system

Abstract

Provide a system. 【Solution means】 Means for inputting organizational goals, Means for inputting assigned tasks, Means for inputting work history, Means for analyzing the input organizational goals, assigned tasks and work history to generate personal goals, Means for determining whether the generated personal goals match the user's grade, Means for modifying the generated personal goals based on the determination result, Means for transmitting the modified personal goals to the user's terminal, Means for saving the final personal goals modified by the user, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In many organizations, the setting of personal goals is often inconsistent among individual employees, lacking unity, which often poses a problem. Also, it is difficult to set appropriate goals according to an individual's grade, and excessive or insufficient goals may be set. As a result, there is a risk of reduced motivation and failure to achieve goals. Based on such a current situation, a system that can automatically set unified personal goals according to an appropriate grade, reflecting an individual's work history and assigned tasks based on the goals of the entire organization, is required.

Means for Solving the Problems

[0005] This system includes means for inputting organizational goals, assigned tasks, and work history, as well as means for analyzing the input data to generate individual goals. Furthermore, it includes means for determining whether the generated individual goals are appropriate for the user's grade, and means for modifying the individual goals as necessary. The final modified goals are sent to the user's terminal, and once the user confirms the final goals, a means for saving those goals is provided. This enables the automatic setting of consistent and appropriate individual goals that reflect each individual's work content and history in relation to organizational goals, thereby standardizing MBO (Management by Objectives).

[0006] "Organizational goals" refer to the specific objectives and results that the entire company or department aims to achieve.

[0007] "Assigned duties" refers to the duties and tasks that each employee is expected to perform on a daily basis.

[0008] "Work history" refers to the records and performance of tasks that an employee has completed in the past.

[0009] "Individual goals" refer to specific objectives that each employee aims to achieve on a yearly or periodic basis.

[0010] "User" refers to employees who use the system or their managers.

[0011] "Grade" refers to a rank or class that evaluates an employee's job title, years of experience, aptitude, etc.

[0012] "Analysis means" refers to a function that analyzes input data, extracts relevant information, and processes it.

[0013] "Generation means" refers to a function that automatically creates new data or outputs based on the analysis results.

[0014] "Determination means" refers to a function that evaluates data and information based on specific conditions and determines whether it is appropriate or not.

[0015] "Correction means" refers to the function of changing and editing the generated information and data as needed.

[0016] "Transmission means" refers to the function of transferring and transmitting data and information within the system to another device or user.

[0017] "Storage means" refers to the function of recording and storing the final data and information in a database or the like.

Brief Description of Drawings

[0018] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0019] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0022] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0031] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0038] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0039] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned duties, and work history, determines whether these goals are appropriate for the employee's grade, and modifies them as necessary.

[0040] System Overview

[0041] This system incorporates several key features, allowing users to input organizational goals, assigned tasks, and work history, and generate individual goals. It also evaluates these goals based on a grade, sends revised goals to users, and saves the final goals, providing a method for consistently achieving overall organizational objectives.

[0042] Program description in natural language

[0043] 1. Data entry

[0044] The terminal receives input.

[0045] The terminal allows users (employees or their managers) to input "organizational goals," "assigned tasks," and "work history." For example, the sales department's organizational goal might be "achieve 100 million yen in annual sales," assigned tasks might be "acquire new customers" and "manage existing customers," and the employee's past performance might be entered in the work history.

[0046] 2. Data transmission and analysis

[0047] The device sends data, and the server receives it.

[0048] The terminal sends the input data to the server. The server receives and stores this data. The server then uses the data stored in the database to analyze it using natural language processing and machine learning algorithms.

[0049] The server generates personal goals.

[0050] Based on this analysis, the server generates individual goals. For example, regarding new customer acquisition, a goal such as "acquire 5 new customers per month" might be generated.

[0051] 3. Automatic determination of grade compatibility

[0052] The server verifies the user's grade information.

[0053] The server retrieves user grade information (job title, years of experience, etc.) from the employee database. It then automatically determines whether the generated goals are appropriate for the user's grade. For example, in the case of a new employee, it checks whether the goals are too high.

[0054] The server will modify the target (if necessary).

[0055] If the generated individual goals are not suitable for the current grade, the server will revise them. For example, the server might determine that the goal of "acquiring 5 new customers per month" is difficult for a new employee to achieve and revise it to "acquiring 3 new customers per month."

[0056] 4. Sending the target and final confirmation

[0057] The server sends the modified target to the terminal.

[0058] The revised personal goals are sent from the server to the user's device.

[0059] The device receives the final target and the user confirms it.

[0060] The device displays this goal to the user. The user is also provided with the ability to review the final goal and make further modifications if necessary.

[0061] The user sends the confirmed goal from their device to the server.

[0062] Once the user has finalized their goal, they send that information from their device to the server.

[0063] 5. Preservation of the final goal

[0064] The server saves the deterministic target.

[0065] The server saves the received final goal to a database so that it can be used later for progress management and evaluation.

[0066] Specific example

[0067] For example, consider the case of Tanaka, a new employee in the sales department, using the system. Tanaka's terminal is populated with the sales department's organizational goal of "achieving 100 million yen in annual sales," and his assigned tasks of "acquiring new customers" and "managing existing customers" are also entered. Since he is a new employee, there is no work history, so the server generates initial individual goals based on this information and checks if they are suitable for Tanaka's grade. The revised goals are sent to Tanaka's terminal, and after Tanaka confirms them, they are registered as final goals.

[0068] The system described above enables efficient management toward achieving overall organizational goals by automatically setting consistent and appropriate individual goals.

[0069] The following describes the processing flow.

[0070] Step 1:

[0071] The terminal receives input.

[0072] Users input "organizational goals," "assigned tasks," and "work history" using a terminal. This includes overall organizational goals, tasks that individual employees are expected to perform, and past work performance. For example, the sales department might enter "achieve 100 million yen in annual sales" as its organizational goal and "acquire new customers" as its assigned task.

[0073] Step 2:

[0074] The device sends data

[0075] The terminal sends the entered data on "organizational goals," "assigned tasks," and "work history" to the server. The data is formatted into the appropriate format before being sent to the server.

[0076] Step 3:

[0077] The server receives the data.

[0078] The server receives data sent from the terminal and stores it in its internal database. This enables centralized data management.

[0079] Step 4:

[0080] The server analyzes the data.

[0081] The server uses natural language processing and machine learning techniques to analyze the received data. For example, it extracts relevant keywords from "organizational goals" and "assigned tasks," and uses them to determine performance indicators.

[0082] Step 5:

[0083] The server generates personal goals.

[0084] Based on the analysis results, the server generates appropriate individual goals for each employee. For example, regarding new customer acquisition, a specific goal such as "acquire 5 new customers per month" is generated.

[0085] Step 6:

[0086] The server verifies the user's grade information.

[0087] The server retrieves user grade information from the employee database and determines whether the generated personal goals are appropriate for that grade. For new employees, it evaluates whether the generated goals are achievable.

[0088] Step 7:

[0089] The server modifies the target.

[0090] Based on the evaluation results, the server modifies the generated goals as needed. For example, if the goal "acquire 5 new customers per month" is too high, it will be modified to "acquire 3 new customers per month."

[0091] Step 8:

[0092] The server sends the modified target to the terminal.

[0093] The revised, final personal goals are sent from the server to the terminal and presented to the user.

[0094] Step 9:

[0095] The device receives the target and the user confirms it.

[0096] The user reviews their personal goals submitted via their device. They can then revise their goals as needed and perform a final review.

[0097] Step 10:

[0098] The user confirms the goal, and the device sends it to the server.

[0099] The user confirms the final goal, and the device sends that information back to the server.

[0100] Step 11:

[0101] The server saves the deterministic target.

[0102] The server saves the finalized goal in a database, which can then be used for progress management and evaluation of goal achievement.

[0103] Through these steps, consistent and appropriate individual goals are automatically generated, modified, and saved, enabling efficient management toward achieving the organization's overall goals.

[0104] (Example 1)

[0105] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0106] In modern organizations, there is a need for efficient management that sets individual goals tailored to each employee's role and capabilities, in order to achieve overall organizational goals. However, traditional systems do not automate goal setting that appropriately considers each employee's work history and grade information, requiring manual adjustments. This leads to problems such as time-consuming goal setting and a lack of consistency and appropriateness.

[0107] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0108] In this invention, the server includes means for transmitting data on organizational goals, assigned tasks, and work history to the server; means for analyzing the received data using natural language processing and machine learning algorithms; and means for obtaining grade information from an employee database and automatically determining the suitability of the generated goals. This enables the automatic generation and modification of consistent and appropriate individual goals for each employee, and allows for efficient management to achieve the goals of the entire organization.

[0109] "Organizational goals" refer to specific objectives or outcomes that the entire organization should achieve.

[0110] "Assigned duties" refers to the specific job responsibilities and tasks assigned to each employee.

[0111] "Work history" refers to a record of an employee's past work performance and achievements.

[0112] "Individual goals" refer to specific achievement targets and performance indicators set for each employee.

[0113] "User grade" refers to qualification information such as employee job title, years of experience, and performance evaluation.

[0114] "Modification" refers to the process of adjusting generated individual goals to optimal goals when they do not match the user's grade level.

[0115] A "terminal" refers to a device used for data entry and goal confirmation, and includes personal computers and smartphones.

[0116] A "server" is a central management system that performs tasks such as data analysis, storage, and transmission.

[0117] "Natural language processing" is an artificial intelligence technology that analyzes human language to understand its meaning.

[0118] A "machine learning algorithm" is a computational method aimed at learning from data and making predictions and decisions.

[0119] An "employee database" is a data storage system used to store employee personal information, grade information, work history, and other data.

[0120] "Suitability" is a criterion used to evaluate whether the generated goals are appropriate for the employee's grade and capabilities.

[0121] "Transmission" refers to the transfer of data from one device to another.

[0122] "Preservation" is the act of recording generated data or information in order to retain it on a permanent basis.

[0123] "Analysis" is the act of analyzing input data in various ways and making decisions based on the results.

[0124] "Automatic generation" refers to the process by which a machine or program generates data or goals without human intervention.

[0125] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned tasks, and work history, determines whether these goals are appropriate for the employee's grade, and modifies them as necessary. This system consists of a server, terminals, and users who access them.

[0126] Data entry

[0127] The terminal receives input.

[0128] Users use a terminal to input "organizational goals," "assigned tasks," and "work history." Specifically, a user might input "achieve 100 million yen in annual sales" as the organizational goal for the sales department, and "acquire new customers" and "manage existing customers" as their assigned tasks. They would also input past performance in the work history section.

[0129] Data transmission and analysis

[0130] The device sends data, and the server receives it.

[0131] The terminal inputs data and sends it to the server using protocols such as HTTP requests. The server receives this data and stores it in its own database. Subsequently, the server analyzes this stored data using natural language processing and machine learning algorithms. Specific software used for this purpose includes Python's NLTK and scikit-learn.

[0132] The server generates personal goals.

[0133] The server generates individual goals for each employee based on the analysis results. For example, regarding new customer acquisition, a goal such as "acquire 5 new customers per month" might be generated. This involves a process that uses machine learning models to calculate predictive results.

[0134] Automatic grade compatibility determination

[0135] The server verifies the user's grade information.

[0136] The server retrieves user grade information (job title, years of experience, etc.) from the employee database. It then automatically determines whether the generated goals are compatible with this grade information. A compatibility determination algorithm is used to determine this compatibility.

[0137] The server will modify the target (if necessary).

[0138] If the generated goal is not suitable for the user's grade, the server will modify the goal. For example, if the goal "acquire 5 new customers per month" is determined to be difficult for a new employee to achieve, it will be modified to "acquire 3 new customers per month."

[0139] Sending the target and final confirmation

[0140] The server sends the modified target to the terminal.

[0141] The revised personal goals are sent from the server to the user's device. This data is typically sent in JSON format.

[0142] The device receives the final target and the user confirms it.

[0143] The device displays the revised goal to the user. The user can review this goal and make further revisions if necessary. When the user finally confirms the goal, confirmation information is sent from the device to the server.

[0144] Preservation of the final goal

[0145] The server saves the deterministic target.

[0146] The server saves the confirmed goals to the database. This information can be used later for progress management and evaluation. The data is inserted into the database using SQL queries or similar methods.

[0147] Specific example

[0148] For example, consider a scenario where a new employee in the sales department uses the system. The new employee enters "Achieve 100 million yen in annual sales" as the organizational goal, and "Acquire new customers" and "Manage existing customers" as their assigned tasks. If there is no work history for the new employee, the server generates initial individual goals based on this information and automatically determines whether they are appropriate for the employee's grade. The revised goals are sent to the new employee's terminal, and after the new employee reviews them, they are registered as the final goals.

[0149] Example of a prompt

[0150] "Design a system that uses user input data (organizational goals, assigned tasks, work history) to generate individual employee goals, determines whether they are appropriate for the employee's grade, makes adjustments accordingly, and then sends them to the user."

[0151] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0152] Step 1:

[0153] Data entry

[0154] The terminal receives input.

[0155] The user uses a terminal to input "organizational goals," "assigned duties," and "work history." Specifically, the user manually enters data for each item into the input form on the terminal. For example, the user enters the organizational goal of "achieving 100 million yen in annual sales," their assigned duties of "acquiring new customers" and "managing existing customers," and their past work history. This entered data will then be used for the next step.

[0156] Step 2:

[0157] Sending data

[0158] The device sends data to the server.

[0159] The terminal sends data entered by the user to the server. Specifically, the data is sent to the server using an HTTP request. The inputs are organizational goals, assigned tasks, and work history, and this data is used in the next analysis step. The output is a notification that the transmission to the server is complete.

[0160] Step 3:

[0161] Data storage and analysis

[0162] The server stores and analyzes the data.

[0163] The server stores the submitted data in a database. Specifically, the server inserts the data into the database using SQL queries. Using the stored data, the server analyzes the data using natural language processing and machine learning algorithms. Libraries used include Python's NLTK and scikit-learn. The input to the analysis is the stored data, and the output is the analysis result.

[0164] Step 4:

[0165] Generating personal goals

[0166] The server generates personal goals.

[0167] Based on the analysis results, the server generates personal goals. Specifically, a machine learning model makes predictions, and goals are set based on those predictions. For example, a specific goal such as "acquire 5 new customers per month" is generated. The input is the analysis results, and the output is the generated personal goals.

[0168] Step 5:

[0169] Obtaining grade information

[0170] The server retrieves the user's grade information.

[0171] The server retrieves user grade information from the employee database. Specifically, it uses SQL queries to retrieve information such as the user's job title and years of experience. The input is the user ID, and the output is the grade information.

[0172] Step 6:

[0173] Grade compatibility determination

[0174] The server determines the suitability of the target.

[0175] The server automatically determines whether the generated personal goals are suitable for the user's grade. Specifically, it executes a suitability determination algorithm. The input is the generated personal goals and grade information, and the output is the suitability determination result.

[0176] Step 7:

[0177] Target revision

[0178] The server will modify the target (if necessary).

[0179] If the generated individual goals are not suitable for the user's grade, the server will revise them. For example, if the goal "acquire 5 new customers per month" is determined to be difficult for a new employee to achieve, it will be revised to "acquire 3 new customers per month." The input is the suitability assessment result, and the output is the revised individual goal.

[0180] Step 8:

[0181] Sending revised targets

[0182] The server sends the modified target to the terminal.

[0183] The revised personal goals are sent from the server to the user's terminal. Specifically, the data is sent in JSON format. The input is the revised personal goals, and the output is a notification that the transmission to the terminal is complete.

[0184] Step 9:

[0185] User verification

[0186] The device receives the final target and the user confirms it.

[0187] The terminal displays the revised goal to the user. The user can review this goal and make further revisions if necessary. Specifically, the revised goal is displayed in a dialog box on the terminal. The input is the revised personal goal, and the output is the user's confirmation result.

[0188] Step 10:

[0189] Final goal confirmed

[0190] The user sends the confirmed goal from their device to the server.

[0191] The user confirms the final goal and sends that information from the terminal to the server. Specifically, this action involves clicking a confirmation button. The input is the user's confirmation result, and the output is a notification to the server that the transmission is complete.

[0192] Step 11:

[0193] Preservation of the final goal

[0194] The server saves the deterministic target.

[0195] The server saves the confirmed goals to the database. Specifically, it uses an SQL query to insert the confirmed goals into the database. The input is the confirmed individual goals, and the output is a notification that the save is complete.

[0196] (Application Example 1)

[0197] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0198] Conventional factory robot management systems make it difficult to set optimal work objectives based on the performance history and capabilities of individual robots, and they lack the means to automatically determine whether those objectives are appropriate and to correct them as needed. As a result, it is difficult to maximize the overall production efficiency of the factory, and the lack of appropriate objective setting can negatively impact robot performance.

[0199] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0200] In this invention, the server includes means for inputting organizational goals, means for inputting assigned tasks, means for inputting work history, means for analyzing the inputted organizational goals, assigned tasks, and work history to generate individual goals, means for determining whether the generated individual goals are appropriate for the user's grade, means for modifying the generated individual goals based on the determination result, means for transmitting the modified individual goals to the user's terminal, means for saving the final individual goals modified by the user, means for optimizing the individual goals based on performance history, determining whether the goals are appropriate for the work capacity, and modifying them as necessary, and means for transmitting the generated goals to another system and performing execution verification. This enables the generation of optimal work goals according to the performance history and work capacity of a factory robot, automatic determination of their appropriateness, and modification of goals as necessary.

[0201] "Organizational goals" refer to specific numerical targets or results that the entire organization aims to achieve.

[0202] "Assigned tasks" refer to specific tasks or work that users or robots are responsible for performing.

[0203] "Work history" refers to a record of tasks and operations performed by users or robots in the past.

[0204] "Individual goals" refer to specific objectives that a particular user or robot should achieve.

[0205] "Grade" refers to a rank or level that represents the job title or capabilities of a user or robot.

[0206] "Performance history" refers to data that records the user's or robot's past work performance and results.

[0207] "Work capability" refers to the range and efficiency of tasks that a user or robot can perform.

[0208] "Final personal goals" refer to the final goals that have been finalized after revisions and confirmations.

[0209] "Execution verification" refers to the process of confirming whether the generated goals are being properly executed.

[0210] System program and hardware / software description

[0211] The system for implementing this invention automatically generates individual work objectives for each robot based on the organization's production goals, the tasks assigned to each robot, and their performance history. It then determines whether these objectives are appropriate for the robot's capabilities and adjusts them as necessary. The system utilizes the following hardware and software.

[0212] Terminal: A device used by factory supervisors and other managers to input "organizational goals," "assigned duties," and "work history." The data entered from the terminal is sent to the server.

[0213] Server: The main computer used for data analysis, goal generation, and optimization. The server stores data in a database and has the capability to analyze the data using machine learning algorithms and natural language processing.

[0214] Database: A storage system for saving organizational goals, assigned tasks, work history, user grade information, etc. The database operates in conjunction with a server.

