receives the request, generates and executes an SQL query to retrieve detailed information about the selected company from the database. It then formats the retrieved information and sends it to the terminal in JSON format.

[1259] Input: Request for detailed information.

[1260] Operation: Generates and executes the query "SELECT FROM company_details WHERE company_id = 'Company A'". Formats it as follows: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500 people', Business Overview: '...', Recent News: '...'}".

[1261] Output: Formatted detailed information in JSON data.

[1262] The device analyzes the received details and displays them in a pop-up window or a new screen, allowing the user to view and analyze the details in more detail.

[1263] Input: Formatted JSON data containing detailed information.

[1264] Function: Displays information such as "Capital of 200 million yen," "Number of employees: 500," and "Business overview" in a pop-up window.

[1265] Output: Detailed information displayed in a pop-up window or new screen.

[1266] Step 8: Storing and using emotional data

[1267] The server stores user sentiment data and uses it to inform future criteria settings. This enables the provision of more personalized search results.

[1268] Input: User sentiment data, criteria data.

[1269] Operation: Saves data on companies selected by the user based on specific criteria, along with their sentiment at the time of selection.

[1270] Output: Sentiment data and criteria data stored on the server.

[1271] The above outlines the specific processing flow of this system, including a detailed explanation of the actions performed at each step.

[1272] (Application Example 2)

[1273] 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."

[1274] In conventional monitoring systems, anomaly detection and alert processing were performed using standard methods, without adaptive processing that took into account the emotions and concentration levels of the monitors. This led to challenges such as monitor fatigue and excessive stress, increasing the risk of missing important anomalies. To address these issues, dynamic alert prioritization that takes monitors' emotional states and personalized display methods are necessary.

[1275] 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.

[1276] In this invention, the server includes means for recognizing and analyzing the user's emotions, means for adjusting the priority of display results based on the user's emotion data, and means for storing the user's emotion data and reflecting it in future search results. This makes it possible to analyze the emotional state of monitors in real time and highlight important alerts at the appropriate time. Furthermore, it enables personalized alert display based on past emotion data, reducing the burden on monitors and lowering the risk of missing important anomalies.

[1277] "Criteria" refers to the conditions or constraints that a user specifies to a system.

[1278] A "database" is a system for storing structured data and for efficiently searching and retrieving it.

[1279] A "search method" is a function for finding information within a database based on specified criteria.

[1280] "Emotion recognition" is a technology that analyzes a user's facial expressions, voice, and behavior to determine their emotional state.

[1281] "Emotional data" refers to information obtained from the user's emotional perception, and this is what the system uses to determine the user's state.

[1282] A "search result" is a collection of information retrieved from a database based on specific criteria.

[1283] "Display means" refers to a function for presenting formatted search results or alert information on the user interface.

[1284] "Emotional analysis" is the process of analyzing a user's emotional state in detail based on data obtained from emotion recognition.

[1285] "Priority adjustment" is the process of dynamically determining the importance of multiple search results and alert information based on sentiment data.

[1286] A "storage method" is a function that records user emotional data and uses it to improve future system operation.

[1287] "Real-time" means analyzing and processing data instantly and reflecting the results immediately.

[1288] The present invention can be applied to a monitoring system that detects anomalies from user input. This system includes means for inputting criteria, means for recognizing and analyzing emotions, means for searching a database, means for formatting and displaying search results, and means for adjusting the priority of display results based on the user's emotion data. The system also has a function to store the user's emotion data and reflect it in future search results.

[1289] Hardware and software to be used

[1290] The system utilizes wearable devices such as smart glasses and head-mounted displays, as well as surveillance cameras. It employs emotion engines like Affectiva for emotion recognition and AI models such as OpenCV and YOLO for real-time video analysis. MySQL is used as the database management system.

[1291] Program Overview

[1292] The server receives the criteria entered by the user and performs a database search based on them. Next, it analyzes the camera footage to detect anomalies and recognizes the user's emotional state in real time. Based on the analyzed emotional data, it adjusts the importance of alerts and presents the results to the user in an appropriate display method. Furthermore, it saves the user's emotional data and reflects it in future searches and displays.

[1293] Specific example

[1294] Let's say a user wears smart glasses and sets a criterion for monitoring area A: "If three or more people appear between 00:00 and 05:00." The system analyzes the surveillance camera footage in real time within the set time and generates an alert if an anomaly is detected. At this time, the system uses Affectiva to recognize the user's emotions, and if, for example, the system determines that the monitor is "tired," it will highlight only the most important alerts. The user's emotion data is also saved and reflected in the display of future alerts.

[1295] Example of a prompt

[1296] "Monitoring area A: An alert will be triggered if three or more people appear between 00:00 and 05:00."

[1297] "Detecting unusual human movement patterns in surveillance area A during the night and generating an alert."

[1298] "When the monitor is fatigued, only the most important alert will be highlighted."

[1299] This system improves the efficiency and accuracy of monitoring operations, reduces the burden on monitors, and lowers the risk of missing important anomalies.

[1300] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1301] Step 1:

[1302] The user enters the monitoring criteria using smart glasses or a terminal. The user enters the criteria through the terminal's interface, such as "Monitoring area A, between 00:00 and 05:00, if three or more people appear." The entered criteria are then sent to the system.

[1303] Step 2:

[1304] The terminal validates the criteria entered by the user. The terminal checks whether the entered data is in the correct format, and if there are any deficiencies, it displays a warning message and prompts the user to re-enter the data. For example, it checks whether the monitoring time and the number of people are entered correctly.

[1305] Step 3:

[1306] The system uses an emotion engine to recognize the user's emotions in real time. The device uses a camera to capture the user's facial expressions, and emotion recognition software such as Affectiva analyzes them to determine emotions like "stressed" or "tired." The analysis results are then sent to the system.

[1307] Step 4:

[1308] The server receives the criteria and sentiment data and begins searching the database. The server filters the surveillance footage in the database based on the specified criteria. For example, it might search for "when three or more people were detected in Area A between 00:00 and 05:00".

[1309] Step 5:

[1310] The server performs real-time video analysis. Using surveillance camera footage, it detects anomalies using AI models such as OpenCV and YOLO. When an anomaly is detected, it generates information as an alert and formats the analysis results.

[1311] Step 6:

[1312] The server adjusts alert priorities based on sentiment data. Taking user sentiment data into account, for example, if the user is "tired," the server will change priorities to highlight only the most important alerts.

[1313] Step 7:

[1314] The formatted alert information is sent to the terminal. The terminal displays the received information on its user interface. For example, it might display specific information such as "An anomaly was detected in Area A at 02:15 AM."

