system

Abstract

We provide the system. [Solution] A means of obtaining activity information from a communication device, A means for analyzing the aforementioned activity information to quantify working hours and results, A means of making suggestions regarding the use of public services by urban residents based on the aforementioned work hours, results, and efficiency scores, A means for visually presenting the aforementioned proposal on a communication device, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The modern workplace environment is rapidly shifting to a hybrid form that combines remote work and commuting to the office. However, in this process, it has become more difficult to manage the performance of employees. While working from home provides flexibility, there are problems such as the difficulty in seeing the work efficiency and deliverables of employees, and the difficulty in making fair evaluations. In addition, self-management by employees themselves is not always effective. For example, they often waste time in emails and meetings and are unable to manage time efficiently. The purpose of the present invention is to provide a system that visualizes the activities of employees in such a hybrid workplace environment and enables accurate and fair performance evaluations.

Means for Solving the Problems

[0005] This invention provides a means for analyzing activity data acquired from a user's terminal device and quantifying work time and output. Specifically, it builds a system that collects activity data including emails, messages, online meeting contributions, and file operation history, and calculates the user's efficiency score based on this data. It also uses natural language processing technology to evaluate the content of contributions and generates indicators that estimate the importance and results of tasks. The calculated efficiency score is then visually presented to the user as a dashboard or report, supporting improved self-management and a fairer evaluation. This makes employee performance more transparent and allows for maximizing the advantages of both remote work and in-office work.

[0006] A "terminal device" is a hardware device used by a user, and includes devices such as computers, laptops, tablets, and smartphones.

[0007] "Activity data" refers to information related to user actions and behaviors, including data such as emails, message exchanges, online meeting comments, and file operation history.

[0008] "Analysis" is the process of thoroughly examining and interpreting acquired data and extracting necessary information.

[0009] "Working hours" refer to the time a user spends engaged in their assigned duties, and are typically recorded as working hours.

[0010] "Output" refers to the deliverables and results generated as a result of a user's work activities, and can take the form of documents, reports, sales figures, completed projects, etc.

[0011] An "efficiency score" is a numerical indicator that quantifies the efficiency of a user's work activities, and is evaluated based on time allocation and the quality of output.

[0012] "Natural language processing technology" refers to AI technology used by computers to understand, interpret, and generate human language.

[0013] "Visual presentation" refers to a technique used to display data and information to users in a graphical format to aid their understanding. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

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

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

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

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

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

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

[0022] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0035] This invention provides a management system for improving work efficiency by acquiring data related to the user's work activities from terminal devices installed in the user's work environment and analyzing it on a server. Specific embodiments thereof are described below.

[0036] First, a dedicated software agent is installed on the terminal device. This agent is configured to collect user activity data from email, messaging applications, online meeting applications, and file management tools. Activity data includes, for example, email sending and receiving times, meeting participation times, and file creation and editing history.

[0037] The terminal device periodically collects activity data and transmits it to the server based on a specific encryption protocol. This ensures data privacy and security.

[0038] The server analyzes the received activity data in real time. The analysis process includes data classification, time measurement, and content evaluation of statements and messages using natural language processing techniques. This analysis quantifies how much time users spent on each task, the importance of their actions, and their efficiency.

[0039] After analysis, the server calculates an efficiency score based on the results obtained, indicating the user's work efficiency. This score takes into account factors such as how work time is used, the quality of task deliverables, and the impact of contributions.

[0040] The server then visually displays the generated efficiency score and analysis results, providing them to users and administrators in a dashboard format. This includes, for example, the time allocation for each task, importance scores, and suggestions for improvement.

[0041] Users can use this dashboard to review their work schedules and consider improvements as needed. Similarly, administrators can understand the overall team performance and provide appropriate feedback.

[0042] This system enables users to achieve effective time management and improved performance, regardless of whether they are working from home or in the office.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device collects activity data based on user actions. This collection is done in real time and includes sending and receiving emails, exchanging messages, participating in online meetings, and creating and editing files. The collected data is temporarily stored in the device's local storage.

[0046] Step 2:

[0047] The device encrypts the collected activity data at predetermined time intervals and transfers it to the server using a secure communication protocol. This process ensures data security and privacy.

[0048] Step 3:

[0049] The server begins analyzing the activity data received from the terminal. First, it categorizes each data point into a specific task category (e.g., responding to emails, attending meetings, creating documents). Next, it calculates the time spent on each task to determine how the user allocates their work time.

[0050] Step 4:

[0051] The server uses natural language processing techniques to further analyze emails, messages, and meeting transcripts. This analysis extracts important keywords and context from the text data and evaluates how much they contribute to business operations.

[0052] Step 5:

[0053] The server calculates the user's efficiency score based on the analysis results. The score is derived by comprehensively evaluating factors such as time allocation, the quality of task deliverables, and the content and frequency of contributions.

[0054] Step 6:

[0055] The server generates dashboards for users and administrators that visualize the analysis results. These dashboards include daily and weekly efficiency scores, time usage percentages, and suggestions for areas where improvement can be made.

[0056] Step 7:

[0057] Users review the provided dashboard and reflect on their daily work activities. This allows them to develop action plans for improving time management and efficient work execution.

[0058] The above outlines the specific processing flow of the program. This system allows users to objectively evaluate their own performance and gain insights into improving work efficiency.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] With the increasing diversification of work environments, there is a growing need to accurately understand users' work efficiency and improve their performance. However, traditional methods have made it difficult to comprehensively and accurately evaluate user activities, and have failed to identify areas for improvement and provide specific advice. Furthermore, data privacy and security have not been adequately ensured.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for transmitting activity information through a protected communication channel, means for analyzing the activity information to quantify work time and work results, and means for creating a visual display that suggests work improvements to the user and administrator based on the analysis results. This makes it possible to more accurately evaluate the user's work efficiency and to present specific improvement measures.

[0064] "Activity information" refers to information generated by user actions, including work time, work results, communication content, network meeting content, and data file operation history.

[0065] An "information processing device" is a terminal device operated by a user to collect and transmit activity information.

[0066] "Work time" refers to the time a user spends on a specific task or activity, and is recorded as activity information.

[0067] "Work output" refers to the quantitative measure of the quality of deliverables or outputs achieved through the user's work.

[0068] A "business efficiency evaluation index" is an index that quantifies business efficiency based on the user's work time and work results.

[0069] A "protected channel" is a communication path that uses encryption technology to protect activity information for secure transmission.

[0070] "Machine learning technology" is artificial intelligence technology that analyzes large amounts of data to evaluate user communication content and work results.

[0071] "Visual display" refers to a visualized data display that intuitively presents analyzed activity information and operational efficiency evaluation indicators to users and administrators.

[0072] This invention is a system that improves work efficiency by collecting and analyzing user activity information using a software agent installed on the user's information processing device.

[0073] The terminal (the user's information processing device) collects activity information such as electronic communications, interactive communications, network meeting content, and data file operation history through a software agent. This agent automatically records data generated during the user's work. For example, the agent logs how much time the user spends processing emails and periodically encrypts this data before sending it to the server.

[0074] The server first decodes the activity information received from the terminal. Next, it analyzes the data using machine learning techniques to evaluate work time and work results. Furthermore, it uses natural language processing techniques to evaluate the user's communication content and meeting contributions, and calculates work efficiency evaluation metrics. Based on these metrics, the server generates advice to improve the user's work efficiency.

[0075] Users can understand their work trends and areas for improvement through visual displays provided by the server. These visual displays include, for example, a heatmap of the user's weekly activities and specific suggestions for improving work efficiency. This allows users to review their work schedules and optimize their workflows as needed.

[0076] For example, when a user reviews a visual summary of their weekly activities, they might realize they're spending a lot of time attending meetings and develop a strategy to improve their work efficiency by reducing that time. An example of a prompt would be, "Please analyze your work activities this week and suggest ways to improve efficiency."

[0077] This system allows users to objectively understand their work efficiency and clearly identify areas for improvement, enabling them to work flexibly even when working from home or on the go.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The terminal activates a software agent to collect user activity information. This agent retrieves user operation data from email clients, scheduling applications, online meeting tools, and other sources. Inputs include sending and receiving emails, joining meetings, sending chat messages, and document editing history. This data is collected and recorded as activity data.

[0081] Step 2:

[0082] The terminal encrypts the collected activity data using a specific encryption method and sends it to the server using a secure communication protocol. The input is the activity data collected in step 1, and the output is the encrypted data. Specifically, the system uses protocols such as TLS to transmit the data, preventing eavesdropping and tampering.

[0083] Step 3:

[0084] The server receives encrypted data sent from the terminal and decrypts it. The input is encrypted activity data, and the output is decrypted activity data. After the decryption process is completed, the data is passed to the analysis engine.

[0085] Step 4:

[0086] The server analyzes the received activity data. Here, natural language processing techniques are used to analyze the content of emails and messages, and time tracking techniques are used to measure the time spent on each task. The input is decoded activity data, and the output is quantified work time and work results as analysis outcomes.

[0087] Step 5:

[0088] The server calculates a business efficiency evaluation index based on the analysis results. Specifically, it evaluates how efficiently users are spending their time on each task. The input is the analysis results obtained in step 4, and the output is the business efficiency evaluation index. A machine learning model is used to analyze user activity patterns.

[0089] Step 6:

[0090] The server generates visualized reports for users and administrators based on operational efficiency metrics and analysis results. The input is operational efficiency metrics, and the output is a visually displayed report. This report includes suggestions for improvement and efficiency scores for each task, and is provided as a dashboard for direct use by users.

[0091] Step 7:

[0092] Users utilize a dashboard provided by the server to check their work efficiency and revise their schedules and work methods as needed. Input is a visually displayed report, and output is improved work performance. In this way, users can optimize their work style more efficiently.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] In smart cities, accurately collecting and analyzing data on daily activities and providing immediate feedback to citizens is necessary to efficiently manage citizens' work and use of public services and to improve the quality of life for them. Conventional systems have challenges such as insufficient collection of activity information and ineffective communication of analysis results to citizens, thus failing to contribute to efficiency improvements.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes means for acquiring activity information from a communication device, means for analyzing the activity information to quantify work time and results, and means for making suggestions to urban residents regarding their use of public services based on the work time, results, and efficiency score. This enables citizens to understand their own activities and choose the optimal lifestyle.

[0098] A "communication device" is a digital device used by a user that enables the acquisition of activity information and the transmission and reception of data via the internet.

[0099] "Activity information" refers to data related to a user's daily work activities and use of public services, and includes communication history, electronic communication messages, statements made in virtual meetings, and data file processing history.

[0100] "Working hours" refers to the time a user spends performing a specific work activity, and it is an important indicator in evaluating efficiency.

[0101] "Results" refer to the specific outputs that users produce as a result of their work activities, and these can be used to measure work efficiency.

[0102] The "efficiency score" is a numerical value calculated based on the user's work time and results, and is an indicator for quantitatively evaluating the user's work efficiency.

[0103] "Suggestions" refer to guidelines and improvement advice for users derived from analyzed activity information and efficiency scores.

[0104] Morphological analysis is a technique in natural language processing that breaks down a sentence into its smallest constituent units and analyzes the meaning and grammatical role of each word.

[0105] The system for realizing this invention uses a combination of a communication device, a server, and advanced data analysis software. The communication device acquires data on the user's daily activities, i.e., information related to work activities and use of public services. This includes communication history, electronic communication messages, statements made in virtual meetings, and data file processing history. This activity information is transmitted to the server using a secure protocol.

[0106] The server uses Python®-based analysis tools (Pandas, Scikit-learn) to analyze the received activity information. Morphological analysis, a natural language processing technique, is used for the analysis, which evaluates the content of statements and quantifies work time and results. Based on the analysis results, an efficiency score is calculated for urban residents.

[0107] Subsequently, the server generates specific suggestions for the user's efficient activities from this information and presents them visually in a dashboard format on the communication device. For example, a user may receive suggestions for the optimal route based on traffic information during their commute, or learn about areas for improvement in their work activities, thereby improving the efficiency of their daily life.

[0108] An example of a generated prompt is, "Suggest the optimal commute route based on citizens' public transport usage data." By inputting this prompt into the generating AI model, it is configured to output appropriate suggestions tailored to the objective.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The communication device collects user activity information. This includes the user's communication history, electronic communication messages, virtual conference statements, and data file processing history. After acquisition, this activity information is sent to the server using the HTTPS protocol. The input data is the user's communication record, and the output data is this record as it has been sent to the server while maintaining confidentiality.