[0215] Machine learning algorithms: Algorithms for generating individual work goals based on received data. Logistic regression and other machine learning models are used.

[0216] Utility software: Software that supports data transmission and reception, display of analysis results, and modification of goals. For example, libraries such as "pandas" and "sklearn" are used.

[0217] Specific examples of data analysis and goal generation

[0218] Data entered from the terminal (e.g., "annual production target of 1000 units," "welding operations," "past performance history," etc.) is sent to the server, which analyzes this data to generate optimal individual targets for each robot. For example, based on the performance history, robot A might be set with a target of "300 welds per month," and robot B with a target of "400 assembles per month." These generated targets are then adjusted to match the capabilities of each robot.

[0219] Example of a prompt

[0220] The following is an example of a prompt statement for using a generative AI model:

[0221] "Considering the role of factory robot assistants, please provide a Python program that generates optimal individual work objectives that align with the overall production goals, based on each robot's assigned tasks and performance history. It should also include a function to evaluate whether the objectives are appropriate for each robot's capabilities and adjust them as needed."

[0222] By using this prompt statement, it is possible to more effectively utilize the generated AI model and achieve automatic generation and optimization of the target settings for factory robots.

[0223] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0224] Step 1:

[0225] The terminal receives input.

[0226] The terminal allows users (such as factory supervisors) to input "organizational goals," "assigned tasks," and "performance history." For example, an annual production target of "1000 units" might be entered, assigned tasks such as "welding" or "assembly" might be entered, and the past performance history of each robot might be entered. This input data is then sent to the server.

[0227] Step 2:

[0228] The server receives and stores the data.

[0229] The server receives input data sent from the terminal and stores it in a database. This database includes organizational goals, assigned tasks, and performance history.

[0230] Step 3:

[0231] The server analyzes the data.

[0232] The server applies machine learning algorithms (e.g., logistic regression models) to analyze the stored data. Based on the input data, it performs data processing to generate optimal individual work goals for each robot. For example, it calculates the sum of the performance history and determines the goals that should be assigned to each robot. As a result of the analysis using the generative AI model, individual work goals for each robot are output.

[0233] Step 4:

[0234] The server determines the suitability of the target.

[0235] The server determines whether the generated personal work objectives are suitable for the work capabilities of each robot. It compares them with the robot's capability data stored in the database (e.g., the type and specifications of each robot) to confirm whether the objectives are achievable.

[0236] Step 5:

[0237] The server modifies the target.

[0238] If the generated individual work objectives do not match the robot's capabilities, the server automatically modifies the objectives. For example, if an objective is set that exceeds the capabilities of robot A, the objective will be lowered to a more realistic target such as "300 welds per month." These modified objectives are then saved in the database.

[0239] Step 6:

[0240] The server sends the target to the terminal.

[0241] The revised work objectives are sent from the server to the terminal. The terminal receives them and displays them to the user. The user reviews the displayed final work objectives and makes any necessary additional corrections.

[0242] Step 7:

[0243] The user makes a final confirmation of the goal and submits it.

[0244] The user reviews the displayed final work objective and confirms it if there are no problems. The objective confirmed by the user is sent from the terminal to the server. The server saves this final objective in its database.

[0245] Step 8:

[0246] The server performs a verification of the target execution.

[0247] The server sends the generated goals to other systems and monitors their execution to verify that they are being properly executed by the configured robots. Once the execution verification is complete, the progress is reported to the user.

[0248] This enables the generation of optimal work objectives based on the performance history and work capabilities of factory robots, automatic determination of their suitability, and further modification of objectives as needed.

[0249] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0250] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned tasks, and work history, determines whether these goals are appropriate for the employee's grade, and modifies them as necessary. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, this system can modify goals based on the user's emotional state.

[0251] System Overview

[0252] This system incorporates several key features, allowing users to input organizational goals, assigned tasks, and work history to generate individual goals. It also evaluates these goals based on a grade, sends revised goals to the user, and saves the final goals, providing a method for consistently achieving overall organizational objectives. Furthermore, by utilizing an emotion engine, it recognizes the user's emotional state (e.g., stress and motivation) and further optimizes individual goals accordingly.

[0253] Program description in natural language

[0254] 1. Data entry

[0255] The terminal receives input.

[0256] Users input "organizational goals," "assigned tasks," and "work history" using a terminal. For example, the sales department might input "achieve 100 million yen in annual sales" as its organizational goal, and "acquire new customers" and "manage existing customers" as its assigned tasks.

[0257] 2. Data transmission and analysis

[0258] The device sends data, and the server receives it.

[0259] The terminal sends the entered data—organizational goals, assigned tasks, and work history—to the server. The server then receives the data and stores it appropriately.

[0260] The server analyzes the data.

[0261] The server uses natural language processing and machine learning techniques to analyze the transmitted data. For example, it evaluates the relationship between "organizational goals" and "assigned tasks" and extracts the information necessary to generate individual goals.

[0262] 3. Setting personal goals

[0263] The server generates personal goals.

[0264] Based on the analysis results, the server generates appropriate individual goals for each employee. For example, a specific goal such as "acquire 5 new customers per month" is generated.

[0265] 4. Determining Grade Compatibility

[0266] The server verifies the user's grade information.

[0267] The server retrieves user grade information from the employee database and determines whether the generated personal goals are appropriate for that grade. For example, in the case of a new employee, it evaluates whether the generated goals are achievable.

[0268] The server modifies the target.

[0269] If necessary, the server will modify the generated goals. For example, if the goal of "acquire 5 new customers per month" is too high for a new employee, it will be modified to "acquire 3 new customers per month."

[0270] 5. Utilizing the Emotion Engine

[0271] The server uses an emotion engine to recognize the user's emotions.

[0272] The emotion engine uses voice analysis and facial recognition technology to collect and analyze user emotional data. For example, it evaluates the stress and motivation that users feel towards their goals.

[0273] The server adjusts its goals based on its emotional state.

[0274] Based on the data obtained from the emotion engine, the server further optimizes the goals. For example, it eases goals for users with high stress levels, while changing them to more challenging goals for highly motivated users.

[0275] 6. Sending the target and final confirmation

[0276] The server sends the modified target to the terminal.

[0277] The revised, final personal goals are sent from the server to the terminal and presented to the user.

[0278] The device receives the target and the user confirms it.

[0279] The user reviews their personal goals submitted via their device. They can then revise their goals as needed and perform a final review.

[0280] The user confirms the goal, and the device sends it to the server.

[0281] The user determines the final goal, and the terminal sends that information to the server again.

[0282] 7. Saving the Final Goal

[0283] The server saves the determined goal

[0284] The server saves the determined final goal in the database so that it can be used for subsequent progress management and evaluation of goal achievement.

[0285] Specific Example

[0286] For example, consider the case where Mr. Tanaka, a new employee in the sales department, uses the system. In Mr. Tanaka's terminal, the organizational goal of "achieving annual sales of 100 million yen" for the sales department is input, and the assigned tasks of "acquiring new customers" and "managing existing customers" are also input. If there is no business history for the new employee, the server generates an initial personal goal based on this information and checks whether it matches Mr. Tanaka's grade. Then, based on the sentiment analysis of the sentiment engine, the goal is optimized according to Mr. Tanaka's stress and motivation. The revised goal is sent to Mr. Tanaka's terminal and registered as the final goal after Mr. Tanaka confirms it.

[0287] With the above system, not only are individual goals with consistency and appropriateness automatically generated, modified, and saved, but also the user's emotional state is considered, enabling efficient management towards the achievement of the overall organizational goal.

[0288] The following explains the process flow.

[0289] Step 1:

[0290] The terminal receives the input

[0291] Users input "organizational goals," "assigned tasks," and "work history" using a terminal. For example, the sales department might enter "achieve 100 million yen in annual sales" as its organizational goal, and "acquire new customers" and "manage existing customers" as its assigned tasks. Past work history is entered in a similar manner.

[0292] Step 2:

[0293] The device sends data

[0294] The terminal formats the "organizational goals," "assigned tasks," and "work history" data entered by the user and sends it to the server.

[0295] Step 3:

[0296] The server receives the data.

[0297] The server receives data sent from the terminal and stores it in the database. This makes the information available throughout the entire system.

[0298] Step 4:

[0299] The server analyzes the data.

[0300] The server uses natural language processing and machine learning techniques to analyze the stored data. For example, it extracts relevant keywords from "organizational goals" and "assigned tasks" and organizes the information necessary to generate individual goals.

[0301] Step 5:

[0302] The server generates personal goals.

[0303] Based on the analysis results, the server generates appropriate individual goals for each employee. For example, a specific goal such as "acquire 5 new customers per month" is generated.

[0304] Step 6:

[0305] The server verifies the user's grade information

[0306] The server obtains the user's grade information from the employee database. It verifies the obtained grade information with the generated personal goal and determines whether the goal matches the user's grade.

[0307] Step 7:

[0308] The server modifies the goal

[0309] If the generated personal goal does not match the user's grade, the server modifies the goal. For example, if the goal of "acquiring 5 new customers per month" is too high for a new employee, it is modified to "acquiring 3 new customers per month".

[0310] Step 8:

[0311] The server uses the emotion engine to recognize the user's emotion

[0312] The server receives data related to the emotion engine (such as a face recognition camera or voice input) from the terminal and analyzes the user's emotional state. It evaluates stress and motivation from the user's expression and voice tone when inputting.

[0313] Step 9:

[0314] The server modifies the goal based on the emotional state

[0315] Based on the emotion data obtained by the emotion engine, the server further optimizes the goal. For example, if the user is feeling high stress, the goal is relaxed; conversely, if high motivation is observed, the goal is made more challenging.

[0316] Step 10:

[0317] The server sends the modified goal to the terminal

[0318] The revised and optimized final personal goals are sent from the server to the terminal and presented to the user.

[0319] Step 11:

[0320] The device receives the target and the user confirms it.

[0321] The user reviews the personal goals presented through their device. They can further modify their goals as needed and then perform a final review.

[0322] Step 12:

[0323] The user confirms the goal, and the device sends it to the server.

[0324] After the user confirms their final goal, the device sends that information to the server.

[0325] Step 13:

[0326] The server saves the deterministic target.

[0327] The server saves the received final goals to a database. The saved goals are then used for progress management and evaluation of goal achievement.

[0328] Through these steps, not only are consistent and appropriate individual goals automatically generated, modified, and saved, but user emotional states are also taken into consideration, enabling efficient management toward achieving the organization's overall goals.

[0329] (Example 2)

[0330] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0331] When setting individual goals for employees, it is necessary to consider each employee's emotional state while maintaining consistency and appropriateness. Traditional systems lacked sufficient coordination between organizational and individual goals, making it difficult to set goals that matched employees' motivation and stress levels. As a result, employee goal achievement may decline, potentially leading to a decrease in overall organizational performance.

[0332] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0333] In this invention, the server includes means for inputting organizational goals, means for inputting assigned tasks, means for inputting work history, means for analyzing the inputted organizational goals, assigned tasks, and work history to generate individual goals, means for determining whether the generated individual goals are appropriate for the user's rank, means for modifying the generated individual goals based on the determination result, means for recognizing the user's emotional state using an emotion engine, means for further modifying the individual goals based on the recognized emotional state, means for transmitting the modified individual goals to the user's terminal, and means for saving the final individual goals modified by the user. This makes it possible to set appropriate goals based on the work content and emotional state of each individual employee.

[0334] "Organizational goals" are specific objectives and indicators that the entire organization should strive to achieve.

[0335] "Assigned duties" refer to the specific tasks and responsibilities that employees are responsible for on a daily basis.

[0336] "Work history" refers to the records and achievements of an employee's past work.

[0337] "Individual goals" are specific objectives that each employee should aim to achieve, and they are set in conjunction with organizational goals.

[0338] "Job rank" refers to a positional rank determined based on an employee's job title and experience, and serves as a basis for evaluation and goal setting.

[0339] The "emotion engine" is a technology for recognizing and analyzing the emotional state of employees, and it collects emotional data using voice analysis and facial recognition.

[0340] "Server" refers to the central processing unit and related software used to analyze input data and generate and modify individual goals.

[0341] A "terminal" is a device used by employees to input data, check goals, and make adjustments.

[0342] A "machine learning model" is an algorithm and framework that learns from data and automatically performs tasks such as goal generation and sentiment analysis.

[0343] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned tasks, and work history, determines whether these goals are appropriate for the employee's rank, and modifies them as necessary. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, this system can modify goals based on the user's emotional state.

[0344] This system incorporates several key mechanisms to input organizational goals, assigned tasks, and work history, and generates individual goals. It also evaluates these goals based on performance ratings, sends revised goals to the user, and saves the final goals, providing a method for consistently achieving overall organizational objectives. Furthermore, by utilizing an emotion engine, it recognizes the user's emotional state (e.g., stress and motivation) and further optimizes individual goals accordingly.

[0345] Specifically, the invention is implemented using the following procedure.

[0346] Users input organizational goals, assigned tasks, and work history from their terminals. The terminals then send this data to the server. The server analyzes the received data using natural language processing (NLP) techniques, specifically the Python libraries "spaCy" and "TENSORFLOW®". From the analyzed data, the server generates appropriate personal goals, using the machine learning library "scikit-learn".

[0347] For each generated individual goal, the server retrieves the user's rank information from the employee database and evaluates whether the generated goal is appropriate for that rank. For example, in the case of a new employee, it evaluates whether the generated goal is achievable and modifies the goal as necessary. After this modification process, the server uses an emotion engine to collect and analyze the user's emotional state from voice analysis and facial recognition technology. This process utilizes IBM Watson® and Microsoft Azure® Cognitive Services.

[0348] Based on the data obtained from the emotion engine, the server further optimizes the goals. For example, it eases goals for users with high stress levels, while changing them to more challenging goals for highly motivated users. The final revised personal goals are sent from the server to the user's device, and after the user reviews and confirms them, they are sent back to the server for final confirmation. The server stores the final confirmed goals in a database, which is then used for progress management and evaluation of goal achievement.

[0349] For example, consider the case of Tanaka, a new employee in the sales department, using the system. Tanaka's terminal is populated with the sales department's organizational goal of "achieving 100 million yen in annual sales," as well as his assigned tasks of "acquiring new customers" and "managing existing customers." Since he is a new employee, there is no work history, so the server generates initial individual goals based on this information and checks if they are appropriate for Tanaka's rank. Then, based on the emotion analysis of the emotion engine, the goals are optimized according to Tanaka's stress and motivation. The revised goals are sent to Tanaka's terminal, and after Tanaka reviews them, they are registered as final goals.

[0350] The following are examples of specific prompts for a generative AI model.

[0351] prompt:

[0352] "Please generate personal goals for Ms. Tanaka, a new employee in the sales department. The current organizational goal is to achieve 100 million yen in annual sales, and her responsibilities include acquiring new customers and managing existing ones. As she is a new employee, she has no work history. Please set optimal goals considering Ms. Tanaka's current stress levels and motivation."

[0353] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0354] Step 1:

[0355] The user enters data into the device.

[0356] Description: Users use a terminal to input "organizational goals," "assigned tasks," and "work history." This data is entered from the user's terminal. For example, if a user inputs the organizational goal of "achieving 100 million yen in annual sales" for the sales department, and also inputs assigned tasks such as "acquiring new customers" and "managing existing customers," this data will be entered into the terminal. The input data is sent from the terminal to the server for use in subsequent processing.

[0357] Step 2:

[0358] The device sends data to the server.

[0359] Description: The terminal sends the entered "organizational goals," "assigned tasks," and "work history" data to the server. Specifically, the terminal makes an API call, and the entered data is sent to the server as a POST request. The server temporarily stores the received data and uses it for later analysis. The input data (organizational goals, assigned tasks, work history) is sent to and stored on the server.

[0360] Step 3:

[0361] The server analyzes the data.

[0362] Description: The server analyzes the received data. This analysis utilizes natural language processing (NLP) and machine learning techniques. Specifically, it uses the Python library "spaCy" and "TensorFlow" to analyze text data and evaluate the relationship between organizational goals and assigned tasks. Based on the input data (organizational goals, assigned tasks, and work history), the analysis results (relevance evaluation and extraction of necessary information) are obtained.

[0363] Step 4:

[0364] The server generates personal goals.

[0365] Description: The server generates appropriate personal goals for each employee based on the results of data analysis. The machine learning library "scikit-learn" is used for this generation. For example, a personal goal such as "acquire 5 new customers per month" might be generated. Based on the input (analysis results), the generated personal goals are obtained as output.

[0366] Step 5:

[0367] The server retrieves the user's rank information.

[0368] Description: The server accesses the employee database and retrieves employee rank information. Specifically, it uses SQL queries to retrieve the required rank information from the database. Based on the input (user ID), the output (rank information) is obtained.

[0369] Step 6:

[0370] The server evaluates and modifies the generated individual goals based on their grade level.

[0371] Description: Based on the acquired grade information, the server evaluates whether the generated individual goals are appropriate for the employee's grade. For example, if the goal of "acquiring 5 new customers per month" is too high for new employee Tanaka, it will be revised to "acquiring 3 new customers per month." Based on the input (generated individual goals, grade information), the revised individual goals are output.

[0372] Step 7:

[0373] The server uses an emotion engine to recognize the user's emotional state.

[0374] Description: The server uses an emotion engine to recognize the user's emotional state through voice analysis and facial recognition technology. IBM Watson and Microsoft Azure Cognitive Services are used to collect and analyze the user's emotional data. Based on the input (voice data, facial data), the emotional state is output.

[0375] Step 8:

[0376] The server further modifies personal goals based on emotional state.

[0377] Description: The server further modifies personal goals based on the recognized emotional state. For example, it eases goals for users with high stress levels and changes them to more challenging goals for highly motivated users. Based on the input (modified personal goals, emotional state), further modified personal goals are output.

[0378] Step 9:

[0379] The server sends the revised personal goals to the device.

[0380] Description: The server sends the final, revised personal goals to the user's device. Specifically, it makes an API call and sends the revised personal goals to the user's device in JSON format. Based on the input (further revised personal goals), the output sent to the user's device is obtained.

[0381] Step 10:

[0382] The device receives the target and the user confirms it.

[0383] Description: The user receives and confirms the revised personal goals via their device. The user provides feedback on the goals and further revises them if necessary. Based on the input (revised personal goals), the user's confirmation and feedback are output.

[0384] Step 11:

[0385] The user confirms the goal, and the device sends it to the server.

[0386] Description: The user confirms the final goal, and the device sends that information back to the server. Specifically, when the confirm button is pressed, an API call is made, and the confirmed goal is sent to the server. Based on the input (user confirmation information), the confirmed goal is sent to the server.

[0387] Step 12:

[0388] The server saves the deterministic target.

[0389] Description: The server saves the finalized goals to the database. This data is used later for progress management and evaluation of goal achievement. Specifically, it uses SQL queries to insert the finalized goals into the database. Based on the input (finalized goals), the output stored in the database is obtained.

