System

Abstract

To provide a system.SOLUTION: A system includes: means for comprehensively acquiring information from business software; means for storing the acquired information in a database; means for analyzing the stored information; means for reading the schedule of a user on the basis of the analysis results; and means for proposing the best action considering time efficiency, movement efficiency, and cost efficiency on the basis of the read schedule.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Traditional work management systems require users to manage their own schedules and tasks, and are unable to automatically provide information to optimize time, travel, and cost efficiency. [Means for solving the problem]

[0005] The system of the present invention comprehensively acquires information from business groupware and reads the user's schedule based on the acquired information. Furthermore, based on the read schedule, it proposes the best course of action, taking into account time efficiency, travel efficiency, and cost efficiency. It also reads tasks from emails and documents, prioritizes the read tasks based on their urgency and importance, and suggests the best course of action. This allows users to efficiently manage their own work. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

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

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

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

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

[0014] [First embodiment]

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

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0027] "Example 1"

[0028] The system of the present invention uses API and scraping technology as a means of comprehensively acquiring information from business groupware.

[0029] It uses the API of business groupware such as Outlook to obtain information such as users' schedules, emails, documents, etc. The obtained information is stored in a database and used as input for analysis.

[0030] "Example 2"

[0031] The system of the present invention uses natural language processing technology as a means for reading the user's schedule based on the acquired information. Specifically, it uses natural language processing services such as Google Cloud Natural Language API and IBM Watson (registered trademark).

[0032] It extracts tasks and events from schedules, emails, and documents, which are then used as input to suggest the best course of action, taking into account time, travel, and cost efficiency.

[0033] "Example 3"

[0034] The system of the present invention uses machine learning technology as a means of proposing the best course of action that takes into account time efficiency, travel efficiency, and cost efficiency. Specifically, it uses machine learning frameworks such as TENSORFLOW (registered trademark) and PyTorch to learn user behavior patterns. The learned model is then used to suggest the best course of action when a new task or event occurs.

[0035] The processing flow of each embodiment will be described below.

[0036] "Example 1"

[0037] Step 1: The system will automatically install Google Calendar and Microsoft Outlook.

[0038] It uses the API of business groupware such as k to obtain information such as users' schedules, emails, documents, etc.

[0039] Step 2: The acquired information is stored in a database, which is used as input for analysis.

[0040] "Example 2"

[0041] Step 1: The system uses natural language processing services such as Google Cloud Natural Language API and IBM Watson to analyze schedules and messages.

[0042] Extract tasks and events from rules and documents.

[0043] Step 2: The extracted tasks and events are used as input to suggest the best course of action, taking into account time, travel, and cost efficiency.

[0044] "Example 3"

[0045] Step 1: The system uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns.

[0046] Step 2: The trained model is used to suggest the best course of action when a new task or event arises.

[0047] Example 1

[0048] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0049] With traditional business software, it was difficult to efficiently manage users' schedules and tasks and suggest the best course of action. Furthermore, information acquisition, storage, and analysis were often done manually, which was time-consuming and labor-intensive. Furthermore, analysis based on the acquired information was insufficient, making it difficult to optimize users' time, travel, and cost efficiency.

[0050] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0051] In this invention, the server includes a means for comprehensively acquiring information from business software, a means for storing the acquired information in a database, and a means for analyzing the stored information, thereby enabling efficient management of user schedules and tasks and proposing optimal actions that take into account time efficiency, travel efficiency, and cost efficiency.

[0052] "Business software" is software used by companies and organizations to carry out their business efficiently.

[0053] "Means for comprehensively acquiring information" refers to methods and technologies for comprehensively collecting necessary information from business software.

[0054] "Means for storing in a database" refers to the methods and techniques used to store the acquired information in a structured format in a database.

[0055] "Analysis tools" are the methods and techniques used to analyze stored information and extract meaningful data and insights.

[0056] A "means for reading a user's schedule" is a method or technique for extracting a user's plans and tasks from the acquired and stored information.

[0057] "Time efficiency" refers to the efficiency with which users can make optimal use of their time.

[0058] "Travel efficiency" refers to the efficiency of optimizing the time and cost required for users to travel.

[0059] "Cost efficiency" refers to efficiency in minimizing the costs involved in conducting business for users.

[0060] "Best action suggestion" refers to methods and technologies for suggesting the most effective action for a user based on the analysis results.

[0061] "Email" is a digital message sent and received over the Internet.

[0062] A "document" is a digital file that contains information such as text and images.

[0063] A "task" refers to a specific task or activity that a user must perform.

[0064] "Urgency" is an index that indicates how urgently the task needs to be performed.

[0065] "Importance" is an indicator of how important a task is to achieving the user's goals.

[0066] A "prioritization method" is a method or technique for determining the order of execution of tasks, taking into account the urgency and importance of the tasks.

[0067] MODE FOR CARRYING OUT THE INVENTION

[0068] The present invention is a system for comprehensively acquiring information from business software, storing the acquired information in a database, and analyzing the stored information. A specific embodiment of this system will be described below.

[0069] 1. Program Generation

[0070] The server generates a program to comprehensively retrieve information from business software. This program uses APIs and scraping technology to retrieve data from the business software. Specifically, it uses APIs such as Google Calendar and Microsoft Outlook.

[0071] 2. Program Processing

[0072] The server uses the APIs of Google Calendar and Microsoft Outlook to obtain information such as user schedules, emails, documents, etc. Below, we will clearly explain the hardware and software used and the methods of data processing and calculation for each.

[0073] API authentication: The server obtains credentials to access the Google Calendar or Microsoft Outlook API using an authentication protocol such as OAuth 2.0.

[0074] Data retrieval: The server uses the authentication information to send a request to the API to retrieve information such as the user's schedule, emails, documents, etc. For example, it uses the Google Calendar API to retrieve the user's schedule.

[0075] Data storage: The acquired information is stored in a database on the server. The database used is a relational database such as MySQL (registered trademark) or PostgreSQL.

[0076] Data analysis: The stored data is used as input for analysis, which can be done using programming languages ​​such as Python or R.

[0077] 3. Specific Examples

[0078] As a specific example, consider the case where a user's schedule is retrieved from Google Calendar.

[0079] The server first performs authentication using OAuth 2.0 to obtain the user's schedule using the Google Calendar API.

[0080] If authentication is successful, the server obtains an access token and uses it to make requests to Google Calendar's API endpoints.

[0081] The server parses the JSON response returned from the API and extracts the user's schedule information.

[0082] The extracted schedule information is stored in the server's MySQL database.

[0083] The saved schedule data is converted into a data frame on the server using Python's Pandas library and used for statistical analysis and training machine learning models.

[0084] Prompt Sentence Examples

[0085] "Use the Google Calendar API to retrieve users' schedules and store them in a MySQL database. Then, analyze the stored data using Python's Pandas library."

[0086] In this way, the server realizes a system that obtains information from business software, stores it in a database, and uses it as input for analysis.

[0087] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0088] Step 1:

[0089] API Authentication

[0090] The server obtains authentication information to access the business software's API. As input, it uses authentication information provided by the user (client ID, client secret, etc.). The server uses this authentication information to send an authentication request to the API provider and obtains an access token as output. Specifically, the server performs authentication using the OAuth 2.0 protocol.

[0091] Step 2:

[0092] Data Acquisition

[0093] The server uses the acquired access token to send a request to the API to retrieve information such as the user's schedule, emails, and documents. The access token and the API endpoint URL are used as input. The server receives the JSON-formatted response returned from the API and extracts the necessary information as output. Specifically, the server sends a request to the Google Calendar API endpoint to retrieve the user's schedule.

[0094] Step 3:

[0095] Data storage

[0096] The server stores the retrieved information in a database. As input, it uses the parsed JSON data. The server converts this into a SQL query and inserts it into the database. As output, it gets the information stored in the database. Specifically, the server connects to the MySQL database and executes an INSERT statement to save the data.

[0097] Step 4:

[0098] Data analysis

[0099] The server analyzes the stored data. It uses data retrieved from the database as input. The server converts this data into a data frame using Python's Pandas library, and performs statistical analysis and trains machine learning models. The output is analysis results and predictive models. Specifically, the server executes SQL queries to retrieve data, converts it into a Pandas data frame, and performs analysis.

[0100] Step 5:

[0101] action suggestion

[0102] The server suggests the best course of action to the user based on the analysis results. It uses the results of the data analysis as input. The server calculates the optimal course of action taking into account time efficiency, travel efficiency, and cost efficiency, and makes a suggestion to the user as output. Specifically, the server runs an algorithm based on the analysis results and sends a notification to the user.

[0103] (Application example 1)

[0104] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0105] Conventional factory robot scheduling is often done manually, making it difficult to optimize work schedules. Furthermore, information acquired from business software is limited and rarely reflected in robot work schedules. This results in reduced time, travel, and cost efficiency, resulting in a loss of overall productivity.

[0106] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0107] In this invention, the server includes means for comprehensively acquiring information from business software, means for reading a user's schedule based on the acquired information, means for proposing the best course of action based on the read schedule, taking into consideration time efficiency, travel efficiency, and cost efficiency, means for optimizing machine work schedules based on the acquired schedule information, and means for saving the optimized schedule in a database. This makes it possible to automatically optimize the work schedules of factory robots and improve overall productivity.

[0108] "Business software" is software used by companies and organizations to carry out their business efficiently.

[0109] "Means for comprehensively acquiring information" refers to methods and technologies for acquiring all necessary information from business software.

[0110] "Means for reading the user's schedule" refers to methods and technologies for analyzing and understanding the user's plans and tasks based on the information obtained.

[0111] "Time efficiency" refers to the efficiency of minimizing wasted time when performing work or tasks.

[0112] "Travel efficiency" refers to the efficiency of minimizing travel time and costs when performing work or tasks.

[0113] "Cost efficiency" refers to the efficiency of minimizing costs when performing work or tasks.

[0114] "Best action suggestion" refers to methods and techniques for suggesting the most efficient and effective action for the user based on the information obtained.

[0115] "Means for optimizing machine work schedules" refers to methods and techniques for adjusting machine work schedules most efficiently based on acquired schedule information.

[0116] "Means for storing in a database" refers to methods and techniques for recording and managing the acquired information and optimized schedules in a database.

[0117] The system for implementing this invention comprehensively acquires information from business software, reads the user's schedule based on the acquired information, and proposes optimal actions. It also has the function of optimizing machine work schedules based on the acquired schedule information and saving the optimized schedule in a database.

[0118] The server obtains information from business software (e.g., Google Calendar, Microsoft Outlook) using APIs. The obtained information is used to analyze the user's schedule and tasks. The analyzed information is used as data to suggest optimal actions, taking into account time efficiency, travel efficiency, and cost efficiency.

[0119] Specifically, the server does the following:

[0120] 1. Data Acquisition: Use the Google Calendar API or Microsoft Outlook API to obtain user schedule information.

[0121] 2. Data processing: Analyze the acquired schedule information and convert it into the required format.

[0122] 3. Schedule optimization: Optimize the work schedule of factory robots based on the acquired schedule information.

[0123] 4. Data storage: The optimized schedule is stored in the database.

[0124] The hardware used is a factory robot and server, and the software uses the Google Calendar API, Microsoft Outlook API, Python, and database modules (e.g., SQLite, PostgreSQL).

[0125] For example, a robot in a factory can automatically start and finish work based on a schedule retrieved from Google Calendar, and the robot can also automatically incorporate maintenance times into its schedule.

[0126] An example of a prompt to input to a generative AI model is as follows:

[0127] "Write a Python program that optimizes the work schedules of factory robots based on schedule information retrieved from Google Calendar. The program will retrieve the schedule information using the Google Calendar API and compare it with the current robot schedule stored in a database to optimize it."

[0128] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0129] Step 1:

[0130] The server uses the API of business software (e.g., Google Calendar, Microsoft Outlook) to obtain the user's schedule information. Specifically, it authenticates using the API key and requests the schedule information. The input is the API key and user ID, and the output is the user's schedule information.

[0131] Step 2:

[0132] The server analyzes the acquired schedule information and converts it into the required format. Specifically, it extracts the start time, end time, event summary, etc. from the schedule information and formats it into a format that can be saved in a database. The input is the acquired schedule information, and the output is the formatted schedule data.

[0133] Step 3:

[0134] The server optimizes the work schedules of the factory robots based on the formatted schedule data. Specifically, it compares the current robot schedule with the newly acquired schedule information and calculates the optimal work sequence and timing. The input is the formatted schedule data and the current robot schedule, and the output is the optimized robot schedule.

[0135] Step 4:

[0136] The server saves the optimized robot schedule to the database by establishing a database connection and inserting or updating the optimized schedule data. The input is the optimized robot schedule, and the output is the result saved to the database.

[0137] Step 5:

[0138] The server sends work instructions to the factory robots based on the saved optimized schedule. Specifically, it sends instructions to the robot control system using an API or dedicated protocol. The input is the optimized robot schedule, and the output is the robot's start of work.

[0139] Step 6:

[0140] Users monitor the work status of factory robots and make adjustments as necessary. Specifically, they use a dedicated monitoring tool to check the robot's status and manually intervene if an abnormality occurs. The input is the robot's work status, and the output is the work status after adjustments.

[0141] Example 2

[0142] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0143] Conventional schedule management systems require users to manually input and manage their schedules, which is time-consuming and labor-intensive. Furthermore, they lack sufficient schedule optimization and efficient action suggestions, making it difficult to maximize users' time, travel, and cost efficiency. Furthermore, it is difficult to integrate and process data from multiple sources, which can lead to inconsistencies in users' schedule management.

[0144] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0145] In this invention, the server includes a means for comprehensively acquiring information from an information processing device, a means for reading a user's schedule based on the acquired information, a means for extracting tasks and events from schedules, correspondence, and documents using natural language processing technology, a means for proposing optimal actions based on the read schedule, taking into account time efficiency, travel efficiency, and cost efficiency, and a means for calculating travel time between the extracted tasks and events. This allows the user to receive efficient action suggestions without having to manually input the schedule. Furthermore, by integrating and processing data from multiple information sources, consistent schedule management can be achieved.

[0146] An "information processing device" is an electronic device for inputting, processing, storing, and outputting data.

[0147] "Means for comprehensively acquiring information" refers to methods and technologies for comprehensively collecting necessary data from multiple sources.

[0148] The "means for reading the user's schedule" refers to a method or technology for analyzing and extracting data related to the user's schedule from the acquired information.

[0149] "Natural language processing technology" is a technology that allows computers to understand, interpret, and generate human language.

[0150] "Means for extracting tasks and events from schedules, correspondence, and documents" refers to methods and techniques for identifying and extracting tasks and events from text data using natural language processing techniques.

[0151] "Time efficiency" refers to the efficiency of achieving maximum results within a given time.

[0152] "Travel efficiency" refers to the efficiency of minimizing travel time and costs and selecting the most optimal route and means.

[0153] "Cost efficiency" refers to efficiency in maximizing cost-effectiveness.

[0154] A "best action suggester" is a method or technique for recommending the most efficient and effective action based on a user's schedule and tasks.

[0155] A "means for calculating travel time" is a method or technology for calculating the time it takes to travel between different locations.

[0156] The present invention is a system for improving the efficiency of a user's schedule management and proposing optimal actions. Specific embodiments of this system will be described below.

[0157] System configuration

[0158] Hardware

[0159] Terminal: A device that allows a user to input information, such as a smartphone, tablet, or computer.

[0160] Server: A central processing unit for processing information and suggesting optimal actions.

[0161] software

[0162] Natural language processing services: Use natural language processing technologies such as Google Cloud Natural Language API and IBM Watson.

[0163] Database: Data storage for saving user schedule information and tasks.

[0164] API Services: External services for calculating travel times, such as the Google Maps API.

[0165] Program processing

[0166] Enter and submit information

[0167] A user uses a device to input information such as schedules, emails, documents, etc. For example, "Meeting tomorrow at 10, followed by lunch meeting at 12." The device then sends this information to the server. Specifically, the device uses an HTTP request to send the input information to the server.

[0168] Performing natural language processing

[0169] The server then sends the received information to the Google Cloud Natural Language API, which creates an API request and sends it to the API to parse the input text. The Google Cloud Natural Language API then parses the text and extracts information related to the task or event.

[0170] Extracting tasks and events

[0171] The server receives the analysis results returned by the Google Cloud Natural Language API. The analysis results include important tasks and events in the text. For example, "Meeting tomorrow at 10:00" and "Lunch meeting at 12:00" are extracted. The server stores this information in a database.

[0172] Best course of action

[0173] The server then suggests the best course of action for the user based on the extracted tasks and events. Specifically, the server uses the Google Maps API to calculate the travel time between each task and event. For example, the server might generate a suggestion such as, "Taking into account the travel time to the lunch meeting location after the meeting, we recommend leaving at 11:45." The suggestion is then sent to the device and displayed to the user.

[0174] Specific examples

[0175] Example 1: Analyzing a schedule

[0176] A user types "Meeting tomorrow at 10am, followed by lunch meeting at 12pm" into their device. The device sends this information to the server, which uses the Google Cloud Natural Language API to parse it and extract the following tasks:

[0177] Meeting tomorrow at 10 o'clock

[0178] Lunch meeting at 12 o'clock

[0179] Example 2: Best course of action

[0180] Based on the extracted tasks, the server makes a suggestion to the user, such as "Taking into account the travel time to the lunch meeting location after the meeting, it is recommended that you leave at 11:45." The suggestion is sent to the terminal and displayed to the user.

[0181] Prompt Sentence Examples

[0182] Examples of prompts to input to a generative AI model might include:

[0183] Enter the user's schedule information. For example, "Meeting tomorrow at 10am, followed by lunch at 12pm."

[0184] Using this prompt, users can input schedule information in natural language, and the system can suggest optimal actions based on that information.

[0185] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0186] Step 1:

[0187] A user inputs schedule information into a terminal. For example, "Meeting tomorrow at 10 o'clock, followed by a lunch meeting at 12 o'clock." The terminal sends this information to a server. Specifically, the terminal uses an HTTP request to send the input information to the server. The input is the user's schedule information, and the output is the data to be sent to the server.

[0188] Step 2:

[0189] The server sends the received information to the natural language processing service. Specifically, the server creates an API request and sends it to the natural language processing service for analysis of the input text. The input is the schedule information received from the device, and the output is the request data for the natural language processing service.

[0190] Step 3:

[0191] The natural language processing service analyzes the received text and extracts information related to tasks and events. For example, "Meeting tomorrow at 10:00" and "Lunch meeting at 12:00" are extracted. The input is text data sent from the server, and the output is the task or event information resulting from the analysis.

[0192] Step 4:

[0193] The server receives the analysis results returned from the natural language processing service. The analysis results include important tasks and events in the text. The server stores this information in a database. The input is the analysis results from the natural language processing service, and the output is the task and event information stored in the database.

[0194] Step 5:

[0195] The server proposes the best course of action to the user based on the extracted tasks and events. Specifically, the server uses an external API service to calculate travel time. For example, the server generates a proposal such as, "Taking into account the travel time to the lunch meeting location after the meeting, it is recommended that you leave at 11:45." The input is task and event information stored in the database, and the output is a proposed action to the user.

[0196] Step 6:

[0197] The server sends the generated action suggestions to the terminal. The terminal displays these suggestions to the user. Specifically, the server uses an HTTP response to send the action suggestions to the terminal. The input is the action suggestions generated by the server, and the output is the data sent to the terminal. The user can check the suggestions through the terminal and take the optimal action.

[0198] (Application example 2)

[0199] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0200] Conventional schedule management systems have difficulty efficiently acquiring users' schedule information and suggesting optimal actions. Furthermore, especially for autonomous vehicles, they lack the functionality to calculate and suggest optimal routes based on the user's schedule. This has led to problems such as reduced time efficiency, travel efficiency, and cost efficiency for users.

[0201] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0202] In this invention, the server includes means for comprehensively acquiring information from business software, means for reading a user's schedule based on the acquired information, means for proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency based on the read schedule, means for being installed in an autonomous vehicle and extracting tasks and events from the user's schedule information and calculating the optimal route, and means for proposing the optimal route based on the extracted tasks and events. This makes it possible to efficiently acquire a user's schedule information and propose optimal actions and routes.

[0203] "Business software" is software used by companies and organizations to carry out their business efficiently.

[0204] "Means for comprehensively acquiring information" refers to methods and techniques for collecting all necessary information without omission.

[0205] "Means for reading the user's schedule" refers to methods and technologies for analyzing and understanding the schedules and tasks set by the user.

[0206] "Time efficiency" refers to the efficiency of making the most of limited time.

[0207] "Travel efficiency" refers to the efficiency of optimizing travel to a destination.

[0208] "Cost efficiency" refers to the efficiency of minimizing costs while maximizing benefits.

[0209] A "best action suggestion" is a method or technique for suggesting the most effective action for a user.

[0210] An "autonomous vehicle" is a vehicle that can navigate autonomously without driver intervention.

[0211] "Means for extracting tasks and events" refers to methods and techniques for extracting specific tasks and events from schedule information.

[0212] "Means for calculating optimal routes" refers to methods or techniques for calculating the most efficient route to a destination.

[0213] "Means for suggesting optimal routes" refers to methods or technologies for showing the calculated optimal route to the user.

[0214] A system for implementing this invention includes means for comprehensively acquiring information from business software and reading a user's schedule. It also includes means for proposing the best course of action based on the read schedule, taking into consideration time efficiency, travel efficiency, and cost efficiency. It also includes means installed in an autonomous vehicle for extracting tasks and events from the user's schedule information and calculating the optimal route, and means for proposing the optimal route based on the extracted tasks and events.

[0215] Hardware and software used

[0216] Hardware: On-board computers for autonomous vehicles

[0217] Software: Google Cloud Natural Language API, Python

[0218] Data processing and calculation

[0219] The server retrieves information from business software, specifically using the Google Cloud Natural Language API to extract tasks and events from emails and documents, and then parses the information into the user's schedule.

[0220] Based on the analyzed schedule information, the device suggests the best course of action, taking into account time, travel, and cost efficiency, including prioritizing tasks based on their urgency and importance.

[0221] The autonomous vehicle calculates the optimal route based on the user's schedule information, and the calculated route is suggested to the user, allowing the user to travel efficiently.

[0222] Specific examples

[0223] For example, if a user has a schedule that says, "Tomorrow I have a meeting at 10:00, lunch at 12:00, and a presentation at 15:00," the server sends this information to the Google Cloud Natural Language API to extract tasks and events. The extracted tasks (meeting, lunch, presentation) are analyzed on the device, and the optimal route is calculated. The calculated route is then suggested to the user as "Optimal route: Meeting -> Lunch -> Presentation."

[0224] Prompt Sentence Examples

[0225] Enter the user's schedule information. For example, "Tomorrow I have a meeting at 10:00, lunch at 12:00, and a presentation at 15:00."

[0226] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0227] Step 1:

[0228] The server comprehensively retrieves information from the business software. Specifically, it uses the Google Cloud Natural Language API to extract tasks and events from emails and documents. It receives business software data as input and generates a list of tasks and events as output.

[0229] Step 2:

[0230] The server reads the user's schedule based on the acquired information. It receives a list of tasks and events as input and generates the user's schedule information as output. Specifically, it analyzes the schedule information using natural language processing technology.

