System

Abstract

To provide a system.SOLUTION: A system includes: means for analyzing data related to management by generative AI; means for recognizing an emotional state of a user; and means for adjusting and outputting the analysis result of the data, based on the emotional state of the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Participants in business strategy planning and decision-making meetings base their discussions on the reports, but there are cases where valuable decision-making opportunities are missed due to a lack of necessary information, wasting the participants' time and the effort of preparing the reports. [Means for solving the problem]

[0005] This issue will be resolved by creating a tool that registers data with AI, summarizes and analyzes it, and extracts information in a timely manner. This system will be useful for strategy planning and decision-making, and will also reduce the time required for reporting and the occurrence of human error, improving work efficiency. [Brief description of the drawings]

[0006] [Figure 1]1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16] FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices 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), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a 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.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0020] 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 of expression that the user 20 can perceive (e.g., voice 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 voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out 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 the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The system of the present invention includes a means for registering information in a database, a means for summarizing and analyzing information by AI, and a means for outputting the summary and analysis results in a timely manner. Specifically, information necessary for planning business strategies and decision-making is registered in a database. This information is, for example, various information necessary for planning business strategies and decision-making, such as market trends, competitors' movements, one's own sales data, and product quality data. Next, the AI ​​summarizes and analyzes this information. The AI ​​summarizes and analyzes the information using natural language processing and machine learning techniques. Then, the summary and analysis results are output in a timely manner. The output format is, for example, displayed on a dashboard, sent by email, or notified to a smartphone application, so that the user can easily check the information. This makes it possible to make quick and accurate decisions without missing the timing for planning business strategies and decision-making.

[0029] "Example 2"

[0030] The system of the present invention includes a means for registering information in a database, a means for summarizing and analyzing information by AI, and a means for outputting the summary and analysis results in a timely manner. Specifically, information necessary for planning business strategies and decision-making is registered in a database. This information is, for example, various information necessary for planning business strategies and decision-making, such as market trends, competitors' movements, one's own sales data, and product quality data. Next, the AI ​​summarizes and analyzes this information. The AI ​​summarizes and analyzes the information using natural language processing and machine learning techniques. Then, the summary and analysis results are output in a timely manner. The output format is, for example, displayed on a dashboard, sent by email, or notified to a smartphone application, so that the user can easily check the information. This makes it possible to make quick and accurate decisions without missing the timing for planning business strategies and decision-making.

[0031] The process flow of each embodiment will be described below.

[0032] "Example 1"

[0033] Step 1: Register the information necessary for planning business strategies and making decisions in a database. This information includes various types of information necessary for planning business strategies and making decisions, such as market trends, the actions of competitors, your own sales data, and product quality data.

[0034] Step 2: AI summarizes and analyzes the information. AI uses natural language processing and machine learning techniques to summarize and analyze the information.

[0035] Step 3: Output summaries and analysis results in a timely manner. The output format is one in which the user can easily check the information, for example, by displaying it on a dashboard, sending it by email, or notifying it via a smartphone application. This makes it possible to make quick and accurate decisions without missing the timing for formulating business strategies and making decisions.

[0036] Example 1

[0037] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0038] Conventional business strategy planning and decision-making requires the manual collection and analysis of vast amounts of information, which is not only time-consuming and labor-intensive, but also carries a high risk of human error. In addition, it is difficult to obtain information in a timely manner and make decisions quickly. This can lead to a decrease in the accuracy of business strategy planning and decision-making, and a loss of competitiveness.

[0039] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0040] In this invention, the server includes a means for registering information, a means for summarizing and analyzing the information by artificial intelligence, a means for outputting the summary and analysis results in a timely manner, a means for storing the information in a database, and a means for displaying the summary and analysis results on a dashboard, sending them by email, or notifying them to a smartphone application. This automates the collection and analysis of information, making it possible to obtain summaries and analysis results in a timely manner. This improves the accuracy of business strategy planning and decision-making, enabling rapid and accurate decision-making.

[0041] The "means for registering information" refers to an interface that allows a user to input information necessary for planning business strategies and decision-making, and to store the information in a database.

[0042] "Means of summarizing and analyzing using artificial intelligence" refers to a system for summarizing and analyzing registered information using natural language processing and machine learning techniques.

[0043] "Means of timely output" refers to a method for quickly providing summaries and analysis results to users, including displaying them on a dashboard, sending emails, or sending notifications to smartphone applications.

[0044] "Means of storing in a database" refers to a system for efficiently managing and storing information entered by users, including relational databases, etc.

[0045] The "means for displaying on the dashboard" refers to an interface for visually providing summaries and analysis results to users, and has the function of updating information in real time.

[0046] "Means for sending by e-mail" refers to a system for sending summaries and analysis results to users as e-mails.

[0047] "Means for notifying smartphone applications" refers to a system for notifying users of summaries and analysis results via smartphone applications.

[0048] This invention is a system for supporting business strategy planning and decision-making, and aims to register information, summarize and analyze it using artificial intelligence, and output the results in a timely manner. A specific embodiment of this system will be described below.

[0049] Registering information

[0050] Users use a dedicated input form to input information necessary for planning business strategies and making decisions. This information includes market trends, competitors' movements, the company's own sales data, product quality data, etc. The input information is sent from the terminal to the server. Specifically, the data is sent using an HTTP POST request.

[0051] Saving to a database

[0052] The server stores the received information in a database. Relational database management systems (RDBMS) such as MySQL (registered trademark) and PostgreSQL are used for the database. This allows information to be managed and stored efficiently.

[0053] Summarizing and analyzing information

[0054] The server periodically retrieves newly registered information from the database. The retrieved information is passed to artificial intelligence (AI). The AI ​​performs natural language processing using Python (registered trademark) libraries NLTK and spaCy, and builds machine learning models using TENSORFLOW (registered trademark) and PyTorch. This allows the information to be summarized and analyzed.

[0055] Output of summary and analysis results

[0056] The server retrieves the summaries and analysis results generated by the AI ​​and provides them to the user in a timely manner. The output formats are as follows:

[0057] Display on a dashboard: Use React.js to update information in real time and provide it visually to the user.

[0058] Send via email: Send the summary or analysis results as email using an SMTP server.

[0059] Notify a smartphone application: Send a push notification using Firebase(R) Cloud Messaging (FCM).

[0060] Examples

[0061] Example 1: Market Trend Summary

[0062] 1. A user enters the latest market trend report into a web form.

[0063] 2. The terminal sends the entered report to the server.

[0064] 3. The server saves the report in a database.

[0065] 4. The server passes the saved report to the AI.

[0066] 5. AI extracts key points from the report and generates a summary.

[0067] 6. The server displays the generated summary on a dashboard.

[0068] 7. Users see summaries on the dashboard and make quick decisions.

[0069] Example 2: Analyzing competitors' activities

[0070] 1. A user fills out a web form with information about a competitor's new product launch.

[0071] 2. The terminal sends the entered information to the server.

[0072] 3. The server stores the information in a database.

[0073] 4. The server passes the stored information to the AI.

[0074] 5. AI uses machine learning models to predict how competitor moves will impact your business.

[0075] 6. The server sends the analysis results to the user via email.

[0076] 7. Users can check the analysis results by email and make decisions quickly.

[0077] Examples of prompt statements

[0078] Below are examples of prompts to be input to the generative AI model.

[0079] "Summarise the latest market trends report. Extract the key points and provide a concise summary."

[0080]

[0081] "Analyze information about your competitors' new product launches and predict how they will affect your company."

[0082] In this way, users are able to make fast and accurate decisions.

[0083] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0084] Step 1: Enter and submit information

[0085] The user uses a dedicated input form to input information necessary for planning business strategies and making decisions. The input information includes market trends, competitors' actions, the company's own sales data, product quality data, etc. The terminal sends the input information to the server using an HTTP POST request. The input data is in the form of text, numeric data, etc.

[0086] Step 2: Saving to the Database

[0087] The server stores the received information in a database. Specifically, it uses a relational database such as MySQL or PostgreSQL to efficiently manage and store information. The server inserts the received data into the appropriate table and creates indexes as necessary. The input data is stored in the database table, and the output is a notification that the data has been saved to the database.

[0088] Step 3: Get the information and pass it on to the AI

[0089] The server periodically retrieves new registered information from the database. The retrieved information is passed to the AI. The server uses SQL queries to extract new data and converts it into a data format to be passed to the AI. The input data is the new information retrieved from the database, and the output is the data passed to the AI.

[0090] Step 4: Summarize and analyze the information

[0091] The AI ​​summarizes and analyzes the information passed from the server. Specifically, it uses Python libraries NLTK and spaCy to perform natural language processing, and builds machine learning models using TensorFlow and PyTorch. The AI ​​analyzes the input data, extracts important points, and generates a summary. The input data is the information passed from the server, and the output is the summary or analysis results.

[0092] Step 5: Obtaining summary and analysis results

[0093] The server retrieves the summary and analysis results generated by the AI. The server receives the output data from the AI ​​and formats it as necessary. The input data is the summary and analysis results from the AI, and the output is the formatted data to provide to the user.

[0094] Step 6: Output summary and analysis results

[0095] The server provides formatted summaries and analysis results to the user in the following ways:

[0096] Display on a dashboard: Use React.js to update information in real time and provide it visually to the user.

[0097] Send via email: Send the summary or analysis results as email using an SMTP server.

[0098] Notify an application on a smartphone: Send a push notification using Firebase Cloud Messaging (FCM).

[0099] The input data is a formatted summary or analysis result, and the output is the information provided to the user.

[0100] Step 7: User review and decision making

[0101] The user checks the provided summary and analysis results and makes a decision quickly. The user checks the information through a dashboard, email, or smartphone notification and takes the necessary action. The input data is the summary and analysis results provided by the server, and the output is the user's decision.

[0102] (Application example 1)

[0103] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0104] Logistics centers are required to grasp information such as inventory status, delivery status, and staff work efficiency in real time, and to make quick and accurate decisions. However, manually collecting and analyzing this information takes time and effort, and there is a high possibility of human error. In addition, there is an issue that it is difficult to respond immediately when important information or abnormalities occur.

[0105] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0106] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data by AI, a means for outputting the summary and analysis results in a timely manner, a means for collecting information such as inventory status, delivery status, and staff work efficiency at the logistics center, and a means for summarizing and analyzing the collected information and notifying important information or abnormalities when they occur. This enables the operation manager of the logistics center to grasp information in real time and make quick and accurate decisions.

[0107] "Means of registering data" refers to a function for saving information such as inventory status, delivery status, and staff work efficiency at the logistics center in a database.

[0108] "Means of summarizing and analyzing using AI" refers to a function for summarizing and analyzing registered data using artificial intelligence technology.

[0109] "Means of timely output" refers to a function for displaying summaries and analysis results in real time and notifying users as necessary.

[0110] "A means of collecting information such as inventory status, delivery status, and staff work efficiency at the logistics center" is a function for automatically collecting various data within the logistics center.

[0111] "Means of summarizing and analyzing collected information and notifying when important information or anomalies occur" refers to a function for summarizing and analyzing collected data and notifying an administrator when important information or anomalies are detected.

[0112] The system for carrying out this invention collects information such as inventory status, delivery status, and staff work efficiency in a logistics center in real time, summarizes and analyzes it using AI, and outputs the results in a timely manner. A specific embodiment of this system is described below.

[0113] 1. System Programming

[0114] The server includes a means for registering data, a means for summarizing and analyzing using AI, a means for outputting in a timely manner, a means for collecting information such as inventory status at the logistics center, delivery status, and staff work efficiency, and a means for summarizing and analyzing the collected information and notifying of important information or the occurrence of abnormalities.

[0115] 2. Program Processing

[0116] The server first registers various data from the logistics center in a database. This includes inventory status, delivery status, and staff work efficiency. Next, AI is used to summarize and analyze this data. AI uses natural language processing and machine learning techniques to summarize the data and detect anomalies. The analysis results are displayed on a dashboard in real time, and if important information or anomalies occur, a push notification is sent to the administrator's smartphone.

[0117] The implementation of this system uses the following hardware and software:

[0118] Hardware: Servers, smartphones

[0119] Software: SQLite (database management system), Python (programming language), scikit-learn (machine learning library), pandas (data analysis library), smtplib (email sending library)

[0120] 3. Specific Examples

[0121] For example, assume that the inventory data for a logistics center is as follows:

[0122] id | info

[0123] 1 | Product A is running low in stock.

[0124] 2 | Product B is in excess stock.

[0125] 3 | Product C has adequate stock.

[0126] By summarizing and analyzing this data, the following clustering results are obtained:

[0127] cluster | info

[0128] 0 | Product A is low in stock.

[0129] 1 | Product B is in excess stock.

[0130] 2 | Product C has adequate stock.

[0131] This will be notified to the administrator via email.

[0132] Example prompts to be input to the generative AI model

[0133] Create a program that summarizes and analyzes inventory data from a distribution center and notifies the manager of important information. The program retrieves data from the database, summarizes and analyzes the data using TF-IDF vectorization and KMeans clustering, and notifies the manager of the results by email.

[0134] In this way, logistics center operators and managers can get real-time information and make fast and accurate decisions.

[0135] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0136] Step 1:

[0137] The server collects information such as inventory status at the logistics center, delivery status, and staff work efficiency. Specifically, it obtains data from various sensors and management systems and registers it in a database. The input is data from the various sensors and management systems, and the output is information stored in the database.

[0138] Step 2:

[0139] The server uses AI to summarize and analyze the information registered in the database. Specifically, it uses natural language processing technology to convert the data into TF-IDF vectors and performs KMeans clustering. The input is information obtained from the database, and the output is the summarized and analyzed data.

[0140] Step 3:

[0141] The server outputs the summary and analysis results in a timely manner. Specifically, the analysis results are displayed on a dashboard, and a push notification is sent to the administrator's smartphone when important information or anomalies occur. The input is the summarized and analyzed data, and the output is the dashboard display and push notification.

[0142] Step 4:

[0143] A user receives a push notification using a smartphone and checks detailed information on a dashboard. Specifically, the user receives the notification through a smartphone application and accesses the dashboard as necessary. The input is the push notification, and the output is the information that the user checks.

[0144] Step 5:

[0145] The server receives user feedback and retrains the data to improve the system performance. Specifically, it collects feedback data from users and updates the machine learning model. The input is the user feedback data and the output is the updated machine learning model.

[0146] Example 2

[0147] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0148] Conventional business strategy planning and decision-making requires the manual collection, summarization, and analysis of vast amounts of information, which takes a great deal of time and effort. In addition, there are issues with human error and the difficulty of obtaining accurate information in a timely manner. This makes it difficult to make quick and accurate decisions, which can reduce the effectiveness of business strategies.

[0149] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0150] In this invention, the server includes a means for collecting and registering data, a means for summarizing and analyzing the data using natural language processing and machine learning techniques, and a means for outputting the summaries and analysis results in a timely manner to a dashboard, email, or a smartphone application, thereby making it possible to quickly and accurately obtain information necessary for planning business strategies and making decisions.

[0151] "Means of collecting and registering data" refers to a function for collecting information necessary for planning business strategies and decision-making, and storing it in a database.

[0152] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0153] "Machine learning technology" refers to algorithms and methods that allow computers to learn from data and make predictions and classifications.

[0154] "Means of summarizing and analyzing" refers to functions for concisely summarizing collected data and analyzing data patterns and trends.

[0155] A "dashboard" is an interface that allows users to visually check information and displays data using graphs, charts, and the like.

[0156] "Email" is a means of communication for sending and receiving text and files over the Internet.

[0157] A "smartphone application" is software that runs on a smartphone and allows users to check and manipulate information.

[0158] "Means of timely output" refers to a function for providing summaries and analysis results to users in real time.

[0159] The present invention is a system for quickly and accurately providing information required for planning business strategies and decision-making. A specific embodiment of this system will be described below.

[0160] Information collection and registration

[0161] The server collects information necessary for planning business strategies and making decisions, and registers it in a database. Specifically, the information includes market trends, the actions of competitors, the company's own sales data, product quality data, etc. A relational database such as MySQL or PostgreSQL is used as the database.

[0162] Examples:

[0163] The server retrieves the latest market trend reports from market research companies via API and stores them in a MySQL database.

[0164] The server imports the company's sales data, provided in CSV file format, into a PostgreSQL database.

[0165] Summarizing and analyzing information

[0166] The AI ​​uses natural language processing (NLP) and machine learning (ML) techniques to summarize and analyze the registered information, specifically using the Python libraries NLTK and Transformers, and Scikit-learn to analyze trends in the sales data.

[0167] Examples:

[0168] AI can be used to summarise competitive landscape reports and extract key takeaways, for example using Transformers' BERT model to generate summaries.

[0169] The AI ​​will analyze the sales data from the past year to identify trends in sales, for example using a linear regression model from Scikit-learn.

[0170] Output of results

[0171] The server outputs summaries and analysis results in a timely manner. The output format is such that the user can easily check the information, for example, by displaying the information on a dashboard, sending it by email, or notifying it to a smartphone application. The terminal displays the dashboard or application so that the user can check the information.

[0172] Examples:

[0173] The server displays the summaries and analysis results in a dashboard that updates in real time, for example by drawing graphs using the JavaScript library D3.js.

[0174] The server then sends important results to management via email, for example using Python's smtplib.

[0175] The device sends a push notification to the smartphone app, for example, by using Firebase Cloud Messaging (FCM).

[0176] Examples of prompt statements

[0177] "Subscribe to our database for the latest market trend reports."

[0178] "Summarize the competitor landscape report and extract key takeaways."

[0179] "Analyze the past year's worth of sales data and identify sales trends."

[0180] "Display summaries and analytics results on a dashboard and email important information to me."

[0181] This system enables users to quickly and accurately obtain the information they need to plan business strategies and make decisions.

[0182] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0183] Step 1:

[0184] Information collection and registration

[0185] The server collects information necessary for planning business strategies and making decisions, and registers it in a database. Input data includes market trends, competitors' actions, the company's own sales data, product quality data, etc. This data is obtained through APIs or provided in CSV file format. The server stores this data in a relational database such as MySQL or PostgreSQL.

[0186] Specific behavior:

[0187] The server retrieves the latest market trend reports from market research companies via API and stores them in a MySQL database.

[0188] The server imports the company's sales data, provided in CSV file format, into a PostgreSQL database.

[0189] Step 2:

[0190] Summarizing and analyzing information

[0191] The AI ​​uses natural language processing (NLP) and machine learning (ML) techniques to summarize and analyze the registered information. The inputs include market trend reports and sales data stored in a database. The AI ​​uses Python libraries NLTK and Transformers to generate summaries and Scikit-learn to analyze trends in the sales data. The output is the summarized reports and analysis results.

[0192] Specific behavior:

[0193] The AI ​​uses Transformers' BERT model to summarize competitor landscape reports and extract key takeaways.

[0194] The AI ​​uses Scikit-learn's linear regression model to analyze sales data from the past year to identify sales trends.

[0195] Step 3:

[0196] Output of results

[0197] The server outputs summaries and analysis results in a timely manner. The input includes summaries and analysis results generated by the AI. The output format is such that users can easily check the information, for example, by displaying it on a dashboard, sending it by email, or notifying it to a smartphone application.

[0198] Specific behavior:

[0199] The server displays summaries and analysis results in a dashboard that updates in real time, for example by drawing graphs using the JavaScript library D3.js.

[0200] The server uses Python's smtplib to email key results to management.

[0201] The device sends push notifications to the smartphone app using Firebase Cloud Messaging (FCM).

[0202] In this way, users can quickly and accurately obtain the information necessary for planning business strategies and making decisions.

[0203] (Application example 2)

[0204] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0205] In conventional manufacturing lines, data collection and analysis was often done manually, which was inefficient and delayed the detection and response of abnormalities. In addition, human error was likely to occur, making it difficult to improve quality control and production efficiency. This caused many problems in the operation of the manufacturing line.

[0206] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0207] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data using AI, a means for outputting the summary and analysis results in a timely manner, a means for collecting various data on the production line, a means for analyzing the collected data and detecting anomalies, and a means for notifying when an anomaly is detected. This makes it possible to improve the efficiency of the production line and quality control.

[0208] A "means for registering data" is any device or software used to store data collected from a production line or other sources in a database.

[0209] "Means of summarizing and analyzing using AI" refers to devices and software that use artificial intelligence technology to summarize and analyze collected data.

[0210] "Means for timely output" refers to devices or software for displaying or notifying analysis results and summary information in real time.

[0211] "Means of collecting various data from the production line" refers to devices and software for collecting data such as production speed, quality score, and machine operating status using sensors and data collection devices installed on the production line.

[0212] The "means for analyzing the collected data and detecting anomalies" refers to a device or software for analyzing the collected data and detecting anomalies.

[0213] The "means for notifying when an abnormality is detected" refers to a device or software for notifying an administrator or relevant parties when an abnormality is detected.

[0214] As an embodiment of the present invention, a "smart manufacturing management system" aimed at improving the efficiency and quality control of a manufacturing line will be described as an example.

[0215] System Overview

[0216] The server collects various data from the production line and registers it in a database. Next, AI is used to summarize and analyze the collected data and detect anomalies. If anomalies are detected or the results of the analysis are sent to the administrator in a timely manner, which makes it possible to improve the efficiency of the production line and quality control.

[0217] Hardware and Software Used

[0218] Data collection devices: Using sensors and data collection devices installed on the production line, data such as production speed, quality scores, and machine operation status is collected in real time.

[0219] Database: Use a database to store the collected data. Specifically, use a relational database such as MySQL or PostgreSQL.

[0220] AI analysis software: To analyze the collected data, we use Python machine learning libraries (e.g., scikit-learn) to build an anomaly detection model and analyze the data.

[0221] Notification system: When an abnormality is detected, an email sending system is used to notify the administrator. Specifically, emails are sent using the smtplib library.

[0222] Processing flow

[0223] 1. Data collection: Collect data from various sensors on the production line and register it in a database.

[0224] 2. Data analysis: Use AI to analyze collected data and detect anomalies.