[0390] (Application Example 2)

[0391] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0392] Conventional individual goal-setting systems were limited to automatically generating individual goals based on organizational goals, assigned tasks, and work history, and could not take into account the user's emotional state. Therefore, flexible goal setting tailored to the user's stress and motivation levels was difficult, hindering the improvement of individual and organizational efficiency and performance. Furthermore, these systems were dependent on specific hardware and software, limiting their applicability.

[0393] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0394] In this invention, the server includes means for inputting organizational goals, means for inputting assigned tasks, means for inputting work history, means for analyzing the inputted organizational goals, assigned tasks, and work history to generate individual goals, means for determining whether the generated individual goals are appropriate for the user's grade, means for modifying the generated individual goals based on the determination result, means for analyzing emotional data, means for further optimizing the individual goals based on the analyzed emotional data, means for transmitting the modified individual goals to the user's terminal, and means for saving the final individual goals modified by the user. This enables flexible setting of individual goals that take into account the user's emotional state, and allows for efficient management toward achieving goals for both individuals and the organization as a whole.

[0395] "Organizational goals" are specific outcomes or achievement criteria that a particular organization aims to accomplish within a certain period of time.

[0396] "Assigned duties" refer to specific tasks or responsibilities that are individually assigned to each employee.

[0397] "Work history" refers to the records of various tasks and duties performed by an employee in the past, as well as the results thereof.

[0398] "Individual goals" refer to specific and achievable objectives set for individual employees based on organizational goals, assigned duties, and work history.

[0399] "User grade" refers to an indicator that shows an employee's position and experience level within an organization.

[0400] "Emotional data" refers to information about a user's feelings and emotional state collected through voice analysis and facial recognition technology.

[0401] "User devices" refer to electronic devices such as tablets, smartphones, and computers used by employees.

[0402] A "server" is a central management system that processes data and provides information via a network.

[0403] This invention relates to a system that generates and modifies personal goals based on organizational goals, assigned tasks, work history, and emotional data, and transmits them to the user's terminal. The operation of this system is described in detail below.

[0404] First, users input data regarding organizational goals, assigned duties, and work history using a device. For example, tablets, smartphones, or computers can be used as devices. This information is transmitted from the device to a server, which receives and appropriately stores the data.

[0405] The server uses natural language processing and machine learning models to analyze the submitted data and generate individual goals. At this time, the server retrieves the user's grade information from the employee database and determines whether the generated individual goals are appropriate for that grade. It then modifies the goals as needed.

[0406] Furthermore, the server utilizes an emotion engine to analyze emotional data. The emotion engine analyzes the user's emotional state through voice analysis and facial recognition technology, evaluating factors such as stress and motivation. The EmotionEngine library is used for this purpose.

[0407] Based on the analyzed emotional data, the server further optimizes personal goals. For example, if a user is experiencing high stress levels, the goals are eased; if they are highly motivated, more challenging goals are set.

[0408] The final revised personal goals are sent from the server to the user's terminal. The user reviews them, makes further revisions as needed, and finalizes them as the final goals. The finalized goals are then sent back to the server and stored in the database.

[0409] As a concrete example, consider the case of a new employee in the manufacturing department using the system. Data such as "production volume 10,000 units / month," "quality inspection," and "no maintenance history for manufacturing line A" are entered into the terminal. The server analyzes this data and generates initial personal goals. If the emotion engine determines that the user's stress level is high, the goal is revised to "perform quality inspections 50 times a day." The revised goal is sent to the user, and after the user confirms it, it is registered as the final goal.

[0410] Examples of prompts to input into a generative AI model include the following:

[0411] "Please generate individual employee goals based on the following data: Organizational goal: Production volume of 10,000 units / month, Job responsibilities: Quality inspection, Maintenance history for production line A, Work history: New. Additionally, for users with a stress level of 80 based on emotional data, please reduce their goals."

[0412] This allows the server to optimize individual goals based on the user's emotional state, enabling efficient management toward achieving the organization's overall goals.

[0413] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0414] Step 1:

[0415] Users input organizational goals, assigned tasks, and work history on their devices. The devices receive this input data, process it into an appropriate format, and send it to the server. Specifically, users use tablets or smartphones to input data such as "production volume 10,000 units / month," "quality inspection," and "no maintenance history for manufacturing line A," and then convert and process this data in JSON format or similar.

[0416] Step 2:

[0417] The server receives data sent from the terminal and stores it in a database. The server first checks the integrity and completeness of the transmitted data, and then stores it in an appropriate database. For example, it might use database software such as MongoDB or MySQL®.

[0418] Step 3:

[0419] The server analyzes the received data and generates individual goals. Using natural language processing and machine learning models, the server evaluates the relevance of organizational goals, assigned tasks, and work history to generate initial individual goals. These generated individual goals are expressed as specific objectives, such as "Perform quality checks 100 times a day."

[0420] Step 4:

[0421] The server retrieves user grade information from the employee database and determines whether the generated individual goals are appropriate for that grade. The server executes database queries to retrieve user grade information and evaluates the appropriateness of the goals. For example, it checks whether goals that are too high have been set for new employees.

[0422] Step 5:

[0423] The server will revise individual goals as needed. Based on the grade compliance check results, the server may revise goals. For example, it might change "perform 100 quality checks per day" to "perform 50 quality checks per day."

[0424] Step 6:

[0425] The server analyzes emotional data. The server uses an emotion engine (such as the EmotionEngine library) to collect and analyze emotional data from the user's voice and facial expressions. For example, it can assess stress levels and motivation based on voice data of the user talking about their goals and facial expression data.

[0426] Step 7:

[0427] The server further optimizes individual goals based on the analyzed emotional data. The server readjusts individual goals according to the emotional state. For example, it may further ease goals for users with high stress levels and set challenging goals for users with high motivation levels.

[0428] Step 8:

[0429] The server sends the final revised personal goals to the user's device. The server converts the final goals into the appropriate format and sends them to the user's device. The user's device receives them and displays them to the user.

[0430] Step 9:

[0431] The user reviews the final goal on their device and makes modifications as needed. The user may then make further modifications based on their own judgment. The modified goal is then sent back to the server.

[0432] Step 10:

[0433] The server stores the final individual goals modified by the user. The server then stores these final goals in a database for later progress tracking and evaluation. This results in an effective goal management system.

[0434] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0435] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0436] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0437] [Second Embodiment]

[0438] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0439] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0440] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0441] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0442] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0443] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0444] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0445] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0446] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0447] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0448] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0449] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0450] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned duties, and work history, determines whether these goals are appropriate for the employee's grade, and modifies them as necessary.

[0451] System Overview

[0452] This system incorporates several key features, allowing users to input organizational goals, assigned tasks, and work history, and generate individual goals. It also evaluates these goals based on a grade, sends revised goals to users, and saves the final goals, providing a method for consistently achieving overall organizational objectives.

[0453] Program description in natural language

[0454] 1. Data entry

[0455] The terminal receives input.

[0456] The terminal allows users (employees or their managers) to input "organizational goals," "assigned tasks," and "work history." For example, the sales department's organizational goal might be "achieve 100 million yen in annual sales," assigned tasks might be "acquire new customers" and "manage existing customers," and the employee's past performance might be entered in the work history.

[0457] 2. Data transmission and analysis

[0458] The device sends data, and the server receives it.

[0459] The terminal sends the input data to the server. The server receives and stores this data. The server then uses the data stored in the database to analyze it using natural language processing and machine learning algorithms.

[0460] The server generates personal goals.

[0461] Based on this analysis, the server generates individual goals. For example, regarding new customer acquisition, a goal such as "acquire 5 new customers per month" might be generated.

[0462] 3. Automatic determination of grade compatibility

[0463] The server verifies the user's grade information.

[0464] The server retrieves user grade information (job title, years of experience, etc.) from the employee database. It then automatically determines whether the generated goals are appropriate for the user's grade. For example, in the case of a new employee, it checks whether the goals are too high.

[0465] The server will modify the target (if necessary).

[0466] If the generated individual goals are not suitable for the current grade, the server will revise them. For example, the server might determine that the goal of "acquiring 5 new customers per month" is difficult for a new employee to achieve and revise it to "acquiring 3 new customers per month."

[0467] 4. Sending the target and final confirmation

[0468] The server sends the modified target to the terminal.

[0469] The revised personal goals are sent from the server to the user's device.

[0470] The device receives the final target and the user confirms it.

[0471] The device displays this goal to the user. The user is also provided with the ability to review the final goal and make further modifications if necessary.

[0472] The user sends the confirmed goal from their device to the server.

[0473] Once the user has finalized their goal, they send that information from their device to the server.

[0474] 5. Preservation of the final goal

[0475] The server saves the deterministic target.

[0476] The server saves the received final goal to a database so that it can be used later for progress management and evaluation.

[0477] Specific example

[0478] For example, consider the case of Tanaka, a new employee in the sales department, using the system. Tanaka's terminal is populated with the sales department's organizational goal of "achieving 100 million yen in annual sales," and his assigned tasks of "acquiring new customers" and "managing existing customers" are also entered. Since he is a new employee, there is no work history, so the server generates initial individual goals based on this information and checks if they are suitable for Tanaka's grade. The revised goals are sent to Tanaka's terminal, and after Tanaka confirms them, they are registered as final goals.

[0479] The system described above enables efficient management toward achieving overall organizational goals by automatically setting consistent and appropriate individual goals.

[0480] The following describes the processing flow.

[0481] Step 1:

[0482] The terminal receives input.

[0483] Users input "organizational goals," "assigned tasks," and "work history" using a terminal. This includes overall organizational goals, tasks that individual employees are expected to perform, and past work performance. For example, the sales department might enter "achieve 100 million yen in annual sales" as its organizational goal and "acquire new customers" as its assigned task.

[0484] Step 2:

[0485] The device sends data

[0486] The terminal sends the entered data on "organizational goals," "assigned tasks," and "work history" to the server. The data is formatted into the appropriate format before being sent to the server.

[0487] Step 3:

[0488] The server receives the data.

[0489] The server receives data sent from the terminal and stores it in its internal database. This enables centralized data management.

[0490] Step 4:

[0491] The server analyzes the data.

[0492] The server uses natural language processing and machine learning techniques to analyze the received data. For example, it extracts relevant keywords from "organizational goals" and "assigned tasks," and uses them to determine performance indicators.

[0493] Step 5:

[0494] The server generates personal goals.

[0495] Based on the analysis results, the server generates appropriate individual goals for each employee. For example, regarding new customer acquisition, a specific goal such as "acquire 5 new customers per month" is generated.

[0496] Step 6:

[0497] The server verifies the user's grade information.

[0498] The server retrieves user grade information from the employee database and determines whether the generated personal goals are appropriate for that grade. For new employees, it evaluates whether the generated goals are achievable.

[0499] Step 7:

[0500] The server modifies the target.

[0501] Based on the evaluation results, the server modifies the generated goals as needed. For example, if the goal "acquire 5 new customers per month" is too high, it will be modified to "acquire 3 new customers per month."

[0502] Step 8:

[0503] The server sends the modified target to the terminal.

[0504] The revised, final personal goals are sent from the server to the terminal and presented to the user.

[0505] Step 9:

[0506] The device receives the target and the user confirms it.

[0507] The user reviews their personal goals submitted via their device. They can then revise their goals as needed and perform a final review.

[0508] Step 10:

[0509] The user confirms the goal, and the device sends it to the server.

[0510] The user confirms the final goal, and the device sends that information back to the server.

[0511] Step 11:

[0512] The server saves the deterministic target.

[0513] The server saves the finalized goal in a database, which can then be used for progress management and evaluation of goal achievement.

[0514] Through these steps, consistent and appropriate individual goals are automatically generated, modified, and saved, enabling efficient management toward achieving the organization's overall goals.

[0515] (Example 1)

[0516] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0517] In modern organizations, there is a need for efficient management that sets individual goals tailored to each employee's role and capabilities, in order to achieve overall organizational goals. However, traditional systems do not automate goal setting that appropriately considers each employee's work history and grade information, requiring manual adjustments. This leads to problems such as time-consuming goal setting and a lack of consistency and appropriateness.

[0518] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0519] In this invention, the server includes means for transmitting data on organizational goals, assigned tasks, and work history to the server; means for analyzing the received data using natural language processing and machine learning algorithms; and means for obtaining grade information from an employee database and automatically determining the suitability of the generated goals. This enables the automatic generation and modification of consistent and appropriate individual goals for each employee, and allows for efficient management to achieve the goals of the entire organization.

[0520] "Organizational goals" refer to specific objectives or outcomes that the entire organization should achieve.

[0521] "Assigned duties" refers to the specific job responsibilities and tasks assigned to each employee.

[0522] "Work history" refers to a record of an employee's past work performance and achievements.

[0523] "Individual goals" refer to specific achievement targets and performance indicators set for each employee.

[0524] "User grade" refers to qualification information such as employee job title, years of experience, and performance evaluation.

[0525] "Modification" refers to the process of adjusting generated individual goals to optimal goals when they do not match the user's grade level.

[0526] A "terminal" refers to a device used for data entry and goal confirmation, and includes personal computers and smartphones.

[0527] A "server" is a central management system that performs tasks such as data analysis, storage, and transmission.

[0528] "Natural language processing" is an artificial intelligence technology that analyzes human language to understand its meaning.

[0529] A "machine learning algorithm" is a computational method aimed at learning from data and making predictions and decisions.

[0530] An "employee database" is a data storage system used to store employee personal information, grade information, work history, and other data.

[0531] "Suitability" is a criterion used to evaluate whether the generated goals are appropriate for the employee's grade and capabilities.

[0532] "Transmission" refers to the transfer of data from one device to another.

[0533] "Preservation" is the act of recording generated data or information in order to retain it on a permanent basis.

[0534] "Analysis" is the act of analyzing input data in various ways and making decisions based on the results.

[0535] "Automatic generation" refers to the process by which a machine or program generates data or goals without human intervention.

[0536] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned tasks, and work history, determines whether these goals are appropriate for the employee's grade, and modifies them as necessary. This system consists of a server, terminals, and users who access them.

[0537] Data entry

[0538] The terminal receives input.

[0539] Users use a terminal to input "organizational goals," "assigned tasks," and "work history." Specifically, a user might input "achieve 100 million yen in annual sales" as the organizational goal for the sales department, and "acquire new customers" and "manage existing customers" as their assigned tasks. They would also input past performance in the work history section.

[0540] Data transmission and analysis

[0541] The device sends data, and the server receives it.

[0542] The terminal inputs data and sends it to the server using protocols such as HTTP requests. The server receives this data and stores it in its own database. Subsequently, the server analyzes this stored data using natural language processing and machine learning algorithms. Specific software used for this purpose includes Python's NLTK and scikit-learn.

[0543] The server generates personal goals.

[0544] The server generates individual goals for each employee based on the analysis results. For example, regarding new customer acquisition, a goal such as "acquire 5 new customers per month" might be generated. This involves a process that uses machine learning models to calculate predictive results.

[0545] Automatic grade compatibility determination

[0546] The server verifies the user's grade information.

[0547] The server retrieves user grade information (job title, years of experience, etc.) from the employee database. It then automatically determines whether the generated goals are compatible with this grade information. A compatibility determination algorithm is used to determine this compatibility.

[0548] The server will modify the target (if necessary).

[0549] If the generated goal is not suitable for the user's grade, the server will modify the goal. For example, if the goal "acquire 5 new customers per month" is determined to be difficult for a new employee to achieve, it will be modified to "acquire 3 new customers per month."

[0550] Sending the target and final confirmation

[0551] The server sends the modified target to the terminal.

[0552] The revised personal goals are sent from the server to the user's device. This data is typically sent in JSON format.

[0553] The device receives the final target and the user confirms it.

[0554] The device displays the revised goal to the user. The user can review this goal and make further revisions if necessary. When the user finally confirms the goal, confirmation information is sent from the device to the server.

[0555] Preservation of the final goal

[0556] The server saves the deterministic target.

[0557] The server saves the confirmed goals to the database. This information can be used later for progress management and evaluation. The data is inserted into the database using SQL queries or similar methods.

[0558] Specific example

[0559] For example, consider a scenario where a new employee in the sales department uses the system. The new employee enters "Achieve 100 million yen in annual sales" as the organizational goal, and "Acquire new customers" and "Manage existing customers" as their assigned tasks. If there is no work history for the new employee, the server generates initial individual goals based on this information and automatically determines whether they are appropriate for the employee's grade. The revised goals are sent to the new employee's terminal, and after the new employee reviews them, they are registered as the final goals.

[0560] Example of a prompt

[0561] "Design a system that uses user input data (organizational goals, assigned tasks, work history) to generate individual employee goals, determines whether they are appropriate for the employee's grade, makes adjustments accordingly, and then sends them to the user."

[0562] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0563] Step 1:

[0564] Data entry

[0565] The terminal receives input.

[0566] The user uses a terminal to input "organizational goals," "assigned duties," and "work history." Specifically, the user manually enters data for each item into the input form on the terminal. For example, the user enters the organizational goal of "achieving 100 million yen in annual sales," their assigned duties of "acquiring new customers" and "managing existing customers," and their past work history. This entered data will then be used for the next step.

[0567] Step 2:

[0568] Sending data

[0569] The device sends data to the server.

[0570] The terminal sends data entered by the user to the server. Specifically, the data is sent to the server using an HTTP request. The inputs are organizational goals, assigned tasks, and work history, and this data is used in the next analysis step. The output is a notification that the transmission to the server is complete.

[0571] Step 3:

[0572] Data storage and analysis

[0573] The server stores and analyzes the data.

[0574] The server stores the submitted data in a database. Specifically, the server inserts the data into the database using SQL queries. Using the stored data, the server analyzes the data using natural language processing and machine learning algorithms. Libraries used include Python's NLTK and scikit-learn. The input to the analysis is the stored data, and the output is the analysis result.

[0575] Step 4:

[0576] Generating personal goals

[0577] The server generates personal goals.

[0578] Based on the analysis results, the server generates personal goals. Specifically, a machine learning model makes predictions, and goals are set based on those predictions. For example, a specific goal such as "acquire 5 new customers per month" is generated. The input is the analysis results, and the output is the generated personal goals.

[0579] Step 5:

[0580] Obtaining grade information

[0581] The server retrieves the user's grade information.

[0582] The server retrieves user grade information from the employee database. Specifically, it uses SQL queries to retrieve information such as the user's job title and years of experience. The input is the user ID, and the output is the grade information.

[0583] Step 6:

[0584] Grade compatibility determination

[0585] The server determines the suitability of the target.

[0586] The server automatically determines whether the generated personal goals are suitable for the user's grade. Specifically, it executes a suitability determination algorithm. The input is the generated personal goals and grade information, and the output is the suitability determination result.

[0587] Step 7:

[0588] Target revision

[0589] The server will modify the target (if necessary).

[0590] If the generated individual goals are not suitable for the user's grade, the server will revise them. For example, if the goal "acquire 5 new customers per month" is determined to be difficult for a new employee to achieve, it will be revised to "acquire 3 new customers per month." The input is the suitability assessment result, and the output is the revised individual goal.

[0591] Step 8:

[0592] Sending revised targets

[0593] The server sends the modified target to the terminal.