[0112] Step 2:

[0113] The server receives the activity information and preprocesses the data. This involves deduplication, imputation of missing values, and categorization. This process transforms the raw input data into well-formed data that can be analyzed.

[0114] Step 3:

[0115] The server manages the preprocessed data as a DataFrame using the Python Pandas library and then uses Scikit-learn to build a model that quantifies work time and results. The input data consists of preprocessed activity information, and the output data is the quantified results of work time and results for each user.

[0116] Step 4:

[0117] The server uses a generative AI model to calculate an efficiency score from work time and results. The input here is quantified work information, and the output is the efficiency score. In this process, the generative AI model extracts rules and patterns from the given data to generate the efficiency score.

[0118] Step 5:

[0119] The server generates specific suggestions for the user based on the calculated efficiency score and displays them visually on the communication device. This is in a dashboard format and includes suggestions for optimal commuting routes and efficient activities. The input is the efficiency score, and the output is the suggestions for the user.

[0120] Step 6:

[0121] Users receive suggestions provided through communication devices and incorporate them into their daily activities. This allows users to receive improvement suggestions based on their actual activities, thereby improving their daily life and work efficiency. The input is visualized suggestions, and the output is the result of the user's improved activities.

[0122] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0123] This invention is a business performance management system that incorporates an emotion engine to recognize user emotions, enabling an integrated understanding of user productivity and emotional state. Specific embodiments are described below.

[0124] First, agent software is installed on the user's device. This agent collects user activity data in real time, namely emails, messages, online meeting comments, and file operation history. The collected data is sent to the server via a defined encryption protocol.

[0125] The server analyzes this activity data to quantify the user's work time and output quality, and further extracts the user's emotional state using an emotion engine. The emotion engine incorporates natural language processing technology to identify emotions such as joy, anger, sadness, and surprise from the content of statements and posts.

[0126] The server calculates a user's efficiency score based on analyzed work and emotional data. This score takes into account not only work progress and the quality of deliverables, but also emotional state. For example, if excessive stress is detected, productivity may be temporarily rated lower.

[0127] The server compiles the generated efficiency scores and sentiment evaluation results and provides them visually to users and administrators in a dashboard format. This dashboard allows users to see their performance and sentiment trends at a glance. For example, tasks where stress levels were high during work hours or tasks that increased feelings of joy are highlighted.

[0128] Based on this information, users can aim for more effective time management and improved emotional well-being. For example, if a particular task is causing stress, taking measures to address it can lead to a healthier work-life balance.

[0129] This system allows companies to achieve comprehensive performance management that takes into account not only employee productivity but also their emotional well-being.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The device collects user activity data. Specifically, it monitors and records logs of emails sent and received by the user, interactions on messaging platforms, comments made during online meetings, and file operations.

[0133] Step 2:

[0134] The device encrypts the collected activity data at predetermined time intervals and sends it to the server using a secure communication protocol. This process ensures the security of the data.

[0135] Step 3:

[0136] The server begins analyzing the received activity data. By classifying each data point into a specific task category, it analyzes how the user allocates their work time.

[0137] Step 4:

[0138] The server uses natural language processing technology to analyze the content of emails, messages, and meeting remarks. This analysis identifies emotions from the text and evaluates the type and intensity of those emotions.

[0139] Step 5:

[0140] The server calculates an efficiency score based on data from the user's work activities and emotional assessments. Emotional states are directly reflected in the score; for example, a high stress level is evaluated as a decrease in efficiency.

[0141] Step 6:

[0142] The server visualizes user efficiency scores and sentiment ratings in a dashboard format and provides it to users and administrators. The dashboard includes user work progress, sentiment trends, and suggestions for improvement.

[0143] Step 7:

[0144] Users can use a dashboard to monitor their work activities and emotional state, and take necessary adjustments to their work schedules or emotional management measures. This allows them to achieve both work efficiency and emotional well-being.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0147] In today's work environment, it is crucial not only to improve employee productivity but also to consider their emotional well-being. However, traditional work management systems have struggled to comprehensively understand users' work performance and emotional states. Furthermore, there has been a lack of means to quantitatively evaluate the impact of emotional states on work efficiency. Therefore, there is a need for a method that simultaneously evaluates and visually presents both work quality and users' emotional states.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for acquiring activity information from an information processing terminal operated by the user, means for analyzing the activity information to quantify the user's work time and deliverables, and means for calculating efficiency figures based on the quality of deliverables and emotional state. This makes it possible to comprehensively evaluate the user's work performance and emotional state and to suggest specific improvement measures.

[0150] An "information processing terminal" refers to an electronic device such as a computer or smartphone operated by a user, which enables the input and acquisition of various types of data.

[0151] "Activity information" refers to information that includes data such as the history of operations on electronic communications, digital messages, virtual conference audio, and electronic data, which are acquired based on user actions.

[0152] "Deliverables" refer to digital files, documents, and other outputs generated, edited, or managed by users through their work activities.

[0153] "Efficiency metrics" are indicators that quantitatively evaluate and quantify users' work performance and emotional state, and are evaluation values ​​that reflect the quality of work and the influence of emotions.

[0154] A "sentiment analysis engine" is a software component equipped with analysis technology used to identify emotions such as joy, anger, sadness, and happiness from a user's linguistic statements and written content.

[0155] A "server" is a backend computing system that centrally analyzes information collected from users and provides evaluation results to administrators and users.

[0156] An "encrypted protocol" refers to a technical method used to ensure the confidentiality and security of data during the transmission and reception of information, and is a communication protocol designed to prevent unauthorized access.

[0157] This invention is a business management system that comprehensively understands a user's work performance and emotional state. An agent software is installed on the user's information processing terminal, which acquires user activity information in real time. This activity information includes the history of electronic communications, digital messages, virtual conference audio, and electronic data operations. The terminal transmits the acquired activity information to a server using encryption technology. To ensure security, encryption protocols such as TLS (Transport Layer Security) are used.

[0158] The server analyzes the transmitted activity information using a variety of data analysis tools. This utilizes distributed data processing systems such as Apache® Spark. Based on the analyzed data, the server quantifies the user's work time and dynamically evaluates the user's emotional state using an emotion analysis engine. This emotion analysis engine leverages natural language processing technologies such as Google® Cloud Natural Language API to determine emotions such as joy, anger, sadness, and happiness from the user's statements and posts.

[0159] The analyzed data is quantified as efficiency figures, calculated to reflect the quality of work deliverables and emotional state. For example, excessive stress may result in lower efficiency figures. The server visualizes these results on a dashboard and provides them to users and administrators. Data visualization tools such as Tableau are used in the design of the dashboard to provide users with an at-a-glance overview of their work and emotional trends.

[0160] For example, if a user is performing the task of "reporting the progress of a new project to their supervisor," and the sentiment analysis engine detects an increase in stress, that information will be visually highlighted on the dashboard. This information can also be used by managers as a basis for taking appropriate intervention.

[0161] This system enables companies to achieve comprehensive management that balances employee productivity improvement with maintaining emotional well-being. Furthermore, by using a generative AI model, it becomes possible to generate prompts tailored to the user's work content, leading to further productivity improvements.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The device collects user activity information.

[0165] Specifically, it monitors in real time the electronic communications sent by the user and the audio from virtual meetings they participate in. Inputs are user operation events and conversation data, while outputs are activity information containing this data. The terminal uses agent software to record this information in a database.

[0166] Step 2:

[0167] The device encrypts the collected activity information and sends it to the server.

[0168] The input is raw activity information stored on the terminal, and the output is transmitted data encrypted using the TLS protocol. The terminal encrypts the data before transmission to ensure secure transmission even over insecure networks.

[0169] Step 3:

[0170] The server analyzes the received activity information and quantifies work time and deliverables.

[0171] The input is encrypted activity information received from the terminal, and the output is analytical data showing the work time and quality of deliverables for each task. The server uses Apache Spark to efficiently process large amounts of data and quantify the work performance of each user.

[0172] Step 4:

[0173] The server uses natural language processing technology to extract the user's emotional state.

[0174] The input is text data within activity information, and the output is the identified emotional state (e.g., joy, anger, sadness). The sentiment analysis engine utilizes the Google Cloud Natural Language API to analyze user posts and conversation content.

[0175] Step 5:

[0176] The server calculates efficiency figures based on business performance and emotional state.

[0177] The input consists of quantified business data and emotional data, and the output is an integrated evaluation as an efficiency score. The server considers the quality of work and emotional state, and calculates the efficiency score in a way that is easy for users and administrators to understand.

[0178] Step 6:

[0179] The server visualizes the generated efficiency figures and sentiment ratings on a dashboard and presents them to users and administrators.

[0180] The input consists of calculated efficiency figures and sentiment evaluation data, while the output is a visually easy-to-understand dashboard. Tools such as Tableau are used for data visualization, providing an overview of user work performance and sentiment trends.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0183] Maintaining both worker efficiency and mental health simultaneously in production environments has been difficult with conventional systems. In particular, there was a risk of compromising efficiency due to the inability to immediately address excessive stress and fatigue. A means to quickly respond to such situations and maintain productivity is needed.

[0184] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0185] In this invention, the server includes means for acquiring activity data from information devices operated by the user, means for analyzing the activity data to quantify the user's work time and deliverables, and means for incorporating an emotion engine for analyzing the user's emotional state. This makes it possible to optimize work assignments and break times, thereby improving efficiency and maintaining mental health.

[0186] "Information equipment" refers to terminal devices that users operate and use to acquire activity data.

[0187] "Activity data" refers to information including a user's electronic communications, phone calls, virtual conference contributions, and file operation history.

[0188] The "efficiency score" is an indicator of productivity calculated based on the user's work time and deliverables.

[0189] An "emotion engine" is software that incorporates technology to analyze a user's emotional state.

[0190] "Natural language processing technology" is a technology that analyzes the content of a user's speech and quantifies their emotional state.

[0191] A "dashboard" is a screen that visually presents users with efficiency scores and emotional states.

[0192] "Task assignment" is the process of assigning appropriate tasks to a user based on their current state.

[0193] "Break timing" refers to the time when users are instructed to take appropriate breaks based on their stress and fatigue levels.

[0194] This invention is a system that collects activity data using user-operated information devices and analyzes the user's work efficiency and emotional state based on that data. The server acquires activity data including the user's electronic communications, phone calls, virtual meeting statements, and file operation history. The acquired data is analyzed using natural language processing technology to quantify the emotional state based on the user's statements. This processing uses natural language processing libraries in a Python environment (e.g., NLTK and Transformers). The results of the emotional analysis, along with an efficiency score, are visually presented to the user in a dashboard format. The dashboard is built with JavaScript® and can be viewed in a web browser. For example, if the user feels fatigued during work, the system detects this state and suggests an appropriate break time. This system aims to maintain both user productivity and mental health by optimizing work assignments and break timings. An example of a prompt to the generative AI model is, "Please tell me how to utilize the emotional analysis engine in the work environment." This prompt provides insights into how to effectively analyze the user's emotional state and link it to improved work efficiency.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The terminal collects activity data in real time, including the user's electronic communications, phone calls, virtual conference contributions, and file operation history. This input data is securely transmitted to the server using encryption protocols established to ensure security.

[0198] Step 2:

[0199] The server stores the received activity data and analyzes it using natural language processing techniques. Specifically, it utilizes Python and natural language processing libraries (e.g., NLTK and Transformers) to extract emotions from the user's statements. This process analyzes the input text data and calculates emotion scores such as joy, anger, sadness, and surprise.

[0200] Step 3:

[0201] The server integrates an efficiency score calculated by quantifying the user's work time and deliverables based on the analyzed emotion score. This data integration process generates an overall efficiency score while considering how emotional state affects productivity.

[0202] Step 4:

[0203] The server builds a user interface on a dashboard to visualize the user's overall efficiency score and emotional state. This dashboard is designed using JavaScript, accessible through the user's browser, and updates information in real time.

[0204] Step 5:

[0205] Users can check their efficiency score and emotional state through a dashboard, which helps them manage their time effectively and self-regulate their emotions. Based on this information, they can optimize their work assignments or insert breaks if fatigue is detected.

