System

Abstract

A system is provided.SOLUTION: A system comprising: means for obtaining electronic messages and schedule information for a specified period of time; means for storing the electronic messages and schedule information in an internal database; means for analyzing the stored electronic messages and schedule information using natural language processing techniques; means for generating a report summarizing business content based on the analysis results; and means for displaying the report to a 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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern workplaces, people are often so busy with meetings and tasks that it's difficult to remember what they've been doing for the past month when they need to reflect on their work. This makes it difficult to identify wasteful tasks and find time to try new things. This invention aims to solve the above problem by providing a system that clearly understands the user's work and supports efficient time management. [Means for solving the problem]

[0005] The present invention provides a system including means for acquiring electronic messages and schedule information for a specified period, means for storing the acquired electronic messages and schedule information in an internal database, means for analyzing the stored electronic messages and schedule information using natural language processing technology, means for generating a report summarizing work content based on the analysis results, and means for displaying the generated report to a user, thereby enabling the user to easily understand the content of past work, eliminate waste, and allocate time more efficiently to new challenges.

[0006] A "specified period" is a specific time range selected by a user for reviewing and analyzing business operations.

[0007] "Electronic Message" refers to emails and other digital communications sent or received by Users.

[0008] "Schedule information" is information about the time and content of meetings, events, etc. that a user registers in their schedule.

[0009] A "means for obtaining" is a mechanism for collecting electronic messages and schedule information, for example, via an API.

[0010] "Means for storage" means the mechanism by which captured electronic messages and schedule information are stored in an internal database for subsequent analysis and reference.

[0011] "Natural language processing technology" is a computational technology for analyzing text data such as electronic messages and schedule information and extracting meaning.

[0012] The "means of analysis" is a mechanism that uses natural language processing technology to analyze stored data and extract patterns and trends in business operations.

[0013] The "means for generating a report" is a mechanism that summarizes the user's past business operations based on the analysis results and creates a report in visual or text format.

[0014] A "means for displaying" is an interface that allows a user to view the generated report. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single 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), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] 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.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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.

[0029] 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 (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" 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 process 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output 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.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. 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."

[0036] The present invention is a system that automatically analyzes work content for a specific period of time, allowing a user to easily understand the work. A specific embodiment of this system will be described.

[0037] Data Acquisition

[0038] Users access the system via a web browser or a dedicated application and log in with their Google account. After logging in, users grant the system permission to access their email and calendar.

[0039] The device initiates the Google OAuth authentication flow in response to the user's actions and obtains the necessary access token, which grants access to retrieve email messages (Gmail) and event information (GCalendar) for a specified period using Google APIs.

[0040] The server uses the acquired token to retrieve electronic messages and schedule information for a specified period via Google API. The period to be retrieved is specified by the user, such as the past month or half year.

[0041] Data storage

[0042] The server stores the captured electronic messages and appointment information in an internal database. This process preserves the body of each message, its metadata, and calendar event details, ensuring the data is available for subsequent analytical processing.

[0043] Analyzing the data

[0044] The server uses text mining techniques to analyze stored electronic messages and schedule information. This involves clustering the content of electronic messages and extracting important keywords and topics. Calendar events are also categorized, for example, into categories such as "Meetings," "Reviews," and "Other."

[0045] Generate reports

[0046] The server analyzes the data and generates a report summarizing the work, including the number of messages in each cluster and the number of events in each category, and may also show trend analysis, such as how much time is spent on specific tasks.

[0047] Viewing Reports

[0048] The terminal provides an interface for displaying the generated reports to the user, for example, as a dashboard viewable via a web browser or as a downloadable PDF report, allowing users to easily check and analyze past business operations.

[0049] Specific examples

[0050] For example, if a user wants to review their work activities over the past month, they first log in to the system and specify the period to retrieve the data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. Next, the system uses text mining technology to analyze the data and generates a report summarizing their work activities based on the results. Users can view this report on their dashboard or download and save it.

[0051] As described above, the present invention provides a system that helps a user accurately understand the content of past work and efficiently review it.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] To log in to the system, users access the system's login page via a web browser or a dedicated application and authenticate using their Google account. If authentication is successful, the necessary permissions are granted to the system.

[0055] Step 2:

[0056] The device initiates the Google OAuth authentication flow when the user logs in. This flow obtains an access token from the user's Google account. This token is required for subsequent data retrieval.

[0057] Step 3:

[0058] The server uses the obtained access token to retrieve electronic messages and schedule information for the specified period via Google APIs. Specifically, it retrieves email data from the Gmail service and calendar data from the G Calendar service.

[0059] Step 4:

[0060] The server stores the retrieved electronic messages and appointment information in an internal database, which includes the body of each message, metadata, and calendar event details.

[0061] Step 5:

[0062] The server uses text mining technology to analyze stored electronic messages and schedule information. It analyzes email content and classifies business activities using clustering technology. It also categorizes and analyzes calendar events.

[0063] Step 6:

[0064] The server generates a report summarizing the user's activities based on the results of text mining and clustering, including the number of messages in each cluster and the number of events in each category.

[0065] Step 7:

[0066] The terminal displays the generated report to the user, either as a dashboard on a web browser or as a downloadable PDF. The user can view this report and check their past work.

[0067] Example 1

[0068] Next, a description will be given of 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."

[0069] In recent years, many companies and businesses have required employees to review and analyze their work. However, manually extracting the necessary information from vast amounts of electronic messages and schedule information is extremely laborious. Rapidly identifying important keywords and topics and grasping work trends requires advanced analytical capabilities. While automated data analysis and summarization are needed to improve the efficiency of such work, existing technologies are not adequately capable.

[0070] 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.

[0071] In this invention, the server includes means for allowing a user to access the system using an account and granting access permission to electronic messages and schedule information for a specified period of time, means for authenticating access to the electronic messages and schedule information and obtaining an access token, means for obtaining the electronic messages and schedule information, means for storing the electronic messages and schedule information in an internal database, means for clustering the stored electronic messages and schedule information using natural language processing techniques, means for extracting important keywords and topics, means for generating a report based on the clustered data, and means for displaying the report to the user, thereby enabling the user to quickly and accurately understand the content of past work and efficiently review it.

[0072] A "user" is a person who uses the system, and is an entity who accesses the system and performs authentication and operations using his or her own account.

[0073] An "electronic message" is a digital communication sent or received in the form of electronic mail.

[0074] "Schedule information" refers to information about events and schedules recorded on an electronic calendar.

[0075] "Access permission" is the permission a user grants through authentication means for the system to access the user's electronic messages and schedule information.

[0076] An "access token" is a token that contains authentication information required for a system to obtain data from an external service.

[0077] "Natural language processing technology" is a technology for analyzing text data and understanding its meaning and structure.

[0078] "Clustering" is the process of grouping data that have similar characteristics.

[0079] A "keyword" is an important word that appears frequently in text data and characterizes its content.

[0080] A "topic" is a subject or theme of content to which multiple keywords are associated.

[0081] A "report" is a written or digital document that summarizes the results of data analysis in an easy-to-understand manner.

[0082] "Internal database" refers to data storage for saving and managing acquired data.

[0083] The present invention provides a system that automatically analyzes the content of work performed over a specific period of time, allowing users to easily understand the work. This system operates through the following steps.

[0084] First, users access the system through a web browser or a dedicated application. Then, they log in using their account and grant permission to access electronic messages and schedule information. The system does this through an authentication process using a Google account.

[0085] The device initiates the Google OAuth authentication flow in response to the user's operation and obtains the necessary access token. This authentication flow uses Google's authentication server to obtain an access token containing the user's authentication information. The obtained token is used to obtain electronic messages (Gmail) and appointment information (GCalendar) for a specified period using the Google API.

[0086] The server then uses the acquired token via Google API to retrieve electronic messages and schedule information for a specified period, such as the past month or the past six months. This data retrieval process is fully automated, minimizing user intervention.

[0087] The captured electronic messages and schedule information are then stored by the server in an internal database. This process preserves the body of each message, its metadata, and calendar event details, ensuring that the data required for subsequent analytical processing is preserved.

[0088] The server then analyzes the stored data using text mining techniques, such as natural language processing libraries like NLTK or spaCy. Specifically, the content of electronic messages is clustered to extract frequently occurring keywords and topics. Calendar events are also classified into categories like "Meetings," "Reviews," and "Other."

[0089] Based on these analyses, the server generates a report summarizing the work, including the number of messages and event categories for each cluster, as well as a trend analysis of the time spent on specific tasks.

[0090] Finally, the terminal provides an interface for displaying the generated reports to the user, who can view them via a web browser, for example in dashboard format, or download them as PDFs.

[0091] Specific examples

[0092] For example, if a user wants to review their work activities over the past month, they first log in to the system and specify the period to retrieve the data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. The data is then analyzed using text mining technology, and a report summarizing the work activities is generated based on the results. The user can view this report on the dashboard or download and save it.

[0093] Prompt Sentence Examples

[0094] "Generate a report that analyzes and summarizes your work based on Gmail and G Calendar data for the past three months."

[0095] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0096] Step 1:

[0097] A user accesses the system and logs in with their Google account.

[0098] Input: User's Google account information (email address, password)

[0099] How it works: A user opens a web browser or a dedicated application and accesses the system's URL. A login screen appears, and the user enters their Google account email address and password and clicks the "Next" button.

[0100] Output: User credentials

[0101] Step 2:

[0102] The device initiates the Google OAuth authentication flow and obtains an access token.

[0103] Input: User credentials

[0104] How it works: The device sends a request to Google's authorization server, initiating the OAuth flow to obtain an access token. The user's credentials are verified, and if successful, Google issues an access token. The device receives this token.

[0105] Output: Access token

[0106] Step 3:

[0107] The server uses the Google API to retrieve electronic messages and schedule information for a specified period.

[0108] Input: Access token, specified period (e.g., last month)

[0109] How it works: The server uses the access token to send a request to the Google API to retrieve email messages (Gmail) and event information (GCalendar) for the specified period. For example, endpoints such as "https: / / www.googleapis.com / gmail / v1 / users / me / messages" and "https: / / www.googleapis.com / calendar / v3 / calendars / primary / events" are used.

[0110] Output: Electronic messages and schedule information for a specified period

[0111] Step 4:

[0112] The server stores the retrieved electronic messages and schedule information in an internal database.

[0113] Input: Electronic messages and schedule information for a specified period

[0114] How it works: The server stores the retrieved data in an internal database. During this storage process, a record is created containing the body of each message, its metadata, and the calendar event details. For example, a SQL query is used to insert the record into the database.

[0115] Output: Data stored in the internal database

[0116] Step 5:

[0117] The server analyzes the stored data using natural language processing technology.

[0118] Input: Data stored in the internal database

[0119] How it works: The server uses natural language processing techniques (such as NLTK or spaCy) to analyze the stored data. Specifically, it tokenizes the content of electronic messages to extract frequently occurring keywords and topics. It also clusters the data and categorizes calendar events into categories such as "Meetings," "Reviews," and "Other."

[0120] Output: Analysis results (important keywords, topics, cluster classification)

[0121] Step 6:

[0122] The server generates a report based on the data analysis results.

[0123] Input: Analysis results

[0124] How it works: The server generates a report based on the analysis results. This report includes the number of messages in each cluster and the number of calendar events per category. It may also include information on the time spent on specific tasks as a trend analysis. The report is generated in PDF, Excel, and other formats.

[0125] Output: Report

[0126] Step 7:

[0127] The terminal displays the generated report to the user.

[0128] Input: Report

[0129] How it works: The device provides an interface via a web browser that displays reports in a dashboard format. Users can view the generated reports and download them as PDFs if desired.

[0130] Output: The report as seen by the user

[0131] (Application example 1)

[0132] Next, a description will be given of Application 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."

[0133] It is important to efficiently manage and analyze the work records and schedules of robots deployed in factories. Conventional methods require manually tracking and recording robot operation history and maintenance information, which is time-consuming and labor-intensive. In addition, it is not easy to extract important keywords and categories from large amounts of data, making it difficult for managers to accurately and quickly understand the work content. This hinders efficient robot operation and optimization of maintenance.

[0134] 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.

[0135] In this invention, the server includes means for acquiring electronic messages and schedule information for a specified period, means for saving the electronic messages and schedule information in an internal database, means for analyzing the saved electronic messages and schedule information using natural language processing technology, means for generating a report summarizing work content based on the analysis results, means for displaying the report to a user, means for automatically analyzing work records and schedules and extracting important keywords and categories, and means for acquiring robot operation history and maintenance information using a Google API. This allows for accurate and rapid understanding of robot work content, enabling efficient operation and optimization of maintenance.

[0136] An "electronic message" is an email or other form of communication sent or received digitally.

[0137] "Schedule information" refers to events and tasks registered in a calendar system or schedule management tool.

[0138] An "internal database" is a digital data storage system that stores and manages acquired data.

[0139] "Natural language processing technology" is a technology that allows computers to process the language that humans use on a daily basis.

[0140] "Text mining technology" is a technology that automatically extracts useful information from text data.

[0141] A "report" is a written or digital document that organizes and reports analytical results.

[0142] A "user" is a person or group that uses the system.

[0143] A "work record" is a log of the specific work performed by a robot.

[0144] A "schedule" is a plan of tasks or events to be carried out within a specific period of time.

[0145] A "keyword" is a word that is considered to be particularly important within a document or data.

[0146] A "category" is a classification item for organizing data and information.

[0147] A "robot" is a mechanical device that performs work automatically.

[0148] "Operation history" is a record of the robot's past actions and instructions.

[0149] "Maintenance information" is data related to the maintenance, inspection, and repair of robots.

[0150] "Google API" is an application programming interface provided by Google and is a means of accessing various data and services.

[0151] This system analyzes the work records and schedules of robots installed in factories, allowing users to easily understand the work content based on the analysis. This system automatically acquires electronic messages and schedule information for a specified period, stores them in an internal database, analyzes them using natural language processing technology, generates reports, and displays them to users, thereby supporting efficient work management.

[0152] System Configuration and Hardware / Software Usage

[0153] This system uses the following hardware and software:

[0154] Hardware

[0155] Robot-integrated processor and storage

[0156] server

[0157] Device (computer or tablet used by the administrator)

[0158] software

[0159] Google API (Google Calendar API, Gmail API)

[0160] Python libraries (pandas, TextBlob)

[0161] Google OAuth 2.0 authentication

[0162] Data Acquisition

[0163] Users access the system using a dedicated terminal and log in with their Google account. After logging in, the terminal initiates the Google OAuth authentication flow to obtain the necessary access token. This token grants access to retrieve email and calendar data for a specified period using Google APIs. The robot uses this access to automatically retrieve work logs and maintenance information.

[0164] Data storage

[0165] The server stores the captured electronic messages and schedule information in an internal database. This storage process preserves the body of each message, its metadata, and calendar event details. This stored data is used for subsequent analysis processes.

[0166] Analyzing the data

[0167] The server analyzes stored electronic messages and schedule information using text mining techniques. This analysis uses pandas data frames and the TextBlob library. The content of messages and events is clustered and key keywords and sentiment analysis is performed. Work records and schedules are organized by category, for example, "assembly," "maintenance," and "inspection."

[0168] Generate reports

[0169] The server generates a report summarizing the activities based on the analysis, including the number of messages and event categories for each cluster, as well as the time and frequency spent on each activity.

[0170] Viewing Reports

[0171] The terminal provides an interface for displaying the generated reports to the user, such as a dashboard viewable via a web browser or a downloadable report in PDF format, allowing the user to easily review and analyze past business operations.

[0172] Specific examples

[0173] For example, to analyze the work of factory robots in January 2023, Google Calendar events and Gmail messages from the past month can be retrieved, and TextBlob can be used to perform sentiment analysis and extract important keywords. A report can then be generated based on the results, displayed on the administrator's device, and downloaded in PDF format.

[0174] Prompt Sentence Examples

[0175] "Analyze the tasks performed by factory robots in January 2023 and perform key keywords and sentiment analysis. Collect data using the Google Calendar API and Gmail API and analyze it using TextBlob."

[0176] This system enables accurate and rapid understanding of the robot's operations, enabling efficient operation and optimization of maintenance.

[0177] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0178] Step 1:

[0179] Users access the system using a dedicated device and log in with their Google account. After logging in, the device initiates the Google OAuth authentication flow to obtain the necessary access token, which provides credentials that allow the server to use Google APIs to access email and calendar data for a specified period of time.

[0180] Input: Google account information, OAuth authentication request

[0181] Output: OAuth access token

[0182] Step 2:

[0183] The server uses the obtained access token to retrieve email and calendar data for the specified period through the Google API by sending an API request and receiving the data in response.

[0184] Input: OAuth access token, API request (specified period)

[0185] Output: Email data, calendar data

[0186] Step 3:

[0187] The server stores the retrieved email and calendar data in an internal database, which stores the body of each message, as well as metadata and calendar event details.

[0188] Input: Email data, calendar data

[0189] Output: Data stored in the internal database

[0190] Step 4:

[0191] The server analyzes email and calendar data stored in an internal database using text mining techniques, specifically converting the data into a data frame using the pandas library and performing sentiment analysis and keyword extraction using the TextBlob library.

[0192] Input: Data from the internal database

[0193] Output: Analysis results (sentiment analysis, keyword extraction)

[0194] Step 5:

[0195] The server generates a report summarizing the work based on the results of the text mining. This report includes the number of messages in each cluster and the number of events in each category. The report is generated in JSON or PDF format.

[0196] Input: Analysis results

[0197] Output: Report (JSON format, PDF format)

[0198] Step 6:

[0199] The terminal provides an interface that displays the generated reports to the user, who can view them via a web browser or download them as PDFs.