[0594] The revised personal goals are sent from the server to the user's terminal. Specifically, the data is sent in JSON format. The input is the revised personal goals, and the output is a notification that the transmission to the terminal is complete.

[0595] Step 9:

[0596] User verification

[0597] The device receives the final target and the user confirms it.

[0598] The terminal displays the revised goal to the user. The user can review this goal and make further revisions if necessary. Specifically, the revised goal is displayed in a dialog box on the terminal. The input is the revised personal goal, and the output is the user's confirmation result.

[0599] Step 10:

[0600] Final goal confirmed

[0601] The user sends the confirmed goal from their device to the server.

[0602] The user confirms the final goal and sends that information from the terminal to the server. Specifically, this action involves clicking a confirmation button. The input is the user's confirmation result, and the output is a notification to the server that the transmission is complete.

[0603] Step 11:

[0604] Preservation of the final goal

[0605] The server saves the deterministic target.

[0606] The server saves the confirmed goals to the database. Specifically, it uses an SQL query to insert the confirmed goals into the database. The input is the confirmed individual goals, and the output is a notification that the save is complete.

[0607] (Application Example 1)

[0608] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0609] Conventional factory robot management systems make it difficult to set optimal work objectives based on the performance history and capabilities of individual robots, and they lack the means to automatically determine whether those objectives are appropriate and to correct them as needed. As a result, it is difficult to maximize the overall production efficiency of the factory, and the lack of appropriate objective setting can negatively impact robot performance.

[0610] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0611] In this invention, the server includes means for inputting organizational goals, means for inputting assigned tasks, means for inputting work history, means for analyzing the inputted organizational goals, assigned tasks, and work history to generate individual goals, means for determining whether the generated individual goals are appropriate for the user's grade, means for modifying the generated individual goals based on the determination result, means for transmitting the modified individual goals to the user's terminal, means for saving the final individual goals modified by the user, means for optimizing the individual goals based on performance history, determining whether the goals are appropriate for the work capacity, and modifying them as necessary, and means for transmitting the generated goals to another system and performing execution verification. This enables the generation of optimal work goals according to the performance history and work capacity of a factory robot, automatic determination of their appropriateness, and modification of goals as necessary.

[0612] "Organizational goals" refer to specific numerical targets or results that the entire organization aims to achieve.

[0613] "Assigned tasks" refer to specific tasks or work that users or robots are responsible for performing.

[0614] "Work history" refers to a record of tasks and operations performed by users or robots in the past.

[0615] "Individual goals" refer to specific objectives that a particular user or robot should achieve.

[0616] "Grade" refers to a rank or level that represents the job title or capabilities of a user or robot.

[0617] "Performance history" refers to data that records the user's or robot's past work performance and results.

[0618] "Work capability" refers to the range and efficiency of tasks that a user or robot can perform.

[0619] "Final personal goals" refer to the final goals that have been finalized after revisions and confirmations.

[0620] "Execution verification" refers to the process of confirming whether the generated goals are being properly executed.

[0621] System program and hardware / software description

[0622] The system for implementing this invention automatically generates individual work objectives for each robot based on the organization's production goals, the tasks assigned to each robot, and their performance history. It then determines whether these objectives are appropriate for the robot's capabilities and adjusts them as necessary. The system utilizes the following hardware and software.

[0623] Terminal: A device used by factory supervisors and other managers to input "organizational goals," "assigned duties," and "work history." The data entered from the terminal is sent to the server.

[0624] Server: The main computer used for data analysis, goal generation, and optimization. The server stores data in a database and has the capability to analyze the data using machine learning algorithms and natural language processing.

[0625] Database: A storage system for saving organizational goals, assigned tasks, work history, user grade information, etc. The database operates in conjunction with a server.

[0626] Machine learning algorithms: Algorithms for generating individual work goals based on received data. Logistic regression and other machine learning models are used.

[0627] Utility software: Software that supports data transmission and reception, display of analysis results, and modification of goals. For example, libraries such as "pandas" and "sklearn" are used.

[0628] Specific examples of data analysis and goal generation

[0629] Data entered from the terminal (e.g., "annual production target of 1000 units," "welding operations," "past performance history," etc.) is sent to the server, which analyzes this data to generate optimal individual targets for each robot. For example, based on the performance history, robot A might be set with a target of "300 welds per month," and robot B with a target of "400 assembles per month." These generated targets are then adjusted to match the capabilities of each robot.

[0630] Example of a prompt

[0631] The following is an example of a prompt statement for using a generative AI model:

[0632] "Considering the role of factory robot assistants, please provide a Python program that generates optimal individual work objectives that align with the overall production goals, based on each robot's assigned tasks and performance history. It should also include a function to evaluate whether the objectives are appropriate for each robot's capabilities and adjust them as needed."

[0633] By using this prompt statement, it is possible to more effectively utilize the generated AI model and achieve automatic generation and optimization of the target settings for factory robots.

[0634] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0635] Step 1:

[0636] The terminal receives input.

[0637] The terminal allows users (such as factory supervisors) to input "organizational goals," "assigned tasks," and "performance history." For example, an annual production target of "1000 units" might be entered, assigned tasks such as "welding" or "assembly" might be entered, and the past performance history of each robot might be entered. This input data is then sent to the server.

[0638] Step 2:

[0639] The server receives and stores the data.

[0640] The server receives input data sent from the terminal and stores it in a database. This database includes organizational goals, assigned tasks, and performance history.

[0641] Step 3:

[0642] The server analyzes the data.

[0643] The server applies machine learning algorithms (e.g., logistic regression models) to analyze the stored data. Based on the input data, it performs data processing to generate optimal individual work goals for each robot. For example, it calculates the sum of the performance history and determines the goals that should be assigned to each robot. As a result of the analysis using the generative AI model, individual work goals for each robot are output.

[0644] Step 4:

[0645] The server determines the suitability of the target.

[0646] The server determines whether the generated personal work objectives are suitable for the work capabilities of each robot. It compares them with the robot's capability data stored in the database (e.g., the type and specifications of each robot) to confirm whether the objectives are achievable.

[0647] Step 5:

[0648] The server modifies the target.

[0649] If the generated individual work objectives do not match the robot's capabilities, the server automatically modifies the objectives. For example, if an objective is set that exceeds the capabilities of robot A, the objective will be lowered to a more realistic target such as "300 welds per month." These modified objectives are then saved in the database.

[0650] Step 6:

[0651] The server sends the target to the terminal.

[0652] The revised work objectives are sent from the server to the terminal. The terminal receives them and displays them to the user. The user reviews the displayed final work objectives and makes any necessary additional corrections.

[0653] Step 7:

[0654] The user makes a final confirmation of the goal and submits it.

[0655] The user reviews the displayed final work objective and confirms it if there are no problems. The objective confirmed by the user is sent from the terminal to the server. The server saves this final objective in its database.

[0656] Step 8:

[0657] The server performs a verification of the target execution.

[0658] The server sends the generated goals to other systems and monitors their execution to verify that they are being properly executed by the configured robots. Once the execution verification is complete, the progress is reported to the user.

[0659] This enables the generation of optimal work objectives based on the performance history and work capabilities of factory robots, automatic determination of their suitability, and further modification of objectives as needed.

[0660] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0661] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned tasks, and work history, determines whether these goals are appropriate for the employee's grade, and modifies them as necessary. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, this system can modify goals based on the user's emotional state.

[0662] System Overview

[0663] This system incorporates several key features, allowing users to input organizational goals, assigned tasks, and work history to generate individual goals. It also evaluates these goals based on a grade, sends revised goals to the user, and saves the final goals, providing a method for consistently achieving overall organizational objectives. Furthermore, by utilizing an emotion engine, it recognizes the user's emotional state (e.g., stress and motivation) and further optimizes individual goals accordingly.

[0664] Program description in natural language

[0665] 1. Data entry

[0666] The terminal receives input.

[0667] Users input "organizational goals," "assigned tasks," and "work history" using a terminal. For example, the sales department might input "achieve 100 million yen in annual sales" as its organizational goal, and "acquire new customers" and "manage existing customers" as its assigned tasks.

[0668] 2. Data transmission and analysis

[0669] The device sends data, and the server receives it.

[0670] The terminal sends the entered data—organizational goals, assigned tasks, and work history—to the server. The server then receives the data and stores it appropriately.

[0671] The server analyzes the data.

[0672] The server uses natural language processing and machine learning techniques to analyze the transmitted data. For example, it evaluates the relationship between "organizational goals" and "assigned tasks" and extracts the information necessary to generate individual goals.

[0673] 3. Setting personal goals

[0674] The server generates personal goals.

[0675] Based on the analysis results, the server generates appropriate individual goals for each employee. For example, a specific goal such as "acquire 5 new customers per month" is generated.

[0676] 4. Determining Grade Compatibility

[0677] The server verifies the user's grade information.

[0678] The server retrieves user grade information from the employee database and determines whether the generated personal goals are appropriate for that grade. For example, in the case of a new employee, it evaluates whether the generated goals are achievable.

[0679] The server modifies the target.

[0680] If necessary, the server will modify the generated goals. For example, if the goal of "acquire 5 new customers per month" is too high for a new employee, it will be modified to "acquire 3 new customers per month."

[0681] 5. Utilizing the Emotion Engine

[0682] The server uses an emotion engine to recognize the user's emotions.

[0683] The emotion engine uses voice analysis and facial recognition technology to collect and analyze user emotional data. For example, it evaluates the stress and motivation that users feel towards their goals.

[0684] The server adjusts its goals based on its emotional state.

[0685] Based on the data obtained from the emotion engine, the server further optimizes the goals. For example, it eases goals for users with high stress levels, while changing them to more challenging goals for highly motivated users.

[0686] 6. Sending the target and final confirmation

[0687] The server sends the modified target to the terminal.

[0688] The revised, final personal goals are sent from the server to the terminal and presented to the user.

[0689] The device receives the target and the user confirms it.

[0690] The user reviews their personal goals submitted via their device. They can then revise their goals as needed and perform a final review.

[0691] The user confirms the goal, and the device sends it to the server.

[0692] The user confirms the final goal, and the device sends that information back to the server.

[0693] 7. Preservation of the final goal

[0694] The server saves the deterministic target.

[0695] The server stores the finalized goal in a database, which can then be used for progress management and evaluation of goal achievement.

[0696] Specific example

[0697] For example, consider the case of Tanaka, a new employee in the sales department, using the system. Tanaka's terminal is populated with the sales department's organizational goal of "achieving 100 million yen in annual sales," as well as his assigned tasks of "acquiring new customers" and "managing existing customers." Since he is a new employee, there is no work history, so the server generates initial individual goals based on this information and checks if they are appropriate for Tanaka's grade. Then, based on the emotion analysis of the emotion engine, the goals are optimized according to Tanaka's stress and motivation. The revised goals are sent to Tanaka's terminal, and after Tanaka reviews them, they are registered as final goals.

[0698] This system not only automatically generates, modifies, and saves individual goals that are consistent and appropriate, but also takes into account the user's emotional state, enabling efficient management toward achieving the organization's overall goals.

[0699] The following describes the processing flow.

[0700] Step 1:

[0701] The terminal receives input.

[0702] Users input "organizational goals," "assigned tasks," and "work history" using a terminal. For example, the sales department might enter "achieve 100 million yen in annual sales" as its organizational goal, and "acquire new customers" and "manage existing customers" as its assigned tasks. Past work history is entered in a similar manner.

[0703] Step 2:

[0704] The device sends data

[0705] The terminal formats the "organizational goals," "assigned tasks," and "work history" data entered by the user and sends it to the server.

[0706] Step 3:

[0707] The server receives the data.

[0708] The server receives data sent from the terminal and stores it in the database. This makes the information available throughout the entire system.

[0709] Step 4:

[0710] The server analyzes the data.

[0711] The server uses natural language processing and machine learning techniques to analyze the stored data. For example, it extracts relevant keywords from "organizational goals" and "assigned tasks" and organizes the information necessary to generate individual goals.

[0712] Step 5:

[0713] The server generates personal goals.

[0714] Based on the analysis results, the server generates appropriate individual goals for each employee. For example, a specific goal such as "acquire 5 new customers per month" is generated.

[0715] Step 6:

[0716] The server verifies the user's grade information.

[0717] The server retrieves user grade information from the employee database. It then compares the retrieved grade information with the generated individual goals to determine if the goals are appropriate for the user's grade.

[0718] Step 7:

[0719] The server modifies the target.

[0720] If the generated individual goals do not match the user's grade, the server will revise the goals. For example, if the goal of "acquiring 5 new customers per month" is too high for a new employee, it will be revised to "acquiring 3 new customers per month."

[0721] Step 8:

[0722] The server uses an emotion engine to recognize the user's emotions.

[0723] The server receives data related to the emotion engine from the terminal (e.g., facial recognition camera or voice input) and analyzes the user's emotional state. It evaluates stress and motivation based on the user's facial expressions and tone of voice when they input data.

[0724] Step 9:

[0725] The server adjusts its goals based on its emotional state.

[0726] Based on the emotional data obtained by the emotion engine, the server further optimizes the goals. For example, if the user is experiencing high stress, the goals are eased; conversely, if high motivation is observed, the goals are made more challenging.

[0727] Step 10:

[0728] The server sends the modified target to the terminal.

[0729] The revised and optimized final personal goals are sent from the server to the terminal and presented to the user.

[0730] Step 11:

[0731] The device receives the target and the user confirms it.

[0732] The user reviews the personal goals presented through their device. They can further modify their goals as needed and then perform a final review.

[0733] Step 12:

[0734] The user confirms the goal, and the device sends it to the server.

[0735] After the user confirms their final goal, the device sends that information to the server.

[0736] Step 13:

[0737] The server saves the deterministic target.

[0738] The server saves the received final goals to a database. The saved goals are then used for progress management and evaluation of goal achievement.

[0739] Through these steps, not only are consistent and appropriate individual goals automatically generated, modified, and saved, but user emotional states are also taken into consideration, enabling efficient management toward achieving the organization's overall goals.

[0740] (Example 2)

[0741] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0742] When setting individual goals for employees, it is necessary to consider each employee's emotional state while maintaining consistency and appropriateness. Traditional systems lacked sufficient coordination between organizational and individual goals, making it difficult to set goals that matched employees' motivation and stress levels. As a result, employee goal achievement may decline, potentially leading to a decrease in overall organizational performance.

[0743] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0744] In this invention, the server includes means for inputting organizational goals, means for inputting assigned tasks, means for inputting work history, means for analyzing the inputted organizational goals, assigned tasks, and work history to generate individual goals, means for determining whether the generated individual goals are appropriate for the user's rank, means for modifying the generated individual goals based on the determination result, means for recognizing the user's emotional state using an emotion engine, means for further modifying the individual goals based on the recognized emotional state, means for transmitting the modified individual goals to the user's terminal, and means for saving the final individual goals modified by the user. This makes it possible to set appropriate goals based on the work content and emotional state of each individual employee.

[0745] "Organizational goals" are specific objectives and indicators that the entire organization should strive to achieve.

[0746] "Assigned duties" refer to the specific tasks and responsibilities that employees are responsible for on a daily basis.

[0747] "Work history" refers to the records and achievements of an employee's past work.

[0748] "Individual goals" are specific objectives that each employee should aim to achieve, and they are set in conjunction with organizational goals.

[0749] "Job rank" refers to a positional rank determined based on an employee's job title and experience, and serves as a basis for evaluation and goal setting.

[0750] The "emotion engine" is a technology for recognizing and analyzing the emotional state of employees, and it collects emotional data using voice analysis and facial recognition.

[0751] "Server" refers to the central processing unit and related software used to analyze input data and generate and modify individual goals.

[0752] A "terminal" is a device used by employees to input data, check goals, and make adjustments.

[0753] A "machine learning model" is an algorithm and framework that learns from data and automatically performs tasks such as goal generation and sentiment analysis.

[0754] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned tasks, and work history, determines whether these goals are appropriate for the employee's rank, and modifies them as necessary. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, this system can modify goals based on the user's emotional state.

[0755] This system incorporates several key mechanisms to input organizational goals, assigned tasks, and work history, and generates individual goals. It also evaluates these goals based on performance ratings, sends revised goals to the user, and saves the final goals, providing a method for consistently achieving overall organizational objectives. Furthermore, by utilizing an emotion engine, it recognizes the user's emotional state (e.g., stress and motivation) and further optimizes individual goals accordingly.

[0756] Specifically, the invention is implemented using the following procedure.

[0757] Users input organizational goals, assigned tasks, and work history from their terminals. The terminals then send this data to a server. The server analyzes the received data using natural language processing (NLP) techniques, specifically utilizing Python's "spaCy" library and "TensorFlow." From the analyzed data, the server generates appropriate personal goals, using the machine learning library "scikit-learn."

[0758] For each generated individual goal, the server retrieves the user's rank information from the employee database and evaluates whether the generated goal is appropriate for that rank. For example, in the case of a new employee, it evaluates whether the generated goal is achievable and modifies the goal as necessary. After this modification process, the server uses an emotion engine to collect and analyze the user's emotional state from voice analysis and facial recognition technology. IBM Watson and Microsoft Azure Cognitive Services are used for this process.

[0759] Based on the data obtained from the emotion engine, the server further optimizes the goals. For example, it eases goals for users with high stress levels, while changing them to more challenging goals for highly motivated users. The final revised personal goals are sent from the server to the user's device, and after the user reviews and confirms them, they are sent back to the server for final confirmation. The server stores the final confirmed goals in a database, which is then used for progress management and evaluation of goal achievement.

[0760] For example, consider the case of Tanaka, a new employee in the sales department, using the system. Tanaka's terminal is populated with the sales department's organizational goal of "achieving 100 million yen in annual sales," as well as his assigned tasks of "acquiring new customers" and "managing existing customers." Since he is a new employee, there is no work history, so the server generates initial individual goals based on this information and checks if they are appropriate for Tanaka's rank. Then, based on the emotion analysis of the emotion engine, the goals are optimized according to Tanaka's stress and motivation. The revised goals are sent to Tanaka's terminal, and after Tanaka reviews them, they are registered as final goals.

[0761] The following are examples of specific prompts for a generative AI model.

[0762] prompt:

[0763] "Please generate personal goals for Ms. Tanaka, a new employee in the sales department. The current organizational goal is to achieve 100 million yen in annual sales, and her responsibilities include acquiring new customers and managing existing ones. As she is a new employee, she has no work history. Please set optimal goals considering Ms. Tanaka's current stress levels and motivation."

[0764] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0765] Step 1:

[0766] The user enters data into the device.

[0767] Description: Users use a terminal to input "organizational goals," "assigned tasks," and "work history." This data is entered from the user's terminal. For example, if a user inputs the organizational goal of "achieving 100 million yen in annual sales" for the sales department, and also inputs assigned tasks such as "acquiring new customers" and "managing existing customers," this data will be entered into the terminal. The input data is sent from the terminal to the server for use in subsequent processing.

[0768] Step 2:

[0769] The device sends data to the server.