[1315] Step 8:

[1316] When a user requests more information, they select a specific alert. A request is then sent to the server to retrieve the details of the alert selected by the user from the database.

[1317] Step 9:

[1318] The server retrieves detailed information, formats it, and sends it to the terminal. For example, the server retrieves "detailed information for company A" again, formats it in JSON format, and sends it.

[1319] Step 10:

[1320] The device displays the received details in a pop-up window or a dedicated screen. The user reviews the displayed details and takes the necessary actions.

[1321] Step 11:

[1322] The server stores user sentiment data and uses it to inform future searches and alerts. For example, it stores monitored sentiment fluctuation data and provides personalized responses based on the next alert.

[1323] 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.

[1324] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1325] 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.

[1326] [Fourth Embodiment]

[1327] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1328] 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.

[1329] 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).

[1330] 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.

[1331] 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.

[1332] 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).

[1333] 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.

[1334] 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.

[1335] 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.

[1336] 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.

[1337] 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.

[1338] 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.

[1339] 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".

[1340] The system of this invention is for automatically selecting target companies for M&A and sales, and mainly consists of a user, a terminal, and a server. The specific processing is described below in natural language.

[1341] Criteria input

[1342] User: Launch the system and input the criteria necessary for selecting M&A targets and sales opportunities. These criteria include sales volume, industry, region, growth rate, and number of employees. Input is performed using the interface on the terminal.

[1343] Specific example: The user enters criteria such as "sales of 1 billion yen or more, industry is IT, and region is Tokyo."

[1344] Criteria validation

[1345] Terminal: Automatically validates the format and content of criteria entered by the user. If there are any deficiencies, it displays a message prompting the user to correct them.

[1346] Validation example: Check if the sales volume is a numerical value and verify that the industry is present in the selection options.

[1347] Database connection

[1348] Server: After successful validation, establish a database connection and prepare search queries based on the criteria. Securely access the database using authentication credentials.

[1349] Specific example: Connect to an SQL database using a JDBC driver.

[1350] Database Search

[1351] Server: Generates search queries and executes them against the database. Queries are generated based on criteria entered by the user.

[1352] Specific example: Generate and execute the query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[1353] Formatting and displaying search results

[1354] Server: Receives search results and formats them into a user-friendly format. Converts them to JSON or XML format and sends them to the terminal.

[1355] Specific example: Format it like this: "[{Company Name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company Name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]".

[1356] Terminal: Analyzes received data and displays it on the user interface in list view or table format. Users then proceed with their selection based on the displayed company information.

[1357] Example of display: Display as follows: "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[1358] Get detailed information

[1359] User: To obtain more detailed information about a selected company, click on that company.

[1360] Server: Retrieves detailed information about the selected company from the database again, formats it similarly, and sends it to the terminal.

[1361] Specific example: If "Company A" is selected, detailed information such as "capital, number of employees, business overview, and recent news" will be retrieved and displayed.

[1362] Device: Receives detailed information and displays it in a pop-up or new screen.

[1363] Example of displaying detailed information: Display detailed information about company A, such as "capital of 200 million yen, 500 employees, business overview, and recent news."

[1364] The above describes a specific embodiment of this system. This system is designed to efficiently execute a series of processes, from inputting criteria to database searching, formatting and displaying results, and obtaining detailed information, enabling users to efficiently select target companies.

[1365] The following describes the processing flow.

[1366] Step 1:

[1367] User: Launch the program and enter criteria for selecting M&A targets or sales opportunities. Criteria include sales volume, industry, region, growth rate, and number of employees.

[1368] Specific example: A user enters "Sales volume: 1 billion yen or more", "Industry: IT", and "Region: Tokyo" into the input field on the screen.

[1369] Step 2:

[1370] Terminal: Receives the entered criteria and validates the content. It verifies that the sales volume is a numerical value, that the industry is present in the selection list, and that the region is in the correct format.

[1371] Specific example: If a value other than a number is entered in the sales volume field, a warning message will be displayed prompting the user to re-enter the value.

[1372] Step 3:

[1373] Terminal: If the criteria are confirmed to be correct, it sends that data to the server. HTTP requests are used for transmission, and the criteria are packaged in JSON format or similar.

[1374] Specific example: The criteria are deemed correct, and the JSON object "{revenue: 'over 1 billion yen', industry: 'IT', location: 'Tokyo'}" is sent to the server.

[1375] Step 4:

[1376] Server: Analyzes the received criteria and establishes a database connection. Securely accesses the database using appropriate authentication credentials.

[1377] Specific example: Connecting to an SQL database using a JDBC driver or ODBC driver.

[1378] Step 5:

[1379] Server: Dynamically generates SQL queries based on criteria. The queries search the database for records that match the conditions.

[1380] Specific example: Generate the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[1381] Step 6:

[1382] Server: Executes SQL queries to retrieve matching company information from the database. The retrieved data is temporarily stored and processed on the server.

[1383] Specific example: The query results will retrieve information on companies A, B, and C.

[1384] Step 7:

[1385] Server: Formats search results into a user-friendly format. Converts the formatted data into JSON or XML format and sends it to the terminal.

[1386] Specific example: Convert to JSON format like this: "[{Company Name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company Name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]".

[1387] Step 8:

[1388] Terminal: Parses received JSON data and displays it on the user interface. It displays the data in list view or table format, making it easy for users to browse and compare.

[1389] Specific display example: The screen will display as follows: "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[1390] Step 9:

[1391] User: Select and click on a company of interest from the displayed company information. Request detailed information.

[1392] Example: Click "Company A" to request detailed information.

[1393] Step 10:

[1394] Server: Receives user requests and generates and executes queries to retrieve detailed information about the selected company from the database again.

[1395] Specific example: Generate a query like "SELECT FROM company_details WHERE company_id = 'Company A'".

[1396] Step 11:

[1397] Server: Reformats the retrieved detailed information and sends it to the terminal in JSON format or similar.

[1398] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[1399] Step 12:

[1400] Terminal: Analyzes received details and displays them in a pop-up window or a new screen. Allows users to view and analyze the details.

[1401] Specific display example: A pop-up window will display information such as "Capital: 200 million yen," "Number of employees: 500," "Business overview," and "Recent news."

[1402] The above outlines the specific processing steps of the system. This allows users to select M&A targets and sales opportunities in an efficient and reliable manner.

[1403] (Example 1)

[1404] 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".

[1405] Traditional M&A and target company selection systems suffered from problems such as difficulty in accurately inputting criteria and insufficient validation of entered criteria, resulting in reduced search accuracy. Furthermore, search results were displayed in a format difficult for users to understand, leading to time-consuming target company selection. Additionally, the process of obtaining detailed information on selected companies was cumbersome, resulting in a poor user experience.