[0200] Input: Report (JSON format, PDF format)

[0201] Output: Displayed report, download link

[0202] Furthermore, an emotion engine that estimates the user's emotion may be 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.

[0203] The present invention is a system that automatically analyzes a user's work content over a specific period of time, allowing the user to easily understand that work. This system also provides a function that includes the user's emotional elements in the analysis of work content by combining it with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this invention are described below.

[0204] Data Acquisition

[0205] Users access the system via a web browser or a dedicated application and log in with their Google account. After logging in, users grant the system permission to access their email and calendar.

[0206] The device initiates the Google OAuth authentication flow in response to the user's actions and obtains the necessary access token, which grants access to retrieve email messages (Gmail) and event information (GCalendar) for a specified period using Google APIs.

[0207] The server uses the acquired token to retrieve electronic messages and schedule information for a specified period via Google API. The period to be retrieved is specified by the user, such as the past month or half year.

[0208] Data storage

[0209] The server stores the captured electronic messages and appointment information in an internal database. This process preserves the body of each message, its metadata, and calendar event details, ensuring the data is available for subsequent analytical processing.

[0210] Analyzing the data

[0211] The server analyzes stored electronic messages and schedule information using text mining technology and an emotion engine. The emotion engine extracts the user's emotional state from each message and event and stores it as data. The server analyzes the content of emails and classifies the work content using clustering technology. Calendar events are also categorized, for example, into categories such as "Meeting," "Review," and "Other." The emotion engine also analyzes the user's emotional tendencies for each cluster and category.

[0212] Generate reports

[0213] The server generates a report summarizing the work based on the data analysis results. This report shows the number of messages in each cluster and the number of events in each category, as well as the emotional trends for each cluster and category extracted by the emotion engine. For example, it can show whether users were feeling stressed about a particular project.

[0214] Viewing Reports

[0215] The terminal provides an interface for displaying the generated report to the user, for example, as a dashboard viewable via a web browser or as a downloadable PDF report, allowing users to easily review and analyze their past work history and understand their own emotional state during work.

[0216] Specific examples

[0217] For example, if a user wants to review the work they performed over the past month and their emotional state during that period, they first log in to the system, specify the period, and retrieve the data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. Next, the system analyzes the data using text mining technology and an emotion engine, and generates a report summarizing their work based on the results. The user can view this report on the dashboard or download and save it. The report also displays trends in the emotions the user felt regarding specific tasks, which can be used as a reference for managing stress and improving work.

[0218] As described above, the present invention provides a system that enables a user to accurately grasp the content of past work and the emotional state during that period, and supports efficient review.

[0219] The processing flow will be explained below.

[0220] The present invention is a system that efficiently analyzes and understands the work content of a user and generates a detailed report based on the user's emotional state. Specific embodiments will be described in detail below, divided into processing steps.

[0221] Processing flow

[0222] Step 1:

[0223] To log in to the system, users access the system's login page using a web browser or a dedicated application and authenticate using their Google account. If authentication is successful, the necessary permissions are granted to the system.

[0224] Step 2:

[0225] The device initiates the Google OAuth authentication flow when the user logs in. This flow obtains an access token and secures the authentication information required for future data retrieval.

[0226] Step 3:

[0227] The server uses the obtained access token to retrieve electronic messages and schedule information for the specified period via the Google API. Specifically, it retrieves electronic message data from the Gmail service and schedule information from the G Calendar service.

[0228] Step 4:

[0229] The server stores the retrieved electronic messages and appointment information in an internal database, including the body of each message, metadata, and calendar event details.

[0230] Step 5:

[0231] The server analyzes the data stored in its internal database using text mining technology. It uses clustering technology to classify the content of electronic messages and extract important keywords and topics. It also categorizes calendar events, dividing them into categories such as "Meetings," "Reviews," and "Other."

[0232] Step 6:

[0233] The server uses an emotion engine to analyze the user's emotional state from the stored electronic messages and schedule information, for example, determining whether the emotional state is positive, negative, or neutral based on the tone and word choice of the electronic messages.

[0234] Step 7:

[0235] Based on the results of the text mining and sentiment engine analysis, the server generates a report summarizing the business activities, including the number of messages in each cluster, the number of events in each category, and the sentiment trends for each cluster and category.

[0236] Step 8:

[0237] The terminal provides an interface for displaying the generated reports to the user, such as a dashboard viewable via a web browser or a downloadable PDF report, allowing the user to easily review and analyze past work and their emotional state during that period.

[0238] Specific examples

[0239] For example, if a user wants to review their work activities and emotional state over the past month, they first log in to the system and specify a period to retrieve data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. Next, the data is analyzed using text mining technology and an emotion engine, and a report summarizing their work activities is generated based on the results. Users can view this report on the dashboard or download and save it. The report also clearly shows emotional trends regarding specific tasks, providing valuable reference information for users to manage stress and improve their work.

[0240] As described above, the present invention provides a system that helps a user accurately understand the content of past work and efficiently review the work based on the user's emotional state during the work.

[0241] Example 2

[0242] Next, a description will be given of 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."

[0243] In today's business environment, users must manage and review numerous tasks, but this process is often laborious and inefficient. Furthermore, it is difficult to incorporate users' emotional states into task analysis, potentially resulting in missed insights for business improvement. This invention aims to solve this problem by automatically analyzing task content and emotional states over a specified period, allowing users to easily understand and manage their tasks.

[0244] 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.

[0245] In this invention, the server includes means for performing user authentication and obtaining access rights to business data for a specific period, means for obtaining electronic messages and schedule information for a specified period, means for storing the electronic messages and schedule information in an internal database, means for analyzing the stored electronic messages and schedule information using text mining technology and a sentiment analysis engine, means for generating a report summarizing business content based on the analysis results, and means for displaying the report to the user, thereby enabling the user to accurately grasp and efficiently review past business content and their emotional state during that period.

[0246] "User authentication" is the process of verifying the identity of users accessing a system and granting them appropriate access rights.

[0247] "Business data" refers to information generated or acquired by a user in connection with their business, including electronic messages and schedule information.

[0248] "Electronic Message" means an email or other digital message sent or received by a User.

[0249] "Schedule information" is data related to the user's schedule and calendar.

[0250] An "internal database" is a storage system for storing acquired data and using it for subsequent processing.

[0251] "Text mining technology" is a technology that uses natural language processing to analyze large amounts of text data and extract useful information.

[0252] An "emotion analysis engine" is a technology for automatically extracting and analyzing a user's emotional state from text data.

[0253] "Analysis Results" means information obtained from the analysis of data obtained using text mining techniques and sentiment analysis engines.

[0254] A "report summarizing business operations" is a report that briefly summarizes the user's business operations based on the analysis results.

[0255] "Means for displaying to the user" refers to an interface that allows the user to view the system operation results and reports.

[0256] MODE FOR CARRYING OUT THE INVENTION

[0257] The present invention is a system that automatically analyzes a user's work content and emotional state, allowing the user to easily understand it. The system acquires the user's electronic messages and schedule information for a specific period and stores them in an internal database. The system then analyzes the stored data using text mining technology and an emotion analysis engine, and generates a report summarizing the work content based on the results and displays it to the user.

[0258] User authentication and data acquisition methods

[0259] Users access the system using a web browser or a dedicated application and log in using their Google account as authentication. After logging in, users grant the system permission to access their electronic messages and schedule information.

[0260] When a user logs in with their Google account, the device initiates the Google OAuth authentication flow and obtains an authentication token. The obtained token is used to retrieve email messages (Gmail) and schedule information (GCalendar) for a specified period via the Google API. This period can be specified by the user as a specific period, such as the past month or half year.

[0261] The server uses the obtained authentication token to access the Google API and collects email and calendar information for the specified period. For example, if the user selects data from the past month, all Gmail and G Calendar events from that period will be collected.

[0262] Data storage format

[0263] The server stores the retrieved electronic messages and schedule information in an internal database, including the body, subject, sender information, received date, and attachment information for each email, and the title, date, and attendee list for each calendar event. The internal database is used to ensure the data necessary for subsequent analysis.

[0264] Forms of data analysis

[0265] The server analyzes the stored electronic messages and schedule information using text mining technology and a sentiment analysis engine. Specifically, it uses natural language processing technology to analyze the content of emails and categorize them by topic using a clustering algorithm.

[0266] Furthermore, a sentiment analysis engine is used to extract the user's emotional state from each email and calendar event. For example, it determines whether the email content is positive or negative, and stores the results as emotional data. Emotional trends by cluster and category are also analyzed.

[0267] Report generation and display formats

[0268] The server analyzes the data and generates a report summarizing the work, including the number of electronic messages in each cluster, the number of calendar events by category, and sentiment trends based on a sentiment analysis engine, such as the user's stress level or satisfaction with a particular project or meeting.

[0269] The terminal provides an interface for displaying the generated report to the user. The user can view the dashboard through a web browser. The generated report can also be downloaded in PDF format. This allows the user to easily check and analyze past work history and understand their own emotional state at work.

[0270] Examples of concrete examples and prompts

[0271] For example, if a user wants to review the work they did over the past month and their emotional state during that period, they first log in to the system and specify the "past month" period. The system automatically collects electronic messages and schedule information and stores it in an internal database. The server then analyzes the data using text mining technology and a sentiment analysis engine to generate a report summarizing their work. The user can view this report on the dashboard or download it as a PDF. The report also displays trends in the emotions the user felt regarding specific tasks, which can be used as a reference for managing their own stress and improving their work.

[0272] An example of a prompt to be input to a generative AI model would be:

[0273] "I want to look back at my work over the past month and my emotional state during that period. I log in with my Google account, specify the period, and retrieve the data. I then generate a report that shows my emotional trends."

[0274] As described above, the present invention provides a system that supports a user in accurately understanding and efficiently reviewing past work details and emotional states.

[0275] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0276] Step 1:

[0277] Users access the system through a web browser or a dedicated application and log in with their Google account. They then grant the system access to their electronic messages and schedule information.

[0278] Input: Google account credentials, grant access

[0279] Output: Get authentication token

[0280] Step 2:

[0281] The device starts the Google OAuth authentication flow when the user logs in. The device follows the OAuth flow to obtain an authentication token from Google.

[0282] Input: Google account credentials, OAuth authentication request

[0283] Output: Get authentication token

[0284] Step 3:

[0285] The device uses the obtained authentication token to send a request to the Google API to retrieve electronic messages (Gmail) and schedule information (GCalendar) for the specified period.

[0286] Input: Authentication token, specified period

[0287] Output: Electronic message and appointment information data

[0288] Step 4:

[0289] The server stores the electronic messages and schedule information sent from the terminals in an internal database, including details such as the body of each email, its subject, sender information, date of receipt, and attachment information, as well as the title, date, and attendee list of each calendar event.

[0290] Input: Data for electronic messages and appointment information

[0291] Output: Save to internal database

[0292] Step 5:

[0293] The server analyzes the stored electronic messages and schedule information using text mining technology and a sentiment analysis engine. Specifically, it uses natural language processing technology to analyze the content of emails and categorize them by topic. The sentiment analysis engine also extracts the emotional state of each message and event and stores it as data. This analysis allows the user's emotional tendencies to be identified for each cluster.

[0294] Input: Data from the internal database

[0295] Output: Text mining and sentiment analysis results

[0296] Step 6:

[0297] The server generates a report based on the analysis, summarizing work content and emotional trends, including the number of electronic messages in each cluster, the number of calendar events by category, and the emotional state of use, providing detailed insight into specific projects or meetings.

[0298] Input: Text mining and sentiment analysis results

[0299] Output: Summary report of work content and sentiment trends

[0300] Step 7:

[0301] The terminal displays the reports generated by the server to the user, which can be viewed in a web browser in dashboard format or downloaded as a PDF. The user can view the reports to understand their past work history and their emotional state during that period.

[0302] Input: Summary report of work and sentiment trends

[0303] Output: Display format for user viewing

[0304] (Application example 2)

[0305] Next, a description will be given of Application 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."

[0306] While traditional work management systems were capable of capturing and storing electronic messages and schedule information for a specified period, they had limitations in quickly and effectively understanding work content. Furthermore, because they did not take into account the user's emotional state, they provided insufficient feedback for stress management and work efficiency. These limitations had a negative impact on the quality of work and employee satisfaction, especially for staff working in remote environments such as virtual stores.

[0307] 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.

[0308] In this invention, the server includes means for acquiring electronic messages and schedule information for a specified period, means for storing the electronic messages and schedule information in an internal database, means for analyzing the stored electronic messages and schedule information using natural language processing technology, means for generating a report summarizing work content based on the analysis results, means for displaying the report to the user, means for analyzing the user's emotional state, means for including the emotional state in the report, and means for displaying the report on a smart terminal, smart glasses, or head-mounted display. This allows the user to quickly and effectively understand the work content and manage their emotional state, which is expected to improve the quality of work and increase employee satisfaction.

[0309] "Electronic Message" means an email sent or received between users.

[0310] "Schedule information" refers to information about a schedule registered in a calendar used by a user.

[0311] "Internal Database" means the digital storage used to store captured electronic messages and schedule information.

[0312] "Natural language processing technology" refers to all technologies that allow computers to analyze, understand, and generate human language.

[0313] A "report" is a document that summarizes the business activities and emotional state that have been captured and analyzed.

[0314] "Emotional state" refers to the type and intensity of emotions a user felt toward a task during a specific period of time.

[0315] A "smart device" is a mobile device with internet connectivity, such as a mobile phone or tablet.

[0316] "Smart glasses" are glasses-type wearable devices that incorporate display functionality.

[0317] A "head-mounted display" is a display device worn on the head, used to display information within the field of vision.

[0318] The present invention is a system that automatically analyzes the work content of a user over a specific period of time and displays emotional elements as well. Specific embodiments will be described below.

[0319] 1. Data Acquisition

[0320] Users access the system via a web browser or a dedicated application and enter their authentication information to log in. After the user logs in, the device uses APIs such as Google APIs to retrieve the user's electronic messages and schedule information for a specified period. Google OAuth authentication is used in this step.

[0321] 2. Data storage

[0322] The captured electronic messages and schedule information are stored on a server that stores them in an internal database, holding the body of each message, its metadata, and details of the calendar event.

[0323] 3. Data Analysis

[0324] The server analyzes the stored electronic messages and schedule information using natural language processing technology (e.g., text mining technology) and an emotion engine. The emotion engine extracts the user's emotional state and stores it as data. This allows the content of the work to be classified and emotional trends to be analyzed.

[0325] 4. Generate reports

[0326] The server then generates a report summarizing the business operations based on the analyzed data, including the emotional state of the users. The report displays the number of messages in each cluster, the number of events in each category, and the emotional trends extracted by the emotion engine.

[0327] 5. View the report

[0328] The terminal displays the generated report to the user. The display method can be a web browser, a smart device, smart glasses, or a head-mounted display (HMD). The user can easily view the report to understand the work and check the emotional state.

[0329] Hardware and software used

[0330] Hardware: Smartphones, smart glasses, head-mounted displays

[0331] Software: Google OAuth authentication, Google API, natural language processing technology (text mining technology), emotion engine

[0332] Specific examples

[0333] For example, if a virtual store staff member wants to review their work over the past month, they log in to the system and specify the period. The server automatically retrieves email and calendar data and stores it in an internal database. It then analyzes the data using text mining technology and an emotion engine to generate a report that includes both their work and their emotional state. This report can be viewed on a smartphone or HMD.

[0334] Example prompt for a generative AI model:

[0335] Summarize the work activities of virtual store staff over the past month and analyze their emotional state during those activities.

[0336] Data Used: Google Mail, Google Calendar

[0337] Extracted information: Business content (clustering), sentiment analysis (positive / negative)

[0338] This system allows users to comprehensively understand their work details and emotional state, which can be used to improve work performance and manage stress.

[0339] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0340] Step 1:

[0341] The user accesses the system via a web browser or a dedicated application and logs in using their Google account.

[0342] Input: Google account information

[0343] Output: Authentication token

[0344] How it works: After entering credentials, the device initiates the Google OAuth authentication flow to obtain the necessary access token.

[0345] Step 2:

[0346] The device uses the obtained authentication token to retrieve electronic messages and schedule information for the specified period from the Google API.

[0347] Input: Authentication token, for a specified period

[0348] Output: Electronic message data and schedule information data for the specified period

[0349] What it does: Retrieves data from Google Mail and Google Calendar for a specified period of time, with access based on an authentication token.

[0350] Step 3:

[0351] The server stores the retrieved electronic messages and schedule information in an internal database.

[0352] Input: Electronic message data and schedule information data for a specified period

[0353] Output: Data stored in the internal database

[0354] What it does: It formats the data it receives and stores the body of each message, along with metadata and calendar event details, in an internal database.

[0355] Step 4:

[0356] The server analyzes the stored electronic messages and schedule information using natural language processing techniques and an emotion engine.

[0357] Input: Data stored in the internal database

[0358] Output: Analysis results (classification of work content, extraction of emotional trends)

[0359] How it works: It uses natural language processing technology to perform text mining and clustering of business content, while simultaneously using an emotion engine to extract the user's emotional state from each message and event.

[0360] Step 5:

[0361] The server generates a report summarizing the work content based on the analysis results and including the emotional state.

[0362] Input: Analysis results

[0363] Output: Summary report of work content (including sentiment analysis results)

[0364] How it works: Consolidates the analysis results and creates a report summarizing the business activities and sentiment trends for each cluster and category.

[0365] Step 6:

[0366] The terminal displays the generated report to the user.

[0367] Input: Business Summary Report

[0368] Output: The report as seen by the user

[0369] How it works: Provides an interface that allows users to view reports using a web browser, smart device, smart glasses, or head-mounted display.