[0770] Description: The terminal sends the entered "organizational goals," "assigned tasks," and "work history" data to the server. Specifically, the terminal makes an API call, and the entered data is sent to the server as a POST request. The server temporarily stores the received data and uses it for later analysis. The input data (organizational goals, assigned tasks, work history) is sent to and stored on the server.

[0771] Step 3:

[0772] The server analyzes the data.

[0773] Description: The server analyzes the received data. This analysis utilizes natural language processing (NLP) and machine learning techniques. Specifically, it uses the Python library "spaCy" and "TensorFlow" to analyze text data and evaluate the relationship between organizational goals and assigned tasks. Based on the input data (organizational goals, assigned tasks, and work history), the analysis results (relevance evaluation and extraction of necessary information) are obtained.

[0774] Step 4:

[0775] The server generates personal goals.

[0776] Description: The server generates appropriate personal goals for each employee based on the results of data analysis. The machine learning library "scikit-learn" is used for this generation. For example, a personal goal such as "acquire 5 new customers per month" might be generated. Based on the input (analysis results), the generated personal goals are obtained as output.

[0777] Step 5:

[0778] The server retrieves the user's rank information.

[0779] Description: The server accesses the employee database and retrieves employee rank information. Specifically, it uses SQL queries to retrieve the required rank information from the database. Based on the input (user ID), the output (rank information) is obtained.

[0780] Step 6:

[0781] The server evaluates and modifies the generated individual goals based on their grade level.

[0782] Description: Based on the acquired grade information, the server evaluates whether the generated individual goals are appropriate for the employee's grade. For example, if the goal of "acquiring 5 new customers per month" is too high for new employee Tanaka, it will be revised to "acquiring 3 new customers per month." Based on the input (generated individual goals, grade information), the revised individual goals are output.

[0783] Step 7:

[0784] The server uses an emotion engine to recognize the user's emotional state.

[0785] Description: The server uses an emotion engine to recognize the user's emotional state through voice analysis and facial recognition technology. IBM Watson and Microsoft Azure Cognitive Services are used to collect and analyze the user's emotional data. Based on the input (voice data, facial data), the emotional state is output.

[0786] Step 8:

[0787] The server further modifies personal goals based on emotional state.

[0788] Description: The server further modifies personal goals based on the recognized emotional state. For example, it eases goals for users with high stress levels and changes them to more challenging goals for highly motivated users. Based on the input (modified personal goals, emotional state), further modified personal goals are output.

[0789] Step 9:

[0790] The server sends the revised personal goals to the device.

[0791] Description: The server sends the final, revised personal goals to the user's device. Specifically, it makes an API call and sends the revised personal goals to the user's device in JSON format. Based on the input (further revised personal goals), the output sent to the user's device is obtained.

[0792] Step 10:

[0793] The device receives the target and the user confirms it.

[0794] Description: The user receives and confirms the revised personal goals via their device. The user provides feedback on the goals and further revises them if necessary. Based on the input (revised personal goals), the user's confirmation and feedback are output.

[0795] Step 11:

[0796] The user confirms the goal, and the device sends it to the server.

[0797] Description: The user confirms the final goal, and the device sends that information back to the server. Specifically, when the confirm button is pressed, an API call is made, and the confirmed goal is sent to the server. Based on the input (user confirmation information), the confirmed goal is sent to the server.

[0798] Step 12:

[0799] The server saves the deterministic target.

[0800] Description: The server saves the finalized goals to the database. This data is used later for progress management and evaluation of goal achievement. Specifically, it uses SQL queries to insert the finalized goals into the database. Based on the input (finalized goals), the output stored in the database is obtained.

[0801] (Application Example 2)

[0802] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0803] Conventional individual goal-setting systems were limited to automatically generating individual goals based on organizational goals, assigned tasks, and work history, and could not take into account the user's emotional state. Therefore, flexible goal setting tailored to the user's stress and motivation levels was difficult, hindering the improvement of individual and organizational efficiency and performance. Furthermore, these systems were dependent on specific hardware and software, limiting their applicability.

[0804] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0805] In this invention, the server includes means for inputting organizational goals, means for inputting assigned tasks, means for inputting work history, means for analyzing the inputted organizational goals, assigned tasks, and work history to generate individual goals, means for determining whether the generated individual goals are appropriate for the user's grade, means for modifying the generated individual goals based on the determination result, means for analyzing emotional data, means for further optimizing the individual goals based on the analyzed emotional data, means for transmitting the modified individual goals to the user's terminal, and means for saving the final individual goals modified by the user. This enables flexible setting of individual goals that take into account the user's emotional state, and allows for efficient management toward achieving goals for both individuals and the organization as a whole.

[0806] "Organizational goals" are specific outcomes or achievement criteria that a particular organization aims to accomplish within a certain period of time.

[0807] "Assigned duties" refer to specific tasks or responsibilities that are individually assigned to each employee.

[0808] "Work history" refers to the records of various tasks and duties performed by an employee in the past, as well as the results thereof.

[0809] "Individual goals" refer to specific and achievable objectives set for individual employees based on organizational goals, assigned duties, and work history.

[0810] "User grade" refers to an indicator that shows an employee's position and experience level within an organization.

[0811] "Emotional data" refers to information about a user's feelings and emotional state collected through voice analysis and facial recognition technology.

[0812] "User devices" refer to electronic devices such as tablets, smartphones, and computers used by employees.

[0813] A "server" is a central management system that processes data and provides information via a network.

[0814] This invention relates to a system that generates and modifies personal goals based on organizational goals, assigned tasks, work history, and emotional data, and transmits them to the user's terminal. The operation of this system is described in detail below.

[0815] First, users input data regarding organizational goals, assigned duties, and work history using a device. For example, tablets, smartphones, or computers can be used as devices. This information is transmitted from the device to a server, which receives and appropriately stores the data.

[0816] The server uses natural language processing and machine learning models to analyze the submitted data and generate individual goals. At this time, the server retrieves the user's grade information from the employee database and determines whether the generated individual goals are appropriate for that grade. It then modifies the goals as needed.

[0817] Furthermore, the server utilizes an emotion engine to analyze emotional data. The emotion engine analyzes the user's emotional state through voice analysis and facial recognition technology, evaluating factors such as stress and motivation. The EmotionEngine library is used for this purpose.

[0818] Based on the analyzed emotional data, the server further optimizes personal goals. For example, if a user is experiencing high stress levels, the goals are eased; if they are highly motivated, more challenging goals are set.

[0819] The final revised personal goals are sent from the server to the user's terminal. The user reviews them, makes further revisions as needed, and finalizes them as the final goals. The finalized goals are then sent back to the server and stored in the database.

[0820] As a concrete example, consider the case of a new employee in the manufacturing department using the system. Data such as "production volume 10,000 units / month," "quality inspection," and "no maintenance history for manufacturing line A" are entered into the terminal. The server analyzes this data and generates initial personal goals. If the emotion engine determines that the user's stress level is high, the goal is revised to "perform quality inspections 50 times a day." The revised goal is sent to the user, and after the user confirms it, it is registered as the final goal.

[0821] Examples of prompts to input into a generative AI model include the following:

[0822] "Please generate individual employee goals based on the following data: Organizational goal: Production volume of 10,000 units / month, Job responsibilities: Quality inspection, Maintenance history for production line A, Work history: New. Additionally, for users with a stress level of 80 based on emotional data, please reduce their goals."

[0823] This allows the server to optimize individual goals based on the user's emotional state, enabling efficient management toward achieving the organization's overall goals.

[0824] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0825] Step 1:

[0826] Users input organizational goals, assigned tasks, and work history on their devices. The devices receive this input data, process it into an appropriate format, and send it to the server. Specifically, users use tablets or smartphones to input data such as "production volume 10,000 units / month," "quality inspection," and "no maintenance history for manufacturing line A," and then convert and process this data in JSON format or similar.

[0827] Step 2:

[0828] The server receives data sent from the terminal and stores it in a database. The server first checks the integrity and completeness of the transmitted data, and then stores it in an appropriate database. For example, it might use database software such as MongoDB or MySQL.

[0829] Step 3:

[0830] The server analyzes the received data and generates individual goals. Using natural language processing and machine learning models, the server evaluates the relevance of organizational goals, assigned tasks, and work history to generate initial individual goals. These generated individual goals are expressed as specific objectives, such as "Perform quality checks 100 times a day."

[0831] Step 4:

[0832] The server retrieves user grade information from the employee database and determines whether the generated individual goals are appropriate for that grade. The server executes database queries to retrieve user grade information and evaluates the appropriateness of the goals. For example, it checks whether goals that are too high have been set for new employees.

[0833] Step 5:

[0834] The server will revise individual goals as needed. Based on the grade compliance check results, the server may revise goals. For example, it might change "perform 100 quality checks per day" to "perform 50 quality checks per day."

[0835] Step 6:

[0836] The server analyzes emotional data. The server uses an emotion engine (such as the EmotionEngine library) to collect and analyze emotional data from the user's voice and facial expressions. For example, it can assess stress levels and motivation based on voice data of the user talking about their goals and facial expression data.

[0837] Step 7:

[0838] The server further optimizes individual goals based on the analyzed emotional data. The server readjusts individual goals according to the emotional state. For example, it may further ease goals for users with high stress levels and set challenging goals for users with high motivation levels.

[0839] Step 8:

[0840] The server sends the final revised personal goals to the user's device. The server converts the final goals into the appropriate format and sends them to the user's device. The user's device receives them and displays them to the user.

[0841] Step 9:

[0842] The user reviews the final goal on their device and makes modifications as needed. The user may then make further modifications based on their own judgment. The modified goal is then sent back to the server.

[0843] Step 10:

[0844] The server stores the final individual goals modified by the user. The server then stores these final goals in a database for later progress tracking and evaluation. This results in an effective goal management system.

[0845] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0846] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0847] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0848] [Third Embodiment]

[0849] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0850] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0851] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0852] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0853] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0854] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0855] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0856] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0857] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0858] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0859] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0860] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0861] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned duties, and work history, determines whether these goals are appropriate for the employee's grade, and modifies them as necessary.

[0862] System Overview

[0863] This system incorporates several key features, allowing users to input organizational goals, assigned tasks, and work history, and generate individual goals. It also evaluates these goals based on a grade, sends revised goals to users, and saves the final goals, providing a method for consistently achieving overall organizational objectives.

[0864] Program description in natural language

[0865] 1. Data entry

[0866] The terminal receives input.

[0867] The terminal allows users (employees or their managers) to input "organizational goals," "assigned tasks," and "work history." For example, the sales department's organizational goal might be "achieve 100 million yen in annual sales," assigned tasks might be "acquire new customers" and "manage existing customers," and the employee's past performance might be entered in the work history.

[0868] 2. Data transmission and analysis

[0869] The device sends data, and the server receives it.

[0870] The terminal sends the input data to the server. The server receives and stores this data. The server then uses the data stored in the database to analyze it using natural language processing and machine learning algorithms.

[0871] The server generates personal goals.

[0872] Based on this analysis, the server generates individual goals. For example, regarding new customer acquisition, a goal such as "acquire 5 new customers per month" might be generated.

[0873] 3. Automatic determination of grade compatibility

[0874] The server verifies the user's grade information.

[0875] The server retrieves user grade information (job title, years of experience, etc.) from the employee database. It then automatically determines whether the generated goals are appropriate for the user's grade. For example, in the case of a new employee, it checks whether the goals are too high.

[0876] The server will modify the target (if necessary).

[0877] If the generated individual goals are not suitable for the current grade, the server will revise them. For example, the server might determine that the goal of "acquiring 5 new customers per month" is difficult for a new employee to achieve and revise it to "acquiring 3 new customers per month."

[0878] 4. Sending the target and final confirmation

[0879] The server sends the modified target to the terminal.

[0880] The revised personal goals are sent from the server to the user's device.

[0881] The device receives the final target and the user confirms it.

[0882] The device displays this goal to the user. The user is also provided with the ability to review the final goal and make further modifications if necessary.

[0883] The user sends the confirmed goal from their device to the server.

[0884] Once the user has finalized their goal, they send that information from their device to the server.

[0885] 5. Preservation of the final goal

[0886] The server saves the deterministic target.

[0887] The server saves the received final goal to a database so that it can be used later for progress management and evaluation.

[0888] Specific example

[0889] For example, consider the case of Tanaka, a new employee in the sales department, using the system. Tanaka's terminal is populated with the sales department's organizational goal of "achieving 100 million yen in annual sales," and his assigned tasks of "acquiring new customers" and "managing existing customers" are also entered. Since he is a new employee, there is no work history, so the server generates initial individual goals based on this information and checks if they are suitable for Tanaka's grade. The revised goals are sent to Tanaka's terminal, and after Tanaka confirms them, they are registered as final goals.

[0890] The system described above enables efficient management toward achieving overall organizational goals by automatically setting consistent and appropriate individual goals.

[0891] The following describes the processing flow.

[0892] Step 1:

[0893] The terminal receives input.

[0894] Users input "organizational goals," "assigned tasks," and "work history" using a terminal. This includes overall organizational goals, tasks that individual employees are expected to perform, and past work performance. For example, the sales department might enter "achieve 100 million yen in annual sales" as its organizational goal and "acquire new customers" as its assigned task.

[0895] Step 2:

[0896] The device sends data

[0897] The terminal sends the entered data on "organizational goals," "assigned tasks," and "work history" to the server. The data is formatted into the appropriate format before being sent to the server.

[0898] Step 3:

[0899] The server receives the data.

[0900] The server receives data sent from the terminal and stores it in its internal database. This enables centralized data management.

[0901] Step 4:

[0902] The server analyzes the data.

[0903] The server uses natural language processing and machine learning techniques to analyze the received data. For example, it extracts relevant keywords from "organizational goals" and "assigned tasks," and uses them to determine performance indicators.

[0904] Step 5:

[0905] The server generates personal goals.

[0906] Based on the analysis results, the server generates appropriate individual goals for each employee. For example, regarding new customer acquisition, a specific goal such as "acquire 5 new customers per month" is generated.

[0907] Step 6:

[0908] The server verifies the user's grade information.

[0909] The server retrieves user grade information from the employee database and determines whether the generated personal goals are appropriate for that grade. For new employees, it evaluates whether the generated goals are achievable.

[0910] Step 7:

[0911] The server modifies the target.

[0912] Based on the evaluation results, the server modifies the generated goals as needed. For example, if the goal "acquire 5 new customers per month" is too high, it will be modified to "acquire 3 new customers per month."

[0913] Step 8:

[0914] The server sends the modified target to the terminal.

[0915] The revised, final personal goals are sent from the server to the terminal and presented to the user.

[0916] Step 9:

[0917] The device receives the target and the user confirms it.

[0918] The user reviews their personal goals submitted via their device. They can then revise their goals as needed and perform a final review.

[0919] Step 10:

[0920] The user confirms the goal, and the device sends it to the server.

[0921] The user confirms the final goal, and the device sends that information back to the server.

[0922] Step 11:

[0923] The server saves the deterministic target.

[0924] The server saves the finalized goal in a database, which can then be used for progress management and evaluation of goal achievement.

[0925] Through these steps, consistent and appropriate individual goals are automatically generated, modified, and saved, enabling efficient management toward achieving the organization's overall goals.

[0926] (Example 1)

[0927] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0928] In modern organizations, there is a need for efficient management that sets individual goals tailored to each employee's role and capabilities, in order to achieve overall organizational goals. However, traditional systems do not automate goal setting that appropriately considers each employee's work history and grade information, requiring manual adjustments. This leads to problems such as time-consuming goal setting and a lack of consistency and appropriateness.

[0929] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0930] In this invention, the server includes means for transmitting data on organizational goals, assigned tasks, and work history to the server; means for analyzing the received data using natural language processing and machine learning algorithms; and means for obtaining grade information from an employee database and automatically determining the suitability of the generated goals. This enables the automatic generation and modification of consistent and appropriate individual goals for each employee, and allows for efficient management to achieve the goals of the entire organization.

[0931] "Organizational goals" refer to specific objectives or outcomes that the entire organization should achieve.

[0932] "Assigned duties" refers to the specific job responsibilities and tasks assigned to each employee.

[0933] "Work history" refers to a record of an employee's past work performance and achievements.

[0934] "Individual goals" refer to specific achievement targets and performance indicators set for each employee.

[0935] "User grade" refers to qualification information such as employee job title, years of experience, and performance evaluation.

[0936] "Modification" refers to the process of adjusting generated individual goals to optimal goals when they do not match the user's grade level.

[0937] A "terminal" refers to a device used for data entry and goal confirmation, and includes personal computers and smartphones.

[0938] A "server" is a central management system that performs tasks such as data analysis, storage, and transmission.

[0939] "Natural language processing" is an artificial intelligence technology that analyzes human language to understand its meaning.

[0940] A "machine learning algorithm" is a computational method aimed at learning from data and making predictions and decisions.

[0941] An "employee database" is a data storage system used to store employee personal information, grade information, work history, and other data.

[0942] "Suitability" is a criterion used to evaluate whether the generated goals are appropriate for the employee's grade and capabilities.

[0943] "Transmission" refers to the transfer of data from one device to another.

[0944] "Preservation" is the act of recording generated data or information in order to retain it on a permanent basis.

[0945] "Analysis" is the act of analyzing input data in various ways and making decisions based on the results.

[0946] "Automatic generation" refers to the process by which a machine or program generates data or goals without human intervention.

[0947] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned tasks, and work history, determines whether these goals are appropriate for the employee's grade, and modifies them as necessary. This system consists of a server, terminals, and users who access them.

[0948] Data entry

[0949] The terminal receives input.

[0950] Users use a terminal to input "organizational goals," "assigned tasks," and "work history." Specifically, a user might input "achieve 100 million yen in annual sales" as the organizational goal for the sales department, and "acquire new customers" and "manage existing customers" as their assigned tasks. They would also input past performance in the work history section.

[0951] Data transmission and analysis

[0952] The device sends data, and the server receives it.

[0953] The terminal inputs data and sends it to the server using protocols such as HTTP requests. The server receives this data and stores it in its own database. Subsequently, the server analyzes this stored data using natural language processing and machine learning algorithms. Specific software used for this purpose includes Python's NLTK and scikit-learn.

[0954] The server generates personal goals.

[0955] The server generates individual goals for each employee based on the analysis results. For example, regarding new customer acquisition, a goal such as "acquire 5 new customers per month" might be generated. This involves a process that uses machine learning models to calculate predictive results.

[0956] Automatic grade compatibility determination

[0957] The server verifies the user's grade information.

[0958] The server retrieves user grade information (job title, years of experience, etc.) from the employee database. It then automatically determines whether the generated goals are compatible with this grade information. A compatibility determination algorithm is used to determine this compatibility.

[0959] The server will modify the target (if necessary).

[0960] If the generated goal is not suitable for the user's grade, the server will modify the goal. For example, if the goal "acquire 5 new customers per month" is determined to be difficult for a new employee to achieve, it will be modified to "acquire 3 new customers per month."

[0961] Sending the target and final confirmation

[0962] The server sends the modified target to the terminal.