[0225] 3. Notification: If an abnormality is detected, the administrator will be notified by email.

[0226] Examples

[0227] For example, if the production speed of a manufacturing line suddenly drops and the quality score falls below the standard value, the AI ​​will determine this to be an abnormality and the server will send an email to the administrator stating, "An abnormality has been detected on the manufacturing line. Please check for details."

[0228] Examples of prompt statements

[0229] Below are examples of prompts to be input to the generative AI model.

[0230] Create a program to collect data from the manufacturing line and use AI to detect anomalies. Data includes production speed, quality score, and machine operation status. If an anomaly is detected, notify the factory manager by email.

[0231] In this way, a system can be constructed to achieve efficient production line and quality control.

[0232] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0233] Step 1:

[0234] The server collects data from sensors installed on the production line. As input, it receives real-time data such as production speed, quality score, and machine operation status. These data are sent directly from the sensors to the server through a data collector. The server registers these data in a database. Specifically, the server periodically polls for data from the sensors and stores the acquired data in the database.

[0235] Step 2:

[0236] The server passes the data registered in the database to the AI ​​analysis software. As input, it uses the production speed, quality score, and machine operation status data stored in the database. The AI ​​analysis software uses these data to run an anomaly detection model and determine whether there are any abnormalities. Specifically, the server uses a Python machine learning library (e.g., scikit-learn) to build an anomaly detection model and analyze the data.

[0237] Step 3:

[0238] The server receives the analysis results from the AI ​​analysis software. As input, it receives the output results of the anomaly detection model. If an anomaly is detected based on the analysis results, the server launches a notification system. Specifically, the server evaluates the analysis results and sets a notification flag if an anomaly is detected.

[0239] Step 4:

[0240] The server uses a notification system to notify the administrator of an anomaly. As input, it uses a flag indicating that an anomaly has been detected. The notification system uses an email sending library (e.g. smtplib) to send an email to notify the administrator of the anomaly. Specifically, the server generates the content of the email and sends it to the administrator's email address.

[0241] Step 5:

[0242] The user (administrator) receives the notification from the server and checks the details of the production line. As input, the user receives the abnormality notification email sent from the server. The user checks the status of the production line based on the contents of the email and takes the necessary action. In concrete terms, the user opens the email, checks the details of the abnormality, and accesses the production line monitoring system to check the status.

[0243] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0244] "Example 1"

[0245] In one embodiment of the present invention, the system further includes an emotion engine that recognizes the emotion of the user. The emotion engine recognizes the user's emotional state from, for example, the user's tone of voice, facial expression, text input, and the like. The emotion engine adjusts the analysis and summarization of data based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, it prioritizes summarizing important information and presents it to the user. Also, if the emotion engine recognizes that the user is feeling relaxed, it provides more detailed information. Furthermore, the emotion engine adjusts the output results based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling happy, it displays positive results with emphasis. On the other hand, if the emotion engine recognizes that the user is feeling sad, it displays negative results with mitigation. This makes it possible to provide optimal information according to the user's emotional state.

[0246] "Example 2"

[0247] In one embodiment of the present invention, the system further includes an emotion engine that recognizes the emotion of the user. The emotion engine recognizes the user's emotional state from, for example, the user's tone of voice, facial expression, text input, and the like. The emotion engine adjusts the analysis and summarization of data based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, it prioritizes summarizing important information and presents it to the user. Also, if the emotion engine recognizes that the user is feeling relaxed, it provides more detailed information. Furthermore, the emotion engine adjusts the output results based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling happy, it displays positive results with emphasis. On the other hand, if the emotion engine recognizes that the user is feeling sad, it displays negative results with mitigation. This makes it possible to provide optimal information according to the user's emotional state.

[0248] The process flow of each embodiment will be described below.

[0249] "Example 1"

[0250] Step 1: Receive input from the user (tone of voice, facial expressions, text input, etc.).

[0251] Step 2: The emotion engine recognizes the user's emotional state, which can be, for example, happy, sad, stressed, relaxed, etc.

[0252] Step 3: The emotion engine adjusts the analysis and summarization of the data based on the user's emotional state. For example, if the emotion engine detects that the user is stressed, it will prioritize summarizing and presenting important information to the user. If the emotion engine detects that the user is relaxed, it will provide more detailed information.

[0253] Step 4: The emotion engine adjusts the output results based on the user's emotional state. For example, if the emotion engine detects that the user is feeling happy, it will highlight the positive results. On the other hand, if the emotion engine detects that the user is feeling sad, it will tone down the negative results.

[0254] Example 1

[0255] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0256] Conventional systems have had difficulty efficiently summarizing and analyzing information necessary for business strategy planning and decision-making, and outputting it in a timely manner. In addition, the system does not provide information that takes into account the user's emotional state, so it is not possible to provide optimal information according to the user's stress and emotions. This can impair the speed and accuracy of decision-making.

[0257] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0258] In this invention, the server includes a means for registering data, a means for summarizing and analyzing by artificial intelligence, a means for outputting the summarization and analysis results in a timely manner, a means for recognizing the emotional state of the user, a means for adjusting the analysis and summarization of the data based on the emotional state, and a means for adjusting the output result based on the emotional state. This makes it possible to provide optimal information according to the emotional state of the user, and to improve the speed and accuracy of business strategy planning and decision-making.

[0259] "Means for registering data" refers to a device or software that allows a user to input information necessary for planning business strategies and decision-making, and store it in a database.

[0260] "Means of summarizing and analyzing using artificial intelligence" refers to devices or software for summarizing and analyzing registered information using natural language processing or machine learning techniques.

[0261] "Means for outputting summaries and analysis results in a timely manner" refers to a device or software for quickly providing summaries and analysis results to users, and includes formats such as dashboard display, email transmission, and smartphone notifications.

[0262] The "means for recognizing the emotional state of the user" refers to a device or software for recognizing the emotional state of the user from the tone of voice, facial expression, text input, and the like.

[0263] A "means for adjusting data analysis or summarization based on emotional state" is a device or software for modifying the way data is analyzed or summarized depending on the recognized emotional state of a user.

[0264] A "means for adjusting an output result based on an emotional state" is a device or software for modifying the content or format of information to be output depending on the recognized emotional state of the user.

[0265] The present invention is a system including a means for registering data, a means for summarizing and analyzing using artificial intelligence, a means for outputting the summary and analysis results in a timely manner, a means for recognizing a user's emotional state, a means for adjusting the analysis and summarization of the data based on the emotional state, and a means for adjusting the output result based on the emotional state.

[0266] Data registration

[0267] A user uses a terminal to input information necessary for planning business strategies and making decisions. Specific information includes market trends, the actions of competitors, the company's own sales data, and product quality data. The terminal stores the input information in a database management system (e.g., MySQL or PostgreSQL). For example, a user inputs "Market trend report for Q3 2023" and registers it in the database.

[0268] Summarizing and analyzing information

[0269] The server retrieves information from the database and summarizes and analyzes the information using Python libraries TensorFlow and PyTorch. Specifically, it uses natural language processing (NLP) techniques to analyze text data and extract key points. For example, it extracts key trends from the "Market Trends Report Q3 2023" and generates a summary.

[0270] Output of summary and analysis results

[0271] The server uses Django or Flask as a web application framework to display summaries and analysis results on a dashboard. Users can access the dashboard through their terminals and check the summaries and analysis results. The server can also send the results by email or notify a smartphone application. For example, when a user accesses the dashboard, a summary of the "Market Trends Report Q3 2023" is displayed.

[0272] Emotion engine adjustment

[0273] The server uses Google Cloud Speech-to-Text API and OpenCV to analyze the user's tone of voice and facial expressions to recognize the user's emotional state. If the emotion engine recognizes that the user is feeling stressed, it prioritizes important information and summarizes it to present to the user. For example, if the user voice-inputs "I want important information to be displayed first," the emotion engine adjusts the summary according to the instruction. If the server recognizes that the user is relaxed, it provides detailed information. Furthermore, the emotion engine adjusts the output results based on the user's emotional state. For example, if the server recognizes that the user is feeling happy, it highlights and displays positive results.

[0274] Examples and prompts

[0275] As a concrete example, a user enters a "Market Trend Report for Q3 2023" into the database. The server uses AI to analyze this data and summarizes the main trends. If the emotion engine recognizes that the user is stressed, the server will prioritize displaying the most important trends. If the user is relaxed, it will provide detailed analysis results.

[0276] An example of a prompt is, "Subscribe to our market trend report Q3 2023 and summarize the key trends. Prioritize important information when the user is stressed and provide more detailed information when the user is relaxed."

[0277] The system enables users to make fast and accurate decisions.

[0278] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0279] Step 1:

[0280] Users use the terminal to input information necessary for planning business strategies and making decisions. The input information includes market trends, competitors' actions, the company's own sales data, product quality data, etc. The terminal stores this information in a database management system. For example, a user inputs "Market trend report for Q3 2023" and registers it in the database.

[0281] Input: Market trends, competitors' actions, company sales data, product quality data

[0282] Output: Information stored in the database

[0283] Step 2:

[0284] The server retrieves information from the database. The retrieved information is summarized and analyzed using Python libraries TensorFlow and PyTorch. Specifically, natural language processing (NLP) techniques are used to analyze text data and extract key points. For example, key trends are extracted from the "Market Trends Report Q3 2023" and a summary is generated.

[0285] Input: Information retrieved from a database

[0286] Output: Summarized information, analysis results

[0287] Step 3:

[0288] The server uses Django or Flask as a web application framework to display summaries and analysis results on a dashboard. Users can access the dashboard through their terminals and check the summaries and analysis results. The server can also send the results by email or notify a smartphone application. For example, when a user accesses the dashboard, a summary of the "Market Trends Report Q3 2023" is displayed.

[0289] Input: Summarised information, analysis results

[0290] Output: Display on dashboard, send email, notify smartphone

[0291] Step 4:

[0292] The server uses Google Cloud Speech-to-Text API and OpenCV to analyze the user's tone of voice and facial expressions to recognize their emotional state. If the emotion engine recognizes that the user is feeling stressed, it prioritizes important information in a summary and presents it to the user. For example, if the user voice-inputs, "I want important information to be displayed first," the emotion engine adjusts the summary accordingly.

[0293] Input: User tone of voice, facial expressions, and text input

[0294] Output: Perceived emotional state

[0295] Step 5:

[0296] The server adjusts the analysis and summarization of data based on the user's emotional state as recognized by the emotion engine. If the user is stressed, important information is prioritized and summarized and presented to the user. If the user is relaxed, detailed information is provided.

[0297] Input: Perceived emotional state

[0298] Output: Tailored summary and analysis results

[0299] Step 6:

[0300] The server adjusts the output results based on the user's emotional state as recognized by the emotion engine: if the user is happy, positive results are emphasized and displayed, and if the user is sad, negative results are toned down and displayed.

[0301] Input: Perceived emotional state

[0302] Output: Adjusted output result

[0303] (Application example 1)

[0304] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0305] In autonomous vehicles, there is a demand for appropriate recognition of passengers' emotional states and for operational and entertainment information to be provided based on those states. However, conventional systems have had difficulty in recognizing passengers' emotional states in real time and providing information accordingly. There was also a lack of technology to adjust the analysis and summarization of information based on passengers' emotional states. This made it difficult to improve passenger comfort and satisfaction.

[0306] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0307] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data by AI, a means for timely outputting the summary and analysis results, a means for recognizing the user's emotions, a means for adjusting the analysis and summarization of the data based on the emotional state, a means for adjusting the output result based on the emotional state, and a means for recognizing the emotional state of passengers using a camera or microphone in the vehicle and providing operation information and entertainment information based on the emotional state. This makes it possible to provide optimal information according to the emotional state of passengers, thereby improving passenger comfort and satisfaction.

[0308] "Means for registering data" refers to a function for saving information required for the system in a database.

[0309] "Means of summarizing and analyzing using AI" refers to a function for summarizing and analyzing registered data using artificial intelligence technology.

[0310] "Means for timely output" refers to a function for quickly providing analysis results and summary information to users.

[0311] "Means for recognizing user emotions" refers to a function that uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice to identify their emotional state.

[0312] The "means for adjusting data analysis and summarization based on the emotional state" is a function for dynamically changing the data analysis method and summarization content according to the recognized emotional state of the user.

[0313] The "means for adjusting the output result based on the emotional state" is a function for changing the format and contents of the information to be output in accordance with the emotional state of the user.

[0314] "Means for recognizing passengers' emotional states using cameras and microphones in the vehicle" refers to a function that uses cameras and microphones installed in the autonomous vehicle to analyze passengers' facial expressions and tone of voice to identify their emotional states.

[0315] The "means for providing operational information and entertainment information" is a function for providing operational information and entertainment content based on the emotional state of passengers.

[0316] The system for carrying out this invention is configured as follows. First, the server has a means for registering data and stores the necessary information in a database. Next, the server has a means for summarizing and analyzing the registered data using artificial intelligence (AI) technology. Furthermore, the server has a means for outputting the analysis results and summary information in a timely manner.

[0317] To recognize the user's emotions, cameras and microphones are installed in the vehicle. These sensors are used to analyze the user's facial expressions and tone of voice to identify the user's emotional state. The server has the means to dynamically change the data analysis method and summary content based on the recognized emotional state. It also has the means to adjust the output results based on the emotional state.

[0318] Specifically, cameras in the vehicle capture passengers' facial expressions using OpenCV and identify their emotions using emotion recognition models (e.g., models using Keras). A microphone captures audio using a speech recognition library and analyzes the emotion of the audio using TextBlob. This allows the system to provide relaxing music if the passenger is stressed, or detailed trip and entertainment information if the passenger is relaxed.

[0319] As a concrete example, consider the following scenario: If the passenger is stressed, the system will play relaxing music. If the passenger is relaxed, the system will provide detailed travel information. In this way, optimal information can be provided according to the passenger's emotional state, improving passenger comfort and satisfaction.

[0320] Examples of prompts to be input to a generative AI model include the following:

[0321] "Create an application that plays relaxing music if passengers are feeling stressed. Include a feature that uses the camera and microphone to recognize emotions and provide appropriate information."

[0322] In this way, the form for carrying out the invention can be concretely shown.

[0323] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0324] Step 1:

[0325] The server captures video data from the camera in the vehicle. The input is the real-time video from the camera, and the output is the captured image frames. Specifically, we use OpenCV to acquire the video data and perform face detection.

[0326] Step 2:

[0327] The server uses the captured image frames to analyze the passengers' facial expressions. The input is the image frame obtained in step 1, and the output is the passenger's emotional state. Specifically, the image data is input to an emotion recognition model using Keras to identify the emotion.

[0328] Step 3:

[0329] The server captures voice data from a microphone in the vehicle. The input is real-time voice from the microphone, and the output is the captured voice data. Specifically, the server obtains the voice data using a voice recognition library.

[0330] Step 4:

[0331] The server uses the captured voice data to analyze the passenger's voice for emotion. The input is the voice data obtained in step 3, and the output is the emotional state of the voice. Specifically, it uses TextBlob to analyze the voice data and identify the emotion.

[0332] Step 5:

[0333] The server integrates the emotional states obtained in steps 2 and 4 to determine the final emotional state of the passenger. The inputs are the facial expression analysis results and the voice analysis results, and the output is the integrated emotional state. Specifically, both emotional states are compared, and the stronger emotion is determined as the final emotional state.

[0334] Step 6:

[0335] The server provides appropriate information based on the final emotional state. The input is the integrated emotional state, and the output is the information provided (e.g., relaxing music, detailed travel information, entertainment information). Specifically, if the emotional state is stressed, relaxing music is played, and if relaxed, detailed travel information is provided.

[0336] Step 7:

[0337] The server presents the provided information to passengers through displays and speakers in the vehicle. The input is the provided information, and the output is the information presented to passengers. Specifically, the server displays trip information on the display and plays music from the speaker.

[0338] In this way, the server can provide optimal information according to the passenger's emotional state, thereby improving passenger comfort and satisfaction.

[0339] Example 2

[0340] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0341] Conventional business strategy planning and decision-making requires the manual collection and analysis of huge amounts of data, which is time-consuming and labor-intensive and prone to human error. In addition, information is provided without taking into account the user's emotional state, making it difficult to provide optimal information according to the user's stress and emotions.

[0342] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0343] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data by artificial intelligence, a means for outputting the summaries and analysis results in a timely manner, a means for recognizing a user's emotions, a means for adjusting the analysis and summarization of the data based on the emotions, and a means for adjusting the output results based on the emotions, thereby enabling not only quick and accurate decision-making but also the provision of optimal information according to the user's emotional state.

[0344] The "means for registering data" refers to an interface that allows users to input information necessary for planning business strategies and decision-making, and transmit it to the server.

[0345] "Artificial intelligence" is a program or system that uses natural language processing and machine learning techniques to summarize and analyze input data.

[0346] "Means of summarization and analysis" refers to the process of using artificial intelligence to summarize input data and generate analytical results.

[0347] "Means for timely output" refers to a method for providing the generated summary and analysis results to the user in real time, and includes displaying a dashboard, sending an email, or sending a smartphone notification.

[0348] "Means for recognizing a user's emotions" refers to technology for detecting an emotional state from a user's tone of voice, facial expression, text input, etc., and includes voice recognition and facial expression recognition.

[0349] "Means for tailoring data analysis and summarization based on emotion" refers to a process that prioritizes summarizing important information or providing detailed information depending on the user's emotional state.

[0350] "Emotionally tailored output" refers to a process that emphasizes positive outcomes or mitigates negative outcomes depending on the user's emotional state.

[0351] This invention is a system including a means for registering data, a means for summarizing and analyzing using artificial intelligence, a means for outputting the summary and analysis results in a timely manner, a means for recognizing a user's emotions, a means for adjusting the analysis and summarization of the data based on the emotions, and a means for adjusting the output results based on the emotions.

[0352] Data registration

[0353] Users use a dedicated input form to input information necessary for planning business strategies and making decisions. For example, they input market trends, the actions of competitors, their own company's sales data, product quality data, etc. The terminal sends the information entered by the user to the server. The HTTPS protocol is used for transmission to ensure the security of the information.

[0354] Summarizing and analyzing information

[0355] The server stores the received information in a database. For example, MySQL or PostgreSQL is used for the database. The server passes the information stored in the database to an artificial intelligence. The artificial intelligence uses natural language processing (NLP) and machine learning (ML) techniques to summarize the information and generate analysis results. For example, the Python libraries NLTK and TensorFlow are used.

[0356] Output of summary and analysis results

[0357] The server outputs summaries and analysis results generated by the AI ​​in a timely manner. The output format is such that users can easily check the information, such as displaying it on a dashboard, sending it by email, or notifying a smartphone application. For example, React.js is used for the front end of the web application, and Node.js is used for the back end.

[0358] Emotion engine adjustment

[0359] The terminal recognizes the user's emotional state from the user's tone of voice, facial expressions, text input, etc. For example, Google Cloud Speech-to-Text API is used for voice recognition, and OpenCV is used for facial expression recognition. The server uses an emotion engine to adjust data analysis and summarization based on the user's emotional state. For example, if the server recognizes that the user is feeling stressed, it prioritizes summarizing important information and presents it to the user. Also, if the server recognizes that the user is relaxed, it provides more detailed information. The server adjusts the output results based on the user's emotional state. For example, if the server recognizes that the user is feeling happy, it emphasizes and displays positive results. On the other hand, if the server recognizes that the user is feeling sad, it displays and mitigates negative results.

[0360] Examples

[0361] For example, consider a case where a user wants to input the latest data on market trends and analyze the movements of competitors. The user inputs the market trend data into a dedicated input form, and the device sends the information to the server. The server stores the information in a database and passes it on to the artificial intelligence. The artificial intelligence summarizes the market trends and analyzes the movements of competitors. The analysis results are displayed in real time on the dashboard. If the emotion engine recognizes that the user is feeling stressed, important trend information will be displayed with priority.

[0362] Examples of prompt statements

[0363] "Enter the latest data on market trends and analyze competitors' moves. Prioritize the most important information, especially when users are stressed."

[0364] This system not only enables users to make quick and accurate decisions, but also provides optimal information according to the user's emotional state.

[0365] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0366] Step 1:

[0367] The user enters the information.

[0368] Users use a dedicated input form to input information necessary for planning business strategies and making decisions. The information input includes market trends, competitors' actions, the company's own sales data, product quality data, etc. The input information is then sent to the terminal.

[0369] Step 2:

[0370] The terminal transmits the information to the server.

[0371] The terminal transmits the information entered by the user to the server using the HTTPS protocol, which ensures the security of the information. The input data reaches the server.

[0372] Step 3:

[0373] The server stores the information in a database.

[0374] The server stores the received information in a database, for example, MySQL or PostgreSQL. The stored data is used for subsequent processing.

[0375] Step 4:

[0376] The server passes the information to the artificial intelligence.

[0377] The server passes the information stored in the database to the AI, which uses natural language processing (NLP) and machine learning (ML) techniques to summarize and analyze the information. The input data is processed by the AI.

[0378] Step 5:

[0379] Artificial intelligence summarizes and analyzes the information.

[0380] Artificial intelligence summarizes the information it receives and generates analytical results. For example, it uses the Python library NLTK or TensorFlow to analyze text data. Summarized information and analytical results are generated.

[0381] Step 6:

[0382] The server outputs the analysis results.

[0383] The server outputs summaries and analysis results generated by the AI ​​in a timely manner. The output format is such that the user can easily check the information, for example, by displaying it on a dashboard, sending it by email, or notifying it via a smartphone application. The output data is provided to the user.

[0384] Step 7:

[0385] The terminal recognizes the user's emotions.

[0386] The device recognizes the user's emotional state from the user's tone of voice, facial expressions, text input, etc. For example, it uses Google Cloud Speech-to-Text API for voice recognition and OpenCV for facial expression recognition. The input data is processed by the emotion recognition engine.

[0387] Step 8:

[0388] The server uses the emotion engine to refine the analysis results.