[1406] 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.

[1407] In this invention, the server includes means for inputting criteria, means for searching a database based on the criteria, means for formatting and displaying the search results, means for retrieving detailed information of a company selected by the user, means for establishing a database connection to retrieve the search results, and means for validating the criteria. This enables the user to efficiently and accurately select a target company.

[1408] "Criteria" refers to the conditions that users enter to select target companies, and include factors such as sales volume, industry, region, growth rate, and number of employees.

[1409] A "database" is a collection of structured data, a system for managing information that can be searched, stored, updated, and deleted.

[1410] A "search query" is a set of instructions executed to retrieve specific information from a database, and is written in languages ​​such as SQL.

[1411] "Formatting" is the process of converting raw data into a format that is easy for users to understand, and includes data format conversion and layout adjustment.

[1412] A "terminal" is a device used by a user to access a system, and includes personal computers, smartphones, tablets, and other similar devices.

[1413] "Validation" is a checking process to ensure that input data is accurate and in the correct format.

[1414] A "server" is a computer system that provides services to clients over a network, and is responsible for database access and processing business logic.

[1415] "JSON" stands for JavaScript Object Notation, and it is a format for representing data in a lightweight text format.

[1416] XML stands for eXtensible Markup Language, and it is a language for representing data in a structured format using tags.

[1417] A "user interface" is the means by which a user interacts with a system, and includes elements such as buttons, forms, and dialog boxes on the screen.

[1418] The system of this invention is for automatically selecting target companies for M&A and sales, and consists of a user, a terminal, and a server. The operation of each component is described in detail below.

[1419] Criteria input

[1420] User: The user launches the system using a terminal. Through the system interface, they input criteria for selecting target companies. These criteria include sales volume, industry, region, growth rate, and number of employees.

[1421] Specific example: A user uses an input form to enter criteria such as "sales volume of 1 billion yen or more, industry is IT, and region is Tokyo."

[1422] Criteria validation

[1423] Terminal: The terminal automatically validates the format and content of the criteria entered by the user. Validation verifies that the sales volume is a numerical value and that the industry is within the given options. If there are any errors, a message prompting the user to correct them is displayed.

[1424] Specific example: If the sales volume is not a number, display the error message "Please enter the sales volume as a number."

[1425] Database connection

[1426] Server: After successful validation, the server establishes a database connection using a JDBC driver, etc. It then securely accesses the database using authentication credentials (username and password).

[1427] Specific example: The server connects to the SQL database in the format "jdbc:mysql: / / localhost:3306 / database_name".

[1428] Database Search

[1429] Server: The server generates search queries based on the criteria entered by the user and executes them against the database. A search query might look something like this: "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[1430] Specific example: The server sends the aforementioned query to the database and searches for companies that match the criteria.

[1431] Formatting and displaying search results

[1432] Server: Formats the search results retrieved from the database. That is, converts the search results into JSON or XML format to make them easy for the user to understand. Sends the formatted data to the terminal.

[1433] Specific example: Convert the search results to the format "[{Company Name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company Name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]".

[1434] Terminal: The terminal analyzes the received data and displays it on the user interface in list view or table format. The user then selects the next action based on the displayed company information.

[1435] Specific example: Company information is displayed in a list format such as "Company A: Sales of 1.2 billion yen, IT, Tokyo" and "Company B: Sales of 1.5 billion yen, IT, Tokyo".

[1436] Get detailed information

[1437] User: If a user wants to get more detailed information about a specific company, they click on that company's entry.

[1438] Specific example: A user clicks on information for "Company A".

[1439] Server: The server retrieves detailed information about the selected company from the database, formats it, and sends it to the terminal.

[1440] Specific example: Retrieve detailed information about company A using an SQL query and convert it into JSON format that includes "capital, number of employees, business overview, and recent news."

[1441] Device: The device receives detailed information and displays it in a pop-up window or a new screen.

[1442] Specific example: Display the following as detailed information: "Company A's capital is 200 million yen, it has 500 employees, and it includes a business overview and recent news."

[1443] Example of a prompt

[1444] 1. Criteria input prompt: "Please search for companies with sales of 1 billion yen or more, in the IT industry, and located in Tokyo."

[1445] 2. Prompt to retrieve detailed information: "Please display detailed information for Company A."

[1446] The above describes a specific embodiment of the target company selection process using the system of the present invention. This system efficiently executes a series of processes, from inputting criteria to database searching, formatting and displaying results, and obtaining detailed information, thereby supporting users in quickly and accurately selecting target companies.

[1447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1448] Step 1:

[1449] Criteria input

[1450] User: The user starts the system using a terminal. An interface for entering criteria is displayed. The user enters the criteria necessary for selecting target companies.

[1451] Input: Conditions such as sales volume, industry, region, growth rate, and number of employees.

[1452] Output: Input criteria data

[1453] Specific action: The user enters "Sales volume of 1 billion yen or more, industry is IT, region is Tokyo" into the input form and clicks the "Submit" button.

[1454] Step 2:

[1455] Criteria validation

[1456] Terminal: The terminal validates the format and content of the criteria entered by the user. It verifies that the sales volume is a numerical value and that the industry is within the selectable options.

[1457] Input: Entered criteria data

[1458] Output: Validation result (success or failure)

[1459] Specific actions: The terminal verifies that the sales volume is a numerical value and checks that the industry is among the available options. If there are any discrepancies, an error message such as "Please enter the sales volume as a numerical value" will be displayed.

[1460] Step 3:

[1461] Database connection

[1462] Server: If validation is successful, the server establishes a database connection. It then securely accesses the database using a JDBC driver or similar.

[1463] Input: Criteria data that successfully validated

[1464] Output: Database connection established and authentication successful.

[1465] Specific operation: The server connects to the SQL database in the format "jdbc:mysql: / / localhost:3306 / database_name" and verifies the authentication information.

[1466] Step 4:

[1467] Database Search

[1468] Server: The server generates search queries based on the input criteria and executes them against the database.

[1469] Input: Criteria data, database connection information

[1470] Output: Search results (list of company information)

[1471] Specific operation: The server sends a query to the database: "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'", and retrieves company information that matches the conditions.

[1472] Step 5:

[1473] Formatting and displaying search results

[1474] Server: Formats search results retrieved from the database and converts them into a user-friendly format. It then converts them to JSON or XML format and sends them to the terminal.