[0370] This series of steps allows users to quickly and effectively grasp their past work history and emotional state.

[0371] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0372] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with 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.

[0373] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0374] [Second embodiment]

[0375] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0376] 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.

[0377] 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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0378] 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.

[0379] 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 audio in accordance with instructions from the processor 46.

[0380] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0381] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0382] Fig. 4 shows an example of the 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0387] The present invention is a system that automatically analyzes work content for a specific period of time, allowing a user to easily understand the work. A specific embodiment of this system will be described.

[0388] Data Acquisition

[0389] Users access the system via a web browser or a dedicated application and log in with their Google account. After logging in, users grant the system permission to access their email and calendar.

[0390] The device initiates the Google OAuth authentication flow in response to the user's actions and obtains the necessary access token, which grants access to retrieve email messages (Gmail) and event information (GCalendar) for a specified period using Google APIs.

[0391] The server uses the acquired token to retrieve electronic messages and schedule information for a specified period via Google API. The period to be retrieved is specified by the user, such as the past month or half year.

[0392] Data storage

[0393] The server stores the captured electronic messages and appointment information in an internal database. This process preserves the body of each message, its metadata, and calendar event details, ensuring the data is available for subsequent analytical processing.

[0394] Analyzing the data

[0395] The server uses text mining techniques to analyze stored electronic messages and schedule information. This involves clustering the content of electronic messages and extracting important keywords and topics. Calendar events are also categorized, for example, into categories such as "Meetings," "Reviews," and "Other."

[0396] Generate reports

[0397] The server analyzes the data and generates a report summarizing the work, including the number of messages in each cluster and the number of events in each category, and may also show trend analysis, such as how much time is spent on specific tasks.

[0398] Viewing Reports

[0399] The terminal provides an interface for displaying the generated reports to the user, for example, as a dashboard viewable via a web browser or as a downloadable PDF report, allowing users to easily check and analyze past business operations.

[0400] Specific examples

[0401] For example, if a user wants to review their work activities over the past month, they first log in to the system and specify the period to retrieve the data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. Next, the system uses text mining technology to analyze the data and generates a report summarizing their work activities based on the results. Users can view this report on their dashboard or download and save it.

[0402] As described above, the present invention provides a system that helps a user accurately understand the content of past work and efficiently review it.

[0403] The processing flow will be explained below.

[0404] Step 1:

[0405] To log in to the system, users access the system's login page via a web browser or a dedicated application and authenticate using their Google account. If authentication is successful, the necessary permissions are granted to the system.

[0406] Step 2:

[0407] The device initiates the Google OAuth authentication flow when the user logs in. This flow obtains an access token from the user's Google account. This token is required for subsequent data retrieval.

[0408] Step 3:

[0409] The server uses the obtained access token to retrieve electronic messages and schedule information for the specified period via Google APIs. Specifically, it retrieves email data from the Gmail service and calendar data from the G Calendar service.

[0410] Step 4:

[0411] The server stores the retrieved electronic messages and appointment information in an internal database, which includes the body of each message, metadata, and calendar event details.

[0412] Step 5:

[0413] The server uses text mining technology to analyze stored electronic messages and schedule information. It analyzes email content and classifies business activities using clustering technology. It also categorizes and analyzes calendar events.

[0414] Step 6:

[0415] The server generates a report summarizing the user's activities based on the results of text mining and clustering, including the number of messages in each cluster and the number of events in each category.

[0416] Step 7:

[0417] The terminal displays the generated report to the user, either as a dashboard on a web browser or as a downloadable PDF. The user can view this report and check their past work.

[0418] Example 1

[0419] Next, a description will be given of 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."

[0420] In recent years, many companies and businesses have required employees to review and analyze their work. However, manually extracting the necessary information from vast amounts of electronic messages and schedule information is extremely laborious. Rapidly identifying important keywords and topics and grasping work trends requires advanced analytical capabilities. While automated data analysis and summarization are needed to improve the efficiency of such work, existing technologies are not adequately capable.

[0421] 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.

[0422] In this invention, the server includes means for allowing a user to access the system using an account and granting access permission to electronic messages and schedule information for a specified period of time, means for authenticating access to the electronic messages and schedule information and obtaining an access token, means for obtaining the electronic messages and schedule information, means for storing the electronic messages and schedule information in an internal database, means for clustering the stored electronic messages and schedule information using natural language processing techniques, means for extracting important keywords and topics, means for generating a report based on the clustered data, and means for displaying the report to the user, thereby enabling the user to quickly and accurately understand the content of past work and efficiently review it.

[0423] A "user" is a person who uses the system, and is an entity who accesses the system and performs authentication and operations using his or her own account.

[0424] An "electronic message" is a digital communication sent or received in the form of electronic mail.

[0425] "Schedule information" refers to information about events and schedules recorded on an electronic calendar.

[0426] "Access permission" is the permission a user grants through authentication means for the system to access the user's electronic messages and schedule information.

[0427] An "access token" is a token that contains authentication information required for a system to obtain data from an external service.

[0428] "Natural language processing technology" is a technology for analyzing text data and understanding its meaning and structure.

[0429] "Clustering" is the process of grouping data that have similar characteristics.

[0430] A "keyword" is an important word that appears frequently in text data and characterizes its content.

[0431] A "topic" is a subject or theme of content to which multiple keywords are associated.

[0432] A "report" is a written or digital document that summarizes the results of data analysis in an easy-to-understand manner.

[0433] "Internal database" refers to data storage for saving and managing acquired data.

[0434] The present invention provides a system that automatically analyzes the content of work performed over a specific period of time, allowing users to easily understand the work. This system operates through the following steps.

[0435] First, users access the system through a web browser or a dedicated application. Then, they log in using their account and grant permission to access electronic messages and schedule information. The system does this through an authentication process using a Google account.

[0436] The device initiates the Google OAuth authentication flow in response to the user's operation and obtains the necessary access token. This authentication flow uses Google's authentication server to obtain an access token containing the user's authentication information. The obtained token is used to obtain electronic messages (Gmail) and appointment information (GCalendar) for a specified period using the Google API.

[0437] The server then uses the acquired token via Google API to retrieve electronic messages and schedule information for a specified period, such as the past month or the past six months. This data retrieval process is fully automated, minimizing user intervention.

[0438] The captured electronic messages and schedule information are then stored by the server in an internal database. This process preserves the body of each message, its metadata, and calendar event details, ensuring that the data required for subsequent analytical processing is preserved.

[0439] The server then analyzes the stored data using text mining techniques, such as natural language processing libraries like NLTK or spaCy. Specifically, the content of electronic messages is clustered to extract frequently occurring keywords and topics. Calendar events are also classified into categories like "Meetings," "Reviews," and "Other."

[0440] Based on these analyses, the server generates a report summarizing the work, including the number of messages and event categories for each cluster, as well as a trend analysis of the time spent on specific tasks.

[0441] Finally, the terminal provides an interface for displaying the generated reports to the user, who can view them via a web browser, for example in dashboard format, or download them as PDFs.

[0442] Specific examples

[0443] For example, if a user wants to review their work activities over the past month, they first log in to the system and specify the period to retrieve the data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. The data is then analyzed using text mining technology, and a report summarizing the work activities is generated based on the results. The user can view this report on the dashboard or download and save it.

[0444] Prompt Sentence Examples

[0445] "Generate a report that analyzes and summarizes your work based on Gmail and G Calendar data for the past three months."

[0446] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0447] Step 1:

[0448] A user accesses the system and logs in with their Google account.

[0449] Input: User's Google account information (email address, password)

[0450] How it works: A user opens a web browser or a dedicated application and accesses the system's URL. A login screen appears, and the user enters their Google account email address and password and clicks the "Next" button.

[0451] Output: User credentials

[0452] Step 2:

[0453] The device initiates the Google OAuth authentication flow and obtains an access token.

[0454] Input: User credentials

[0455] How it works: The device sends a request to Google's authorization server, initiating the OAuth flow to obtain an access token. The user's credentials are verified, and if successful, Google issues an access token. The device receives this token.

[0456] Output: Access token

[0457] Step 3:

[0458] The server uses the Google API to retrieve electronic messages and schedule information for a specified period.

[0459] Input: Access token, specified period (e.g., last month)

[0460] How it works: The server uses the access token to send a request to the Google API to retrieve email messages (Gmail) and event information (GCalendar) for the specified period. For example, endpoints such as "https: / / www.googleapis.com / gmail / v1 / users / me / messages" and "https: / / www.googleapis.com / calendar / v3 / calendars / primary / events" are used.

[0461] Output: Electronic messages and schedule information for a specified period

[0462] Step 4:

[0463] The server stores the retrieved electronic messages and schedule information in an internal database.

[0464] Input: Electronic messages and schedule information for a specified period

[0465] How it works: The server stores the retrieved data in an internal database. During this storage process, a record is created containing the body of each message, its metadata, and the calendar event details. For example, a SQL query is used to insert the record into the database.

[0466] Output: Data stored in the internal database

[0467] Step 5:

[0468] The server analyzes the stored data using natural language processing technology.

[0469] Input: Data stored in the internal database

[0470] How it works: The server uses natural language processing techniques (such as NLTK or spaCy) to analyze the stored data. Specifically, it tokenizes the content of electronic messages to extract frequently occurring keywords and topics. It also clusters the data and categorizes calendar events into categories such as "Meetings," "Reviews," and "Other."

[0471] Output: Analysis results (important keywords, topics, cluster classification)

[0472] Step 6:

[0473] The server generates a report based on the data analysis results.

[0474] Input: Analysis results

[0475] How it works: The server generates a report based on the analysis results. This report includes the number of messages in each cluster and the number of calendar events per category. It may also include information on the time spent on specific tasks as a trend analysis. The report is generated in PDF, Excel, and other formats.

[0476] Output: Report

[0477] Step 7:

[0478] The terminal displays the generated report to the user.

[0479] Input: Report

[0480] How it works: The device provides an interface via a web browser that displays reports in a dashboard format. Users can view the generated reports and download them as PDFs if desired.

[0481] Output: The report as seen by the user

[0482] (Application example 1)

[0483] Next, a description will be given of Application 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."

[0484] It is important to efficiently manage and analyze the work records and schedules of robots deployed in factories. Conventional methods require manually tracking and recording robot operation history and maintenance information, which is time-consuming and labor-intensive. In addition, it is not easy to extract important keywords and categories from large amounts of data, making it difficult for managers to accurately and quickly understand the work content. This hinders efficient robot operation and optimization of maintenance.

[0485] 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.

[0486] In this invention, the server includes means for acquiring electronic messages and schedule information for a specified period, means for saving the electronic messages and schedule information in an internal database, means for analyzing the saved electronic messages and schedule information using natural language processing technology, means for generating a report summarizing work content based on the analysis results, means for displaying the report to a user, means for automatically analyzing work records and schedules and extracting important keywords and categories, and means for acquiring robot operation history and maintenance information using a Google API. This allows for accurate and rapid understanding of robot work content, enabling efficient operation and optimization of maintenance.

[0487] An "electronic message" is an email or other form of communication sent or received digitally.

[0488] "Schedule information" refers to events and tasks registered in a calendar system or schedule management tool.

[0489] An "internal database" is a digital data storage system that stores and manages acquired data.

[0490] "Natural language processing technology" is a technology that allows computers to process the language that humans use on a daily basis.

[0491] "Text mining technology" is a technology that automatically extracts useful information from text data.

[0492] A "report" is a written or digital document that organizes and reports analytical results.

[0493] A "user" is a person or group that uses the system.

[0494] A "work record" is a log of the specific work performed by a robot.

[0495] A "schedule" is a plan of tasks or events to be carried out within a specific period of time.

[0496] A "keyword" is a word that is considered to be particularly important within a document or data.

[0497] A "category" is a classification item for organizing data and information.

[0498] A "robot" is a mechanical device that performs work automatically.

[0499] "Operation history" is a record of the robot's past actions and instructions.

[0500] "Maintenance information" is data related to the maintenance, inspection, and repair of robots.

[0501] "Google API" is an application programming interface provided by Google and is a means of accessing various data and services.

[0502] This system analyzes the work records and schedules of robots installed in factories, allowing users to easily understand the work content based on the analysis. This system automatically acquires electronic messages and schedule information for a specified period, stores them in an internal database, analyzes them using natural language processing technology, generates reports, and displays them to users, thereby supporting efficient work management.

[0503] System Configuration and Hardware / Software Usage

[0504] This system uses the following hardware and software:

[0505] Hardware

[0506] Robot-integrated processor and storage

[0507] server

[0508] Device (computer or tablet used by the administrator)

[0509] software

[0510] Google API (Google Calendar API, Gmail API)

[0511] Python libraries (pandas, TextBlob)

[0512] Google OAuth 2.0 authentication

[0513] Data Acquisition

[0514] Users access the system using a dedicated terminal and log in with their Google account. After logging in, the terminal initiates the Google OAuth authentication flow to obtain the necessary access token. This token grants access to retrieve email and calendar data for a specified period using Google APIs. The robot uses this access to automatically retrieve work logs and maintenance information.

[0515] Data storage

[0516] The server stores the captured electronic messages and schedule information in an internal database. This storage process preserves the body of each message, its metadata, and calendar event details. This stored data is used for subsequent analysis processes.

[0517] Analyzing the data

[0518] The server analyzes stored electronic messages and schedule information using text mining techniques. This analysis uses pandas data frames and the TextBlob library. The content of messages and events is clustered and key keywords and sentiment analysis is performed. Work records and schedules are organized by category, for example, "assembly," "maintenance," and "inspection."

[0519] Generate reports

[0520] The server generates a report summarizing the activities based on the analysis, including the number of messages and event categories for each cluster, as well as the time and frequency spent on each activity.

[0521] Viewing Reports

[0522] The terminal provides an interface for displaying the generated reports to the user, such as a dashboard viewable via a web browser or a downloadable report in PDF format, allowing the user to easily review and analyze past business operations.

[0523] Specific examples

[0524] For example, to analyze the work of factory robots in January 2023, Google Calendar events and Gmail messages from the past month can be retrieved, and TextBlob can be used to perform sentiment analysis and extract important keywords. A report can then be generated based on the results, displayed on the administrator's device, and downloaded in PDF format.

[0525] Prompt Sentence Examples

[0526] "Analyze the tasks performed by factory robots in January 2023 and perform key keywords and sentiment analysis. Collect data using the Google Calendar API and Gmail API and analyze it using TextBlob."

[0527] This system enables accurate and rapid understanding of the robot's operations, enabling efficient operation and optimization of maintenance.

[0528] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0529] Step 1:

[0530] Users access the system using a dedicated device and log in with their Google account. After logging in, the device initiates the Google OAuth authentication flow to obtain the necessary access token, which provides credentials that allow the server to use Google APIs to access email and calendar data for a specified period of time.

[0531] Input: Google account information, OAuth authentication request

[0532] Output: OAuth access token

[0533] Step 2:

[0534] The server uses the obtained access token to retrieve email and calendar data for the specified period through the Google API by sending an API request and receiving the data in response.

[0535] Input: OAuth access token, API request (specified period)

[0536] Output: Email data, calendar data

[0537] Step 3:

[0538] The server stores the retrieved email and calendar data in an internal database, which stores the body of each message, as well as metadata and calendar event details.

[0539] Input: Email data, calendar data

[0540] Output: Data stored in the internal database

[0541] Step 4:

[0542] The server analyzes email and calendar data stored in an internal database using text mining techniques, specifically converting the data into a data frame using the pandas library and performing sentiment analysis and keyword extraction using the TextBlob library.

[0543] Input: Data from the internal database

[0544] Output: Analysis results (sentiment analysis, keyword extraction)

[0545] Step 5:

[0546] The server generates a report summarizing the work based on the results of the text mining. This report includes the number of messages in each cluster and the number of events in each category. The report is generated in JSON or PDF format.

[0547] Input: Analysis results

[0548] Output: Report (JSON format, PDF format)

[0549] Step 6:

[0550] The terminal provides an interface that displays the generated reports to the user, who can view them via a web browser or download them as PDFs.

[0551] Input: Report (JSON format, PDF format)

[0552] Output: Displayed report, download link

[0553] Furthermore, 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.

[0554] The present invention is a system that automatically analyzes a user's work content over a specific period of time, allowing the user to easily understand that work. This system also provides a function that includes the user's emotional elements in the analysis of work content by combining it with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this invention are described below.

[0555] Data Acquisition

[0556] Users access the system via a web browser or a dedicated application and log in with their Google account. After logging in, users grant the system permission to access their email and calendar.

[0557] The device initiates the Google OAuth authentication flow in response to the user's actions and obtains the necessary access token, which grants access to retrieve email messages (Gmail) and event information (GCalendar) for a specified period using Google APIs.

[0558] The server uses the acquired token to retrieve electronic messages and schedule information for a specified period via Google API. The period to be retrieved is specified by the user, such as the past month or half year.

[0559] Data storage

[0560] The server stores the captured electronic messages and appointment information in an internal database. This process preserves the body of each message, its metadata, and calendar event details, ensuring the data is available for subsequent analytical processing.

[0561] Analyzing the data

[0562] The server analyzes stored electronic messages and schedule information using text mining technology and an emotion engine. The emotion engine extracts the user's emotional state from each message and event and stores it as data. The server analyzes the content of emails and classifies the work content using clustering technology. Calendar events are also categorized, for example, into categories such as "Meeting," "Review," and "Other." The emotion engine also analyzes the user's emotional tendencies for each cluster and category.

[0563] Generate reports

[0564] The server generates a report summarizing the work based on the data analysis results. This report shows the number of messages in each cluster and the number of events in each category, as well as the emotional trends for each cluster and category extracted by the emotion engine. For example, it can show whether users were feeling stressed about a particular project.