[0389] The server uses the emotion engine to adjust the analysis and summarization of data based on the user's emotional state. For example, if the server recognizes that the user is stressed, it will prioritize summarizing and presenting important information to the user. If the server recognizes that the user is relaxed, it will provide more detailed information. The adjusted data is then provided to the user.

[0390] Step 9:

[0391] The server displays the output according to the emotion.

[0392] The server adjusts the output results based on the user's emotional state. For example, if the server recognizes that the user is feeling happy, it emphasizes the positive results. On the other hand, if the server recognizes that the user is feeling sad, it softens the negative results. The final output data is provided to the user.

[0393] (Application example 2)

[0394] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0395] Conventional systems make it difficult to obtain the information necessary for planning business strategies and making decisions in a timely manner, and real-time information acquisition is required especially in the operation of physical stores. In addition, since information is not provided according to the user's emotional state, there is a problem that appropriate information cannot be provided according to stress or relaxation state. Furthermore, there is a lack of notification functions using smartphones and smart glasses, making it difficult to check information immediately.

[0396] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0397] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data by AI, a means for outputting the summary and analysis results in a timely manner, an emotion engine for recognizing the emotional state of the user, and a means for adjusting the output results based on the emotion engine. This makes it possible to obtain information necessary for planning business strategies and decision-making in real time, and to provide appropriate information according to the emotional state of the user. In addition, a notification function using a smartphone or smart glasses makes it possible to check information immediately.

[0398] "Means for registering data" refers to a function for saving information required for the system in a database.

[0399] "Means of summarizing and analyzing using AI" refers to a function for summarizing and analyzing registered data using artificial intelligence.

[0400] "Means for timely output" refers to a function for providing analysis results to the user in real time.

[0401] The "emotion engine that recognizes the user's emotional state" is a function for recognizing emotions from the user's tone of voice, facial expressions, text input, etc.

[0402] The "means for adjusting the output result based on the emotion engine" is a function for adjusting the content and format of the information output according to the recognized emotional state.

[0403] "Reference information for formulating business strategies and making decisions" is information that is necessary when formulating a company's business strategies and making decisions.

[0404] "The effect of reducing the time required for reporting tasks and the occurrence of human errors" refers to the effect of shortening the time required for reporting tasks and reducing human error.

[0405] "Information necessary for running a physical store" refers to various information necessary for running a store, such as store sales data, inventory information, and customer feedback.

[0406] "Means of notifying a smartphone or smart glasses" refers to a function for notifying a smartphone or smart glasses of analysis results and important information.

[0407] A system for implementing the present invention includes means for registering data, means for summarizing and analyzing using AI, means for outputting in a timely manner, an emotion engine for recognizing the emotional state of a user, and means for adjusting the output results based on the emotion engine.

[0408] System Configuration

[0409] 1. How to register data

[0410] The server registers information such as store sales data, inventory information, and customer feedback in a database. This database uses a relational database such as SQLite.

[0411] 2. AI-based methods for summarizing and analyzing

[0412] The server summarizes and analyzes the registered data using AI models. Specifically, it uses the Transformers library to perform natural language processing. For example, it uses generative AI models such as BERT and GPT-3 (registered trademark) to summarize customer feedback.

[0413] 3. Means of timely output

[0414] The server provides the analysis results to the user in real time. The output format can be displayed on a dashboard or sent to a smartphone or smart glasses, allowing the user to check the information instantly.

[0415] 4. Emotion engine that recognizes the user's emotional state

[0416] The server uses an emotion recognition library to recognize emotions from the user's tone of voice, facial expressions, text input, etc. This allows the server to grasp the user's emotional state in real time.

[0417] 5. A way to adjust the output based on the emotion engine

[0418] The server then adjusts the content and format of the information output depending on the user's recognized emotional state, for example prioritizing important information if the user is stressed, and providing more detailed information if the user is relaxed.

[0419] Examples

[0420] For example, if a store staff member is feeling stressed, the system will prioritize displaying "important information." Conversely, if the staff member is feeling relaxed, the system will provide detailed information. In this way, it is possible to provide optimal information according to the user's emotional state.

[0421] Examples of prompt statements

[0422] Here are some example prompts to feed to a generative AI model:

[0423] Summarise your store's sales data and customer feedback and tailor the output based on your staff's emotional state - prioritise important information if your staff is stressed, or provide more detailed information if they're relaxed.

[0424] In this way, a smart store assistant system that supports the operation of physical stores can be realized.

[0425] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0426] Step 1:

[0427] The server registers information such as store sales data, inventory information, and customer feedback in a database. As input, various data collected from the stores is stored in a relational database such as SQLite. As output, the information registered in the database is obtained.

[0428] Step 2:

[0429] The server summarizes and analyzes the registered data using an AI model. The inputs include sales data and customer feedback obtained from the database, which are then processed using the Transformers library for natural language processing. Specifically, the data is summarized using generative AI models such as BERT and GPT-3. The output is summarized text and analysis results.

[0430] Step 3:

[0431] The server provides the analysis results to the user in real time. The inputs are the summary and analysis results obtained in step 2, which are displayed on a dashboard or sent to a smartphone or smart glasses. The output is information that the user can check immediately.

[0432] Step 4:

[0433] The server uses an emotion recognition library to recognize emotions from the user's tone of voice, facial expressions, text input, etc. The inputs are the user's voice data, facial expression data, and text data, which are analyzed to recognize the user's emotional state. The output is the user's emotional state.

[0434] Step 5:

[0435] The server adjusts the content and format of the information output according to the recognized emotional state. The inputs are the emotional state obtained in step 4 and the summary and analysis results obtained in step 2, and the priority and level of detail of the information are adjusted based on this. Specifically, if the user is feeling stressed, important information is displayed first, and if the user is relaxed, detailed information is provided. The adjusted information is obtained as the output.

[0436] Step 6:

[0437] The server notifies the smartphone or smart glasses of the adjusted information. As input, the adjusted information from step 5 is sent to the user's device. As output, a notification is displayed on the user's smartphone or smart glasses.

[0438] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0439] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0440] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0441] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0442] [Second embodiment]

[0443] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0444] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0445] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0446] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0447] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0448] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0449] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0450] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0451] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0452] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0453] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0454] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0455] "Example 1"

[0456] The system of the present invention includes a means for registering information in a database, a means for summarizing and analyzing information by AI, and a means for outputting the summary and analysis results in a timely manner. Specifically, information necessary for planning business strategies and decision-making is registered in a database. This information is, for example, various information necessary for planning business strategies and decision-making, such as market trends, competitors' movements, one's own sales data, and product quality data. Next, the AI ​​summarizes and analyzes this information. The AI ​​summarizes and analyzes the information using natural language processing and machine learning techniques. Then, the summary and analysis results are output in a timely manner. The output format is, for example, displayed on a dashboard, sent by email, or notified to a smartphone application, so that the user can easily check the information. This makes it possible to make quick and accurate decisions without missing the timing for planning business strategies and decision-making.

[0457] "Example 2"

[0458] The system of the present invention includes a means for registering information in a database, a means for summarizing and analyzing information by AI, and a means for outputting the summary and analysis results in a timely manner. Specifically, information necessary for planning business strategies and decision-making is registered in a database. This information is, for example, various information necessary for planning business strategies and decision-making, such as market trends, competitors' movements, one's own sales data, and product quality data. Next, the AI ​​summarizes and analyzes this information. The AI ​​summarizes and analyzes the information using natural language processing and machine learning techniques. Then, the summary and analysis results are output in a timely manner. The output format is, for example, displayed on a dashboard, sent by email, or notified to a smartphone application, so that the user can easily check the information. This makes it possible to make quick and accurate decisions without missing the timing for planning business strategies and decision-making.

[0459] The process flow of each embodiment will be described below.

[0460] "Example 1"

[0461] Step 1: Register the information necessary for planning business strategies and making decisions in a database. This information includes various types of information necessary for planning business strategies and making decisions, such as market trends, the actions of competitors, your own sales data, and product quality data.

[0462] Step 2: AI summarizes and analyzes the information. AI uses natural language processing and machine learning techniques to summarize and analyze the information.

[0463] Step 3: Output summaries and analysis results in a timely manner. The output format is one in which the user can easily check the information, for example, by displaying it on a dashboard, sending it by email, or notifying it via a smartphone application. This makes it possible to make quick and accurate decisions without missing the timing for formulating business strategies and making decisions.

[0464] Example 1

[0465] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0466] Conventional business strategy planning and decision-making requires the manual collection and analysis of vast amounts of information, which is not only time-consuming and labor-intensive, but also carries a high risk of human error. In addition, it is difficult to obtain information in a timely manner and make decisions quickly. This can lead to a decrease in the accuracy of business strategy planning and decision-making, and a loss of competitiveness.

[0467] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0468] In this invention, the server includes a means for registering information, a means for summarizing and analyzing the information by artificial intelligence, a means for outputting the summary and analysis results in a timely manner, a means for storing the information in a database, and a means for displaying the summary and analysis results on a dashboard, sending them by email, or notifying them to a smartphone application. This automates the collection and analysis of information, making it possible to obtain summaries and analysis results in a timely manner. This improves the accuracy of business strategy planning and decision-making, enabling rapid and accurate decision-making.

[0469] The "means for registering information" refers to an interface that allows a user to input information necessary for planning business strategies and decision-making, and to store the information in a database.

[0470] "Means of summarizing and analyzing using artificial intelligence" refers to a system for summarizing and analyzing registered information using natural language processing and machine learning techniques.

[0471] "Means of timely output" refers to a method for quickly providing summaries and analysis results to users, including displaying them on a dashboard, sending emails, or sending notifications to smartphone applications.

[0472] "Means of storing in a database" refers to a system for efficiently managing and storing information entered by users, including relational databases, etc.

[0473] The "means for displaying on the dashboard" refers to an interface for visually providing summaries and analysis results to users, and has the function of updating information in real time.

[0474] "Means for sending by e-mail" refers to a system for sending summaries and analysis results to users as e-mails.

[0475] "Means for notifying smartphone applications" refers to a system for notifying users of summaries and analysis results via smartphone applications.

[0476] This invention is a system for supporting business strategy planning and decision-making, and aims to register information, summarize and analyze it using artificial intelligence, and output the results in a timely manner. A specific embodiment of this system will be described below.

[0477] Registering information

[0478] Users use a dedicated input form to input information necessary for planning business strategies and making decisions. This information includes market trends, competitors' movements, the company's own sales data, product quality data, etc. The input information is sent from the terminal to the server. Specifically, the data is sent using an HTTP POST request.

[0479] Saving to a database

[0480] The server stores the received information in a database. Relational database management systems (RDBMS) such as MySQL and PostgreSQL are used for the database. This allows information to be managed and stored efficiently.

[0481] Summarizing and analyzing information

[0482] The server periodically retrieves newly registered information from the database. The retrieved information is passed to artificial intelligence (AI). The AI ​​uses Python libraries NLTK and spaCy to process natural language, and builds machine learning models using TensorFlow and PyTorch. This allows the information to be summarized and analyzed.

[0483] Summary and analysis output

[0484] The server retrieves the summaries and analysis results generated by the AI ​​and provides them to the user in a timely manner. The output formats are as follows:

[0485] Display on a dashboard: Use React.js to update information in real time and provide it visually to the user.

[0486] Send via email: Send the summary or analysis results as email using an SMTP server.

[0487] Notify an application on a smartphone: Send a push notification using Firebase Cloud Messaging (FCM).

[0488] Examples

[0489] Example 1: Market Trend Summary

[0490] 1. A user enters the latest market trend report into a web form.

[0491] 2. The terminal sends the entered report to the server.

[0492] 3. The server saves the report in a database.

[0493] 4. The server passes the saved report to the AI.

[0494] 5. AI extracts key points from the report and generates a summary.

[0495] 6. The server displays the generated summary on a dashboard.

[0496] 7. Users see summaries on the dashboard and make quick decisions.

[0497] Example 2: Analyzing competitors' activities

[0498] 1. A user fills out a web form with information about a competitor's new product launch.

[0499] 2. The terminal sends the entered information to the server.

[0500] 3. The server stores the information in a database.

[0501] 4. The server passes the stored information to the AI.

[0502] 5. AI uses machine learning models to predict how competitor moves will impact your business.

[0503] 6. The server sends the analysis results to the user via email.

[0504] 7. Users can check the analysis results by email and make decisions quickly.

[0505] Examples of prompt statements

[0506] Below are examples of prompts to be input to the generative AI model.

[0507] "Summarise the latest market trends report. Extract the key points and provide a concise summary."

[0508]

[0509] "Analyze information about your competitors' new product launches and predict how they will affect your company."

[0510] In this way, users are able to make fast and accurate decisions.

[0511] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0512] Step 1: Enter and submit information

[0513] The user uses a dedicated input form to input information necessary for planning business strategies and making decisions. The input information includes market trends, competitors' actions, the company's own sales data, product quality data, etc. The terminal sends the input information to the server using an HTTP POST request. The input data is in the form of text, numeric data, etc.

[0514] Step 2: Saving to the Database

[0515] The server stores the received information in a database. Specifically, it uses a relational database such as MySQL or PostgreSQL to efficiently manage and store information. The server inserts the received data into the appropriate table and creates indexes as necessary. The input data is stored in the database table, and the output is a notification that the data has been saved to the database.

[0516] Step 3: Get the information and pass it on to the AI

[0517] The server periodically retrieves new registered information from the database. The retrieved information is passed to the AI. The server uses SQL queries to extract new data and converts it into a data format to be passed to the AI. The input data is the new information retrieved from the database, and the output is the data passed to the AI.

[0518] Step 4: Summarize and analyze the information

[0519] The AI ​​summarizes and analyzes the information passed from the server. Specifically, it uses Python libraries NLTK and spaCy to perform natural language processing, and builds machine learning models using TensorFlow and PyTorch. The AI ​​analyzes the input data, extracts important points, and generates a summary. The input data is the information passed from the server, and the output is the summary or analysis results.

[0520] Step 5: Obtaining summary and analysis results

[0521] The server retrieves the summary and analysis results generated by the AI. The server receives the output data from the AI ​​and formats it as necessary. The input data is the summary and analysis results from the AI, and the output is the formatted data to provide to the user.

[0522] Step 6: Output summary and analysis results

[0523] The server provides formatted summaries and analysis results to the user in the following ways:

[0524] Display on a dashboard: Use React.js to update information in real time and provide it visually to the user.

[0525] Send via email: Send the summary or analysis results as email using an SMTP server.

[0526] Notify an application on a smartphone: Send a push notification using Firebase Cloud Messaging (FCM).

[0527] The input data is a formatted summary or analysis result, and the output is the information provided to the user.

[0528] Step 7: User review and decision making

[0529] The user checks the provided summary and analysis results and makes a decision quickly. The user checks the information through a dashboard, email, or smartphone notification and takes the necessary action. The input data is the summary and analysis results provided by the server, and the output is the user's decision.

[0530] (Application example 1)

[0531] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0532] Logistics centers are required to grasp information such as inventory status, delivery status, and staff work efficiency in real time, and to make quick and accurate decisions. However, manually collecting and analyzing this information takes time and effort, and there is a high possibility of human error. In addition, there is an issue that it is difficult to respond immediately when important information or abnormalities occur.

[0533] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0534] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data by AI, a means for outputting the summary and analysis results in a timely manner, a means for collecting information such as inventory status, delivery status, and staff work efficiency at the logistics center, and a means for summarizing and analyzing the collected information and notifying important information or abnormalities when they occur. This enables the operation manager of the logistics center to grasp information in real time and make quick and accurate decisions.

[0535] "Means of registering data" refers to a function for saving information such as inventory status, delivery status, and staff work efficiency at the logistics center in a database.

[0536] "Means of summarizing and analyzing using AI" refers to a function for summarizing and analyzing registered data using artificial intelligence technology.

[0537] "Means of timely output" refers to a function for displaying summaries and analysis results in real time and notifying users as necessary.

[0538] "A means of collecting information such as inventory status, delivery status, and staff work efficiency at the logistics center" is a function for automatically collecting various data within the logistics center.

[0539] "Means of summarizing and analyzing collected information and notifying when important information or anomalies occur" refers to a function for summarizing and analyzing collected data and notifying an administrator when important information or anomalies are detected.

[0540] The system for carrying out this invention collects information such as inventory status, delivery status, and staff work efficiency in a logistics center in real time, summarizes and analyzes it using AI, and outputs the results in a timely manner. A specific embodiment of this system is described below.

[0541] 1. System Programming

[0542] The server includes a means for registering data, a means for summarizing and analyzing using AI, a means for outputting in a timely manner, a means for collecting information such as inventory status at the logistics center, delivery status, and staff work efficiency, and a means for summarizing and analyzing the collected information and notifying of important information or the occurrence of abnormalities.

[0543] 2. Program Processing

[0544] The server first registers various data from the logistics center in a database. This includes inventory status, delivery status, and staff work efficiency. Next, AI is used to summarize and analyze this data. AI uses natural language processing and machine learning techniques to summarize the data and detect anomalies. The analysis results are displayed on a dashboard in real time, and if important information or anomalies occur, a push notification is sent to the administrator's smartphone.

[0545] The implementation of this system uses the following hardware and software:

[0546] Hardware: Servers, smartphones

[0547] Software: SQLite (database management system), Python (programming language), scikit-learn (machine learning library), pandas (data analysis library), smtplib (email sending library)

[0548] 3. Specific Examples

[0549] For example, assume that the inventory data for a logistics center is as follows:

[0550] id | info

[0551] 1 | Product A is running low in stock.

[0552] 2 | Product B is in excess stock.

[0553] 3 | Product C has adequate stock.

[0554] By summarizing and analyzing this data, the following clustering results are obtained:

[0555] cluster | info

[0556] 0 | Product A is low in stock.

[0557] 1 | Product B is in excess stock.

[0558] 2 | Product C has adequate stock.

[0559] This will be notified to the administrator via email.

[0560] Example prompts to be input to the generative AI model

[0561] Create a program that summarizes and analyzes inventory data from a distribution center and notifies the manager of important information. The program retrieves data from the database, summarizes and analyzes the data using TF-IDF vectorization and KMeans clustering, and notifies the manager of the results by email.

[0562] In this way, logistics center operators and managers can get real-time information and make fast and accurate decisions.

[0563] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0564] Step 1:

[0565] The server collects information such as inventory status at the logistics center, delivery status, and staff work efficiency. Specifically, it obtains data from various sensors and management systems and registers it in a database. The input is data from the various sensors and management systems, and the output is information stored in the database.

[0566] Step 2:

[0567] The server uses AI to summarize and analyze the information registered in the database. Specifically, it uses natural language processing technology to convert the data into TF-IDF vectors and performs KMeans clustering. The input is information obtained from the database, and the output is the summarized and analyzed data.

[0568] Step 3:

[0569] The server outputs the summary and analysis results in a timely manner. Specifically, the analysis results are displayed on a dashboard, and a push notification is sent to the administrator's smartphone when important information or anomalies occur. The input is the summarized and analyzed data, and the output is the dashboard display and push notification.

[0570] Step 4:

[0571] A user receives a push notification using a smartphone and checks detailed information on a dashboard. Specifically, the user receives the notification through a smartphone application and accesses the dashboard as necessary. The input is the push notification, and the output is the information that the user checks.

[0572] Step 5:

[0573] The server receives user feedback and retrains the data to improve the system performance. Specifically, it collects feedback data from users and updates the machine learning model. The input is the user feedback data and the output is the updated machine learning model.

[0574] Example 2

[0575] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0576] Conventional business strategy planning and decision-making requires the manual collection, summarization, and analysis of vast amounts of information, which takes a great deal of time and effort. In addition, there are issues with human error and the difficulty of obtaining accurate information in a timely manner. This makes it difficult to make quick and accurate decisions, which can reduce the effectiveness of business strategies.

[0577] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0578] In this invention, the server includes a means for collecting and registering data, a means for summarizing and analyzing the data using natural language processing and machine learning techniques, and a means for outputting the summaries and analysis results in a timely manner to a dashboard, email, or a smartphone application, thereby making it possible to quickly and accurately obtain information necessary for planning business strategies and making decisions.

[0579] "Means of collecting and registering data" refers to a function for collecting information necessary for planning business strategies and decision-making, and storing it in a database.

[0580] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0581] "Machine learning technology" refers to algorithms and methods that allow computers to learn from data and make predictions and classifications.

[0582] "Means of summarizing and analyzing" refers to functions for concisely summarizing collected data and analyzing data patterns and trends.

[0583] A "dashboard" is an interface that allows users to visually check information and displays data using graphs, charts, and the like.

[0584] "Email" is a means of communication for sending and receiving text and files over the Internet.

[0585] A "smartphone application" is software that runs on a smartphone and allows users to check and manipulate information.

[0586] "Means of timely output" refers to a function for providing summaries and analysis results to users in real time.

[0587] The present invention is a system for quickly and accurately providing information required for planning business strategies and decision-making. A specific embodiment of this system will be described below.

[0588] Information collection and registration

[0589] The server collects information necessary for planning business strategies and making decisions, and registers it in a database. Specifically, the information includes market trends, the actions of competitors, the company's own sales data, product quality data, etc. A relational database such as MySQL or PostgreSQL is used as the database.

[0590] Examples:

[0591] The server retrieves the latest market trend reports from market research companies via API and stores them in a MySQL database.

[0592] The server imports the company's sales data, provided in CSV file format, into a PostgreSQL database.

[0593] Summarizing and analyzing information

[0594] The AI ​​uses natural language processing (NLP) and machine learning (ML) techniques to summarize and analyze the registered information, specifically using the Python libraries NLTK and Transformers, and Scikit-learn to analyze trends in the sales data.

[0595] Examples:

[0596] AI can be used to summarise competitive landscape reports and extract key takeaways, for example using Transformers' BERT model to generate summaries.

[0597] The AI ​​will analyze the sales data from the past year to identify trends in sales, for example using a linear regression model from Scikit-learn.