[0206] Step 6:

[0207] The server generates a prompt for the generated AI model, "Please tell me how to utilize the sentiment analysis engine in the work environment," providing guidance for users and administrators to understand and further improve how to use the system. This prompt provides insights into how to effectively analyze the user's emotional state and link it to improved work efficiency.

[0208] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0209] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0210] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0214] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0215] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0216] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0217] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0218] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0219] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0220] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0221] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0222] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0223] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0224] This invention provides a management system for improving work efficiency by acquiring data related to the user's work activities from terminal devices installed in the user's work environment and analyzing it on a server. Specific embodiments thereof are described below.

[0225] First, a dedicated software agent is installed on the terminal device. This agent is configured to collect user activity data from email, messaging applications, online meeting applications, and file management tools. Activity data includes, for example, email sending and receiving times, meeting participation times, and file creation and editing history.

[0226] The terminal device periodically collects activity data and transmits it to the server based on a specific encryption protocol. This ensures data privacy and security.

[0227] The server analyzes the received activity data in real time. The analysis process includes data classification, time measurement, and content evaluation of statements and messages using natural language processing techniques. This analysis quantifies how much time users spent on each task, the importance of their actions, and their efficiency.

[0228] After analysis, the server calculates an efficiency score based on the results obtained, indicating the user's work efficiency. This score takes into account factors such as how work time is used, the quality of task deliverables, and the impact of contributions.

[0229] The server then visually displays the generated efficiency score and analysis results, providing them to users and administrators in a dashboard format. This includes, for example, the time allocation for each task, importance scores, and suggestions for improvement.

[0230] Users can use this dashboard to review their work schedules and consider improvements as needed. Similarly, administrators can understand the overall team performance and provide appropriate feedback.

[0231] This system enables users to achieve effective time management and improved performance, regardless of whether they are working from home or in the office.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The device collects activity data based on user actions. This collection is done in real time and includes sending and receiving emails, exchanging messages, participating in online meetings, and creating and editing files. The collected data is temporarily stored in the device's local storage.

[0235] Step 2:

[0236] The device encrypts the collected activity data at predetermined time intervals and transfers it to the server using a secure communication protocol. This process ensures data security and privacy.

[0237] Step 3:

[0238] The server begins analyzing the activity data received from the terminal. First, it categorizes each data point into a specific task category (e.g., responding to emails, attending meetings, creating documents). Next, it calculates the time spent on each task to determine how the user allocates their work time.

[0239] Step 4:

[0240] The server uses natural language processing techniques to further analyze emails, messages, and meeting transcripts. This analysis extracts important keywords and context from the text data and evaluates how much they contribute to business operations.

[0241] Step 5:

[0242] The server calculates the user's efficiency score based on the analysis results. The score is derived by comprehensively evaluating factors such as time allocation, the quality of task deliverables, and the content and frequency of contributions.

[0243] Step 6:

[0244] The server generates dashboards for users and administrators that visualize the analysis results. These dashboards include daily and weekly efficiency scores, time usage percentages, and suggestions for areas where improvement can be made.

[0245] Step 7:

[0246] Users review the provided dashboard and reflect on their daily work activities. This allows them to develop action plans for improving time management and efficient work execution.

[0247] The above outlines the specific processing flow of the program. This system allows users to objectively evaluate their own performance and gain insights into improving work efficiency.

[0248] (Example 1)

[0249] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0250] With the increasing diversification of work environments, there is a growing need to accurately understand users' work efficiency and improve their performance. However, traditional methods have made it difficult to comprehensively and accurately evaluate user activities, and have failed to identify areas for improvement and provide specific advice. Furthermore, data privacy and security have not been adequately ensured.

[0251] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0252] In this invention, the server includes means for transmitting activity information through a protected communication channel, means for analyzing the activity information to quantify work time and work results, and means for creating a visual display that suggests work improvements to the user and administrator based on the analysis results. This makes it possible to more accurately evaluate the user's work efficiency and to present specific improvement measures.

[0253] "Activity information" refers to information generated by user actions, including work time, work results, communication content, network meeting content, and data file operation history.

[0254] An "information processing device" is a terminal device operated by a user to collect and transmit activity information.

[0255] "Work time" refers to the time a user spends on a specific task or activity, and is recorded as activity information.

[0256] "Work output" refers to the quantitative measure of the quality of deliverables or outputs achieved through the user's work.

[0257] A "business efficiency evaluation index" is an index that quantifies business efficiency based on the user's work time and work results.

[0258] A "protected channel" is a communication path that uses encryption technology to protect activity information for secure transmission.

[0259] "Machine learning technology" is artificial intelligence technology that analyzes large amounts of data to evaluate user communication content and work results.

[0260] "Visual display" refers to a visualized data display that intuitively presents analyzed activity information and operational efficiency evaluation indicators to users and administrators.

[0261] This invention is a system that improves work efficiency by collecting and analyzing user activity information using a software agent installed on the user's information processing device.

[0262] The terminal (the user's information processing device) collects activity information such as electronic communications, interactive communications, network meeting content, and data file operation history through a software agent. This agent automatically records data generated during the user's work. For example, the agent logs how much time the user spends processing emails and periodically encrypts this data before sending it to the server.

[0263] The server first decodes the activity information received from the terminal. Next, it analyzes the data using machine learning techniques to evaluate work time and work results. Furthermore, it uses natural language processing techniques to evaluate the user's communication content and meeting contributions, and calculates work efficiency evaluation metrics. Based on these metrics, the server generates advice to improve the user's work efficiency.

[0264] Users can understand their work trends and areas for improvement through visual displays provided by the server. These visual displays include, for example, a heatmap of the user's weekly activities and specific suggestions for improving work efficiency. This allows users to review their work schedules and optimize their workflows as needed.

[0265] For example, when a user reviews a visual summary of their weekly activities, they might realize they're spending a lot of time attending meetings and develop a strategy to improve their work efficiency by reducing that time. An example of a prompt would be, "Please analyze your work activities this week and suggest ways to improve efficiency."

[0266] This system allows users to objectively understand their work efficiency and clearly identify areas for improvement, enabling them to work flexibly even when working from home or on the go.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The terminal activates a software agent to collect user activity information. This agent retrieves user operation data from email clients, scheduling applications, online meeting tools, and other sources. Inputs include sending and receiving emails, joining meetings, sending chat messages, and document editing history. This data is collected and recorded as activity data.

[0270] Step 2:

[0271] The terminal encrypts the collected activity data using a specific encryption method and sends it to the server using a secure communication protocol. The input is the activity data collected in step 1, and the output is the encrypted data. Specifically, the system uses protocols such as TLS to transmit the data, preventing eavesdropping and tampering.

[0272] Step 3:

[0273] The server receives encrypted data sent from the terminal and decrypts it. The input is encrypted activity data, and the output is decrypted activity data. After the decryption process is completed, the data is passed to the analysis engine.

[0274] Step 4:

[0275] The server analyzes the received activity data. Here, natural language processing techniques are used to analyze the content of emails and messages, and time tracking techniques are used to measure the time spent on each task. The input is decoded activity data, and the output is quantified work time and work results as analysis outcomes.

[0276] Step 5:

[0277] The server calculates a business efficiency evaluation index based on the analysis results. Specifically, it evaluates how efficiently users are spending their time on each task. The input is the analysis results obtained in step 4, and the output is the business efficiency evaluation index. A machine learning model is used to analyze user activity patterns.

[0278] Step 6:

[0279] The server generates visualized reports for users and administrators based on operational efficiency metrics and analysis results. The input is operational efficiency metrics, and the output is a visually displayed report. This report includes suggestions for improvement and efficiency scores for each task, and is provided as a dashboard for direct use by users.

[0280] Step 7:

[0281] Users utilize a dashboard provided by the server to check their work efficiency and revise their schedules and work methods as needed. Input is a visually displayed report, and output is improved work performance. In this way, users can optimize their work style more efficiently.

[0282] (Application Example 1)

[0283] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0284] In a smart city, in order to efficiently manage citizens' business and public service usage and improve the quality of citizens' lives, it is necessary to accurately collect and analyze data on daily activities and immediately provide feedback on the results to citizens. In conventional systems, there are problems such as insufficient collection of activity information, and the analysis results are not effectively communicated to citizens, thus not contributing to efficiency improvement.

[0285] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for acquiring activity information from a communication device, means for analyzing the activity information to quantify business time and results, and means for making proposals regarding the use of public services by urban residents based on the business time, results, and efficiency score. Thereby, citizens can understand their own activities and select an optimal lifestyle.

[0287] The "communication device" is a digital device used by a user, and is a device that enables acquisition of activity information and transmission / reception of data via the Internet.

[0288] The "activity information" is data related to users' daily business activities and public service usage, and includes communication history, electronic communication messages, statements in virtual meetings, and processing history of data files.

[0289] The "business time" refers to the time spent by a user in performing a specific business activity, and is an important indicator in the evaluation of efficiency.

[0290] The "result" is the specific output produced by a user as a result of business activities, and based on this, business efficiency can be measured.

[0291] The "efficiency score" is a numerical value calculated based on a user's business time and results, and is an indicator for quantitatively evaluating a user's business efficiency.

[0292] "Suggestions" refer to guidelines and improvement advice for users derived from analyzed activity information and efficiency scores.

[0293] Morphological analysis is a technique in natural language processing that breaks down a sentence into its smallest constituent units and analyzes the meaning and grammatical role of each word.

[0294] The system for realizing this invention uses a combination of a communication device, a server, and advanced data analysis software. The communication device acquires data on the user's daily activities, i.e., information related to work activities and use of public services. This includes communication history, electronic communication messages, statements made in virtual meetings, and data file processing history. This activity information is transmitted to the server using a secure protocol.

[0295] The server uses Python-based analysis tools (Pandas, Scikit-learn) to analyze the received activity information. Morphological analysis, a natural language processing technique, is used for the analysis, which evaluates the content of statements and quantifies work time and results. Based on the analysis results, an efficiency score is calculated for urban residents.

[0296] Subsequently, the server generates specific suggestions for the user's efficient activities from this information and presents them visually in a dashboard format on the communication device. For example, a user may receive suggestions for the optimal route based on traffic information during their commute, or learn about areas for improvement in their work activities, thereby improving the efficiency of their daily life.

[0297] An example of a generated prompt is, "Suggest the optimal commute route based on citizens' public transport usage data." By inputting this prompt into the generating AI model, it is configured to output appropriate suggestions tailored to the objective.

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The communication device collects the user's activity information. This includes the user's communication history, electronic communication messages, virtual meeting speeches, and data file processing history. After obtaining these activity information, they are transmitted to the server using the HTTPS protocol. The input data is the user's communication record, and the output data is the state where this record is transmitted to the server while maintaining confidentiality.

[0301] Step 2:

[0302] The server receives the received activity information and performs preprocessing on the data. Here, the data is deduplicated, missing values are imputed, and categorized. Through this process, the input raw data is converted into analyzable structured data.

[0303] Step 3:

[0304] The server manages the preprocessed data as a data frame using the Pandas library in Python, and further constructs a model for quantifying working hours and achievements using Scikit-learn. The input data is the preprocessed activity information, and the output data is the quantification results of working hours and achievements for each user.

[0305] Step 4:

[0306] The server calculates an efficiency score from the working hours and achievements using a generative AI model. The input here is the quantified business information, and the output is the efficiency score. In this process, the generative AI model extracts rules and patterns from the given data to generate the efficiency score.

[0307] Step 5:

[0308] The server generates specific suggestions for the user based on the calculated efficiency score and displays them visually on the communication device. This is in a dashboard format and includes suggestions for optimal commuting routes and efficient activities. The input is the efficiency score, and the output is the suggestions for the user.

[0309] Step 6:

[0310] Users receive suggestions provided through communication devices and incorporate them into their daily activities. This allows users to receive improvement suggestions based on their actual activities, thereby improving their daily life and work efficiency. The input is visualized suggestions, and the output is the result of the user's improved activities.

[0311] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0312] This invention is a business performance management system that incorporates an emotion engine to recognize user emotions, enabling an integrated understanding of user productivity and emotional state. Specific embodiments are described below.

[0313] First, agent software is installed on the user's device. This agent collects user activity data in real time, namely emails, messages, online meeting comments, and file operation history. The collected data is sent to the server via a defined encryption protocol.

[0314] The server analyzes this activity data to quantify the user's work time and output quality, and further extracts the user's emotional state using an emotion engine. The emotion engine incorporates natural language processing technology to identify emotions such as joy, anger, sadness, and surprise from the content of statements and posts.