[0963] The revised personal goals are sent from the server to the user's device. This data is typically sent in JSON format.

[0964] The device receives the final target and the user confirms it.

[0965] The device displays the revised goal to the user. The user can review this goal and make further revisions if necessary. When the user finally confirms the goal, confirmation information is sent from the device to the server.

[0966] Preservation of the final goal

[0967] The server saves the deterministic target.

[0968] The server saves the confirmed goals to the database. This information can be used later for progress management and evaluation. The data is inserted into the database using SQL queries or similar methods.

[0969] Specific example

[0970] For example, consider a scenario where a new employee in the sales department uses the system. The new employee enters "Achieve 100 million yen in annual sales" as the organizational goal, and "Acquire new customers" and "Manage existing customers" as their assigned tasks. If there is no work history for the new employee, the server generates initial individual goals based on this information and automatically determines whether they are appropriate for the employee's grade. The revised goals are sent to the new employee's terminal, and after the new employee reviews them, they are registered as the final goals.

[0971] Example of a prompt

[0972] "Design a system that uses user input data (organizational goals, assigned tasks, work history) to generate individual employee goals, determines whether they are appropriate for the employee's grade, makes adjustments accordingly, and then sends them to the user."

[0973] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0974] Step 1:

[0975] Data entry

[0976] The terminal receives input.

[0977] The user uses a terminal to input "organizational goals," "assigned duties," and "work history." Specifically, the user manually enters data for each item into the input form on the terminal. For example, the user enters the organizational goal of "achieving 100 million yen in annual sales," their assigned duties of "acquiring new customers" and "managing existing customers," and their past work history. This entered data will then be used for the next step.

[0978] Step 2:

[0979] Sending data

[0980] The device sends data to the server.

[0981] The terminal sends data entered by the user to the server. Specifically, the data is sent to the server using an HTTP request. The inputs are organizational goals, assigned tasks, and work history, and this data is used in the next analysis step. The output is a notification that the transmission to the server is complete.

[0982] Step 3:

[0983] Data storage and analysis

[0984] The server stores and analyzes the data.

[0985] The server stores the submitted data in a database. Specifically, the server inserts the data into the database using SQL queries. Using the stored data, the server analyzes the data using natural language processing and machine learning algorithms. Libraries used include Python's NLTK and scikit-learn. The input to the analysis is the stored data, and the output is the analysis result.

[0986] Step 4:

[0987] Generating personal goals

[0988] The server generates personal goals.

[0989] Based on the analysis results, the server generates personal goals. Specifically, a machine learning model makes predictions, and goals are set based on those predictions. For example, a specific goal such as "acquire 5 new customers per month" is generated. The input is the analysis results, and the output is the generated personal goals.

[0990] Step 5:

[0991] Obtaining grade information

[0992] The server retrieves the user's grade information.

[0993] The server retrieves user grade information from the employee database. Specifically, it uses SQL queries to retrieve information such as the user's job title and years of experience. The input is the user ID, and the output is the grade information.

[0994] Step 6:

[0995] Grade compatibility determination

[0996] The server determines the suitability of the target.

[0997] The server automatically determines whether the generated personal goals are suitable for the user's grade. Specifically, it executes a suitability determination algorithm. The input is the generated personal goals and grade information, and the output is the suitability determination result.

[0998] Step 7:

[0999] Target revision

[1000] The server will modify the target (if necessary).

[1001] If the generated individual goals are not suitable for the user's grade, the server will revise them. For example, if the goal "acquire 5 new customers per month" is determined to be difficult for a new employee to achieve, it will be revised to "acquire 3 new customers per month." The input is the suitability assessment result, and the output is the revised individual goal.

[1002] Step 8:

[1003] Sending revised targets

[1004] The server sends the modified target to the terminal.

[1005] The revised personal goals are sent from the server to the user's terminal. Specifically, the data is sent in JSON format. The input is the revised personal goals, and the output is a notification that the transmission to the terminal is complete.

[1006] Step 9:

[1007] User verification

[1008] The device receives the final target and the user confirms it.

[1009] The terminal displays the revised goal to the user. The user can review this goal and make further revisions if necessary. Specifically, the revised goal is displayed in a dialog box on the terminal. The input is the revised personal goal, and the output is the user's confirmation result.

[1010] Step 10:

[1011] Final goal confirmed

[1012] The user sends the confirmed goal from their device to the server.

[1013] The user confirms the final goal and sends that information from the terminal to the server. Specifically, this action involves clicking a confirmation button. The input is the user's confirmation result, and the output is a notification to the server that the transmission is complete.

[1014] Step 11:

[1015] Preservation of the final goal

[1016] The server saves the deterministic target.

[1017] The server saves the confirmed goals to the database. Specifically, it uses an SQL query to insert the confirmed goals into the database. The input is the confirmed individual goals, and the output is a notification that the save is complete.

[1018] (Application Example 1)

[1019] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1020] Conventional factory robot management systems make it difficult to set optimal work objectives based on the performance history and capabilities of individual robots, and they lack the means to automatically determine whether those objectives are appropriate and to correct them as needed. As a result, it is difficult to maximize the overall production efficiency of the factory, and the lack of appropriate objective setting can negatively impact robot performance.

[1021] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1022] In this invention, the server includes means for inputting organizational goals, means for inputting assigned tasks, means for inputting work history, means for analyzing the inputted organizational goals, assigned tasks, and work history to generate individual goals, means for determining whether the generated individual goals are appropriate for the user's grade, means for modifying the generated individual goals based on the determination result, means for transmitting the modified individual goals to the user's terminal, means for saving the final individual goals modified by the user, means for optimizing the individual goals based on performance history, determining whether the goals are appropriate for the work capacity, and modifying them as necessary, and means for transmitting the generated goals to another system and performing execution verification. This enables the generation of optimal work goals according to the performance history and work capacity of a factory robot, automatic determination of their appropriateness, and modification of goals as necessary.

[1023] "Organizational goals" refer to specific numerical targets or results that the entire organization aims to achieve.

[1024] "Assigned tasks" refer to specific tasks or work that users or robots are responsible for performing.

[1025] "Work history" refers to a record of tasks and operations performed by users or robots in the past.

[1026] "Individual goals" refer to specific objectives that a particular user or robot should achieve.

[1027] "Grade" refers to a rank or level that represents the job title or capabilities of a user or robot.

[1028] "Performance history" refers to data that records the user's or robot's past work performance and results.

[1029] "Work capability" refers to the range and efficiency of tasks that a user or robot can perform.

[1030] "Final personal goals" refer to the final goals that have been finalized after revisions and confirmations.

[1031] "Execution verification" refers to the process of confirming whether the generated goals are being properly executed.

[1032] System program and hardware / software description

[1033] The system for implementing this invention automatically generates individual work objectives for each robot based on the organization's production goals, the tasks assigned to each robot, and their performance history. It then determines whether these objectives are appropriate for the robot's capabilities and adjusts them as necessary. The system utilizes the following hardware and software.

[1034] Terminal: A device used by factory supervisors and other managers to input "organizational goals," "assigned duties," and "work history." The data entered from the terminal is sent to the server.

[1035] Server: The main computer used for data analysis, goal generation, and optimization. The server stores data in a database and has the capability to analyze the data using machine learning algorithms and natural language processing.

[1036] Database: A storage system for saving organizational goals, assigned tasks, work history, user grade information, etc. The database operates in conjunction with a server.

[1037] Machine learning algorithms: Algorithms for generating individual work goals based on received data. Logistic regression and other machine learning models are used.

[1038] Utility software: Software that supports data transmission and reception, display of analysis results, and modification of goals. For example, libraries such as "pandas" and "sklearn" are used.

[1039] Specific examples of data analysis and goal generation

[1040] Data entered from the terminal (e.g., "annual production target of 1000 units," "welding operations," "past performance history," etc.) is sent to the server, which analyzes this data to generate optimal individual targets for each robot. For example, based on the performance history, robot A might be set with a target of "300 welds per month," and robot B with a target of "400 assembles per month." These generated targets are then adjusted to match the capabilities of each robot.

[1041] Example of a prompt

[1042] The following is an example of a prompt statement for using a generative AI model:

[1043] "Considering the role of factory robot assistants, please provide a Python program that generates optimal individual work objectives that align with the overall production goals, based on each robot's assigned tasks and performance history. It should also include a function to evaluate whether the objectives are appropriate for each robot's capabilities and adjust them as needed."

[1044] By using this prompt statement, it is possible to more effectively utilize the generated AI model and achieve automatic generation and optimization of the target settings for factory robots.

[1045] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1046] Step 1:

[1047] The terminal receives input.

[1048] The terminal allows users (such as factory supervisors) to input "organizational goals," "assigned tasks," and "performance history." For example, an annual production target of "1000 units" might be entered, assigned tasks such as "welding" or "assembly" might be entered, and the past performance history of each robot might be entered. This input data is then sent to the server.

[1049] Step 2:

[1050] The server receives and stores the data.

[1051] The server receives input data sent from the terminal and stores it in a database. This database includes organizational goals, assigned tasks, and performance history.

[1052] Step 3:

[1053] The server analyzes the data.

[1054] The server applies machine learning algorithms (e.g., logistic regression models) to analyze the stored data. Based on the input data, it performs data processing to generate optimal individual work goals for each robot. For example, it calculates the sum of the performance history and determines the goals that should be assigned to each robot. As a result of the analysis using the generative AI model, individual work goals for each robot are output.

[1055] Step 4:

[1056] The server determines the suitability of the target.

[1057] The server determines whether the generated personal work objectives are suitable for the work capabilities of each robot. It compares them with the robot's capability data stored in the database (e.g., the type and specifications of each robot) to confirm whether the objectives are achievable.

[1058] Step 5:

[1059] The server modifies the target.

[1060] If the generated individual work objectives do not match the robot's capabilities, the server automatically modifies the objectives. For example, if an objective is set that exceeds the capabilities of robot A, the objective will be lowered to a more realistic target such as "300 welds per month." These modified objectives are then saved in the database.

[1061] Step 6:

[1062] The server sends the target to the terminal.

[1063] The revised work objectives are sent from the server to the terminal. The terminal receives them and displays them to the user. The user reviews the displayed final work objectives and makes any necessary additional corrections.

[1064] Step 7:

[1065] The user makes a final confirmation of the goal and submits it.

[1066] The user reviews the displayed final work objective and confirms it if there are no problems. The objective confirmed by the user is sent from the terminal to the server. The server saves this final objective in its database.

[1067] Step 8:

[1068] The server performs a verification of the target execution.

[1069] The server sends the generated goals to other systems and monitors their execution to verify that they are being properly executed by the configured robots. Once the execution verification is complete, the progress is reported to the user.

[1070] This enables the generation of optimal work objectives based on the performance history and work capabilities of factory robots, automatic determination of their suitability, and further modification of objectives as needed.

[1071] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1072] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned tasks, and work history, determines whether these goals are appropriate for the employee's grade, and modifies them as necessary. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, this system can modify goals based on the user's emotional state.

[1073] System Overview

[1074] This system incorporates several key features, allowing users to input organizational goals, assigned tasks, and work history to generate individual goals. It also evaluates these goals based on a grade, sends revised goals to the user, and saves the final goals, providing a method for consistently achieving overall organizational objectives. Furthermore, by utilizing an emotion engine, it recognizes the user's emotional state (e.g., stress and motivation) and further optimizes individual goals accordingly.

[1075] Program description in natural language

[1076] 1. Data entry

[1077] The terminal receives input.

[1078] Users input "organizational goals," "assigned tasks," and "work history" using a terminal. For example, the sales department might input "achieve 100 million yen in annual sales" as its organizational goal, and "acquire new customers" and "manage existing customers" as its assigned tasks.

[1079] 2. Data transmission and analysis

[1080] The device sends data, and the server receives it.

[1081] The terminal sends the entered data—organizational goals, assigned tasks, and work history—to the server. The server then receives the data and stores it appropriately.

[1082] The server analyzes the data.

[1083] The server uses natural language processing and machine learning techniques to analyze the transmitted data. For example, it evaluates the relationship between "organizational goals" and "assigned tasks" and extracts the information necessary to generate individual goals.

[1084] 3. Setting personal goals

[1085] The server generates personal goals.

[1086] Based on the analysis results, the server generates appropriate individual goals for each employee. For example, a specific goal such as "acquire 5 new customers per month" is generated.

[1087] 4. Determining Grade Compatibility

[1088] The server verifies the user's grade information.

[1089] The server retrieves user grade information from the employee database and determines whether the generated personal goals are appropriate for that grade. For example, in the case of a new employee, it evaluates whether the generated goals are achievable.

[1090] The server modifies the target.

[1091] If necessary, the server will modify the generated goals. For example, if the goal of "acquire 5 new customers per month" is too high for a new employee, it will be modified to "acquire 3 new customers per month."

[1092] 5. Utilizing the Emotion Engine

[1093] The server uses an emotion engine to recognize the user's emotions.

[1094] The emotion engine uses voice analysis and facial recognition technology to collect and analyze user emotional data. For example, it evaluates the stress and motivation that users feel towards their goals.

[1095] The server adjusts its goals based on its emotional state.

[1096] Based on the data obtained from the emotion engine, the server further optimizes the goals. For example, it eases goals for users with high stress levels, while changing them to more challenging goals for highly motivated users.

[1097] 6. Sending the target and final confirmation

[1098] The server sends the modified target to the terminal.

[1099] The revised, final personal goals are sent from the server to the terminal and presented to the user.

[1100] The device receives the target and the user confirms it.

[1101] The user reviews their personal goals submitted via their device. They can then revise their goals as needed and perform a final review.

[1102] The user confirms the goal, and the device sends it to the server.

[1103] The user confirms the final goal, and the device sends that information back to the server.

[1104] 7. Preservation of the final goal

[1105] The server saves the deterministic target.

[1106] The server stores the finalized goal in a database, which can then be used for progress management and evaluation of goal achievement.

[1107] Specific example

[1108] For example, consider the case of Tanaka, a new employee in the sales department, using the system. Tanaka's terminal is populated with the sales department's organizational goal of "achieving 100 million yen in annual sales," as well as his assigned tasks of "acquiring new customers" and "managing existing customers." Since he is a new employee, there is no work history, so the server generates initial individual goals based on this information and checks if they are appropriate for Tanaka's grade. Then, based on the emotion analysis of the emotion engine, the goals are optimized according to Tanaka's stress and motivation. The revised goals are sent to Tanaka's terminal, and after Tanaka reviews them, they are registered as final goals.

[1109] This system not only automatically generates, modifies, and saves individual goals that are consistent and appropriate, but also takes into account the user's emotional state, enabling efficient management toward achieving the organization's overall goals.

[1110] The following describes the processing flow.

[1111] Step 1:

[1112] The terminal receives input.

[1113] Users input "organizational goals," "assigned tasks," and "work history" using a terminal. For example, the sales department might enter "achieve 100 million yen in annual sales" as its organizational goal, and "acquire new customers" and "manage existing customers" as its assigned tasks. Past work history is entered in a similar manner.

[1114] Step 2:

[1115] The device sends data

[1116] The terminal formats the "organizational goals," "assigned tasks," and "work history" data entered by the user and sends it to the server.

[1117] Step 3:

[1118] The server receives the data.

[1119] The server receives data sent from the terminal and stores it in the database. This makes the information available throughout the entire system.

[1120] Step 4:

[1121] The server analyzes the data.

[1122] The server uses natural language processing and machine learning techniques to analyze the stored data. For example, it extracts relevant keywords from "organizational goals" and "assigned tasks" and organizes the information necessary to generate individual goals.

[1123] Step 5:

[1124] The server generates personal goals.

[1125] Based on the analysis results, the server generates appropriate individual goals for each employee. For example, a specific goal such as "acquire 5 new customers per month" is generated.

[1126] Step 6:

[1127] The server verifies the user's grade information.

[1128] The server retrieves user grade information from the employee database. It then compares the retrieved grade information with the generated individual goals to determine if the goals are appropriate for the user's grade.

[1129] Step 7:

[1130] The server modifies the target.

[1131] If the generated individual goals do not match the user's grade, the server will revise the goals. For example, if the goal of "acquiring 5 new customers per month" is too high for a new employee, it will be revised to "acquiring 3 new customers per month."

[1132] Step 8:

[1133] The server uses an emotion engine to recognize the user's emotions.

[1134] The server receives data related to the emotion engine from the terminal (e.g., facial recognition camera or voice input) and analyzes the user's emotional state. It evaluates stress and motivation based on the user's facial expressions and tone of voice when they input data.

[1135] Step 9:

[1136] The server adjusts its goals based on its emotional state.

[1137] Based on the emotional data obtained by the emotion engine, the server further optimizes the goals. For example, if the user is experiencing high stress, the goals are eased; conversely, if high motivation is observed, the goals are made more challenging.

[1138] Step 10:

[1139] The server sends the modified target to the terminal.

[1140] The revised and optimized final personal goals are sent from the server to the terminal and presented to the user.

[1141] Step 11:

[1142] The device receives the target and the user confirms it.

[1143] The user reviews the personal goals presented through their device. They can further modify their goals as needed and then perform a final review.

[1144] Step 12:

[1145] The user confirms the goal, and the device sends it to the server.

[1146] After the user confirms their final goal, the device sends that information to the server.

[1147] Step 13:

[1148] The server saves the deterministic target.

[1149] The server saves the received final goals to a database. The saved goals are then used for progress management and evaluation of goal achievement.

[1150] Through these steps, not only are consistent and appropriate individual goals automatically generated, modified, and saved, but user emotional states are also taken into consideration, enabling efficient management toward achieving the organization's overall goals.

[1151] (Example 2)

[1152] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1153] When setting individual goals for employees, it is necessary to consider each employee's emotional state while maintaining consistency and appropriateness. Traditional systems lacked sufficient coordination between organizational and individual goals, making it difficult to set goals that matched employees' motivation and stress levels. As a result, employee goal achievement may decline, potentially leading to a decrease in overall organizational performance.

[1154] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1155] In this invention, the server includes means for inputting organizational goals, means for inputting assigned tasks, means for inputting work history, means for analyzing the inputted organizational goals, assigned tasks, and work history to generate individual goals, means for determining whether the generated individual goals are appropriate for the user's rank, means for modifying the generated individual goals based on the determination result, means for recognizing the user's emotional state using an emotion engine, means for further modifying the individual goals based on the recognized emotional state, means for transmitting the modified individual goals to the user's terminal, and means for saving the final individual goals modified by the user. This makes it possible to set appropriate goals based on the work content and emotional state of each individual employee.

[1156] "Organizational goals" are specific objectives and indicators that the entire organization should strive to achieve.

[1157] "Assigned duties" refer to the specific tasks and responsibilities that employees are responsible for on a daily basis.

[1158] "Work history" refers to the records and achievements of an employee's past work.

[1159] "Individual goals" are specific objectives that each employee should aim to achieve, and they are set in conjunction with organizational goals.

[1160] "Job rank" refers to a positional rank determined based on an employee's job title and experience, and serves as a basis for evaluation and goal setting.