[0598] Output of results

[0599] The server outputs summaries and analysis results in a timely manner. The output format is such that the user can easily check the information, for example, by displaying the information on a dashboard, sending it by email, or notifying it to a smartphone application. The terminal displays the dashboard or application so that the user can check the information.

[0600] Examples:

[0601] The server displays summaries and analysis results in a dashboard that updates in real time, for example by drawing graphs using the JavaScript library D3.js.

[0602] The server then sends important results to management via email, for example using Python's smtplib.

[0603] The device sends a push notification to the smartphone app, for example, by using Firebase Cloud Messaging (FCM).

[0604] Examples of prompt statements

[0605] "Subscribe to our database for the latest market trend reports."

[0606] "Summarize the competitor landscape report and extract key takeaways."

[0607] "Analyze the past year's worth of sales data and identify sales trends."

[0608] "Display summaries and analytics results on a dashboard and email important information to me."

[0609] This system enables users to quickly and accurately obtain the information they need to plan business strategies and make decisions.

[0610] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0611] Step 1:

[0612] Information collection and registration

[0613] The server collects information necessary for planning business strategies and making decisions, and registers it in a database. Input data includes market trends, competitors' actions, the company's own sales data, product quality data, etc. This data is obtained through APIs or provided in CSV file format. The server stores this data in a relational database such as MySQL or PostgreSQL.

[0614] Specific behavior:

[0615] The server retrieves the latest market trend reports from market research companies via API and stores them in a MySQL database.

[0616] The server imports the company's sales data, provided in CSV file format, into a PostgreSQL database.

[0617] Step 2:

[0618] Summarizing and analyzing information

[0619] The AI ​​uses natural language processing (NLP) and machine learning (ML) techniques to summarize and analyze the registered information. The inputs include market trend reports and sales data stored in a database. The AI ​​uses Python libraries NLTK and Transformers to generate summaries and Scikit-learn to analyze trends in the sales data. The output is the summarized reports and analysis results.

[0620] Specific behavior:

[0621] The AI ​​uses Transformers' BERT model to summarize competitor landscape reports and extract key takeaways.

[0622] The AI ​​uses Scikit-learn's linear regression model to analyze sales data from the past year to identify sales trends.

[0623] Step 3:

[0624] Output of results

[0625] The server outputs summaries and analysis results in a timely manner. The input includes summaries and analysis results generated by the AI. The output format is such that users can easily check the information, for example, by displaying it on a dashboard, sending it by email, or notifying it to a smartphone application.

[0626] Specific behavior:

[0627] The server displays summaries and analysis results in a dashboard that updates in real time, for example by drawing graphs using the JavaScript library D3.js.

[0628] The server uses Python's smtplib to email key results to management.

[0629] The device sends push notifications to the smartphone app using Firebase Cloud Messaging (FCM).

[0630] In this way, users can quickly and accurately obtain the information necessary for planning business strategies and making decisions.

[0631] (Application example 2)

[0632] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[0633] In conventional manufacturing lines, data collection and analysis was often done manually, which was inefficient and delayed the detection and response of abnormalities. In addition, human error was likely to occur, making it difficult to improve quality control and production efficiency. This caused many problems in the operation of the manufacturing line.

[0634] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0635] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data using AI, a means for outputting the summary and analysis results in a timely manner, a means for collecting various data on the production line, a means for analyzing the collected data and detecting anomalies, and a means for notifying when an anomaly is detected. This makes it possible to improve the efficiency of the production line and quality control.

[0636] A "means for registering data" is any device or software used to store data collected from a production line or other sources in a database.

[0637] "Means of summarizing and analyzing using AI" refers to devices and software that use artificial intelligence technology to summarize and analyze collected data.

[0638] "Means for timely output" refers to devices or software for displaying or notifying analysis results and summary information in real time.

[0639] "Means of collecting various data from the production line" refers to devices and software for collecting data such as production speed, quality score, and machine operating status using sensors and data collection devices installed on the production line.

[0640] The "means for analyzing the collected data and detecting anomalies" refers to a device or software for analyzing the collected data and detecting anomalies.

[0641] The "means for notifying when an abnormality is detected" refers to a device or software for notifying an administrator or relevant parties when an abnormality is detected.

[0642] As an embodiment of the present invention, a "smart manufacturing management system" aimed at improving the efficiency and quality control of a manufacturing line will be described as an example.

[0643] System Overview

[0644] The server collects various data from the production line and registers it in a database. Next, AI is used to summarize and analyze the collected data and detect anomalies. If anomalies are detected or the results of the analysis are sent to the administrator in a timely manner, which makes it possible to improve the efficiency of the production line and quality control.

[0645] Hardware and Software Used

[0646] Data collection devices: Using sensors and data collection devices installed on the production line, data such as production speed, quality scores, and machine operation status is collected in real time.

[0647] Database: Use a database to store the collected data. Specifically, use a relational database such as MySQL or PostgreSQL.

[0648] AI analysis software: To analyze the collected data, we use Python machine learning libraries (e.g., scikit-learn) to build an anomaly detection model and analyze the data.

[0649] Notification system: When an abnormality is detected, an email sending system is used to notify the administrator. Specifically, emails are sent using the smtplib library.

[0650] Processing flow

[0651] 1. Data collection: Collect data from various sensors on the production line and register it in a database.

[0652] 2. Data analysis: Use AI to analyze collected data and detect anomalies.

[0653] 3. Notification: If an abnormality is detected, the administrator will be notified by email.

[0654] Examples

[0655] For example, if the production speed of a manufacturing line suddenly drops and the quality score falls below the standard value, the AI ​​will determine this to be an abnormality and the server will send an email to the administrator stating, "An abnormality has been detected on the manufacturing line. Please check for details."

[0656] Examples of prompt statements

[0657] Below are examples of prompts to be input to the generative AI model.

[0658] Create a program to collect data from the manufacturing line and use AI to detect anomalies. Data includes production speed, quality score, and machine operation status. If an anomaly is detected, notify the factory manager by email.

[0659] In this way, a system can be constructed to achieve efficient production line and quality control.

[0660] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0661] Step 1:

[0662] The server collects data from sensors installed on the production line. As input, it receives real-time data such as production speed, quality score, and machine operation status. These data are sent directly from the sensors to the server through a data collector. The server registers these data in a database. Specifically, the server periodically polls for data from the sensors and stores the acquired data in the database.

[0663] Step 2:

[0664] The server passes the data registered in the database to the AI ​​analysis software. As input, it uses the production speed, quality score, and machine operation status data stored in the database. The AI ​​analysis software uses these data to run an anomaly detection model and determine whether there are any abnormalities. Specifically, the server uses a Python machine learning library (e.g., scikit-learn) to build an anomaly detection model and analyze the data.

[0665] Step 3:

[0666] The server receives the analysis results from the AI ​​analysis software. As input, it receives the output results of the anomaly detection model. If an anomaly is detected based on the analysis results, the server launches a notification system. Specifically, the server evaluates the analysis results and sets a notification flag if an anomaly is detected.

[0667] Step 4:

[0668] The server uses a notification system to notify the administrator of an anomaly. As input, it uses a flag indicating that an anomaly has been detected. The notification system uses an email sending library (e.g. smtplib) to send an email to notify the administrator of the anomaly. Specifically, the server generates the content of the email and sends it to the administrator's email address.

[0669] Step 5:

[0670] The user (administrator) receives the notification from the server and checks the details of the production line. As input, the user receives the abnormality notification email sent from the server. The user checks the status of the production line based on the contents of the email and takes the necessary action. In concrete terms, the user opens the email, checks the details of the abnormality, and accesses the production line monitoring system to check the status.

[0671] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0672] "Example 1"

[0673] In one embodiment of the present invention, the system further includes an emotion engine that recognizes the emotion of the user. The emotion engine recognizes the user's emotional state from, for example, the user's tone of voice, facial expression, text input, and the like. The emotion engine adjusts the analysis and summarization of data based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, it prioritizes summarizing important information and presents it to the user. Also, if the emotion engine recognizes that the user is feeling relaxed, it provides more detailed information. Furthermore, the emotion engine adjusts the output results based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling happy, it displays positive results with emphasis. On the other hand, if the emotion engine recognizes that the user is feeling sad, it displays negative results with mitigation. This makes it possible to provide optimal information according to the user's emotional state.

[0674] "Example 2"

[0675] In one embodiment of the present invention, the system further includes an emotion engine that recognizes the emotion of the user. The emotion engine recognizes the user's emotional state from, for example, the user's tone of voice, facial expression, text input, and the like. The emotion engine adjusts the analysis and summarization of data based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, it prioritizes summarizing important information and presents it to the user. Also, if the emotion engine recognizes that the user is feeling relaxed, it provides more detailed information. Furthermore, the emotion engine adjusts the output results based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling happy, it displays positive results with emphasis. On the other hand, if the emotion engine recognizes that the user is feeling sad, it displays negative results with mitigation. This makes it possible to provide optimal information according to the user's emotional state.

[0676] The process flow of each embodiment will be described below.

[0677] "Example 1"

[0678] Step 1: Receive input from the user (tone of voice, facial expressions, text input, etc.).

[0679] Step 2: The emotion engine recognizes the user's emotional state, which can be, for example, happy, sad, stressed, relaxed, etc.

[0680] Step 3: The emotion engine adjusts the analysis and summarization of the data based on the user's emotional state. For example, if the emotion engine detects that the user is stressed, it will prioritize summarizing and presenting important information to the user. If the emotion engine detects that the user is relaxed, it will provide more detailed information.

[0681] Step 4: The emotion engine adjusts the output results based on the user's emotional state. For example, if the emotion engine detects that the user is feeling happy, it will highlight the positive results. On the other hand, if the emotion engine detects that the user is feeling sad, it will tone down the negative results.

[0682] Example 1

[0683] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0684] Conventional systems have had difficulty efficiently summarizing and analyzing information necessary for business strategy planning and decision-making, and outputting it in a timely manner. In addition, the system does not provide information that takes into account the user's emotional state, so it is not possible to provide optimal information according to the user's stress and emotions. This can impair the speed and accuracy of decision-making.

[0685] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0686] In this invention, the server includes a means for registering data, a means for summarizing and analyzing by artificial intelligence, a means for outputting the summarization and analysis results in a timely manner, a means for recognizing the emotional state of the user, a means for adjusting the analysis and summarization of the data based on the emotional state, and a means for adjusting the output result based on the emotional state. This makes it possible to provide optimal information according to the emotional state of the user, and to improve the speed and accuracy of business strategy planning and decision-making.

[0687] "Means for registering data" refers to a device or software that allows a user to input information necessary for planning business strategies and decision-making, and store it in a database.

[0688] "Means of summarizing and analyzing using artificial intelligence" refers to devices or software for summarizing and analyzing registered information using natural language processing or machine learning techniques.

[0689] "Means for outputting summaries and analysis results in a timely manner" refers to a device or software for quickly providing summaries and analysis results to users, and includes formats such as dashboard display, email transmission, and smartphone notifications.

[0690] The "means for recognizing the emotional state of the user" refers to a device or software for recognizing the emotional state of the user from the tone of voice, facial expression, text input, and the like.

[0691] A "means for adjusting data analysis or summarization based on emotional state" is a device or software for modifying the way data is analyzed or summarized depending on the recognized emotional state of a user.

[0692] A "means for adjusting an output result based on an emotional state" is a device or software for modifying the content or format of information to be output depending on the recognized emotional state of the user.

[0693] The present invention is a system including a means for registering data, a means for summarizing and analyzing using artificial intelligence, a means for outputting the summary and analysis results in a timely manner, a means for recognizing a user's emotional state, a means for adjusting the analysis and summarization of the data based on the emotional state, and a means for adjusting the output result based on the emotional state.

[0694] Data registration

[0695] A user uses a terminal to input information necessary for planning business strategies and making decisions. Specific information includes market trends, the actions of competitors, the company's own sales data, and product quality data. The terminal stores the input information in a database management system (e.g., MySQL or PostgreSQL). For example, a user inputs "Market trend report for Q3 2023" and registers it in the database.

[0696] Summarizing and analyzing information

[0697] The server retrieves information from the database and summarizes and analyzes the information using Python libraries TensorFlow and PyTorch. Specifically, it uses natural language processing (NLP) techniques to analyze text data and extract key points. For example, it extracts key trends from the "Market Trends Report Q3 2023" and generates a summary.

[0698] Summary and analysis output

[0699] The server uses Django or Flask as a web application framework to display summaries and analysis results on a dashboard. Users can access the dashboard through their terminals and check the summaries and analysis results. The server can also send the results by email or notify a smartphone application. For example, when a user accesses the dashboard, a summary of the "Market Trends Report Q3 2023" is displayed.

[0700] Emotion engine adjustment

[0701] The server uses Google Cloud Speech-to-Text API and OpenCV to analyze the user's tone of voice and facial expressions to recognize their emotional state. If the emotion engine recognizes that the user is feeling stressed, it prioritizes and summarizes important information and presents it to the user. For example, if the user voice-inputs that they want important information to be displayed first, the emotion engine adjusts the summary according to that instruction. If the server recognizes that the user is relaxed, it provides more detailed information. Furthermore, the emotion engine adjusts the output results based on the user's emotional state. For example, if the server recognizes that the user is feeling happy, it highlights and displays positive results.

[0702] Examples and prompts

[0703] As a concrete example, a user enters a "Market Trend Report for Q3 2023" into the database. The server uses AI to analyze this data and summarizes the main trends. If the emotion engine recognizes that the user is stressed, the server will prioritize displaying the most important trends. If the user is relaxed, it will provide detailed analysis results.

[0704] An example of a prompt is, "Subscribe to our market trend report Q3 2023 and summarize the key trends. Prioritize important information when the user is stressed and provide more detailed information when the user is relaxed."

[0705] The system enables users to make fast and accurate decisions.

[0706] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0707] Step 1:

[0708] Users use the terminal to input information necessary for planning business strategies and making decisions. The input information includes market trends, competitors' actions, the company's own sales data, product quality data, etc. The terminal stores this information in a database management system. For example, a user inputs "Market trend report for Q3 2023" and registers it in the database.

[0709] Input: Market trends, competitors' actions, company sales data, product quality data

[0710] Output: Information stored in the database

[0711] Step 2:

[0712] The server retrieves information from the database. The retrieved information is summarized and analyzed using Python libraries TensorFlow and PyTorch. Specifically, natural language processing (NLP) techniques are used to analyze text data and extract key points. For example, key trends are extracted from the "Market Trends Report Q3 2023" and a summary is generated.

[0713] Input: Information retrieved from a database

[0714] Output: Summarized information, analysis results

[0715] Step 3:

[0716] The server uses Django or Flask as a web application framework to display summaries and analysis results on a dashboard. Users can access the dashboard through their terminals and check the summaries and analysis results. The server can also send the results by email or notify a smartphone application. For example, when a user accesses the dashboard, a summary of the "Market Trends Report Q3 2023" is displayed.

[0717] Input: Summarised information, analysis results

[0718] Output: Display on dashboard, send email, notify smartphone

[0719] Step 4:

[0720] The server uses Google Cloud Speech-to-Text API and OpenCV to analyze the user's tone of voice and facial expressions to recognize their emotional state. If the emotion engine recognizes that the user is feeling stressed, it prioritizes important information in a summary and presents it to the user. For example, if the user voice-inputs, "I want important information to be displayed first," the emotion engine adjusts the summary accordingly.

[0721] Input: User tone of voice, facial expressions, and text input

[0722] Output: Perceived emotional state

[0723] Step 5:

[0724] The server adjusts the analysis and summarization of data based on the user's emotional state as recognized by the emotion engine. If the user is stressed, important information is prioritized and summarized and presented to the user. If the user is relaxed, detailed information is provided.

[0725] Input: Perceived emotional state

[0726] Output: Tailored summary and analysis results

[0727] Step 6:

[0728] The server adjusts the output results based on the user's emotional state as recognized by the emotion engine: if the user is happy, positive results are emphasized and displayed, and if the user is sad, negative results are toned down and displayed.

[0729] Input: Perceived emotional state

[0730] Output: Adjusted output result

[0731] (Application example 1)

[0732] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0733] In autonomous vehicles, there is a demand for appropriate recognition of passengers' emotional states and for operational and entertainment information to be provided based on those states. However, conventional systems have had difficulty in recognizing passengers' emotional states in real time and providing information accordingly. There was also a lack of technology to adjust the analysis and summarization of information based on passengers' emotional states. This made it difficult to improve passenger comfort and satisfaction.

[0734] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0735] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data by AI, a means for timely outputting the summary and analysis results, a means for recognizing the user's emotions, a means for adjusting the analysis and summarization of the data based on the emotional state, a means for adjusting the output result based on the emotional state, and a means for recognizing the emotional state of passengers using a camera or microphone in the vehicle and providing operation information and entertainment information based on the emotional state. This makes it possible to provide optimal information according to the emotional state of passengers, thereby improving passenger comfort and satisfaction.

[0736] "Means for registering data" refers to a function for saving information required for the system in a database.

[0737] "Means of summarizing and analyzing using AI" refers to a function for summarizing and analyzing registered data using artificial intelligence technology.

[0738] "Means for timely output" refers to a function for quickly providing analysis results and summary information to users.

[0739] "Means for recognizing user emotions" refers to a function that uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice to identify their emotional state.

[0740] The "means for adjusting data analysis and summarization based on the emotional state" is a function for dynamically changing the data analysis method and summarization content according to the recognized emotional state of the user.

[0741] The "means for adjusting the output result based on the emotional state" is a function for changing the format and contents of the information to be output in accordance with the emotional state of the user.

[0742] "Means for recognizing passengers' emotional states using cameras and microphones in the vehicle" refers to a function that uses cameras and microphones installed in the autonomous vehicle to analyze passengers' facial expressions and tone of voice to identify their emotional states.

[0743] The "means for providing operational information and entertainment information" is a function for providing operational information and entertainment content based on the emotional state of passengers.

[0744] The system for carrying out this invention is configured as follows. First, the server has a means for registering data and stores the necessary information in a database. Next, the server has a means for summarizing and analyzing the registered data using artificial intelligence (AI) technology. Furthermore, the server has a means for outputting the analysis results and summary information in a timely manner.

[0745] To recognize the user's emotions, cameras and microphones are installed in the vehicle. These sensors are used to analyze the user's facial expressions and tone of voice to identify the user's emotional state. The server has the means to dynamically change the data analysis method and summary content based on the recognized emotional state. It also has the means to adjust the output results based on the emotional state.

[0746] Specifically, cameras in the vehicle capture passengers' facial expressions using OpenCV and identify their emotions using emotion recognition models (e.g., models using Keras). A microphone captures audio using a speech recognition library and analyzes the emotion of the audio using TextBlob. This allows the system to provide relaxing music if the passenger is stressed, or detailed trip and entertainment information if the passenger is relaxed.

[0747] As a concrete example, consider the following scenario: If the passenger is stressed, the system will play relaxing music. If the passenger is relaxed, the system will provide detailed travel information. In this way, optimal information can be provided according to the passenger's emotional state, improving passenger comfort and satisfaction.

[0748] Examples of prompts to be input to a generative AI model include the following:

[0749] "Create an application that plays relaxing music if passengers are feeling stressed. Include a feature that uses the camera and microphone to recognize emotions and provide appropriate information."

[0750] In this way, the form for carrying out the invention can be concretely shown.

[0751] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0752] Step 1:

[0753] The server captures video data from the camera in the vehicle. The input is real-time video from the camera, and the output is the captured image frames. Specifically, we use OpenCV to acquire the video data and perform face detection.

[0754] Step 2:

[0755] The server uses the captured image frames to analyze the passengers' facial expressions. The input is the image frame obtained in step 1, and the output is the passenger's emotional state. Specifically, the image data is input to an emotion recognition model using Keras to identify the emotion.

[0756] Step 3:

[0757] The server captures voice data from a microphone in the vehicle. The input is real-time voice from the microphone, and the output is the captured voice data. Specifically, the server obtains the voice data using a voice recognition library.

[0758] Step 4:

[0759] The server uses the captured voice data to analyze the passenger's voice for emotion. The input is the voice data obtained in step 3, and the output is the emotional state of the voice. Specifically, it uses TextBlob to analyze the voice data and identify the emotion.

[0760] Step 5:

[0761] The server integrates the emotional states obtained in steps 2 and 4 to determine the final emotional state of the passenger. The inputs are the facial expression analysis results and the voice analysis results, and the output is the integrated emotional state. Specifically, both emotional states are compared, and the stronger emotion is determined as the final emotional state.

[0762] Step 6:

[0763] The server provides appropriate information based on the final emotional state. The input is the integrated emotional state, and the output is the information provided (e.g., relaxing music, detailed travel information, entertainment information). Specifically, if the emotional state is stressed, relaxing music is played, and if relaxed, detailed travel information is provided.

[0764] Step 7:

[0765] The server presents the provided information to passengers through displays and speakers in the vehicle. The input is the provided information, and the output is the information presented to passengers. Specifically, the server displays trip information on the display and plays music from the speaker.

[0766] In this way, the server can provide optimal information according to the passenger's emotional state, thereby improving passenger comfort and satisfaction.

[0767] Example 2

[0768] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0769] Conventional business strategy planning and decision-making requires the manual collection and analysis of huge amounts of data, which is time-consuming and labor-intensive and prone to human error. In addition, information is provided without taking into account the user's emotional state, making it difficult to provide optimal information according to the user's stress and emotions.

[0770] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0771] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data by artificial intelligence, a means for outputting the summaries and analysis results in a timely manner, a means for recognizing a user's emotions, a means for adjusting the analysis and summarization of the data based on the emotions, and a means for adjusting the output results based on the emotions, thereby enabling not only quick and accurate decision-making but also the provision of optimal information according to the user's emotional state.

[0772] The "means for registering data" refers to an interface that allows users to input information necessary for planning business strategies and decision-making, and transmit it to the server.

[0773] "Artificial intelligence" is a program or system that uses natural language processing and machine learning techniques to summarize and analyze input data.

[0774] "Means of summarization and analysis" refers to the process of using artificial intelligence to summarize input data and generate analytical results.