[0565] Viewing Reports

[0566] The terminal provides an interface for displaying the generated report to the user, for example, as a dashboard viewable via a web browser or as a downloadable PDF report, allowing users to easily review and analyze their past work history and understand their own emotional state during work.

[0567] Specific examples

[0568] For example, if a user wants to review the work they performed over the past month and their emotional state during that period, they first log in to the system, specify the period, and retrieve the data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. Next, the system analyzes the data using text mining technology and an emotion engine, and generates a report summarizing their work based on the results. The user can view this report on the dashboard or download and save it. The report also displays trends in the emotions the user felt regarding specific tasks, which can be used as a reference for managing stress and improving work.

[0569] As described above, the present invention provides a system that enables a user to accurately grasp the content of past work and the emotional state during that period, and supports efficient review.

[0570] The processing flow will be explained below.

[0571] The present invention is a system that efficiently analyzes and understands the work content of a user and generates a detailed report based on the user's emotional state. Specific embodiments will be described in detail below, divided into processing steps.

[0572] Processing flow

[0573] Step 1:

[0574] To log in to the system, users access the system's login page using a web browser or a dedicated application and authenticate using their Google account. If authentication is successful, the necessary permissions are granted to the system.

[0575] Step 2:

[0576] The device initiates the Google OAuth authentication flow when the user logs in. This flow obtains an access token and secures the authentication information required for future data retrieval.

[0577] Step 3:

[0578] The server uses the obtained access token to retrieve electronic messages and schedule information for the specified period via the Google API. Specifically, it retrieves electronic message data from the Gmail service and schedule information from the G Calendar service.

[0579] Step 4:

[0580] The server stores the retrieved electronic messages and appointment information in an internal database, including the body of each message, metadata, and calendar event details.

[0581] Step 5:

[0582] The server analyzes the data stored in its internal database using text mining technology. It uses clustering technology to classify the content of electronic messages and extract important keywords and topics. It also categorizes calendar events, dividing them into categories such as "Meetings," "Reviews," and "Other."

[0583] Step 6:

[0584] The server uses an emotion engine to analyze the user's emotional state from the stored electronic messages and schedule information, for example, determining whether the emotional state is positive, negative, or neutral based on the tone and word choice of the electronic messages.

[0585] Step 7:

[0586] Based on the results of the text mining and sentiment engine analysis, the server generates a report summarizing the business activities, including the number of messages in each cluster, the number of events in each category, and the sentiment trends for each cluster and category.

[0587] Step 8:

[0588] The terminal provides an interface for displaying the generated reports to the user, such as a dashboard viewable via a web browser or a downloadable PDF report, allowing the user to easily review and analyze past work and their emotional state during that period.

[0589] Specific examples

[0590] For example, if a user wants to review their work activities and emotional state over the past month, they first log in to the system and specify a period to retrieve data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. Next, the data is analyzed using text mining technology and an emotion engine, and a report summarizing their work activities is generated based on the results. Users can view this report on the dashboard or download and save it. The report also clearly shows emotional trends regarding specific tasks, providing valuable reference information for users to manage stress and improve their work.

[0591] As described above, the present invention provides a system that helps a user accurately understand the content of past work and efficiently review the work based on the user's emotional state during the work.

[0592] Example 2

[0593] Next, a description will be given of 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."

[0594] In today's business environment, users must manage and review numerous tasks, but this process is often laborious and inefficient. Furthermore, it is difficult to incorporate users' emotional states into task analysis, potentially resulting in missed insights for business improvement. This invention aims to solve this problem by automatically analyzing task content and emotional states over a specified period, allowing users to easily understand and manage their tasks.

[0595] 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.

[0596] In this invention, the server includes means for performing user authentication and obtaining access rights to business data for a specific period, means for obtaining electronic messages and schedule information for a specified period, means for storing the electronic messages and schedule information in an internal database, means for analyzing the stored electronic messages and schedule information using text mining technology and a sentiment analysis engine, means for generating a report summarizing business content based on the analysis results, and means for displaying the report to the user, thereby enabling the user to accurately grasp and efficiently review past business content and their emotional state during that period.

[0597] "User authentication" is the process of verifying the identity of users accessing a system and granting them appropriate access rights.

[0598] "Business data" refers to information generated or acquired by a user in connection with their business, including electronic messages and schedule information.

[0599] "Electronic Message" means an email or other digital message sent or received by a User.

[0600] "Schedule information" is data related to the user's schedule and calendar.

[0601] An "internal database" is a storage system for storing acquired data and using it for subsequent processing.

[0602] "Text mining technology" is a technology that uses natural language processing to analyze large amounts of text data and extract useful information.

[0603] An "emotion analysis engine" is a technology for automatically extracting and analyzing a user's emotional state from text data.

[0604] "Analysis Results" means information obtained from the analysis of data obtained using text mining techniques and sentiment analysis engines.

[0605] A "report summarizing business operations" is a report that briefly summarizes the user's business operations based on the analysis results.

[0606] "Means for displaying to the user" refers to an interface that allows the user to view the system operation results and reports.

[0607] MODE FOR CARRYING OUT THE INVENTION

[0608] The present invention is a system that automatically analyzes a user's work content and emotional state, allowing the user to easily understand it. The system acquires the user's electronic messages and schedule information for a specific period and stores them in an internal database. The system then analyzes the stored data using text mining technology and an emotion analysis engine, and generates a report summarizing the work content based on the results and displays it to the user.

[0609] User authentication and data acquisition methods

[0610] Users access the system using a web browser or a dedicated application and log in using their Google account as authentication. After logging in, users grant the system permission to access their electronic messages and schedule information.

[0611] When a user logs in with their Google account, the device initiates the Google OAuth authentication flow and obtains an authentication token. The obtained token is used to retrieve email messages (Gmail) and schedule information (GCalendar) for a specified period via the Google API. This period can be specified by the user as a specific period, such as the past month or half year.

[0612] The server uses the obtained authentication token to access the Google API and collects email and calendar information for the specified period. For example, if the user selects data from the past month, all Gmail and G Calendar events from that period will be collected.

[0613] Data storage format

[0614] The server stores the retrieved electronic messages and schedule information in an internal database, including the body, subject, sender information, received date, and attachment information for each email, and the title, date, and attendee list for each calendar event. The internal database is used to ensure the data necessary for subsequent analysis.

[0615] Forms of data analysis

[0616] The server analyzes the stored electronic messages and schedule information using text mining technology and a sentiment analysis engine. Specifically, it uses natural language processing technology to analyze the content of emails and categorize them by topic using a clustering algorithm.

[0617] Furthermore, a sentiment analysis engine is used to extract the user's emotional state from each email and calendar event. For example, it determines whether the email content is positive or negative, and stores the results as emotional data. Emotional trends by cluster and category are also analyzed.

[0618] Report generation and display formats

[0619] The server analyzes the data and generates a report summarizing the work, including the number of electronic messages in each cluster, the number of calendar events by category, and sentiment trends based on a sentiment analysis engine, such as the user's stress level or satisfaction with a particular project or meeting.

[0620] The terminal provides an interface for displaying the generated report to the user. The user can view the dashboard through a web browser. The generated report can also be downloaded in PDF format. This allows the user to easily check and analyze past work history and understand their own emotional state at work.

[0621] Examples of concrete examples and prompts

[0622] For example, if a user wants to review the work they did over the past month and their emotional state during that period, they first log in to the system and specify the "past month" period. The system automatically collects electronic messages and schedule information and stores it in an internal database. The server then analyzes the data using text mining technology and a sentiment analysis engine to generate a report summarizing their work. The user can view this report on the dashboard or download it as a PDF. The report also displays trends in the emotions the user felt regarding specific tasks, which can be used as a reference for managing their own stress and improving their work.

[0623] An example of a prompt to be input to a generative AI model would be:

[0624] "I want to look back at my work over the past month and my emotional state during that period. I log in with my Google account, specify the period, and retrieve the data. I then generate a report that shows my emotional trends."

[0625] As described above, the present invention provides a system that supports a user in accurately understanding and efficiently reviewing past work details and emotional states.

[0626] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0627] Step 1:

[0628] Users access the system through a web browser or a dedicated application and log in with their Google account. They then grant the system access to their electronic messages and schedule information.

[0629] Input: Google account credentials, grant access

[0630] Output: Get authentication token

[0631] Step 2:

[0632] The device starts the Google OAuth authentication flow when the user logs in. The device follows the OAuth flow to obtain an authentication token from Google.

[0633] Input: Google account credentials, OAuth authentication request

[0634] Output: Get authentication token

[0635] Step 3:

[0636] The device uses the obtained authentication token to send a request to the Google API to retrieve electronic messages (Gmail) and schedule information (GCalendar) for the specified period.

[0637] Input: Authentication token, specified period

[0638] Output: Electronic message and appointment information data

[0639] Step 4:

[0640] The server stores the electronic messages and schedule information sent from the terminals in an internal database, including details such as the body of each email, its subject, sender information, date of receipt, and attachment information, as well as the title, date, and attendee list of each calendar event.

[0641] Input: Data for electronic messages and appointment information

[0642] Output: Save to internal database

[0643] Step 5:

[0644] The server analyzes the stored electronic messages and schedule information using text mining technology and a sentiment analysis engine. Specifically, it uses natural language processing technology to analyze the content of emails and categorize them by topic. The sentiment analysis engine also extracts the emotional state of each message and event and stores it as data. This analysis allows the user's emotional tendencies to be identified for each cluster.

[0645] Input: Data from the internal database

[0646] Output: Text mining and sentiment analysis results

[0647] Step 6:

[0648] The server generates a report based on the analysis, summarizing work content and emotional trends, including the number of electronic messages in each cluster, the number of calendar events by category, and the emotional state of use, providing detailed insight into specific projects or meetings.

[0649] Input: Text mining and sentiment analysis results

[0650] Output: Summary report of work content and sentiment trends

[0651] Step 7:

[0652] The terminal displays the reports generated by the server to the user, which can be viewed in a web browser in dashboard format or downloaded as a PDF. The user can view the reports to understand their past work history and their emotional state during that period.

[0653] Input: Summary report of work and sentiment trends

[0654] Output: Display format for user viewing

[0655] (Application example 2)

[0656] Next, a description will be given of Application 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."

[0657] While traditional work management systems were capable of capturing and storing electronic messages and schedule information for a specified period, they had limitations in quickly and effectively understanding work content. Furthermore, because they did not take into account the user's emotional state, they provided insufficient feedback for stress management and work efficiency. These limitations had a negative impact on the quality of work and employee satisfaction, especially for staff working in remote environments such as virtual stores.

[0658] 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.

[0659] In this invention, the server includes means for acquiring electronic messages and schedule information for a specified period, means for storing the electronic messages and schedule information in an internal database, means for analyzing the stored electronic messages and schedule information using natural language processing technology, means for generating a report summarizing work content based on the analysis results, means for displaying the report to the user, means for analyzing the user's emotional state, means for including the emotional state in the report, and means for displaying the report on a smart terminal, smart glasses, or head-mounted display. This allows the user to quickly and effectively understand the work content and manage their emotional state, which is expected to improve the quality of work and increase employee satisfaction.

[0660] "Electronic Message" means an email sent or received between users.

[0661] "Schedule information" refers to information about a schedule registered in a calendar used by a user.

[0662] "Internal Database" means the digital storage used to store captured electronic messages and schedule information.

[0663] "Natural language processing technology" refers to all technologies that allow computers to analyze, understand, and generate human language.

[0664] A "report" is a document that summarizes the business activities and emotional state that have been captured and analyzed.

[0665] "Emotional state" refers to the type and intensity of emotions a user felt toward a task during a specific period of time.

[0666] A "smart device" is a mobile device with internet connectivity, such as a mobile phone or tablet.

[0667] "Smart glasses" are glasses-type wearable devices that incorporate display functionality.

[0668] A "head-mounted display" is a display device worn on the head, used to display information within the field of vision.

[0669] The present invention is a system that automatically analyzes the work content of a user over a specific period of time and displays emotional elements as well. Specific embodiments will be described below.

[0670] 1. Data Acquisition

[0671] Users access the system via a web browser or a dedicated application and enter their authentication information to log in. After the user logs in, the device uses APIs such as Google APIs to retrieve the user's electronic messages and schedule information for a specified period. Google OAuth authentication is used in this step.

[0672] 2. Data storage

[0673] The captured electronic messages and schedule information are stored on a server that stores them in an internal database, holding the body of each message, its metadata, and details of the calendar event.

[0674] 3. Data Analysis

[0675] The server analyzes the stored electronic messages and schedule information using natural language processing technology (e.g., text mining technology) and an emotion engine. The emotion engine extracts the user's emotional state and stores it as data. This allows the content of the work to be classified and emotional trends to be analyzed.

[0676] 4. Generate reports

[0677] The server then generates a report summarizing the business operations based on the analyzed data, including the emotional state of the users. The report displays the number of messages in each cluster, the number of events in each category, and the emotional trends extracted by the emotion engine.

[0678] 5. View the report

[0679] The terminal displays the generated report to the user. The display method can be a web browser, a smart device, smart glasses, or a head-mounted display (HMD). The user can easily view the report to understand the work and check the emotional state.

[0680] Hardware and software used

[0681] Hardware: Smartphones, smart glasses, head-mounted displays

[0682] Software: Google OAuth authentication, Google API, natural language processing technology (text mining technology), emotion engine

[0683] Specific examples

[0684] For example, if a virtual store staff member wants to review their work over the past month, they log in to the system and specify the period. The server automatically retrieves email and calendar data and stores it in an internal database. It then analyzes the data using text mining technology and an emotion engine to generate a report that includes both their work and their emotional state. This report can be viewed on a smartphone or HMD.

[0685] Example prompt for a generative AI model:

[0686] Summarize the work activities of virtual store staff over the past month and analyze their emotional state during those activities.

[0687] Data Used: Google Mail, Google Calendar

[0688] Extracted information: Business content (clustering), sentiment analysis (positive / negative)

[0689] This system allows users to comprehensively understand their work details and emotional state, which can be used to improve work performance and manage stress.

[0690] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0691] Step 1:

[0692] The user accesses the system via a web browser or a dedicated application and logs in using their Google account.

[0693] Input: Google account information

[0694] Output: Authentication token

[0695] How it works: After entering credentials, the device initiates the Google OAuth authentication flow to obtain the necessary access token.

[0696] Step 2:

[0697] The device uses the obtained authentication token to retrieve electronic messages and schedule information for the specified period from the Google API.

[0698] Input: Authentication token, for a specified period

[0699] Output: Electronic message data and schedule information data for the specified period

[0700] What it does: Retrieves data from Google Mail and Google Calendar for a specified period of time, with access based on an authentication token.

[0701] Step 3:

[0702] The server stores the retrieved electronic messages and schedule information in an internal database.

[0703] Input: Electronic message data and schedule information data for a specified period

[0704] Output: Data stored in the internal database

[0705] What it does: It formats the data it receives and stores the body of each message, along with metadata and calendar event details, in an internal database.

[0706] Step 4:

[0707] The server analyzes the stored electronic messages and schedule information using natural language processing techniques and an emotion engine.

[0708] Input: Data stored in the internal database

[0709] Output: Analysis results (classification of work content, extraction of emotional trends)

[0710] How it works: It uses natural language processing technology to perform text mining and clustering of business content, while simultaneously using an emotion engine to extract the user's emotional state from each message and event.

[0711] Step 5:

[0712] The server generates a report summarizing the work content based on the analysis results and including the emotional state.

[0713] Input: Analysis results

[0714] Output: Summary report of work content (including sentiment analysis results)

[0715] How it works: Consolidates the analysis results and creates a report summarizing the business activities and sentiment trends for each cluster and category.

[0716] Step 6:

[0717] The terminal displays the generated report to the user.

[0718] Input: Business Summary Report

[0719] Output: The report as seen by the user

[0720] How it works: Provides an interface that allows users to view reports using a web browser, smart device, smart glasses, or head-mounted display.

[0721] This series of steps allows users to quickly and effectively grasp their past work history and emotional state.

[0722] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0723] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with 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.

[0724] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0725] [Third embodiment]

[0726] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0727] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0728] 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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0729] 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.

[0730] 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 audio in accordance with instructions from the processor 46.

[0731] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0732] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0733] Fig. 6 shows an example of the 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.

[0734] 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.

[0735] 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.

[0736] In the headset type terminal 314, a 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.

[0737] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0738] The present invention is a system that automatically analyzes work content for a specific period of time, allowing a user to easily understand the work. A specific embodiment of this system will be described.

[0739] Data Acquisition

[0740] Users access the system via a web browser or a dedicated application and log in with their Google account. After logging in, users grant the system permission to access their email and calendar.

[0741] The device initiates the Google OAuth authentication flow in response to the user's actions and obtains the necessary access token, which grants access to retrieve email messages (Gmail) and event information (GCalendar) for a specified period using Google APIs.

[0742] The server uses the acquired token to retrieve electronic messages and schedule information for a specified period via Google API. The period to be retrieved is specified by the user, such as the past month or half year.

[0743] Data storage

[0744] The server stores the captured electronic messages and appointment information in an internal database. This process preserves the body of each message, its metadata, and calendar event details, ensuring the data is available for subsequent analytical processing.

[0745] Analyzing the data

[0746] The server uses text mining techniques to analyze stored electronic messages and schedule information. This involves clustering the content of electronic messages and extracting important keywords and topics. Calendar events are also categorized, for example, into categories such as "Meetings," "Reviews," and "Other."

[0747] Generate reports

[0748] The server analyzes the data and generates a report summarizing the work, including the number of messages in each cluster and the number of events in each category, and may also show trend analysis, such as how much time is spent on specific tasks.