[0231] Step 3:

[0232] The device then proposes the best course of action based on the schedule information it reads, taking into account time, travel, and cost efficiency. It receives the user's schedule information as input and generates the best course of action as output. Specifically, it prioritizes tasks by taking into account their urgency and importance.

[0233] Step 4:

[0234] The autonomous vehicle extracts tasks and events from the user's schedule information and calculates the optimal route. It receives the user's schedule information as input and generates the optimal route as output. Specifically, it calculates the optimal route using a map API.

[0235] Step 5:

[0236] The autonomous vehicle proposes the calculated optimal route to the user. It receives the optimal route information as input and generates a route proposal to present to the user as output. Specifically, the route is shown to the user through a display and voice guidance.

[0237] Example 3

[0238] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0239] Conventional systems have had difficulty efficiently learning users' behavioral patterns and suggesting optimal actions that take into account time, travel, and cost efficiency. Furthermore, they lacked support for users to take optimal actions due to insufficient prioritization based on task urgency and importance.

[0240] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0241] In this invention, the server includes means for comprehensively acquiring data from the information processing device, means for learning the user's behavioral patterns based on the acquired data, means for proposing the best action taking into consideration time efficiency, travel efficiency, and cost efficiency based on the learned behavioral patterns, and means for presenting the proposed action plan to the user. This makes it possible to efficiently learn the user's behavioral patterns and propose optimal actions.

[0242] An "information processing device" is a device for collecting, processing, storing, and transmitting data.

[0243] "Data" refers to information necessary to learn user behavior patterns, and includes emails, documents, travel history, schedule information, and the like.

[0244] A "behavioral pattern" is a collection of data that indicates the tendencies and habits of a user's past behavior.

[0245] "Time efficiency" refers to minimizing the time required for a user to complete a task or event.

[0246] "Travel efficiency" refers to the user's selection of the optimal means of transportation and route to reach their destination.

[0247] "Cost-effective" refers to minimizing the expense required for a user to complete a task or event.

[0248] "Best action" refers to the most appropriate action for a user, taking into consideration time efficiency, travel efficiency, and cost efficiency.

[0249] "Suggestion" refers to showing the user the optimal plan of action based on learned behavioral patterns.

[0250] A "task" refers to a specific task or activity that a user must perform.

[0251] "Urgency" is an indicator of how quickly a task needs to be addressed.

[0252] "Importance" is an indicator of how important a task is to the user.

[0253] "Priority" is the order used to determine which of multiple tasks should be performed first.

[0254] The present invention is a system that learns a user's behavioral patterns and proposes the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency. This system comprehensively acquires data from an information processing device, learns the user's behavioral patterns based on the acquired data, and proposes the best course of action based on the learned behavioral patterns.

[0255] Server Roles

[0256] The server first comprehensively acquires data from the information processing device. Specifically, it collects data such as the user's movement history, schedule information, emails, documents, etc. This data is stored in a database and used for subsequent processing.

[0257] The server then preprocesses the acquired data, which includes imputing missing values, normalizing the data, encoding categorical data, etc. The preprocessed data is then used to train machine learning models.

[0258] The server uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns based on preprocessed data. The learned model is then used to predict user behavior patterns and suggest optimal actions.

[0259] Finally, the server deploys the trained model to the device, allowing the device to use the trained model to suggest optimal actions for the user.

[0260] Device Role

[0261] The device receives the trained model sent from the server, which enables the device to suggest optimal actions for new tasks and events.

[0262] When a user inputs new task or event information, the device inputs that information into the trained model. For example, if a user inputs "What is the best route to get to a meeting tomorrow?", the device inputs that information into the model and generates the best route.

[0263] The generated action plan is then proposed to the user via the terminal, who can then confirm the proposed action plan and act accordingly.

[0264] User Roles

[0265] Users input new task or event information into the device, such as "optimize my shopping list this week," and the device feeds that information into the trained model to generate an optimal plan of action.

[0266] The user can then view the proposed action plan on the device and act accordingly, for example, by following the proposed travel route to the destination.

[0267] Specific examples

[0268] Example 1

[0269] The user inputs into the device, "Tell me the best route to get to tomorrow's meeting." The device inputs this information into the trained model and suggests the best route. For example, it may suggest a specific route such as "30 minutes by train, 10 minutes by bus, and 5 minutes on foot."

[0270] Example 2

[0271] The user inputs "optimize this week's shopping list" into the device. The device inputs this information into the trained model and suggests the optimal shopping route and timing. For example, the device might make a specific suggestion such as "Buy vegetables at supermarket A on Monday and meat at supermarket B on Wednesday."

[0272] Prompt Sentence Examples

[0273] "What's the best route to get to tomorrow's meeting?"

[0274] "Optimize my shopping list this week"

[0275] "Suggest the best travel plan for your next vacation"

[0276] The above is an embodiment of the system of the present invention. This system allows users to efficiently plan their activities, optimizing time efficiency, travel efficiency, and cost efficiency. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[0277] Step 1: Data collection

[0278] The server comprehensively acquires data from the information processing device. Specifically, it collects data such as the user's movement history, schedule information, emails, documents, etc. This data is then stored in a database.

[0279] Input: User travel history, schedule information, emails, documents

[0280] Output: User behavior data stored in a database

[0281] Step 2: Data Preprocessing

[0282] The server preprocesses the collected data, specifically imputing missing values, normalizing the data, encoding categorical data, etc. The preprocessed data is then used to train machine learning models.

[0283] Input: User behavior data stored in a database

[0284] Output: Preprocessed data

[0285] Step 3: Model training

[0286] The server uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns based on preprocessed data. The learned model is then used to predict user behavior patterns and suggest optimal actions.

[0287] Input: Preprocessed data

[0288] Output: Trained model

[0289] Step 4: Model evaluation

[0290] The server evaluates the performance of the trained model. Specifically, it calculates the model's precision, recall, F1 score, etc. using test data. Based on the evaluation results, it retrains the model or adjusts its hyperparameters as necessary.

[0291] Input: trained model, test data

[0292] Output: Model evaluation results

[0293] Step 5: Model deployment

[0294] The server deploys the evaluated model to the device, allowing the device to use the trained model to suggest optimal actions to the user.

[0295] Input: Evaluated model

[0296] Output: The trained model deployed to the device.

[0297] Step 6: Receiving User Input

[0298] The device receives new tasks and events from the user, for example, "What is the best route to get to my meeting tomorrow?"

[0299] Input: User task or event information

[0300] Output: Received task and event information

[0301] Step 7: Input to the model

[0302] The device inputs the received user input into the trained model, which then generates an optimal action plan.

[0303] Input: Received task and event information, trained model

[0304] Output: Optimal action plan

[0305] Step 8: Propose an action plan

[0306] The device then presents the user with an optimal action plan output from the model, specifically displaying the results of optimization of travel routes and schedules.

[0307] Input: Optimal action plan

[0308] Output: A suggested action plan for the user

[0309] Step 9: Review the proposal and take action

[0310] The user can then view the proposed action plan on the device and act accordingly, for example, by following the proposed travel route to the destination.

[0311] Input: User's proposed plan of action

[0312] Output: Actual behavior

[0313] The above is the specific processing flow of the program of this system.

[0314] (Application example 3)

[0315] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0316] In conventional business groupware systems, users manage their schedules and tasks individually, making it difficult to comprehensively optimize time, travel, and cost efficiency. Furthermore, particularly in food delivery services, there is a lack of systems that can suggest optimal routes and actions in real time for delivery personnel to efficiently handle multiple orders. This has led to a demand for more efficient delivery operations.

[0317] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0318] In this invention, the server includes means for comprehensively acquiring information from business groupware, means for reading a user's schedule based on the acquired information, means for proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency based on the read schedule, means for proposing the best delivery route taking into consideration past behavioral data in delivery work, current traffic conditions, and order priority, and means for predicting the best course of action when a new task or event occurs. This enables efficient management of user schedules and food delivery work.

[0319] "Business groupware" is software that streamlines information sharing and communication within a company or organization.

[0320] "Means for comprehensively acquiring information" refers to methods and techniques for collecting all necessary information without omission.

[0321] "Means for reading the user's schedule" refers to methods and technologies for understanding the user's plans and tasks.

[0322] "Time efficiency" refers to the efficiency of making the most of limited time.

[0323] "Travel efficiency" refers to the efficiency of minimizing travel time and costs.

[0324] "Cost efficiency" refers to efficiency in maximizing cost-effectiveness.

[0325] A "best action suggestion" is a method or technique for suggesting the most appropriate action for a user.

[0326] "Delivery work" refers to the work of delivering products and services to customers.

[0327] "Past behavioral data" is a record of past behavior of users or systems.

[0328] "Current traffic conditions" refers to the real-time state of roads and transportation.

[0329] "Order priority" is an index that indicates which of multiple orders should be given priority in processing.

[0330] A "means for proposing the optimal delivery route" is a method or technology for showing the most efficient route in delivery operations.

[0331] "Means for predicting the best course of action when a new task or event occurs" refers to methods or techniques for predicting the most appropriate action when a new task or event occurs.

[0332] To implement this invention, the following system configuration is required: The server comprehensively acquires information from business groupware and reads the user's schedule based on the acquired information. Based on the read schedule, the server proposes the best course of action, taking into account time efficiency, travel efficiency, and cost efficiency. The server also proposes the best delivery route, taking into account past behavioral data in delivery work, current traffic conditions, and order priority, and predicts the best course of action when a new task or event occurs.

[0333] Hardware and software used

[0334] The server uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns. Data is collected using business groupware and traffic information APIs. An application is installed on the delivery person's smartphone that suggests the optimal delivery route in real time.

[0335] Data processing and calculation

[0336] The server analyzes information obtained from the business groupware to read the user's schedule. It then calculates the optimal delivery route, taking into account past behavioral data, current traffic conditions, and order priority. To do this, it uses machine learning models and applies algorithms to maximize time, travel, and cost efficiency.

[0337] Specific examples

[0338] For example, if a delivery person receives multiple orders at the same time, the server will consider past delivery data and current traffic conditions to suggest the most efficient delivery order, and the optimal route will be displayed in real time on the delivery person's smartphone.

[0339] Prompt Sentence Examples

[0340] "Consider past delivery data and current traffic conditions to suggest the most efficient delivery route."

[0341] In this way, users can manage their schedules and food delivery operations more efficiently.

[0342] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0343] Step 1:

[0344] The server comprehensively acquires information from the business groupware.

[0345] Input: User schedule and task information obtained from the business groupware API.

[0346] Data processing: Analyze the acquired information and extract the user's schedule and tasks.

[0347] Output: A list of the user's schedule and task information.

[0348] Step 2:

[0349] The server reads the user's schedule based on the obtained information.

[0350] Input: The user's schedule and task information extracted in step 1.

[0351] Data processing: Analyze schedule information and identify the start time, end time, location, etc. of each task.

[0352] Output: Detailed information about each task (start time, end time, location, etc.).

[0353] Step 3:

[0354] The server then suggests the best course of action based on the read schedule, taking into account time efficiency, travel efficiency, and cost efficiency.

[0355] Input: Detailed information for each task obtained in step 2.

[0356] Data calculation: Using machine learning models, we calculate the priority of each task and determine the optimal order of actions.

[0357] Output: A list of optimal action sequences.

[0358] Step 4:

[0359] The server proposes the optimal delivery route by taking into account past behavioral data from delivery operations, current traffic conditions, and order priority.

[0360] Inputs: Delivery driver historical behavior data, real-time traffic information, and order priority.

[0361] Data computation: Using machine learning models to calculate optimal delivery routes.

[0362] Output: A list of optimal delivery routes.

[0363] Step 5:

[0364] The server predicts the best course of action when a new task or event arises.

[0365] Input: Information about new tasks or events.

[0366] Data Computing: Using machine learning models to predict optimal actions for new tasks and events.

[0367] Output: Best action suggestions for new tasks or events.

[0368] Step 6:

[0369] The terminal displays the optimal order of actions and delivery route received from the server to the delivery person.

[0370] Input: A list of optimal action sequences and delivery routes sent from the server.

[0371] Specific operation: Through a smartphone application, delivery personnel are shown the optimal order of actions and delivery routes in real time.

[0372] Output: Specific action instructions for the delivery person to carry out.

[0373] In this way, users can manage their schedules and food delivery operations more efficiently.

[0374] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0375] "Example 1"

[0376] In one embodiment of the present invention, a system incorporating an emotion engine is provided. The emotion engine recognizes a user's emotions and adjusts task priorities based on the user's emotional state. Specifically, the emotion engine analyzes the user's tone of voice, facial expressions, behavioral patterns, etc. to recognize the user's emotional state. For example, if the system determines that the user is feeling stressed, it prioritizes tasks that promote relaxation.

[0377] "Example 2"

[0378] The emotion engine also suggests the best course of action based on the user's emotional state, taking into account the user's emotional state and the urgency and importance of the task.

[0379] For example, if the system determines that the user is feeling happy and there is an important but non-urgent task, it will suggest that task, thereby making the most of the user's emotional state to achieve efficient task management.

[0380] "Example 3"

[0381] In one embodiment of the present invention, a system incorporating an emotion engine is provided. The emotion engine recognizes a user's emotions and adjusts task priorities based on the user's emotional state. Specifically, the emotion engine analyzes the user's tone of voice, facial expressions, behavioral patterns, etc. to recognize the user's emotional state. For example, if the system determines that the user is feeling stressed, it prioritizes tasks that promote relaxation.

[0382] The processing flow of each embodiment will be described below.

[0383] "Example 1"

[0384] Step 1: The system recognizes the user's emotional state by analyzing their tone of voice, facial expressions, behavioral patterns, etc.

[0385] Step 2: The emotion engine adjusts task priorities based on the perceived emotional state. For example, if it determines that the user is feeling stressed, it will prioritize tasks that promote relaxation.

[0386] "Example 2"

[0387] Step 1: The emotion engine recognizes the user's emotional state.

[0388] Step 2: The emotion engine suggests the best course of action, taking into account the perceived emotional state and the urgency and importance of the task.

[0389] Step 3: For example, if the user is judged to be feeling joyful and there is an important but non-urgent task, the system will suggest that task, thereby making the most of the user's emotional state to achieve efficient task management.

[0390] Example 1

[0391] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0392] While conventional business software systems can efficiently manage users' schedules and tasks, they do not prioritize or suggest tasks that take into account the user's emotional state. As a result, when users are stressed or in a state where appropriate task management is not possible in response to their emotional state, work efficiency can decline.

[0393] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0394] In this invention, the server includes means for comprehensively acquiring information from business software, means for reading a user's schedule based on the acquired information, means for proposing the best course of action based on the read schedule, taking into account time efficiency, travel efficiency, and cost efficiency, means for recognizing the user's emotional state, means for adjusting task priorities based on the recognized emotional state, and means for notifying the user of prioritized tasks. This enables task management that takes the user's emotional state into account, and is expected to improve business efficiency.

[0395] "Business software" is software used by companies and organizations to carry out their business efficiently.

[0396] "Means for comprehensively acquiring information" refers to methods and technologies for comprehensively collecting necessary information from business software.

[0397] "Means for reading the user's schedule" refers to methods and technologies for analyzing and understanding the user's plans and tasks from the information obtained.

[0398] "Time efficiency" refers to the efficiency of achieving maximum results within a limited time.

[0399] "Travel efficiency" refers to the efficiency of minimizing the time and cost required for users to travel.

[0400] "Cost efficiency" refers to the efficiency of minimizing costs and maximizing effectiveness in carrying out business.

[0401] "Best action suggestion" refers to methods and techniques for suggesting the most effective action to a user based on the user's schedule and tasks.

[0402] "Means for recognizing the user's emotional state" refers to methods or technologies for analyzing the user's tone of voice, facial expressions, behavioral patterns, etc. to determine the user's emotions.

[0403] "Means for adjusting task priorities" are methods and techniques for optimizing the order in which tasks are executed, taking into account the user's emotional state and the urgency and importance of the tasks.

[0404] "Means for notifying users of prioritized tasks" refers to methods and techniques for notifying users of the adjusted task priorities.

[0405] MODE FOR CARRYING OUT THE INVENTION

[0406] This invention is a system that comprehensively acquires information from business software and adjusts task priorities by taking into account the user's emotional state. This system operates in cooperation with the server, the terminal, and the user.

[0407] Obtaining information

[0408] The server uses the business software's API to obtain information such as user schedules, emails, and documents. Specifically, it uses the Google Calendar API or Microsoft Outlook API. The obtained information is stored in a database on the server. For example, the Google Calendar API can be used to obtain user schedules and store them in a MySQL database.

[0409] Recognition of emotional states

[0410] The device captures the user's tone of voice, facial expressions, behavioral patterns, etc. in real time using hardware such as a microphone and camera. The captured data is sent from the device to a server. The server uses an emotion engine to analyze the user's emotional state from the received data. The emotion engine recognizes the user's emotions using voice analysis and image analysis technology.

[0411] Task prioritization

[0412] The server adjusts task priorities based on the acquired schedule information and the analysis results of the emotion engine. For example, if the user is feeling stressed, it will prioritize tasks that promote relaxation. The server notifies the user of prioritized tasks. Notifications are sent via push notifications, emails, and other means.

[0413] Specific examples

[0414] If the user uses Google Calendar, the server uses the Google Calendar API to obtain the user's schedule information. The obtained schedule information is stored in a database. The device then captures the user's tone of voice and facial expressions and sends them to the server. The server uses an emotion engine to analyze the user's emotional state and determines that the user is feeling stressed. Based on this information, the server prioritizes tasks that promote relaxation (e.g., taking a short break) and notifies the user.

[0415] Prompt Sentence Examples

[0416] "Use the Google Calendar API to obtain the user's schedule information and save it in a database. Then, capture the user's tone of voice and facial expression data on the device, send it to the server, and use the emotion engine to analyze the user's emotional state. If the user is feeling stressed, prioritize tasks that promote relaxation and notify the user."

[0417] In this way, the system of the present invention can improve a user's work efficiency by obtaining information from business software and adjusting task priorities based on the user's emotional state using an emotion engine.

[0418] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0419] Step 1:

[0420] The server uses the business software's API to obtain information such as the user's schedule, emails, and documents. Specifically, it uses the Google Calendar API and Microsoft Outlook API. It sends an API request as input and receives JSON-formatted data as output. This data includes details of the user's schedules and tasks.

[0421] Step 2:

[0422] The server saves the retrieved information in a database. It receives JSON format data as input and generates records stored in the database as output. Specifically, it uses a MySQL database to insert schedule information. This saves the user's schedule information in the database.

[0423] Step 3:

[0424] The device captures the user's tone of voice, facial expressions, and behavioral patterns in real time, taking data from a microphone and camera as input and generating audio and image data as output using voice recording and facial recognition software.

[0425] Step 4:

[0426] The device sends the captured audio and image data to the server, which receives the audio and image data as input and sends an HTTP POST request as output, allowing the server to receive data for analyzing the user's emotional state.

[0427] Step 5:

[0428] The server uses an emotion engine to analyze the user's emotional state from the received data. It receives voice and image data as input and generates the user's emotional state (e.g., stress, joy, fatigue, etc.) as output. The emotion engine recognizes emotions using voice and image analysis techniques.

[0429] Step 6:

[0430] The server adjusts task priorities based on the acquired schedule information and the analysis results of the emotion engine. It receives schedule information and emotional state as input and generates a prioritized task list as output. For example, if the user is feeling stressed, it will prioritize tasks that promote relaxation.

[0431] Step 7:

[0432] The server notifies the user of the prioritized tasks. It receives a prioritized task list as input and sends push notifications or emails as output, so that the user knows which tasks should be performed first.

[0433] In this way, by clarifying the specific operations and data flow at each step, it is possible to understand how the processing of the entire system is carried out.

[0434] (Application example 1)

[0435] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0436] While conventional business groupware systems can manage users' schedules and tasks, they have the problem of not being able to adjust task priorities or suggest relaxation methods that take into account the user's emotional state. In particular, in factories and other workplaces, where workers' stress and fatigue can affect work efficiency and safety, there is a need to understand their emotional state in real time and take appropriate action based on that.

[0437] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0438] In this invention, the server includes means for comprehensively acquiring information from business groupware, means for reading a user's schedule based on the acquired information, means for proposing the best course of action based on the read schedule taking into account time efficiency, travel efficiency, and cost efficiency, means for recognizing the user's emotional state, means for adjusting task priorities based on the recognized emotional state, and means for proposing tasks that promote relaxation. This makes it possible to adjust task priorities and suggest relaxation activities taking into account the user's emotional state.

[0439] "Business groupware" is software used to manage schedules, send and receive emails, share documents, and more within a company or organization.

[0440] "Means for comprehensively acquiring information" refers to methods and technologies for acquiring all necessary information from business groupware.

[0441] "Means for reading the user's schedule" refers to methods and technologies for analyzing and understanding the user's plans and tasks from the information obtained.

[0442] "Means for suggesting the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency" refers to methods and technologies for suggesting the most efficient course of action based on the user's schedule.

[0443] "Means for recognizing the user's emotional state" refers to methods or technologies for analyzing the user's tone of voice, facial expressions, behavioral patterns, etc. to determine their emotions.

[0444] A "means for adjusting task priorities" is a method or technology for reevaluating the importance and urgency of tasks and setting priorities based on the user's emotional state.

[0445] A "means for suggesting tasks to promote relaxation" is a method or technology that suggests tasks to promote relaxation when a user is feeling stressed.

[0446] A system for implementing the present invention has the following configuration.

[0447] Program Generation

[0448] The system includes a program that comprehensively retrieves information from business groupware and reads the user's schedule and tasks, as well as a program that recognizes the user's emotional state, adjusts task priorities based on that state, and suggests tasks that promote relaxation.

[0449] Program processing explanation

[0450] Data Acquisition

[0451] The server obtains information from business groupware (e.g., Google Calendar, Microsoft Outlook) using APIs and scraping technology. The obtained information includes users' schedules, emails, documents, etc. This information is then stored in a database.

[0452] emotion recognition

[0453] The device (e.g., smart glasses, head-mounted display, microphone) uses an emotion recognition engine (e.g., Affectiva, Microsoft Azure® Emotion API) to analyze the user's tone of voice, facial expressions, and behavioral patterns in real time, thereby recognizing the user's emotional state.

[0454] Task priority adjustment

[0455] The server then adjusts task priorities using a task management system (e.g., Trello, Asana) based on the user's emotional state. Specifically, if the user is feeling stressed, it prioritizes tasks that promote relaxation.

[0456] Relaxation task suggestions

[0457] The device (e.g., smartphone, smart glasses) suggests relaxation tasks to the user through relaxation apps (e.g., Calm, Headspace), allowing the user to perform appropriate relaxation tasks even when feeling stressed.

[0458] Specific examples

[0459] For example, if the emotion engine recognizes that factory worker A is feeling stressed, the following processing will occur.

[0460] 1. The server retrieves worker A's current tasks from his schedule and email.

[0461] 2. Using its emotion engine, the device determines that Mr. A is feeling stressed based on his tone of voice and facial expression.

[0462] 3. The server uses a task management system to adjust the priority of Mr. A's current tasks and suggest a relaxation task.

[0463] 4. The device notifies Mr. A's smartphone of deep breathing and short meditation tasks through a relaxation app.