[0775] "Means for timely output" refers to a method for providing the generated summary and analysis results to the user in real time, and includes displaying a dashboard, sending an email, or sending a smartphone notification.

[0776] "Means for recognizing a user's emotions" refers to technology for detecting an emotional state from a user's tone of voice, facial expression, text input, etc., and includes voice recognition and facial expression recognition.

[0777] "Means for tailoring data analysis and summarization based on emotion" refers to a process that prioritizes summarizing important information or providing detailed information depending on the user's emotional state.

[0778] "Emotionally tailored output" refers to a process that emphasizes positive outcomes or mitigates negative outcomes depending on the user's emotional state.

[0779] This invention is a system including a means for registering data, a means for summarizing and analyzing using artificial intelligence, a means for outputting the summary and analysis results in a timely manner, a means for recognizing a user's emotions, a means for adjusting the analysis and summarization of the data based on the emotions, and a means for adjusting the output results based on the emotions.

[0780] Data registration

[0781] Users use a dedicated input form to input information necessary for planning business strategies and making decisions. For example, they input market trends, the actions of competitors, their own company's sales data, product quality data, etc. The terminal sends the information entered by the user to the server. The HTTPS protocol is used for transmission to ensure the security of the information.

[0782] Summarizing and analyzing information

[0783] The server stores the received information in a database. For example, MySQL or PostgreSQL is used for the database. The server passes the information stored in the database to an artificial intelligence. The artificial intelligence uses natural language processing (NLP) and machine learning (ML) techniques to summarize the information and generate analysis results. For example, the Python libraries NLTK and TensorFlow are used.

[0784] Output of summary and analysis results

[0785] The server outputs summaries and analysis results generated by the AI ​​in a timely manner. The output format is such that users can easily check the information, such as displaying it on a dashboard, sending it by email, or notifying a smartphone application. For example, React.js is used for the front end of the web application, and Node.js is used for the back end.

[0786] Emotion engine adjustment

[0787] The terminal recognizes the user's emotional state from the user's tone of voice, facial expressions, text input, etc. For example, Google Cloud Speech-to-Text API is used for voice recognition, and OpenCV is used for facial expression recognition. The server uses an emotion engine to adjust data analysis and summarization based on the user's emotional state. For example, if the server recognizes that the user is feeling stressed, it prioritizes summarizing important information and presents it to the user. Also, if the server recognizes that the user is relaxed, it provides more detailed information. The server adjusts the output results based on the user's emotional state. For example, if the server recognizes that the user is feeling happy, it emphasizes and displays positive results. On the other hand, if the server recognizes that the user is feeling sad, it displays and mitigates negative results.

[0788] Examples

[0789] For example, consider a case where a user wants to input the latest data on market trends and analyze the movements of competitors. The user inputs the market trend data into a dedicated input form, and the device sends the information to the server. The server stores the information in a database and passes it on to the artificial intelligence. The artificial intelligence summarizes the market trends and analyzes the movements of competitors. The analysis results are displayed in real time on the dashboard. If the emotion engine recognizes that the user is feeling stressed, important trend information will be displayed with priority.

[0790] Examples of prompt statements

[0791] "Enter the latest data on market trends and analyze competitors' moves. Prioritize the most important information, especially when users are stressed."

[0792] This system not only enables users to make quick and accurate decisions, but also provides optimal information according to the user's emotional state.

[0793] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0794] Step 1:

[0795] The user enters the information.

[0796] Users use a dedicated input form to input information necessary for planning business strategies and making decisions. The information input includes market trends, competitors' actions, the company's own sales data, product quality data, etc. The input information is then sent to the terminal.

[0797] Step 2:

[0798] The terminal transmits the information to the server.

[0799] The terminal transmits the information entered by the user to the server using the HTTPS protocol, which ensures the security of the information. The input data reaches the server.

[0800] Step 3:

[0801] The server stores the information in a database.

[0802] The server stores the received information in a database, for example, MySQL or PostgreSQL. The stored data is used for subsequent processing.

[0803] Step 4:

[0804] The server passes the information to the artificial intelligence.

[0805] The server passes the information stored in the database to the AI, which uses natural language processing (NLP) and machine learning (ML) techniques to summarize and analyze the information. The input data is processed by the AI.

[0806] Step 5:

[0807] Artificial intelligence summarizes and analyzes the information.

[0808] Artificial intelligence summarizes the information it receives and generates analytical results. For example, it uses the Python library NLTK or TensorFlow to analyze text data. Summarized information and analytical results are generated.

[0809] Step 6:

[0810] The server outputs the analysis results.

[0811] The server outputs summaries and analysis results generated by the AI ​​in a timely manner. The output format is such that the user can easily check the information, for example, by displaying it on a dashboard, sending it by email, or notifying it via a smartphone application. The output data is provided to the user.

[0812] Step 7:

[0813] The terminal recognizes the user's emotions.

[0814] The device recognizes the user's emotional state from the user's tone of voice, facial expressions, text input, etc. For example, it uses Google Cloud Speech-to-Text API for voice recognition and OpenCV for facial expression recognition. The input data is processed by the emotion recognition engine.

[0815] Step 8:

[0816] The server uses the emotion engine to refine the analysis results.

[0817] The server uses the emotion engine to adjust the analysis and summarization of data based on the user's emotional state. For example, if the server recognizes that the user is stressed, it will prioritize summarizing and presenting important information to the user. If the server recognizes that the user is relaxed, it will provide more detailed information. The adjusted data is then provided to the user.

[0818] Step 9:

[0819] The server displays the output according to the emotion.

[0820] The server adjusts the output results based on the user's emotional state. For example, if the server recognizes that the user is feeling happy, it emphasizes the positive results. On the other hand, if the server recognizes that the user is feeling sad, it softens the negative results. The final output data is provided to the user.

[0821] (Application example 2)

[0822] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[0823] Conventional systems make it difficult to obtain the information necessary for planning business strategies and making decisions in a timely manner, and real-time information acquisition is required especially in the operation of physical stores. In addition, since information is not provided according to the user's emotional state, there is a problem that appropriate information cannot be provided according to stress or relaxation state. Furthermore, there is a lack of notification functions using smartphones and smart glasses, making it difficult to check information immediately.

[0824] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0825] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data by AI, a means for outputting the summary and analysis results in a timely manner, an emotion engine for recognizing the emotional state of the user, and a means for adjusting the output results based on the emotion engine. This makes it possible to obtain information necessary for planning business strategies and decision-making in real time, and to provide appropriate information according to the emotional state of the user. In addition, a notification function using a smartphone or smart glasses makes it possible to check information immediately.

[0826] "Means for registering data" refers to a function for saving information required for the system in a database.

[0827] "Means of summarizing and analyzing using AI" refers to a function for summarizing and analyzing registered data using artificial intelligence.

[0828] "Means for timely output" refers to a function for providing analysis results to the user in real time.

[0829] The "emotion engine that recognizes the user's emotional state" is a function for recognizing emotions from the user's tone of voice, facial expressions, text input, etc.

[0830] The "means for adjusting the output result based on the emotion engine" is a function for adjusting the content and format of the information output according to the recognized emotional state.

[0831] "Reference information for formulating business strategies and making decisions" is information that is necessary when formulating a company's business strategies and making decisions.

[0832] "The effect of reducing the time required for reporting tasks and the occurrence of human errors" refers to the effect of shortening the time required for reporting tasks and reducing human error.

[0833] "Information necessary for running a physical store" refers to various information necessary for running a store, such as store sales data, inventory information, and customer feedback.

[0834] "Means of notifying a smartphone or smart glasses" refers to a function for notifying a smartphone or smart glasses of analysis results and important information.

[0835] A system for implementing the present invention includes means for registering data, means for summarizing and analyzing using AI, means for outputting in a timely manner, an emotion engine for recognizing the emotional state of a user, and means for adjusting the output results based on the emotion engine.

[0836] System Configuration

[0837] 1. How to register data

[0838] The server registers information such as store sales data, inventory information, and customer feedback in a database. This database uses a relational database such as SQLite.

[0839] 2. AI-based methods for summarizing and analyzing

[0840] The server uses AI models to summarize and analyze the registered data. Specifically, it uses the Transformers library to perform natural language processing. For example, it uses generative AI models such as BERT and GPT-3 to summarize customer feedback.

[0841] 3. Means of timely output

[0842] The server provides the analysis results to the user in real time. The output format can be displayed on a dashboard or sent to a smartphone or smart glasses, allowing the user to check the information instantly.

[0843] 4. Emotion engine that recognizes the user's emotional state

[0844] The server uses an emotion recognition library to recognize emotions from the user's tone of voice, facial expressions, text input, etc. This allows the server to grasp the user's emotional state in real time.

[0845] 5. A way to adjust the output based on the emotion engine

[0846] The server then adjusts the content and format of the information output depending on the user's recognized emotional state, for example prioritizing important information if the user is stressed, and providing more detailed information if the user is relaxed.

[0847] Examples

[0848] For example, if a store staff member is feeling stressed, the system will prioritize displaying "important information." Conversely, if the staff member is feeling relaxed, the system will provide detailed information. In this way, it is possible to provide optimal information according to the user's emotional state.

[0849] Examples of prompt statements

[0850] Here are some example prompts to feed to a generative AI model:

[0851] Summarise your store's sales data and customer feedback and tailor the output based on your staff's emotional state - prioritise important information if your staff is stressed, or provide more detailed information if they're relaxed.

[0852] In this way, a smart store assistant system that supports the operation of physical stores can be realized.

[0853] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0854] Step 1:

[0855] The server registers information such as store sales data, inventory information, and customer feedback in a database. As input, various data collected from the stores is stored in a relational database such as SQLite. As output, the information registered in the database is obtained.

[0856] Step 2:

[0857] The server summarizes and analyzes the registered data using an AI model. The inputs include sales data and customer feedback obtained from the database, which are then processed using the Transformers library for natural language processing. Specifically, the data is summarized using generative AI models such as BERT and GPT-3. The output is summarized text and analysis results.

[0858] Step 3:

[0859] The server provides the analysis results to the user in real time. The inputs are the summary and analysis results obtained in step 2, which are displayed on a dashboard or sent to a smartphone or smart glasses. The output is information that the user can check immediately.

[0860] Step 4:

[0861] The server uses an emotion recognition library to recognize emotions from the user's tone of voice, facial expressions, text input, etc. The inputs are the user's voice data, facial expression data, and text data, which are analyzed to recognize the user's emotional state. The output is the user's emotional state.

[0862] Step 5:

[0863] The server adjusts the content and format of the information output according to the recognized emotional state. The inputs are the emotional state obtained in step 4 and the summary and analysis results obtained in step 2, and the priority and level of detail of the information are adjusted based on this. Specifically, if the user is feeling stressed, important information is displayed first, and if the user is relaxed, detailed information is provided. The adjusted information is obtained as the output.

[0864] Step 6:

[0865] The server notifies the smartphone or smart glasses of the adjusted information. As input, the adjusted information from step 5 is sent to the user's device. As output, a notification is displayed on the user's smartphone or smart glasses.

[0866] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0867] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0868] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0869] 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 the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0870] [Third embodiment]

[0871] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0872] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0873] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0874] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0875] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0876] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0877] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0878] Fig. 6 shows an example of main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0879] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0880] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0881] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0882] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0883] "Example 1"

[0884] The system of the present invention includes a means for registering information in a database, a means for summarizing and analyzing information by AI, and a means for outputting the summary and analysis results in a timely manner. Specifically, information necessary for planning business strategies and decision-making is registered in a database. This information is, for example, various information necessary for planning business strategies and decision-making, such as market trends, competitors' movements, one's own sales data, and product quality data. Next, the AI ​​summarizes and analyzes this information. The AI ​​summarizes and analyzes the information using natural language processing and machine learning techniques. Then, the summary and analysis results are output in a timely manner. The output format is, for example, displayed on a dashboard, sent by email, or notified to a smartphone application, so that the user can easily check the information. This makes it possible to make quick and accurate decisions without missing the timing for planning business strategies and decision-making.

[0885] "Example 2"

[0886] The system of the present invention includes a means for registering information in a database, a means for summarizing and analyzing information by AI, and a means for outputting the summary and analysis results in a timely manner. Specifically, information necessary for planning business strategies and decision-making is registered in a database. This information is, for example, various information necessary for planning business strategies and decision-making, such as market trends, competitors' movements, one's own sales data, and product quality data. Next, the AI ​​summarizes and analyzes this information. The AI ​​summarizes and analyzes the information using natural language processing and machine learning techniques. Then, the summary and analysis results are output in a timely manner. The output format is, for example, displayed on a dashboard, sent by email, or notified to a smartphone application, so that the user can easily check the information. This makes it possible to make quick and accurate decisions without missing the timing for planning business strategies and decision-making.

[0887] The process flow of each embodiment will be described below.

[0888] "Example 1"

[0889] Step 1: Register the information necessary for planning business strategies and making decisions in a database. This information includes various types of information necessary for planning business strategies and making decisions, such as market trends, the actions of competitors, your own sales data, and product quality data.

[0890] Step 2: AI summarizes and analyzes the information. AI uses natural language processing and machine learning techniques to summarize and analyze the information.

[0891] Step 3: Output summaries and analysis results in a timely manner. The output format is one in which the user can easily check the information, for example, by displaying it on a dashboard, sending it by email, or notifying it via a smartphone application. This makes it possible to make quick and accurate decisions without missing the timing for formulating business strategies and making decisions.

[0892] Example 1

[0893] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[0894] Conventional business strategy planning and decision-making requires the manual collection and analysis of vast amounts of information, which is not only time-consuming and labor-intensive, but also carries a high risk of human error. In addition, it is difficult to obtain information in a timely manner and make decisions quickly. This can lead to a decrease in the accuracy of business strategy planning and decision-making, and a loss of competitiveness.

[0895] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0896] In this invention, the server includes a means for registering information, a means for summarizing and analyzing the information by artificial intelligence, a means for outputting the summary and analysis results in a timely manner, a means for storing the information in a database, and a means for displaying the summary and analysis results on a dashboard, sending them by email, or notifying them to a smartphone application. This automates the collection and analysis of information, making it possible to obtain summaries and analysis results in a timely manner. This improves the accuracy of business strategy planning and decision-making, enabling rapid and accurate decision-making.

[0897] The "means for registering information" refers to an interface that allows a user to input information necessary for planning business strategies and decision-making, and to store the information in a database.

[0898] "Means of summarizing and analyzing using artificial intelligence" refers to a system for summarizing and analyzing registered information using natural language processing and machine learning techniques.

[0899] "Means of timely output" refers to a method for quickly providing summaries and analysis results to users, including displaying them on a dashboard, sending emails, or sending notifications to smartphone applications.

[0900] "Means of storing in a database" refers to a system for efficiently managing and storing information entered by users, including relational databases, etc.

[0901] The "means for displaying on the dashboard" refers to an interface for visually providing summaries and analysis results to users, and has the function of updating information in real time.

[0902] "Means for sending by e-mail" refers to a system for sending summaries and analysis results to users as e-mails.

[0903] "Means for notifying smartphone applications" refers to a system for notifying users of summaries and analysis results via smartphone applications.

[0904] This invention is a system for supporting business strategy planning and decision-making, and aims to register information, summarize and analyze it using artificial intelligence, and output the results in a timely manner. A specific embodiment of this system will be described below.

[0905] Registering information

[0906] Users use a dedicated input form to input information necessary for planning business strategies and making decisions. This information includes market trends, competitors' movements, the company's own sales data, product quality data, etc. The input information is sent from the terminal to the server. Specifically, the data is sent using an HTTP POST request.

[0907] Saving to a database

[0908] The server stores the received information in a database. Relational database management systems (RDBMS) such as MySQL and PostgreSQL are used for the database. This allows information to be managed and stored efficiently.

[0909] Summarizing and analyzing information

[0910] The server periodically retrieves newly registered information from the database. The retrieved information is passed to artificial intelligence (AI). The AI ​​uses Python libraries NLTK and spaCy to process natural language, and builds machine learning models using TensorFlow and PyTorch. This allows the information to be summarized and analyzed.

[0911] Summary and analysis output

[0912] The server retrieves the summaries and analysis results generated by the AI ​​and provides them to the user in a timely manner. The output formats are as follows:

[0913] Display on a dashboard: Use React.js to update information in real time and provide it visually to the user.

[0914] Send via email: Send the summary or analysis results as email using an SMTP server.

[0915] Notify an application on a smartphone: Send a push notification using Firebase Cloud Messaging (FCM).

[0916] Examples

[0917] Example 1: Market Trend Summary

[0918] 1. A user enters the latest market trend report into a web form.

[0919] 2. The terminal sends the entered report to the server.

[0920] 3. The server saves the report in a database.

[0921] 4. The server passes the saved report to the AI.

[0922] 5. AI extracts key points from the report and generates a summary.

[0923] 6. The server displays the generated summary on a dashboard.

[0924] 7. Users see summaries on the dashboard and make quick decisions.

[0925] Example 2: Analyzing competitors' activities

[0926] 1. A user fills out a web form with information about a competitor's new product launch.

[0927] 2. The terminal sends the entered information to the server.

[0928] 3. The server stores the information in a database.

[0929] 4. The server passes the stored information to the AI.

[0930] 5. AI uses machine learning models to predict how competitor moves will impact your business.

[0931] 6. The server sends the analysis results to the user via email.

[0932] 7. Users can check the analysis results by email and make decisions quickly.

[0933] Examples of prompt statements

[0934] Below are examples of prompts to be input to the generative AI model.

[0935] "Summarise the latest market trends report. Extract the key points and provide a concise summary."

[0936]

[0937] "Analyze information about your competitors' new product launches and predict how they will affect your company."

[0938] In this way, users are able to make fast and accurate decisions.

[0939] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0940] Step 1: Enter and submit information

[0941] The user uses a dedicated input form to input information necessary for planning business strategies and making decisions. The input information includes market trends, competitors' actions, the company's own sales data, product quality data, etc. The terminal sends the input information to the server using an HTTP POST request. The input data is in the form of text, numeric data, etc.

[0942] Step 2: Saving to the Database

[0943] The server stores the received information in a database. Specifically, it uses a relational database such as MySQL or PostgreSQL to efficiently manage and store information. The server inserts the received data into the appropriate table and creates indexes as necessary. The input data is stored in the database table, and the output is a notification that the data has been saved to the database.

[0944] Step 3: Get the information and pass it on to the AI

[0945] The server periodically retrieves new registered information from the database. The retrieved information is passed to the AI. The server uses SQL queries to extract new data and converts it into a data format to be passed to the AI. The input data is the new information retrieved from the database, and the output is the data passed to the AI.

[0946] Step 4: Summarize and analyze the information

[0947] The AI ​​summarizes and analyzes the information passed from the server. Specifically, it uses Python libraries NLTK and spaCy to perform natural language processing, and builds machine learning models using TensorFlow and PyTorch. The AI ​​analyzes the input data, extracts important points, and generates a summary. The input data is the information passed from the server, and the output is the summary or analysis results.

[0948] Step 5: Obtaining summary and analysis results

[0949] The server retrieves the summary and analysis results generated by the AI. The server receives the output data from the AI ​​and formats it as necessary. The input data is the summary and analysis results from the AI, and the output is the formatted data to provide to the user.

[0950] Step 6: Output summary and analysis results

[0951] The server provides formatted summaries and analysis results to the user in the following ways:

[0952] Display on a dashboard: Use React.js to update information in real time and provide it visually to the user.

[0953] Send via email: Send the summary or analysis results as email using an SMTP server.

[0954] Notify an application on a smartphone: Send a push notification using Firebase Cloud Messaging (FCM).

[0955] The input data is a formatted summary or analysis result, and the output is the information provided to the user.

[0956] Step 7: User review and decision making

[0957] The user checks the provided summary and analysis results and makes a decision quickly. The user checks the information through a dashboard, email, or smartphone notification and takes the necessary action. The input data is the summary and analysis results provided by the server, and the output is the user's decision.

[0958] (Application example 1)

[0959] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[0960] Logistics centers are required to grasp information such as inventory status, delivery status, and staff work efficiency in real time, and to make quick and accurate decisions. However, manually collecting and analyzing this information takes time and effort, and there is a high possibility of human error. In addition, there is an issue that it is difficult to respond immediately when important information or abnormalities occur.

[0961] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0962] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data by AI, a means for outputting the summary and analysis results in a timely manner, a means for collecting information such as inventory status, delivery status, and staff work efficiency at the logistics center, and a means for summarizing and analyzing the collected information and notifying important information or abnormalities when they occur. This enables the operation manager of the logistics center to grasp information in real time and make quick and accurate decisions.

[0963] "Means of registering data" refers to a function for saving information such as inventory status, delivery status, and staff work efficiency at the logistics center in a database.

[0964] "Means of summarizing and analyzing using AI" refers to a function for summarizing and analyzing registered data using artificial intelligence technology.

[0965] "Means of timely output" refers to a function for displaying summaries and analysis results in real time and notifying users as necessary.

[0966] "A means of collecting information such as inventory status, delivery status, and staff work efficiency at the logistics center" is a function for automatically collecting various data within the logistics center.

[0967] "Means of summarizing and analyzing collected information and notifying when important information or anomalies occur" refers to a function for summarizing and analyzing collected data and notifying an administrator when important information or anomalies are detected.

[0968] The system for carrying out this invention collects information such as inventory status, delivery status, and staff work efficiency in a logistics center in real time, summarizes and analyzes it using AI, and outputs the results in a timely manner. A specific embodiment of this system is described below.

[0969] 1. System Programming

[0970] The server includes a means for registering data, a means for summarizing and analyzing using AI, a means for outputting in a timely manner, a means for collecting information such as inventory status at the logistics center, delivery status, and staff work efficiency, and a means for summarizing and analyzing the collected information and notifying of important information or the occurrence of abnormalities.