[0749] Viewing Reports

[0750] The terminal provides an interface for displaying the generated reports to the user, for example, as a dashboard viewable via a web browser or as a downloadable PDF report, allowing users to easily check and analyze past business operations.

[0751] Specific examples

[0752] For example, if a user wants to review their work activities over the past month, they first log in to the system and specify the period to retrieve the data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. Next, the system uses text mining technology to analyze the data and generates a report summarizing their work activities based on the results. Users can view this report on their dashboard or download and save it.

[0753] As described above, the present invention provides a system that helps a user accurately understand the content of past work and efficiently review it.

[0754] The processing flow will be explained below.

[0755] Step 1:

[0756] To log in to the system, users access the system's login page via a web browser or a dedicated application and authenticate using their Google account. If authentication is successful, the necessary permissions are granted to the system.

[0757] Step 2:

[0758] The device initiates the Google OAuth authentication flow when the user logs in. This flow obtains an access token from the user's Google account. This token is required for subsequent data retrieval.

[0759] Step 3:

[0760] The server uses the obtained access token to retrieve electronic messages and schedule information for the specified period via Google APIs. Specifically, it retrieves email data from the Gmail service and calendar data from the G Calendar service.

[0761] Step 4:

[0762] The server stores the retrieved electronic messages and appointment information in an internal database, which includes the body of each message, metadata, and calendar event details.

[0763] Step 5:

[0764] The server uses text mining technology to analyze stored electronic messages and schedule information. It analyzes email content and classifies business activities using clustering technology. It also categorizes and analyzes calendar events.

[0765] Step 6:

[0766] The server generates a report summarizing the user's activities based on the results of text mining and clustering, including the number of messages in each cluster and the number of events in each category.

[0767] Step 7:

[0768] The terminal displays the generated report to the user, either as a dashboard on a web browser or as a downloadable PDF. The user can view this report and check their past work.

[0769] Example 1

[0770] Next, a description will be given of 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."

[0771] In recent years, many companies and businesses have required employees to review and analyze their work. However, manually extracting the necessary information from vast amounts of electronic messages and schedule information is extremely laborious. Rapidly identifying important keywords and topics and grasping work trends requires advanced analytical capabilities. While automated data analysis and summarization are needed to improve the efficiency of such work, existing technologies are not adequately capable.

[0772] 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.

[0773] In this invention, the server includes means for allowing a user to access the system using an account and granting access permission to electronic messages and schedule information for a specified period of time, means for authenticating access to the electronic messages and schedule information and obtaining an access token, means for obtaining the electronic messages and schedule information, means for storing the electronic messages and schedule information in an internal database, means for clustering the stored electronic messages and schedule information using natural language processing techniques, means for extracting important keywords and topics, means for generating a report based on the clustered data, and means for displaying the report to the user, thereby enabling the user to quickly and accurately understand the content of past work and efficiently review it.

[0774] A "user" is a person who uses the system, and is an entity who accesses the system and performs authentication and operations using his or her own account.

[0775] An "electronic message" is a digital communication sent or received in the form of electronic mail.

[0776] "Schedule information" refers to information about events and schedules recorded on an electronic calendar.

[0777] "Access permission" is the permission a user grants through authentication means for the system to access the user's electronic messages and schedule information.

[0778] An "access token" is a token that contains authentication information required for a system to obtain data from an external service.

[0779] "Natural language processing technology" is a technology for analyzing text data and understanding its meaning and structure.

[0780] "Clustering" is the process of grouping data that have similar characteristics.

[0781] A "keyword" is an important word that appears frequently in text data and characterizes its content.

[0782] A "topic" is a subject or theme of content to which multiple keywords are associated.

[0783] A "report" is a written or digital document that summarizes the results of data analysis in an easy-to-understand manner.

[0784] "Internal database" refers to data storage for saving and managing acquired data.

[0785] The present invention provides a system that automatically analyzes the content of work performed over a specific period of time, allowing users to easily understand the work. This system operates through the following steps.

[0786] First, users access the system through a web browser or a dedicated application. Then, they log in using their account and grant permission to access electronic messages and schedule information. The system does this through an authentication process using a Google account.

[0787] The device initiates the Google OAuth authentication flow in response to the user's operation and obtains the necessary access token. This authentication flow uses Google's authentication server to obtain an access token containing the user's authentication information. The obtained token is used to obtain electronic messages (Gmail) and appointment information (GCalendar) for a specified period using the Google API.

[0788] The server then uses the acquired token via Google API to retrieve electronic messages and schedule information for a specified period, such as the past month or the past six months. This data retrieval process is fully automated, minimizing user intervention.

[0789] The captured electronic messages and schedule information are then stored by the server in an internal database. This process preserves the body of each message, its metadata, and calendar event details, ensuring that the data required for subsequent analytical processing is preserved.

[0790] The server then analyzes the stored data using text mining techniques, such as natural language processing libraries like NLTK or spaCy. Specifically, the content of electronic messages is clustered to extract frequently occurring keywords and topics. Calendar events are also classified into categories like "Meetings," "Reviews," and "Other."

[0791] Based on these analyses, the server generates a report summarizing the work, including the number of messages and event categories for each cluster, as well as a trend analysis of the time spent on specific tasks.

[0792] Finally, the terminal provides an interface for displaying the generated reports to the user, who can view them via a web browser, for example in dashboard format, or download them as PDFs.

[0793] Specific examples

[0794] For example, if a user wants to review their work activities over the past month, they first log in to the system and specify the period to retrieve the data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. The data is then analyzed using text mining technology, and a report summarizing the work activities is generated based on the results. The user can view this report on the dashboard or download and save it.

[0795] Prompt Sentence Examples

[0796] "Generate a report that analyzes and summarizes your work based on Gmail and G Calendar data for the past three months."

[0797] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0798] Step 1:

[0799] A user accesses the system and logs in with their Google account.

[0800] Input: User's Google account information (email address, password)

[0801] How it works: A user opens a web browser or a dedicated application and accesses the system's URL. A login screen appears, and the user enters their Google account email address and password and clicks the "Next" button.

[0802] Output: User credentials

[0803] Step 2:

[0804] The device initiates the Google OAuth authentication flow and obtains an access token.

[0805] Input: User credentials

[0806] How it works: The device sends a request to Google's authorization server, initiating the OAuth flow to obtain an access token. The user's credentials are verified, and if successful, Google issues an access token. The device receives this token.

[0807] Output: Access token

[0808] Step 3:

[0809] The server uses the Google API to retrieve electronic messages and schedule information for a specified period.

[0810] Input: Access token, specified period (e.g., last month)

[0811] How it works: The server uses the access token to send a request to the Google API to retrieve email messages (Gmail) and event information (GCalendar) for the specified period. For example, endpoints such as "https: / / www.googleapis.com / gmail / v1 / users / me / messages" and "https: / / www.googleapis.com / calendar / v3 / calendars / primary / events" are used.

[0812] Output: Electronic messages and schedule information for a specified period

[0813] Step 4:

[0814] The server stores the retrieved electronic messages and schedule information in an internal database.

[0815] Input: Electronic messages and schedule information for a specified period

[0816] How it works: The server stores the retrieved data in an internal database. During this storage process, a record is created containing the body of each message, its metadata, and the calendar event details. For example, a SQL query is used to insert the record into the database.

[0817] Output: Data stored in the internal database

[0818] Step 5:

[0819] The server analyzes the stored data using natural language processing technology.

[0820] Input: Data stored in the internal database

[0821] How it works: The server uses natural language processing techniques (such as NLTK or spaCy) to analyze the stored data. Specifically, it tokenizes the content of electronic messages to extract frequently occurring keywords and topics. It also clusters the data and categorizes calendar events into categories such as "Meetings," "Reviews," and "Other."

[0822] Output: Analysis results (important keywords, topics, cluster classification)

[0823] Step 6:

[0824] The server generates a report based on the data analysis results.

[0825] Input: Analysis results

[0826] How it works: The server generates a report based on the analysis results. This report includes the number of messages in each cluster and the number of calendar events per category. It may also include information on the time spent on specific tasks as a trend analysis. The report is generated in PDF, Excel, and other formats.

[0827] Output: Report

[0828] Step 7:

[0829] The terminal displays the generated report to the user.

[0830] Input: Report

[0831] How it works: The device provides an interface via a web browser that displays reports in a dashboard format. Users can view the generated reports and download them as PDFs if desired.

[0832] Output: The report as seen by the user

[0833] (Application example 1)

[0834] Next, a description will be given of Application 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."

[0835] It is important to efficiently manage and analyze the work records and schedules of robots deployed in factories. Conventional methods require manually tracking and recording robot operation history and maintenance information, which is time-consuming and labor-intensive. In addition, it is not easy to extract important keywords and categories from large amounts of data, making it difficult for managers to accurately and quickly understand the work content. This hinders efficient robot operation and optimization of maintenance.

[0836] 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.

[0837] In this invention, the server includes means for acquiring electronic messages and schedule information for a specified period, means for saving the electronic messages and schedule information in an internal database, means for analyzing the saved electronic messages and schedule information using natural language processing technology, means for generating a report summarizing work content based on the analysis results, means for displaying the report to a user, means for automatically analyzing work records and schedules and extracting important keywords and categories, and means for acquiring robot operation history and maintenance information using a Google API. This allows for accurate and rapid understanding of robot work content, enabling efficient operation and optimization of maintenance.

[0838] An "electronic message" is an email or other form of communication sent or received digitally.

[0839] "Schedule information" refers to events and tasks registered in a calendar system or schedule management tool.

[0840] An "internal database" is a digital data storage system that stores and manages acquired data.

[0841] "Natural language processing technology" is a technology that allows computers to process the language that humans use on a daily basis.

[0842] "Text mining technology" is a technology that automatically extracts useful information from text data.

[0843] A "report" is a written or digital document that organizes and reports analytical results.

[0844] A "user" is a person or group that uses the system.

[0845] A "work record" is a log of the specific work performed by a robot.

[0846] A "schedule" is a plan of tasks or events to be carried out within a specific period of time.

[0847] A "keyword" is a word that is considered to be particularly important within a document or data.

[0848] A "category" is a classification item for organizing data and information.

[0849] A "robot" is a mechanical device that performs work automatically.

[0850] "Operation history" is a record of the robot's past actions and instructions.

[0851] "Maintenance information" is data related to the maintenance, inspection, and repair of robots.

[0852] "Google API" is an application programming interface provided by Google and is a means of accessing various data and services.

[0853] This system analyzes the work records and schedules of robots installed in factories, allowing users to easily understand the work content based on the analysis. This system automatically acquires electronic messages and schedule information for a specified period, stores them in an internal database, analyzes them using natural language processing technology, generates reports, and displays them to users, thereby supporting efficient work management.

[0854] System Configuration and Hardware / Software Usage

[0855] This system uses the following hardware and software:

[0856] Hardware

[0857] Robot-integrated processor and storage

[0858] server

[0859] Device (computer or tablet used by the administrator)

[0860] software

[0861] Google API (Google Calendar API, Gmail API)

[0862] Python libraries (pandas, TextBlob)

[0863] Google OAuth 2.0 authentication

[0864] Data Acquisition

[0865] Users access the system using a dedicated terminal and log in with their Google account. After logging in, the terminal initiates the Google OAuth authentication flow to obtain the necessary access token. This token grants access to retrieve email and calendar data for a specified period using Google APIs. The robot uses this access to automatically retrieve work logs and maintenance information.

[0866] Data storage

[0867] The server stores the captured electronic messages and schedule information in an internal database. This storage process preserves the body of each message, its metadata, and calendar event details. This stored data is used for subsequent analysis processes.

[0868] Analyzing the data

[0869] The server analyzes stored electronic messages and schedule information using text mining techniques. This analysis uses pandas data frames and the TextBlob library. The content of messages and events is clustered and key keywords and sentiment analysis is performed. Work records and schedules are organized by category, for example, "assembly," "maintenance," and "inspection."

[0870] Generate reports

[0871] The server generates a report summarizing the activities based on the analysis, including the number of messages and event categories for each cluster, as well as the time and frequency spent on each activity.

[0872] Viewing Reports

[0873] The terminal provides an interface for displaying the generated reports to the user, such as a dashboard viewable via a web browser or a downloadable report in PDF format, allowing the user to easily review and analyze past business operations.

[0874] Specific examples

[0875] For example, to analyze the work of factory robots in January 2023, Google Calendar events and Gmail messages from the past month can be retrieved, and TextBlob can be used to perform sentiment analysis and extract important keywords. A report can then be generated based on the results, displayed on the administrator's device, and downloaded in PDF format.

[0876] Prompt Sentence Examples

[0877] "Analyze the tasks performed by factory robots in January 2023 and perform key keywords and sentiment analysis. Collect data using the Google Calendar API and Gmail API and analyze it using TextBlob."

[0878] This system enables accurate and rapid understanding of the robot's operations, enabling efficient operation and optimization of maintenance.

[0879] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0880] Step 1:

[0881] Users access the system using a dedicated device and log in with their Google account. After logging in, the device initiates the Google OAuth authentication flow to obtain the necessary access token, which provides credentials that allow the server to use Google APIs to access email and calendar data for a specified period of time.

[0882] Input: Google account information, OAuth authentication request

[0883] Output: OAuth access token

[0884] Step 2:

[0885] The server uses the obtained access token to retrieve email and calendar data for the specified period through the Google API by sending an API request and receiving the data in response.

[0886] Input: OAuth access token, API request (specified period)

[0887] Output: Email data, calendar data

[0888] Step 3:

[0889] The server stores the retrieved email and calendar data in an internal database, which stores the body of each message, as well as metadata and calendar event details.

[0890] Input: Email data, calendar data

[0891] Output: Data stored in the internal database

[0892] Step 4:

[0893] The server analyzes email and calendar data stored in an internal database using text mining techniques, specifically converting the data into a data frame using the pandas library and performing sentiment analysis and keyword extraction using the TextBlob library.

[0894] Input: Data from the internal database

[0895] Output: Analysis results (sentiment analysis, keyword extraction)

[0896] Step 5:

[0897] The server generates a report summarizing the work based on the results of the text mining. This report includes the number of messages in each cluster and the number of events in each category. The report is generated in JSON or PDF format.

[0898] Input: Analysis results

[0899] Output: Report (JSON format, PDF format)

[0900] Step 6:

[0901] The terminal provides an interface that displays the generated reports to the user, who can view them via a web browser or download them as PDFs.

[0902] Input: Report (JSON format, PDF format)

[0903] Output: Displayed report, download link

[0904] Furthermore, 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.

[0905] The present invention is a system that automatically analyzes a user's work content over a specific period of time, allowing the user to easily understand that work. This system also provides a function that includes the user's emotional elements in the analysis of work content by combining it with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this invention are described below.

[0906] Data Acquisition

[0907] Users access the system via a web browser or a dedicated application and log in with their Google account. After logging in, users grant the system permission to access their email and calendar.

[0908] The device initiates the Google OAuth authentication flow in response to the user's actions and obtains the necessary access token, which grants access to retrieve email messages (Gmail) and event information (GCalendar) for a specified period using Google APIs.

[0909] The server uses the acquired token to retrieve electronic messages and schedule information for a specified period via Google API. The period to be retrieved is specified by the user, such as the past month or half year.

[0910] Data storage

[0911] The server stores the captured electronic messages and appointment information in an internal database. This process preserves the body of each message, its metadata, and calendar event details, ensuring the data is available for subsequent analytical processing.

[0912] Analyzing the data

[0913] The server analyzes stored electronic messages and schedule information using text mining technology and an emotion engine. The emotion engine extracts the user's emotional state from each message and event and stores it as data. The server analyzes the content of emails and classifies the work content using clustering technology. Calendar events are also categorized, for example, into categories such as "Meeting," "Review," and "Other." The emotion engine also analyzes the user's emotional tendencies for each cluster and category.

[0914] Generate reports

[0915] The server generates a report summarizing the work based on the data analysis results. This report shows the number of messages in each cluster and the number of events in each category, as well as the emotional trends for each cluster and category extracted by the emotion engine. For example, it can show whether users were feeling stressed about a particular project.

[0916] Viewing Reports

[0917] The terminal provides an interface for displaying the generated report to the user, for example, as a dashboard viewable via a web browser or as a downloadable PDF report, allowing users to easily review and analyze their past work history and understand their own emotional state during work.

[0918] Specific examples

[0919] For example, if a user wants to review the work they performed over the past month and their emotional state during that period, they first log in to the system, specify the period, and retrieve the data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. Next, the system analyzes the data using text mining technology and an emotion engine, and generates a report summarizing their work based on the results. The user can view this report on the dashboard or download and save it. The report also displays trends in the emotions the user felt regarding specific tasks, which can be used as a reference for managing stress and improving work.

[0920] As described above, the present invention provides a system that enables a user to accurately grasp the content of past work and the emotional state during that period, and supports efficient review.

[0921] The processing flow will be explained below.

[0922] The present invention is a system that efficiently analyzes and understands the work content of a user and generates a detailed report based on the user's emotional state. Specific embodiments will be described in detail below, divided into processing steps.

[0923] Processing flow

[0924] Step 1:

[0925] To log in to the system, users access the system's login page using a web browser or a dedicated application and authenticate using their Google account. If authentication is successful, the necessary permissions are granted to the system.

[0926] Step 2:

[0927] The device initiates the Google OAuth authentication flow when the user logs in. This flow obtains an access token and secures the authentication information required for future data retrieval.

[0928] Step 3:

[0929] The server uses the obtained access token to retrieve electronic messages and schedule information for the specified period via the Google API. Specifically, it retrieves electronic message data from the Gmail service and schedule information from the G Calendar service.