[0464] Prompt Sentence Examples

[0465] "Get Worker A's schedule and emails, and analyze his emotional state using the emotion engine. If he is feeling stressed, suggest relaxation tasks as a priority and notify him on his smartphone."

[0466] In this way, the system can understand the user's emotional state in real time, improving work efficiency and safety.

[0467] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0468] Step 1:

[0469] The server obtains information from business groupware (e.g., Google Calendar, Microsoft Outlook) using APIs and scraping technology. The input is the user's authentication information and the groupware's API endpoint. The output is information such as the user's schedule, emails, and documents. This information is stored in a database.

[0470] Step 2:

[0471] The server analyzes the acquired information and reads the user's schedule and tasks. The input is the schedule, email, and document information stored in the database. The output is a list of the analyzed schedule and tasks. This clarifies the user's schedule and tasks.

[0472] Step 3:

[0473] The device uses an emotion recognition engine (e.g., Affectiva, Microsoft Azure Emotion API) to analyze the user's tone of voice, facial expressions, and behavioral patterns in real time. The input is the user's audio and video data. The output is the user's emotional state (e.g., stressed, relaxed). This allows the user's emotional state to be recognized.

[0474] Step 4:

[0475] The server adjusts task priorities using a task management system (e.g., Trello, Asana) based on the recognized emotional state. The input is the analyzed schedule and task list, and the user's emotional state. The output is a list of tasks with adjusted priorities. This allows the priorities of tasks to be set according to the user's emotional state.

[0476] Step 5:

[0477] The device suggests relaxation tasks to the user through a relaxation app (e.g., Calm, Headspace). The input is a list of prioritized tasks and the user's emotional state. The output is a notification of the relaxation task. This allows the user to perform appropriate relaxation even when feeling stressed.

[0478] In this way, the system can understand the user's emotional state in real time, improving work efficiency and safety.

[0479] Example 2

[0480] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0481] Conventional schedule management systems simply display a user's schedule and tasks, and have the problem of being unable to suggest optimal actions that take into account time, travel, and cost efficiency. Furthermore, task management does not take into account the user's emotional state, making it difficult to suggest optimal actions based on the user's emotions.

[0482] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0483] In this invention, the server includes means for comprehensively acquiring information from an information processing device, means for reading a user's schedule based on the acquired information, means for proposing the best course of action based on the read schedule, taking into consideration time efficiency, travel efficiency, and cost efficiency, and means for analyzing the user's emotional state and proposing the best course of action in consideration of the emotional state and the urgency and importance of the task. This not only enables efficient schedule management for the user, but also makes it possible to propose the best course of action according to the user's emotional state.

[0484] An "information processing device" is a device that acquires and processes information such as a user's schedule, e-mail, and documents.

[0485] "Means for comprehensively obtaining information" refers to means for comprehensively collecting necessary information from various applications and devices used by users.

[0486] "Means for reading the user's schedule" refers to means for analyzing and extracting the user's plans and tasks from the acquired information.

[0487] "A means of proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency" is a means of optimizing the user's schedule and providing an efficient plan of action.

[0488] "Means for analyzing the user's emotional state" refers to means for inferring the user's emotions based on the user's past behavior and current input data.

[0489] "A means for suggesting the best course of action taking into consideration the emotional state and the urgency and importance of the task" is a means for suggesting the optimal course of action based on the user's emotional state and task priority.

[0490] "Means for reading tasks from emails and documents" refers to means for analyzing the contents of emails and documents and extracting tasks and events.

[0491] The "means for prioritizing tasks by taking into consideration urgency and importance" is a means for classifying extracted tasks and events based on urgency and importance, and setting priorities.

[0492] MODE FOR CARRYING OUT THE INVENTION

[0493] The present invention is a system for improving the efficiency of a user's schedule management and proposing optimal actions according to the user's emotional state. Specific embodiments of this system will be described below.

[0494] Information acquisition

[0495] The server retrieves information such as the user's schedule, emails, documents, etc. This information is then synced to the cloud from the user's device, specifically data from Google Calendar and email applications.

[0496] Analysis using natural language processing

[0497] The server then sends the acquired information to a natural language processing service, such as Google Cloud Natural Language API or IBM Watson, to extract tasks and events from the text data.

[0498] Example: If a user's email contains the content "I have a meeting tomorrow at 10am," the server retrieves this information and uses a natural language processing service to extract the task "Meeting."

[0499] Categorizing and prioritizing tasks

[0500] The server categorizes and prioritizes the extracted tasks and events using algorithms that assess the urgency and importance of the tasks.

[0501] Example: A server categorizes and prioritizes tasks such as "meeting," "lunch," and "presentation" based on urgency and importance.

[0502] Emotional state analysis

[0503] The server uses an emotion engine to analyze the user's emotional state, which is inferred from the user's past behavior and current input data.

[0504] Example: The server analyzes a user's past emails and social media posts to determine their current emotional state as "joy," "sadness," "stress," etc.

[0505] Suggesting the best course of action

[0506] The server considers the user's emotional state and task priorities to suggest optimal actions, including schedule rearrangements that take into account time, travel, and cost efficiency.

[0507] Example: If the server determines that the user is feeling "joy," it will suggest "preparing for next week's presentation." This allows the user to manage their tasks efficiently.

[0508] Prompt Sentence Examples

[0509] Below are some example prompts to input to the generative AI model:

[0510] Prompt: "Please describe the process steps of a system that extracts tasks from a user's email and schedule, and suggests the best course of action based on their emotional state. Include specific examples of using natural language processing services and emotion engines."

[0511] Using this prompt, the generative AI model can generate a detailed description of the system.

[0512] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0513] Step 1: Get the information

[0514] The server retrieves information such as the user's schedule, emails, and documents. As input, it receives data synchronized to the cloud from the user's device. Specifically, the server retrieves data from Google Calendar and email applications. As output, it obtains the retrieved schedule, email, and document data.

[0515] Step 2: Natural Language Processing Analysis

[0516] The server sends the acquired information to a natural language processing service. The schedule, email, and document data acquired in step 1 are used as input. Specifically, the server uses services such as Google Cloud Natural Language API and IBM Watson to extract tasks and events from the text data. The output is a list of the extracted tasks and events.

[0517] Step 3: Categorize and prioritize tasks

[0518] The server categorizes and prioritizes the extracted tasks and events. As input, it uses the list of tasks and events extracted in step 2. Specifically, it applies an algorithm to evaluate the urgency and importance of the tasks. As output, it obtains a list of categorized and prioritized tasks and events.

[0519] Step 4: Analyze emotional state

[0520] The server uses an emotion engine to analyze the user's emotional state. It uses the user's past actions and current input data as input. Specifically, the server analyzes the user's past emails and social media posts to estimate their current emotional state. The output is the user's emotional state (e.g., joy, sadness, stress).

[0521] Step 5: Recommend the best course of action

[0522] The server proposes optimal actions by taking into account the user's emotional state and task priorities. As input, it uses the categorized and prioritized list of tasks and events obtained in step 3 and the user's emotional state obtained in step 4. Specifically, the server rearranges the schedule taking into account time efficiency, travel efficiency, and cost efficiency. As output, it obtains the optimal action plan proposed to the user.

[0523] (Application example 2)

[0524] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0525] Conventional schedule management systems only manage users' schedules and tasks, and are unable to suggest optimal actions that take into account the user's emotional state or real-time circumstances. Furthermore, autonomous vehicles, in particular, require optimal route suggestions based on the user's emotional state and the urgency of tasks, but no system has been able to achieve this. This has resulted in insufficient time management and stress reduction for users.

[0526] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0527] In this invention, the server includes means for comprehensively acquiring information from business software, means for reading a user's schedule based on the acquired information, means for proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency based on the read schedule, means for analyzing the user's emotional state, means for proposing the best course of action taking into consideration the user's emotional state and the urgency and importance of the task, and means for proposing the best route for an autonomous vehicle. This makes it possible to propose optimal actions taking into consideration the user's schedule management and emotional state, and in particular, for an autonomous vehicle, it is possible to propose optimal routes according to the user's emotional state and the urgency of the task.

[0528] "Business software" is software used by companies and organizations to carry out their business efficiently.

[0529] "Means for comprehensively acquiring information" refers to methods and techniques for collecting all necessary information without omission.

[0530] "Means for reading a user's schedule" refers to methods and technologies for analyzing and understanding a user's plans and tasks.

[0531] "Time efficiency" refers to the efficiency of making the most of limited time.

[0532] "Travel efficiency" refers to the efficiency of minimizing travel time and costs.

[0533] "Cost efficiency" refers to efficiency in maximizing cost-effectiveness.

[0534] "Means for suggesting best actions" are methods and techniques for suggesting the most appropriate actions for users.

[0535] "Means for analyzing a user's emotional state" refers to methods or techniques for analyzing a user's emotions and understanding their state.

[0536] "Task urgency" is an indicator of how quickly a task needs to be addressed.

[0537] "Task importance" is an index that indicates how important a task is.

[0538] An "autonomous vehicle" is a vehicle that is capable of driving autonomously without driver intervention.

[0539] "Means for suggesting optimal routes" are methods or technologies for suggesting the most efficient route to reach a destination.

[0540] A system for implementing this invention includes a means for comprehensively acquiring information from business software and reading a user's schedule. Specifically, a server uses natural language processing technologies such as Google Cloud Natural Language API and IBM Watson to extract tasks and events from emails and documents. This allows the user's schedule to be automatically analyzed.

[0541] The server then includes a means for analyzing the user's emotional state. The user's voice and facial expressions are captured by a camera and microphone and analyzed by an emotion engine. This emotion engine grasps the user's emotional state in real time and suggests the best course of action in combination with the urgency and importance of the task.

[0542] The system also includes a means for suggesting the optimal route for the autonomous vehicle. The server obtains real-time traffic information and uses a route optimization algorithm to suggest the optimal route based on the user's emotional state and the urgency of the task, enabling the user to travel efficiently and stress-free.

[0543] As a concrete example, consider a user on their way to an important meeting, but wanting to relax on the way. The server extracts the meeting time and location from the schedule, and the emotion engine determines that the user wants to relax. The route optimization algorithm then suggests a scenic route that will get you to the meeting on time.

[0544] An example of a prompt for a generative AI model is:

[0545] Develop an application that suggests optimal routes based on the user's schedule and emotional state. Use Google Cloud Natural Language API and IBM Watson to extract tasks from schedules and emails, and analyze the user's emotional state with an emotion engine. Use a route optimization algorithm to suggest optimal actions based on real-time traffic information.

[0546] In this way, a smart driving assistant for self-driving vehicles can be realized that takes into account the user's schedule management and emotional state and suggests optimal actions.

[0547] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0548] Step 1:

[0549] The server comprehensively retrieves information from business software. Specifically, it uses Google Cloud Natural Language API and IBM Watson to extract tasks and events from emails and documents. The input is the user's emails and documents, and the output is a list of extracted tasks and events.

[0550] Step 2:

[0551] The server reads the user's schedule based on the acquired information. Specifically, it analyzes the extracted tasks and events and constructs the user's schedule. The input is the list of tasks and events obtained in step 1, and the output is the user's schedule.

[0552] Step 3:

[0553] The server analyzes the user's emotional state. Specifically, it captures the user's voice and facial expressions with a camera and microphone and analyzes them with an emotion engine. The input is the user's voice data and video data, and the output is the user's emotional state.

[0554] Step 4:

[0555] The server proposes the best course of action by taking into account the user's emotional state and the urgency and importance of the task. Specifically, it prioritizes the task by combining the emotional state and the urgency and importance of the task. The inputs are the user's emotional state and schedule, and the output is a prioritized task list.

[0556] Step 5:

[0557] The server proposes the optimal route for the autonomous vehicle. Specifically, it obtains real-time traffic information and calculates the optimal route using a route optimization algorithm. The inputs are the user's schedule, emotional state, and real-time traffic information, and the output is the optimal route.

[0558] Step 6:

[0559] The terminal receives the optimal route proposal from the server and presents it to the user. Specifically, it guides the user along the optimal route through the vehicle's display or voice assistant. The input is the optimal route information from the server, and the output is route guidance to the user.

[0560] In this way, optimal action suggestions are realized that take into account the user's schedule management and emotional state.

[0561] Example 3

[0562] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0563] Conventional schedule management systems are limited to simple task management without taking into account the user's behavioral patterns or emotional state. This makes it difficult to optimize the user's time, travel, and cost efficiency, and they are unable to reduce the user's stress and burden. They also lack the ability to prioritize tasks based on urgency and importance. This means that they provide insufficient support for users in choosing the best course of action.

[0564] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0565] In this invention, the server includes means for comprehensively acquiring information from an information processing device, means for reading a user's schedule based on the acquired information, means for proposing the best course of action taking into account time efficiency, travel efficiency, and cost efficiency based on the read schedule, means for recognizing the user's emotional state and adjusting task priorities based on the emotional state, means for learning the user's behavioral patterns using machine learning technology, and means for proposing the best course of action when a new task or event occurs using the learned model. This enables optimal schedule management taking into account the user's behavioral patterns and emotional state, thereby improving time efficiency, travel efficiency, and cost efficiency and reducing stress and burden on the user.

[0566] An "information processing device" is an electronic device that has functions such as data collection, analysis, storage, and communication.

[0567] "Means for comprehensively acquiring information" refers to a method or device for comprehensively collecting data such as a user's schedule, travel history, and expenditure history.

[0568] "Means for reading a user's schedule" refers to a method or device for analyzing and understanding a user's plans and tasks from the acquired data.

[0569] "Means for proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency" refers to a method or device for calculating and proposing the most efficient course of action based on a user's schedule and behavioral patterns.

[0570] "Means for recognizing the user's emotional state" refers to a method or device for analyzing the user's tone of voice, facial expressions, behavioral patterns, etc. to determine the user's emotions.

[0571] A "means for adjusting task priority" is a method or device for determining the order in which tasks are executed, taking into consideration the emotional state of the user and the urgency and importance of the tasks.

[0572] "Means for learning user behavior patterns using machine learning technology" refers to methods and devices for analyzing user behavior patterns based on past data and building predictive models.

[0573] "Means for using a learned model to suggest the best course of action when a new task or event occurs" refers to a method or device that uses a machine learning model to calculate and suggest the best course of action for a newly input task or event.

[0574] The present invention is a system for optimizing time efficiency, travel efficiency, and cost efficiency by taking into account the behavioral patterns and emotional state of a user. Specific embodiments of this system will be described below.

[0575] System configuration

[0576] The server is equipped with a means for comprehensively acquiring information from information processing devices. Specifically, it collects calendar app data, GPS data, electronic money usage history, etc. from users' smartphones and PCs. It also includes a means for reading tasks from emails and documents.

[0577] The server has a means for reading the user's schedule based on the acquired information, which allows it to analyze and understand the user's plans and tasks.

[0578] Furthermore, the server is equipped with a means to suggest the best course of action based on the read schedule, taking into account time efficiency, travel efficiency, and cost efficiency. Specifically, it uses machine learning technology to learn the user's behavioral patterns, and uses the learned model to suggest the best course of action when a new task or event occurs.

[0579] Hardware and software used

[0580] This system uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns and build predictive models. It also incorporates an emotion engine to analyze the user's tone of voice, facial expressions, and behavioral patterns to recognize their emotional state.

[0581] Specific examples

[0582] If the user inputs "I want to optimize tomorrow's schedule," the server processes it as follows:

[0583] 1. Collect users' past schedule data, travel history, and expenditure history.

[0584] 2. The server preprocesses this data, imputes missing values, and removes outliers.

[0585] 3. Use TensorFlow and PyTorch to analyze this data and learn user behavior patterns.

[0586] 4. A user enters a task to "attend a meeting tomorrow."

[0587] 5. The server uses the trained model to suggest the optimal route and departure time, taking into account the meeting location and time, the user's current location, transportation method, etc.

[0588] 6. The server analyzes the user's tone of voice and facial expressions, and if the user is feeling stressed, it will prioritize tasks that promote relaxation.

[0589] Prompt Sentence Examples

[0590] "I'd like you to optimize my schedule for tomorrow. My meeting starts at 10:00, lunch starts at 12:00, and I'll be using public transportation to get around."

[0591] In this way, the server provides a system that suggests optimal actions based on user input. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0592] Step 1:

[0593] The server collects user data. Specifically, it obtains calendar app data, GPS data, electronic money usage history, etc. from the user's smartphone or PC. The input includes data from the user's various devices. The output is stored on the server.

[0594] Step 2:

[0595] The server preprocesses the collected data by imputing missing values ​​and removing outliers. It also standardizes the data and converts it into a format suitable for machine learning models. The input includes the data collected in step 1. The output is the preprocessed data.

[0596] Step 3:

[0597] The server uses the preprocessed data to train a machine learning model. It uses TensorFlow or PyTorch to learn user behavior patterns. The input includes the preprocessed data. The output is a trained model.

[0598] Step 4:

[0599] A user inputs a new task or event into the system. For example, the user inputs a task such as "Attend a meeting tomorrow." The input includes the task or event entered by the user. The output is the task information sent to the server.

[0600] Step 5:

[0601] The server uses the trained model to suggest optimal actions to the user. Specifically, it calculates the optimal travel route and departure time by taking into account the location and time of the meeting, the user's current location, and the mode of transportation. The input includes the task information entered by the user and the trained model. The output is the optimal action suggestion.

[0602] Step 6:

[0603] The server uses an emotion engine to recognize the user's emotional state. Specifically, it analyzes the user's tone of voice, facial expressions, behavioral patterns, etc. Inputs include the user's audio and video data. The output is the user's emotional state.

[0604] Step 7:

[0605] The server adjusts task priorities based on the user's emotional state. For example, if the server determines that the user is feeling stressed, it will prioritize tasks that promote relaxation. Inputs include the user's emotional state and task information. The output is the adjusted task priorities.

[0606] (Application example 3)

[0607] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0608] Conventional business software and schedule management systems are limited to simple task management and schedule suggestions without taking into account the user's behavioral patterns or emotional state. This makes it difficult to optimize the user's time, travel, and cost efficiency, and they are unable to respond appropriately, especially in stressful situations. Furthermore, certain tasks, such as food delivery, require optimization of delivery routes and timing, but conventional systems were unable to adequately address this.

[0609] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0610] In this invention, the server includes means for comprehensively acquiring information from business software, means for reading a user's schedule based on the acquired information, means for proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency based on the read schedule, means for recognizing the user's emotional state and adjusting task priorities based on the emotional state, means for collecting data such as delivery routes, delivery times, and break times and proposing optimal delivery routes and timings, means for analyzing the user's tone of voice and facial expressions to recognize the user's emotional state, and means for suggesting breaks when the emotional state is high in stress. This enables optimal schedule management and action suggestions that take into consideration the user's behavioral patterns and emotional state, enabling efficient and stress-free work, particularly in specific tasks such as food delivery.

[0611] "Business software" is software used by companies and organizations to carry out their business efficiently.

[0612] "Means for comprehensively acquiring information" refers to methods and techniques for collecting all necessary information without omission.

[0613] "Means for reading the user's schedule" refers to methods and technologies for understanding the user's plans and tasks.

[0614] "Time efficiency" refers to the efficiency of making the most of limited time.

[0615] "Travel efficiency" refers to the efficiency of minimizing travel time and costs.

[0616] "Cost efficiency" refers to efficiency in maximizing cost-effectiveness.

[0617] "Means for suggesting best actions" are methods and techniques for suggesting the most appropriate actions for users.

[0618] "Means for recognizing a user's emotional state" refers to methods or techniques for understanding a user's emotions.

[0619] A "means for adjusting task priorities" is a method or technique for setting priorities based on the importance or urgency of tasks.

[0620] "Delivery route" refers to the route taken when making a delivery.

[0621] "Delivery Time" means the time required to make a Delivery.

[0622] "Break time" refers to the time taken to rest between work tasks.

[0623] "Means of collecting data" are the methods and techniques used to collect the necessary data.

[0624] "Tone of voice" refers to the vocal characteristics that express the pitch, intensity, and emotion of the speaker's voice.

[0625] "Facial expression" refers to emotions and intentions expressed through the movement of facial muscles.

[0626] The "means for suggesting a break when the emotional state is high stress" is a method or technology for recommending a break when the user is determined to be in a high stress state.

[0627] As an embodiment of the present invention, the following system is constructed.

[0628] System Overview

[0629] The system comprehensively retrieves information from business software to read the user's schedule, and then suggests the best course of action, taking into account time, travel, and cost efficiency. It also recognizes the user's emotional state and adjusts task priorities based on that emotional state. It also collects data such as delivery routes, delivery times, and break times to suggest optimal delivery routes and timings.

[0630] Hardware and software used

[0631] Hardware: Smartphones, smart glasses, head-mounted displays

[0632] Software: TensorFlow, Python, speech recognition software, facial recognition software

[0633] Data processing and calculation

[0634] Data collection

[0635] The server comprehensively retrieves information such as emails and documents from the business software, collecting data to understand the user's schedule and tasks, as well as data such as delivery routes, delivery times, and break times.

[0636] Data Preprocessing

[0637] The server preprocesses the collected data and converts it into a format suitable for machine learning models, specifically by cleaning and normalizing text data and formatting time series data.

[0638] Model training

[0639] The server uses TensorFlow and PyTorch to learn user behavior patterns, processes time-series data using LSTM (Long Short-Term Memory), and builds a model to predict optimal behavior.

[0640] Emotion Engine

[0641] The server uses voice and facial recognition software to analyze the user's tone of voice and facial expressions to recognize their emotional state, and if the emotional state indicates high stress, suggests taking a break.

[0642] Specific examples

[0643] For example, if a food delivery driver is feeling stressed, the server will analyze their voice tone and facial expression data to suggest they take a break. If their delivery route is congested, the server will suggest an optimal alternative route.

[0644] Prompt Sentence Examples

[0645] Below are some example prompts to input to the generative AI model:

[0646] Develop an application that learns the behavioral patterns of delivery personnel and suggests optimal delivery routes and timings. Also incorporate a function to recognize the emotional state of delivery personnel by analyzing their tone of voice and facial expressions. It is also necessary to have a function to suggest breaks when stress levels are high.

[0647] In this way, optimal schedule management and action suggestions can be made possible, taking into account the user's behavioral patterns and emotional state, enabling efficient and stress-free work, especially in specific tasks such as food delivery.

[0648] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0649] Step 1:

[0650] The server comprehensively obtains information from business software. Specifically, it collects data such as emails and documents and extracts information about the user's schedule and tasks. The input is data from the business software, and the output is the extracted schedule and task information.

[0651] Step 2:

[0652] The server preprocesses the acquired information. Specifically, it cleans and normalizes the text data and converts it into a format suitable for machine learning models. The input is the extracted schedule and task information, and the output is the preprocessed data.

[0653] Step 3:

[0654] The server uses the preprocessed data to learn user behavior patterns. It builds an LSTM model using TensorFlow or PyTorch to process time-series data. The input is the preprocessed data, and the output is the learned behavior pattern model.

[0655] Step 4:

[0656] The server uses voice recognition software and facial recognition software to analyze the user's tone of voice and facial expressions to recognize their emotional state. The input is the user's voice data and image data, and the output is the recognized emotional state.