[1161] The "emotion engine" is a technology for recognizing and analyzing the emotional state of employees, and it collects emotional data using voice analysis and facial recognition.

[1162] "Server" refers to the central processing unit and related software used to analyze input data and generate and modify individual goals.

[1163] A "terminal" is a device used by employees to input data, check goals, and make adjustments.

[1164] A "machine learning model" is an algorithm and framework that learns from data and automatically performs tasks such as goal generation and sentiment analysis.

[1165] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned tasks, and work history, determines whether these goals are appropriate for the employee's rank, and modifies them as necessary. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, this system can modify goals based on the user's emotional state.

[1166] This system incorporates several key mechanisms to input organizational goals, assigned tasks, and work history, and generates individual goals. It also evaluates these goals based on performance ratings, sends revised goals to the user, and saves the final goals, providing a method for consistently achieving overall organizational objectives. Furthermore, by utilizing an emotion engine, it recognizes the user's emotional state (e.g., stress and motivation) and further optimizes individual goals accordingly.

[1167] Specifically, the invention is implemented using the following procedure.

[1168] Users input organizational goals, assigned tasks, and work history from their terminals. The terminals then send this data to a server. The server analyzes the received data using natural language processing (NLP) techniques, specifically utilizing Python's "spaCy" library and "TensorFlow." From the analyzed data, the server generates appropriate personal goals, using the machine learning library "scikit-learn."

[1169] For each generated individual goal, the server retrieves the user's rank information from the employee database and evaluates whether the generated goal is appropriate for that rank. For example, in the case of a new employee, it evaluates whether the generated goal is achievable and modifies the goal as necessary. After this modification process, the server uses an emotion engine to collect and analyze the user's emotional state from voice analysis and facial recognition technology. IBM Watson and Microsoft Azure Cognitive Services are used for this process.

[1170] Based on the data obtained from the emotion engine, the server further optimizes the goals. For example, it eases goals for users with high stress levels, while changing them to more challenging goals for highly motivated users. The final revised personal goals are sent from the server to the user's device, and after the user reviews and confirms them, they are sent back to the server for final confirmation. The server stores the final confirmed goals in a database, which is then used for progress management and evaluation of goal achievement.

[1171] For example, consider the case of Tanaka, a new employee in the sales department, using the system. Tanaka's terminal is populated with the sales department's organizational goal of "achieving 100 million yen in annual sales," as well as his assigned tasks of "acquiring new customers" and "managing existing customers." Since he is a new employee, there is no work history, so the server generates initial individual goals based on this information and checks if they are appropriate for Tanaka's rank. Then, based on the emotion analysis of the emotion engine, the goals are optimized according to Tanaka's stress and motivation. The revised goals are sent to Tanaka's terminal, and after Tanaka reviews them, they are registered as final goals.

[1172] The following are examples of specific prompts for a generative AI model.

[1173] prompt:

[1174] "Please generate personal goals for Ms. Tanaka, a new employee in the sales department. The current organizational goal is to achieve 100 million yen in annual sales, and her responsibilities include acquiring new customers and managing existing ones. As she is a new employee, she has no work history. Please set optimal goals considering Ms. Tanaka's current stress levels and motivation."

[1175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1176] Step 1:

[1177] The user enters data into the device.

[1178] Description: Users use a terminal to input "organizational goals," "assigned tasks," and "work history." This data is entered from the user's terminal. For example, if a user inputs the organizational goal of "achieving 100 million yen in annual sales" for the sales department, and also inputs assigned tasks such as "acquiring new customers" and "managing existing customers," this data will be entered into the terminal. The input data is sent from the terminal to the server for use in subsequent processing.

[1179] Step 2:

[1180] The device sends data to the server.

[1181] Description: The terminal sends the entered "organizational goals," "assigned tasks," and "work history" data to the server. Specifically, the terminal makes an API call, and the entered data is sent to the server as a POST request. The server temporarily stores the received data and uses it for later analysis. The input data (organizational goals, assigned tasks, work history) is sent to and stored on the server.

[1182] Step 3:

[1183] The server analyzes the data.

[1184] Description: The server analyzes the received data. This analysis utilizes natural language processing (NLP) and machine learning techniques. Specifically, it uses the Python library "spaCy" and "TensorFlow" to analyze text data and evaluate the relationship between organizational goals and assigned tasks. Based on the input data (organizational goals, assigned tasks, and work history), the analysis results (relevance evaluation and extraction of necessary information) are obtained.

[1185] Step 4:

[1186] The server generates personal goals.

[1187] Description: The server generates appropriate personal goals for each employee based on the results of data analysis. The machine learning library "scikit-learn" is used for this generation. For example, a personal goal such as "acquire 5 new customers per month" might be generated. Based on the input (analysis results), the generated personal goals are obtained as output.

[1188] Step 5:

[1189] The server retrieves the user's rank information.

[1190] Description: The server accesses the employee database and retrieves employee rank information. Specifically, it uses SQL queries to retrieve the required rank information from the database. Based on the input (user ID), the output (rank information) is obtained.

[1191] Step 6:

[1192] The server evaluates and modifies the generated individual goals based on their grade level.

[1193] Description: Based on the acquired grade information, the server evaluates whether the generated individual goals are appropriate for the employee's grade. For example, if the goal of "acquiring 5 new customers per month" is too high for new employee Tanaka, it will be revised to "acquiring 3 new customers per month." Based on the input (generated individual goals, grade information), the revised individual goals are output.

[1194] Step 7:

[1195] The server uses an emotion engine to recognize the user's emotional state.

[1196] Description: The server uses an emotion engine to recognize the user's emotional state through voice analysis and facial recognition technology. IBM Watson and Microsoft Azure Cognitive Services are used to collect and analyze the user's emotional data. Based on the input (voice data, facial data), the emotional state is output.

[1197] Step 8:

[1198] The server further modifies personal goals based on emotional state.

[1199] Description: The server further modifies personal goals based on the recognized emotional state. For example, it eases goals for users with high stress levels and changes them to more challenging goals for highly motivated users. Based on the input (modified personal goals, emotional state), further modified personal goals are output.

[1200] Step 9:

[1201] The server sends the revised personal goals to the device.

[1202] Description: The server sends the final, revised personal goals to the user's device. Specifically, it makes an API call and sends the revised personal goals to the user's device in JSON format. Based on the input (further revised personal goals), the output sent to the user's device is obtained.

[1203] Step 10:

[1204] The device receives the target and the user confirms it.

[1205] Description: The user receives and confirms the revised personal goals via their device. The user provides feedback on the goals and further revises them if necessary. Based on the input (revised personal goals), the user's confirmation and feedback are output.

[1206] Step 11:

[1207] The user confirms the goal, and the device sends it to the server.

[1208] Description: The user confirms the final goal, and the device sends that information back to the server. Specifically, when the confirm button is pressed, an API call is made, and the confirmed goal is sent to the server. Based on the input (user confirmation information), the confirmed goal is sent to the server.

[1209] Step 12:

[1210] The server saves the deterministic target.

[1211] Description: The server saves the finalized goals to the database. This data is used later for progress management and evaluation of goal achievement. Specifically, it uses SQL queries to insert the finalized goals into the database. Based on the input (finalized goals), the output stored in the database is obtained.

[1212] (Application Example 2)

[1213] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1214] Conventional individual goal-setting systems were limited to automatically generating individual goals based on organizational goals, assigned tasks, and work history, and could not take into account the user's emotional state. Therefore, flexible goal setting tailored to the user's stress and motivation levels was difficult, hindering the improvement of individual and organizational efficiency and performance. Furthermore, these systems were dependent on specific hardware and software, limiting their applicability.

[1215] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1216] In this invention, the server includes means for inputting organizational goals, means for inputting assigned tasks, means for inputting work history, means for analyzing the inputted organizational goals, assigned tasks, and work history to generate individual goals, means for determining whether the generated individual goals are appropriate for the user's grade, means for modifying the generated individual goals based on the determination result, means for analyzing emotional data, means for further optimizing the individual goals based on the analyzed emotional data, means for transmitting the modified individual goals to the user's terminal, and means for saving the final individual goals modified by the user. This enables flexible setting of individual goals that take into account the user's emotional state, and allows for efficient management toward achieving goals for both individuals and the organization as a whole.

[1217] "Organizational goals" are specific outcomes or achievement criteria that a particular organization aims to accomplish within a certain period of time.

[1218] "Assigned duties" refer to specific tasks or responsibilities that are individually assigned to each employee.

[1219] "Work history" refers to the records of various tasks and duties performed by an employee in the past, as well as the results thereof.

[1220] "Individual goals" refer to specific and achievable objectives set for individual employees based on organizational goals, assigned duties, and work history.

[1221] "User grade" refers to an indicator that shows an employee's position and experience level within an organization.

[1222] "Emotional data" refers to information about a user's feelings and emotional state collected through voice analysis and facial recognition technology.

[1223] "User devices" refer to electronic devices such as tablets, smartphones, and computers used by employees.

[1224] A "server" is a central management system that processes data and provides information via a network.

[1225] This invention relates to a system that generates and modifies personal goals based on organizational goals, assigned tasks, work history, and emotional data, and transmits them to the user's terminal. The operation of this system is described in detail below.

[1226] First, users input data regarding organizational goals, assigned duties, and work history using a device. For example, tablets, smartphones, or computers can be used as devices. This information is transmitted from the device to a server, which receives and appropriately stores the data.

[1227] The server uses natural language processing and machine learning models to analyze the submitted data and generate individual goals. At this time, the server retrieves the user's grade information from the employee database and determines whether the generated individual goals are appropriate for that grade. It then modifies the goals as needed.

[1228] Furthermore, the server utilizes an emotion engine to analyze emotional data. The emotion engine analyzes the user's emotional state through voice analysis and facial recognition technology, evaluating factors such as stress and motivation. The EmotionEngine library is used for this purpose.

[1229] Based on the analyzed emotional data, the server further optimizes personal goals. For example, if a user is experiencing high stress levels, the goals are eased; if they are highly motivated, more challenging goals are set.

[1230] The final revised personal goals are sent from the server to the user's terminal. The user reviews them, makes further revisions as needed, and finalizes them as the final goals. The finalized goals are then sent back to the server and stored in the database.

[1231] As a concrete example, consider the case of a new employee in the manufacturing department using the system. Data such as "production volume 10,000 units / month," "quality inspection," and "no maintenance history for manufacturing line A" are entered into the terminal. The server analyzes this data and generates initial personal goals. If the emotion engine determines that the user's stress level is high, the goal is revised to "perform quality inspections 50 times a day." The revised goal is sent to the user, and after the user confirms it, it is registered as the final goal.

[1232] Examples of prompts to input into a generative AI model include the following:

[1233] "Please generate individual employee goals based on the following data: Organizational goal: Production volume of 10,000 units / month, Job responsibilities: Quality inspection, Maintenance history for production line A, Work history: New. Additionally, for users with a stress level of 80 based on emotional data, please reduce their goals."

[1234] This allows the server to optimize individual goals based on the user's emotional state, enabling efficient management toward achieving the organization's overall goals.

[1235] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1236] Step 1:

[1237] Users input organizational goals, assigned tasks, and work history on their devices. The devices receive this input data, process it into an appropriate format, and send it to the server. Specifically, users use tablets or smartphones to input data such as "production volume 10,000 units / month," "quality inspection," and "no maintenance history for manufacturing line A," and then convert and process this data in JSON format or similar.

[1238] Step 2:

[1239] The server receives data sent from the terminal and stores it in a database. The server first checks the integrity and completeness of the transmitted data, and then stores it in an appropriate database. For example, it might use database software such as MongoDB or MySQL.

[1240] Step 3:

[1241] The server analyzes the received data and generates individual goals. Using natural language processing and machine learning models, the server evaluates the relevance of organizational goals, assigned tasks, and work history to generate initial individual goals. These generated individual goals are expressed as specific objectives, such as "Perform quality checks 100 times a day."

[1242] Step 4:

[1243] The server retrieves user grade information from the employee database and determines whether the generated individual goals are appropriate for that grade. The server executes database queries to retrieve user grade information and evaluates the appropriateness of the goals. For example, it checks whether goals that are too high have been set for new employees.

[1244] Step 5:

[1245] The server will revise individual goals as needed. Based on the grade compliance check results, the server may revise goals. For example, it might change "perform 100 quality checks per day" to "perform 50 quality checks per day."

[1246] Step 6:

[1247] The server analyzes emotional data. The server uses an emotion engine (such as the EmotionEngine library) to collect and analyze emotional data from the user's voice and facial expressions. For example, it can assess stress levels and motivation based on voice data of the user talking about their goals and facial expression data.

[1248] Step 7:

[1249] The server further optimizes individual goals based on the analyzed emotional data. The server readjusts individual goals according to the emotional state. For example, it may further ease goals for users with high stress levels and set challenging goals for users with high motivation levels.

[1250] Step 8:

[1251] The server sends the final revised personal goals to the user's device. The server converts the final goals into the appropriate format and sends them to the user's device. The user's device receives them and displays them to the user.

[1252] Step 9:

[1253] The user reviews the final goal on their device and makes modifications as needed. The user may then make further modifications based on their own judgment. The modified goal is then sent back to the server.

[1254] Step 10:

[1255] The server stores the final individual goals modified by the user. The server then stores these final goals in a database for later progress tracking and evaluation. This results in an effective goal management system.

[1256] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1257] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1258] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1259] [Fourth Embodiment]

[1260] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1261] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1262] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1263] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1264] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1265] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1266] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1267] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1268] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1269] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1270] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1271] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1272] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1273] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned duties, and work history, determines whether these goals are appropriate for the employee's grade, and modifies them as necessary.

[1274] System Overview

[1275] This system incorporates several key features, allowing users to input organizational goals, assigned tasks, and work history, and generate individual goals. It also evaluates these goals based on a grade, sends revised goals to users, and saves the final goals, providing a method for consistently achieving overall organizational objectives.

[1276] Program description in natural language

[1277] 1. Data entry

[1278] The terminal receives input.

[1279] The terminal allows users (employees or their managers) to input "organizational goals," "assigned tasks," and "work history." For example, the sales department's organizational goal might be "achieve 100 million yen in annual sales," assigned tasks might be "acquire new customers" and "manage existing customers," and the employee's past performance might be entered in the work history.

[1280] 2. Data transmission and analysis

[1281] The device sends data, and the server receives it.

[1282] The terminal sends the input data to the server. The server receives and stores this data. The server then uses the data stored in the database to analyze it using natural language processing and machine learning algorithms.

[1283] The server generates personal goals.

[1284] Based on this analysis, the server generates individual goals. For example, regarding new customer acquisition, a goal such as "acquire 5 new customers per month" might be generated.

[1285] 3. Automatic determination of grade compatibility

[1286] The server verifies the user's grade information.

[1287] The server retrieves user grade information (job title, years of experience, etc.) from the employee database. It then automatically determines whether the generated goals are appropriate for the user's grade. For example, in the case of a new employee, it checks whether the goals are too high.

[1288] The server will modify the target (if necessary).

[1289] If the generated individual goals are not suitable for the current grade, the server will revise them. For example, the server might determine that the goal of "acquiring 5 new customers per month" is difficult for a new employee to achieve and revise it to "acquiring 3 new customers per month."

[1290] 4. Sending the target and final confirmation

[1291] The server sends the modified target to the terminal.

[1292] The revised personal goals are sent from the server to the user's device.

[1293] The device receives the final target and the user confirms it.

[1294] The device displays this goal to the user. The user is also provided with the ability to review the final goal and make further modifications if necessary.

[1295] The user sends the confirmed goal from their device to the server.

[1296] Once the user has finalized their goal, they send that information from their device to the server.

[1297] 5. Preservation of the final goal

[1298] The server saves the deterministic target.

[1299] The server saves the received final goal to a database so that it can be used later for progress management and evaluation.

[1300] Specific example

[1301] For example, consider the case of Tanaka, a new employee in the sales department, using the system. Tanaka's terminal is populated with the sales department's organizational goal of "achieving 100 million yen in annual sales," and his assigned tasks of "acquiring new customers" and "managing existing customers" are also entered. Since he is a new employee, there is no work history, so the server generates initial individual goals based on this information and checks if they are suitable for Tanaka's grade. The revised goals are sent to Tanaka's terminal, and after Tanaka confirms them, they are registered as final goals.

[1302] The system described above enables efficient management toward achieving overall organizational goals by automatically setting consistent and appropriate individual goals.

[1303] The following describes the processing flow.

[1304] Step 1:

[1305] The terminal receives input.

[1306] Users input "organizational goals," "assigned tasks," and "work history" using a terminal. This includes overall organizational goals, tasks that individual employees are expected to perform, and past work performance. For example, the sales department might enter "achieve 100 million yen in annual sales" as its organizational goal and "acquire new customers" as its assigned task.

[1307] Step 2:

[1308] The device sends data

[1309] The terminal sends the entered data on "organizational goals," "assigned tasks," and "work history" to the server. The data is formatted into the appropriate format before being sent to the server.

[1310] Step 3:

[1311] The server receives the data.

[1312] The server receives data sent from the terminal and stores it in its internal database. This enables centralized data management.

[1313] Step 4:

[1314] The server analyzes the data.

[1315] The server uses natural language processing and machine learning techniques to analyze the received data. For example, it extracts relevant keywords from "organizational goals" and "assigned tasks," and uses them to determine performance indicators.

[1316] Step 5:

[1317] The server generates personal goals.

[1318] Based on the analysis results, the server generates appropriate individual goals for each employee. For example, regarding new customer acquisition, a specific goal such as "acquire 5 new customers per month" is generated.

[1319] Step 6:

[1320] The server verifies the user's grade information.

[1321] The server retrieves user grade information from the employee database and determines whether the generated personal goals are appropriate for that grade. For new employees, it evaluates whether the generated goals are achievable.

[1322] Step 7:

[1323] The server modifies the target.

[1324] Based on the evaluation results, the server modifies the generated goals as needed. For example, if the goal "acquire 5 new customers per month" is too high, it will be modified to "acquire 3 new customers per month."

[1325] Step 8:

[1326] The server sends the modified target to the terminal.

[1327] The revised, final personal goals are sent from the server to the terminal and presented to the user.

[1328] Step 9:

[1329] The device receives the target and the user confirms it.

[1330] The user reviews their personal goals submitted via their device. They can then revise their goals as needed and perform a final review.

[1331] Step 10:

[1332] The user confirms the goal, and the device sends it to the server.

[1333] The user confirms the final goal, and the device sends that information back to the server.

[1334] Step 11:

[1335] The server saves the deterministic target.

[1336] The server saves the finalized goal in a database, which can then be used for progress management and evaluation of goal achievement.

[1337] Through these steps, consistent and appropriate individual goals are automatically generated, modified, and saved, enabling efficient management toward achieving the organization's overall goals.

[1338] (Example 1)

[1339] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1340] In modern organizations, there is a need for efficient management that sets individual goals tailored to each employee's role and capabilities, in order to achieve overall organizational goals. However, traditional systems do not automate goal setting that appropriately considers each employee's work history and grade information, requiring manual adjustments. This leads to problems such as time-consuming goal setting and a lack of consistency and appropriateness.