[0930] Step 4:

[0931] The server stores the retrieved electronic messages and appointment information in an internal database, including the body of each message, metadata, and calendar event details.

[0932] Step 5:

[0933] The server analyzes the data stored in its internal database using text mining technology. It uses clustering technology to classify the content of electronic messages and extract important keywords and topics. It also categorizes calendar events, dividing them into categories such as "Meetings," "Reviews," and "Other."

[0934] Step 6:

[0935] The server uses an emotion engine to analyze the user's emotional state from the stored electronic messages and schedule information, for example, determining whether the emotional state is positive, negative, or neutral based on the tone and word choice of the electronic messages.

[0936] Step 7:

[0937] Based on the results of the text mining and sentiment engine analysis, the server generates a report summarizing the business activities, including the number of messages in each cluster, the number of events in each category, and the sentiment trends for each cluster and category.

[0938] Step 8:

[0939] The terminal provides an interface for displaying the generated reports to the user, such as a dashboard viewable via a web browser or a downloadable PDF report, allowing the user to easily review and analyze past work and their emotional state during that period.

[0940] Specific examples

[0941] For example, if a user wants to review their work activities and emotional state over the past month, they first log in to the system and specify a period to retrieve data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. Next, the data is analyzed using text mining technology and an emotion engine, and a report summarizing their work activities is generated based on the results. Users can view this report on the dashboard or download and save it. The report also clearly shows emotional trends regarding specific tasks, providing valuable reference information for users to manage stress and improve their work.

[0942] As described above, the present invention provides a system that helps a user accurately understand the content of past work and efficiently review the work based on the user's emotional state during the work.

[0943] Example 2

[0944] Next, a description will be given of 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."

[0945] In today's business environment, users must manage and review numerous tasks, but this process is often laborious and inefficient. Furthermore, it is difficult to incorporate users' emotional states into task analysis, potentially resulting in missed insights for business improvement. This invention aims to solve this problem by automatically analyzing task content and emotional states over a specified period, allowing users to easily understand and manage their tasks.

[0946] 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.

[0947] In this invention, the server includes means for performing user authentication and obtaining access rights to business data for a specific period, means for obtaining electronic messages and schedule information for a specified period, means for storing the electronic messages and schedule information in an internal database, means for analyzing the stored electronic messages and schedule information using text mining technology and a sentiment analysis engine, means for generating a report summarizing business content based on the analysis results, and means for displaying the report to the user, thereby enabling the user to accurately grasp and efficiently review past business content and their emotional state during that period.

[0948] "User authentication" is the process of verifying the identity of users accessing a system and granting them appropriate access rights.

[0949] "Business data" refers to information generated or acquired by a user in connection with their business, including electronic messages and schedule information.

[0950] "Electronic Message" means an email or other digital message sent or received by a User.

[0951] "Schedule information" is data related to the user's schedule and calendar.

[0952] An "internal database" is a storage system for storing acquired data and using it for subsequent processing.

[0953] "Text mining technology" is a technology that uses natural language processing to analyze large amounts of text data and extract useful information.

[0954] An "emotion analysis engine" is a technology for automatically extracting and analyzing a user's emotional state from text data.

[0955] "Analysis Results" means information obtained from the analysis of data obtained using text mining techniques and sentiment analysis engines.

[0956] A "report summarizing business operations" is a report that briefly summarizes the user's business operations based on the analysis results.

[0957] "Means for displaying to the user" refers to an interface that allows the user to view the system operation results and reports.

[0958] MODE FOR CARRYING OUT THE INVENTION

[0959] The present invention is a system that automatically analyzes a user's work content and emotional state, allowing the user to easily understand it. The system acquires the user's electronic messages and schedule information for a specific period and stores them in an internal database. The system then analyzes the stored data using text mining technology and an emotion analysis engine, and generates a report summarizing the work content based on the results and displays it to the user.

[0960] User authentication and data acquisition methods

[0961] Users access the system using a web browser or a dedicated application and log in using their Google account as authentication. After logging in, users grant the system permission to access their electronic messages and schedule information.

[0962] When a user logs in with their Google account, the device initiates the Google OAuth authentication flow and obtains an authentication token. The obtained token is used to retrieve email messages (Gmail) and schedule information (GCalendar) for a specified period via the Google API. This period can be specified by the user as a specific period, such as the past month or half year.

[0963] The server uses the obtained authentication token to access the Google API and collects email and calendar information for the specified period. For example, if the user selects data from the past month, all Gmail and G Calendar events from that period will be collected.

[0964] Data storage format

[0965] The server stores the retrieved electronic messages and schedule information in an internal database, including the body, subject, sender information, received date, and attachment information for each email, and the title, date, and attendee list for each calendar event. The internal database is used to ensure the data necessary for subsequent analysis.

[0966] Forms of data analysis

[0967] The server analyzes the stored electronic messages and schedule information using text mining technology and a sentiment analysis engine. Specifically, it uses natural language processing technology to analyze the content of emails and categorize them by topic using a clustering algorithm.

[0968] Furthermore, a sentiment analysis engine is used to extract the user's emotional state from each email and calendar event. For example, it determines whether the email content is positive or negative, and stores the results as emotional data. Emotional trends by cluster and category are also analyzed.

[0969] Report generation and display formats

[0970] The server analyzes the data and generates a report summarizing the work, including the number of electronic messages in each cluster, the number of calendar events by category, and sentiment trends based on a sentiment analysis engine, such as the user's stress level or satisfaction with a particular project or meeting.

[0971] The terminal provides an interface for displaying the generated report to the user. The user can view the dashboard through a web browser. The generated report can also be downloaded in PDF format. This allows the user to easily check and analyze past work history and understand their own emotional state at work.

[0972] Examples of concrete examples and prompts

[0973] For example, if a user wants to review the work they did over the past month and their emotional state during that period, they first log in to the system and specify the "past month" period. The system automatically collects electronic messages and schedule information and stores it in an internal database. The server then analyzes the data using text mining technology and a sentiment analysis engine to generate a report summarizing their work. The user can view this report on the dashboard or download it as a PDF. The report also displays trends in the emotions the user felt regarding specific tasks, which can be used as a reference for managing their own stress and improving their work.

[0974] An example of a prompt to be input to a generative AI model would be:

[0975] "I want to look back at my work over the past month and my emotional state during that period. I log in with my Google account, specify the period, and retrieve the data. I then generate a report that shows my emotional trends."

[0976] As described above, the present invention provides a system that supports a user in accurately understanding and efficiently reviewing past work details and emotional states.

[0977] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0978] Step 1:

[0979] Users access the system through a web browser or a dedicated application and log in with their Google account. They then grant the system access to their electronic messages and schedule information.

[0980] Input: Google account credentials, grant access

[0981] Output: Get authentication token

[0982] Step 2:

[0983] The device starts the Google OAuth authentication flow when the user logs in. The device follows the OAuth flow to obtain an authentication token from Google.

[0984] Input: Google account credentials, OAuth authentication request

[0985] Output: Get authentication token

[0986] Step 3:

[0987] The device uses the obtained authentication token to send a request to the Google API to retrieve electronic messages (Gmail) and schedule information (GCalendar) for the specified period.

[0988] Input: Authentication token, specified period

[0989] Output: Electronic message and appointment information data

[0990] Step 4:

[0991] The server stores the electronic messages and schedule information sent from the terminals in an internal database, including details such as the body of each email, its subject, sender information, date of receipt, and attachment information, as well as the title, date, and attendee list of each calendar event.

[0992] Input: Data for electronic messages and appointment information

[0993] Output: Save to internal database

[0994] Step 5:

[0995] The server analyzes the stored electronic messages and schedule information using text mining technology and a sentiment analysis engine. Specifically, it uses natural language processing technology to analyze the content of emails and categorize them by topic. The sentiment analysis engine also extracts the emotional state of each message and event and stores it as data. This analysis allows the user's emotional tendencies to be identified for each cluster.

[0996] Input: Data from the internal database

[0997] Output: Text mining and sentiment analysis results

[0998] Step 6:

[0999] The server generates a report based on the analysis, summarizing work content and emotional trends, including the number of electronic messages in each cluster, the number of calendar events by category, and the emotional state of use, providing detailed insight into specific projects or meetings.

[1000] Input: Text mining and sentiment analysis results

[1001] Output: Summary report of work content and sentiment trends

[1002] Step 7:

[1003] The terminal displays the reports generated by the server to the user, which can be viewed in a web browser in dashboard format or downloaded as a PDF. The user can view the reports to understand their past work history and their emotional state during that period.

[1004] Input: Summary report of work and sentiment trends

[1005] Output: Display format for user viewing

[1006] (Application example 2)

[1007] Next, a description will be given of Application 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."

[1008] While traditional work management systems were capable of capturing and storing electronic messages and schedule information for a specified period, they had limitations in quickly and effectively understanding work content. Furthermore, because they did not take into account the user's emotional state, they provided insufficient feedback for stress management and work efficiency. These limitations had a negative impact on the quality of work and employee satisfaction, especially for staff working in remote environments such as virtual stores.

[1009] 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.

[1010] In this invention, the server includes means for acquiring electronic messages and schedule information for a specified period, means for storing the electronic messages and schedule information in an internal database, means for analyzing the stored electronic messages and schedule information using natural language processing technology, means for generating a report summarizing work content based on the analysis results, means for displaying the report to the user, means for analyzing the user's emotional state, means for including the emotional state in the report, and means for displaying the report on a smart terminal, smart glasses, or head-mounted display. This allows the user to quickly and effectively understand the work content and manage their emotional state, which is expected to improve the quality of work and increase employee satisfaction.

[1011] "Electronic Message" means an email sent or received between users.

[1012] "Schedule information" refers to information about a schedule registered in a calendar used by a user.

[1013] "Internal Database" means the digital storage used to store captured electronic messages and schedule information.

[1014] "Natural language processing technology" refers to all technologies that allow computers to analyze, understand, and generate human language.

[1015] A "report" is a document that summarizes the business activities and emotional state that have been captured and analyzed.

[1016] "Emotional state" refers to the type and intensity of emotions a user felt toward a task during a specific period of time.

[1017] A "smart device" is a mobile device with internet connectivity, such as a mobile phone or tablet.

[1018] "Smart glasses" are glasses-type wearable devices that incorporate display functionality.

[1019] A "head-mounted display" is a display device worn on the head, used to display information within the field of vision.

[1020] The present invention is a system that automatically analyzes the work content of a user over a specific period of time and displays emotional elements as well. Specific embodiments will be described below.

[1021] 1. Data Acquisition

[1022] Users access the system via a web browser or a dedicated application and enter their authentication information to log in. After the user logs in, the device uses APIs such as Google APIs to retrieve the user's electronic messages and schedule information for a specified period. Google OAuth authentication is used in this step.

[1023] 2. Data storage

[1024] The captured electronic messages and schedule information are stored on a server that stores them in an internal database, holding the body of each message, its metadata, and details of the calendar event.

[1025] 3. Data Analysis

[1026] The server analyzes the stored electronic messages and schedule information using natural language processing technology (e.g., text mining technology) and an emotion engine. The emotion engine extracts the user's emotional state and stores it as data. This allows the content of the work to be classified and emotional trends to be analyzed.

[1027] 4. Generate reports

[1028] The server then generates a report summarizing the business operations based on the analyzed data, including the emotional state of the users. The report displays the number of messages in each cluster, the number of events in each category, and the emotional trends extracted by the emotion engine.

[1029] 5. View the report

[1030] The terminal displays the generated report to the user. The display method can be a web browser, a smart device, smart glasses, or a head-mounted display (HMD). The user can easily view the report to understand the work and check the emotional state.

[1031] Hardware and software used

[1032] Hardware: Smartphones, smart glasses, head-mounted displays

[1033] Software: Google OAuth authentication, Google API, natural language processing technology (text mining technology), emotion engine

[1034] Specific examples

[1035] For example, if a virtual store staff member wants to review their work over the past month, they log in to the system and specify the period. The server automatically retrieves email and calendar data and stores it in an internal database. It then analyzes the data using text mining technology and an emotion engine to generate a report that includes both their work and their emotional state. This report can be viewed on a smartphone or HMD.

[1036] Example prompt for a generative AI model:

[1037] Summarize the work activities of virtual store staff over the past month and analyze their emotional state during those activities.

[1038] Data Used: Google Mail, Google Calendar

[1039] Extracted information: Business content (clustering), sentiment analysis (positive / negative)

[1040] This system allows users to comprehensively understand their work details and emotional state, which can be used to improve work performance and manage stress.

[1041] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1042] Step 1:

[1043] The user accesses the system via a web browser or a dedicated application and logs in using their Google account.

[1044] Input: Google account information

[1045] Output: Authentication token

[1046] How it works: After entering credentials, the device initiates the Google OAuth authentication flow to obtain the necessary access token.

[1047] Step 2:

[1048] The device uses the obtained authentication token to retrieve electronic messages and schedule information for the specified period from the Google API.

[1049] Input: Authentication token, for a specified period

[1050] Output: Electronic message data and schedule information data for the specified period

[1051] What it does: Retrieves data from Google Mail and Google Calendar for a specified period of time, with access based on an authentication token.

[1052] Step 3:

[1053] The server stores the retrieved electronic messages and schedule information in an internal database.

[1054] Input: Electronic message data and schedule information data for a specified period

[1055] Output: Data stored in the internal database

[1056] What it does: It formats the data it receives and stores the body of each message, along with metadata and calendar event details, in an internal database.

[1057] Step 4:

[1058] The server analyzes the stored electronic messages and schedule information using natural language processing techniques and an emotion engine.

[1059] Input: Data stored in the internal database

[1060] Output: Analysis results (classification of work content, extraction of emotional trends)

[1061] How it works: It uses natural language processing technology to perform text mining and clustering of business content, while simultaneously using an emotion engine to extract the user's emotional state from each message and event.

[1062] Step 5:

[1063] The server generates a report summarizing the work content based on the analysis results and including the emotional state.

[1064] Input: Analysis results

[1065] Output: Summary report of work content (including sentiment analysis results)

[1066] How it works: Consolidates the analysis results and creates a report summarizing the business activities and sentiment trends for each cluster and category.

[1067] Step 6:

[1068] The terminal displays the generated report to the user.

[1069] Input: Business Summary Report

[1070] Output: The report as seen by the user

[1071] How it works: Provides an interface that allows users to view reports using a web browser, smart device, smart glasses, or head-mounted display.

[1072] This series of steps allows users to quickly and effectively grasp their past work history and emotional state.

[1073] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[1074] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with 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.

[1075] 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.

[1076] [Fourth embodiment]

[1077] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1078] 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.

[1079] 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 WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1080] 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1081] 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 audio in accordance with instructions from the processor 46.

[1082] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1083] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1084] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[1085] Fig. 8 shows an example of the 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.

[1086] 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.

[1087] 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.

[1088] In the robot 414, the processor 46 performs the reception output process. 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.

[1089] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. 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."

[1090] The present invention is a system that automatically analyzes work content for a specific period of time, allowing a user to easily understand the work. A specific embodiment of this system will be described.

[1091] Data Acquisition

[1092] Users access the system via a web browser or a dedicated application and log in with their Google account. After logging in, users grant the system permission to access their email and calendar.

[1093] The device initiates the Google OAuth authentication flow in response to the user's actions and obtains the necessary access token, which grants access to retrieve email messages (Gmail) and event information (GCalendar) for a specified period using Google APIs.

[1094] The server uses the acquired token to retrieve electronic messages and schedule information for a specified period via Google API. The period to be retrieved is specified by the user, such as the past month or half year.

[1095] Data storage

[1096] The server stores the captured electronic messages and appointment information in an internal database. This process preserves the body of each message, its metadata, and calendar event details, ensuring the data is available for subsequent analytical processing.

[1097] Analyzing the data

[1098] The server uses text mining techniques to analyze stored electronic messages and schedule information. This involves clustering the content of electronic messages and extracting important keywords and topics. Calendar events are also categorized, for example, into categories such as "Meetings," "Reviews," and "Other."

[1099] Generate reports

[1100] The server analyzes the data and generates a report summarizing the work, including the number of messages in each cluster and the number of events in each category, and may also show trend analysis, such as how much time is spent on specific tasks.

[1101] Viewing Reports

[1102] The terminal provides an interface for displaying the generated reports to the user, for example, as a dashboard viewable via a web browser or as a downloadable PDF report, allowing users to easily check and analyze past business operations.

[1103] Specific examples

[1104] For example, if a user wants to review their work activities over the past month, they first log in to the system and specify the period to retrieve the data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. Next, the system uses text mining technology to analyze the data and generates a report summarizing their work activities based on the results. Users can view this report on their dashboard or download and save it.

[1105] As described above, the present invention provides a system that helps a user accurately understand the content of past work and efficiently review it.

[1106] The processing flow will be explained below.

[1107] Step 1:

[1108] To log in to the system, users access the system's login page via a web browser or a dedicated application and authenticate using their Google account. If authentication is successful, the necessary permissions are granted to the system.

[1109] Step 2:

[1110] The device initiates the Google OAuth authentication flow when the user logs in. This flow obtains an access token from the user's Google account. This token is required for subsequent data retrieval.

[1111] Step 3:

[1112] The server uses the obtained access token to retrieve electronic messages and schedule information for the specified period via Google APIs. Specifically, it retrieves email data from the Gmail service and calendar data from the G Calendar service.

[1113] Step 4:

[1114] The server stores the retrieved electronic messages and appointment information in an internal database, which includes the body of each message, metadata, and calendar event details.

[1115] Step 5:

[1116] The server uses text mining technology to analyze stored electronic messages and schedule information. It analyzes email content and classifies business activities using clustering technology. It also categorizes and analyzes calendar events.