[0657] Step 5:

[0658] The server proposes optimal actions based on the learned behavioral pattern model and the recognized emotional state. Specifically, it proposes the most appropriate action for the user in real time, taking into account time efficiency, travel efficiency, and cost efficiency. The input is the behavioral pattern model and the emotional state, and the output is the proposed action.

[0659] Step 6:

[0660] The server collects data such as delivery routes, delivery times, and break times, and proposes optimal delivery routes and timings. The input is delivery-related data, and the output is the optimal delivery route and timing.

[0661] Step 7:

[0662] The server suggests a break if the emotional state is high stress. Specifically, it analyzes voice tone and facial expression data and recommends a break if it determines that the user is in a high stress state. The input is emotional state data, and the output is a break suggestion.

[0663] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0664] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0665] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0666] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0667] [Second embodiment]

[0668] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0669] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0670] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0671] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0672] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0673] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0674] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0675] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0676] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0677] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0678] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0679] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0680] "Example 1"

[0681] The system of the present invention uses APIs and scraping technology as a means of comprehensively acquiring information from business groupware. Specifically, it uses the APIs of business groupware such as Google Calendar and Microsoft Outlook to acquire information such as users' schedules, emails, and documents. The acquired information is stored in a database and used as input for analysis.

[0682] "Example 2"

[0683] The system of the present invention uses natural language processing technology as a means of reading the user's schedule based on the acquired information. Specifically, it uses natural language processing services such as Google Cloud Natural Language API and IBM Watson.

[0684] It extracts tasks and events from schedules, emails, and documents, which are then used as input to suggest the best course of action, taking into account time, travel, and cost efficiency.

[0685] "Example 3"

[0686] The system of the present invention uses machine learning technology as a means of proposing the best course of action that takes into account time, travel, and cost efficiency. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns. The learned model is then used to suggest the best course of action when a new task or event occurs.

[0687] The processing flow of each embodiment will be described below.

[0688] "Example 1"

[0689] Step 1: The system uses the APIs of business groupware such as Google Calendar and Microsoft Outlook to obtain information such as users' schedules, emails, and documents.

[0690] Step 2: The acquired information is stored in a database, which is used as input for analysis.

[0691] "Example 2"

[0692] Step 1: The system uses natural language processing services such as Google Cloud Natural Language API and IBM Watson to analyze schedules and messages.

[0693] Extract tasks and events from rules and documents.

[0694] Step 2: The extracted tasks and events are used as input to suggest the best course of action, taking into account time, travel, and cost efficiency.

[0695] "Example 3"

[0696] Step 1: The system uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns.

[0697] Step 2: The trained model is used to suggest the best course of action when a new task or event arises.

[0698] Example 1

[0699] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0700] With traditional business software, it was difficult to efficiently manage users' schedules and tasks and suggest the best course of action. Furthermore, information acquisition, storage, and analysis were often done manually, which was time-consuming and labor-intensive. Furthermore, analysis based on the acquired information was insufficient, making it difficult to optimize users' time, travel, and cost efficiency.

[0701] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0702] In this invention, the server includes a means for comprehensively acquiring information from business software, a means for storing the acquired information in a database, and a means for analyzing the stored information, thereby enabling efficient management of user schedules and tasks and proposing optimal actions that take into account time efficiency, travel efficiency, and cost efficiency.

[0703] "Business software" is software used by companies and organizations to carry out their business efficiently.

[0704] "Means for comprehensively acquiring information" refers to methods and technologies for comprehensively collecting necessary information from business software.

[0705] "Means for storing in a database" refers to the methods and techniques used to store the acquired information in a structured format in a database.

[0706] "Analysis tools" are the methods and techniques used to analyze stored information and extract meaningful data and insights.

[0707] A "means for reading a user's schedule" is a method or technique for extracting a user's plans and tasks from the acquired and stored information.

[0708] "Time efficiency" refers to the efficiency with which users can make optimal use of their time.

[0709] "Travel efficiency" refers to the efficiency of optimizing the time and cost required for users to travel.

[0710] "Cost efficiency" refers to efficiency in minimizing the costs involved in conducting business for users.

[0711] "Best action suggestion" refers to methods and technologies for suggesting the most effective action for a user based on the analysis results.

[0712] "Email" is a digital message sent and received over the Internet.

[0713] A "document" is a digital file that contains information such as text and images.

[0714] A "task" refers to a specific task or activity that a user must perform.

[0715] "Urgency" is an index that indicates how urgently the task needs to be performed.

[0716] "Importance" is an indicator of how important a task is to achieving the user's goals.

[0717] A "prioritization method" is a method or technique for determining the order of execution of tasks, taking into account the urgency and importance of the tasks.

[0718] MODE FOR CARRYING OUT THE INVENTION

[0719] The present invention is a system for comprehensively acquiring information from business software, storing the acquired information in a database, and analyzing the stored information. A specific embodiment of this system will be described below.

[0720] 1. Program Generation

[0721] The server generates a program to comprehensively retrieve information from business software. This program uses APIs and scraping technology to retrieve data from the business software. Specifically, it uses APIs such as Google Calendar and Microsoft Outlook.

[0722] 2. Program Processing

[0723] The server uses the APIs of Google Calendar and Microsoft Outlook to obtain information such as user schedules, emails, documents, etc. Below, we will clearly explain the hardware and software used and the methods of data processing and calculation for each.

[0724] API authentication: The server obtains credentials to access the Google Calendar or Microsoft Outlook API using an authentication protocol such as OAuth 2.0.

[0725] Data retrieval: The server uses the authentication information to send a request to the API to retrieve information such as the user's schedule, emails, documents, etc. For example, it uses the Google Calendar API to retrieve the user's schedule.

[0726] Data storage: The acquired information is stored in a database on the server, using a relational database such as MySQL or PostgreSQL.

[0727] Data analysis: The stored data is used as input for analysis, which can be done using programming languages ​​such as Python or R.

[0728] 3. Specific Examples

[0729] As a specific example, consider the case where a user's schedule is retrieved from Google Calendar.

[0730] The server first performs authentication using OAuth 2.0 to obtain the user's schedule using the Google Calendar API.

[0731] If authentication is successful, the server obtains an access token and uses it to make requests to Google Calendar's API endpoints.

[0732] The server parses the JSON response returned from the API and extracts the user's schedule information.

[0733] The extracted schedule information is stored in the server's MySQL database.

[0734] The saved schedule data is converted into a data frame on the server using Python's Pandas library and used for statistical analysis and training of machine learning models.

[0735] Prompt Sentence Examples

[0736] "Use the Google Calendar API to retrieve users' schedules and store them in a MySQL database. Then, analyze the stored data using Python's Pandas library."

[0737] In this way, the server realizes a system that obtains information from business software, stores it in a database, and uses it as input for analysis.

[0738] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0739] Step 1:

[0740] API Authentication

[0741] The server obtains authentication information to access the business software's API. As input, it uses authentication information provided by the user (client ID, client secret, etc.). The server uses this authentication information to send an authentication request to the API provider and obtains an access token as output. Specifically, the server performs authentication using the OAuth 2.0 protocol.

[0742] Step 2:

[0743] Data Acquisition

[0744] The server uses the acquired access token to send a request to the API to retrieve information such as the user's schedule, emails, and documents. The access token and the API endpoint URL are used as input. The server receives the JSON-formatted response returned from the API and extracts the necessary information as output. Specifically, the server sends a request to the Google Calendar API endpoint to retrieve the user's schedule.

[0745] Step 3:

[0746] Data storage

[0747] The server stores the retrieved information in a database. As input, it uses the parsed JSON data. The server converts this into a SQL query and inserts it into the database. As output, it gets the information stored in the database. Specifically, the server connects to the MySQL database and executes an INSERT statement to save the data.

[0748] Step 4:

[0749] Data analysis

[0750] The server analyzes the stored data. It uses data retrieved from the database as input. The server converts this data into a data frame using Python's Pandas library, and performs statistical analysis and trains machine learning models. The output is analysis results and predictive models. Specifically, the server executes SQL queries to retrieve data, converts it into a Pandas data frame, and performs analysis.

[0751] Step 5:

[0752] action suggestion

[0753] The server suggests the best course of action to the user based on the analysis results. It uses the results of the data analysis as input. The server calculates the optimal course of action taking into account time efficiency, travel efficiency, and cost efficiency, and makes a suggestion to the user as output. Specifically, the server runs an algorithm based on the analysis results and sends a notification to the user.

[0754] (Application example 1)

[0755] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0756] Conventional factory robot scheduling is often done manually, making it difficult to optimize work schedules. Furthermore, information acquired from business software is limited and rarely reflected in robot work schedules. This results in reduced time, travel, and cost efficiency, resulting in a loss of overall productivity.

[0757] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0758] In this invention, the server includes means for comprehensively acquiring information from business software, means for reading a user's schedule based on the acquired information, means for proposing the best course of action based on the read schedule, taking into consideration time efficiency, travel efficiency, and cost efficiency, means for optimizing machine work schedules based on the acquired schedule information, and means for saving the optimized schedule in a database. This makes it possible to automatically optimize the work schedules of factory robots and improve overall productivity.

[0759] "Business software" is software used by companies and organizations to carry out their business efficiently.

[0760] "Means for comprehensively acquiring information" refers to methods and technologies for acquiring all necessary information from business software.

[0761] "Means for reading the user's schedule" refers to methods and technologies for analyzing and understanding the user's plans and tasks based on the information obtained.

[0762] "Time efficiency" refers to the efficiency of minimizing wasted time when performing work or tasks.

[0763] "Travel efficiency" refers to the efficiency of minimizing travel time and costs when performing work or tasks.

[0764] "Cost efficiency" refers to the efficiency of minimizing costs when performing work or tasks.

[0765] "Best action suggestion" refers to methods and techniques for suggesting the most efficient and effective action for the user based on the information obtained.

[0766] "Means for optimizing machine work schedules" refers to methods and techniques for adjusting machine work schedules most efficiently based on acquired schedule information.

[0767] "Means for storing in a database" refers to methods and techniques for recording and managing the acquired information and optimized schedules in a database.

[0768] The system for implementing this invention comprehensively acquires information from business software, reads the user's schedule based on the acquired information, and proposes optimal actions. It also has the function of optimizing machine work schedules based on the acquired schedule information and saving the optimized schedule in a database.

[0769] The server obtains information from business software (e.g., Google Calendar, Microsoft Outlook) using APIs. The obtained information is used to analyze the user's schedule and tasks. The analyzed information is used as data to suggest optimal actions, taking into account time efficiency, travel efficiency, and cost efficiency.

[0770] Specifically, the server does the following:

[0771] 1. Data Acquisition: Use the Google Calendar API or Microsoft Outlook API to obtain user schedule information.

[0772] 2. Data processing: Analyze the acquired schedule information and convert it into the required format.

[0773] 3. Schedule optimization: Optimize the work schedule of factory robots based on the acquired schedule information.

[0774] 4. Data storage: The optimized schedule is stored in the database.

[0775] The hardware used is a factory robot and server, and the software uses the Google Calendar API, Microsoft Outlook API, Python, and database modules (e.g., SQLite, PostgreSQL).

[0776] For example, a robot in a factory can automatically start and finish work based on a schedule retrieved from Google Calendar, and the robot can also automatically incorporate maintenance times into its schedule.

[0777] An example of a prompt to input to a generative AI model is as follows:

[0778] "Write a Python program that optimizes the work schedules of factory robots based on schedule information retrieved from Google Calendar. The program will retrieve the schedule information using the Google Calendar API and compare it with the current robot schedule stored in a database to optimize it."

[0779] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0780] Step 1:

[0781] The server uses the API of business software (e.g., Google Calendar, Microsoft Outlook) to obtain the user's schedule information. Specifically, it authenticates using the API key and requests the schedule information. The input is the API key and user ID, and the output is the user's schedule information.

[0782] Step 2:

[0783] The server analyzes the acquired schedule information and converts it into the required format. Specifically, it extracts the start time, end time, event summary, etc. from the schedule information and formats it into a format that can be saved in a database. The input is the acquired schedule information, and the output is the formatted schedule data.

[0784] Step 3:

[0785] The server optimizes the work schedules of the factory robots based on the formatted schedule data. Specifically, it compares the current robot schedule with the newly acquired schedule information and calculates the optimal work sequence and timing. The input is the formatted schedule data and the current robot schedule, and the output is the optimized robot schedule.

[0786] Step 4:

[0787] The server saves the optimized robot schedule to the database by establishing a database connection and inserting or updating the optimized schedule data. The input is the optimized robot schedule, and the output is the result saved to the database.

[0788] Step 5:

[0789] The server sends work instructions to the factory robots based on the saved optimized schedule. Specifically, it sends instructions to the robot control system using an API or dedicated protocol. The input is the optimized robot schedule, and the output is the robot's start of work.

[0790] Step 6:

[0791] Users monitor the work status of factory robots and make adjustments as necessary. Specifically, they use a dedicated monitoring tool to check the robot's status and manually intervene if an abnormality occurs. The input is the robot's work status, and the output is the work status after adjustments.

[0792] Example 2

[0793] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0794] Conventional schedule management systems require users to manually input and manage their schedules, which is time-consuming and labor-intensive. Furthermore, they lack sufficient schedule optimization and efficient action suggestions, making it difficult to maximize users' time, travel, and cost efficiency. Furthermore, it is difficult to integrate and process data from multiple sources, which can lead to inconsistencies in users' schedule management.

[0795] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0796] In this invention, the server includes a means for comprehensively acquiring information from an information processing device, a means for reading a user's schedule based on the acquired information, a means for extracting tasks and events from schedules, correspondence, and documents using natural language processing technology, a means for proposing optimal actions based on the read schedule, taking into account time efficiency, travel efficiency, and cost efficiency, and a means for calculating travel time between the extracted tasks and events. This allows the user to receive efficient action suggestions without having to manually input the schedule. Furthermore, by integrating and processing data from multiple information sources, consistent schedule management can be achieved.

[0797] An "information processing device" is an electronic device for inputting, processing, storing, and outputting data.

[0798] "Means for comprehensively acquiring information" refers to methods and technologies for comprehensively collecting necessary data from multiple sources.

[0799] The "means for reading the user's schedule" refers to a method or technology for analyzing and extracting data related to the user's schedule from the acquired information.

[0800] "Natural language processing technology" is a technology that allows computers to understand, interpret, and generate human language.

[0801] "Means for extracting tasks and events from schedules, correspondence, and documents" refers to methods and techniques for identifying and extracting tasks and events from text data using natural language processing techniques.

[0802] "Time efficiency" refers to the efficiency of achieving maximum results within a given time.

[0803] "Travel efficiency" refers to the efficiency of minimizing travel time and costs and selecting the most optimal route and means.

[0804] "Cost efficiency" refers to efficiency in maximizing cost-effectiveness.

[0805] A "best action suggester" is a method or technique for recommending the most efficient and effective action based on a user's schedule and tasks.

[0806] A "means for calculating travel time" is a method or technology for calculating the time it takes to travel between different locations.

[0807] The present invention is a system for improving the efficiency of a user's schedule management and proposing optimal actions. Specific embodiments of this system will be described below.

[0808] System configuration

[0809] Hardware

[0810] Terminal: A device that allows a user to input information, such as a smartphone, tablet, or computer.

[0811] Server: A central processing unit for processing information and suggesting optimal actions.

[0812] software

[0813] Natural language processing services: Use natural language processing technologies such as Google Cloud Natural Language API and IBM Watson.

[0814] Database: Data storage for saving user schedule information and tasks.

[0815] API Services: External services for calculating travel times, such as the Google Maps API.

[0816] Program processing

[0817] Enter and submit information

[0818] A user uses a device to input information such as schedules, emails, documents, etc. For example, "Meeting tomorrow at 10, followed by lunch meeting at 12." The device then sends this information to the server. Specifically, the device uses an HTTP request to send the input information to the server.

[0819] Performing natural language processing

[0820] The server then sends the received information to the Google Cloud Natural Language API, which creates an API request and sends it to the API to parse the input text. The Google Cloud Natural Language API then parses the text and extracts information related to the task or event.

[0821] Extracting tasks and events

[0822] The server receives the analysis results returned by the Google Cloud Natural Language API. The analysis results include important tasks and events in the text. For example, "Meeting tomorrow at 10:00" and "Lunch meeting at 12:00" are extracted. The server stores this information in a database.

[0823] Best course of action

[0824] The server then suggests the best course of action for the user based on the extracted tasks and events. Specifically, the server uses the Google Maps API to calculate the travel time between each task and event. For example, the server might generate a suggestion such as, "Taking into account the travel time to the lunch meeting location after the meeting, we recommend leaving at 11:45." The suggestion is then sent to the device and displayed to the user.

[0825] Specific examples

[0826] Example 1: Analyzing a schedule

[0827] A user types "Meeting tomorrow at 10am, followed by lunch meeting at 12pm" into their device. The device sends this information to the server, which uses the Google Cloud Natural Language API to parse it and extract the following tasks:

[0828] Meeting tomorrow at 10 o'clock

[0829] Lunch meeting at 12 o'clock

[0830] Example 2: Best course of action

[0831] Based on the extracted tasks, the server makes a suggestion to the user, such as "Taking into account the travel time to the lunch meeting location after the meeting, it is recommended that you leave at 11:45." The suggestion is sent to the terminal and displayed to the user.

[0832] Prompt Sentence Examples

[0833] Examples of prompts to input to a generative AI model might include:

[0834] Enter the user's schedule information. For example, "Meeting tomorrow at 10am, followed by lunch at 12pm."

[0835] Using this prompt, users can input schedule information in natural language, and the system can suggest optimal actions based on that information.

[0836] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0837] Step 1:

[0838] A user inputs schedule information into a terminal. For example, "Meeting tomorrow at 10 o'clock, followed by a lunch meeting at 12 o'clock." The terminal sends this information to a server. Specifically, the terminal uses an HTTP request to send the input information to the server. The input is the user's schedule information, and the output is the data to be sent to the server.

[0839] Step 2:

[0840] The server sends the received information to the natural language processing service. Specifically, the server creates an API request and sends it to the natural language processing service for analysis of the input text. The input is the schedule information received from the device, and the output is the request data for the natural language processing service.

[0841] Step 3:

[0842] The natural language processing service analyzes the received text and extracts information related to tasks and events. For example, "Meeting tomorrow at 10:00" and "Lunch meeting at 12:00" are extracted. The input is text data sent from the server, and the output is the task or event information resulting from the analysis.

[0843] Step 4:

[0844] The server receives the analysis results returned from the natural language processing service. The analysis results include important tasks and events in the text. The server stores this information in a database. The input is the analysis results from the natural language processing service, and the output is the task and event information stored in the database.

[0845] Step 5:

[0846] The server proposes the best course of action to the user based on the extracted tasks and events. Specifically, the server uses an external API service to calculate travel time. For example, the server generates a proposal such as, "Taking into account the travel time to the lunch meeting location after the meeting, it is recommended that you leave at 11:45." The input is task and event information stored in the database, and the output is a proposed action to the user.

[0847] Step 6:

[0848] The server sends the generated action suggestions to the terminal. The terminal displays these suggestions to the user. Specifically, the server uses an HTTP response to send the action suggestions to the terminal. The input is the action suggestions generated by the server, and the output is the data sent to the terminal. The user can check the suggestions through the terminal and take the optimal action.

[0849] (Application example 2)

[0850] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0851] Conventional schedule management systems have difficulty efficiently acquiring users' schedule information and suggesting optimal actions. Furthermore, especially for autonomous vehicles, they lack the functionality to calculate and suggest optimal routes based on the user's schedule. This has led to problems such as reduced time efficiency, travel efficiency, and cost efficiency for users.

[0852] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0853] In this invention, the server includes means for comprehensively acquiring information from business software, means for reading a user's schedule based on the acquired information, means for proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency based on the read schedule, means for being installed in an autonomous vehicle and extracting tasks and events from the user's schedule information and calculating the optimal route, and means for proposing the optimal route based on the extracted tasks and events. This makes it possible to efficiently acquire a user's schedule information and propose optimal actions and routes.

[0854] "Business software" is software used by companies and organizations to carry out their business efficiently.

[0855] "Means for comprehensively acquiring information" refers to methods and techniques for collecting all necessary information without omission.

[0856] "Means for reading the user's schedule" refers to methods and technologies for analyzing and understanding the schedules and tasks set by the user.

[0857] "Time efficiency" refers to the efficiency of making the most of limited time.

[0858] "Travel efficiency" refers to the efficiency of optimizing travel to a destination.

[0859] "Cost efficiency" refers to the efficiency of minimizing costs while maximizing benefits.

[0860] A "best action suggestion" is a method or technique for suggesting the most effective action for a user.

[0861] An "autonomous vehicle" is a vehicle that can navigate autonomously without driver intervention.

[0862] "Means for extracting tasks and events" refers to methods and techniques for extracting specific tasks and events from schedule information.

[0863] "Means for calculating optimal routes" refers to methods or techniques for calculating the most efficient route to a destination.

[0864] "Means for suggesting optimal routes" refers to methods or technologies for showing the calculated optimal route to the user.

[0865] A system for implementing this invention includes means for comprehensively acquiring information from business software and reading a user's schedule. It also includes means for proposing the best course of action based on the read schedule, taking into consideration time efficiency, travel efficiency, and cost efficiency. It also includes means installed in an autonomous vehicle for extracting tasks and events from the user's schedule information and calculating the optimal route, and means for proposing the optimal route based on the extracted tasks and events.

[0866] Hardware and software used

[0867] Hardware: On-board computers for autonomous vehicles

[0868] Software: Google Cloud Natural Language API, Python

[0869] Data processing and calculation

[0870] The server retrieves information from business software, specifically using the Google Cloud Natural Language API to extract tasks and events from emails and documents, and then parses the information into the user's schedule.

[0871] Based on the analyzed schedule information, the device suggests the best course of action, taking into account time, travel, and cost efficiency, including prioritizing tasks based on their urgency and importance.

[0872] The autonomous vehicle calculates the optimal route based on the user's schedule information, and the calculated route is suggested to the user, allowing the user to travel efficiently.

[0873] Specific examples

[0874] For example, if a user has a schedule that says, "Tomorrow I have a meeting at 10:00, lunch at 12:00, and a presentation at 15:00," the server sends this information to the Google Cloud Natural Language API to extract tasks and events. The extracted tasks (meeting, lunch, presentation) are analyzed on the device, and the optimal route is calculated. The calculated route is then suggested to the user as "Optimal route: Meeting -> Lunch -> Presentation."

[0875] Prompt Sentence Examples

[0876] Enter the user's schedule information. For example, "Tomorrow I have a meeting at 10:00, lunch at 12:00, and a presentation at 15:00."

[0877] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0878] Step 1:

[0879] The server comprehensively retrieves information from the business software. Specifically, it uses the Google Cloud Natural Language API to extract tasks and events from emails and documents. It receives business software data as input and generates a list of tasks and events as output.

[0880] Step 2:

[0881] The server reads the user's schedule based on the acquired information. It receives a list of tasks and events as input and generates the user's schedule information as output. Specifically, it analyzes the schedule information using natural language processing technology.

[0882] Step 3:

[0883] The device then proposes the best course of action based on the schedule information it reads, taking into account time, travel, and cost efficiency. It receives the user's schedule information as input and generates the best course of action as output. Specifically, it prioritizes tasks by taking into account their urgency and importance.