[0315] The server calculates a user's efficiency score based on analyzed work and emotional data. This score takes into account not only work progress and the quality of deliverables, but also emotional state. For example, if excessive stress is detected, productivity may be temporarily rated lower.

[0316] The server compiles the generated efficiency scores and sentiment evaluation results and provides them visually to users and administrators in a dashboard format. This dashboard allows users to see their performance and sentiment trends at a glance. For example, tasks where stress levels were high during work hours or tasks that increased feelings of joy are highlighted.

[0317] Based on this information, users can aim for more effective time management and improved emotional well-being. For example, if a particular task is causing stress, taking measures to address it can lead to a healthier work-life balance.

[0318] This system allows companies to achieve comprehensive performance management that takes into account not only employee productivity but also their emotional well-being.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The device collects user activity data. Specifically, it monitors and records logs of emails sent and received by the user, interactions on messaging platforms, comments made during online meetings, and file operations.

[0322] Step 2:

[0323] The device encrypts the collected activity data at predetermined time intervals and sends it to the server using a secure communication protocol. This process ensures the security of the data.

[0324] Step 3:

[0325] The server begins analyzing the received activity data. By classifying each data point into a specific task category, it analyzes how the user allocates their work time.

[0326] Step 4:

[0327] The server uses natural language processing technology to analyze the content of emails, messages, and meeting remarks. This analysis identifies emotions from the text and evaluates the type and intensity of those emotions.

[0328] Step 5:

[0329] The server calculates an efficiency score based on data from the user's work activities and emotional assessments. Emotional states are directly reflected in the score; for example, a high stress level is evaluated as a decrease in efficiency.

[0330] Step 6:

[0331] The server visualizes user efficiency scores and sentiment ratings in a dashboard format and provides it to users and administrators. The dashboard includes user work progress, sentiment trends, and suggestions for improvement.

[0332] Step 7:

[0333] Users can use a dashboard to monitor their work activities and emotional state, and take necessary adjustments to their work schedules or emotional management measures. This allows them to achieve both work efficiency and emotional well-being.

[0334] (Example 2)

[0335] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0336] In today's work environment, it is crucial not only to improve employee productivity but also to consider their emotional well-being. However, traditional work management systems have struggled to comprehensively understand users' work performance and emotional states. Furthermore, there has been a lack of means to quantitatively evaluate the impact of emotional states on work efficiency. Therefore, there is a need for a method that simultaneously evaluates and visually presents both work quality and users' emotional states.

[0337] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0338] In this invention, the server includes means for acquiring activity information from an information processing terminal operated by the user, means for analyzing the activity information to quantify the user's work time and deliverables, and means for calculating efficiency figures based on the quality of deliverables and emotional state. This makes it possible to comprehensively evaluate the user's work performance and emotional state and to suggest specific improvement measures.

[0339] An "information processing terminal" refers to an electronic device such as a computer or smartphone operated by a user, which enables the input and acquisition of various types of data.

[0340] "Activity information" refers to information that includes data such as the history of operations on electronic communications, digital messages, virtual conference audio, and electronic data, which are acquired based on user actions.

[0341] "Deliverables" refer to digital files, documents, and other outputs generated, edited, or managed by users through their work activities.

[0342] "Efficiency metrics" are indicators that quantitatively evaluate and quantify users' work performance and emotional state, and are evaluation values ​​that reflect the quality of work and the influence of emotions.

[0343] A "sentiment analysis engine" is a software component equipped with analysis technology used to identify emotions such as joy, anger, sadness, and happiness from a user's linguistic statements and written content.

[0344] A "server" is a backend computing system that centrally analyzes information collected from users and provides evaluation results to administrators and users.

[0345] An "encrypted protocol" refers to a technical method used to ensure the confidentiality and security of data during the transmission and reception of information, and is a communication protocol designed to prevent unauthorized access.

[0346] This invention is a business management system that comprehensively understands a user's work performance and emotional state. An agent software is installed on the user's information processing terminal, which acquires user activity information in real time. This activity information includes the history of electronic communications, digital messages, virtual conference audio, and electronic data operations. The terminal transmits the acquired activity information to a server using encryption technology. To ensure security, encryption protocols such as TLS (Transport Layer Security) are used.

[0347] The server analyzes the transmitted activity information using a variety of data analysis tools. Distributed data processing systems such as Apache Spark are used for this purpose. Based on the analyzed data, the server quantifies the user's work time and dynamically evaluates the user's emotional state using an emotion analysis engine. This emotion analysis engine utilizes natural language processing technologies such as the Google Cloud Natural Language API to determine emotions such as joy, anger, sadness, and happiness from the user's statements and posts.

[0348] The analyzed data is quantified as efficiency figures, calculated to reflect the quality of work deliverables and emotional state. For example, excessive stress may result in lower efficiency figures. The server visualizes these results on a dashboard and provides them to users and administrators. Data visualization tools such as Tableau are used in the design of the dashboard to provide users with an at-a-glance overview of their work and emotional trends.

[0349] For example, if a user is performing the task of "reporting the progress of a new project to their supervisor," and the sentiment analysis engine detects an increase in stress, that information will be visually highlighted on the dashboard. This information can also be used by managers as a basis for taking appropriate intervention.

[0350] This system enables companies to achieve comprehensive management that balances employee productivity improvement with maintaining emotional well-being. Furthermore, by using a generative AI model, it becomes possible to generate prompts tailored to the user's work content, leading to further productivity improvements.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] The device collects user activity information.

[0354] Specifically, it monitors in real time the electronic communications sent by the user and the audio from virtual meetings they participate in. Inputs are user operation events and conversation data, while outputs are activity information containing this data. The terminal uses agent software to record this information in a database.

[0355] Step 2:

[0356] The device encrypts the collected activity information and sends it to the server.

[0357] The input is raw activity information stored on the terminal, and the output is transmitted data encrypted using the TLS protocol. The terminal encrypts the data before transmission to ensure secure transmission even over insecure networks.

[0358] Step 3:

[0359] The server analyzes the received activity information and quantifies work time and deliverables.

[0360] The input is encrypted activity information received from the terminal, and the output is analytical data showing the work time and quality of deliverables for each task. The server uses Apache Spark to efficiently process large amounts of data and quantify the work performance of each user.

[0361] Step 4:

[0362] The server uses natural language processing technology to extract the user's emotional state.

[0363] The input is text data within activity information, and the output is the identified emotional state (e.g., joy, anger, sadness). The sentiment analysis engine utilizes the Google Cloud Natural Language API to analyze user posts and conversation content.

[0364] Step 5:

[0365] The server calculates efficiency figures based on business performance and emotional state.

[0366] The input consists of quantified business data and emotional data, and the output is an integrated evaluation as an efficiency score. The server considers the quality of work and emotional state, and calculates the efficiency score in a way that is easy for users and administrators to understand.

[0367] Step 6:

[0368] The server visualizes the generated efficiency figures and sentiment ratings on a dashboard and presents them to users and administrators.

[0369] The input consists of calculated efficiency figures and sentiment evaluation data, while the output is a visually easy-to-understand dashboard. Tools such as Tableau are used for data visualization, providing an overview of user work performance and sentiment trends.

[0370] (Application Example 2)

[0371] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0372] Maintaining both worker efficiency and mental health simultaneously in production environments has been difficult with conventional systems. In particular, there was a risk of compromising efficiency due to the inability to immediately address excessive stress and fatigue. A means to quickly respond to such situations and maintain productivity is needed.

[0373] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0374] In this invention, the server includes means for acquiring activity data from information devices operated by the user, means for analyzing the activity data to quantify the user's work time and deliverables, and means for incorporating an emotion engine for analyzing the user's emotional state. This makes it possible to optimize work assignments and break times, thereby improving efficiency and maintaining mental health.

[0375] "Information equipment" refers to terminal devices that users operate and use to acquire activity data.

[0376] "Activity data" refers to information including a user's electronic communications, phone calls, virtual conference contributions, and file operation history.

[0377] The "efficiency score" is an indicator of productivity calculated based on the user's work time and deliverables.

[0378] An "emotion engine" is software that incorporates technology to analyze a user's emotional state.

[0379] "Natural language processing technology" is a technology that analyzes the content of a user's speech and quantifies their emotional state.

[0380] A "dashboard" is a screen that visually presents users with efficiency scores and emotional states.

[0381] "Task assignment" is the process of assigning appropriate tasks to a user based on their current state.

[0382] "Break timing" refers to the time when users are instructed to take appropriate breaks based on their stress and fatigue levels.

[0383] This invention is a system that collects activity data using user-operated information devices and analyzes the user's work efficiency and emotional state based on that data. The server acquires activity data including the user's electronic communications, phone calls, virtual meeting statements, and file operation history. The acquired data is analyzed using natural language processing technology to quantify the emotional state based on the user's statements. This processing uses natural language processing libraries in a Python environment (e.g., NLTK and Transformers). The results of the emotional analysis, along with an efficiency score, are visually presented to the user in a dashboard format. The dashboard is built with JavaScript and can be viewed in a web browser. For example, if the user feels fatigued during work, the system detects this state and suggests an appropriate break time. This system aims to maintain both user productivity and mental health by optimizing work assignments and break timings. An example of a prompt to the generative AI model is, "Please tell me how to utilize the emotional analysis engine in the work environment." This prompt provides insights into how to effectively analyze the user's emotional state and link it to improved work efficiency.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The terminal collects activity data in real time, including the user's electronic communications, phone calls, virtual conference contributions, and file operation history. This input data is securely transmitted to the server using encryption protocols established to ensure security.

[0387] Step 2:

[0388] The server stores the received activity data and analyzes it using natural language processing techniques. Specifically, it utilizes Python and natural language processing libraries (e.g., NLTK and Transformers) to extract emotions from the user's statements. This process analyzes the input text data and calculates emotion scores such as joy, anger, sadness, and surprise.

[0389] Step 3:

[0390] The server integrates an efficiency score calculated by quantifying the user's work time and deliverables based on the analyzed emotion score. This data integration process generates an overall efficiency score while considering how emotional state affects productivity.

[0391] Step 4:

[0392] The server builds a user interface on a dashboard to visualize the user's overall efficiency score and emotional state. This dashboard is designed using JavaScript, accessible through the user's browser, and updates information in real time.

[0393] Step 5:

[0394] Users can check their efficiency score and emotional state through a dashboard, which helps them manage their time effectively and self-regulate their emotions. Based on this information, they can optimize their work assignments or insert breaks if fatigue is detected.

[0395] Step 6:

[0396] The server generates a prompt for the generated AI model, "Please tell me how to utilize the sentiment analysis engine in the work environment," providing guidance for users and administrators to understand and further improve how to use the system. This prompt provides insights into how to effectively analyze the user's emotional state and link it to improved work efficiency.

[0397] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0398] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0399] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0403] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0404] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0405] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0406] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0407] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0408] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0409] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0410] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0411] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0412] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0413] This invention provides a management system for improving work efficiency by acquiring data related to the user's work activities from terminal devices installed in the user's work environment and analyzing it on a server. Specific embodiments thereof are described below.

[0414] First, a dedicated software agent is installed on the terminal device. This agent is configured to collect user activity data from email, messaging applications, online meeting applications, and file management tools. Activity data includes, for example, email sending and receiving times, meeting participation times, and file creation and editing history.

[0415] The terminal device periodically collects activity data and transmits it to the server based on a specific encryption protocol. This ensures data privacy and security.

[0416] The server analyzes the received activity data in real time. The analysis process includes data classification, time measurement, and content evaluation of statements and messages using natural language processing techniques. This analysis quantifies how much time users spent on each task, the importance of their actions, and their efficiency.

[0417] After analysis, the server calculates an efficiency score based on the results obtained, indicating the user's work efficiency. This score takes into account factors such as how work time is used, the quality of task deliverables, and the impact of contributions.

[0418] The server then visually displays the generated efficiency score and analysis results, providing them to users and administrators in a dashboard format. This includes, for example, the time allocation for each task, importance scores, and suggestions for improvement.

[0419] Users can use this dashboard to review their work schedules and consider improvements as needed. Similarly, administrators can understand the overall team performance and provide appropriate feedback.

[0420] This system enables users to achieve effective time management and improved performance, regardless of whether they are working from home or in the office.

[0421] The following describes the processing flow.