[1117] Step 6:

[1118] The server generates a report summarizing the user's activities based on the results of text mining and clustering, including the number of messages in each cluster and the number of events in each category.

[1119] Step 7:

[1120] The terminal displays the generated report to the user, either as a dashboard on a web browser or as a downloadable PDF. The user can view this report and check their past work.

[1121] Example 1

[1122] Next, a description will be given of 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."

[1123] In recent years, many companies and businesses have required employees to review and analyze their work. However, manually extracting the necessary information from vast amounts of electronic messages and schedule information is extremely laborious. Rapidly identifying important keywords and topics and grasping work trends requires advanced analytical capabilities. While automated data analysis and summarization are needed to improve the efficiency of such work, existing technologies are not adequately capable.

[1124] 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.

[1125] In this invention, the server includes means for allowing a user to access the system using an account and granting access permission to electronic messages and schedule information for a specified period of time, means for authenticating access to the electronic messages and schedule information and obtaining an access token, means for obtaining the electronic messages and schedule information, means for storing the electronic messages and schedule information in an internal database, means for clustering the stored electronic messages and schedule information using natural language processing techniques, means for extracting important keywords and topics, means for generating a report based on the clustered data, and means for displaying the report to the user, thereby enabling the user to quickly and accurately understand the content of past work and efficiently review it.

[1126] A "user" is a person who uses the system, and is an entity who accesses the system and performs authentication and operations using his or her own account.

[1127] An "electronic message" is a digital communication sent or received in the form of electronic mail.

[1128] "Schedule information" refers to information about events and schedules recorded on an electronic calendar.

[1129] "Access permission" is the permission a user grants through authentication means for the system to access the user's electronic messages and schedule information.

[1130] An "access token" is a token that contains authentication information required for a system to obtain data from an external service.

[1131] "Natural language processing technology" is a technology for analyzing text data and understanding its meaning and structure.

[1132] "Clustering" is the process of grouping data that have similar characteristics.

[1133] A "keyword" is an important word that appears frequently in text data and characterizes its content.

[1134] A "topic" is a subject or theme of content to which multiple keywords are associated.

[1135] A "report" is a written or digital document that summarizes the results of data analysis in an easy-to-understand manner.

[1136] "Internal database" refers to data storage for saving and managing acquired data.

[1137] The present invention provides a system that automatically analyzes the content of work performed over a specific period of time, allowing users to easily understand the work. This system operates through the following steps.

[1138] First, users access the system through a web browser or a dedicated application. Then, they log in using their account and grant permission to access electronic messages and schedule information. The system does this through an authentication process using a Google account.

[1139] The device initiates the Google OAuth authentication flow in response to the user's operation and obtains the necessary access token. This authentication flow uses Google's authentication server to obtain an access token containing the user's authentication information. The obtained token is used to obtain electronic messages (Gmail) and appointment information (GCalendar) for a specified period using the Google API.

[1140] The server then uses the acquired token via Google API to retrieve electronic messages and schedule information for a specified period, such as the past month or the past six months. This data retrieval process is fully automated, minimizing user intervention.

[1141] The captured electronic messages and schedule information are then stored by the server in an internal database. This process preserves the body of each message, its metadata, and calendar event details, ensuring that the data required for subsequent analytical processing is preserved.

[1142] The server then analyzes the stored data using text mining techniques, such as natural language processing libraries like NLTK or spaCy. Specifically, the content of electronic messages is clustered to extract frequently occurring keywords and topics. Calendar events are also classified into categories like "Meetings," "Reviews," and "Other."

[1143] Based on these analyses, the server generates a report summarizing the work, including the number of messages and event categories for each cluster, as well as a trend analysis of the time spent on specific tasks.

[1144] Finally, the terminal provides an interface for displaying the generated reports to the user, who can view them via a web browser, for example in dashboard format, or download them as PDFs.

[1145] Specific examples

[1146] For example, if a user wants to review their work activities over the past month, they first log in to the system and specify the period to retrieve the data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. The data is then analyzed using text mining technology, and a report summarizing the work activities is generated based on the results. The user can view this report on the dashboard or download and save it.

[1147] Prompt Sentence Examples

[1148] "Generate a report that analyzes and summarizes your work based on Gmail and G Calendar data for the past three months."

[1149] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1150] Step 1:

[1151] A user accesses the system and logs in with their Google account.

[1152] Input: User's Google account information (email address, password)

[1153] How it works: A user opens a web browser or a dedicated application and accesses the system's URL. A login screen appears, and the user enters their Google account email address and password and clicks the "Next" button.

[1154] Output: User credentials

[1155] Step 2:

[1156] The device initiates the Google OAuth authentication flow and obtains an access token.

[1157] Input: User credentials

[1158] How it works: The device sends a request to Google's authorization server, initiating the OAuth flow to obtain an access token. The user's credentials are verified, and if successful, Google issues an access token. The device receives this token.

[1159] Output: Access token

[1160] Step 3:

[1161] The server uses the Google API to retrieve electronic messages and schedule information for a specified period.

[1162] Input: Access token, specified period (e.g., last month)

[1163] How it works: The server uses the access token to send a request to the Google API to retrieve email messages (Gmail) and event information (GCalendar) for the specified period. For example, endpoints such as "https: / / www.googleapis.com / gmail / v1 / users / me / messages" and "https: / / www.googleapis.com / calendar / v3 / calendars / primary / events" are used.

[1164] Output: Electronic messages and schedule information for a specified period

[1165] Step 4:

[1166] The server stores the retrieved electronic messages and schedule information in an internal database.

[1167] Input: Electronic messages and schedule information for a specified period

[1168] How it works: The server stores the retrieved data in an internal database. During this storage process, a record is created containing the body of each message, its metadata, and the calendar event details. For example, a SQL query is used to insert the record into the database.

[1169] Output: Data stored in the internal database

[1170] Step 5:

[1171] The server analyzes the stored data using natural language processing technology.

[1172] Input: Data stored in the internal database

[1173] How it works: The server uses natural language processing techniques (such as NLTK or spaCy) to analyze the stored data. Specifically, it tokenizes the content of electronic messages to extract frequently occurring keywords and topics. It also clusters the data and categorizes calendar events into categories such as "Meetings," "Reviews," and "Other."

[1174] Output: Analysis results (important keywords, topics, cluster classification)

[1175] Step 6:

[1176] The server generates a report based on the data analysis results.

[1177] Input: Analysis results

[1178] How it works: The server generates a report based on the analysis results. This report includes the number of messages in each cluster and the number of calendar events per category. It may also include information on the time spent on specific tasks as a trend analysis. The report is generated in PDF, Excel, and other formats.

[1179] Output: Report

[1180] Step 7:

[1181] The terminal displays the generated report to the user.

[1182] Input: Report

[1183] How it works: The device provides an interface via a web browser that displays reports in a dashboard format. Users can view the generated reports and download them as PDFs if desired.

[1184] Output: The report as seen by the user

[1185] (Application example 1)

[1186] Next, a description will be given of Application 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."

[1187] It is important to efficiently manage and analyze the work records and schedules of robots deployed in factories. Conventional methods require manually tracking and recording robot operation history and maintenance information, which is time-consuming and labor-intensive. In addition, it is not easy to extract important keywords and categories from large amounts of data, making it difficult for managers to accurately and quickly understand the work content. This hinders efficient robot operation and optimization of maintenance.

[1188] 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.

[1189] In this invention, the server includes means for acquiring electronic messages and schedule information for a specified period, means for saving the electronic messages and schedule information in an internal database, means for analyzing the saved electronic messages and schedule information using natural language processing technology, means for generating a report summarizing work content based on the analysis results, means for displaying the report to a user, means for automatically analyzing work records and schedules and extracting important keywords and categories, and means for acquiring robot operation history and maintenance information using a Google API. This allows for accurate and rapid understanding of robot work content, enabling efficient operation and optimization of maintenance.

[1190] An "electronic message" is an email or other form of communication sent or received digitally.

[1191] "Schedule information" refers to events and tasks registered in a calendar system or schedule management tool.

[1192] An "internal database" is a digital data storage system that stores and manages acquired data.

[1193] "Natural language processing technology" is a technology that allows computers to process the language that humans use on a daily basis.

[1194] "Text mining technology" is a technology that automatically extracts useful information from text data.

[1195] A "report" is a written or digital document that organizes and reports analytical results.

[1196] A "user" is a person or group that uses the system.

[1197] A "work record" is a log of the specific work performed by a robot.

[1198] A "schedule" is a plan of tasks or events to be carried out within a specific period of time.

[1199] A "keyword" is a word that is considered to be particularly important within a document or data.

[1200] A "category" is a classification item for organizing data and information.

[1201] A "robot" is a mechanical device that performs work automatically.

[1202] "Operation history" is a record of the robot's past actions and instructions.

[1203] "Maintenance information" is data related to the maintenance, inspection, and repair of robots.

[1204] "Google API" is an application programming interface provided by Google and is a means of accessing various data and services.

[1205] This system analyzes the work records and schedules of robots installed in factories, allowing users to easily understand the work content based on the analysis. This system automatically acquires electronic messages and schedule information for a specified period, stores them in an internal database, analyzes them using natural language processing technology, generates reports, and displays them to users, thereby supporting efficient work management.

[1206] System Configuration and Hardware / Software Usage

[1207] This system uses the following hardware and software:

[1208] Hardware

[1209] Robot-integrated processor and storage

[1210] server

[1211] Device (computer or tablet used by the administrator)

[1212] software

[1213] Google API (Google Calendar API, Gmail API)

[1214] Python libraries (pandas, TextBlob)

[1215] Google OAuth 2.0 authentication

[1216] Data Acquisition

[1217] Users access the system using a dedicated terminal and log in with their Google account. After logging in, the terminal initiates the Google OAuth authentication flow to obtain the necessary access token. This token grants access to retrieve email and calendar data for a specified period using Google APIs. The robot uses this access to automatically retrieve work logs and maintenance information.

[1218] Data storage

[1219] The server stores the captured electronic messages and schedule information in an internal database. This storage process preserves the body of each message, its metadata, and calendar event details. This stored data is used for subsequent analysis processes.

[1220] Analyzing the data

[1221] The server analyzes stored electronic messages and schedule information using text mining techniques. This analysis uses pandas data frames and the TextBlob library. The content of messages and events is clustered and key keywords and sentiment analysis is performed. Work records and schedules are organized by category, for example, "assembly," "maintenance," and "inspection."

[1222] Generate reports

[1223] The server generates a report summarizing the activities based on the analysis, including the number of messages and event categories for each cluster, as well as the time and frequency spent on each activity.

[1224] Viewing Reports

[1225] The terminal provides an interface for displaying the generated reports to the user, such as a dashboard viewable via a web browser or a downloadable report in PDF format, allowing the user to easily review and analyze past business operations.

[1226] Specific examples

[1227] For example, to analyze the work of factory robots in January 2023, Google Calendar events and Gmail messages from the past month can be retrieved, and TextBlob can be used to perform sentiment analysis and extract important keywords. A report can then be generated based on the results, displayed on the administrator's device, and downloaded in PDF format.

[1228] Prompt Sentence Examples

[1229] "Analyze the tasks performed by factory robots in January 2023 and perform key keywords and sentiment analysis. Collect data using the Google Calendar API and Gmail API and analyze it using TextBlob."

[1230] This system enables accurate and rapid understanding of the robot's operations, enabling efficient operation and optimization of maintenance.

[1231] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1232] Step 1:

[1233] Users access the system using a dedicated device and log in with their Google account. After logging in, the device initiates the Google OAuth authentication flow to obtain the necessary access token, which provides credentials that allow the server to use Google APIs to access email and calendar data for a specified period of time.

[1234] Input: Google account information, OAuth authentication request

[1235] Output: OAuth access token

[1236] Step 2:

[1237] The server uses the obtained access token to retrieve email and calendar data for the specified period through the Google API by sending an API request and receiving the data in response.

[1238] Input: OAuth access token, API request (specified period)

[1239] Output: Email data, calendar data

[1240] Step 3:

[1241] The server stores the retrieved email and calendar data in an internal database, which stores the body of each message, as well as metadata and calendar event details.

[1242] Input: Email data, calendar data

[1243] Output: Data stored in the internal database

[1244] Step 4:

[1245] The server analyzes email and calendar data stored in an internal database using text mining techniques, specifically converting the data into a data frame using the pandas library and performing sentiment analysis and keyword extraction using the TextBlob library.

[1246] Input: Data from the internal database

[1247] Output: Analysis results (sentiment analysis, keyword extraction)

[1248] Step 5:

[1249] The server generates a report summarizing the work based on the results of the text mining. This report includes the number of messages in each cluster and the number of events in each category. The report is generated in JSON or PDF format.

[1250] Input: Analysis results

[1251] Output: Report (JSON format, PDF format)

[1252] Step 6:

[1253] The terminal provides an interface that displays the generated reports to the user, who can view them via a web browser or download them as PDFs.

[1254] Input: Report (JSON format, PDF format)

[1255] Output: Displayed report, download link

[1256] Furthermore, 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.

[1257] The present invention is a system that automatically analyzes a user's work content over a specific period of time, allowing the user to easily understand that work. This system also provides a function that includes the user's emotional elements in the analysis of work content by combining it with an emotion engine that recognizes the user's emotions. Specific embodiments for implementing this invention are described below.

[1258] Data Acquisition

[1259] Users access the system via a web browser or a dedicated application and log in with their Google account. After logging in, users grant the system permission to access their email and calendar.

[1260] The device initiates the Google OAuth authentication flow in response to the user's actions and obtains the necessary access token, which grants access to retrieve email messages (Gmail) and event information (GCalendar) for a specified period using Google APIs.

[1261] The server uses the acquired token to retrieve electronic messages and schedule information for a specified period via Google API. The period to be retrieved is specified by the user, such as the past month or half year.

[1262] Data storage

[1263] The server stores the captured electronic messages and appointment information in an internal database. This process preserves the body of each message, its metadata, and calendar event details, ensuring the data is available for subsequent analytical processing.

[1264] Analyzing the data

[1265] The server analyzes stored electronic messages and schedule information using text mining technology and an emotion engine. The emotion engine extracts the user's emotional state from each message and event and stores it as data. The server analyzes the content of emails and classifies the work content using clustering technology. Calendar events are also categorized, for example, into categories such as "Meeting," "Review," and "Other." The emotion engine also analyzes the user's emotional tendencies for each cluster and category.

[1266] Generate reports

[1267] The server generates a report summarizing the work based on the data analysis results. This report shows the number of messages in each cluster and the number of events in each category, as well as the emotional trends for each cluster and category extracted by the emotion engine. For example, it can show whether users were feeling stressed about a particular project.

[1268] Viewing Reports

[1269] The terminal provides an interface for displaying the generated report to the user, for example, as a dashboard viewable via a web browser or as a downloadable PDF report, allowing users to easily review and analyze their past work history and understand their own emotional state during work.

[1270] Specific examples

[1271] For example, if a user wants to review the work they performed over the past month and their emotional state during that period, they first log in to the system, specify the period, and retrieve the data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. Next, the system analyzes the data using text mining technology and an emotion engine, and generates a report summarizing their work based on the results. The user can view this report on the dashboard or download and save it. The report also displays trends in the emotions the user felt regarding specific tasks, which can be used as a reference for managing stress and improving work.

[1272] As described above, the present invention provides a system that enables a user to accurately grasp the content of past work and the emotional state during that period, and supports efficient review.

[1273] The processing flow will be explained below.

[1274] The present invention is a system that efficiently analyzes and understands the work content of a user and generates a detailed report based on the user's emotional state. Specific embodiments will be described in detail below, divided into processing steps.

[1275] Processing flow

[1276] Step 1:

[1277] To log in to the system, users access the system's login page using a web browser or a dedicated application and authenticate using their Google account. If authentication is successful, the necessary permissions are granted to the system.

[1278] Step 2:

[1279] The device initiates the Google OAuth authentication flow when the user logs in. This flow obtains an access token and secures the authentication information required for future data retrieval.

[1280] Step 3:

[1281] The server uses the obtained access token to retrieve electronic messages and schedule information for the specified period via the Google API. Specifically, it retrieves electronic message data from the Gmail service and schedule information from the G Calendar service.

[1282] Step 4:

[1283] The server stores the retrieved electronic messages and appointment information in an internal database, including the body of each message, metadata, and calendar event details.

[1284] Step 5:

[1285] The server analyzes the data stored in its internal database using text mining technology. It uses clustering technology to classify the content of electronic messages and extract important keywords and topics. It also categorizes calendar events, dividing them into categories such as "Meetings," "Reviews," and "Other."

[1286] Step 6:

[1287] The server uses an emotion engine to analyze the user's emotional state from the stored electronic messages and schedule information, for example, determining whether the emotional state is positive, negative, or neutral based on the tone and word choice of the electronic messages.

[1288] Step 7:

[1289] Based on the results of the text mining and sentiment engine analysis, the server generates a report summarizing the business activities, including the number of messages in each cluster, the number of events in each category, and the sentiment trends for each cluster and category.

[1290] Step 8:

[1291] The terminal provides an interface for displaying the generated reports to the user, such as a dashboard viewable via a web browser or a downloadable PDF report, allowing the user to easily review and analyze past work and their emotional state during that period.

[1292] Specific examples

[1293] For example, if a user wants to review their work activities and emotional state over the past month, they first log in to the system and specify a period to retrieve data. The server then automatically collects electronic messages and schedule information and stores it in an internal database. Next, the data is analyzed using text mining technology and an emotion engine, and a report summarizing their work activities is generated based on the results. Users can view this report on the dashboard or download and save it. The report also clearly shows emotional trends regarding specific tasks, providing valuable reference information for users to manage stress and improve their work.