[1475] Input: Search results retrieved from the database

[1476] Output: Formatted search result data (JSON or XML format)

[1477] Specific operation: The server formats the search results as follows: "[{Company name: 'Company A', Sales: '1.2 billion yen', Industry: 'IT', Location: 'Tokyo'}, {Company name: 'Company B', Sales: '1.5 billion yen', Industry: 'IT', Location: 'Tokyo'}]" and sends it to the terminal.

[1478] Terminal: The terminal analyzes the received data and displays it on the user interface in a list view or table format.

[1479] Input: Formatted search result data

[1480] Output: List of companies displayed on the screen

[1481] Specific operation: The terminal displays company information in a list format such as "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[1482] Step 6:

[1483] Get detailed information

[1484] User: To get detailed information about a specific company, click on that company's entry.

[1485] Input: User click-through information

[1486] Output: Request for detailed information about the selected companies

[1487] Specific action: The user clicks on the item "Company A".

[1488] Server: The server retrieves detailed information about the selected company from the database, formats it, and sends it to the terminal.

[1489] Input: Request for detailed information, database connection information

[1490] Output: Formatted detailed information data

[1491] Specific operation: The server executes the SQL query again and converts the detailed information of company A, including "capital, number of employees, business overview, and recent news," into JSON format.

[1492] Device: The device receives detailed information and displays it in a pop-up window or a new screen.

[1493] Input: Formatted detailed information data

[1494] Output: Detailed information displayed on the screen

[1495] Specific action: The device displays detailed information about "Company A: capital of 200 million yen, number of employees of 500, business overview, and recent news" in a new window or pop-up.

[1496] (Application Example 1)

[1497] 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".

[1498] The present invention aims to streamline sales activities within a factory and provide a system that enables sales representatives to immediately select appropriate target companies and present information. In particular, it aims to automate sales support within the factory and expedite on-site information acquisition and proposal activities.

[1499] 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.

[1500] In this invention, the server includes means for inputting criteria, means for searching a database based on the criteria, means for formatting and displaying the search results, means for a factory robot that performs an action based on the displayed search results, and means for providing information to factory staff's smart devices using the displayed results. This enables sales representatives to quickly select target companies on-site and make appropriate sales proposals.

[1501] A "means for inputting criteria" refers to an interface that allows users to input specific conditions or criteria (such as sales volume, industry, or region) into a terminal.

[1502] "Means for searching the database based on the aforementioned criteria" refers to a function that accesses the database based on the input criteria and executes a process to extract suitable data.

[1503] "Methods for formatting and displaying search results" refers to functions that convert the results obtained from a database search into a format that is easy for the user to understand and then display them.

[1504] "Factory robot means" refers to a function that includes robots operating within a factory to perform specific actions based on search results.

[1505] "Means of providing information to factory staff's smart devices" refers to a function that transmits formatted search results and detailed information to smart devices (such as smart glasses) within the factory, making them available for use by on-site staff.

[1506] "Means of validation" refers to verification functions that confirm that the input criteria are in the correct format and content.

[1507] "Means for obtaining detailed information again" refers to a function that retrieves and displays more detailed information from the database regarding the search results selected by the user.

[1508] The system of this invention is designed to streamline sales activities within a factory. The system mainly consists of users, terminals, and servers, each of which works in cooperation with the others.

[1509] Criteria input and validation

[1510] The user inputs criteria (e.g., sales volume, industry, region) through a sales support robot using an interface on their smart device. The terminal receives the input and validates it. Specifically, it checks that the sales volume is a numerical value and that the industry and region are in the correct format. If there are any errors, a message prompting the user to correct them is displayed.

[1511] Database connection and search

[1512] After successful validation on the terminal, the server securely establishes a database connection. Using a JDBC driver or similar, it connects to the SQL database and generates a search query based on the criteria entered by the user. The server then executes the query against the database and extracts the relevant company information.

[1513] Formatting and displaying search results

[1514] The server receives the search results and formats them into a user-friendly format (e.g., JSON). The formatted data is sent to the terminal, where the user can view the search results in a list view or table format on a smart device (e.g., smart glasses or a head-mounted display).

[1515] Actions performed by factory robots

[1516] Based on the displayed search results, the sales support robots in the factory perform specific actions. For example, they automatically prepare proposal materials for specific companies or organize data on potential customers.

[1517] Get detailed information

[1518] When a user selects a specific company from the displayed search results, the server accesses the database again to retrieve detailed information about that company. This data is also formatted and displayed on the smart device as a pop-up or in a new screen. This allows the user to proceed with sales activities based on more in-depth information.

[1519] Example prompt statement

[1520] "Please search for companies with sales of 1 billion yen or more, in the manufacturing industry, and located in the Nagoya area."

[1521] By inputting such prompt statements into the AI ​​model, the system selects the appropriate company and executes the function.

[1522] The system of this invention efficiently executes a series of processes, from inputting criteria to database searching, formatting and displaying results, executing actions by factory robots, and obtaining detailed information. This enables users to quickly and effectively select target companies and smoothly advance their sales activities.

[1523] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1524] Step 1: The user accesses the sales support robot using a smart device and enters criteria. For example, sales volume, industry, region, etc., are entered through the smart glasses interface. This input will be used as data in the next step.

[1525] Step 2: The terminal validates the criteria entered by the user. Specifically, it verifies that the sales volume is a number, the industry is a string, and the region is in the correct format. If there are any inappropriate criteria, the terminal displays a message prompting the user to correct them and waits until it receives the correct input.

[1526] Step 3: Upon receiving the criteria that have passed validation, the terminal sends them to the server. At this point, the criteria are formatted in JSON or another format to ensure data consistency.

[1527] Step 4: The server establishes a database connection based on the received criteria. It securely and efficiently connects to the SQL database using tools such as a JDBC driver. Through this connection, query generation and execution become possible.

[1528] Step 5: The server generates an SQL query based on the criteria and executes it against the database. For example, it generates a query such as "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'manufacturing' AND location = 'Nagoya'" and sends it to the database. Executing this query retrieves information about the relevant companies.

[1529] Step 6: The server formats the search results retrieved from the database and converts them into a user-friendly format (e.g., JSON). This makes it easier to display them on terminals and smart devices.

[1530] Step 7: The server sends the formatted data to the terminal. The terminal analyzes the received data and displays it in a list view or table format on the user interface. This allows the user to quickly review the search results in the field.

[1531] Step 8: Once the user selects a company they are interested in, the device requests detailed information about that company from the server. This request is then sent back to the database via the server.

[1532] Step 9: The server retrieves detailed company information from the database, reformats it, and sends it to the terminal. The terminal displays this information to the user in a pop-up or new screen. This allows the user to conduct sales activities based on more in-depth information.