[0422] Step 1:

[0423] The device collects activity data based on user actions. This collection is done in real time and includes sending and receiving emails, exchanging messages, participating in online meetings, and creating and editing files. The collected data is temporarily stored in the device's local storage.

[0424] Step 2:

[0425] The device encrypts the collected activity data at predetermined time intervals and transfers it to the server using a secure communication protocol. This process ensures data security and privacy.

[0426] Step 3:

[0427] The server begins analyzing the activity data received from the terminal. First, it categorizes each data point into a specific task category (e.g., responding to emails, attending meetings, creating documents). Next, it calculates the time spent on each task to determine how the user allocates their work time.

[0428] Step 4:

[0429] The server uses natural language processing techniques to further analyze emails, messages, and meeting transcripts. This analysis extracts important keywords and context from the text data and evaluates how much they contribute to business operations.

[0430] Step 5:

[0431] The server calculates the user's efficiency score based on the analysis results. The score is derived by comprehensively evaluating factors such as time allocation, the quality of task deliverables, and the content and frequency of contributions.

[0432] Step 6:

[0433] The server generates dashboards for users and administrators that visualize the analysis results. These dashboards include daily and weekly efficiency scores, time usage percentages, and suggestions for areas where improvement can be made.

[0434] Step 7:

[0435] Users review the provided dashboard and reflect on their daily work activities. This allows them to develop action plans for improving time management and efficient work execution.

[0436] The above outlines the specific processing flow of the program. This system allows users to objectively evaluate their own performance and gain insights into improving work efficiency.

[0437] (Example 1)

[0438] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0439] With the increasing diversification of work environments, there is a growing need to accurately understand users' work efficiency and improve their performance. However, traditional methods have made it difficult to comprehensively and accurately evaluate user activities, and have failed to identify areas for improvement and provide specific advice. Furthermore, data privacy and security have not been adequately ensured.

[0440] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0441] In this invention, the server includes means for transmitting activity information through a protected communication channel, means for analyzing the activity information to quantify work time and work results, and means for creating a visual display that suggests work improvements to the user and administrator based on the analysis results. This makes it possible to more accurately evaluate the user's work efficiency and to present specific improvement measures.

[0442] "Activity information" refers to information generated by user actions, including work time, work results, communication content, network meeting content, and data file operation history.

[0443] An "information processing device" is a terminal device operated by a user to collect and transmit activity information.

[0444] "Work time" refers to the time a user spends on a specific task or activity, and is recorded as activity information.

[0445] "Work output" refers to the quantitative measure of the quality of deliverables or outputs achieved through the user's work.

[0446] A "business efficiency evaluation index" is an index that quantifies business efficiency based on the user's work time and work results.

[0447] A "protected channel" is a communication path that uses encryption technology to protect activity information for secure transmission.

[0448] "Machine learning technology" is artificial intelligence technology that analyzes large amounts of data to evaluate user communication content and work results.

[0449] "Visual display" refers to a visualized data display that intuitively presents analyzed activity information and operational efficiency evaluation indicators to users and administrators.

[0450] This invention is a system that improves work efficiency by collecting and analyzing user activity information using a software agent installed on the user's information processing device.

[0451] The terminal (the user's information processing device) collects activity information such as electronic communications, interactive communications, network meeting content, and data file operation history through a software agent. This agent automatically records data generated during the user's work. For example, the agent logs how much time the user spends processing emails and periodically encrypts this data before sending it to the server.

[0452] The server first decodes the activity information received from the terminal. Next, it analyzes the data using machine learning techniques to evaluate work time and work results. Furthermore, it uses natural language processing techniques to evaluate the user's communication content and meeting contributions, and calculates work efficiency evaluation metrics. Based on these metrics, the server generates advice to improve the user's work efficiency.

[0453] Users can understand their work trends and areas for improvement through visual displays provided by the server. These visual displays include, for example, a heatmap of the user's weekly activities and specific suggestions for improving work efficiency. This allows users to review their work schedules and optimize their workflows as needed.

[0454] For example, when a user reviews a visual summary of their weekly activities, they might realize they're spending a lot of time attending meetings and develop a strategy to improve their work efficiency by reducing that time. An example of a prompt would be, "Please analyze your work activities this week and suggest ways to improve efficiency."

[0455] This system allows users to objectively understand their work efficiency and clearly identify areas for improvement, enabling them to work flexibly even when working from home or on the go.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The terminal activates a software agent to collect user activity information. This agent retrieves user operation data from email clients, scheduling applications, online meeting tools, and other sources. Inputs include sending and receiving emails, joining meetings, sending chat messages, and document editing history. This data is collected and recorded as activity data.

[0459] Step 2:

[0460] The terminal encrypts the collected activity data using a specific encryption method and sends it to the server using a secure communication protocol. The input is the activity data collected in step 1, and the output is the encrypted data. Specifically, the system uses protocols such as TLS to transmit the data, preventing eavesdropping and tampering.

[0461] Step 3:

[0462] The server receives encrypted data sent from the terminal and decrypts it. The input is encrypted activity data, and the output is decrypted activity data. After the decryption process is completed, the data is passed to the analysis engine.

[0463] Step 4:

[0464] The server analyzes the received activity data. Here, natural language processing techniques are used to analyze the content of emails and messages, and time tracking techniques are used to measure the time spent on each task. The input is decoded activity data, and the output is quantified work time and work results as analysis outcomes.

[0465] Step 5:

[0466] The server calculates a business efficiency evaluation index based on the analysis results. Specifically, it evaluates how efficiently users are spending their time on each task. The input is the analysis results obtained in step 4, and the output is the business efficiency evaluation index. A machine learning model is used to analyze user activity patterns.

[0467] Step 6:

[0468] The server generates visualized reports for users and administrators based on operational efficiency metrics and analysis results. The input is operational efficiency metrics, and the output is a visually displayed report. This report includes suggestions for improvement and efficiency scores for each task, and is provided as a dashboard for direct use by users.

[0469] Step 7:

[0470] Users utilize a dashboard provided by the server to check their work efficiency and revise their schedules and work methods as needed. Input is a visually displayed report, and output is improved work performance. In this way, users can optimize their work style more efficiently.

[0471] (Application Example 1)

[0472] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0473] In smart cities, accurately collecting and analyzing data on daily activities and providing immediate feedback to citizens is necessary to efficiently manage citizens' work and use of public services and to improve the quality of life for them. Conventional systems have challenges such as insufficient collection of activity information and ineffective communication of analysis results to citizens, thus failing to contribute to efficiency improvements.

[0474] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0475] In this invention, the server includes means for acquiring activity information from a communication device, means for analyzing the activity information to quantify work time and results, and means for making suggestions to urban residents regarding their use of public services based on the work time, results, and efficiency score. This enables citizens to understand their own activities and choose the optimal lifestyle.

[0476] A "communication device" is a digital device used by a user that enables the acquisition of activity information and the transmission and reception of data via the internet.

[0477] "Activity information" refers to data related to a user's daily work activities and use of public services, and includes communication history, electronic communication messages, statements made in virtual meetings, and data file processing history.

[0478] "Working hours" refers to the time a user spends performing a specific work activity, and it is an important indicator in evaluating efficiency.

[0479] "Results" refer to the specific outputs that users produce as a result of their work activities, and these can be used to measure work efficiency.

[0480] The "efficiency score" is a numerical value calculated based on the user's work time and results, and is an indicator for quantitatively evaluating the user's work efficiency.

[0481] "Suggestions" refer to guidelines and improvement advice for users derived from analyzed activity information and efficiency scores.

[0482] Morphological analysis is a technique in natural language processing that breaks down a sentence into its smallest constituent units and analyzes the meaning and grammatical role of each word.

[0483] The system for realizing this invention uses a combination of a communication device, a server, and advanced data analysis software. The communication device acquires data on the user's daily activities, i.e., information related to work activities and use of public services. This includes communication history, electronic communication messages, statements made in virtual meetings, and data file processing history. This activity information is transmitted to the server using a secure protocol.

[0484] The server uses Python-based analysis tools (Pandas, Scikit-learn) to analyze the received activity information. Morphological analysis, a natural language processing technique, is used for the analysis, which evaluates the content of statements and quantifies work time and results. Based on the analysis results, an efficiency score is calculated for urban residents.

[0485] Subsequently, the server generates specific suggestions for the user's efficient activities from this information and presents them visually in a dashboard format on the communication device. For example, a user may receive suggestions for the optimal route based on traffic information during their commute, or learn about areas for improvement in their work activities, thereby improving the efficiency of their daily life.

[0486] An example of a generated prompt is, "Suggest the optimal commute route based on citizens' public transport usage data." By inputting this prompt into the generating AI model, it is configured to output appropriate suggestions tailored to the objective.

[0487] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0488] Step 1:

[0489] The communication device collects user activity information. This includes the user's communication history, electronic communication messages, virtual conference statements, and data file processing history. After acquisition, this activity information is sent to the server using the HTTPS protocol. The input data is the user's communication record, and the output data is this record as it has been sent to the server while maintaining confidentiality.

[0490] Step 2:

[0491] The server receives the activity information and preprocesses the data. This involves deduplication, imputation of missing values, and categorization. This process transforms the raw input data into well-formed data that can be analyzed.

[0492] Step 3:

[0493] The server manages the preprocessed data as a DataFrame using the Python Pandas library and then uses Scikit-learn to build a model that quantifies work time and results. The input data consists of preprocessed activity information, and the output data is the quantified results of work time and results for each user.

[0494] Step 4:

[0495] The server uses a generative AI model to calculate an efficiency score from work time and results. The input here is quantified work information, and the output is the efficiency score. In this process, the generative AI model extracts rules and patterns from the given data to generate the efficiency score.

[0496] Step 5:

[0497] The server generates specific suggestions for the user based on the calculated efficiency score and displays them visually on the communication device. This is in a dashboard format and includes suggestions for optimal commuting routes and efficient activities. The input is the efficiency score, and the output is the suggestions for the user.

[0498] Step 6:

[0499] Users receive suggestions provided through communication devices and incorporate them into their daily activities. This allows users to receive improvement suggestions based on their actual activities, thereby improving their daily life and work efficiency. The input is visualized suggestions, and the output is the result of the user's improved activities.

[0500] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0501] This invention is a business performance management system that incorporates an emotion engine to recognize user emotions, enabling an integrated understanding of user productivity and emotional state. Specific embodiments are described below.

[0502] First, agent software is installed on the user's device. This agent collects user activity data in real time, namely emails, messages, online meeting comments, and file operation history. The collected data is sent to the server via a defined encryption protocol.

[0503] The server analyzes this activity data to quantify the user's work time and output quality, and further extracts the user's emotional state using an emotion engine. The emotion engine incorporates natural language processing technology to identify emotions such as joy, anger, sadness, and surprise from the content of statements and posts.

[0504] The server calculates a user's efficiency score based on analyzed work and emotional data. This score takes into account not only work progress and the quality of deliverables, but also emotional state. For example, if excessive stress is detected, productivity may be temporarily rated lower.

[0505] The server compiles the generated efficiency scores and sentiment evaluation results and provides them visually to users and administrators in a dashboard format. This dashboard allows users to see their performance and sentiment trends at a glance. For example, tasks where stress levels were high during work hours or tasks that increased feelings of joy are highlighted.

[0506] Based on this information, users can aim for more effective time management and improved emotional well-being. For example, if a particular task is causing stress, taking measures to address it can lead to a healthier work-life balance.

[0507] This system allows companies to achieve comprehensive performance management that takes into account not only employee productivity but also their emotional well-being.

[0508] The following describes the processing flow.

[0509] Step 1:

[0510] The device collects user activity data. Specifically, it monitors and records logs of emails sent and received by the user, interactions on messaging platforms, comments made during online meetings, and file operations.

[0511] Step 2:

[0512] The device encrypts the collected activity data at predetermined time intervals and sends it to the server using a secure communication protocol. This process ensures the security of the data.

[0513] Step 3:

[0514] The server begins analyzing the received activity data. By classifying each data point into a specific task category, it analyzes how the user allocates their work time.

[0515] Step 4:

[0516] The server uses natural language processing technology to analyze the content of emails, messages, and meeting remarks. This analysis identifies emotions from the text and evaluates the type and intensity of those emotions.

[0517] Step 5:

[0518] The server calculates an efficiency score based on data from the user's work activities and emotional assessments. Emotional states are directly reflected in the score; for example, a high stress level is evaluated as a decrease in efficiency.