[0884] Step 4:

[0885] The autonomous vehicle extracts tasks and events from the user's schedule information and calculates the optimal route. It receives the user's schedule information as input and generates the optimal route as output. Specifically, it calculates the optimal route using a map API.

[0886] Step 5:

[0887] The autonomous vehicle proposes the calculated optimal route to the user. It receives the optimal route information as input and generates a route proposal to present to the user as output. Specifically, the route is shown to the user through a display and voice guidance.

[0888] Example 3

[0889] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0890] Conventional systems have had difficulty efficiently learning users' behavioral patterns and suggesting optimal actions that take into account time, travel, and cost efficiency. Furthermore, they lacked support for users to take optimal actions due to insufficient prioritization based on task urgency and importance.

[0891] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0892] In this invention, the server includes means for comprehensively acquiring data from the information processing device, means for learning the user's behavioral patterns based on the acquired data, means for proposing the best action taking into consideration time efficiency, travel efficiency, and cost efficiency based on the learned behavioral patterns, and means for presenting the proposed action plan to the user. This makes it possible to efficiently learn the user's behavioral patterns and propose optimal actions.

[0893] An "information processing device" is a device for collecting, processing, storing, and transmitting data.

[0894] "Data" refers to information necessary to learn user behavior patterns, and includes emails, documents, travel history, schedule information, and the like.

[0895] A "behavioral pattern" is a collection of data that indicates the tendencies and habits of a user's past behavior.

[0896] "Time efficiency" refers to minimizing the time required for a user to complete a task or event.

[0897] "Travel efficiency" refers to the user's selection of the optimal means of transportation and route to reach their destination.

[0898] "Cost-effective" refers to minimizing the expense required for a user to complete a task or event.

[0899] "Best action" refers to the most appropriate action for a user, taking into consideration time efficiency, travel efficiency, and cost efficiency.

[0900] "Suggestion" refers to showing the user the optimal plan of action based on learned behavioral patterns.

[0901] A "task" refers to a specific task or activity that a user must perform.

[0902] "Urgency" is an indicator of how quickly a task needs to be addressed.

[0903] "Importance" is an indicator of how important a task is to the user.

[0904] "Priority" is the order used to determine which of multiple tasks should be performed first.

[0905] The present invention is a system that learns a user's behavioral patterns and proposes the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency. This system comprehensively acquires data from an information processing device, learns the user's behavioral patterns based on the acquired data, and proposes the best course of action based on the learned behavioral patterns.

[0906] Server Roles

[0907] The server first comprehensively acquires data from the information processing device. Specifically, it collects data such as the user's movement history, schedule information, emails, documents, etc. This data is stored in a database and used for subsequent processing.

[0908] The server then preprocesses the acquired data, which includes imputing missing values, normalizing the data, encoding categorical data, etc. The preprocessed data is then used to train machine learning models.

[0909] The server uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns based on preprocessed data. The learned model is then used to predict user behavior patterns and suggest optimal actions.

[0910] Finally, the server deploys the trained model to the device, allowing the device to use the trained model to suggest optimal actions for the user.

[0911] Device Role

[0912] The device receives the trained model sent from the server, which enables the device to suggest optimal actions for new tasks and events.

[0913] When a user inputs new task or event information, the device inputs that information into the trained model. For example, if a user inputs "What is the best route to get to a meeting tomorrow?", the device inputs that information into the model and generates the best route.

[0914] The generated action plan is then proposed to the user via the terminal, who can then confirm the proposed action plan and act accordingly.

[0915] User Roles

[0916] Users input new task or event information into the device, such as "optimize my shopping list this week," and the device feeds that information into the trained model to generate an optimal plan of action.

[0917] The user can then view the proposed action plan on the device and act accordingly, for example, by following the proposed travel route to the destination.

[0918] Specific examples

[0919] Example 1

[0920] The user inputs into the device, "Tell me the best route to get to tomorrow's meeting." The device inputs this information into the trained model and suggests the best route. For example, it may suggest a specific route such as "30 minutes by train, 10 minutes by bus, and 5 minutes on foot."

[0921] Example 2

[0922] The user inputs "optimize this week's shopping list" into the device. The device inputs this information into the trained model and suggests the optimal shopping route and timing. For example, the device might make a specific suggestion such as "Buy vegetables at supermarket A on Monday and meat at supermarket B on Wednesday."

[0923] Prompt Sentence Examples

[0924] "What's the best route to get to tomorrow's meeting?"

[0925] "Optimize my shopping list this week"

[0926] "Suggest the best travel plan for your next vacation"

[0927] The above is an embodiment of the system of the present invention. This system allows users to efficiently plan their activities, optimizing time efficiency, travel efficiency, and cost efficiency. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[0928] Step 1: Data collection

[0929] The server comprehensively acquires data from the information processing device. Specifically, it collects data such as the user's movement history, schedule information, emails, documents, etc. This data is then stored in a database.

[0930] Input: User travel history, schedule information, emails, documents

[0931] Output: User behavior data stored in a database

[0932] Step 2: Data Preprocessing

[0933] The server preprocesses the collected data, specifically imputing missing values, normalizing the data, encoding categorical data, etc. The preprocessed data is then used to train machine learning models.

[0934] Input: User behavior data stored in a database

[0935] Output: Preprocessed data

[0936] Step 3: Model training

[0937] The server uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns based on preprocessed data. The learned model is then used to predict user behavior patterns and suggest optimal actions.

[0938] Input: Preprocessed data

[0939] Output: Trained model

[0940] Step 4: Model evaluation

[0941] The server evaluates the performance of the trained model. Specifically, it calculates the model's precision, recall, F1 score, etc. using test data. Based on the evaluation results, it retrains the model or adjusts its hyperparameters as necessary.

[0942] Input: trained model, test data

[0943] Output: Model evaluation results

[0944] Step 5: Model deployment

[0945] The server deploys the evaluated model to the device, allowing the device to use the trained model to suggest optimal actions to the user.

[0946] Input: Evaluated model

[0947] Output: The trained model deployed to the device.

[0948] Step 6: Receiving User Input

[0949] The device receives new tasks and events from the user, for example, "What is the best route to get to my meeting tomorrow?"

[0950] Input: User task or event information

[0951] Output: Received task and event information

[0952] Step 7: Input to the model

[0953] The device inputs the received user input into the trained model, which then generates an optimal action plan.

[0954] Input: Received task and event information, trained model

[0955] Output: Optimal action plan

[0956] Step 8: Propose an action plan

[0957] The device then presents the user with an optimal action plan output from the model, specifically displaying the results of optimization of travel routes and schedules.

[0958] Input: Optimal action plan

[0959] Output: A suggested action plan for the user

[0960] Step 9: Review the proposal and take action

[0961] The user can then view the proposed action plan on the device and act accordingly, for example, by following the proposed travel route to the destination.

[0962] Input: User's proposed plan of action

[0963] Output: Actual behavior

[0964] The above is the specific processing flow of the program of this system.

[0965] (Application example 3)

[0966] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0967] In conventional business groupware systems, users manage their schedules and tasks individually, making it difficult to comprehensively optimize time, travel, and cost efficiency. Furthermore, particularly in food delivery services, there is a lack of systems that can suggest optimal routes and actions in real time for delivery personnel to efficiently handle multiple orders. This has led to a demand for more efficient delivery operations.

[0968] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0969] In this invention, the server includes means for comprehensively acquiring information from business groupware, means for reading a user's schedule based on the acquired information, means for proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency based on the read schedule, means for proposing the best delivery route taking into consideration past behavioral data in delivery work, current traffic conditions, and order priority, and means for predicting the best course of action when a new task or event occurs. This enables efficient management of user schedules and food delivery work.

[0970] "Business groupware" is software that streamlines information sharing and communication within a company or organization.

[0971] "Means for comprehensively acquiring information" refers to methods and techniques for collecting all necessary information without omission.

[0972] "Means for reading the user's schedule" refers to methods and technologies for understanding the user's plans and tasks.

[0973] "Time efficiency" refers to the efficiency of making the most of limited time.

[0974] "Travel efficiency" refers to the efficiency of minimizing travel time and costs.

[0975] "Cost efficiency" refers to efficiency in maximizing cost-effectiveness.

[0976] A "best action suggestion" is a method or technique for suggesting the most appropriate action for a user.

[0977] "Delivery work" refers to the work of delivering products and services to customers.

[0978] "Past behavioral data" is a record of past behavior of users or systems.

[0979] "Current traffic conditions" refers to the real-time state of roads and transportation.

[0980] "Order priority" is an index that indicates which of multiple orders should be given priority in processing.

[0981] A "means for proposing the optimal delivery route" is a method or technology for showing the most efficient route in delivery operations.

[0982] "Means for predicting the best course of action when a new task or event occurs" refers to methods or techniques for predicting the most appropriate action when a new task or event occurs.

[0983] To implement this invention, the following system configuration is required: The server comprehensively acquires information from business groupware and reads the user's schedule based on the acquired information. Based on the read schedule, the server proposes the best course of action, taking into account time efficiency, travel efficiency, and cost efficiency. The server also proposes the best delivery route, taking into account past behavioral data in delivery work, current traffic conditions, and order priority, and predicts the best course of action when a new task or event occurs.

[0984] Hardware and software used

[0985] The server uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns. Data is collected using business groupware and traffic information APIs. An application is installed on the delivery person's smartphone that suggests the optimal delivery route in real time.

[0986] Data processing and calculation

[0987] The server analyzes information obtained from the business groupware to read the user's schedule. It then calculates the optimal delivery route, taking into account past behavioral data, current traffic conditions, and order priority. To do this, it uses machine learning models and applies algorithms to maximize time, travel, and cost efficiency.

[0988] Specific examples

[0989] For example, if a delivery person receives multiple orders at the same time, the server will consider past delivery data and current traffic conditions to suggest the most efficient delivery order, and the optimal route will be displayed in real time on the delivery person's smartphone.

[0990] Prompt Sentence Examples

[0991] "Consider past delivery data and current traffic conditions to suggest the most efficient delivery route."

[0992] In this way, users can manage their schedules and food delivery operations more efficiently.

[0993] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0994] Step 1:

[0995] The server comprehensively acquires information from the business groupware.

[0996] Input: User schedule and task information obtained from the business groupware API.

[0997] Data processing: Analyze the acquired information and extract the user's schedule and tasks.

[0998] Output: A list of the user's schedule and task information.

[0999] Step 2:

[1000] The server reads the user's schedule based on the obtained information.

[1001] Input: The user's schedule and task information extracted in step 1.

[1002] Data processing: Analyze schedule information and identify the start time, end time, location, etc. of each task.

[1003] Output: Detailed information about each task (start time, end time, location, etc.).

[1004] Step 3:

[1005] The server then suggests the best course of action based on the read schedule, taking into account time efficiency, travel efficiency, and cost efficiency.

[1006] Input: Detailed information for each task obtained in step 2.

[1007] Data calculation: Using machine learning models, we calculate the priority of each task and determine the optimal order of actions.

[1008] Output: A list of optimal action sequences.

[1009] Step 4:

[1010] The server proposes the optimal delivery route by taking into account past behavioral data from delivery operations, current traffic conditions, and order priority.

[1011] Inputs: Delivery driver historical behavior data, real-time traffic information, and order priority.

[1012] Data computation: Using machine learning models to calculate optimal delivery routes.

[1013] Output: A list of optimal delivery routes.

[1014] Step 5:

[1015] The server predicts the best course of action when a new task or event arises.

[1016] Input: Information about new tasks or events.

[1017] Data Computing: Using machine learning models to predict optimal actions for new tasks and events.

[1018] Output: Best action suggestions for new tasks or events.

[1019] Step 6:

[1020] The terminal displays the optimal order of actions and delivery route received from the server to the delivery person.

[1021] Input: A list of optimal action sequences and delivery routes sent from the server.

[1022] Specific operation: Through a smartphone application, delivery personnel are shown the optimal order of actions and delivery routes in real time.

[1023] Output: Specific action instructions for the delivery person to carry out.

[1024] In this way, users can manage their schedules and food delivery operations more efficiently.

[1025] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1026] "Example 1"

[1027] In one embodiment of the present invention, a system incorporating an emotion engine is provided. The emotion engine recognizes a user's emotions and adjusts task priorities based on the user's emotional state. Specifically, the emotion engine analyzes the user's tone of voice, facial expressions, behavioral patterns, etc. to recognize the user's emotional state. For example, if the system determines that the user is feeling stressed, it prioritizes tasks that promote relaxation.

[1028] "Example 2"

[1029] The emotion engine also suggests the best course of action based on the user's emotional state. Specifically, it takes into account the user's emotional state and the urgency and importance of the task to suggest the best course of action. For example, if it determines that the user is feeling happy and there is an important but not urgent task, it will suggest that task. This makes the most of the user's emotional state to achieve efficient task management.

[1030] "Example 3"

[1031] In one embodiment of the present invention, a system incorporating an emotion engine is provided. The emotion engine recognizes a user's emotions and adjusts task priorities based on the user's emotional state. Specifically, the emotion engine analyzes the user's tone of voice, facial expressions, behavioral patterns, etc. to recognize the user's emotional state. For example, if the system determines that the user is feeling stressed, it prioritizes tasks that promote relaxation.

[1032] The processing flow of each embodiment will be described below.

[1033] "Example 1"

[1034] Step 1: The system recognizes the user's emotional state by analyzing their tone of voice, facial expressions, behavioral patterns, etc.

[1035] Step 2: The emotion engine adjusts task priorities based on the perceived emotional state. For example, if it determines that the user is feeling stressed, it will prioritize tasks that promote relaxation.

[1036] "Example 2"

[1037] Step 1: The emotion engine recognizes the user's emotional state.

[1038] Step 2: The emotion engine suggests the best course of action, taking into account the perceived emotional state and the urgency and importance of the task.

[1039] Step 3: For example, if the user is judged to be feeling joyful and there is an important but non-urgent task, the system will suggest that task, thereby making the most of the user's emotional state to achieve efficient task management.

[1040] Example 1

[1041] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1042] While conventional business software systems can efficiently manage users' schedules and tasks, they do not prioritize or suggest tasks that take into account the user's emotional state. As a result, when users are stressed or in a state where appropriate task management is not possible in response to their emotional state, work efficiency can decline.

[1043] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1044] In this invention, the server includes means for comprehensively acquiring information from business software, means for reading a user's schedule based on the acquired information, means for proposing the best course of action based on the read schedule, taking into account time efficiency, travel efficiency, and cost efficiency, means for recognizing the user's emotional state, means for adjusting task priorities based on the recognized emotional state, and means for notifying the user of prioritized tasks. This enables task management that takes the user's emotional state into account, and is expected to improve business efficiency.

[1045] "Business software" is software used by companies and organizations to carry out their business efficiently.

[1046] "Means for comprehensively acquiring information" refers to methods and technologies for comprehensively collecting necessary information from business software.

[1047] "Means for reading the user's schedule" refers to methods and technologies for analyzing and understanding the user's plans and tasks from the information obtained.

[1048] "Time efficiency" refers to the efficiency of achieving maximum results within a limited time.

[1049] "Travel efficiency" refers to the efficiency of minimizing the time and cost required for users to travel.

[1050] "Cost efficiency" refers to the efficiency of minimizing costs and maximizing effectiveness in carrying out business.

[1051] "Best action suggestion" refers to methods and techniques for suggesting the most effective action to a user based on the user's schedule and tasks.

[1052] "Means for recognizing the user's emotional state" refers to methods or technologies for analyzing the user's tone of voice, facial expressions, behavioral patterns, etc. to determine the user's emotions.

[1053] "Means for adjusting task priorities" are methods and techniques for optimizing the order in which tasks are executed, taking into account the user's emotional state and the urgency and importance of the tasks.

[1054] "Means for notifying users of prioritized tasks" refers to methods and techniques for notifying users of the adjusted task priorities.

[1055] MODE FOR CARRYING OUT THE INVENTION

[1056] This invention is a system that comprehensively acquires information from business software and adjusts task priorities by taking into account the user's emotional state. This system operates in cooperation with the server, the terminal, and the user.

[1057] Obtaining information

[1058] The server uses the business software's API to obtain information such as user schedules, emails, and documents. Specifically, it uses the Google Calendar API or Microsoft Outlook API. The obtained information is stored in a database on the server. For example, the Google Calendar API can be used to obtain user schedules and store them in a MySQL database.

[1059] Recognition of emotional states

[1060] The device captures the user's tone of voice, facial expressions, behavioral patterns, etc. in real time using hardware such as a microphone and camera. The captured data is sent from the device to a server. The server uses an emotion engine to analyze the user's emotional state from the received data. The emotion engine recognizes the user's emotions using voice analysis and image analysis technology.

[1061] Task prioritization

[1062] The server adjusts task priorities based on the acquired schedule information and the analysis results of the emotion engine. For example, if the user is feeling stressed, it will prioritize tasks that promote relaxation. The server notifies the user of prioritized tasks. Notifications are sent via push notifications, emails, and other means.

[1063] Specific examples

[1064] If the user uses Google Calendar, the server uses the Google Calendar API to obtain the user's schedule information. The obtained schedule information is stored in a database. The device then captures the user's tone of voice and facial expressions and sends them to the server. The server uses an emotion engine to analyze the user's emotional state and determines that the user is feeling stressed. Based on this information, the server prioritizes tasks that promote relaxation (e.g., taking a short break) and notifies the user.

[1065] Prompt Sentence Examples

[1066] "Use the Google Calendar API to obtain the user's schedule information and save it in a database. Then, capture the user's tone of voice and facial expression data on the device, send it to the server, and use the emotion engine to analyze the user's emotional state. If the user is feeling stressed, prioritize tasks that promote relaxation and notify the user."

[1067] In this way, the system of the present invention can improve a user's work efficiency by obtaining information from business software and adjusting task priorities based on the user's emotional state using an emotion engine.

[1068] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1069] Step 1:

[1070] The server uses the business software's API to obtain information such as the user's schedule, emails, and documents. Specifically, it uses the Google Calendar API and Microsoft Outlook API. It sends an API request as input and receives JSON-formatted data as output. This data includes details of the user's schedules and tasks.

[1071] Step 2:

[1072] The server saves the retrieved information in a database. It receives JSON format data as input and generates records stored in the database as output. Specifically, it uses a MySQL database to insert schedule information. This saves the user's schedule information in the database.

[1073] Step 3:

[1074] The device captures the user's tone of voice, facial expressions, and behavioral patterns in real time, taking data from a microphone and camera as input and generating audio and image data as output using voice recording and facial recognition software.

[1075] Step 4:

[1076] The device sends the captured audio and image data to the server, which receives the audio and image data as input and sends an HTTP POST request as output, allowing the server to receive data for analyzing the user's emotional state.

[1077] Step 5:

[1078] The server uses an emotion engine to analyze the user's emotional state from the received data. It receives voice and image data as input and generates the user's emotional state (e.g., stress, joy, fatigue, etc.) as output. The emotion engine recognizes emotions using voice and image analysis techniques.

[1079] Step 6:

[1080] The server adjusts task priorities based on the acquired schedule information and the analysis results of the emotion engine. It receives schedule information and emotional state as input and generates a prioritized task list as output. For example, if the user is feeling stressed, it will prioritize tasks that promote relaxation.

[1081] Step 7:

[1082] The server notifies the user of the prioritized tasks. It receives a prioritized task list as input and sends push notifications or emails as output, so that the user knows which tasks should be performed first.

[1083] In this way, by clarifying the specific operations and data flow at each step, it is possible to understand how the processing of the entire system is carried out.

[1084] (Application example 1)

[1085] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1086] While conventional business groupware systems can manage users' schedules and tasks, they have the problem of not being able to adjust task priorities or suggest relaxation methods that take into account the user's emotional state. In particular, in factories and other workplaces, where workers' stress and fatigue can affect work efficiency and safety, there is a need to understand their emotional state in real time and take appropriate action based on that.

[1087] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1088] In this invention, the server includes means for comprehensively acquiring information from business groupware, means for reading a user's schedule based on the acquired information, means for proposing the best course of action based on the read schedule taking into account time efficiency, travel efficiency, and cost efficiency, means for recognizing the user's emotional state, means for adjusting task priorities based on the recognized emotional state, and means for proposing tasks that promote relaxation. This makes it possible to adjust task priorities and suggest relaxation activities taking into account the user's emotional state.

[1089] "Business groupware" is software used to manage schedules, send and receive emails, share documents, and more within a company or organization.

[1090] "Means for comprehensively acquiring information" refers to methods and technologies for acquiring all necessary information from business groupware.

[1091] "Means for reading the user's schedule" refers to methods and technologies for analyzing and understanding the user's plans and tasks from the information obtained.

[1092] "Means for suggesting the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency" refers to methods and technologies for suggesting the most efficient course of action based on the user's schedule.

[1093] "Means for recognizing the user's emotional state" refers to methods or technologies for analyzing the user's tone of voice, facial expressions, behavioral patterns, etc. to determine their emotions.

[1094] A "means for adjusting task priorities" is a method or technology for reevaluating the importance and urgency of tasks and setting priorities based on the user's emotional state.

[1095] A "means for suggesting tasks to promote relaxation" is a method or technology that suggests tasks to promote relaxation when a user is feeling stressed.

[1096] A system for implementing the present invention has the following configuration.

[1097] Program Generation

[1098] The system includes a program that comprehensively retrieves information from business groupware and reads the user's schedule and tasks, as well as a program that recognizes the user's emotional state, adjusts task priorities based on that state, and suggests tasks that promote relaxation.

[1099] Program processing explanation

[1100] Data Acquisition

[1101] The server obtains information from business groupware (e.g., Google Calendar, Microsoft Outlook) using APIs and scraping technology. The obtained information includes users' schedules, emails, documents, etc. This information is then stored in a database.

[1102] emotion recognition

[1103] The device (e.g., smart glasses, head-mounted display, microphone) uses an emotion recognition engine (e.g., Affectiva, Microsoft Azure Emotion API) to analyze the user's tone of voice, facial expressions, and behavioral patterns in real time, thereby recognizing the user's emotional state.

[1104] Task priority adjustment

[1105] The server then adjusts task priorities using a task management system (e.g., Trello, Asana) based on the user's emotional state. Specifically, if the user is feeling stressed, it prioritizes tasks that promote relaxation.

[1106] Relaxation task suggestions

[1107] The device (e.g., smartphone, smart glasses) suggests relaxation tasks to the user through relaxation apps (e.g., Calm, Headspace), allowing the user to perform appropriate relaxation tasks even when feeling stressed.

[1108] Specific examples

[1109] For example, if the emotion engine recognizes that factory worker A is feeling stressed, the following processing will occur.

[1110] 1. The server retrieves worker A's current tasks from his schedule and email.

[1111] 2. Using its emotion engine, the device determines that Mr. A is feeling stressed based on his tone of voice and facial expression.

[1112] 3. The server uses a task management system to adjust the priority of Mr. A's current tasks and suggest a relaxation task.

[1113] 4. The device notifies Mr. A's smartphone of deep breathing and short meditation tasks through a relaxation app.

[1114] Prompt Sentence Examples

[1115] "Get Worker A's schedule and emails, and analyze his emotional state using the emotion engine. If he is feeling stressed, suggest relaxation tasks as a priority and notify him on his smartphone."