[1533] Step 10: Based on the search results and detailed information, the factory robot performs specific actions. For example, it might prepare proposal materials for a specific company or organize data about potential customers. This makes sales support more efficient and automated.

[1534] 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.

[1535] The system of this invention is for automatically selecting M&A and sales targets, and mainly consists of a user, a terminal, a server, and an emotion engine. The specific processing is described below in natural language.

[1536] Criteria input

[1537] User: Launch the system and input the criteria necessary for selecting M&A targets and sales opportunities. These criteria include sales volume, industry, region, growth rate, and number of employees. Input is performed using the interface on the terminal.

[1538] Specific example: The user enters criteria such as "sales of 1 billion yen or more, industry is IT, and region is Tokyo."

[1539] Criteria validation

[1540] Terminal: Automatically validates the format and content of criteria entered by the user. If there are any deficiencies, it displays a message prompting the user to correct them.

[1541] Validation example: If a value other than a number is entered in the sales volume field, a warning message will be displayed, prompting the user to re-enter the value.

[1542] User emotion recognition

[1543] Emotion Engine: This engine analyzes the user's facial expressions, voice, input speed, and patterns to recognize their emotions. For example, it can capture the user's face with a camera and use facial recognition technology to determine their emotions.

[1544] Specific example: When a user enters criteria, the camera captures the user's facial expression and recognizes emotions such as "excited" or "confused."

[1545] Database connection and search

[1546] Server: Receives criteria and user sentiment data, and establishes a database connection. Securely accesses the database using appropriate authentication credentials.

[1547] Specific example: Connecting to an SQL database using a JDBC driver or ODBC driver.

[1548] Server: Dynamically generates and executes SQL queries based on criteria, and searches the database for records that match the conditions.

[1549] Specific example: Generate and execute the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[1550] Formatting and displaying search results

[1551] Server: Receives search results and adjusts the display order of results based on user sentiment data. Converts the formatted data into JSON or XML format and sends it to the terminal.

[1552] Specific example: If the user is in an "excited" state, the display order will be adjusted to prioritize positive information.

[1553] Terminal: Parses received JSON data and displays it on the user interface. It displays the data in list view or table format, making it easy for users to browse and compare.

[1554] Specific display example: The screen will display as follows: "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[1555] Get detailed information

[1556] User: Select and click on a company of interest from the displayed company information. Request detailed information.

[1557] Example: Click "Company A" to request detailed information.

[1558] Server: Receives user requests and generates and executes queries to retrieve detailed information about the selected company from the database again.

[1559] Specific example: Generate a query like "SELECT FROM company_details WHERE company_id = 'Company A'".

[1560] Server: Reformats the retrieved detailed information and sends it to the terminal in JSON format or similar.

[1561] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[1562] Terminal: Analyzes received details and displays them in a pop-up window or a new screen. Allows users to view and analyze the details.

[1563] Specific display example: A pop-up window will display information such as "Capital: 200 million yen," "Number of employees: 500," "Business overview," and "Recent news."

[1564] Storage and use of emotional data

[1565] Server: Stores user sentiment data and uses it to inform future criteria settings. This allows for more personalized search results by considering the user's past emotional states and choices.

[1566] Specific example: Save the companies a user selects based on specific criteria, along with their sentiment data, to use as reference for future searches.

[1567] The above is a specific embodiment of a system that combines an emotion engine. This system efficiently executes a series of processes, from inputting criteria and searching the database to formatting and displaying results, obtaining detailed information, and even utilizing emotion data, thereby providing users with a more personalized service.

[1568] The following describes the processing flow.

[1569] Step 1:

[1570] User: Launch the program and enter criteria for selecting M&A targets or sales opportunities. Criteria include sales volume, industry, region, growth rate, and number of employees.

[1571] Specific example: A user enters "Sales volume: 1 billion yen or more", "Industry: IT", and "Region: Tokyo" into the input field on the screen.

[1572] Step 2:

[1573] Terminal: Receives the entered criteria and validates the content. It verifies that the sales volume is a numerical value, that the industry is present in the selection list, and that the region is in the correct format.

[1574] Specific example: If a value other than a number is entered in the sales volume field, a warning message will be displayed prompting the user to re-enter the value.

[1575] Step 3:

[1576] Terminal: If the criteria are confirmed to be correct, it sends that data to the server. HTTP requests are used for transmission, and the criteria are packaged in JSON format or similar.

[1577] Specific example: The criteria are deemed correct, and the JSON object "{revenue: 'over 1 billion yen', industry: 'IT', location: 'Tokyo'}" is sent to the server.

[1578] Step 4:

[1579] Server: Analyzes the received criteria and establishes a database connection. Securely accesses the database using appropriate authentication credentials.

[1580] Specific example: Connecting to an SQL database using a JDBC driver or ODBC driver.

[1581] Step 5:

[1582] Emotion Engine: Recognizes user emotions by analyzing user facial expressions, voice, input speed and patterns, etc. It uses cameras and microphones to determine emotions.

[1583] Specific example: When a user performs an input task, their face is captured by a camera, and emotions such as "excited" or "confused" are recognized.

[1584] Step 6:

[1585] Server: Dynamically generates SQL queries based on criteria. Uses the generated queries to search the database for records that match the conditions.

[1586] Specific example: Generate the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[1587] Step 7:

[1588] Server: Executes SQL queries to retrieve matching company information from the database. The retrieved data is temporarily stored and processed on the server.

[1589] Specific example: The query results will retrieve information on "Company A, Company B, Company C".

[1590] Step 8:

[1591] Server: Formats search results and adjusts the display order based on user sentiment data. Converts the formatted data to JSON or XML format and sends it to the terminal.

[1592] Specific example: If the user's emotional state is "excited," prioritize displaying positive information.

[1593] Step 9:

[1594] Terminal: Parses received JSON data and displays it in the user interface. It displays the data in list view or table format, making it easy for users to browse and compare.

[1595] Specific display examples: The screen will display as follows: "Company A: Sales 1.2 billion yen, Industry: IT, Location: Tokyo" and "Company B: Sales 1.5 billion yen, Industry: IT, Location: Tokyo".

[1596] Step 10:

[1597] User: Select and click on a company of interest from the displayed company information. Request detailed information.

[1598] Example: Click "Company A" to request detailed information.

[1599] Step 11:

[1600] Server: Receives user requests and generates and executes queries to retrieve detailed information about the selected company from the database again.

[1601] Specific example: Generate a query like "SELECT FROM company_details WHERE company_id = 'Company A'".

[1602] Step 12:

[1603] Server: Formats the retrieved detailed information and sends it to the terminal in JSON format or similar.