[0519] Step 6:

[0520] The server visualizes user efficiency scores and sentiment ratings in a dashboard format and provides it to users and administrators. The dashboard includes user work progress, sentiment trends, and suggestions for improvement.

[0521] Step 7:

[0522] Users can use a dashboard to monitor their work activities and emotional state, and take necessary adjustments to their work schedules or emotional management measures. This allows them to achieve both work efficiency and emotional well-being.

[0523] (Example 2)

[0524] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0525] In today's work environment, it is crucial not only to improve employee productivity but also to consider their emotional well-being. However, traditional work management systems have struggled to comprehensively understand users' work performance and emotional states. Furthermore, there has been a lack of means to quantitatively evaluate the impact of emotional states on work efficiency. Therefore, there is a need for a method that simultaneously evaluates and visually presents both work quality and users' emotional states.

[0526] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0527] In this invention, the server includes means for acquiring activity information from an information processing terminal operated by the user, means for analyzing the activity information to quantify the user's work time and deliverables, and means for calculating efficiency figures based on the quality of deliverables and emotional state. This makes it possible to comprehensively evaluate the user's work performance and emotional state and to suggest specific improvement measures.

[0528] An "information processing terminal" refers to an electronic device such as a computer or smartphone operated by a user, which enables the input and acquisition of various types of data.

[0529] "Activity information" refers to information that includes data such as the history of operations on electronic communications, digital messages, virtual conference audio, and electronic data, which are acquired based on user actions.

[0530] "Deliverables" refer to digital files, documents, and other outputs generated, edited, or managed by users through their work activities.

[0531] "Efficiency metrics" are indicators that quantitatively evaluate and quantify users' work performance and emotional state, and are evaluation values ​​that reflect the quality of work and the influence of emotions.

[0532] A "sentiment analysis engine" is a software component equipped with analysis technology used to identify emotions such as joy, anger, sadness, and happiness from a user's linguistic statements and written content.

[0533] A "server" is a backend computing system that centrally analyzes information collected from users and provides evaluation results to administrators and users.

[0534] An "encrypted protocol" refers to a technical method used to ensure the confidentiality and security of data during the transmission and reception of information, and is a communication protocol designed to prevent unauthorized access.

[0535] This invention is a business management system that comprehensively understands a user's work performance and emotional state. An agent software is installed on the user's information processing terminal, which acquires user activity information in real time. This activity information includes the history of electronic communications, digital messages, virtual conference audio, and electronic data operations. The terminal transmits the acquired activity information to a server using encryption technology. To ensure security, encryption protocols such as TLS (Transport Layer Security) are used.

[0536] The server analyzes the transmitted activity information using a variety of data analysis tools. Distributed data processing systems such as Apache Spark are used for this purpose. Based on the analyzed data, the server quantifies the user's work time and dynamically evaluates the user's emotional state using an emotion analysis engine. This emotion analysis engine utilizes natural language processing technologies such as the Google Cloud Natural Language API to determine emotions such as joy, anger, sadness, and happiness from the user's statements and posts.

[0537] The analyzed data is quantified as efficiency figures, calculated to reflect the quality of work deliverables and emotional state. For example, excessive stress may result in lower efficiency figures. The server visualizes these results on a dashboard and provides them to users and administrators. Data visualization tools such as Tableau are used in the design of the dashboard to provide users with an at-a-glance overview of their work and emotional trends.

[0538] For example, if a user is performing the task of "reporting the progress of a new project to their supervisor," and the sentiment analysis engine detects an increase in stress, that information will be visually highlighted on the dashboard. This information can also be used by managers as a basis for taking appropriate intervention.

[0539] This system enables companies to achieve comprehensive management that balances employee productivity improvement with maintaining emotional well-being. Furthermore, by using a generative AI model, it becomes possible to generate prompts tailored to the user's work content, leading to further productivity improvements.

[0540] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0541] Step 1:

[0542] The device collects user activity information.

[0543] Specifically, it monitors in real time the electronic communications sent by the user and the audio from virtual meetings they participate in. Inputs are user operation events and conversation data, while outputs are activity information containing this data. The terminal uses agent software to record this information in a database.

[0544] Step 2:

[0545] The device encrypts the collected activity information and sends it to the server.

[0546] The input is raw activity information stored on the terminal, and the output is transmitted data encrypted using the TLS protocol. The terminal encrypts the data before transmission to ensure secure transmission even over insecure networks.

[0547] Step 3:

[0548] The server analyzes the received activity information and quantifies work time and deliverables.

[0549] The input is encrypted activity information received from the terminal, and the output is analytical data showing the work time and quality of deliverables for each task. The server uses Apache Spark to efficiently process large amounts of data and quantify the work performance of each user.

[0550] Step 4:

[0551] The server uses natural language processing technology to extract the user's emotional state.

[0552] The input is text data within activity information, and the output is the identified emotional state (e.g., joy, anger, sadness). The sentiment analysis engine utilizes the Google Cloud Natural Language API to analyze user posts and conversation content.

[0553] Step 5:

[0554] The server calculates efficiency figures based on business performance and emotional state.

[0555] The input consists of quantified business data and emotional data, and the output is an integrated evaluation as an efficiency score. The server considers the quality of work and emotional state, and calculates the efficiency score in a way that is easy for users and administrators to understand.

[0556] Step 6:

[0557] The server visualizes the generated efficiency figures and sentiment ratings on a dashboard and presents them to users and administrators.

[0558] The input consists of calculated efficiency figures and sentiment evaluation data, while the output is a visually easy-to-understand dashboard. Tools such as Tableau are used for data visualization, providing an overview of user work performance and sentiment trends.

[0559] (Application Example 2)

[0560] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0561] Maintaining both worker efficiency and mental health simultaneously in production environments has been difficult with conventional systems. In particular, there was a risk of compromising efficiency due to the inability to immediately address excessive stress and fatigue. A means to quickly respond to such situations and maintain productivity is needed.

[0562] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0563] In this invention, the server includes means for acquiring activity data from information devices operated by the user, means for analyzing the activity data to quantify the user's work time and deliverables, and means for incorporating an emotion engine for analyzing the user's emotional state. This makes it possible to optimize work assignments and break times, thereby improving efficiency and maintaining mental health.

[0564] "Information equipment" refers to terminal devices that users operate and use to acquire activity data.

[0565] "Activity data" refers to information including a user's electronic communications, phone calls, virtual conference contributions, and file operation history.

[0566] The "efficiency score" is an indicator of productivity calculated based on the user's work time and deliverables.

[0567] An "emotion engine" is software that incorporates technology to analyze a user's emotional state.

[0568] "Natural language processing technology" is a technology that analyzes the content of a user's speech and quantifies their emotional state.

[0569] A "dashboard" is a screen that visually presents users with efficiency scores and emotional states.

[0570] "Task assignment" is the process of assigning appropriate tasks to a user based on their current state.

[0571] "Break timing" refers to the time when users are instructed to take appropriate breaks based on their stress and fatigue levels.

[0572] This invention is a system that collects activity data using user-operated information devices and analyzes the user's work efficiency and emotional state based on that data. The server acquires activity data including the user's electronic communications, phone calls, virtual meeting statements, and file operation history. The acquired data is analyzed using natural language processing technology to quantify the emotional state based on the user's statements. This processing uses natural language processing libraries in a Python environment (e.g., NLTK and Transformers). The results of the emotional analysis, along with an efficiency score, are visually presented to the user in a dashboard format. The dashboard is built with JavaScript and can be viewed in a web browser. For example, if the user feels fatigued during work, the system detects this state and suggests an appropriate break time. This system aims to maintain both user productivity and mental health by optimizing work assignments and break timings. An example of a prompt to the generative AI model is, "Please tell me how to utilize the emotional analysis engine in the work environment." This prompt provides insights into how to effectively analyze the user's emotional state and link it to improved work efficiency.

[0573] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0574] Step 1:

[0575] The terminal collects activity data in real time, including the user's electronic communications, phone calls, virtual conference contributions, and file operation history. This input data is securely transmitted to the server using encryption protocols established to ensure security.

[0576] Step 2:

[0577] The server stores the received activity data and analyzes it using natural language processing techniques. Specifically, it utilizes Python and natural language processing libraries (e.g., NLTK and Transformers) to extract emotions from the user's statements. This process analyzes the input text data and calculates emotion scores such as joy, anger, sadness, and surprise.

[0578] Step 3:

[0579] The server integrates an efficiency score calculated by quantifying the user's work time and deliverables based on the analyzed emotion score. This data integration process generates an overall efficiency score while considering how emotional state affects productivity.

[0580] Step 4:

[0581] The server builds a user interface on a dashboard to visualize the user's overall efficiency score and emotional state. This dashboard is designed using JavaScript, accessible through the user's browser, and updates information in real time.

[0582] Step 5:

[0583] Users can check their efficiency score and emotional state through a dashboard, which helps them manage their time effectively and self-regulate their emotions. Based on this information, they can optimize their work assignments or insert breaks if fatigue is detected.

[0584] Step 6:

[0585] The server generates a prompt for the generated AI model, "Please tell me how to utilize the sentiment analysis engine in the work environment," providing guidance for users and administrators to understand and further improve how to use the system. This prompt provides insights into how to effectively analyze the user's emotional state and link it to improved work efficiency.

[0586] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0587] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0588] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0589] [Fourth Embodiment]

[0590] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0591] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0592] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0593] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0594] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0595] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0596] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0597] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0598] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0599] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0600] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0601] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0602] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0603] This invention provides a management system for improving work efficiency by acquiring data related to the user's work activities from terminal devices installed in the user's work environment and analyzing it on a server. Specific embodiments thereof are described below.

[0604] First, a dedicated software agent is installed on the terminal device. This agent is configured to collect user activity data from email, messaging applications, online meeting applications, and file management tools. Activity data includes, for example, email sending and receiving times, meeting participation times, and file creation and editing history.

[0605] The terminal device periodically collects activity data and transmits it to the server based on a specific encryption protocol. This ensures data privacy and security.

[0606] The server analyzes the received activity data in real time. The analysis process includes data classification, time measurement, and content evaluation of statements and messages using natural language processing techniques. This analysis quantifies how much time users spent on each task, the importance of their actions, and their efficiency.

[0607] After analysis, the server calculates an efficiency score based on the results obtained, indicating the user's work efficiency. This score takes into account factors such as how work time is used, the quality of task deliverables, and the impact of contributions.

[0608] The server then visually displays the generated efficiency score and analysis results, providing them to users and administrators in a dashboard format. This includes, for example, the time allocation for each task, importance scores, and suggestions for improvement.

[0609] Users can use this dashboard to review their work schedules and consider improvements as needed. Similarly, administrators can understand the overall team performance and provide appropriate feedback.

[0610] This system enables users to achieve effective time management and improved performance, regardless of whether they are working from home or in the office.

[0611] The following describes the processing flow.

[0612] Step 1:

[0613] The device collects activity data based on user actions. This collection is done in real time and includes sending and receiving emails, exchanging messages, participating in online meetings, and creating and editing files. The collected data is temporarily stored in the device's local storage.

[0614] Step 2:

[0615] The device encrypts the collected activity data at predetermined time intervals and transfers it to the server using a secure communication protocol. This process ensures data security and privacy.

[0616] Step 3:

[0617] The server begins analyzing the activity data received from the terminal. First, it categorizes each data point into a specific task category (e.g., responding to emails, attending meetings, creating documents). Next, it calculates the time spent on each task to determine how the user allocates their work time.

[0618] Step 4:

[0619] The server uses natural language processing techniques to further analyze emails, messages, and meeting transcripts. This analysis extracts important keywords and context from the text data and evaluates how much they contribute to business operations.

[0620] Step 5:

[0621] The server calculates the user's efficiency score based on the analysis results. The score is derived by comprehensively evaluating factors such as time allocation, the quality of task deliverables, and the content and frequency of contributions.

[0622] Step 6:

[0623] The server generates dashboards for users and administrators that visualize the analysis results. These dashboards include daily and weekly efficiency scores, time usage percentages, and suggestions for areas where improvement can be made.

[0624] Step 7:

[0625] Users review the provided dashboard and reflect on their daily work activities. This allows them to develop action plans for improving time management and efficient work execution.

[0626] The above outlines the specific processing flow of the program. This system allows users to objectively evaluate their own performance and gain insights into improving work efficiency.