[1294] As described above, the present invention provides a system that helps a user accurately understand the content of past work and efficiently review the work based on the user's emotional state during the work.

[1295] Example 2

[1296] Next, a description will be given of 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."

[1297] In today's business environment, users must manage and review numerous tasks, but this process is often laborious and inefficient. Furthermore, it is difficult to incorporate users' emotional states into task analysis, potentially resulting in missed insights for business improvement. This invention aims to solve this problem by automatically analyzing task content and emotional states over a specified period, allowing users to easily understand and manage their tasks.

[1298] 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.

[1299] In this invention, the server includes means for performing user authentication and obtaining access rights to business data for a specific period, means for obtaining electronic messages and schedule information for a specified period, means for storing the electronic messages and schedule information in an internal database, means for analyzing the stored electronic messages and schedule information using text mining technology and a sentiment analysis engine, means for generating a report summarizing business content based on the analysis results, and means for displaying the report to the user, thereby enabling the user to accurately grasp and efficiently review past business content and their emotional state during that period.

[1300] "User authentication" is the process of verifying the identity of users accessing a system and granting them appropriate access rights.

[1301] "Business data" refers to information generated or acquired by a user in connection with their business, including electronic messages and schedule information.

[1302] "Electronic Message" means an email or other digital message sent or received by a User.

[1303] "Schedule information" is data related to the user's schedule and calendar.

[1304] An "internal database" is a storage system for storing acquired data and using it for subsequent processing.

[1305] "Text mining technology" is a technology that uses natural language processing to analyze large amounts of text data and extract useful information.

[1306] An "emotion analysis engine" is a technology for automatically extracting and analyzing a user's emotional state from text data.

[1307] "Analysis Results" means information obtained from the analysis of data obtained using text mining techniques and sentiment analysis engines.

[1308] A "report summarizing business operations" is a report that briefly summarizes the user's business operations based on the analysis results.

[1309] "Means for displaying to the user" refers to an interface that allows the user to view the system operation results and reports.

[1310] MODE FOR CARRYING OUT THE INVENTION

[1311] The present invention is a system that automatically analyzes a user's work content and emotional state, allowing the user to easily understand it. The system acquires the user's electronic messages and schedule information for a specific period and stores them in an internal database. The system then analyzes the stored data using text mining technology and an emotion analysis engine, and generates a report summarizing the work content based on the results and displays it to the user.

[1312] User authentication and data acquisition methods

[1313] Users access the system using a web browser or a dedicated application and log in using their Google account as authentication. After logging in, users grant the system permission to access their electronic messages and schedule information.

[1314] When a user logs in with their Google account, the device initiates the Google OAuth authentication flow and obtains an authentication token. The obtained token is used to retrieve email messages (Gmail) and schedule information (GCalendar) for a specified period via the Google API. This period can be specified by the user as a specific period, such as the past month or half year.

[1315] The server uses the obtained authentication token to access the Google API and collects email and calendar information for the specified period. For example, if the user selects data from the past month, all Gmail and G Calendar events from that period will be collected.

[1316] Data storage format

[1317] The server stores the retrieved electronic messages and schedule information in an internal database, including the body, subject, sender information, received date, and attachment information for each email, and the title, date, and attendee list for each calendar event. The internal database is used to ensure the data necessary for subsequent analysis.

[1318] Forms of data analysis

[1319] The server analyzes the stored electronic messages and schedule information using text mining technology and a sentiment analysis engine. Specifically, it uses natural language processing technology to analyze the content of emails and categorize them by topic using a clustering algorithm.

[1320] Furthermore, a sentiment analysis engine is used to extract the user's emotional state from each email and calendar event. For example, it determines whether the email content is positive or negative, and stores the results as emotional data. Emotional trends by cluster and category are also analyzed.

[1321] Report generation and display formats

[1322] The server analyzes the data and generates a report summarizing the work, including the number of electronic messages in each cluster, the number of calendar events by category, and sentiment trends based on a sentiment analysis engine, such as the user's stress level or satisfaction with a particular project or meeting.

[1323] The terminal provides an interface for displaying the generated report to the user. The user can view the dashboard through a web browser. The generated report can also be downloaded in PDF format. This allows the user to easily check and analyze past work history and understand their own emotional state at work.

[1324] Examples of concrete examples and prompts

[1325] For example, if a user wants to review the work they did over the past month and their emotional state during that period, they first log in to the system and specify the "past month" period. The system automatically collects electronic messages and schedule information and stores it in an internal database. The server then analyzes the data using text mining technology and a sentiment analysis engine to generate a report summarizing their work. The user can view this report on the dashboard or download it as a PDF. The report also displays trends in the emotions the user felt regarding specific tasks, which can be used as a reference for managing their own stress and improving their work.

[1326] An example of a prompt to be input to a generative AI model would be:

[1327] "I want to look back at my work over the past month and my emotional state during that period. I log in with my Google account, specify the period, and retrieve the data. I then generate a report that shows my emotional trends."

[1328] As described above, the present invention provides a system that supports a user in accurately understanding and efficiently reviewing past work details and emotional states.

[1329] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1330] Step 1:

[1331] Users access the system through a web browser or a dedicated application and log in with their Google account. They then grant the system access to their electronic messages and schedule information.

[1332] Input: Google account credentials, grant access

[1333] Output: Get authentication token

[1334] Step 2:

[1335] The device starts the Google OAuth authentication flow when the user logs in. The device follows the OAuth flow to obtain an authentication token from Google.

[1336] Input: Google account credentials, OAuth authentication request

[1337] Output: Get authentication token

[1338] Step 3:

[1339] The device uses the obtained authentication token to send a request to the Google API to retrieve electronic messages (Gmail) and schedule information (GCalendar) for the specified period.

[1340] Input: Authentication token, specified period

[1341] Output: Electronic message and appointment information data

[1342] Step 4:

[1343] The server stores the electronic messages and schedule information sent from the terminals in an internal database, including details such as the body of each email, its subject, sender information, date of receipt, and attachment information, as well as the title, date, and attendee list of each calendar event.

[1344] Input: Data for electronic messages and appointment information

[1345] Output: Save to internal database

[1346] Step 5:

[1347] The server analyzes the stored electronic messages and schedule information using text mining technology and a sentiment analysis engine. Specifically, it uses natural language processing technology to analyze the content of emails and categorize them by topic. The sentiment analysis engine also extracts the emotional state of each message and event and stores it as data. This analysis allows the user's emotional tendencies to be identified for each cluster.

[1348] Input: Data from the internal database

[1349] Output: Text mining and sentiment analysis results

[1350] Step 6:

[1351] The server generates a report based on the analysis, summarizing work content and emotional trends, including the number of electronic messages in each cluster, the number of calendar events by category, and the emotional state of use, providing detailed insight into specific projects or meetings.

[1352] Input: Text mining and sentiment analysis results

[1353] Output: Summary report of work content and sentiment trends

[1354] Step 7:

[1355] The terminal displays the reports generated by the server to the user, which can be viewed in a web browser in dashboard format or downloaded as a PDF. The user can view the reports to understand their past work history and their emotional state during that period.

[1356] Input: Summary report of work and sentiment trends

[1357] Output: Display format for user viewing

[1358] (Application example 2)

[1359] Next, a description will be given of Application 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."

[1360] While traditional work management systems were capable of capturing and storing electronic messages and schedule information for a specified period, they had limitations in quickly and effectively understanding work content. Furthermore, because they did not take into account the user's emotional state, they provided insufficient feedback for stress management and work efficiency. These limitations had a negative impact on the quality of work and employee satisfaction, especially for staff working in remote environments such as virtual stores.

[1361] 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.

[1362] In this invention, the server includes means for acquiring electronic messages and schedule information for a specified period, means for storing the electronic messages and schedule information in an internal database, means for analyzing the stored electronic messages and schedule information using natural language processing technology, means for generating a report summarizing work content based on the analysis results, means for displaying the report to the user, means for analyzing the user's emotional state, means for including the emotional state in the report, and means for displaying the report on a smart terminal, smart glasses, or head-mounted display. This allows the user to quickly and effectively understand the work content and manage their emotional state, which is expected to improve the quality of work and increase employee satisfaction.

[1363] "Electronic Message" means an email sent or received between users.

[1364] "Schedule information" refers to information about a schedule registered in a calendar used by a user.

[1365] "Internal Database" means the digital storage used to store captured electronic messages and schedule information.

[1366] "Natural language processing technology" refers to all technologies that allow computers to analyze, understand, and generate human language.

[1367] A "report" is a document that summarizes the business activities and emotional state that have been captured and analyzed.

[1368] "Emotional state" refers to the type and intensity of emotions a user felt toward a task during a specific period of time.

[1369] A "smart device" is a mobile device with internet connectivity, such as a mobile phone or tablet.

[1370] "Smart glasses" are glasses-type wearable devices that incorporate display functionality.

[1371] A "head-mounted display" is a display device worn on the head, used to display information within the field of vision.

[1372] The present invention is a system that automatically analyzes the work content of a user over a specific period of time and displays emotional elements as well. Specific embodiments will be described below.

[1373] 1. Data Acquisition

[1374] Users access the system via a web browser or a dedicated application and enter their authentication information to log in. After the user logs in, the device uses APIs such as Google APIs to retrieve the user's electronic messages and schedule information for a specified period. Google OAuth authentication is used in this step.

[1375] 2. Data storage

[1376] The captured electronic messages and schedule information are stored on a server that stores them in an internal database, holding the body of each message, its metadata, and details of the calendar event.

[1377] 3. Data Analysis

[1378] The server analyzes the stored electronic messages and schedule information using natural language processing technology (e.g., text mining technology) and an emotion engine. The emotion engine extracts the user's emotional state and stores it as data. This allows the content of the work to be classified and emotional trends to be analyzed.

[1379] 4. Generate reports

[1380] The server then generates a report summarizing the business operations based on the analyzed data, including the emotional state of the users. The report displays the number of messages in each cluster, the number of events in each category, and the emotional trends extracted by the emotion engine.

[1381] 5. View the report

[1382] The terminal displays the generated report to the user. The display method can be a web browser, a smart device, smart glasses, or a head-mounted display (HMD). The user can easily view the report to understand the work and check the emotional state.

[1383] Hardware and software used

[1384] Hardware: Smartphones, smart glasses, head-mounted displays

[1385] Software: Google OAuth authentication, Google API, natural language processing technology (text mining technology), emotion engine

[1386] Specific examples

[1387] For example, if a virtual store staff member wants to review their work over the past month, they log in to the system and specify the period. The server automatically retrieves email and calendar data and stores it in an internal database. It then analyzes the data using text mining technology and an emotion engine to generate a report that includes both their work and their emotional state. This report can be viewed on a smartphone or HMD.

[1388] Example prompt for a generative AI model:

[1389] Summarize the work activities of virtual store staff over the past month and analyze their emotional state during those activities.

[1390] Data Used: Google Mail, Google Calendar

[1391] Extracted information: Business content (clustering), sentiment analysis (positive / negative)

[1392] This system allows users to comprehensively understand their work details and emotional state, which can be used to improve work performance and manage stress.

[1393] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1394] Step 1:

[1395] The user accesses the system via a web browser or a dedicated application and logs in using their Google account.

[1396] Input: Google account information

[1397] Output: Authentication token

[1398] How it works: After entering credentials, the device initiates the Google OAuth authentication flow to obtain the necessary access token.

[1399] Step 2:

[1400] The device uses the obtained authentication token to retrieve electronic messages and schedule information for the specified period from the Google API.

[1401] Input: Authentication token, for a specified period

[1402] Output: Electronic message data and schedule information data for the specified period

[1403] What it does: Retrieves data from Google Mail and Google Calendar for a specified period of time, with access based on an authentication token.

[1404] Step 3:

[1405] The server stores the retrieved electronic messages and schedule information in an internal database.

[1406] Input: Electronic message data and schedule information data for a specified period

[1407] Output: Data stored in the internal database

[1408] What it does: It formats the data it receives and stores the body of each message, along with metadata and calendar event details, in an internal database.

[1409] Step 4:

[1410] The server analyzes the stored electronic messages and schedule information using natural language processing techniques and an emotion engine.

[1411] Input: Data stored in the internal database

[1412] Output: Analysis results (classification of work content, extraction of emotional trends)

[1413] How it works: It uses natural language processing technology to perform text mining and clustering of business content, while simultaneously using an emotion engine to extract the user's emotional state from each message and event.

[1414] Step 5:

[1415] The server generates a report summarizing the work content based on the analysis results and including the emotional state.

[1416] Input: Analysis results

[1417] Output: Summary report of work content (including sentiment analysis results)

[1418] How it works: Consolidates the analysis results and creates a report summarizing the business activities and sentiment trends for each cluster and category.

[1419] Step 6:

[1420] The terminal displays the generated report to the user.

[1421] Input: Business Summary Report

[1422] Output: The report as seen by the user

[1423] How it works: Provides an interface that allows users to view reports using a web browser, smart device, smart glasses, or head-mounted display.

[1424] This series of steps allows users to quickly and effectively grasp their past work history and emotional state.

[1425] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding 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.

[1426] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with 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.

[1427] 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 robot 414.

[1428] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1429] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1430] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1431] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1432] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1433] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1434] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1435] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1436] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1437] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1438] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1439] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1440] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1441] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1442] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1443] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1444] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1445] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1446] The following is further disclosed regarding the above embodiment.

[1447] (Claim 1)

[1448] means for obtaining electronic messages and schedule information for a specified period of time;

[1449] means for storing said electronic messages and schedule information in an internal database;

[1450] means for analyzing the stored electronic messages and schedule information using natural language processing techniques;

[1451] A means for generating a report summarizing the work based on the analysis results;

[1452] means for displaying said report to a user;

[1453] A system including:

[1454] (Claim 2)

[1455] 10. The system of claim 1, wherein the electronic message and schedule information is email and calendar data.

[1456] (Claim 3)

[1457] 2. The system of claim 1, wherein the natural language processing technology includes text mining technology.

[1458] "Example 1"

[1459] (Claim 1)

[1460] a means by which users access the system using accounts and grant access to electronic messages and schedule information for specified periods of time;

[1461] means for authenticating access to said electronic messages and schedule information and obtaining an access token;

[1462] means for obtaining said electronic message and schedule information;

[1463] means for storing said electronic messages and schedule information in an internal database;

[1464] means for clustering the stored electronic messages and schedule information using natural language processing techniques;

[1465] A means of extracting important keywords and topics,

[1466] means for generating a report based on the clustered data;

[1467] means for displaying said report to a user;

[1468] A system including:

[1469] (Claim 2)

[1470] 10. The system of claim 1, wherein the electronic message and schedule information is email and calendar data.

[1471] (Claim 3)

[1472] 2. The system of claim 1, wherein the natural language processing technology includes text mining technology.

[1473] "Application Example 1"

[1474] (Claim 1)

[1475] means for obtaining electronic messages and schedule information for a specified period of time;

[1476] means for storing said electronic messages and schedule information in an internal database;

[1477] means for analyzing the stored electronic messages and schedule information using natural language processing techniques;

[1478] A means for generating a report summarizing the work based on the analysis results;

[1479] means for displaying said report to a user;

[1480] A method for automatically analyzing work records and schedules and extracting important keywords and categories;

[1481] A system that includes a means of obtaining robot operation history and maintenance information using Google API.

[1482] (Claim 2)

[1483] 10. The system of claim 1, wherein the electronic messages and schedule information are email and calendar data.

[1484] (Claim 3)

[1485] 2. The system of claim 1, wherein the natural language processing technology includes text mining technology.

[1486] "Example 2: Combining Emotion Engines"

[1487] (Claim 1)

[1488] a means for performing user authentication and obtaining access to business data for a specific period of time;

[1489] means for obtaining electronic messages and schedule information for a specified period of time;

[1490] means for storing said electronic messages and schedule information in an internal database;

[1491] means for analyzing the stored electronic messages and schedule information using text mining techniques and a sentiment analysis engine;

[1492] A means for generating a report summarizing the work based on the analysis results;

[1493] means for displaying said report to a user;

[1494] A system including:

[1495] (Claim 2)

[1496] 10. The system of claim 1, wherein the electronic message and schedule information is email and calendar data.

[1497] (Claim 3)

[1498] 2. The system according to claim 1, wherein the text mining technology and the emotion analysis engine are used to extract the user's emotional state and analyze the emotional tendency for each cluster and category.

[1499] "Application example 2 when combining emotion engines"

[1500] (Claim 1)

[1501] means for obtaining electronic messages and schedule information for a specified period of time;

[1502] means for storing said electronic messages and schedule information in an internal database;

[1503] means for analyzing the stored electronic messages and schedule information using natural language processing techniques;

[1504] A means for generating a report summarizing the work based on the analysis results;

[1505] means for displaying said report to a user;

[1506] means for analyzing the emotional state of a user;

[1507] means for including said emotional state in a report;

[1508] a means for displaying on a smart device, smart glasses, or head-mounted display;

[1509] A system including:

[1510] (Claim 2)

[1511] 10. The system of claim 1, wherein the electronic message and schedule information is email and calendar data.

[1512] (Claim 3)

[1513] 2. The system of claim 1, wherein the natural language processing technology includes text mining technology. [Explanation of symbols]

[1514] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for obtaining electronic messages and schedule information for a specified period of time; means for storing said electronic messages and schedule information in an internal database; means for analyzing the stored electronic messages and schedule information using natural language processing techniques; A means for generating a report summarizing the work based on the analysis results; means for displaying said report to a user; A system including:

2. 2. The system of claim 1, wherein the electronic message and schedule information is email and calendar data.

3. The system of claim 1 , wherein the natural language processing techniques include text mining techniques.

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