[1604] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[1605] Step 13:

[1606] Terminal: Analyzes received details and displays them in a pop-up window or a new screen. Allows users to view and analyze the details.

[1607] Examples of display: Display "Capital of 200 million yen," "Number of employees: 500," "Business overview," "Recent news," etc.

[1608] Step 14:

[1609] Server: Stores user emotional data and uses it to inform future criteria settings. This allows for more personalized service by considering the user's past emotional states and choices.

[1610] Specific example: In the next search, reflect the company data selected by the user while they were "excited" during their previous search.

[1611] (Example 2)

[1612] 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".

[1613] Traditional M&A and sales target selection systems search a database based on user-inputted criteria and display search results. However, they lack the ability to consider the user's emotional state, resulting in a failure to provide personalized search results. Furthermore, validation of the input criteria and retrieval of detailed information had to be done manually, which was inefficient.

[1614] 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.

[1615] In this invention, the server includes means for inputting criteria, means for searching a database based on the criteria, means for recognizing the user's emotions, means for formatting and displaying the search results, and means for adjusting the search results based on the user's emotions. This makes it possible to provide personalized search results that match the user's emotional state.

[1616] "Criteria" refers to the evaluation criteria specified by the user when selecting M&A or sales targets, and includes items such as sales volume, industry, region, growth rate, and number of employees.

[1617] A "database" is a system that efficiently manages, searches, and retrieves a collection of information, and provides information based on specific criteria.

[1618] "User emotions" refers to the user's mental state, analyzed from facial expressions, voice, and other factors, and is used by the system to personalize search results.

[1619] "Means of recognizing emotions" refers to technologies that use input devices such as cameras and microphones to analyze a user's facial expressions and voice to determine their emotions.

[1620] "Search results" refer to a list of companies and potential clients retrieved from the database based on the criteria entered by the user.

[1621] "Formatting" refers to the process of converting search results obtained from a database into a format that is easy for users to understand.

[1622] "Means of display" refers to methods for graphically displaying search results on a user interface, and includes display in list view or table format.

[1623] "Methods of adjusting based on emotions" refer to methods of adjusting the display order and content of search results while taking into account the user's emotions.

[1624] The system of this invention automatically selects M&A targets and sales opportunities, and mainly consists of a user, a terminal, a server, and an emotion engine.

[1625] Criteria input

[1626] The user launches the system and inputs the criteria necessary for M&A and selecting sales targets via an interface on the terminal. These criteria include sales volume, industry, region, growth rate, and number of employees. The entered data is used as a basis for processing within the system.

[1627] Specific example: The user enters criteria such as "sales of 1 billion yen or more, industry is IT, and region is Tokyo."

[1628] Criteria validation

[1629] The terminal has a function to automatically validate the format and content of the criteria entered by the user. If there are any deficiencies, a message prompting correction will be displayed. This ensures that the criteria are set correctly.

[1630] Specific example: If a value other than a number is entered in the sales volume field, a warning message will be displayed prompting the user to re-enter the value.

[1631] User emotion recognition

[1632] The emotion engine analyzes the user's facial expressions, voice, input speed, and patterns to recognize their emotions. Specifically, it captures the user's face with a camera and uses facial recognition technology to determine their emotions. It can also perform voice analysis using a microphone.

[1633] Specific example: When a user enters criteria, the camera captures the user's facial expression and recognizes emotions such as "excited" or "confused."

[1634] Database connection and search

[1635] The server receives the criteria and sentiment data entered by the user and uses JDBC or ODBC drivers to establish a database connection. Once a secure connection is established, it dynamically generates SQL queries based on the criteria and searches the database for records that match the conditions.

[1636] Specific example: Generate and execute the SQL query "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[1637] Formatting and displaying search results

[1638] The server receives the search results and adjusts the display order of the results based on the user's sentiment data. The formatted data is converted into JSON or XML format and sent to the terminal. This process results in search results that reflect the user's sentiment.

[1639] Specific example: If the user is in an "excited" state, the display order will be adjusted to prioritize positive information.

[1640] The terminal parses the received JSON data and displays it on the user interface in a list view or table format. This allows users to easily view and compare the results.

[1641] Example display: The screen will show "Company A: Sales 1.2 billion yen, IT, Tokyo" and "Company B: Sales 1.5 billion yen, IT, Tokyo".

[1642] Get detailed information

[1643] Users can request detailed information by selecting a company they are interested in from the displayed company information and clicking on it.

[1644] Example: Click on "Company A" to request more information.

[1645] The server receives the user's request, generates and executes a query to retrieve detailed information about the selected company from the database again. Then, it reformats the retrieved information and sends it to the terminal in JSON format or another appropriate format.

[1646] Specific example: Generate and execute the query "SELECT FROM company_details WHERE company_id = 'Company A'".

[1647] Example: Format it like this: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500', Business Overview: '...', Recent News: '...'}".

[1648] The device analyzes the received details and displays them in a pop-up window or a new screen. This allows the user to view and analyze the details in more detail.

[1649] Example display: The pop-up window will show information such as "Capital: 200 million yen," "Number of employees: 500," "Business overview," and "Recent news."

[1650] Storage and use of emotional data

[1651] The server stores user sentiment data and uses it to inform future criteria settings. This allows for more personalized search results by considering the user's past emotional states and choices.

[1652] Specific example: Save the companies a user selects based on specific criteria, along with their sentiment data, to use as reference for future searches.

[1653] Examples of prompt statements:

[1654] "We are looking for IT companies in the Tokyo area with sales of over 1 billion yen."

[1655] "Please enter the following criteria: sales scale of 1 billion yen, industry: IT, region: Tokyo."

[1656] This system takes user emotions into consideration and executes a series of processes, from inputting criteria and searching the database to formatting and displaying results, obtaining detailed information, and even utilizing emotional data. This allows for the provision of more personalized services to users.

[1657] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1658] Step 1: Enter the criteria

[1659] The user launches the system and uses the interface on the terminal to input the criteria necessary for M&A and selecting potential clients. The input criteria include sales volume, industry, region, growth rate, and number of employees.

[1660] Input: Criteria such as sales volume, industry, region, growth rate, and number of employees.

[1661] Operation: Enter "Sales volume: 1 billion yen or more", "Industry: IT", and "Region: Tokyo" into the form on the terminal.

[1662] Output: Criteria data is input into the system.

[1663] Step 2: Criteria Validation

[1664] The terminal automatically validates the criteria entered by the user. If there are any errors, it displays a warning message about the input error and prompts the user to correct it.

[1665] Input: Criteria entered by the user.

[1666] Action: If anything other than a number is entered in the sales volume field, a warning message "Please enter the sales volume as a number" will be displayed, prompting the user to re-enter the value.