[0627] (Example 1)

[0628] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0629] With the increasing diversification of work environments, there is a growing need to accurately understand users' work efficiency and improve their performance. However, traditional methods have made it difficult to comprehensively and accurately evaluate user activities, and have failed to identify areas for improvement and provide specific advice. Furthermore, data privacy and security have not been adequately ensured.

[0630] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0631] In this invention, the server includes means for transmitting activity information through a protected communication channel, means for analyzing the activity information to quantify work time and work results, and means for creating a visual display that suggests work improvements to the user and administrator based on the analysis results. This makes it possible to more accurately evaluate the user's work efficiency and to present specific improvement measures.

[0632] "Activity information" refers to information generated by user actions, including work time, work results, communication content, network meeting content, and data file operation history.

[0633] An "information processing device" is a terminal device operated by a user to collect and transmit activity information.

[0634] "Work time" refers to the time a user spends on a specific task or activity, and is recorded as activity information.

[0635] "Work output" refers to the quantitative measure of the quality of deliverables or outputs achieved through the user's work.

[0636] A "business efficiency evaluation index" is an index that quantifies business efficiency based on the user's work time and work results.

[0637] A "protected channel" is a communication path that uses encryption technology to protect activity information for secure transmission.

[0638] "Machine learning technology" is artificial intelligence technology that analyzes large amounts of data to evaluate user communication content and work results.

[0639] "Visual display" refers to a visualized data display that intuitively presents analyzed activity information and operational efficiency evaluation indicators to users and administrators.

[0640] This invention is a system that improves work efficiency by collecting and analyzing user activity information using a software agent installed on the user's information processing device.

[0641] The terminal (the user's information processing device) collects activity information such as electronic communications, interactive communications, network meeting content, and data file operation history through a software agent. This agent automatically records data generated during the user's work. For example, the agent logs how much time the user spends processing emails and periodically encrypts this data before sending it to the server.

[0642] The server first decodes the activity information received from the terminal. Next, it analyzes the data using machine learning techniques to evaluate work time and work results. Furthermore, it uses natural language processing techniques to evaluate the user's communication content and meeting contributions, and calculates work efficiency evaluation metrics. Based on these metrics, the server generates advice to improve the user's work efficiency.

[0643] Users can understand their work trends and areas for improvement through visual displays provided by the server. These visual displays include, for example, a heatmap of the user's weekly activities and specific suggestions for improving work efficiency. This allows users to review their work schedules and optimize their workflows as needed.

[0644] For example, when a user reviews a visual summary of their weekly activities, they might realize they're spending a lot of time attending meetings and develop a strategy to improve their work efficiency by reducing that time. An example of a prompt would be, "Please analyze your work activities this week and suggest ways to improve efficiency."

[0645] This system allows users to objectively understand their work efficiency and clearly identify areas for improvement, enabling them to work flexibly even when working from home or on the go.

[0646] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0647] Step 1:

[0648] The terminal activates a software agent to collect user activity information. This agent retrieves user operation data from email clients, scheduling applications, online meeting tools, and other sources. Inputs include sending and receiving emails, joining meetings, sending chat messages, and document editing history. This data is collected and recorded as activity data.

[0649] Step 2:

[0650] The terminal encrypts the collected activity data using a specific encryption method and sends it to the server using a secure communication protocol. The input is the activity data collected in step 1, and the output is the encrypted data. Specifically, the system uses protocols such as TLS to transmit the data, preventing eavesdropping and tampering.

[0651] Step 3:

[0652] The server receives encrypted data sent from the terminal and decrypts it. The input is encrypted activity data, and the output is decrypted activity data. After the decryption process is completed, the data is passed to the analysis engine.

[0653] Step 4:

[0654] The server analyzes the received activity data. Here, natural language processing techniques are used to analyze the content of emails and messages, and time tracking techniques are used to measure the time spent on each task. The input is decoded activity data, and the output is quantified work time and work results as analysis outcomes.

[0655] Step 5:

[0656] The server calculates a business efficiency evaluation index based on the analysis results. Specifically, it evaluates how efficiently users are spending their time on each task. The input is the analysis results obtained in step 4, and the output is the business efficiency evaluation index. A machine learning model is used to analyze user activity patterns.

[0657] Step 6:

[0658] The server generates visualized reports for users and administrators based on operational efficiency metrics and analysis results. The input is operational efficiency metrics, and the output is a visually displayed report. This report includes suggestions for improvement and efficiency scores for each task, and is provided as a dashboard for direct use by users.

[0659] Step 7:

[0660] Users utilize a dashboard provided by the server to check their work efficiency and revise their schedules and work methods as needed. Input is a visually displayed report, and output is improved work performance. In this way, users can optimize their work style more efficiently.

[0661] (Application Example 1)

[0662] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0663] In smart cities, accurately collecting and analyzing data on daily activities and providing immediate feedback to citizens is necessary to efficiently manage citizens' work and use of public services and to improve the quality of life for them. Conventional systems have challenges such as insufficient collection of activity information and ineffective communication of analysis results to citizens, thus failing to contribute to efficiency improvements.

[0664] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0665] In this invention, the server includes means for acquiring activity information from a communication device, means for analyzing the activity information to quantify work time and results, and means for making suggestions to urban residents regarding their use of public services based on the work time, results, and efficiency score. This enables citizens to understand their own activities and choose the optimal lifestyle.

[0666] A "communication device" is a digital device used by a user that enables the acquisition of activity information and the transmission and reception of data via the internet.

[0667] "Activity information" refers to data related to a user's daily work activities and use of public services, and includes communication history, electronic communication messages, statements made in virtual meetings, and data file processing history.

[0668] "Working hours" refers to the time a user spends performing a specific work activity, and it is an important indicator in evaluating efficiency.

[0669] "Results" refer to the specific outputs that users produce as a result of their work activities, and these can be used to measure work efficiency.

[0670] The "efficiency score" is a numerical value calculated based on the user's work time and results, and is an indicator for quantitatively evaluating the user's work efficiency.

[0671] "Suggestions" refer to guidelines and improvement advice for users derived from analyzed activity information and efficiency scores.

[0672] Morphological analysis is a technique in natural language processing that breaks down a sentence into its smallest constituent units and analyzes the meaning and grammatical role of each word.

[0673] The system for realizing this invention uses a combination of a communication device, a server, and advanced data analysis software. The communication device acquires data on the user's daily activities, i.e., information related to work activities and use of public services. This includes communication history, electronic communication messages, statements made in virtual meetings, and data file processing history. This activity information is transmitted to the server using a secure protocol.

[0674] The server uses Python-based analysis tools (Pandas, Scikit-learn) to analyze the received activity information. Morphological analysis, a natural language processing technique, is used for the analysis, which evaluates the content of statements and quantifies work time and results. Based on the analysis results, an efficiency score is calculated for urban residents.

[0675] Subsequently, the server generates specific suggestions for the user's efficient activities from this information and presents them visually in a dashboard format on the communication device. For example, a user may receive suggestions for the optimal route based on traffic information during their commute, or learn about areas for improvement in their work activities, thereby improving the efficiency of their daily life.

[0676] An example of a generated prompt is, "Suggest the optimal commute route based on citizens' public transport usage data." By inputting this prompt into the generating AI model, it is configured to output appropriate suggestions tailored to the objective.

[0677] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0678] Step 1:

[0679] The communication device collects user activity information. This includes the user's communication history, electronic communication messages, virtual conference statements, and data file processing history. After acquisition, this activity information is sent to the server using the HTTPS protocol. The input data is the user's communication record, and the output data is this record as it has been sent to the server while maintaining confidentiality.

[0680] Step 2:

[0681] The server receives the activity information and preprocesses the data. This involves deduplication, imputation of missing values, and categorization. This process transforms the raw input data into well-formed data that can be analyzed.

[0682] Step 3:

[0683] The server manages the preprocessed data as a DataFrame using the Python Pandas library and then uses Scikit-learn to build a model that quantifies work time and results. The input data consists of preprocessed activity information, and the output data is the quantified results of work time and results for each user.

[0684] Step 4:

[0685] The server uses a generative AI model to calculate an efficiency score from work time and results. The input here is quantified work information, and the output is the efficiency score. In this process, the generative AI model extracts rules and patterns from the given data to generate the efficiency score.

[0686] Step 5:

[0687] The server generates specific suggestions for the user based on the calculated efficiency score and displays them visually on the communication device. This is in a dashboard format and includes suggestions for optimal commuting routes and efficient activities. The input is the efficiency score, and the output is the suggestions for the user.

[0688] Step 6:

[0689] Users receive suggestions provided through communication devices and incorporate them into their daily activities. This allows users to receive improvement suggestions based on their actual activities, thereby improving their daily life and work efficiency. The input is visualized suggestions, and the output is the result of the user's improved activities.

[0690] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0691] This invention is a business performance management system that incorporates an emotion engine to recognize user emotions, enabling an integrated understanding of user productivity and emotional state. Specific embodiments are described below.

[0692] First, agent software is installed on the user's device. This agent collects user activity data in real time, namely emails, messages, online meeting comments, and file operation history. The collected data is sent to the server via a defined encryption protocol.

[0693] The server analyzes this activity data to quantify the user's work time and output quality, and further extracts the user's emotional state using an emotion engine. The emotion engine incorporates natural language processing technology to identify emotions such as joy, anger, sadness, and surprise from the content of statements and posts.

[0694] The server calculates a user's efficiency score based on analyzed work and emotional data. This score takes into account not only work progress and the quality of deliverables, but also emotional state. For example, if excessive stress is detected, productivity may be temporarily rated lower.

[0695] The server compiles the generated efficiency scores and sentiment evaluation results and provides them visually to users and administrators in a dashboard format. This dashboard allows users to see their performance and sentiment trends at a glance. For example, tasks where stress levels were high during work hours or tasks that increased feelings of joy are highlighted.

[0696] Based on this information, users can aim for more effective time management and improved emotional well-being. For example, if a particular task is causing stress, taking measures to address it can lead to a healthier work-life balance.

[0697] This system allows companies to achieve comprehensive performance management that takes into account not only employee productivity but also their emotional well-being.

[0698] The following describes the processing flow.

[0699] Step 1:

[0700] The device collects user activity data. Specifically, it monitors and records logs of emails sent and received by the user, interactions on messaging platforms, comments made during online meetings, and file operations.

[0701] Step 2:

[0702] The device encrypts the collected activity data at predetermined time intervals and sends it to the server using a secure communication protocol. This process ensures the security of the data.

[0703] Step 3:

[0704] The server begins analyzing the received activity data. By classifying each data point into a specific task category, it analyzes how the user allocates their work time.

[0705] Step 4:

[0706] The server uses natural language processing technology to analyze the content of emails, messages, and meeting remarks. This analysis identifies emotions from the text and evaluates the type and intensity of those emotions.

[0707] Step 5:

[0708] The server calculates an efficiency score based on data from the user's work activities and emotional assessments. Emotional states are directly reflected in the score; for example, a high stress level is evaluated as a decrease in efficiency.

[0709] Step 6:

[0710] The server visualizes user efficiency scores and sentiment ratings in a dashboard format and provides it to users and administrators. The dashboard includes user work progress, sentiment trends, and suggestions for improvement.

[0711] Step 7:

[0712] Users can use a dashboard to monitor their work activities and emotional state, and take necessary adjustments to their work schedules or emotional management measures. This allows them to achieve both work efficiency and emotional well-being.

[0713] (Example 2)

[0714] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0715] In today's work environment, it is crucial not only to improve employee productivity but also to consider their emotional well-being. However, traditional work management systems have struggled to comprehensively understand users' work performance and emotional states. Furthermore, there has been a lack of means to quantitatively evaluate the impact of emotional states on work efficiency. Therefore, there is a need for a method that simultaneously evaluates and visually presents both work quality and users' emotional states.

[0716] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0717] In this invention, the server includes means for acquiring activity information from an information processing terminal operated by the user, means for analyzing the activity information to quantify the user's work time and deliverables, and means for calculating efficiency figures based on the quality of deliverables and emotional state. This makes it possible to comprehensively evaluate the user's work performance and emotional state and to suggest specific improvement measures.

[0718] An "information processing terminal" refers to an electronic device such as a computer or smartphone operated by a user, which enables the input and acquisition of various types of data.

[0719] "Activity information" refers to information that includes data such as the history of operations on electronic communications, digital messages, virtual conference audio, and electronic data, which are acquired based on user actions.