[0971] 2. Program Processing

[0972] The server first registers various data from the logistics center in a database. This includes inventory status, delivery status, and staff work efficiency. Next, AI is used to summarize and analyze this data. AI uses natural language processing and machine learning techniques to summarize the data and detect anomalies. The analysis results are displayed on a dashboard in real time, and if important information or anomalies occur, a push notification is sent to the administrator's smartphone.

[0973] The implementation of this system uses the following hardware and software:

[0974] Hardware: Servers, smartphones

[0975] Software: SQLite (database management system), Python (programming language), scikit-learn (machine learning library), pandas (data analysis library), smtplib (email sending library)

[0976] 3. Specific Examples

[0977] For example, assume that the inventory data for a logistics center is as follows:

[0978] id | info

[0979] 1 | Product A is running low in stock.

[0980] 2 | Product B is in excess stock.

[0981] 3 | Product C has adequate stock.

[0982] By summarizing and analyzing this data, the following clustering results are obtained:

[0983] cluster | info

[0984] 0 | Product A is low in stock.

[0985] 1 | Product B is in excess stock.

[0986] 2 | Product C has adequate stock.

[0987] This will be notified to the administrator via email.

[0988] Example prompts to be input to the generative AI model

[0989] Create a program that summarizes and analyzes inventory data from a distribution center and notifies the manager of important information. The program retrieves data from the database, summarizes and analyzes the data using TF-IDF vectorization and KMeans clustering, and notifies the manager of the results by email.

[0990] In this way, logistics center operators and managers can get real-time information and make fast and accurate decisions.

[0991] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0992] Step 1:

[0993] The server collects information such as inventory status at the logistics center, delivery status, and staff work efficiency. Specifically, it obtains data from various sensors and management systems and registers it in a database. The input is data from the various sensors and management systems, and the output is information stored in the database.

[0994] Step 2:

[0995] The server uses AI to summarize and analyze the information registered in the database. Specifically, it uses natural language processing technology to convert the data into TF-IDF vectors and performs KMeans clustering. The input is information obtained from the database, and the output is the summarized and analyzed data.

[0996] Step 3:

[0997] The server outputs the summary and analysis results in a timely manner. Specifically, the analysis results are displayed on a dashboard, and a push notification is sent to the administrator's smartphone when important information or anomalies occur. The input is the summarized and analyzed data, and the output is the dashboard display and push notification.

[0998] Step 4:

[0999] A user receives a push notification using a smartphone and checks detailed information on a dashboard. Specifically, the user receives the notification through a smartphone application and accesses the dashboard as necessary. The input is the push notification, and the output is the information that the user checks.

[1000] Step 5:

[1001] The server receives user feedback and retrains the data to improve the system performance. Specifically, it collects feedback data from users and updates the machine learning model. The input is the user feedback data and the output is the updated machine learning model.

[1002] Example 2

[1003] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1004] Conventional business strategy planning and decision-making requires the manual collection, summarization, and analysis of vast amounts of information, which takes a great deal of time and effort. In addition, there are issues with human error and the difficulty of obtaining accurate information in a timely manner. This makes it difficult to make quick and accurate decisions, which can reduce the effectiveness of business strategies.

[1005] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1006] In this invention, the server includes a means for collecting and registering data, a means for summarizing and analyzing the data using natural language processing and machine learning techniques, and a means for outputting the summaries and analysis results in a timely manner to a dashboard, email, or a smartphone application, thereby making it possible to quickly and accurately obtain information necessary for planning business strategies and making decisions.

[1007] "Means of collecting and registering data" refers to a function for collecting information necessary for planning business strategies and decision-making, and storing it in a database.

[1008] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[1009] "Machine learning technology" refers to algorithms and methods that allow computers to learn from data and make predictions and classifications.

[1010] "Means of summarizing and analyzing" refers to functions for concisely summarizing collected data and analyzing data patterns and trends.

[1011] A "dashboard" is an interface that allows users to visually check information and displays data using graphs, charts, and the like.

[1012] "Email" is a means of communication for sending and receiving text and files over the Internet.

[1013] A "smartphone application" is software that runs on a smartphone and allows users to check and manipulate information.

[1014] "Means of timely output" refers to a function for providing summaries and analysis results to users in real time.

[1015] The present invention is a system for quickly and accurately providing information required for planning business strategies and decision-making. A specific embodiment of this system will be described below.

[1016] Information collection and registration

[1017] The server collects information necessary for planning business strategies and making decisions, and registers it in a database. Specifically, the information includes market trends, the actions of competitors, the company's own sales data, product quality data, etc. A relational database such as MySQL or PostgreSQL is used as the database.

[1018] Examples:

[1019] The server retrieves the latest market trend reports from market research companies via API and stores them in a MySQL database.

[1020] The server imports the company's sales data, provided in CSV file format, into a PostgreSQL database.

[1021] Summarizing and analyzing information

[1022] The AI ​​uses natural language processing (NLP) and machine learning (ML) techniques to summarize and analyze the registered information, specifically using the Python libraries NLTK and Transformers, and Scikit-learn to analyze trends in the sales data.

[1023] Examples:

[1024] AI can be used to summarise competitive landscape reports and extract key takeaways, for example using Transformers' BERT model to generate summaries.

[1025] The AI ​​will analyze the sales data from the past year to identify trends in sales, for example using a linear regression model from Scikit-learn.

[1026] Output of results

[1027] The server outputs summaries and analysis results in a timely manner. The output format is such that the user can easily check the information, for example, by displaying the information on a dashboard, sending it by email, or notifying it to a smartphone application. The terminal displays the dashboard or application so that the user can check the information.

[1028] Examples:

[1029] The server displays summaries and analysis results in a dashboard that updates in real time, for example by drawing graphs using the JavaScript library D3.js.

[1030] The server then sends important results to management via email, for example using Python's smtplib.

[1031] The device sends a push notification to the smartphone app, for example, by using Firebase Cloud Messaging (FCM).

[1032] Examples of prompt statements

[1033] "Subscribe to our database for the latest market trend reports."

[1034] "Summarize the competitor landscape report and extract key takeaways."

[1035] "Analyze the past year's worth of sales data and identify sales trends."

[1036] "Display summaries and analytics results on a dashboard and email important information to me."

[1037] This system enables users to quickly and accurately obtain the information they need to plan business strategies and make decisions.

[1038] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1039] Step 1:

[1040] Information collection and registration

[1041] The server collects information necessary for planning business strategies and making decisions, and registers it in a database. Input data includes market trends, competitors' actions, the company's own sales data, product quality data, etc. This data is obtained through APIs or provided in CSV file format. The server stores this data in a relational database such as MySQL or PostgreSQL.

[1042] Specific behavior:

[1043] The server retrieves the latest market trend reports from market research companies via API and stores them in a MySQL database.

[1044] The server imports the company's sales data, provided in CSV file format, into a PostgreSQL database.

[1045] Step 2:

[1046] Summarizing and analyzing information

[1047] The AI ​​uses natural language processing (NLP) and machine learning (ML) techniques to summarize and analyze the registered information. The inputs include market trend reports and sales data stored in a database. The AI ​​uses Python libraries NLTK and Transformers to generate summaries and Scikit-learn to analyze trends in the sales data. The output is the summarized reports and analysis results.

[1048] Specific behavior:

[1049] The AI ​​uses Transformers' BERT model to summarize competitor landscape reports and extract key takeaways.

[1050] The AI ​​uses Scikit-learn's linear regression model to analyze sales data from the past year to identify sales trends.

[1051] Step 3:

[1052] Output of results

[1053] The server outputs summaries and analysis results in a timely manner. The input includes summaries and analysis results generated by the AI. The output format is such that users can easily check the information, for example, by displaying it on a dashboard, sending it by email, or notifying it to a smartphone application.

[1054] Specific behavior:

[1055] The server displays summaries and analysis results in a dashboard that updates in real time, for example by drawing graphs using the JavaScript library D3.js.

[1056] The server uses Python's smtplib to email key results to management.

[1057] The device sends push notifications to the smartphone app using Firebase Cloud Messaging (FCM).

[1058] In this way, users can quickly and accurately obtain the information necessary for planning business strategies and making decisions.

[1059] (Application example 2)

[1060] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1061] In conventional manufacturing lines, data collection and analysis was often done manually, which was inefficient and delayed the detection and response of abnormalities. In addition, human error was likely to occur, making it difficult to improve quality control and production efficiency. This caused many problems in the operation of the manufacturing line.

[1062] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1063] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data using AI, a means for outputting the summary and analysis results in a timely manner, a means for collecting various data on the production line, a means for analyzing the collected data and detecting anomalies, and a means for notifying when an anomaly is detected. This makes it possible to improve the efficiency of the production line and quality control.

[1064] A "means for registering data" is any device or software used to store data collected from a production line or other sources in a database.

[1065] "Means of summarizing and analyzing using AI" refers to devices and software that use artificial intelligence technology to summarize and analyze collected data.

[1066] "Means for timely output" refers to devices or software for displaying or notifying analysis results and summary information in real time.

[1067] "Means of collecting various data from the production line" refers to devices and software for collecting data such as production speed, quality score, and machine operating status using sensors and data collection devices installed on the production line.

[1068] The "means for analyzing the collected data and detecting anomalies" refers to a device or software for analyzing the collected data and detecting anomalies.

[1069] The "means for notifying when an abnormality is detected" refers to a device or software for notifying an administrator or relevant parties when an abnormality is detected.

[1070] As an embodiment of the present invention, a "smart manufacturing management system" aimed at improving the efficiency and quality control of a manufacturing line will be described as an example.

[1071] System Overview

[1072] The server collects various data from the production line and registers it in a database. Next, AI is used to summarize and analyze the collected data and detect anomalies. If anomalies are detected or the results of the analysis are sent to the administrator in a timely manner, which makes it possible to improve the efficiency of the production line and quality control.

[1073] Hardware and Software Used

[1074] Data collection devices: Using sensors and data collection devices installed on the production line, data such as production speed, quality scores, and machine operation status is collected in real time.

[1075] Database: Use a database to store the collected data. Specifically, use a relational database such as MySQL or PostgreSQL.

[1076] AI analysis software: To analyze the collected data, we use Python machine learning libraries (e.g., scikit-learn) to build an anomaly detection model and analyze the data.

[1077] Notification system: When an abnormality is detected, an email sending system is used to notify the administrator. Specifically, emails are sent using the smtplib library.

[1078] Processing flow

[1079] 1. Data collection: Collect data from various sensors on the production line and register it in a database.

[1080] 2. Data analysis: Use AI to analyze collected data and detect anomalies.

[1081] 3. Notification: If an abnormality is detected, the administrator will be notified by email.

[1082] Examples

[1083] For example, if the production speed of a manufacturing line suddenly drops and the quality score falls below the standard value, the AI ​​will determine this to be an abnormality and the server will send an email to the administrator stating, "An abnormality has been detected on the manufacturing line. Please check for details."

[1084] Examples of prompt statements

[1085] Below are examples of prompts to be input to the generative AI model.

[1086] Create a program to collect data from the manufacturing line and use AI to detect anomalies. Data includes production speed, quality score, and machine operation status. If an anomaly is detected, notify the factory manager by email.

[1087] In this way, a system can be constructed to achieve efficient production line and quality control.

[1088] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1089] Step 1:

[1090] The server collects data from sensors installed on the production line. As input, it receives real-time data such as production speed, quality score, and machine operation status. These data are sent directly from the sensors to the server through a data collector. The server registers these data in a database. Specifically, the server periodically polls for data from the sensors and stores the acquired data in the database.

[1091] Step 2:

[1092] The server passes the data registered in the database to the AI ​​analysis software. As input, it uses the production speed, quality score, and machine operation status data stored in the database. The AI ​​analysis software uses these data to run an anomaly detection model and determine whether there are any abnormalities. Specifically, the server uses a Python machine learning library (e.g., scikit-learn) to build an anomaly detection model and analyze the data.

[1093] Step 3:

[1094] The server receives the analysis results from the AI ​​analysis software. As input, it receives the output results of the anomaly detection model. If an anomaly is detected based on the analysis results, the server launches a notification system. Specifically, the server evaluates the analysis results and sets a notification flag if an anomaly is detected.

[1095] Step 4:

[1096] The server uses a notification system to notify the administrator of an anomaly. As input, it uses a flag indicating that an anomaly has been detected. The notification system uses an email sending library (e.g. smtplib) to send an email to notify the administrator of the anomaly. Specifically, the server generates the content of the email and sends it to the administrator's email address.

[1097] Step 5:

[1098] The user (administrator) receives the notification from the server and checks the details of the production line. As input, the user receives the abnormality notification email sent from the server. The user checks the status of the production line based on the contents of the email and takes the necessary action. In concrete terms, the user opens the email, checks the details of the abnormality, and accesses the production line monitoring system to check the status.

[1099] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1100] "Example 1"

[1101] In one embodiment of the present invention, the system further includes an emotion engine that recognizes the emotion of the user. The emotion engine recognizes the user's emotional state from, for example, the user's tone of voice, facial expression, text input, and the like. The emotion engine adjusts the analysis and summarization of data based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, it prioritizes summarizing important information and presents it to the user. Also, if the emotion engine recognizes that the user is feeling relaxed, it provides more detailed information. Furthermore, the emotion engine adjusts the output results based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling happy, it displays positive results with emphasis. On the other hand, if the emotion engine recognizes that the user is feeling sad, it displays negative results with mitigation. This makes it possible to provide optimal information according to the user's emotional state.

[1102] "Example 2"

[1103] In one embodiment of the present invention, the system further includes an emotion engine that recognizes the emotion of the user. The emotion engine recognizes the user's emotional state from, for example, the user's tone of voice, facial expression, text input, and the like. The emotion engine adjusts the analysis and summarization of data based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, it prioritizes summarizing important information and presents it to the user. Also, if the emotion engine recognizes that the user is feeling relaxed, it provides more detailed information. Furthermore, the emotion engine adjusts the output results based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling happy, it displays positive results with emphasis. On the other hand, if the emotion engine recognizes that the user is feeling sad, it displays negative results with mitigation. This makes it possible to provide optimal information according to the user's emotional state.

[1104] The process flow of each embodiment will be described below.

[1105] "Example 1"

[1106] Step 1: Receive input from the user (tone of voice, facial expressions, text input, etc.).

[1107] Step 2: The emotion engine recognizes the user's emotional state, which can be, for example, happy, sad, stressed, relaxed, etc.

[1108] Step 3: The emotion engine adjusts the analysis and summarization of the data based on the user's emotional state. For example, if the emotion engine detects that the user is stressed, it will prioritize summarizing and presenting important information to the user. If the emotion engine detects that the user is relaxed, it will provide more detailed information.

[1109] Step 4: The emotion engine adjusts the output results based on the user's emotional state. For example, if the emotion engine detects that the user is feeling happy, it will highlight the positive results. On the other hand, if the emotion engine detects that the user is feeling sad, it will tone down the negative results.

[1110] Example 1

[1111] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1112] Conventional systems have had difficulty efficiently summarizing and analyzing information necessary for business strategy planning and decision-making, and outputting it in a timely manner. In addition, the system does not provide information that takes into account the user's emotional state, so it is not possible to provide optimal information according to the user's stress and emotions. This can impair the speed and accuracy of decision-making.

[1113] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1114] In this invention, the server includes a means for registering data, a means for summarizing and analyzing by artificial intelligence, a means for outputting the summarization and analysis results in a timely manner, a means for recognizing the emotional state of the user, a means for adjusting the analysis and summarization of the data based on the emotional state, and a means for adjusting the output result based on the emotional state. This makes it possible to provide optimal information according to the emotional state of the user, and to improve the speed and accuracy of business strategy planning and decision-making.

[1115] "Means for registering data" refers to a device or software that allows a user to input information necessary for planning business strategies and decision-making, and store it in a database.

[1116] "Means of summarizing and analyzing using artificial intelligence" refers to devices or software for summarizing and analyzing registered information using natural language processing or machine learning techniques.

[1117] "Means for outputting summaries and analysis results in a timely manner" refers to a device or software for quickly providing summaries and analysis results to users, and includes formats such as dashboard display, email transmission, and smartphone notifications.

[1118] The "means for recognizing the emotional state of the user" refers to a device or software for recognizing the emotional state of the user from the tone of voice, facial expression, text input, and the like.

[1119] A "means for adjusting data analysis or summarization based on emotional state" is a device or software for modifying the way data is analyzed or summarized depending on the recognized emotional state of a user.

[1120] A "means for adjusting an output result based on an emotional state" is a device or software for modifying the content or format of information to be output depending on the recognized emotional state of the user.

[1121] The present invention is a system including a means for registering data, a means for summarizing and analyzing using artificial intelligence, a means for outputting the summary and analysis results in a timely manner, a means for recognizing a user's emotional state, a means for adjusting the analysis and summarization of the data based on the emotional state, and a means for adjusting the output result based on the emotional state.

[1122] Data registration

[1123] A user uses a terminal to input information necessary for planning business strategies and making decisions. Specific information includes market trends, the actions of competitors, the company's own sales data, and product quality data. The terminal stores the input information in a database management system (e.g., MySQL or PostgreSQL). For example, a user inputs "Market trend report for Q3 2023" and registers it in the database.

[1124] Summarizing and analyzing information

[1125] The server retrieves information from the database and summarizes and analyzes the information using Python libraries TensorFlow and PyTorch. Specifically, it uses natural language processing (NLP) techniques to analyze text data and extract key points. For example, it extracts key trends from the "Market Trends Report Q3 2023" and generates a summary.

[1126] Output of summary and analysis results

[1127] The server uses Django or Flask as a web application framework to display summaries and analysis results on a dashboard. Users can access the dashboard through their terminals and check the summaries and analysis results. The server can also send the results by email or notify a smartphone application. For example, when a user accesses the dashboard, a summary of the "Market Trends Report Q3 2023" is displayed.

[1128] Emotion engine adjustment

[1129] The server uses Google Cloud Speech-to-Text API and OpenCV to analyze the user's tone of voice and facial expressions to recognize their emotional state. If the emotion engine recognizes that the user is feeling stressed, it prioritizes and summarizes important information and presents it to the user. For example, if the user voice-inputs that they want important information to be displayed first, the emotion engine adjusts the summary according to that instruction. If the server recognizes that the user is relaxed, it provides more detailed information. Furthermore, the emotion engine adjusts the output results based on the user's emotional state. For example, if the server recognizes that the user is feeling happy, it highlights and displays positive results.

[1130] Examples and prompts

[1131] As a concrete example, a user enters a "Market Trend Report for Q3 2023" into the database. The server uses AI to analyze this data and summarizes the main trends. If the emotion engine recognizes that the user is stressed, the server will prioritize displaying the most important trends. If the user is relaxed, it will provide detailed analysis results.

[1132] An example of a prompt is, "Subscribe to our market trend report Q3 2023 and summarize the key trends. Prioritize important information when the user is stressed and provide more detailed information when the user is relaxed."

[1133] The system enables users to make fast and accurate decisions.

[1134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1135] Step 1:

[1136] Users use the terminal to input information necessary for planning business strategies and making decisions. The input information includes market trends, competitors' actions, the company's own sales data, product quality data, etc. The terminal stores this information in a database management system. For example, a user inputs "Market trend report for Q3 2023" and registers it in the database.

[1137] Input: Market trends, competitors' actions, company sales data, product quality data

[1138] Output: Information stored in the database

[1139] Step 2:

[1140] The server retrieves information from the database. The retrieved information is summarized and analyzed using Python libraries TensorFlow and PyTorch. Specifically, natural language processing (NLP) techniques are used to analyze text data and extract key points. For example, key trends are extracted from the "Market Trends Report Q3 2023" and a summary is generated.

[1141] Input: Information retrieved from a database

[1142] Output: Summarized information, analysis results

[1143] Step 3:

[1144] The server uses Django or Flask as a web application framework to display summaries and analysis results on a dashboard. Users can access the dashboard through their terminals and check the summaries and analysis results. The server can also send the results by email or notify a smartphone application. For example, when a user accesses the dashboard, a summary of the "Market Trends Report Q3 2023" is displayed.

[1145] Input: Summarised information, analysis results

[1146] Output: Display on dashboard, send email, notify smartphone

[1147] Step 4:

[1148] The server uses Google Cloud Speech-to-Text API and OpenCV to analyze the user's tone of voice and facial expressions to recognize their emotional state. If the emotion engine recognizes that the user is feeling stressed, it prioritizes important information in a summary and presents it to the user. For example, if the user voice-inputs, "I want important information to be displayed first," the emotion engine adjusts the summary accordingly.

[1149] Input: User tone of voice, facial expressions, and text input

[1150] Output: Perceived emotional state

[1151] Step 5:

[1152] The server adjusts the analysis and summarization of data based on the user's emotional state as recognized by the emotion engine. If the user is stressed, important information is prioritized and summarized and presented to the user. If the user is relaxed, detailed information is provided.

[1153] Input: Perceived emotional state

[1154] Output: Tailored summary and analysis results

[1155] Step 6:

[1156] The server adjusts the output results based on the user's emotional state as recognized by the emotion engine: if the user is happy, positive results are emphasized and displayed, and if the user is sad, negative results are toned down and displayed.

[1157] Input: Perceived emotional state

[1158] Output: Adjusted output result

[1159] (Application example 1)

[1160] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1161] In autonomous vehicles, there is a demand for appropriate recognition of passengers' emotional states and for operational and entertainment information to be provided based on those states. However, conventional systems have had difficulty in recognizing passengers' emotional states in real time and providing information accordingly. There was also a lack of technology to adjust the analysis and summarization of information based on passengers' emotional states. This made it difficult to improve passenger comfort and satisfaction.

[1162] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1163] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data by AI, a means for timely outputting the summary and analysis results, a means for recognizing the user's emotions, a means for adjusting the analysis and summarization of the data based on the emotional state, a means for adjusting the output result based on the emotional state, and a means for recognizing the emotional state of passengers using a camera or microphone in the vehicle and providing operation information and entertainment information based on the emotional state. This makes it possible to provide optimal information according to the emotional state of passengers, thereby improving passenger comfort and satisfaction.

[1164] "Means for registering data" refers to a function for saving information required for the system in a database.