[1667] Output: Accurate criteria data that has passed validation.

[1668] Step 3: User emotion recognition

[1669] The emotion engine analyzes the user's facial expressions and voice to recognize their emotional state. Specifically, it captures the user's face with a camera and uses facial recognition technology to determine their emotions.

[1670] Input: User facial expression data, voice data.

[1671] Operation: When criteria are entered, the camera captures the user's facial expression and recognizes emotions such as "excited" or "confused."

[1672] Output: User sentiment data.

[1673] Step 4: Database connection and query generation

[1674] The server receives precise criteria data and user sentiment data, and connects to the database using JDBC or ODBC drivers. It then generates SQL queries based on the criteria and searches the database for records that match the conditions.

[1675] Input: Accurate criteria data, user sentiment data.

[1676] Operation: Connect to "jdbc:mysql: / / localhost:3306 / ma_database" and generate the following query: "SELECT FROM companies WHERE revenue >= 1 billion AND industry = 'IT' AND location = 'Tokyo'".

[1677] Output: Records from the search results.

[1678] Step 5: Format and reorder search results

[1679] The server receives the search results and formats and adjusts the display order of the results based on the user's sentiment data. The formatted data is converted to JSON format and sent to the terminal.

[1680] Input: Search result records, user sentiment data.

[1681] Function: Ranks search results based on sentiment data, formats them, and converts them to JSON format.

[1682] Output: Formatted JSON data.

[1683] Step 6: Displaying the results

[1684] The terminal parses the received JSON data and displays it on the user interface in a list view or table format, making it easy for users to view and compare the data.

[1685] Input: Formatted JSON data.

[1686] Operation: Displays results such as "Company A: Sales of 1.2 billion yen, IT, Tokyo" and "Company B: Sales of 1.5 billion yen, IT, Tokyo".

[1687] Output: Search results displayed on the user interface.

[1688] Step 7: Obtaining detailed information

[1689] Users select a company they are interested in from the displayed company information and click to request more details.

[1690] Input: The company selected by the user.

[1691] Action: Click "Company A" to request detailed information.

[1692] Output: A request for more information is sent to the server.

[1693] The server receives the request, generates and executes an SQL query to retrieve detailed information about the selected company from the database. It then formats the retrieved information and sends it to the terminal in JSON format.

[1694] Input: Request for detailed information.

[1695] Operation: Generates and executes the query "SELECT FROM company_details WHERE company_id = 'Company A'". Formats it as follows: "{Company Name: 'Company A', Capital: '200 million yen', Number of Employees: '500 people', Business Overview: '...', Recent News: '...'}".

[1696] Output: Formatted detailed information in JSON data.

[1697] The device analyzes the received details and displays them in a pop-up window or a new screen, allowing the user to view and analyze the details in more detail.

[1698] Input: Formatted JSON data containing detailed information.

[1699] Function: Displays information such as "Capital of 200 million yen," "Number of employees: 500," and "Business overview" in a pop-up window.

[1700] Output: Detailed information displayed in a pop-up window or new screen.

[1701] Step 8: Storing and using emotional data

[1702] The server stores user sentiment data and uses it to inform future criteria settings. This enables the provision of more personalized search results.

[1703] Input: User sentiment data, criteria data.

[1704] Operation: Saves data on companies selected by the user based on specific criteria, along with their sentiment at the time of selection.

[1705] Output: Sentiment data and criteria data stored on the server.

[1706] The above outlines the specific processing flow of this system, including a detailed explanation of the actions performed at each step.

[1707] (Application Example 2)

[1708] 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".

[1709] In conventional monitoring systems, anomaly detection and alert processing were performed using standard methods, without adaptive processing that took into account the emotions and concentration levels of the monitors. This led to challenges such as monitor fatigue and excessive stress, increasing the risk of missing important anomalies. To address these issues, dynamic alert prioritization that takes monitors' emotional states and personalized display methods are necessary.

[1710] 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.

[1711] In this invention, the server includes means for recognizing and analyzing the user's emotions, means for adjusting the priority of display results based on the user's emotion data, and means for storing the user's emotion data and reflecting it in future search results. This makes it possible to analyze the emotional state of monitors in real time and highlight important alerts at the appropriate time. Furthermore, it enables personalized alert display based on past emotion data, reducing the burden on monitors and lowering the risk of missing important anomalies.

[1712] "Criteria" refers to the conditions or constraints that a user specifies to a system.

[1713] A "database" is a system for storing structured data and for efficiently searching and retrieving it.

[1714] A "search method" is a function for finding information within a database based on specified criteria.

[1715] "Emotion recognition" is a technology that analyzes a user's facial expressions, voice, and behavior to determine their emotional state.

[1716] "Emotional data" refers to information obtained from the user's emotional perception, and this is what the system uses to determine the user's state.

[1717] A "search result" is a collection of information retrieved from a database based on specific criteria.

[1718] "Display means" refers to a function for presenting formatted search results or alert information on the user interface.

[1719] "Emotional analysis" is the process of analyzing a user's emotional state in detail based on data obtained from emotion recognition.

[1720] "Priority adjustment" is the process of dynamically determining the importance of multiple search results and alert information based on sentiment data.

[1721] A "storage method" is a function that records user emotional data and uses it to improve future system operation.

[1722] "Real-time" means analyzing and processing data instantly and reflecting the results immediately.

[1723] The present invention can be applied to a monitoring system that detects anomalies from user input. This system includes means for inputting criteria, means for recognizing and analyzing emotions, means for searching a database, means for formatting and displaying search results, and means for adjusting the priority of display results based on the user's emotion data. The system also has a function to store the user's emotion data and reflect it in future search results.

[1724] Hardware and software to be used

[1725] The system utilizes wearable devices such as smart glasses and head-mounted displays, as well as surveillance cameras. It employs emotion engines like Affectiva for emotion recognition and AI models such as OpenCV and YOLO for real-time video analysis. MySQL is used as the database management system.

[1726] Program Overview

[1727] The server receives the criteria entered by the user and performs a database search based on them. Next, it analyzes the camera footage to detect anomalies and recognizes the user's emotional state in real time. Based on the analyzed emotional data, it adjusts the importance of alerts and presents the results to the user in an appropriate display method. Furthermore, it saves the user's emotional data and reflects it in future searches and displays.

[1728] Specific example

[1729] Let's say a user wears smart glasses and sets a criterion for monitoring area A: "If three or more people appear between 00:00 and 05:00." The system analyzes the surveillance camera footage in real time within the set time and generates an alert if an anomaly is detected. At this time, the system uses Affectiva to recognize the user's emotions, and if, for example, the system determines that the monitor is "tired," it will highlight only the most important alerts. The user's emotion data is also saved and reflected in the display of future alerts.