[1116] In this way, the system can understand the user's emotional state in real time, improving work efficiency and safety.

[1117] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1118] Step 1:

[1119] The server obtains information from business groupware (e.g., Google Calendar, Microsoft Outlook) using APIs and scraping technology. The input is the user's authentication information and the groupware's API endpoint. The output is information such as the user's schedule, emails, and documents. This information is stored in a database.

[1120] Step 2:

[1121] The server analyzes the acquired information and reads the user's schedule and tasks. The input is the schedule, email, and document information stored in the database. The output is a list of the analyzed schedule and tasks. This clarifies the user's schedule and tasks.

[1122] Step 3:

[1123] The device uses an emotion recognition engine (e.g., Affectiva, Microsoft Azure Emotion API) to analyze the user's tone of voice, facial expressions, and behavioral patterns in real time. The input is the user's audio and video data. The output is the user's emotional state (e.g., stressed, relaxed). This allows the user's emotional state to be recognized.

[1124] Step 4:

[1125] The server adjusts task priorities using a task management system (e.g., Trello, Asana) based on the recognized emotional state. The input is the analyzed schedule and task list, and the user's emotional state. The output is a list of tasks with adjusted priorities. This allows the priorities of tasks to be set according to the user's emotional state.

[1126] Step 5:

[1127] The device suggests relaxation tasks to the user through a relaxation app (e.g., Calm, Headspace). The input is a list of prioritized tasks and the user's emotional state. The output is a notification of the relaxation task. This allows the user to perform appropriate relaxation even when feeling stressed.

[1128] In this way, the system can understand the user's emotional state in real time, improving work efficiency and safety.

[1129] Example 2

[1130] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1131] Conventional schedule management systems simply display a user's schedule and tasks, and have the problem of being unable to suggest optimal actions that take into account time, travel, and cost efficiency. Furthermore, task management does not take into account the user's emotional state, making it difficult to suggest optimal actions based on the user's emotions.

[1132] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1133] In this invention, the server includes means for comprehensively acquiring information from an information processing device, means for reading a user's schedule based on the acquired information, means for proposing the best course of action based on the read schedule, taking into consideration time efficiency, travel efficiency, and cost efficiency, and means for analyzing the user's emotional state and proposing the best course of action in consideration of the emotional state and the urgency and importance of the task. This not only enables efficient schedule management for the user, but also makes it possible to propose the best course of action according to the user's emotional state.

[1134] An "information processing device" is a device that acquires and processes information such as a user's schedule, e-mail, and documents.

[1135] "Means for comprehensively obtaining information" refers to means for comprehensively collecting necessary information from various applications and devices used by users.

[1136] "Means for reading the user's schedule" refers to means for analyzing and extracting the user's plans and tasks from the acquired information.

[1137] "A means of proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency" is a means of optimizing the user's schedule and providing an efficient plan of action.

[1138] "Means for analyzing the user's emotional state" refers to means for inferring the user's emotions based on the user's past behavior and current input data.

[1139] "A means for suggesting the best course of action taking into consideration the emotional state and the urgency and importance of the task" is a means for suggesting the optimal course of action based on the user's emotional state and task priority.

[1140] "Means for reading tasks from emails and documents" refers to means for analyzing the contents of emails and documents and extracting tasks and events.

[1141] The "means for prioritizing tasks by taking into consideration urgency and importance" is a means for classifying extracted tasks and events based on urgency and importance, and setting priorities.

[1142] MODE FOR CARRYING OUT THE INVENTION

[1143] The present invention is a system for improving the efficiency of a user's schedule management and proposing optimal actions according to the user's emotional state. Specific embodiments of this system will be described below.

[1144] Information acquisition

[1145] The server retrieves information such as the user's schedule, emails, documents, etc. This information is then synced to the cloud from the user's device, specifically data from Google Calendar and email applications.

[1146] Analysis using natural language processing

[1147] The server then sends the acquired information to a natural language processing service, such as Google Cloud Natural Language API or IBM Watson, to extract tasks and events from the text data.

[1148] Example: If a user's email contains the content "I have a meeting tomorrow at 10am," the server retrieves this information and uses a natural language processing service to extract the task "Meeting."

[1149] Categorizing and prioritizing tasks

[1150] The server categorizes and prioritizes the extracted tasks and events using algorithms that assess the urgency and importance of the tasks.

[1151] Example: A server categorizes and prioritizes tasks such as "meeting," "lunch," and "presentation" based on urgency and importance.

[1152] Emotional state analysis

[1153] The server uses an emotion engine to analyze the user's emotional state, which is inferred from the user's past behavior and current input data.

[1154] Example: The server analyzes a user's past emails and social media posts to determine their current emotional state as "joy," "sadness," "stress," etc.

[1155] Suggesting the best course of action

[1156] The server considers the user's emotional state and task priorities to suggest optimal actions, including schedule rearrangements that take into account time, travel, and cost efficiency.

[1157] Example: If the server determines that the user is feeling "joy," it will suggest "preparing for next week's presentation." This allows the user to manage their tasks efficiently.

[1158] Prompt Sentence Examples

[1159] Below are some example prompts to input to the generative AI model:

[1160] Prompt: "Please describe the process steps of a system that extracts tasks from a user's email and schedule, and suggests the best course of action based on their emotional state. Include specific examples of using natural language processing services and emotion engines."

[1161] Using this prompt, the generative AI model can generate a detailed description of the system.

[1162] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1163] Step 1: Get the information

[1164] The server retrieves information such as the user's schedule, emails, and documents. As input, it receives data synchronized to the cloud from the user's device. Specifically, the server retrieves data from Google Calendar and email applications. As output, it obtains the retrieved schedule, email, and document data.

[1165] Step 2: Natural Language Processing Analysis

[1166] The server sends the acquired information to a natural language processing service. The schedule, email, and document data acquired in step 1 are used as input. Specifically, the server uses services such as Google Cloud Natural Language API and IBM Watson to extract tasks and events from the text data. The output is a list of the extracted tasks and events.

[1167] Step 3: Categorize and prioritize tasks

[1168] The server categorizes and prioritizes the extracted tasks and events. As input, it uses the list of tasks and events extracted in step 2. Specifically, it applies an algorithm to evaluate the urgency and importance of the tasks. As output, it obtains a list of categorized and prioritized tasks and events.

[1169] Step 4: Analyze emotional state

[1170] The server uses an emotion engine to analyze the user's emotional state. It uses the user's past actions and current input data as input. Specifically, the server analyzes the user's past emails and social media posts to estimate their current emotional state. The output is the user's emotional state (e.g., joy, sadness, stress).

[1171] Step 5: Recommend the best course of action

[1172] The server proposes optimal actions by taking into account the user's emotional state and task priorities. As input, it uses the categorized and prioritized list of tasks and events obtained in step 3 and the user's emotional state obtained in step 4. Specifically, the server rearranges the schedule taking into account time efficiency, travel efficiency, and cost efficiency. As output, it obtains the optimal action plan proposed to the user.

[1173] (Application example 2)

[1174] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1175] Conventional schedule management systems only manage users' schedules and tasks, and are unable to suggest optimal actions that take into account the user's emotional state or real-time circumstances. Furthermore, autonomous vehicles, in particular, require optimal route suggestions based on the user's emotional state and the urgency of tasks, but no system has been able to achieve this. This has resulted in insufficient time management and stress reduction for users.

[1176] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1177] In this invention, the server includes means for comprehensively acquiring information from business software, means for reading a user's schedule based on the acquired information, means for proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency based on the read schedule, means for analyzing the user's emotional state, means for proposing the best course of action taking into consideration the user's emotional state and the urgency and importance of the task, and means for proposing the best route for an autonomous vehicle. This makes it possible to propose optimal actions taking into consideration the user's schedule management and emotional state, and in particular, for an autonomous vehicle, it is possible to propose optimal routes according to the user's emotional state and the urgency of the task.

[1178] "Business software" is software used by companies and organizations to carry out their business efficiently.

[1179] "Means for comprehensively acquiring information" refers to methods and techniques for collecting all necessary information without omission.

[1180] "Means for reading a user's schedule" refers to methods and technologies for analyzing and understanding a user's plans and tasks.

[1181] "Time efficiency" refers to the efficiency of making the most of limited time.

[1182] "Travel efficiency" refers to the efficiency of minimizing travel time and costs.

[1183] "Cost efficiency" refers to efficiency in maximizing cost-effectiveness.

[1184] "Means for suggesting best actions" are methods and techniques for suggesting the most appropriate actions for users.

[1185] "Means for analyzing a user's emotional state" refers to methods or techniques for analyzing a user's emotions and understanding their state.

[1186] "Task urgency" is an indicator of how quickly a task needs to be addressed.

[1187] "Task importance" is an index that indicates how important a task is.

[1188] An "autonomous vehicle" is a vehicle that is capable of driving autonomously without driver intervention.

[1189] "Means for suggesting optimal routes" are methods or technologies for suggesting the most efficient route to reach a destination.

[1190] A system for implementing this invention includes a means for comprehensively acquiring information from business software and reading a user's schedule. Specifically, a server uses natural language processing technologies such as Google Cloud Natural Language API and IBM Watson to extract tasks and events from emails and documents. This allows the user's schedule to be automatically analyzed.

[1191] The server then includes a means for analyzing the user's emotional state. The user's voice and facial expressions are captured by a camera and microphone and analyzed by an emotion engine. This emotion engine grasps the user's emotional state in real time and suggests the best course of action in combination with the urgency and importance of the task.

[1192] The system also includes a means for suggesting the optimal route for the autonomous vehicle. The server obtains real-time traffic information and uses a route optimization algorithm to suggest the optimal route based on the user's emotional state and the urgency of the task, enabling the user to travel efficiently and stress-free.

[1193] As a concrete example, consider a user on their way to an important meeting, but wanting to relax on the way. The server extracts the meeting time and location from the schedule, and the emotion engine determines that the user wants to relax. The route optimization algorithm then suggests a scenic route that will get you to the meeting on time.

[1194] An example of a prompt for a generative AI model is:

[1195] Develop an application that suggests optimal routes based on the user's schedule and emotional state. Use Google Cloud Natural Language API and IBM Watson to extract tasks from schedules and emails, and analyze the user's emotional state with an emotion engine. Use a route optimization algorithm to suggest optimal actions based on real-time traffic information.

[1196] In this way, a smart driving assistant for self-driving vehicles can be realized that takes into account the user's schedule management and emotional state and suggests optimal actions.

[1197] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1198] Step 1:

[1199] The server comprehensively retrieves information from business software. Specifically, it uses Google Cloud Natural Language API and IBM Watson to extract tasks and events from emails and documents. The input is the user's emails and documents, and the output is a list of extracted tasks and events.

[1200] Step 2:

[1201] The server reads the user's schedule based on the acquired information. Specifically, it analyzes the extracted tasks and events and constructs the user's schedule. The input is the list of tasks and events obtained in step 1, and the output is the user's schedule.

[1202] Step 3:

[1203] The server analyzes the user's emotional state. Specifically, it captures the user's voice and facial expressions with a camera and microphone and analyzes them with an emotion engine. The input is the user's voice data and video data, and the output is the user's emotional state.

[1204] Step 4:

[1205] The server proposes the best course of action by taking into account the user's emotional state and the urgency and importance of the task. Specifically, it prioritizes the task by combining the emotional state and the urgency and importance of the task. The inputs are the user's emotional state and schedule, and the output is a prioritized task list.

[1206] Step 5:

[1207] The server proposes the optimal route for the autonomous vehicle. Specifically, it obtains real-time traffic information and calculates the optimal route using a route optimization algorithm. The inputs are the user's schedule, emotional state, and real-time traffic information, and the output is the optimal route.

[1208] Step 6:

[1209] The terminal receives the optimal route proposal from the server and presents it to the user. Specifically, it guides the user along the optimal route through the vehicle's display or voice assistant. The input is the optimal route information from the server, and the output is route guidance to the user.

[1210] In this way, optimal action suggestions are realized that take into account the user's schedule management and emotional state.

[1211] Example 3

[1212] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1213] Conventional schedule management systems are limited to simple task management without taking into account the user's behavioral patterns or emotional state. This makes it difficult to optimize the user's time, travel, and cost efficiency, and they are unable to reduce the user's stress and burden. They also lack the ability to prioritize tasks based on urgency and importance. This means that they provide insufficient support for users in choosing the best course of action.

[1214] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1215] In this invention, the server includes means for comprehensively acquiring information from an information processing device, means for reading a user's schedule based on the acquired information, means for proposing the best course of action taking into account time efficiency, travel efficiency, and cost efficiency based on the read schedule, means for recognizing the user's emotional state and adjusting task priorities based on the emotional state, means for learning the user's behavioral patterns using machine learning technology, and means for proposing the best course of action when a new task or event occurs using the learned model. This enables optimal schedule management taking into account the user's behavioral patterns and emotional state, thereby improving time efficiency, travel efficiency, and cost efficiency and reducing stress and burden on the user.

[1216] An "information processing device" is an electronic device that has functions such as data collection, analysis, storage, and communication.

[1217] "Means for comprehensively acquiring information" refers to a method or device for comprehensively collecting data such as a user's schedule, travel history, and expenditure history.

[1218] "Means for reading a user's schedule" refers to a method or device for analyzing and understanding a user's plans and tasks from the acquired data.

[1219] "Means for proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency" refers to a method or device for calculating and proposing the most efficient course of action based on a user's schedule and behavioral patterns.

[1220] "Means for recognizing the user's emotional state" refers to a method or device for analyzing the user's tone of voice, facial expressions, behavioral patterns, etc. to determine the user's emotions.

[1221] A "means for adjusting task priority" is a method or device for determining the order in which tasks are executed, taking into consideration the emotional state of the user and the urgency and importance of the tasks.

[1222] "Means for learning user behavior patterns using machine learning technology" refers to methods and devices for analyzing user behavior patterns based on past data and building predictive models.

[1223] "Means for using a learned model to suggest the best course of action when a new task or event occurs" refers to a method or device that uses a machine learning model to calculate and suggest the best course of action for a newly input task or event.

[1224] The present invention is a system for optimizing time efficiency, travel efficiency, and cost efficiency by taking into account the behavioral patterns and emotional state of a user. Specific embodiments of this system will be described below.

[1225] System configuration

[1226] The server is equipped with a means for comprehensively acquiring information from information processing devices. Specifically, it collects calendar app data, GPS data, electronic money usage history, etc. from users' smartphones and PCs. It also includes a means for reading tasks from emails and documents.

[1227] The server has a means for reading the user's schedule based on the acquired information, which allows it to analyze and understand the user's plans and tasks.

[1228] Furthermore, the server is equipped with a means to suggest the best course of action based on the read schedule, taking into account time efficiency, travel efficiency, and cost efficiency. Specifically, it uses machine learning technology to learn the user's behavioral patterns, and uses the learned model to suggest the best course of action when a new task or event occurs.

[1229] Hardware and software used

[1230] This system uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns and build predictive models. It also incorporates an emotion engine to analyze the user's tone of voice, facial expressions, and behavioral patterns to recognize their emotional state.

[1231] Specific examples

[1232] If the user inputs "I want to optimize tomorrow's schedule," the server processes it as follows:

[1233] 1. Collect users' past schedule data, travel history, and expenditure history.

[1234] 2. The server preprocesses this data, imputes missing values, and removes outliers.

[1235] 3. Use TensorFlow and PyTorch to analyze this data and learn user behavior patterns.

[1236] 4. A user enters a task to "attend a meeting tomorrow."

[1237] 5. The server uses the trained model to suggest the optimal route and departure time, taking into account the meeting location and time, the user's current location, transportation method, etc.

[1238] 6. The server analyzes the user's tone of voice and facial expressions, and if the user is feeling stressed, it will prioritize tasks that promote relaxation.

[1239] Prompt Sentence Examples

[1240] "I'd like you to optimize my schedule for tomorrow. My meeting starts at 10:00, lunch starts at 12:00, and I'll be using public transportation to get around."

[1241] In this way, the server provides a system that suggests optimal actions based on user input. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1242] Step 1:

[1243] The server collects user data. Specifically, it obtains calendar app data, GPS data, electronic money usage history, etc. from the user's smartphone or PC. The input includes data from the user's various devices. The output is stored on the server.

[1244] Step 2:

[1245] The server preprocesses the collected data by imputing missing values ​​and removing outliers. It also standardizes the data and converts it into a format suitable for machine learning models. The input includes the data collected in step 1. The output is the preprocessed data.

[1246] Step 3:

[1247] The server uses the preprocessed data to train a machine learning model. It uses TensorFlow or PyTorch to learn user behavior patterns. The input includes the preprocessed data. The output is a trained model.

[1248] Step 4:

[1249] A user inputs a new task or event into the system. For example, the user inputs a task such as "Attend a meeting tomorrow." The input includes the task or event entered by the user. The output is the task information sent to the server.

[1250] Step 5:

[1251] The server uses the trained model to suggest optimal actions to the user. Specifically, it calculates the optimal travel route and departure time by taking into account the location and time of the meeting, the user's current location, and the mode of transportation. The input includes the task information entered by the user and the trained model. The output is the optimal action suggestion.

[1252] Step 6:

[1253] The server uses an emotion engine to recognize the user's emotional state. Specifically, it analyzes the user's tone of voice, facial expressions, behavioral patterns, etc. Inputs include the user's audio and video data. The output is the user's emotional state.

[1254] Step 7:

[1255] The server adjusts task priorities based on the user's emotional state. For example, if the server determines that the user is feeling stressed, it will prioritize tasks that promote relaxation. Inputs include the user's emotional state and task information. The output is the adjusted task priorities.

[1256] (Application example 3)

[1257] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1258] Conventional business software and schedule management systems are limited to simple task management and schedule suggestions without taking into account the user's behavioral patterns or emotional state. This makes it difficult to optimize the user's time, travel, and cost efficiency, and they are unable to respond appropriately, especially in stressful situations. Furthermore, certain tasks, such as food delivery, require optimization of delivery routes and timing, but conventional systems were unable to adequately address this.

[1259] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1260] In this invention, the server includes means for comprehensively acquiring information from business software, means for reading a user's schedule based on the acquired information, means for proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency based on the read schedule, means for recognizing the user's emotional state and adjusting task priorities based on the emotional state, means for collecting data such as delivery routes, delivery times, and break times and proposing optimal delivery routes and timings, means for analyzing the user's tone of voice and facial expressions to recognize the user's emotional state, and means for suggesting breaks when the emotional state is high in stress. This enables optimal schedule management and action suggestions that take into consideration the user's behavioral patterns and emotional state, enabling efficient and stress-free work, particularly in specific tasks such as food delivery.

[1261] "Business software" is software used by companies and organizations to carry out their business efficiently.

[1262] "Means for comprehensively acquiring information" refers to methods and techniques for collecting all necessary information without omission.

[1263] "Means for reading the user's schedule" refers to methods and technologies for understanding the user's plans and tasks.

[1264] "Time efficiency" refers to the efficiency of making the most of limited time.

[1265] "Travel efficiency" refers to the efficiency of minimizing travel time and costs.

[1266] "Cost efficiency" refers to efficiency in maximizing cost-effectiveness.

[1267] "Means for suggesting best actions" are methods and techniques for suggesting the most appropriate actions for users.

[1268] "Means for recognizing a user's emotional state" refers to methods or techniques for understanding a user's emotions.

[1269] A "means for adjusting task priorities" is a method or technique for setting priorities based on the importance or urgency of tasks.

[1270] "Delivery route" refers to the route taken when making a delivery.

[1271] "Delivery Time" means the time required to make a Delivery.

[1272] "Break time" refers to the time taken to rest between work tasks.

[1273] "Means of collecting data" are the methods and techniques used to collect the necessary data.

[1274] "Tone of voice" refers to the vocal characteristics that express the pitch, intensity, and emotion of the speaker's voice.

[1275] "Facial expression" refers to emotions and intentions expressed through the movement of facial muscles.

[1276] The "means for suggesting a break when the emotional state is high stress" is a method or technology for recommending a break when the user is determined to be in a high stress state.

[1277] As an embodiment of the present invention, the following system is constructed.

[1278] System Overview

[1279] The system comprehensively retrieves information from business software to read the user's schedule, and then suggests the best course of action, taking into account time, travel, and cost efficiency. It also recognizes the user's emotional state and adjusts task priorities based on that emotional state. It also collects data such as delivery routes, delivery times, and break times to suggest optimal delivery routes and timings.

[1280] Hardware and software used

[1281] Hardware: Smartphones, smart glasses, head-mounted displays

[1282] Software: TensorFlow, Python, speech recognition software, facial recognition software

[1283] Data processing and calculation

[1284] Data collection

[1285] The server comprehensively retrieves information such as emails and documents from the business software, collecting data to understand the user's schedule and tasks, as well as data such as delivery routes, delivery times, and break times.

[1286] Data Preprocessing

[1287] The server preprocesses the collected data and converts it into a format suitable for machine learning models, specifically by cleaning and normalizing text data and formatting time series data.

[1288] Model training

[1289] The server uses TensorFlow and PyTorch to learn user behavior patterns, processes time-series data using LSTM (Long Short-Term Memory), and builds a model to predict optimal behavior.

[1290] Emotion Engine

[1291] The server uses voice and facial recognition software to analyze the user's tone of voice and facial expressions to recognize their emotional state, and if the emotional state indicates high stress, suggests taking a break.

[1292] Specific examples

[1293] For example, if a food delivery driver is feeling stressed, the server will analyze their voice tone and facial expression data to suggest they take a break. If their delivery route is congested, the server will suggest an optimal alternative route.

[1294] Prompt Sentence Examples

[1295] Below are some example prompts to input to the generative AI model:

[1296] Develop an application that learns the behavioral patterns of delivery personnel and suggests optimal delivery routes and timings. Also incorporate a function to recognize the emotional state of delivery personnel by analyzing their tone of voice and facial expressions. It is also necessary to have a function to suggest breaks when stress levels are high.

[1297] In this way, optimal schedule management and action suggestions can be made possible, taking into account the user's behavioral patterns and emotional state, enabling efficient and stress-free work, especially in specific tasks such as food delivery.

[1298] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1299] Step 1:

[1300] The server comprehensively obtains information from business software. Specifically, it collects data such as emails and documents and extracts information about the user's schedule and tasks. The input is data from the business software, and the output is the extracted schedule and task information.

[1301] Step 2:

[1302] The server preprocesses the acquired information. Specifically, it cleans and normalizes the text data and converts it into a format suitable for machine learning models. The input is the extracted schedule and task information, and the output is the preprocessed data.

[1303] Step 3:

[1304] The server uses the preprocessed data to learn user behavior patterns. It builds an LSTM model using TensorFlow or PyTorch to process time-series data. The input is the preprocessed data, and the output is the learned behavior pattern model.

[1305] Step 4:

[1306] The server uses voice recognition software and facial recognition software to analyze the user's tone of voice and facial expressions to recognize their emotional state. The input is the user's voice data and image data, and the output is the recognized emotional state.

[1307] Step 5:

[1308] The server proposes optimal actions based on the learned behavioral pattern model and the recognized emotional state. Specifically, it proposes the most appropriate action for the user in real time, taking into account time efficiency, travel efficiency, and cost efficiency. The input is the behavioral pattern model and the emotional state, and the output is the proposed action.