[1165] "Means of summarizing and analyzing using AI" refers to a function for summarizing and analyzing registered data using artificial intelligence technology.

[1166] "Means for timely output" refers to a function for quickly providing analysis results and summary information to users.

[1167] "Means for recognizing user emotions" refers to a function that uses sensors such as a camera and microphone to analyze the user's facial expressions and tone of voice to identify their emotional state.

[1168] The "means for adjusting data analysis and summarization based on the emotional state" is a function for dynamically changing the data analysis method and summarization content according to the recognized emotional state of the user.

[1169] The "means for adjusting the output result based on the emotional state" is a function for changing the format and contents of the information to be output in accordance with the emotional state of the user.

[1170] "Means for recognizing passengers' emotional states using cameras and microphones in the vehicle" refers to a function that uses cameras and microphones installed in the autonomous vehicle to analyze passengers' facial expressions and tone of voice to identify their emotional states.

[1171] The "means for providing operational information and entertainment information" is a function for providing operational information and entertainment content based on the emotional state of passengers.

[1172] The system for carrying out this invention is configured as follows. First, the server has a means for registering data and stores the necessary information in a database. Next, the server has a means for summarizing and analyzing the registered data using artificial intelligence (AI) technology. Furthermore, the server has a means for outputting the analysis results and summary information in a timely manner.

[1173] To recognize the user's emotions, cameras and microphones are installed in the vehicle. These sensors are used to analyze the user's facial expressions and tone of voice to identify the user's emotional state. The server has the means to dynamically change the data analysis method and summary content based on the recognized emotional state. It also has the means to adjust the output results based on the emotional state.

[1174] Specifically, cameras in the vehicle capture passengers' facial expressions using OpenCV and identify their emotions using emotion recognition models (e.g., models using Keras). A microphone captures audio using a speech recognition library and analyzes the emotion of the audio using TextBlob. This allows the system to provide relaxing music if the passenger is stressed, or detailed trip and entertainment information if the passenger is relaxed.

[1175] As a concrete example, consider the following scenario: If the passenger is stressed, the system will play relaxing music. If the passenger is relaxed, the system will provide detailed travel information. In this way, optimal information can be provided according to the passenger's emotional state, improving passenger comfort and satisfaction.

[1176] Examples of prompts to be input to a generative AI model include the following:

[1177] "Create an application that plays relaxing music if passengers are feeling stressed. Include a feature that uses the camera and microphone to recognize emotions and provide appropriate information."

[1178] In this way, the form for carrying out the invention can be concretely shown.

[1179] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1180] Step 1:

[1181] The server captures video data from the camera in the vehicle. The input is the real-time video from the camera, and the output is the captured image frames. Specifically, we use OpenCV to acquire the video data and perform face detection.

[1182] Step 2:

[1183] The server uses the captured image frames to analyze the passengers' facial expressions. The input is the image frame obtained in step 1, and the output is the passenger's emotional state. Specifically, the image data is input to an emotion recognition model using Keras to identify the emotion.

[1184] Step 3:

[1185] The server captures voice data from a microphone in the vehicle. The input is real-time voice from the microphone, and the output is the captured voice data. Specifically, the server obtains the voice data using a voice recognition library.

[1186] Step 4:

[1187] The server uses the captured voice data to analyze the passenger's voice for emotion. The input is the voice data obtained in step 3, and the output is the emotional state of the voice. Specifically, it uses TextBlob to analyze the voice data and identify the emotion.

[1188] Step 5:

[1189] The server integrates the emotional states obtained in steps 2 and 4 to determine the final emotional state of the passenger. The inputs are the facial expression analysis results and the voice analysis results, and the output is the integrated emotional state. Specifically, both emotional states are compared, and the stronger emotion is determined as the final emotional state.

[1190] Step 6:

[1191] The server provides appropriate information based on the final emotional state. The input is the integrated emotional state, and the output is the information provided (e.g., relaxing music, detailed travel information, entertainment information). Specifically, if the emotional state is stressed, relaxing music is played, and if relaxed, detailed travel information is provided.

[1192] Step 7:

[1193] The server presents the provided information to passengers through displays and speakers in the vehicle. The input is the provided information, and the output is the information presented to passengers. Specifically, the server displays trip information on the display and plays music from the speaker.

[1194] In this way, the server can provide optimal information according to the passenger's emotional state, thereby improving passenger comfort and satisfaction.

[1195] Example 2

[1196] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1197] Conventional business strategy planning and decision-making requires the manual collection and analysis of huge amounts of data, which is time-consuming and labor-intensive and prone to human error. In addition, information is provided without taking into account the user's emotional state, making it difficult to provide optimal information according to the user's stress and emotions.

[1198] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1199] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data by artificial intelligence, a means for outputting the summaries and analysis results in a timely manner, a means for recognizing a user's emotions, a means for adjusting the analysis and summarization of the data based on the emotions, and a means for adjusting the output results based on the emotions, thereby enabling not only quick and accurate decision-making but also the provision of optimal information according to the user's emotional state.

[1200] The "means for registering data" refers to an interface that allows users to input information necessary for planning business strategies and decision-making, and transmit it to the server.

[1201] "Artificial intelligence" is a program or system that uses natural language processing and machine learning techniques to summarize and analyze input data.

[1202] "Means of summarization and analysis" refers to the process of using artificial intelligence to summarize input data and generate analytical results.

[1203] "Means for timely output" refers to a method for providing the generated summary and analysis results to the user in real time, and includes displaying a dashboard, sending an email, or sending a smartphone notification.

[1204] "Means for recognizing a user's emotions" refers to technology for detecting an emotional state from a user's tone of voice, facial expression, text input, etc., and includes voice recognition and facial expression recognition.

[1205] "Means for tailoring data analysis and summarization based on emotion" refers to a process that prioritizes summarizing important information or providing detailed information depending on the user's emotional state.

[1206] "Emotionally tailored output" refers to a process that emphasizes positive outcomes or mitigates negative outcomes depending on the user's emotional state.

[1207] This invention is a system including a means for registering data, a means for summarizing and analyzing using artificial intelligence, a means for outputting the summary and analysis results in a timely manner, a means for recognizing a user's emotions, a means for adjusting the analysis and summarization of the data based on the emotions, and a means for adjusting the output results based on the emotions.

[1208] Data registration

[1209] Users use a dedicated input form to input information necessary for planning business strategies and making decisions. For example, they input market trends, the actions of competitors, their own company's sales data, product quality data, etc. The terminal sends the information entered by the user to the server. The HTTPS protocol is used for transmission to ensure the security of the information.

[1210] Summarizing and analyzing information

[1211] The server stores the received information in a database. For example, MySQL or PostgreSQL is used for the database. The server passes the information stored in the database to an artificial intelligence. The artificial intelligence uses natural language processing (NLP) and machine learning (ML) techniques to summarize the information and generate analysis results. For example, the Python libraries NLTK and TensorFlow are used.

[1212] Summary and analysis output

[1213] The server outputs summaries and analysis results generated by the AI ​​in a timely manner. The output format is such that users can easily check the information, such as displaying it on a dashboard, sending it by email, or notifying a smartphone application. For example, React.js is used for the front end of the web application, and Node.js is used for the back end.

[1214] Emotion engine adjustment

[1215] The terminal recognizes the user's emotional state from the user's tone of voice, facial expressions, text input, etc. For example, Google Cloud Speech-to-Text API is used for voice recognition, and OpenCV is used for facial expression recognition. The server uses an emotion engine to adjust data analysis and summarization based on the user's emotional state. For example, if the server recognizes that the user is feeling stressed, it prioritizes summarizing important information and presents it to the user. Also, if the server recognizes that the user is relaxed, it provides more detailed information. The server adjusts the output results based on the user's emotional state. For example, if the server recognizes that the user is feeling happy, it emphasizes and displays positive results. On the other hand, if the server recognizes that the user is feeling sad, it displays and mitigates negative results.

[1216] Examples

[1217] For example, consider a case where a user wants to input the latest data on market trends and analyze the movements of competitors. The user inputs the market trend data into a dedicated input form, and the device sends the information to the server. The server stores the information in a database and passes it on to the artificial intelligence. The artificial intelligence summarizes the market trends and analyzes the movements of competitors. The analysis results are displayed in real time on the dashboard. If the emotion engine recognizes that the user is feeling stressed, important trend information will be displayed with priority.

[1218] Examples of prompt statements

[1219] "Enter the latest data on market trends and analyze competitors' moves. Prioritize the most important information, especially when users are stressed."

[1220] This system not only enables users to make quick and accurate decisions, but also provides optimal information according to the user's emotional state.

[1221] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1222] Step 1:

[1223] The user enters the information.

[1224] Users use a dedicated input form to input information necessary for planning business strategies and making decisions. The information input includes market trends, competitors' actions, the company's own sales data, product quality data, etc. The input information is then sent to the terminal.

[1225] Step 2:

[1226] The terminal transmits the information to the server.

[1227] The terminal transmits the information entered by the user to the server using the HTTPS protocol, which ensures the security of the information. The input data reaches the server.

[1228] Step 3:

[1229] The server stores the information in a database.

[1230] The server stores the received information in a database, for example, MySQL or PostgreSQL. The stored data is used for subsequent processing.

[1231] Step 4:

[1232] The server passes the information to the artificial intelligence.

[1233] The server passes the information stored in the database to the AI, which uses natural language processing (NLP) and machine learning (ML) techniques to summarize and analyze the information. The input data is processed by the AI.

[1234] Step 5:

[1235] Artificial intelligence summarizes and analyzes the information.

[1236] Artificial intelligence summarizes the information it receives and generates analytical results. For example, it uses the Python library NLTK or TensorFlow to analyze text data. Summarized information and analytical results are generated.

[1237] Step 6:

[1238] The server outputs the analysis results.

[1239] The server outputs summaries and analysis results generated by the AI ​​in a timely manner. The output format is such that the user can easily check the information, for example, by displaying it on a dashboard, sending it by email, or notifying it via a smartphone application. The output data is provided to the user.

[1240] Step 7:

[1241] The terminal recognizes the user's emotions.

[1242] The device recognizes the user's emotional state from the user's tone of voice, facial expressions, text input, etc. For example, it uses Google Cloud Speech-to-Text API for voice recognition and OpenCV for facial expression recognition. The input data is processed by the emotion recognition engine.

[1243] Step 8:

[1244] The server uses the emotion engine to refine the analysis results.

[1245] The server uses the emotion engine to adjust the analysis and summarization of data based on the user's emotional state. For example, if the server recognizes that the user is stressed, it will prioritize summarizing and presenting important information to the user. If the server recognizes that the user is relaxed, it will provide more detailed information. The adjusted data is then provided to the user.

[1246] Step 9:

[1247] The server displays the output according to the emotion.

[1248] The server adjusts the output results based on the user's emotional state. For example, if the server recognizes that the user is feeling happy, it emphasizes the positive results. On the other hand, if the server recognizes that the user is feeling sad, it softens the negative results. The final output data is provided to the user.

[1249] (Application example 2)

[1250] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1251] Conventional systems make it difficult to obtain the information necessary for planning business strategies and making decisions in a timely manner, and real-time information acquisition is required especially in the operation of physical stores. In addition, since information is not provided according to the user's emotional state, there is a problem that appropriate information cannot be provided according to stress or relaxation state. Furthermore, there is a lack of notification functions using smartphones and smart glasses, making it difficult to check information immediately.

[1252] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1253] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data by AI, a means for outputting the summary and analysis results in a timely manner, an emotion engine for recognizing the emotional state of the user, and a means for adjusting the output results based on the emotion engine. This makes it possible to obtain information necessary for planning business strategies and decision-making in real time, and to provide appropriate information according to the emotional state of the user. In addition, a notification function using a smartphone or smart glasses makes it possible to check information immediately.

[1254] "Means for registering data" refers to a function for saving information required for the system in a database.

[1255] "Means of summarizing and analyzing using AI" refers to a function for summarizing and analyzing registered data using artificial intelligence.

[1256] "Means for timely output" refers to a function for providing analysis results to the user in real time.

[1257] The "emotion engine that recognizes the user's emotional state" is a function for recognizing emotions from the user's tone of voice, facial expressions, text input, etc.

[1258] The "means for adjusting the output result based on the emotion engine" is a function for adjusting the content and format of the information output according to the recognized emotional state.

[1259] "Reference information for formulating business strategies and making decisions" is information that is necessary when formulating a company's business strategies and making decisions.

[1260] "The effect of reducing the time required for reporting tasks and the occurrence of human errors" refers to the effect of shortening the time required for reporting tasks and reducing human error.

[1261] "Information necessary for running a physical store" refers to various information necessary for running a store, such as store sales data, inventory information, and customer feedback.

[1262] "Means of notifying a smartphone or smart glasses" refers to a function for notifying a smartphone or smart glasses of analysis results and important information.

[1263] A system for implementing the present invention includes means for registering data, means for summarizing and analyzing using AI, means for outputting in a timely manner, an emotion engine for recognizing the emotional state of a user, and means for adjusting the output results based on the emotion engine.

[1264] System Configuration

[1265] 1. How to register data

[1266] The server registers information such as store sales data, inventory information, and customer feedback in a database. This database uses a relational database such as SQLite.

[1267] 2. AI-based methods for summarizing and analyzing

[1268] The server uses AI models to summarize and analyze the registered data. Specifically, it uses the Transformers library to perform natural language processing. For example, it uses generative AI models such as BERT and GPT-3 to summarize customer feedback.

[1269] 3. Means of timely output

[1270] The server provides the analysis results to the user in real time. The output format can be displayed on a dashboard or sent to a smartphone or smart glasses, allowing the user to check the information instantly.

[1271] 4. Emotion engine that recognizes the user's emotional state

[1272] The server uses an emotion recognition library to recognize emotions from the user's tone of voice, facial expressions, text input, etc. This allows the server to grasp the user's emotional state in real time.

[1273] 5. A way to adjust the output based on the emotion engine

[1274] The server then adjusts the content and format of the information output depending on the user's recognized emotional state, for example prioritizing important information if the user is stressed, and providing more detailed information if the user is relaxed.

[1275] Examples

[1276] For example, if a store staff member is feeling stressed, the system will prioritize displaying "important information." Conversely, if the staff member is feeling relaxed, the system will provide detailed information. In this way, it is possible to provide optimal information according to the user's emotional state.

[1277] Examples of prompt statements

[1278] Here are some example prompts to feed to a generative AI model:

[1279] Summarise your store's sales data and customer feedback and tailor the output based on your staff's emotional state - prioritise important information if your staff is stressed, or provide more detailed information if they're relaxed.

[1280] In this way, a smart store assistant system that supports the operation of physical stores can be realized.

[1281] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1282] Step 1:

[1283] The server registers information such as store sales data, inventory information, and customer feedback in a database. As input, various data collected from the stores is stored in a relational database such as SQLite. As output, the information registered in the database is obtained.

[1284] Step 2:

[1285] The server summarizes and analyzes the registered data using an AI model. The inputs include sales data and customer feedback obtained from the database, which are then processed using the Transformers library for natural language processing. Specifically, the data is summarized using generative AI models such as BERT and GPT-3. The output is summarized text and analysis results.

[1286] Step 3:

[1287] The server provides the analysis results to the user in real time. The inputs are the summary and analysis results obtained in step 2, which are displayed on a dashboard or sent to a smartphone or smart glasses. The output is information that the user can check immediately.

[1288] Step 4:

[1289] The server uses an emotion recognition library to recognize emotions from the user's tone of voice, facial expressions, text input, etc. The inputs are the user's voice data, facial expression data, and text data, which are analyzed to recognize the user's emotional state. The output is the user's emotional state.

[1290] Step 5:

[1291] The server adjusts the content and format of the information output according to the recognized emotional state. The inputs are the emotional state obtained in step 4 and the summary and analysis results obtained in step 2, and the priority and level of detail of the information are adjusted based on this. Specifically, if the user is feeling stressed, important information is displayed first, and if the user is relaxed, detailed information is provided. The adjusted information is obtained as the output.

[1292] Step 6:

[1293] The server notifies the smartphone or smart glasses of the adjusted information. As input, the adjusted information from step 5 is sent to the user's device. As output, a notification is displayed on the user's smartphone or smart glasses.

[1294] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1295] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1296] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1297] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1298] [Fourth embodiment]

[1299] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1300] 7, a 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.

[1301] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[1302] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1303] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[1304] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1305] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1306] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1307] Fig. 8 shows an example of main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1308] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1309] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1310] In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1311] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1312] "Example 1"

[1313] The system of the present invention includes a means for registering information in a database, a means for summarizing and analyzing information by AI, and a means for outputting the summary and analysis results in a timely manner. Specifically, information necessary for planning business strategies and decision-making is registered in a database. This information is, for example, various information necessary for planning business strategies and decision-making, such as market trends, competitors' movements, one's own sales data, and product quality data. Next, the AI ​​summarizes and analyzes this information. The AI ​​summarizes and analyzes the information using natural language processing and machine learning techniques. Then, the summary and analysis results are output in a timely manner. The output format is, for example, displayed on a dashboard, sent by email, or notified to a smartphone application, so that the user can easily check the information. This makes it possible to make quick and accurate decisions without missing the timing for planning business strategies and decision-making.

[1314] "Example 2"

[1315] The system of the present invention includes a means for registering information in a database, a means for summarizing and analyzing information by AI, and a means for outputting the summary and analysis results in a timely manner. Specifically, information necessary for planning business strategies and decision-making is registered in a database. This information is, for example, various information necessary for planning business strategies and decision-making, such as market trends, competitors' movements, one's own sales data, and product quality data. Next, the AI ​​summarizes and analyzes this information. The AI ​​summarizes and analyzes the information using natural language processing and machine learning techniques. Then, the summary and analysis results are output in a timely manner. The output format is, for example, displayed on a dashboard, sent by email, or notified to a smartphone application, so that the user can easily check the information. This makes it possible to make quick and accurate decisions without missing the timing for planning business strategies and decision-making.

[1316] The process flow of each embodiment will be described below.

[1317] "Example 1"

[1318] Step 1: Register the information necessary for planning business strategies and making decisions in a database. This information includes various types of information necessary for planning business strategies and making decisions, such as market trends, the actions of competitors, your own sales data, and product quality data.

[1319] Step 2: AI summarizes and analyzes the information. AI uses natural language processing and machine learning techniques to summarize and analyze the information.

[1320] Step 3: Output summaries and analysis results in a timely manner. The output format is one in which the user can easily check the information, for example, by displaying it on a dashboard, sending it by email, or notifying it via a smartphone application. This makes it possible to make quick and accurate decisions without missing the timing for formulating business strategies and making decisions.

[1321] Example 1

[1322] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1323] Conventional business strategy planning and decision-making requires the manual collection and analysis of vast amounts of information, which is not only time-consuming and labor-intensive, but also carries a high risk of human error. In addition, it is difficult to obtain information in a timely manner and make decisions quickly. This can lead to a decrease in the accuracy of business strategy planning and decision-making, and a loss of competitiveness.

[1324] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1325] In this invention, the server includes a means for registering information, a means for summarizing and analyzing the information by artificial intelligence, a means for outputting the summary and analysis results in a timely manner, a means for storing the information in a database, and a means for displaying the summary and analysis results on a dashboard, sending them by email, or notifying them to a smartphone application. This automates the collection and analysis of information, making it possible to obtain summaries and analysis results in a timely manner. This improves the accuracy of business strategy planning and decision-making, enabling rapid and accurate decision-making.

[1326] The "means for registering information" refers to an interface that allows a user to input information necessary for planning business strategies and decision-making, and to store the information in a database.

[1327] "Means of summarizing and analyzing using artificial intelligence" refers to a system for summarizing and analyzing registered information using natural language processing and machine learning techniques.

[1328] "Means of timely output" refers to a method for quickly providing summaries and analysis results to users, including displaying them on a dashboard, sending emails, or sending notifications to smartphone applications.

[1329] "Means of storing in a database" refers to a system for efficiently managing and storing information entered by users, including relational databases, etc.

[1330] The "means for displaying on the dashboard" refers to an interface for visually providing summaries and analysis results to users, and has the function of updating information in real time.

[1331] "Means for sending by e-mail" refers to a system for sending summaries and analysis results to users as e-mails.

[1332] "Means for notifying smartphone applications" refers to a system for notifying users of summaries and analysis results via smartphone applications.

[1333] This invention is a system for supporting business strategy planning and decision-making, and aims to register information, summarize and analyze it using artificial intelligence, and output the results in a timely manner. A specific embodiment of this system will be described below.

[1334] Registering information

[1335] Users use a dedicated input form to input information necessary for planning business strategies and making decisions. This information includes market trends, competitors' movements, the company's own sales data, product quality data, etc. The input information is sent from the terminal to the server. Specifically, the data is sent using an HTTP POST request.

[1336] Saving to a database

[1337] The server stores the received information in a database. Relational database management systems (RDBMS) such as MySQL and PostgreSQL are used for the database. This allows information to be managed and stored efficiently.

[1338] Summarizing and analyzing information

[1339] The server periodically retrieves newly registered information from the database. The retrieved information is passed to artificial intelligence (AI). The AI ​​uses Python libraries NLTK and spaCy to process natural language, and builds machine learning models using TensorFlow and PyTorch. This allows the information to be summarized and analyzed.

[1340] Summary and analysis output

[1341] The server retrieves the summaries and analysis results generated by the AI ​​and provides them to the user in a timely manner. The output formats are as follows:

[1342] Display on a dashboard: Use React.js to update information in real time and provide it visually to the user.

[1343] Send via email: Send the summary or analysis results as email using an SMTP server.

[1344] Notify an application on a smartphone: Send a push notification using Firebase Cloud Messaging (FCM).

[1345] Examples

[1346] Example 1: Market Trend Summary

[1347] 1. A user enters the latest market trend report into a web form.

[1348] 2. The terminal sends the entered report to the server.

[1349] 3. The server saves the report in a database.

[1350] 4. The server passes the saved report to the AI.