[1730] Example of a prompt

[1731] "Monitoring area A: An alert will be triggered if three or more people appear between 00:00 and 05:00."

[1732] "Detecting unusual human movement patterns in surveillance area A during the night and generating an alert."

[1733] "When the monitor is fatigued, only the most important alert will be highlighted."

[1734] This system improves the efficiency and accuracy of monitoring operations, reduces the burden on monitors, and lowers the risk of missing important anomalies.

[1735] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1736] Step 1:

[1737] The user enters the monitoring criteria using smart glasses or a terminal. The user enters the criteria through the terminal's interface, such as "Monitoring area A, between 00:00 and 05:00, if three or more people appear." The entered criteria are then sent to the system.

[1738] Step 2:

[1739] The terminal validates the criteria entered by the user. The terminal checks whether the entered data is in the correct format, and if there are any deficiencies, it displays a warning message and prompts the user to re-enter the data. For example, it checks whether the monitoring time and the number of people are entered correctly.

[1740] Step 3:

[1741] The system uses an emotion engine to recognize the user's emotions in real time. The device uses a camera to capture the user's facial expressions, and emotion recognition software such as Affectiva analyzes them to determine emotions like "stressed" or "tired." The analysis results are then sent to the system.

[1742] Step 4:

[1743] The server receives the criteria and sentiment data and begins searching the database. The server filters the surveillance footage in the database based on the specified criteria. For example, it might search for "when three or more people were detected in Area A between 00:00 and 05:00".

[1744] Step 5:

[1745] The server performs real-time video analysis. Using surveillance camera footage, it detects anomalies using AI models such as OpenCV and YOLO. When an anomaly is detected, it generates information as an alert and formats the analysis results.

[1746] Step 6:

[1747] The server adjusts alert priorities based on sentiment data. Taking user sentiment data into account, for example, if the user is "tired," the server will change priorities to highlight only the most important alerts.

[1748] Step 7:

[1749] The formatted alert information is sent to the terminal. The terminal displays the received information on its user interface. For example, it might display specific information such as "An anomaly was detected in Area A at 02:15 AM."

[1750] Step 8:

[1751] When a user requests more information, they select a specific alert. A request is then sent to the server to retrieve the details of the alert selected by the user from the database.

[1752] Step 9:

[1753] The server retrieves detailed information, formats it, and sends it to the terminal. For example, the server retrieves "detailed information for company A" again, formats it in JSON format, and sends it.

[1754] Step 10:

[1755] The device displays the received details in a pop-up window or a dedicated screen. The user reviews the displayed details and takes the necessary actions.

[1756] Step 11:

[1757] The server stores user sentiment data and uses it to inform future searches and alerts. For example, it stores monitored sentiment fluctuation data and provides personalized responses based on the next alert.

[1758] 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.

[1759] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1760] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1761] 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.

[1762] 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.

[1763] 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.

[1764] 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.

[1765] 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.

[1766] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1767] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1768] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1769] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1770] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1771] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1772] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1773] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1774] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1775] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1776] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1777] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1778] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1779] The following is further disclosed regarding the embodiments described above.

[1780] (Claim 1)

[1781] A means of inputting criteria,

[1782] A means for searching a database based on the aforementioned criteria,

[1783] Methods for formatting and displaying search results,

[1784] A system that includes this.

[1785] (Claim 2)

[1786] The system according to claim 1, further comprising means for performing validation on the criteria.

[1787] (Claim 3)

[1788] The system according to claim 1, further comprising means for obtaining detailed information again when the user selects the aforementioned search result.

[1789] "Example 1"

[1790] (Claim 1)

[1791] A means of inputting criteria,

[1792] A means for searching a database based on the aforementioned criteria,

[1793] Methods for formatting and displaying search results,

[1794] A means to retrieve detailed information about the company selected by the user,

[1795] Means for establishing a database connection in order to obtain the aforementioned search results,

[1796] Means for performing validation on the aforementioned criteria,

[1797] A system that includes this.

[1798] (Claim 2)

[1799] The system according to claim 1, further comprising means for using an interface on a terminal when inputting the aforementioned criteria.

[1800] (Claim 3)

[1801] The system according to claim 1, further comprising means for converting the data into JSON or XML format when formatting the aforementioned test results.

[1802] "Application Example 1"

[1803] (Claim 1)

[1804] A means of inputting criteria,

[1805] A means for searching a database based on the aforementioned criteria,

[1806] Methods for formatting and displaying search results,

[1807] A factory robot means that performs an action based on the displayed search results,

[1808] A means of providing information to factory staff's smart devices using the displayed results,

[1809] A system that includes this.

[1810] (Claim 2)

[1811] The system according to claim 1, further comprising means for performing validation on the criteria.

[1812] (Claim 3)

[1813] The system according to claim 1, further comprising means for obtaining detailed information again when the user selects the aforementioned search result.

[1814] "Example 2 of combining an emotion engine"

[1815] (Claim 1)

[1816] A means of inputting criteria,

[1817] A means for searching a database based on the aforementioned criteria,

[1818] Means of recognizing user emotions,

[1819] Methods for formatting and displaying search results,

[1820] A means of adjusting search results based on user sentiment,

[1821] ...

[1822] A system that includes this.

[1823] (Claim 2)

[1824] The system according to claim 1, further comprising means for performing validation on the criteria.

[1825] (Claim 3)

[1826] The system according to claim 1, further comprising means for obtaining detailed information again when the user selects the aforementioned search result.

[1827] "Application example 2 when combining with an emotional engine"

[1828] (Claim 1)

[1829] A means of inputting criteria,

[1830] A means for searching a database based on the aforementioned criteria,

[1831] Methods for formatting and displaying search results,

[1832] A means of recognizing and analyzing user emotions,

[1833] A means for adjusting the priority of display results based on user sentiment data,

[1834] A means of saving user sentiment data and reflecting it in future search results,

[1835] A system that includes this.

[1836] (Claim 2)

[1837] The system according to claim 1, further comprising means for performing validation on the criteria.

[1838] (Claim 3)

[1839] The system according to claim 1, further comprising means for obtaining detailed information again when the user selects the aforementioned search result. [Explanation of symbols]

[1840] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting criteria, A means for searching a database based on the aforementioned criteria, Methods for formatting and displaying search results, A system that includes this.

2. The system according to claim 1, further comprising means for performing validation on the criteria.

3. The system according to claim 1, further comprising means for obtaining detailed information again when the user selects the aforementioned search result.

Images