[0720] "Deliverables" refer to digital files, documents, and other outputs generated, edited, or managed by users through their work activities.

[0721] "Efficiency metrics" are indicators that quantitatively evaluate and quantify users' work performance and emotional state, and are evaluation values ​​that reflect the quality of work and the influence of emotions.

[0722] A "sentiment analysis engine" is a software component equipped with analysis technology used to identify emotions such as joy, anger, sadness, and happiness from a user's linguistic statements and written content.

[0723] A "server" is a backend computing system that centrally analyzes information collected from users and provides evaluation results to administrators and users.

[0724] An "encrypted protocol" refers to a technical method used to ensure the confidentiality and security of data during the transmission and reception of information, and is a communication protocol designed to prevent unauthorized access.

[0725] This invention is a business management system that comprehensively understands a user's work performance and emotional state. An agent software is installed on the user's information processing terminal, which acquires user activity information in real time. This activity information includes the history of electronic communications, digital messages, virtual conference audio, and electronic data operations. The terminal transmits the acquired activity information to a server using encryption technology. To ensure security, encryption protocols such as TLS (Transport Layer Security) are used.

[0726] The server analyzes the transmitted activity information using a variety of data analysis tools. Distributed data processing systems such as Apache Spark are used for this purpose. Based on the analyzed data, the server quantifies the user's work time and dynamically evaluates the user's emotional state using an emotion analysis engine. This emotion analysis engine utilizes natural language processing technologies such as the Google Cloud Natural Language API to determine emotions such as joy, anger, sadness, and happiness from the user's statements and posts.

[0727] The analyzed data is quantified as efficiency figures, calculated to reflect the quality of work deliverables and emotional state. For example, excessive stress may result in lower efficiency figures. The server visualizes these results on a dashboard and provides them to users and administrators. Data visualization tools such as Tableau are used in the design of the dashboard to provide users with an at-a-glance overview of their work and emotional trends.

[0728] For example, if a user is performing the task of "reporting the progress of a new project to their supervisor," and the sentiment analysis engine detects an increase in stress, that information will be visually highlighted on the dashboard. This information can also be used by managers as a basis for taking appropriate intervention.

[0729] This system enables companies to achieve comprehensive management that balances employee productivity improvement with maintaining emotional well-being. Furthermore, by using a generative AI model, it becomes possible to generate prompts tailored to the user's work content, leading to further productivity improvements.

[0730] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0731] Step 1:

[0732] The device collects user activity information.

[0733] Specifically, it monitors in real time the electronic communications sent by the user and the audio from virtual meetings they participate in. Inputs are user operation events and conversation data, while outputs are activity information containing this data. The terminal uses agent software to record this information in a database.

[0734] Step 2:

[0735] The device encrypts the collected activity information and sends it to the server.

[0736] The input is raw activity information stored on the terminal, and the output is transmitted data encrypted using the TLS protocol. The terminal encrypts the data before transmission to ensure secure transmission even over insecure networks.

[0737] Step 3:

[0738] The server analyzes the received activity information and quantifies work time and deliverables.

[0739] The input is encrypted activity information received from the terminal, and the output is analytical data showing the work time and quality of deliverables for each task. The server uses Apache Spark to efficiently process large amounts of data and quantify the work performance of each user.

[0740] Step 4:

[0741] The server uses natural language processing technology to extract the user's emotional state.

[0742] The input is text data within activity information, and the output is the identified emotional state (e.g., joy, anger, sadness). The sentiment analysis engine utilizes the Google Cloud Natural Language API to analyze user posts and conversation content.

[0743] Step 5:

[0744] The server calculates efficiency figures based on business performance and emotional state.

[0745] The input consists of quantified business data and emotional data, and the output is an integrated evaluation as an efficiency score. The server considers the quality of work and emotional state, and calculates the efficiency score in a way that is easy for users and administrators to understand.

[0746] Step 6:

[0747] The server visualizes the generated efficiency figures and sentiment ratings on a dashboard and presents them to users and administrators.

[0748] The input consists of calculated efficiency figures and sentiment evaluation data, while the output is a visually easy-to-understand dashboard. Tools such as Tableau are used for data visualization, providing an overview of user work performance and sentiment trends.

[0749] (Application Example 2)

[0750] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0751] Maintaining both worker efficiency and mental health simultaneously in production environments has been difficult with conventional systems. In particular, there was a risk of compromising efficiency due to the inability to immediately address excessive stress and fatigue. A means to quickly respond to such situations and maintain productivity is needed.

[0752] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0753] In this invention, the server includes means for acquiring activity data from information devices operated by the user, means for analyzing the activity data to quantify the user's work time and deliverables, and means for incorporating an emotion engine for analyzing the user's emotional state. This makes it possible to optimize work assignments and break times, thereby improving efficiency and maintaining mental health.

[0754] "Information equipment" refers to terminal devices that users operate and use to acquire activity data.

[0755] "Activity data" refers to information including a user's electronic communications, phone calls, virtual conference contributions, and file operation history.

[0756] The "efficiency score" is an indicator of productivity calculated based on the user's work time and deliverables.

[0757] An "emotion engine" is software that incorporates technology to analyze a user's emotional state.

[0758] "Natural language processing technology" is a technology that analyzes the content of a user's speech and quantifies their emotional state.

[0759] A "dashboard" is a screen that visually presents users with efficiency scores and emotional states.

[0760] "Task assignment" is the process of assigning appropriate tasks to a user based on their current state.

[0761] "Break timing" refers to the time when users are instructed to take appropriate breaks based on their stress and fatigue levels.

[0762] This invention is a system that collects activity data using user-operated information devices and analyzes the user's work efficiency and emotional state based on that data. The server acquires activity data including the user's electronic communications, phone calls, virtual meeting statements, and file operation history. The acquired data is analyzed using natural language processing technology to quantify the emotional state based on the user's statements. This processing uses natural language processing libraries in a Python environment (e.g., NLTK and Transformers). The results of the emotional analysis, along with an efficiency score, are visually presented to the user in a dashboard format. The dashboard is built with JavaScript and can be viewed in a web browser. For example, if the user feels fatigued during work, the system detects this state and suggests an appropriate break time. This system aims to maintain both user productivity and mental health by optimizing work assignments and break timings. An example of a prompt to the generative AI model is, "Please tell me how to utilize the emotional analysis engine in the work environment." This prompt provides insights into how to effectively analyze the user's emotional state and link it to improved work efficiency.

[0763] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0764] Step 1:

[0765] The terminal collects activity data in real time, including the user's electronic communications, phone calls, virtual conference contributions, and file operation history. This input data is securely transmitted to the server using encryption protocols established to ensure security.

[0766] Step 2:

[0767] The server stores the received activity data and analyzes it using natural language processing techniques. Specifically, it utilizes Python and natural language processing libraries (e.g., NLTK and Transformers) to extract emotions from the user's statements. This process analyzes the input text data and calculates emotion scores such as joy, anger, sadness, and surprise.

[0768] Step 3:

[0769] The server integrates an efficiency score calculated by quantifying the user's work time and deliverables based on the analyzed emotion score. This data integration process generates an overall efficiency score while considering how emotional state affects productivity.

[0770] Step 4:

[0771] The server builds a user interface on a dashboard to visualize the user's overall efficiency score and emotional state. This dashboard is designed using JavaScript, accessible through the user's browser, and updates information in real time.

[0772] Step 5:

[0773] Users can check their efficiency score and emotional state through a dashboard, which helps them manage their time effectively and self-regulate their emotions. Based on this information, they can optimize their work assignments or insert breaks if fatigue is detected.

[0774] Step 6:

[0775] The server generates a prompt for the generated AI model, "Please tell me how to utilize the sentiment analysis engine in the work environment," providing guidance for users and administrators to understand and further improve how to use the system. This prompt provides insights into how to effectively analyze the user's emotional state and link it to improved work efficiency.

[0776] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0777] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0778] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0779] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0780] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0781] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0782] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0783] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0784] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0785] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0786] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0787] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0788] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0789] 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.

[0790] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0791] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0792] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0793] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0794] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0795] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0796] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0797] The following is further disclosed regarding the embodiments described above.

[0798] (Claim 1)

[0799] A means of acquiring activity data from a terminal device operated by a user,

[0800] A means for analyzing the aforementioned activity data to quantify the user's working hours and output,

[0801] A means for calculating an efficiency score based on the aforementioned work hours and output,

[0802] A means for visually presenting the efficiency score to the user,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, wherein the activity data includes emails, messages, online meeting comments, and file operation history.

[0806] (Claim 3)

[0807] The system according to claim 1, wherein the analysis means evaluates the content of the user's statements using natural language processing technology.

[0808] "Example 1"

[0809] (Claim 1)

[0810] A means for collecting activity information from an information processing device operated by the user,

[0811] A means for analyzing the aforementioned activity information to quantify the user's work time and work results,

[0812] A means for calculating a business efficiency evaluation index based on the aforementioned work time and work results,

[0813] A means of visualizing the aforementioned business efficiency evaluation indicators and providing them to the user,

[0814] Means for transmitting the aforementioned activity information through a protected communication channel,

[0815] A means for creating a visual display that suggests business improvement to users and administrators based on the aforementioned analysis results,

[0816] A system that includes this.

[0817] (Claim 2)

[0818] The system according to claim 1, wherein the activity information includes the contents of electronic communications, interactive communications, network conferences, and data file operation history.

[0819] (Claim 3)

[0820] The system according to claim 1, wherein the analysis means evaluates the user's communication content and work results using machine learning technology.

[0821] "Application Example 1"

[0822] (Claim 1)

[0823] A means of obtaining activity information from a communication device,

[0824] A means for analyzing the aforementioned activity information to quantify working hours and results,

[0825] A means of making suggestions regarding the use of public services by urban residents based on the aforementioned work hours, results, and efficiency scores,

[0826] A means for visually presenting the aforementioned proposal on a communication device,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, wherein the activity information includes communications, electronic communication messages, virtual conference speeches, and data file processing history.

[0830] (Claim 3)

[0831] The system according to claim 1, wherein the analysis means evaluates the content of the statement using morphological analysis technology and reflects it in the proposal.

[0832] "Example 2 of combining an emotion engine"

[0833] (Claim 1)

[0834] A means of obtaining activity information from an information processing terminal operated by a user,

[0835] A means for analyzing the aforementioned activity information to quantify the user's working hours and deliverables,

[0836] A means for calculating efficiency figures based on the quality and emotional state of the aforementioned deliverables,

[0837] A means for visually presenting the aforementioned efficiency figures and emotional evaluations to the user,

[0838] A means of presenting the generated efficiency figures and sentiment evaluations to the administrator,

[0839] A means of sending data to a server using an encrypted protocol,

[0840] A method for extracting emotional states in real time using an emotion analysis engine,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, wherein the activity information includes electronic communications, digital messages, audio from virtual meetings, and operation history of electronic data.

[0844] (Claim 3)

[0845] The system according to claim 1, wherein the analysis means evaluates the user's spoken content and written content using language processing technology.

[0846] "Application example 2 when combining with an emotional engine"

[0847] (Claim 1)

[0848] A means of acquiring activity data from information devices operated by the user,

[0849] A means for analyzing the aforementioned activity data to quantify the user's working time and deliverables,

[0850] A means for calculating an efficiency score based on the aforementioned work time and deliverables,

[0851] A means for visually presenting the efficiency score to the user,

[0852] A means of incorporating an emotion engine to analyze the user's emotional state,

[0853] A means for optimizing work assignments and break times based on the aforementioned emotional state,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, wherein the activity data includes electronic communications, phone calls, virtual conference speeches, and file operation history.

[0857] (Claim 3)

[0858] The system according to claim 1, wherein the analysis means evaluates the content of the user's statements using natural language processing technology and quantifies the emotional state. [Explanation of Symbols]

[0859] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining activity information from a communication device, A means for analyzing the aforementioned activity information to quantify working hours and results, A means of making suggestions regarding the use of public services by urban residents based on the aforementioned work hours, results, and efficiency scores, A means for visually presenting the aforementioned proposal on a communication device, A system that includes this.

2. The system according to claim 1, wherein the activity information includes communications, electronic communication messages, virtual conference speeches, and data file processing history.

3. The system according to claim 1, wherein the analysis means evaluates the content of the statement using morphological analysis technology and reflects it in the proposal.

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