[1351] 5. AI extracts key points from the report and generates a summary.

[1352] 6. The server displays the generated summary on a dashboard.

[1353] 7. Users see summaries on the dashboard and make quick decisions.

[1354] Example 2: Analyzing competitors' activities

[1355] 1. A user fills out a web form with information about a competitor's new product launch.

[1356] 2. The terminal sends the entered information to the server.

[1357] 3. The server stores the information in a database.

[1358] 4. The server passes the stored information to the AI.

[1359] 5. AI uses machine learning models to predict how competitor moves will impact your business.

[1360] 6. The server sends the analysis results to the user via email.

[1361] 7. Users can check the analysis results by email and make decisions quickly.

[1362] Examples of prompt statements

[1363] Below are examples of prompts to be input to the generative AI model.

[1364] "Summarise the latest market trends report. Extract the key points and provide a concise summary."

[1365]

[1366] "Analyze information about your competitors' new product launches and predict how they will affect your company."

[1367] In this way, users are able to make fast and accurate decisions.

[1368] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1369] Step 1: Enter and submit information

[1370] The user uses a dedicated input form to input information necessary for planning business strategies and making decisions. The input information includes market trends, competitors' actions, the company's own sales data, product quality data, etc. The terminal sends the input information to the server using an HTTP POST request. The input data is in the form of text, numeric data, etc.

[1371] Step 2: Saving to the Database

[1372] The server stores the received information in a database. Specifically, it uses a relational database such as MySQL or PostgreSQL to efficiently manage and store information. The server inserts the received data into the appropriate table and creates indexes as necessary. The input data is stored in the database table, and the output is a notification that the data has been saved to the database.

[1373] Step 3: Get the information and pass it on to the AI

[1374] The server periodically retrieves new registered information from the database. The retrieved information is passed to the AI. The server uses SQL queries to extract new data and converts it into a data format to be passed to the AI. The input data is the new information retrieved from the database, and the output is the data passed to the AI.

[1375] Step 4: Summarize and analyze the information

[1376] The AI ​​summarizes and analyzes the information passed from the server. Specifically, it uses Python libraries NLTK and spaCy to perform natural language processing, and builds machine learning models using TensorFlow and PyTorch. The AI ​​analyzes the input data, extracts important points, and generates a summary. The input data is the information passed from the server, and the output is the summary or analysis results.

[1377] Step 5: Obtaining summary and analysis results

[1378] The server retrieves the summary and analysis results generated by the AI. The server receives the output data from the AI ​​and formats it as necessary. The input data is the summary and analysis results from the AI, and the output is the formatted data to provide to the user.

[1379] Step 6: Output summary and analysis results

[1380] The server provides formatted summaries and analysis results to the user in the following ways:

[1381] Display on a dashboard: Use React.js to update information in real time and provide it visually to the user.

[1382] Send via email: Send the summary or analysis results as email using an SMTP server.

[1383] Notify an application on a smartphone: Send a push notification using Firebase Cloud Messaging (FCM).

[1384] The input data is a formatted summary or analysis result, and the output is the information provided to the user.

[1385] Step 7: User review and decision making

[1386] The user checks the provided summary and analysis results and makes a decision quickly. The user checks the information through a dashboard, email, or smartphone notification and takes the necessary action. The input data is the summary and analysis results provided by the server, and the output is the user's decision.

[1387] (Application example 1)

[1388] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1389] Logistics centers are required to grasp information such as inventory status, delivery status, and staff work efficiency in real time, and to make quick and accurate decisions. However, manually collecting and analyzing this information takes time and effort, and there is a high possibility of human error. In addition, there is an issue that it is difficult to respond immediately when important information or abnormalities occur.

[1390] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1391] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data by AI, a means for outputting the summary and analysis results in a timely manner, a means for collecting information such as inventory status, delivery status, and staff work efficiency at the logistics center, and a means for summarizing and analyzing the collected information and notifying important information or abnormalities when they occur. This enables the operation manager of the logistics center to grasp information in real time and make quick and accurate decisions.

[1392] "Means of registering data" refers to a function for saving information such as inventory status, delivery status, and staff work efficiency at the logistics center in a database.

[1393] "Means of summarizing and analyzing using AI" refers to a function for summarizing and analyzing registered data using artificial intelligence technology.

[1394] "Means of timely output" refers to a function for displaying summaries and analysis results in real time and notifying users as necessary.

[1395] "A means of collecting information such as inventory status, delivery status, and staff work efficiency at the logistics center" is a function for automatically collecting various data within the logistics center.

[1396] "Means of summarizing and analyzing collected information and notifying when important information or anomalies occur" refers to a function for summarizing and analyzing collected data and notifying an administrator when important information or anomalies are detected.

[1397] The system for carrying out this invention collects information such as inventory status, delivery status, and staff work efficiency in a logistics center in real time, summarizes and analyzes it using AI, and outputs the results in a timely manner. A specific embodiment of this system is described below.

[1398] 1. System Programming

[1399] The server includes a means for registering data, a means for summarizing and analyzing using AI, a means for outputting in a timely manner, a means for collecting information such as inventory status at the logistics center, delivery status, and staff work efficiency, and a means for summarizing and analyzing the collected information and notifying of important information or the occurrence of abnormalities.

[1400] 2. Program Processing

[1401] The server first registers various data from the logistics center in a database. This includes inventory status, delivery status, and staff work efficiency. Next, AI is used to summarize and analyze this data. AI uses natural language processing and machine learning techniques to summarize the data and detect anomalies. The analysis results are displayed on a dashboard in real time, and if important information or anomalies occur, a push notification is sent to the administrator's smartphone.

[1402] The implementation of this system uses the following hardware and software:

[1403] Hardware: Servers, smartphones

[1404] Software: SQLite (database management system), Python (programming language), scikit-learn (machine learning library), pandas (data analysis library), smtplib (email sending library)

[1405] 3. Specific Examples

[1406] For example, assume that the inventory data for a logistics center is as follows:

[1407] id | info

[1408] 1 | Product A is running low in stock.

[1409] 2 | Product B is in excess stock.

[1410] 3 | Product C has adequate stock.

[1411] By summarizing and analyzing this data, the following clustering results are obtained:

[1412] cluster | info

[1413] 0 | Product A is low in stock.

[1414] 1 | Product B is in excess stock.

[1415] 2 | Product C has adequate stock.

[1416] This will be notified to the administrator via email.

[1417] Example prompts to be input to the generative AI model

[1418] Create a program that summarizes and analyzes inventory data from a distribution center and notifies the manager of important information. The program retrieves data from the database, summarizes and analyzes the data using TF-IDF vectorization and KMeans clustering, and notifies the manager of the results by email.

[1419] In this way, logistics center operators and managers can get real-time information and make fast and accurate decisions.

[1420] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1421] Step 1:

[1422] The server collects information such as inventory status at the logistics center, delivery status, and staff work efficiency. Specifically, it obtains data from various sensors and management systems and registers it in a database. The input is data from the various sensors and management systems, and the output is information stored in the database.

[1423] Step 2:

[1424] The server uses AI to summarize and analyze the information registered in the database. Specifically, it uses natural language processing technology to convert the data into TF-IDF vectors and performs KMeans clustering. The input is information obtained from the database, and the output is the summarized and analyzed data.

[1425] Step 3:

[1426] The server outputs the summary and analysis results in a timely manner. Specifically, the analysis results are displayed on a dashboard, and a push notification is sent to the administrator's smartphone when important information or anomalies occur. The input is the summarized and analyzed data, and the output is the dashboard display and push notification.

[1427] Step 4:

[1428] A user receives a push notification using a smartphone and checks detailed information on a dashboard. Specifically, the user receives the notification through a smartphone application and accesses the dashboard as necessary. The input is the push notification, and the output is the information that the user checks.

[1429] Step 5:

[1430] The server receives user feedback and retrains the data to improve the system performance. Specifically, it collects feedback data from users and updates the machine learning model. The input is the user feedback data and the output is the updated machine learning model.

[1431] Example 2

[1432] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1433] Conventional business strategy planning and decision-making requires the manual collection, summarization, and analysis of vast amounts of information, which takes a great deal of time and effort. In addition, there are issues with human error and the difficulty of obtaining accurate information in a timely manner. This makes it difficult to make quick and accurate decisions, which can reduce the effectiveness of business strategies.

[1434] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1435] In this invention, the server includes a means for collecting and registering data, a means for summarizing and analyzing the data using natural language processing and machine learning techniques, and a means for outputting the summaries and analysis results in a timely manner to a dashboard, email, or a smartphone application, thereby making it possible to quickly and accurately obtain information necessary for planning business strategies and making decisions.

[1436] "Means of collecting and registering data" refers to a function for collecting information necessary for planning business strategies and decision-making, and storing it in a database.

[1437] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[1438] "Machine learning technology" refers to algorithms and methods that allow computers to learn from data and make predictions and classifications.

[1439] "Means of summarizing and analyzing" refers to functions for concisely summarizing collected data and analyzing data patterns and trends.

[1440] A "dashboard" is an interface that allows users to visually check information and displays data using graphs, charts, and the like.

[1441] "Email" is a means of communication for sending and receiving text and files over the Internet.

[1442] A "smartphone application" is software that runs on a smartphone and allows users to check and manipulate information.

[1443] "Means of timely output" refers to a function for providing summaries and analysis results to users in real time.

[1444] The present invention is a system for quickly and accurately providing information required for planning business strategies and decision-making. A specific embodiment of this system will be described below.

[1445] Information collection and registration

[1446] The server collects information necessary for planning business strategies and making decisions, and registers it in a database. Specifically, the information includes market trends, the actions of competitors, the company's own sales data, product quality data, etc. A relational database such as MySQL or PostgreSQL is used as the database.

[1447] Examples:

[1448] The server retrieves the latest market trend reports from market research companies via API and stores them in a MySQL database.

[1449] The server imports the company's sales data, provided in CSV file format, into a PostgreSQL database.

[1450] Summarizing and analyzing information

[1451] The AI ​​uses natural language processing (NLP) and machine learning (ML) techniques to summarize and analyze the registered information, specifically using the Python libraries NLTK and Transformers, and Scikit-learn to analyze trends in the sales data.

[1452] Examples:

[1453] AI can be used to summarise competitive landscape reports and extract key takeaways, for example using Transformers' BERT model to generate summaries.

[1454] The AI ​​will analyze the sales data from the past year to identify trends in sales, for example using a linear regression model from Scikit-learn.

[1455] Output of results

[1456] The server outputs summaries and analysis results in a timely manner. The output format is such that the user can easily check the information, for example, by displaying the information on a dashboard, sending it by email, or notifying it to a smartphone application. The terminal displays the dashboard or application so that the user can check the information.

[1457] Examples:

[1458] The server displays summaries and analysis results in a dashboard that updates in real time, for example by drawing graphs using the JavaScript library D3.js.

[1459] The server then sends important results to management via email, for example using Python's smtplib.

[1460] The device sends a push notification to the smartphone app, for example, by using Firebase Cloud Messaging (FCM).

[1461] Examples of prompt statements

[1462] "Subscribe to our database for the latest market trend reports."

[1463] "Summarize the competitor landscape report and extract key takeaways."

[1464] "Analyze the past year's worth of sales data and identify sales trends."

[1465] "Display summaries and analytics results on a dashboard and email important information to me."

[1466] This system enables users to quickly and accurately obtain the information they need to plan business strategies and make decisions.

[1467] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1468] Step 1:

[1469] Information collection and registration

[1470] The server collects information necessary for planning business strategies and making decisions, and registers it in a database. Input data includes market trends, competitors' actions, the company's own sales data, product quality data, etc. This data is obtained through APIs or provided in CSV file format. The server stores this data in a relational database such as MySQL or PostgreSQL.

[1471] Specific behavior:

[1472] The server retrieves the latest market trend reports from market research companies via API and stores them in a MySQL database.

[1473] The server imports the company's sales data, provided in CSV file format, into a PostgreSQL database.

[1474] Step 2:

[1475] Summarizing and analyzing information

[1476] The AI ​​uses natural language processing (NLP) and machine learning (ML) techniques to summarize and analyze the registered information. The inputs include market trend reports and sales data stored in a database. The AI ​​uses Python libraries NLTK and Transformers to generate summaries and Scikit-learn to analyze trends in the sales data. The output is the summarized reports and analysis results.

[1477] Specific behavior:

[1478] The AI ​​uses Transformers' BERT model to summarize competitor landscape reports and extract key takeaways.

[1479] The AI ​​uses Scikit-learn's linear regression model to analyze sales data from the past year to identify sales trends.

[1480] Step 3:

[1481] Output of results

[1482] The server outputs summaries and analysis results in a timely manner. The input includes summaries and analysis results generated by the AI. The output format is such that users can easily check the information, for example, by displaying it on a dashboard, sending it by email, or notifying it to a smartphone application.

[1483] Specific behavior:

[1484] The server displays summaries and analysis results in a dashboard that updates in real time, for example by drawing graphs using the JavaScript library D3.js.

[1485] The server uses Python's smtplib to email key results to management.

[1486] The device sends push notifications to the smartphone app using Firebase Cloud Messaging (FCM).

[1487] In this way, users can quickly and accurately obtain the information necessary for planning business strategies and making decisions.

[1488] (Application example 2)

[1489] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1490] In conventional manufacturing lines, data collection and analysis was often done manually, which was inefficient and delayed the detection and response of abnormalities. In addition, human error was likely to occur, making it difficult to improve quality control and production efficiency. This caused many problems in the operation of the manufacturing line.

[1491] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1492] In this invention, the server includes a means for registering data, a means for summarizing and analyzing the data using AI, a means for outputting the summary and analysis results in a timely manner, a means for collecting various data on the production line, a means for analyzing the collected data and detecting anomalies, and a means for notifying when an anomaly is detected. This makes it possible to improve the efficiency of the production line and quality control.

[1493] A "means for registering data" is any device or software used to store data collected from a production line or other sources in a database.

[1494] "Means of summarizing and analyzing using AI" refers to devices and software that use artificial intelligence technology to summarize and analyze collected data.

[1495] "Means for timely output" refers to devices or software for displaying or notifying analysis results and summary information in real time.

[1496] "Means of collecting various data from the production line" refers to devices and software for collecting data such as production speed, quality score, and machine operating status using sensors and data collection devices installed on the production line.

[1497] The "means for analyzing the collected data and detecting anomalies" refers to a device or software for analyzing the collected data and detecting anomalies.

[1498] The "means for notifying when an abnormality is detected" refers to a device or software for notifying an administrator or relevant parties when an abnormality is detected.

[1499] As an embodiment of the present invention, a "smart manufacturing management system" aimed at improving the efficiency and quality control of a manufacturing line will be described as an example.

[1500] System Overview

[1501] The server collects various data from the production line and registers it in a database. Next, AI is used to summarize and analyze the collected data and detect anomalies. If anomalies are detected or the results of the analysis are sent to the administrator in a timely manner, this makes it possible to improve the efficiency of the production line and quality control.

[1502] Hardware and Software Used

[1503] Data collection devices: Using sensors and data collection devices installed on the production line, data such as production speed, quality scores, and machine operation status is collected in real time.

[1504] Database: Use a database to store the collected data. Specifically, use a relational database such as MySQL or PostgreSQL.

[1505] AI analysis software: To analyze the collected data, we use Python machine learning libraries (e.g., scikit-learn) to build an anomaly detection model and analyze the data.

[1506] Notification system: When an abnormality is detected, an email sending system is used to notify the administrator. Specifically, emails are sent using the smtplib library.

[1507] Processing flow

[1508] 1. Data collection: Collect data from various sensors on the production line and register it in a database.

[1509] 2. Data analysis: Use AI to analyze collected data and detect anomalies.

[1510] 3. Notification: If an abnormality is detected, the administrator will be notified by email.

[1511] Examples

[1512] For example, if the production speed of a manufacturing line suddenly drops and the quality score falls below the standard value, the AI ​​will determine this to be an abnormality and the server will send an email to the administrator stating, "An abnormality has been detected on the manufacturing line. Please check for details."

[1513] Examples of prompt statements

[1514] Below are examples of prompts to be input to the generative AI model.

[1515] Create a program to collect data from the manufacturing line and use AI to detect anomalies. Data includes production speed, quality score, and machine operation status. If an anomaly is detected, notify the factory manager by email.

[1516] In this way, a system can be constructed to achieve efficient production line and quality control.

[1517] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1518] Step 1:

[1519] The server collects data from sensors installed on the production line. As input, it receives real-time data such as production speed, quality score, and machine operation status. These data are sent directly from the sensors to the server through a data collector. The server registers these data in a database. Specifically, the server periodically polls for data from the sensors and stores the acquired data in the database.

[1520] Step 2:

[1521] The server passes the data registered in the database to the AI ​​analysis software. As input, it uses the production speed, quality score, and machine operation status data stored in the database. The AI ​​analysis software uses these data to run an anomaly detection model and determine whether there are any abnormalities. Specifically, the server uses a Python machine learning library (e.g., scikit-learn) to build an anomaly detection model and analyze the data.

[1522] Step 3:

[1523] The server receives the analysis results from the AI ​​analysis software. As input, it receives the output results of the anomaly detection model. If an anomaly is detected based on the analysis results, the server launches a notification system. Specifically, the server evaluates the analysis results and sets a notification flag if an anomaly is detected.

[1524] Step 4:

[1525] The server uses a notification system to notify the administrator of an anomaly. As input, it uses a flag indicating that an anomaly has been detected. The notification system uses an email sending library (e.g. smtplib) to send an email to notify the administrator of the anomaly. Specifically, the server generates the content of the email and sends it to the administrator's email address.

[1526] Step 5:

[1527] The user (administrator) receives the notification from the server and checks the details of the production line. As input, the user receives the abnormality notification email sent from the server. The user checks the status of the production line based on the contents of the email and takes the necessary action. In concrete terms, the user opens the email, checks the details of the abnormality, and accesses the production line monitoring system to check the status.

[1528] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1529] "Example 1"

[1530] In one embodiment of the present invention, the system further includes an emotion engine that recognizes the emotion of the user. The emotion engine recognizes the user's emotional state from, for example, the user's tone of voice, facial expression, text input, and the like. The emotion engine adjusts the analysis and summarization of data based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, it prioritizes summarizing important information and presents it to the user. Also, if the emotion engine recognizes that the user is feeling relaxed, it provides more detailed information. Furthermore, the emotion engine adjusts the output results based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling happy, it displays positive results with emphasis. On the other hand, if the emotion engine recognizes that the user is feeling sad, it displays negative results with mitigation. This makes it possible to provide optimal information according to the user's emotional state.

[1531] "Example 2"

[1532] In one embodiment of the present invention, the system further includes an emotion engine that recognizes the emotion of the user. The emotion engine recognizes the user's emotional state from, for example, the user's tone of voice, facial expression, text input, and the like. The emotion engine adjusts the analysis and summarization of data based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, it prioritizes summarizing important information and presents it to the user. Also, if the emotion engine recognizes that the user is feeling relaxed, it provides more detailed information. Furthermore, the emotion engine adjusts the output results based on the user's emotional state. For example, if the emotion engine recognizes that the user is feeling happy, it displays positive results with emphasis. On the other hand, if the emotion engine recognizes that the user is feeling sad, it displays negative results with mitigation. This makes it possible to provide optimal information according to the user's emotional state.

[1533] The process flow of each embodiment will be described below.

[1534] "Example 1"

[1535] Step 1: Receive input from the user (tone of voice, facial expressions, text input, etc.).

[1536] Step 2: The emotion engine recognizes the user's emotional state, which can be, for example, happy, sad, stressed, relaxed, etc.

[1537] Step 3: The emotion engine adjusts the analysis and summarization of the data based on the user's emotional state. For example, if the emotion engine detects that the user is stressed, it will prioritize summarizing and presenting important information to the user. If the emotion engine detects that the user is relaxed, it will provide more detailed information.

[1538] Step 4: The emotion engine adjusts the output results based on the user's emotional state. For example, if the emotion engine detects that the user is feeling happy, it will highlight the positive results. On the other hand, if the emotion engine detects that the user is feeling sad, it will tone down the negative results.

[1539] Example 1

[1540] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1541] Conventional systems have had difficulty efficiently summarizing and analyzing information necessary for business strategy planning and decision-making, and outputting it in a timely manner. In addition, the system does not provide information that takes into account the user's emotional state, so it is not possible to provide optimal information according to the user's stress and emotions. This can impair the speed and accuracy of decision-making.

[1542] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1543] In this invention, the server includes a means for registering data, a means for summarizing and analyzing by artificial intelligence, a means for outputting the summarization and analysis results in a timely manner, a means for recognizing the emotional state of the user, a means for adjusting the analysis and summarization of the data based on the emotional state, and a means for adjusting the output result based on the emotional state. This makes it possible to provide optimal information according to the emotional state of the user, and to improve the speed and accuracy of business strategy planning and decision-making.

[1544] "Means for registering data" refers to a device or software that allows a user to input information necessary for planning business strategies and decision-making, and store it in a database.

[1545] "Means of summarizing and analyzing using artificial intelligence" refers to devices or software for summarizing and analyzing registered information using natural language processing or machine learning techniques.

[1546] "Means for outputting summaries and analysis results in a timely manner" refers to a device or software for quickly providing summaries and analysis results to users, and includes formats such as dashboard display, email transmission, and smartphone notifications.

[1547] The "means for recognizing the emotional state of the user" refers to a device or software for recognizing the emotional state of the user from the tone of voice, facial expression, text input, and the like.

[1548] A "means for adjusting data analysis or summarization based on emotional state" is a device or software for modifying the way data is analyzed or summarized depending on th...

Claims

1. A means for analyzing data related to management by a generating AI; a means for recognizing an emotional state of a user; means for adjusting and outputting the analysis result of the data based on the emotional state of the user; A system including:

2. The output means adjusts the analysis result of the data using a generation AI based on a prompt sentence corresponding to the emotional state of the user. The system of claim 1 .

3. The output means outputs the analysis result in a timely manner. The system of claim 1 .

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