[1309] Step 6:

[1310] The server collects data such as delivery routes, delivery times, and break times, and proposes optimal delivery routes and timings. The input is delivery-related data, and the output is the optimal delivery route and timing.

[1311] Step 7:

[1312] The server suggests a break if the emotional state is high stress. Specifically, it analyzes voice tone and facial expression data and recommends a break if it determines that the user is in a high stress state. The input is emotional state data, and the output is a break suggestion.

[1313] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1314] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1315] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1316] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1317] [Third embodiment]

[1318] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1319] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1320] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1321] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1322] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1323] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1324] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1325] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1326] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1327] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1328] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1329] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1330] "Example 1"

[1331] The system of the present invention uses APIs and scraping technology as a means of comprehensively acquiring information from business groupware. Specifically, it uses the APIs of business groupware such as Google Calendar and Microsoft Outlook to acquire information such as users' schedules, emails, and documents. The acquired information is stored in a database and used as input for analysis.

[1332] "Example 2"

[1333] The system of the present invention uses natural language processing technology as a means of reading the user's schedule based on the acquired information. Specifically, it uses natural language processing services such as Google Cloud Natural Language API and IBM Watson.

[1334] It extracts tasks and events from schedules, emails, and documents, which are then used as input to suggest the best course of action, taking into account time, travel, and cost efficiency.

[1335] "Example 3"

[1336] The system of the present invention uses machine learning technology as a means of proposing the best course of action that takes into account time, travel, and cost efficiency. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns. The learned model is then used to suggest the best course of action when a new task or event occurs.

[1337] The processing flow of each embodiment will be described below.

[1338] "Example 1"

[1339] Step 1: The system uses the APIs of business groupware such as Google Calendar and Microsoft Outlook to obtain information such as users' schedules, emails, and documents.

[1340] Step 2: The acquired information is stored in a database, which is used as input for analysis.

[1341] "Example 2"

[1342] Step 1: The system uses natural language processing services such as Google Cloud Natural Language API and IBM Watson to analyze schedules and messages.

[1343] Extract tasks and events from rules and documents.

[1344] Step 2: The extracted tasks and events are used as input to suggest the best course of action, taking into account time, travel, and cost efficiency.

[1345] "Example 3"

[1346] Step 1: The system uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns.

[1347] Step 2: The trained model is used to suggest the best course of action when a new task or event arises.

[1348] Example 1

[1349] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1350] With traditional business software, it was difficult to efficiently manage users' schedules and tasks and suggest the best course of action. Furthermore, information acquisition, storage, and analysis were often done manually, which was time-consuming and labor-intensive. Furthermore, analysis based on the acquired information was insufficient, making it difficult to optimize users' time, travel, and cost efficiency.

[1351] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1352] In this invention, the server includes a means for comprehensively acquiring information from business software, a means for storing the acquired information in a database, and a means for analyzing the stored information, thereby enabling efficient management of user schedules and tasks and proposing optimal actions that take into account time efficiency, travel efficiency, and cost efficiency.

[1353] "Business software" is software used by companies and organizations to carry out their business efficiently.

[1354] "Means for comprehensively acquiring information" refers to methods and technologies for comprehensively collecting necessary information from business software.

[1355] "Means for storing in a database" refers to the methods and techniques used to store the acquired information in a structured format in a database.

[1356] "Analysis tools" are the methods and techniques used to analyze stored information and extract meaningful data and insights.

[1357] A "means for reading a user's schedule" is a method or technique for extracting a user's plans and tasks from the acquired and stored information.

[1358] "Time efficiency" refers to the efficiency with which users can make optimal use of their time.

[1359] "Travel efficiency" refers to the efficiency of optimizing the time and cost required for users to travel.

[1360] "Cost efficiency" refers to efficiency in minimizing the costs involved in conducting business for users.

[1361] "Best action suggestion" refers to methods and technologies for suggesting the most effective action for a user based on the analysis results.

[1362] "Email" is a digital message sent and received over the Internet.

[1363] A "document" is a digital file that contains information such as text and images.

[1364] A "task" refers to a specific task or activity that a user must perform.

[1365] "Urgency" is an index that indicates how urgently the task needs to be performed.

[1366] "Importance" is an indicator of how important a task is to achieving the user's goals.

[1367] A "prioritization method" is a method or technique for determining the order of execution of tasks, taking into account the urgency and importance of the tasks.

[1368] MODE FOR CARRYING OUT THE INVENTION

[1369] The present invention is a system for comprehensively acquiring information from business software, storing the acquired information in a database, and analyzing the stored information. A specific embodiment of this system will be described below.

[1370] 1. Program Generation

[1371] The server generates a program to comprehensively retrieve information from business software. This program uses APIs and scraping technology to retrieve data from the business software. Specifically, it uses APIs such as Google Calendar and Microsoft Outlook.

[1372] 2. Program Processing

[1373] The server uses the APIs of Google Calendar and Microsoft Outlook to obtain information such as user schedules, emails, documents, etc. Below, we will clearly explain the hardware and software used and the methods of data processing and calculation for each.

[1374] API authentication: The server obtains credentials to access the Google Calendar or Microsoft Outlook API using an authentication protocol such as OAuth 2.0.

[1375] Data retrieval: The server uses the authentication information to send a request to the API to retrieve information such as the user's schedule, emails, documents, etc. For example, it uses the Google Calendar API to retrieve the user's schedule.

[1376] Data storage: The acquired information is stored in a database on the server, using a relational database such as MySQL or PostgreSQL.

[1377] Data analysis: The stored data is used as input for analysis, which can be done using programming languages ​​such as Python or R.

[1378] 3. Specific Examples

[1379] As a specific example, consider the case where a user's schedule is retrieved from Google Calendar.

[1380] The server first performs authentication using OAuth 2.0 to obtain the user's schedule using the Google Calendar API.

[1381] If authentication is successful, the server obtains an access token and uses it to make requests to Google Calendar's API endpoints.

[1382] The server parses the JSON response returned from the API and extracts the user's schedule information.

[1383] The extracted schedule information is stored in the server's MySQL database.

[1384] The saved schedule data is converted into a data frame on the server using Python's Pandas library and used for statistical analysis and training of machine learning models.

[1385] Prompt Sentence Examples

[1386] "Use the Google Calendar API to retrieve users' schedules and store them in a MySQL database. Then, analyze the stored data using Python's Pandas library."

[1387] In this way, the server realizes a system that obtains information from business software, stores it in a database, and uses it as input for analysis.

[1388] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1389] Step 1:

[1390] API Authentication

[1391] The server obtains authentication information to access the business software's API. As input, it uses authentication information provided by the user (client ID, client secret, etc.). The server uses this authentication information to send an authentication request to the API provider and obtains an access token as output. Specifically, the server performs authentication using the OAuth 2.0 protocol.

[1392] Step 2:

[1393] Data Acquisition

[1394] The server uses the acquired access token to send a request to the API to retrieve information such as the user's schedule, emails, and documents. The access token and the API endpoint URL are used as input. The server receives the JSON-formatted response returned from the API and extracts the necessary information as output. Specifically, the server sends a request to the Google Calendar API endpoint to retrieve the user's schedule.

[1395] Step 3:

[1396] Data storage

[1397] The server stores the retrieved information in a database. As input, it uses the parsed JSON data. The server converts this into a SQL query and inserts it into the database. As output, it gets the information stored in the database. Specifically, the server connects to the MySQL database and executes an INSERT statement to save the data.

[1398] Step 4:

[1399] Data analysis

[1400] The server analyzes the stored data. It uses data retrieved from the database as input. The server converts this data into a data frame using Python's Pandas library, and performs statistical analysis and trains machine learning models. The output is analysis results and predictive models. Specifically, the server executes SQL queries to retrieve data, converts it into a Pandas data frame, and performs analysis.

[1401] Step 5:

[1402] action suggestion

[1403] The server suggests the best course of action to the user based on the analysis results. It uses the results of the data analysis as input. The server calculates the optimal course of action taking into account time efficiency, travel efficiency, and cost efficiency, and makes a suggestion to the user as output. Specifically, the server runs an algorithm based on the analysis results and sends a notification to the user.

[1404] (Application example 1)

[1405] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1406] Conventional factory robot scheduling is often done manually, making it difficult to optimize work schedules. Furthermore, information acquired from business software is limited and rarely reflected in robot work schedules. This results in reduced time, travel, and cost efficiency, resulting in a loss of overall productivity.

[1407] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1408] In this invention, the server includes means for comprehensively acquiring information from business software, means for reading a user's schedule based on the acquired information, means for proposing the best course of action based on the read schedule, taking into consideration time efficiency, travel efficiency, and cost efficiency, means for optimizing machine work schedules based on the acquired schedule information, and means for saving the optimized schedule in a database. This makes it possible to automatically optimize the work schedules of factory robots and improve overall productivity.

[1409] "Business software" is software used by companies and organizations to carry out their business efficiently.

[1410] "Means for comprehensively acquiring information" refers to methods and technologies for acquiring all necessary information from business software.

[1411] "Means for reading the user's schedule" refers to methods and technologies for analyzing and understanding the user's plans and tasks based on the information obtained.

[1412] "Time efficiency" refers to the efficiency of minimizing wasted time when performing work or tasks.

[1413] "Travel efficiency" refers to the efficiency of minimizing travel time and costs when performing work or tasks.

[1414] "Cost efficiency" refers to the efficiency of minimizing costs when performing work or tasks.

[1415] "Best action suggestion" refers to methods and techniques for suggesting the most efficient and effective action for the user based on the information obtained.

[1416] "Means for optimizing machine work schedules" refers to methods and techniques for adjusting machine work schedules most efficiently based on acquired schedule information.

[1417] "Means for storing in a database" refers to methods and techniques for recording and managing the acquired information and optimized schedules in a database.

[1418] The system for implementing this invention comprehensively acquires information from business software, reads the user's schedule based on the acquired information, and proposes optimal actions. It also has the function of optimizing machine work schedules based on the acquired schedule information and saving the optimized schedule in a database.

[1419] The server obtains information from business software (e.g., Google Calendar, Microsoft Outlook) using APIs. The obtained information is used to analyze the user's schedule and tasks. The analyzed information is used as data to suggest optimal actions, taking into account time efficiency, travel efficiency, and cost efficiency.

[1420] Specifically, the server does the following:

[1421] 1. Data Acquisition: Use the Google Calendar API or Microsoft Outlook API to obtain user schedule information.

[1422] 2. Data processing: Analyze the acquired schedule information and convert it into the required format.

[1423] 3. Schedule optimization: Optimize the work schedule of factory robots based on the acquired schedule information.

[1424] 4. Data storage: The optimized schedule is stored in the database.

[1425] The hardware used is a factory robot and server, and the software uses the Google Calendar API, Microsoft Outlook API, Python, and database modules (e.g., SQLite, PostgreSQL).

[1426] For example, a robot in a factory can automatically start and finish work based on a schedule retrieved from Google Calendar, and the robot can also automatically incorporate maintenance times into its schedule.

[1427] An example of a prompt to input to a generative AI model is as follows:

[1428] "Write a Python program that optimizes the work schedules of factory robots based on schedule information retrieved from Google Calendar. The program will retrieve the schedule information using the Google Calendar API and compare it with the current robot schedule stored in a database to optimize it."

[1429] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1430] Step 1:

[1431] The server uses the API of business software (e.g., Google Calendar, Microsoft Outlook) to obtain the user's schedule information. Specifically, it authenticates using the API key and requests the schedule information. The input is the API key and user ID, and the output is the user's schedule information.

[1432] Step 2:

[1433] The server analyzes the acquired schedule information and converts it into the required format. Specifically, it extracts the start time, end time, and event summary of the schedule information, and formats it into a format that can be saved in the database. The input is the acquired schedule information, and the output is the formatted schedule data.

[1434] Step 3:

[1435] The server optimizes the work schedules of the factory robots based on the formatted schedule data. Specifically, it compares the current robot schedule with the newly acquired schedule information and calculates the optimal work sequence and timing. The input is the formatted schedule data and the current robot schedule, and the output is the optimized robot schedule.

[1436] Step 4:

[1437] The server saves the optimized robot schedule to the database by establishing a database connection and inserting or updating the optimized schedule data. The input is the optimized robot schedule, and the output is the result saved to the database.

[1438] Step 5:

[1439] The server sends work instructions to the factory robots based on the saved optimized schedule. Specifically, it sends instructions to the robot control system using an API or dedicated protocol. The input is the optimized robot schedule, and the output is the robot's start of work.

[1440] Step 6:

[1441] Users monitor the work status of factory robots and make adjustments as necessary. Specifically, they use a dedicated monitoring tool to check the robot's status and manually intervene if an abnormality occurs. The input is the robot's work status, and the output is the work status after adjustments.

[1442] Example 2

[1443] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1444] Conventional schedule management systems require users to manually input and manage their schedules, which is time-consuming and labor-intensive. Furthermore, they lack sufficient schedule optimization and efficient action suggestions, making it difficult to maximize users' time, travel, and cost efficiency. Furthermore, it is difficult to integrate and process data from multiple sources, which can lead to inconsistencies in users' schedule management.

[1445] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1446] In this invention, the server includes a means for comprehensively acquiring information from an information processing device, a means for reading a user's schedule based on the acquired information, a means for extracting tasks and events from schedules, correspondence, and documents using natural language processing technology, a means for proposing optimal actions based on the read schedule, taking into account time efficiency, travel efficiency, and cost efficiency, and a means for calculating travel time between the extracted tasks and events. This allows the user to receive efficient action suggestions without having to manually input the schedule. Furthermore, by integrating and processing data from multiple information sources, consistent schedule management can be achieved.

[1447] An "information processing device" is an electronic device for inputting, processing, storing, and outputting data.

[1448] "Means for comprehensively acquiring information" refers to methods and technologies for comprehensively collecting necessary data from multiple sources.

[1449] The "means for reading the user's schedule" refers to a method or technology for analyzing and extracting data related to the user's schedule from the acquired information.

[1450] "Natural language processing technology" is a technology that allows computers to understand, interpret, and generate human language.

[1451] "Means for extracting tasks and events from schedules, correspondence, and documents" refers to methods and techniques for identifying and extracting tasks and events from text data using natural language processing techniques.

[1452] "Time efficiency" refers to the efficiency of achieving maximum results within a given time.

[1453] "Travel efficiency" refers to the efficiency of minimizing travel time and costs and selecting the most optimal route and means.

[1454] "Cost efficiency" refers to efficiency in maximizing cost-effectiveness.

[1455] A "best action suggester" is a method or technique for recommending the most efficient and effective action based on a user's schedule and tasks.

[1456] A "means for calculating travel time" is a method or technology for calculating the time it takes to travel between different locations.

[1457] The present invention is a system for improving the efficiency of a user's schedule management and proposing optimal actions. Specific embodiments of this system will be described below.

[1458] System configuration

[1459] Hardware

[1460] Terminal: A device that allows a user to input information, such as a smartphone, tablet, or computer.

[1461] Server: A central processing unit for processing information and suggesting optimal actions.

[1462] software

[1463] Natural language processing services: Use natural language processing technologies such as Google Cloud Natural Language API and IBM Watson.

[1464] Database: Data storage for saving user schedule information and tasks.

[1465] API Services: External services for calculating travel times, such as the Google Maps API.

[1466] Program processing

[1467] Enter and submit information

[1468] A user uses a device to input information such as schedules, emails, documents, etc. For example, "Meeting tomorrow at 10, followed by lunch meeting at 12." The device then sends this information to the server. Specifically, the device uses an HTTP request to send the input information to the server.

[1469] Performing natural language processing

[1470] The server then sends the received information to the Google Cloud Natural Language API, which creates an API request and sends it to the API to parse the input text. The Google Cloud Natural Language API then parses the text and extracts information related to the task or event.

[1471] Extracting tasks and events

[1472] The server receives the analysis results returned by the Google Cloud Natural Language API. The analysis results include important tasks and events in the text. For example, "Meeting tomorrow at 10:00" and "Lunch meeting at 12:00" are extracted. The server stores this information in a database.

[1473] Best course of action

[1474] The server then suggests the best course of action for the user based on the extracted tasks and events. Specifically, the server uses the Google Maps API to calculate the travel time between each task and event. For example, the server might generate a suggestion such as, "Taking into account the travel time to the lunch meeting location after the meeting, we recommend leaving at 11:45." The suggestion is then sent to the device and displayed to the user.

[1475] Specific examples

[1476] Example 1: Analyzing a schedule

[1477] A user types "Meeting tomorrow at 10am, followed by lunch meeting at 12pm" into their device. The device sends this information to the server, which uses the Google Cloud Natural Language API to parse it and extract the following tasks:

[1478] Meeting tomorrow at 10 o'clock

[1479] Lunch meeting at 12 o'clock

[1480] Example 2: Best course of action

[1481] Based on the extracted tasks, the server makes a suggestion to the user, such as "Taking into account the travel time to the lunch meeting location after the meeting, it is recommended that you leave at 11:45." The suggestion is sent to the terminal and displayed to the user.

[1482] Prompt Sentence Examples

[1483] Examples of prompts to input to a generative AI model might include:

[1484] Enter the user's schedule information. For example, "Meeting tomorrow at 10am, followed by lunch at 12pm."

[1485] Using this prompt, users can input schedule information in natural language, and the system can suggest optimal actions based on that information.

[1486] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1487] Step 1:

[1488] A user inputs schedule information into a terminal. For example, "Meeting tomorrow at 10 o'clock, followed by a lunch meeting at 12 o'clock." The terminal sends this information to a server. Specifically, the terminal uses an HTTP request to send the input information to the server. The input is the user's schedule information, and the output is the data to be sent to the server.

[1489] Step 2:

[1490] The server sends the received information to the natural language processing service. Specifically, the server creates an API request and sends it to the natural language processing service for analysis of the input text. The input is the schedule information received from the device, and the output is the request data for the natural language processing service.

[1491] Step 3:

[1492] The natural language processing service analyzes the received text and extracts information related to tasks and events. For example, "Meeting tomorrow at 10:00" and "Lunch meeting at 12:00" are extracted. The input is text data sent from the server, and the output is the task or event information resulting from the analysis.

[1493] Step 4:

[1494] The server receives the analysis results returned from the natural language processing service. The analysis results include important tasks and events in the text. The server stores this information in a database. The input is the analysis results from the natural language processing service, and the output is the task and event information stored in the database.

[1495] Step 5:

[1496] The server proposes the best course of action to the user based on the extracted tasks and events. Specifically, the server uses an external API service to calculate travel time. For example, the server generates a proposal such as, "Taking into account the travel time to the lunch meeting location after the meeting, it is recommended that you leave at 11:45." The input is task and event information stored in the database, and the output is a proposed action to the user.

[1497] Step 6:

[1498] The server sends the generated action suggestions to the terminal. The terminal displays these suggestions to the user. Specifically, the server uses an HTTP response to send the action suggestions to the terminal. The input is the action suggestions generated by the server, and the output is the data sent to the terminal. The user can check the suggestions through the terminal and take the optimal action.

[1499] (Application example 2)

[1500] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1501] Conventional schedule management systems have difficulty efficiently acquiring users' schedule information and suggesting optimal actions. Furthermore, especially for autonomous vehicles, they lack the functionality to calculate and suggest optimal routes based on the user's schedule. This has led to problems such as reduced time efficiency, travel efficiency, and cost efficiency for users.

[1502] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1503] In this invention, the server includes: means for comprehensively acquiring information from business software; means for reading a user's schedule based on the acquired information; means for proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency based on the read schedule; means for being installed in an autonomous vehicle and extracting tasks and events from the user's schedule information and calculating the optimal route; and means for proposing the optimal route based on the extracted tasks and events. This makes it possible to efficiently acquire a user's schedule information and propose optimal actions and routes.

[1504] "Business software" is software used by companies and organizations to carry out their business efficiently.

[1505] "Means for comprehensively acquiring information" refers to methods and techniques for collecting all necessary information without omission.

[1506] "Means for reading the user's schedule" refers to methods and technologies for analyzing and understanding the schedules and tasks set by the user.

[1507] "Time efficiency" refers to the efficiency of making the most of limited time.

[1508] "Travel efficiency" refers to the efficiency of optimizing travel to a destination.

[1509] "Cost efficiency" refers to the efficiency of minimizing costs while maximizing benefits.

[1510] A "best action suggestion" is a method or technique for suggesting the most effective action for a user.

[1511] An "autonomous vehicle" is a vehicle that can navigate autonomously without driver intervention.

[1512] "Means for extracting tasks and events" refers to methods and techniques for extracting specific tasks and events from schedule information.

[1513] "Means for calculating optimal routes" refers to methods or techniques for calculating the most efficient route to a destination.

[1514] "Means for suggesting optimal routes" refers to methods or technologies for showing the calculated optimal route to the user.

[1515] A system for implementing this invention includes means for comprehensively acquiring information from business software and reading a user's schedule. It also includes means for proposing the best course of action based on the read schedule, taking into consideration time efficiency, travel efficiency, and cost efficiency. It also includes means installed in an autonomous vehicle for extracting tasks and events from the user's schedule information and calculating the optimal route, and means for proposing the optimal route based on the extracted tasks and events.

[1516] Hardware and software used

[1517] Hardware: On-board computers for autonomous vehicles

[1518] Software: Google Cloud Natural Language API, Python

[1519] Data processing and calculation

[1520] The server retrieves information from business software, specifically using the Google Cloud Natural Language API to extract tasks and events from emails and documents, and then parses the information into the user's schedule.

[1521] Based on the analyzed schedule information, the device suggests the best course of action, taking into account time, travel, and cost efficiency, including prioritizing tasks based on their urgency and importance.

[1522] The autonomous vehicle calculates the optimal route based on the user's schedule information, and the calculated route is suggested to the user, allowing the user to travel efficiently.

[1523] Specific examples

[1524] For example, if a user has a schedule that says, "Tomorrow I have a meeting at 10:00, lunch at 12:00, and a presentation at 15:00," the server sends this information to the Google Cloud Natural Language API to extract tasks and events. The extracted tasks (meeting, lunch, presentation) are analyzed on the device, and the optimal route is calculated. The calculated route is then suggested to the user as "Optimal route: Meeting -> Lunch -> Presentation."

[1525] Prompt Sentence Examples

[1526] Enter the user's schedule information. For example, "Tomorrow I have a meeting at 10:00, lunch at 12:00, and a presentation at 15:00."

[1527] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1528] Step 1:

[1529] The server comprehensively retrieves information from the business software. Specifically, it uses the Google Cloud Natural Language API to extract tasks and events from emails and documents. It receives business software data as input and generates a list of tasks and events as output.

[1530] Step 2:

[1531] The server reads the user's schedule based on the acquired information. It receives a list of tasks and events as input and generates the user's schedule information as output. Specifically, it analyzes the schedule information using natural language processing technology.

[1532] Step 3:

[1533] The device then proposes the best course of action based on the schedule information it reads, taking into account time, travel, and cost efficiency. It receives the user's schedule information as input and generates the best course of action as output. Specifically, it prioritizes tasks by taking into account their urgency and importance.

[1534] Step 4:

[1535] The autonomous vehicle extracts tasks and events from the user's schedule information and calculates the optimal route. It receives the user's schedule information as input and generates the optimal route as output. Specifically, it calculates the optimal route using a map API.

[1536] Step 5:

[1537] The autonomous vehicle proposes the calculated optimal route to the user. It receives the optimal route information as input and generates a route proposal to present to the user as output. Specifically, it shows the route to the user through a display and voice guidance.

[1538] Example 3

[1539] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1540] Conventional systems have had difficulty efficiently learning users' behavioral patterns and suggesting optimal actions that take into account time, travel, and cost efficiency. Furthermore, they lacked support for users to take optimal actions due to insufficient prioritization based on task urgency and importance.

[1541] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1542] In this invention, the server includes means for comprehensively acquiring data from the information processing device, means for learning the user's behavioral patterns based on the acquired data, means for proposing the best action taking into consideration time efficiency, travel efficiency, and cost efficiency based on the learned behavioral patterns, and means for presenting the proposed action plan to the user. This makes it possible to efficiently learn the user's behavioral patterns and propose optimal actions.

[1543] An "information processing device" is a device for collecting, processing, storing, and transmitting data.

[1544] "Data" refers to information necessary to learn user behavior patterns, and includes emails, documents, travel history, schedule information, and the like.

[1545] A "behavioral pattern" is a collection of data that indicates the tendencies and habits of a user's past behavior.

[1546] "Time efficiency" refers to minimizing the time required for a user to complete a task or event.

[1547] "Travel efficiency" refers to the user's selection of the optimal means of transportation and route to reach their destination.

[1548] "Cost-effective" refers to minimizing the expense required for a user to complete a task or event.

[1549] "Best action" refers to the most appropriate action for a user, taking into consideration time efficiency, travel efficiency, and cost efficiency.

[1550] "Suggestion" refers to showing the user the optimal plan of action based on learned behavioral patterns.

[1551] A "task" refers to a specific task or activity that a user must perform.

[1552] "Urgency" is an indicator of how quickly a task needs to be addressed.

[1553] "Importance" is an indicator of how important a task is to the user.

[1554] "Priority" is the order used to determine which of multiple tasks should be performed first.

[1555] The present invention is a system that learns a user's behavioral patterns and proposes the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency. This system comprehensively acquires data from an information processing device, learns the user's behavioral patterns based on the acquired data, and proposes the best course of action based on the learned behavioral patterns.

[1556] Server Roles

[1557] The server first comprehensively acquires data from the information processing device. Specifically, it collects data such as the user's movement history, schedule information, emails, documents, etc. This data is stored in a database and used for subsequent processing.

[1558] The server then preprocesses the acquired data, which includes imputing missing values, normalizing data, encoding categorical data, etc. The preprocessed data is then used to train machine learning models.

[1559] The server uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns based on preprocessed data. The learned model is then used to predict user behavior patterns and suggest optimal actions.

[1560] Finally, the server deploys the trained model to the device, allowing the device to use the trained model to suggest optimal actions for the user.

[1561] Device Role

[1562] The device receives the trained model sent from the server, which enables the device to suggest optimal actions for new tasks and events.

[1563] When a user inputs new task or event information, the device inputs that information into the trained model. For example, if a user inputs "What is the best route to get to a meeting tomorrow?", the device inputs that information into the model and generates the best route.

[1564] The generated action plan is then proposed to the user via the terminal, who can then confirm the proposed action plan and act accordingly.

[1565] User Roles

[1566] Users input new task or event information into the device, such as "optimize my shopping list this week," and the device feeds that information into the trained model to generate an optimal plan of action.

[1567] The user can then view the proposed action plan on the device and act accordingly, for example, by following the proposed travel route to the destination.

[1568] Specific examples

[1569] Example 1

[1570] The user types into the device, "Tell me the best route to get to tomorrow's meeting." The device inputs this information into the trained model and suggests the best route. For example, it might suggest a specific route such as "30 minutes by train, 10 minutes by bus, and 5 minutes on foot."

[1571] Example 2

[1572] The user inputs "optimize this week's shopping list" into the device. The device inputs this information into the trained model and suggests the optimal shopping route and timing. For example, the device might make a specific suggestion such as "Buy vegetables at supermarket A on Monday and meat at supermarket B on Wednesday."

[1573] Prompt Sentence Examples

[1574] "What's the best route to get to tomorrow's meeting?"

[1575] "Optimize my shopping list this week"

[1576] "Suggest the best travel plan for your next vacation"

[1577] The above is an embodiment of the system of the present invention. This system allows users to efficiently plan their activities, optimizing time efficiency, travel efficiency, and cost efficiency. The flow of the identification process in Example 3 will be described with reference to FIG. 15.

[1578] Step 1: Data collection

[1579] The server comprehensively acquires data from the information processing device. Specifically, it collects data such as the user's movement history, schedule information, emails, documents, etc. This data is then stored in a database.

[1580] Input: User travel history, schedule information, emails, documents

[1581] Output: User behavior data stored in a database

[1582] Step 2: Data Preprocessing

[1583] The server preprocesses the collected data, specifically imputing missing values, normalizing the data, encoding categorical data, etc. The preprocessed data is then used to train machine learning models.

[1584] Input: User behavior data stored in a database

[1585] Output: Preprocessed data

[1586] Step 3: Model training

[1587] The server uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns based on preprocessed data. The learned model is then used to predict user behavior patterns and suggest optimal actions.

[1588] Input: Preprocessed data

[1589] Output: Trained model

[1590] Step 4: Model evaluation

[1591] The server evaluates the performance of the trained model. Specifically, it calculates the model's precision, recall, F1 score, etc. using test data. Based on the evaluation results, it retrains the model or adjusts its hyperparameters as necessary.

[1592] Input: trained model, test data

[1593] Output: Model evaluation results

[1594] Step 5: Model deployment

[1595] The server deploys the evaluated model to the device, allowing the device to use the trained model to suggest optimal actions to the user.

[1596] Input: Evaluated model

[1597] Output: The trained model deployed to the device.

[1598] Step 6: Receiving User Input

[1599] The device receives new tasks and events from the user, for example, "What is the best route to get to my meeting tomorrow?"

[1600] Input: User task or event information

[1601] Output: Received task and event information

[1602] Step 7: Input to the model

[1603] The device inputs the received user input into the trained model, which then generates an optimal action plan.

[1604] Input: Received task and event information, trained model

[1605] Output: Optimal action plan

[1606] Step 8: Propose an action plan

[1607] The device then presents the user with an optimal action plan output from the model, specifically displaying the results of optimization of travel routes and schedules.

[1608] Input: Optimal action plan

[1609] Output: A suggested action plan for the user

[1610] Step 9: Review the proposal and take action

[1611] The user can then view the proposed action plan on the device and act accordingly, for example, by following the proposed travel route to the destination.

[1612] Input: User's proposed plan of action

[1613] Output: Actual behavior

[1614] The above is the specific processing flow of the program of this system.

[1615] (Application example 3)

[1616] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1617] In conventional business groupware systems, users manage their schedules and tasks individually, making it difficult to comprehensively optimize time, travel, and cost efficiency. Furthermore, particularly in food delivery services, there is a lack of systems that can suggest optimal routes and actions in real time for delivery personnel to efficiently handle multiple orders. This has led to a demand for more efficient delivery operations.

[1618] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1619] In this invention, the server includes means for comprehensively acquiring information from business groupware, means for reading a user's schedule based on the acquired information, means for proposing the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency based on the read schedule, means for proposing the best delivery route taking into consideration past behavioral data in delivery work, current traffic conditions, and order priority, and means for predicting the best course of action when a new task or event occurs. This enables efficient management of user schedules and food delivery work.

[1620] "Business groupware" is software that streamlines information sharing and communication within a company or organization.

[1621] "Means for comprehensively acquiring information" refers to methods and techniques for collecting all necessary information without omission.

[1622] "Means for reading the user's schedule" refers to methods and technologies for understanding the user's plans and tasks.

[1623] "Time efficiency" refers to the efficiency of making the most of limited time.

[1624] "Travel efficiency" refers to the efficiency of minimizing travel time and costs.

[1625] "Cost efficiency" refers to efficiency in maximizing cost-effectiveness.

[1626] A "best action suggestion" is a method or technique for suggesting the most appropriate action for a user.

[1627] "Delivery work" refers to the work of delivering products and services to customers.

[1628] "Past behavioral data" is a record of past behavior of users or systems.

[1629] "Current traffic conditions" refers to the real-time state of roads and transportation.

[1630] "Order priority" is an index that indicates which of multiple orders should be given priority in processing.

[1631] A "means for proposing the optimal delivery route" is a method or technology for showing the most efficient route in delivery operations.

[1632] "Means for predicting the best course of action when a new task or event occurs" refers to methods or techniques for predicting the most appropriate action when a new task or event occurs.

[1633] To implement this invention, the following system configuration is required: The server comprehensively acquires information from business groupware and reads the user's schedule based on the acquired information. Based on the read schedule, the server proposes the best course of action, taking into account time efficiency, travel efficiency, and cost efficiency. The server also proposes the best delivery route, taking into account past behavioral data in delivery work, current traffic conditions, and order priority, and predicts the best course of action when a new task or event occurs.

[1634] Hardware and software used

[1635] The server uses machine learning frameworks such as TensorFlow and PyTorch to learn user behavior patterns. Data is collected using business groupware and traffic information APIs. An application is installed on the delivery person's smartphone that suggests the optimal delivery route in real time.

[1636] Data processing and calculation

[1637] The server analyzes information obtained from the business groupware to read the user's schedule. It then calculates the optimal delivery route, taking into account past behavioral data, current traffic conditions, and order priority. To do this, it uses machine learning models and applies algorithms to maximize time, travel, and cost efficiency.

[1638] Specific examples

[1639] For example, if a delivery person receives multiple orders at the same time, the server will consider past delivery data and current traffic conditions to suggest the most efficient delivery order, and the optimal route will be displayed in real time on the delivery person's smartphone.

[1640] Prompt Sentence Examples

[1641] "Consider past delivery data and current traffic conditions to suggest the most efficient delivery route."

[1642] In this way, users can manage their schedules and food delivery operations more efficiently.

[1643] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1644] Step 1:

[1645] The server comprehensively acquires information from the business groupware.

[1646] Input: User schedule and task information obtained from the business groupware API.

[1647] Data processing: Analyze the acquired information and extract the user's schedule and tasks.

[1648] Output: A list of the user's schedule and task information.

[1649] Step 2:

[1650] The server reads the user's schedule based on the obtained information.

[1651] Input: The user's schedule and task information extracted in step 1.

[1652] Data processing: Analyze schedule information and identify the start time, end time, location, etc. of each task.

[1653] Output: Detailed information about each task (start time, end time, location, etc.).

[1654] Step 3:

[1655] The server then suggests the best course of action based on the read schedule, taking into account time efficiency, travel efficiency, and cost efficiency.

[1656] Input: Detailed information for each task obtained in step 2.

[1657] Data calculation: Using machine learning models, we calculate the priority of each task and determine the optimal order of actions.

[1658] Output: A list of optimal action sequences.

[1659] Step 4:

[1660] The server proposes the optimal delivery route by taking into account past behavioral data from delivery operations, current traffic conditions, and order priority.

[1661] Inputs: Delivery driver historical behavior data, real-time traffic information, and order priority.

[1662] Data computation: Using machine learning models to calculate optimal delivery routes.

[1663] Output: A list of optimal delivery routes.

[1664] Step 5:

[1665] The server predicts the best course of action when a new task or event arises.

[1666] Input: Information about new tasks or events.

[1667] Data Computing: Using machine learning models to predict optimal actions for new tasks and events.

[1668] Output: Best action suggestions for new tasks or events.

[1669] Step 6:

[1670] The terminal displays the optimal order of actions and delivery route received from the server to the delivery person.

[1671] Input: A list of optimal action sequences and delivery routes sent from the server.

[1672] Specific operation: Through a smartphone application, delivery personnel are shown the optimal order of actions and delivery routes in real time.

[1673] Output: Specific action instructions for the delivery person to carry out.

[1674] In this way, users can manage their schedules and food delivery operations more efficiently.

[1675] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1676] "Example 1"

[1677] In one embodiment of the present invention, a system incorporating an emotion engine is provided. The emotion engine recognizes a user's emotions and adjusts task priorities based on the user's emotional state. Specifically, the emotion engine analyzes the user's tone of voice, facial expressions, behavioral patterns, etc. to recognize the user's emotional state. For example, if the system determines that the user is feeling stressed, it prioritizes tasks that promote relaxation.

[1678] "Example 2"

[1679] The emotion engine also suggests the best course of action based on the user's emotional state. Specifically, it takes into account the user's emotional state and the urgency and importance of the task to suggest the best course of action. For example, if it determines that the user is feeling happy and there is an important but not urgent task, it will suggest that task. This makes the most of the user's emotional state to achieve efficient task management.

[1680] "Example 3"

[1681] In one embodiment of the present invention, a system incorporating an emotion engine is provided. The emotion engine recognizes a user's emotions and adjusts task priorities based on the user's emotional state. Specifically, the emotion engine analyzes the user's tone of voice, facial expressions, behavioral patterns, etc. to recognize the user's emotional state. For example, if the system determines that the user is feeling stressed, it prioritizes tasks that promote relaxation.

[1682] The processing flow of each embodiment will be described below.

[1683] "Example 1"

[1684] Step 1: The system recognizes the user's emotional state by analyzing their tone of voice, facial expressions, behavioral patterns, etc.

[1685] Step 2: The emotion engine adjusts task priorities based on the perceived emotional state. For example, if it determines that the user is feeling stressed, it will prioritize tasks that promote relaxation.

[1686] "Example 2"

[1687] Step 1: The emotion engine recognizes the user's emotional state.

[1688] Step 2: The emotion engine suggests the best course of action, taking into account the perceived emotional state and the urgency and importance of the task.

[1689] Step 3: For example, if the user is judged to be feeling joyful and there is an important but non-urgent task, the system will suggest that task, thereby making the most of the user's emotional state to achieve efficient task management.

[1690] Example 1

[1691] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1692] While conventional business software systems can efficiently manage users' schedules and tasks, they do not prioritize or suggest tasks that take into account the user's emotional state. As a result, when users are stressed or in a state where appropriate task management is not possible in response to their emotional state, work efficiency can decline.

[1693] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1694] In this invention, the server includes means for comprehensively acquiring information from business software, means for reading a user's schedule based on the acquired information, means for proposing the best course of action based on the read schedule, taking into account time efficiency, travel efficiency, and cost efficiency, means for recognizing the user's emotional state, means for adjusting task priorities based on the recognized emotional state, and means for notifying the user of prioritized tasks. This enables task management that takes the user's emotional state into account, and is expected to improve business efficiency.

[1695] "Business software" is software used by companies and organizations to carry out their business efficiently.

[1696] "Means for comprehensively acquiring information" refers to methods and technologies for comprehensively collecting necessary information from business software.

[1697] "Means for reading the user's schedule" refers to methods and technologies for analyzing and understanding the user's plans and tasks from the information obtained.

[1698] "Time efficiency" refers to the efficiency of achieving maximum results within a limited time.

[1699] "Travel efficiency" refers to the efficiency of minimizing the time and cost required for users to travel.

[1700] "Cost efficiency" refers to the efficiency of minimizing costs and maximizing effectiveness in carrying out business.

[1701] "Best action suggestion" refers to methods and techniques for suggesting the most effective action to a user based on the user's schedule and tasks.

[1702] "Means for recognizing the user's emotional state" refers to methods or technologies for analyzing the user's tone of voice, facial expressions, behavioral patterns, etc. to determine the user's emotions.

[1703] "Means for adjusting task priorities" are methods and techniques for optimizing the order in which tasks are executed, taking into account the user's emotional state and the urgency and importance of the tasks.

[1704] "Means for notifying users of prioritized tasks" refers to methods and techniques for notifying users of the adjusted task priorities.

[1705] MODE FOR CARRYING OUT THE INVENTION

[1706] This invention is a system that comprehensively acquires information from business software and adjusts task priorities by taking into account the user's emotional state. This system operates in cooperation with the server, the terminal, and the user.

[1707] Obtaining information

[1708] The server uses the business software's API to obtain information such as user schedules, emails, and documents. Specifically, it uses the Google Calendar API or Microsoft Outlook API. The obtained information is stored in a database on the server. For example, the Google Calendar API can be used to obtain user schedules and store them in a MySQL database.

[1709] Recognition of emotional states

[1710] The device captures the user's tone of voice, facial expressions, behavioral patterns, etc. in real time using hardware such as a microphone and camera. The captured data is sent from the device to a server. The server uses an emotion engine to analyze the user's emotional state from the received data. The emotion engine recognizes the user's emotions using voice analysis and image analysis technology.

[1711] Task prioritization

[1712] The server adjusts task priorities based on the acquired schedule information and the analysis results of the emotion engine. For example, if the user is feeling stressed, it will prioritize tasks that promote relaxation. The server notifies the user of prioritized tasks. Notifications are sent via push notifications, emails, and other means.

[1713] Specific examples

[1714] If the user uses Google Calendar, the server uses the Google Calendar API to obtain the user's schedule information. The obtained schedule information is stored in a database. The device then captures the user's tone of voice and facial expressions and sends them to the server. The server uses an emotion engine to analyze the user's emotional state and determines that the user is feeling stressed. Based on this information, the server prioritizes tasks that promote relaxation (e.g., taking a short break) and notifies the user.

[1715] Prompt Sentence Examples

[1716] "Use the Google Calendar API to obtain the user's schedule information and save it in a database. Then, capture the user's tone of voice and facial expression data on the device, send it to the server, and use the emotion engine to analyze the user's emotional state. If the user is feeling stressed, prioritize tasks that promote relaxation and notify the user."

[1717] In this way, the system of the present invention can improve a user's work efficiency by obtaining information from business software and adjusting task priorities based on the user's emotional state using an emotion engine.

[1718] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1719] Step 1:

[1720] The server uses the business software's API to obtain information such as the user's schedule, emails, and documents. Specifically, it uses the Google Calendar API and Microsoft Outlook API. It sends an API request as input and receives JSON-formatted data as output. This data includes details of the user's schedules and tasks.

[1721] Step 2:

[1722] The server saves the retrieved information in a database. It receives JSON format data as input and generates records stored in the database as output. Specifically, it uses a MySQL database to insert schedule information. This saves the user's schedule information in the database.

[1723] Step 3:

[1724] The device captures the user's tone of voice, facial expressions, and behavioral patterns in real time, taking data from a microphone and camera as input and generating audio and image data as output using voice recording and facial recognition software.

[1725] Step 4:

[1726] The device sends the captured audio and image data to the server, which receives the audio and image data as input and sends an HTTP POST request as output, allowing the server to receive data for analyzing the user's emotional state.

[1727] Step 5:

[1728] The server uses an emotion engine to analyze the user's emotional state from the received data. It receives voice and image data as input and generates the user's emotional state (e.g., stress, joy, fatigue, etc.) as output. The emotion engine recognizes emotions using voice and image analysis techniques.

[1729] Step 6:

[1730] The server adjusts task priorities based on the acquired schedule information and the analysis results of the emotion engine. It receives schedule information and emotional state as input and generates a prioritized task list as output. For example, if the user is feeling stressed, it will prioritize tasks that promote relaxation.

[1731] Step 7:

[1732] The server notifies the user of the prioritized tasks. It receives a prioritized task list as input and sends push notifications or emails as output, so that the user knows which tasks should be performed first.

[1733] In this way, by clarifying the specific operations and data flow at each step, it is possible to understand how the processing of the entire system is carried out.

[1734] (Application example 1)

[1735] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1736] While conventional business groupware systems can manage users' schedules and tasks, they have the problem of not being able to adjust task priorities or suggest relaxation methods that take into account the user's emotional state. In particular, in factories and other workplaces, where workers' stress and fatigue can affect work efficiency and safety, there is a need to understand their emotional state in real time and take appropriate action based on that.

[1737] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1738] In this invention, the server includes means for comprehensively acquiring information from business groupware, means for reading a user's schedule based on the acquired information, means for proposing the best course of action based on the read schedule taking into account time efficiency, travel efficiency, and cost efficiency, means for recognizing the user's emotional state, means for adjusting task priorities based on the recognized emotional state, and means for proposing tasks that promote relaxation. This makes it possible to adjust task priorities and suggest relaxation activities taking into account the user's emotional state.

[1739] "Business groupware" is software used to manage schedules, send and receive emails, share documents, and more within a company or organization.

[1740] "Means for comprehensively acquiring information" refers to methods and technologies for acquiring all necessary information from business groupware.

[1741] "Means for reading the user's schedule" refers to methods and technologies for analyzing and understanding the user's plans and tasks from the information obtained.

[1742] "Means for suggesting the best course of action taking into consideration time efficiency, travel efficiency, and cost efficiency" refers to methods and technologies for suggesting the most efficient course of action based on the user's schedule.

[1743] "Means for recognizing the user's emotional state" refers to methods or technologies for analyzing the user's tone of voice, facial expressions, behavioral patterns, etc. to determine their emotions.

[1744] A "means for adjusting task priorities" is a method or technology for reevaluating the importance and urgency of tasks and setting priorities based on the user's emotional state.

[1745] A "means for suggesting tasks to promote relaxation" is a method or technology that suggests tasks to promote relaxation when a user is feeling stressed.

[1746] A system for implementing the present invention has the following configuration.

[1747] Generating a Program

[1748] The system includes a program that comprehensively retrieves information from business groupware and reads the user's schedule and tasks, as well as a program that recognizes the user's emotional state, adjusts task priorities based on that state, and suggests tasks that promote relaxation.

[1749] Program processing explanation

[1750] Data Acquisition

[1751] The server obtains information from business groupware (e.g., Google Calendar, Microsoft Outlook) using APIs and scraping technology. The obtained information includes users' schedules, emails, documents, etc. This information is then stored in a database.

[1752] emotion recognition

[1753] The device (e.g., smart glasses, head-mounted display, microphone) uses an emotion recognition engine (e.g., Affectiva, Microsoft Azure Emotion API) to analyze the user's tone of voice, facial expressions, and behavioral patterns in real time, thereby recognizing the user's emotional state.

[1754] Task priority adjustment

[1755] The server adjusts task priorities using a task management system (e.g., Trello, Asana) based on the recognized emotional state. Specifically, if the user is feeling stressed, it prioritizes tasks that promote relaxation.

[1756] Relaxation task suggestions

[1757] The device (e.g., smartphone, smart glasses) suggests relaxation tasks to the user through relaxation apps (e.g., Calm, Headspace), allowing the user to perform appropriate relaxation tasks even when feeling stressed.

[1758] Specific examples

[1759] For example, if the emotion engine recognizes that factory worker A is feeling stressed, the followi...

Claims

1. A means of comprehensively acquiring information from business software; A means for storing the acquired information in a database; means for analyzing the stored information; A means for reading a user's schedule based on the analysis result of the information; A means for proposing the best action for the user in consideration of time efficiency, travel efficiency, and cost efficiency based on the read schedule of the user; A system including:

2. The system of claim 1 , wherein the means for obtaining further comprises means for reading the task from an email or a document.

3. The system according to claim 1 , wherein the suggesting means further comprises means for prioritizing the read tasks by taking into consideration urgency and importance of the tasks and indicating the best course of action.

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