[1341] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1342] In this invention, the server includes means for transmitting data on organizational goals, assigned tasks, and work history to the server; means for analyzing the received data using natural language processing and machine learning algorithms; and means for obtaining grade information from an employee database and automatically determining the suitability of the generated goals. This enables the automatic generation and modification of consistent and appropriate individual goals for each employee, and allows for efficient management to achieve the goals of the entire organization.

[1343] "Organizational goals" refer to specific objectives or outcomes that the entire organization should achieve.

[1344] "Assigned duties" refers to the specific job responsibilities and tasks assigned to each employee.

[1345] "Work history" refers to a record of an employee's past work performance and achievements.

[1346] "Individual goals" refer to specific achievement targets and performance indicators set for each employee.

[1347] "User grade" refers to qualification information such as employee job title, years of experience, and performance evaluation.

[1348] "Modification" refers to the process of adjusting generated individual goals to optimal goals when they do not match the user's grade level.

[1349] A "terminal" refers to a device used for data entry and goal confirmation, and includes personal computers and smartphones.

[1350] A "server" is a central management system that performs tasks such as data analysis, storage, and transmission.

[1351] "Natural language processing" is an artificial intelligence technology that analyzes human language to understand its meaning.

[1352] A "machine learning algorithm" is a computational method aimed at learning from data and making predictions and decisions.

[1353] An "employee database" is a data storage system used to store employee personal information, grade information, work history, and other data.

[1354] "Suitability" is a criterion used to evaluate whether the generated goals are appropriate for the employee's grade and capabilities.

[1355] "Transmission" refers to the transfer of data from one device to another.

[1356] "Preservation" is the act of recording generated data or information in order to retain it on a permanent basis.

[1357] "Analysis" is the act of analyzing input data in various ways and making decisions based on the results.

[1358] "Automatic generation" refers to the process by which a machine or program generates data or goals without human intervention.

[1359] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned tasks, and work history, determines whether these goals are appropriate for the employee's grade, and modifies them as necessary. This system consists of a server, terminals, and users who access them.

[1360] Data entry

[1361] The terminal receives input.

[1362] Users use a terminal to input "organizational goals," "assigned tasks," and "work history." Specifically, a user might input "achieve 100 million yen in annual sales" as the organizational goal for the sales department, and "acquire new customers" and "manage existing customers" as their assigned tasks. They would also input past performance in the work history section.

[1363] Data transmission and analysis

[1364] The device sends data, and the server receives it.

[1365] The terminal inputs data and sends it to the server using protocols such as HTTP requests. The server receives this data and stores it in its own database. Subsequently, the server analyzes this stored data using natural language processing and machine learning algorithms. Specific software used for this purpose includes Python's NLTK and scikit-learn.

[1366] The server generates personal goals.

[1367] The server generates individual goals for each employee based on the analysis results. For example, regarding new customer acquisition, a goal such as "acquire 5 new customers per month" might be generated. This involves a process that uses machine learning models to calculate predictive results.

[1368] Automatic grade compatibility determination

[1369] The server verifies the user's grade information.

[1370] The server retrieves user grade information (job title, years of experience, etc.) from the employee database. It then automatically determines whether the generated goals are compatible with this grade information. A compatibility determination algorithm is used to determine this compatibility.

[1371] The server will modify the target (if necessary).

[1372] If the generated goal is not suitable for the user's grade, the server will modify the goal. For example, if the goal "acquire 5 new customers per month" is determined to be difficult for a new employee to achieve, it will be modified to "acquire 3 new customers per month."

[1373] Sending the target and final confirmation

[1374] The server sends the modified target to the terminal.

[1375] The revised personal goals are sent from the server to the user's device. This data is typically sent in JSON format.

[1376] The device receives the final target and the user confirms it.

[1377] The device displays the revised goal to the user. The user can review this goal and make further revisions if necessary. When the user finally confirms the goal, confirmation information is sent from the device to the server.

[1378] Preservation of the final goal

[1379] The server saves the deterministic target.

[1380] The server saves the confirmed goals to the database. This information can be used later for progress management and evaluation. The data is inserted into the database using SQL queries or similar methods.

[1381] Specific example

[1382] For example, consider a scenario where a new employee in the sales department uses the system. The new employee enters "Achieve 100 million yen in annual sales" as the organizational goal, and "Acquire new customers" and "Manage existing customers" as their assigned tasks. If there is no work history for the new employee, the server generates initial individual goals based on this information and automatically determines whether they are appropriate for the employee's grade. The revised goals are sent to the new employee's terminal, and after the new employee reviews them, they are registered as the final goals.

[1383] Example of a prompt

[1384] "Design a system that uses user input data (organizational goals, assigned tasks, work history) to generate individual employee goals, determines whether they are appropriate for the employee's grade, makes adjustments accordingly, and then sends them to the user."

[1385] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1386] Step 1:

[1387] Data entry

[1388] The terminal receives input.

[1389] The user uses a terminal to input "organizational goals," "assigned duties," and "work history." Specifically, the user manually enters data for each item into the input form on the terminal. For example, the user enters the organizational goal of "achieving 100 million yen in annual sales," their assigned duties of "acquiring new customers" and "managing existing customers," and their past work history. This entered data will then be used for the next step.

[1390] Step 2:

[1391] Sending data

[1392] The device sends data to the server.

[1393] The terminal sends data entered by the user to the server. Specifically, the data is sent to the server using an HTTP request. The inputs are organizational goals, assigned tasks, and work history, and this data is used in the next analysis step. The output is a notification that the transmission to the server is complete.

[1394] Step 3:

[1395] Data storage and analysis

[1396] The server stores and analyzes the data.

[1397] The server stores the submitted data in a database. Specifically, the server inserts the data into the database using SQL queries. Using the stored data, the server analyzes the data using natural language processing and machine learning algorithms. Libraries used include Python's NLTK and scikit-learn. The input to the analysis is the stored data, and the output is the analysis result.

[1398] Step 4:

[1399] Generating personal goals

[1400] The server generates personal goals.

[1401] Based on the analysis results, the server generates personal goals. Specifically, a machine learning model makes predictions, and goals are set based on those predictions. For example, a specific goal such as "acquire 5 new customers per month" is generated. The input is the analysis results, and the output is the generated personal goals.

[1402] Step 5:

[1403] Obtaining grade information

[1404] The server retrieves the user's grade information.

[1405] The server retrieves user grade information from the employee database. Specifically, it uses SQL queries to retrieve information such as the user's job title and years of experience. The input is the user ID, and the output is the grade information.

[1406] Step 6:

[1407] Grade compatibility determination

[1408] The server determines the suitability of the target.

[1409] The server automatically determines whether the generated personal goals are suitable for the user's grade. Specifically, it executes a suitability determination algorithm. The input is the generated personal goals and grade information, and the output is the suitability determination result.

[1410] Step 7:

[1411] Target revision

[1412] The server will modify the target (if necessary).

[1413] If the generated individual goals are not suitable for the user's grade, the server will revise them. For example, if the goal "acquire 5 new customers per month" is determined to be difficult for a new employee to achieve, it will be revised to "acquire 3 new customers per month." The input is the suitability assessment result, and the output is the revised individual goal.

[1414] Step 8:

[1415] Sending revised targets

[1416] The server sends the modified target to the terminal.

[1417] The revised personal goals are sent from the server to the user's terminal. Specifically, the data is sent in JSON format. The input is the revised personal goals, and the output is a notification that the transmission to the terminal is complete.

[1418] Step 9:

[1419] User verification

[1420] The device receives the final target and the user confirms it.

[1421] The terminal displays the revised goal to the user. The user can review this goal and make further revisions if necessary. Specifically, the revised goal is displayed in a dialog box on the terminal. The input is the revised personal goal, and the output is the user's confirmation result.

[1422] Step 10:

[1423] Final goal confirmed

[1424] The user sends the confirmed goal from their device to the server.

[1425] The user confirms the final goal and sends that information from the terminal to the server. Specifically, this action involves clicking a confirmation button. The input is the user's confirmation result, and the output is a notification to the server that the transmission is complete.

[1426] Step 11:

[1427] Preservation of the final goal

[1428] The server saves the deterministic target.

[1429] The server saves the confirmed goals to the database. Specifically, it uses an SQL query to insert the confirmed goals into the database. The input is the confirmed individual goals, and the output is a notification that the save is complete.

[1430] (Application Example 1)

[1431] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1432] Conventional factory robot management systems make it difficult to set optimal work objectives based on the performance history and capabilities of individual robots, and they lack the means to automatically determine whether those objectives are appropriate and to correct them as needed. As a result, it is difficult to maximize the overall production efficiency of the factory, and the lack of appropriate objective setting can negatively impact robot performance.

[1433] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1434] In this invention, the server includes means for inputting organizational goals, means for inputting assigned tasks, means for inputting work history, means for analyzing the inputted organizational goals, assigned tasks, and work history to generate individual goals, means for determining whether the generated individual goals are appropriate for the user's grade, means for modifying the generated individual goals based on the determination result, means for transmitting the modified individual goals to the user's terminal, means for saving the final individual goals modified by the user, means for optimizing the individual goals based on performance history, determining whether the goals are appropriate for the work capacity, and modifying them as necessary, and means for transmitting the generated goals to another system and performing execution verification. This enables the generation of optimal work goals according to the performance history and work capacity of a factory robot, automatic determination of their appropriateness, and modification of goals as necessary.

[1435] "Organizational goals" refer to specific numerical targets or results that the entire organization aims to achieve.

[1436] "Assigned tasks" refer to specific tasks or work that users or robots are responsible for performing.

[1437] "Work history" refers to a record of tasks and operations performed by users or robots in the past.

[1438] "Individual goals" refer to specific objectives that a particular user or robot should achieve.

[1439] "Grade" refers to a rank or level that represents the job title or capabilities of a user or robot.

[1440] "Performance history" refers to data that records the user's or robot's past work performance and results.

[1441] "Work capability" refers to the range and efficiency of tasks that a user or robot can perform.

[1442] "Final personal goals" refer to the final goals that have been finalized after revisions and confirmations.

[1443] "Execution verification" refers to the process of confirming whether the generated goals are being properly executed.

[1444] System program and hardware / software description

[1445] The system for implementing this invention automatically generates individual work objectives for each robot based on the organization's production goals, the tasks assigned to each robot, and their performance history. It then determines whether these objectives are appropriate for the robot's capabilities and adjusts them as necessary. The system utilizes the following hardware and software.

[1446] Terminal: A device used by factory supervisors and other managers to input "organizational goals," "assigned duties," and "work history." The data entered from the terminal is sent to the server.

[1447] Server: The main computer used for data analysis, goal generation, and optimization. The server stores data in a database and has the capability to analyze the data using machine learning algorithms and natural language processing.

[1448] Database: A storage system for saving organizational goals, assigned tasks, work history, user grade information, etc. The database operates in conjunction with a server.

[1449] Machine learning algorithms: Algorithms for generating individual work goals based on received data. Logistic regression and other machine learning models are used.

[1450] Utility software: Software that supports data transmission and reception, display of analysis results, and modification of goals. For example, libraries such as "pandas" and "sklearn" are used.

[1451] Specific examples of data analysis and goal generation

[1452] Data entered from the terminal (e.g., "annual production target of 1000 units," "welding operations," "past performance history," etc.) is sent to the server, which analyzes this data to generate optimal individual targets for each robot. For example, based on the performance history, robot A might be set with a target of "300 welds per month," and robot B with a target of "400 assembles per month." These generated targets are then adjusted to match the capabilities of each robot.

[1453] Example of a prompt

[1454] The following is an example of a prompt statement for using a generative AI model:

[1455] "Considering the role of factory robot assistants, please provide a Python program that generates optimal individual work objectives that align with the overall production goals, based on each robot's assigned tasks and performance history. It should also include a function to evaluate whether the objectives are appropriate for each robot's capabilities and adjust them as needed."

[1456] By using this prompt statement, it is possible to more effectively utilize the generated AI model and achieve automatic generation and optimization of the target settings for factory robots.

[1457] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1458] Step 1:

[1459] The terminal receives input.

[1460] The terminal allows users (such as factory supervisors) to input "organizational goals," "assigned tasks," and "performance history." For example, an annual production target of "1000 units" might be entered, assigned tasks such as "welding" or "assembly" might be entered, and the past performance history of each robot might be entered. This input data is then sent to the server.

[1461] Step 2:

[1462] The server receives and stores the data.

[1463] The server receives input data sent from the terminal and stores it in a database. This database includes organizational goals, assigned tasks, and performance history.

[1464] Step 3:

[1465] The server analyzes the data.

[1466] The server applies machine learning algorithms (e.g., logistic regression models) to analyze the stored data. Based on the input data, it performs data processing to generate optimal individual work goals for each robot. For example, it calculates the sum of the performance history and determines the goals that should be assigned to each robot. As a result of the analysis using the generative AI model, individual work goals for each robot are output.

[1467] Step 4:

[1468] The server determines the suitability of the target.

[1469] The server determines whether the generated personal work objectives are suitable for the work capabilities of each robot. It compares them with the robot's capability data stored in the database (e.g., the type and specifications of each robot) to confirm whether the objectives are achievable.

[1470] Step 5:

[1471] The server modifies the target.

[1472] If the generated individual work objectives do not match the robot's capabilities, the server automatically modifies the objectives. For example, if an objective is set that exceeds the capabilities of robot A, the objective will be lowered to a more realistic target such as "300 welds per month." These modified objectives are then saved in the database.

[1473] Step 6:

[1474] The server sends the target to the terminal.

[1475] The revised work objectives are sent from the server to the terminal. The terminal receives them and displays them to the user. The user reviews the displayed final work objectives and makes any necessary additional corrections.

[1476] Step 7:

[1477] The user makes a final confirmation of the goal and submits it.

[1478] The user reviews the displayed final work objective and confirms it if there are no problems. The objective confirmed by the user is sent from the terminal to the server. The server saves this final objective in its database.

[1479] Step 8:

[1480] The server performs a verification of the target execution.

[1481] The server sends the generated goals to other systems and monitors their execution to verify that they are being properly executed by the configured robots. Once the execution verification is complete, the progress is reported to the user.

[1482] This enables the generation of optimal work objectives based on the performance history and work capabilities of factory robots, automatic determination of their suitability, and further modification of objectives as needed.

[1483] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1484] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned tasks, and work history, determines whether these goals are appropriate for the employee's grade, and modifies them as necessary. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, this system can modify goals based on the user's emotional state.

[1485] System Overview

[1486] This system incorporates several key features, allowing users to input organizational goals, assigned tasks, and work history to generate individual goals. It also evaluates these goals based on a grade, sends revised goals to the user, and saves the final goals, providing a method for consistently achieving overall organizational objectives. Furthermore, by utilizing an emotion engine, it recognizes the user's emotional state (e.g., stress and motivation) and further optimizes individual goals accordingly.

[1487] Program description in natural language

[1488] 1. Data entry

[1489] The terminal receives input.

[1490] Users input "organizational goals," "assigned tasks," and "work history" using a terminal. For example, the sales department might input "achieve 100 million yen in annual sales" as its organizational goal, and "acquire new customers" and "manage existing customers" as its assigned tasks.

[1491] 2. Data transmission and analysis

[1492] The device sends data, and the server receives it.

[1493] The terminal sends the entered data—organizational goals, assigned tasks, and work history—to the server. The server then receives the data and stores it appropriately.

[1494] The server analyzes the data.

[1495] The server uses natural language processing and machine learning techniques to analyze the transmitted data. For example, it evaluates the relationship between "organizational goals" and "assigned tasks" and extracts the information necessary to generate individual goals.

[1496] 3. Setting personal goals

[1497] The server generates personal goals.

[1498] Based on the analysis results, the server generates appropriate individual goals for each employee. For example, a specific goal such as "acquire 5 new customers per month" is generated.

[1499] 4. Determining Grade Compatibility

[1500] The server verifies the user's grade information.

[1501] The server retrieves user grade information from the employee database and determines whether the generated personal goals are appropriate for that grade. For example, in the case of a new employee, it evaluates whether the generated goals are achievable.

[1502] The server modifies the target.

[1503] If necessary, the server will modify the generated goals. For example, if the goal of "acquire 5 new customers per month" is too high for a new employee, it will be modified to "acquire 3 new customers per month."

[1504] 5. Utilizing the Emotion Engine

[1505] The server uses an emotion engine to recognize the user's emotions.

[1506] The emotion engine uses voice analysis and facial recognition technology to collect and analyze user emotional data. For example, it evaluates the stress and motivation that users feel towards their goals.

[1507] The server adjusts its goals based on its emotional state.

[1508] Based on the data obtained from the emotion engine, the server further optimizes the goals. For example, it eases goals for users with high stress levels, while changing them to more challenging goals for highly motivated users.

[1509] 6. Sending the target and final confirmation

[1510] The server sends the modified target to the terminal.

[1511] The revised, final personal goals are sent from the server to the terminal and presented to the user.

[1512] The device receives the target and the user confirms it.

[1513] The user reviews their personal goals submitted via their device. They can then revise their goals as needed and perform a final review.

[1514] The user confirms the goal, and the device sends it to the server.

[1515] The user confirms the final goal, and the device sends that information back to the server.

[1516] 7. Preservation of the final goal

[1517] The server saves the deterministic target.

[1518] The server stores the finalized goal in a database, which can then be used for progress management and evaluation of goal achievement.

[1519] Specific example

[1520] For example, consider the case of Tanaka, a new employee in the sales department, using the system. Tanaka's terminal is populated with the sales department's organizational goal of "achieving 100 million yen in annual sales," as well as his assigned tasks of "acquiring new customers" and "managing existing customers." Since he is a new employee, there is no work history, so the server generates initial individual goals based on this information and checks if they are appropriate for Tanaka's grade. Then, based on the emotion analysis of the emotion engine, the goals are optimized according to Tanaka's stress and motivation. The revised goals are sent to Tanaka's terminal, and after Tanaka reviews them, they are registered as final goals.

[1521] This system not only automatically generates, modifies, and saves individual goals that are consistent and appropriate, but also takes into account the user's emotional state, enabling efficient management toward achieving the organization's overall goals.

[1522] The following describes the processing flow.

[1523] Step 1:

[1524] The terminal receives input.

[1525] Users input "organizational goals," "assigned tasks," and "work history" using a terminal. For example, the sales department might enter "achieve 100 million yen in annual sales" as its organizational goal, and "acquire new customers" and "manage existing customers" as its assigned tasks. Past work history is entered in a similar manner.

[1526] Step 2:

[1527] The device sends data

[1528] The terminal formats the "organizational goals," "assigned tasks," and "work history" data entered by the user and sends it to the server.

[1529] Step 3:

[1530] The server receives the data.

[1531] The server receives data sent from the terminal and stores it in the database. This makes the information available throughout the entire system.

[1532] Step 4:

[1533] The server analyzes the data.

[1534] The server uses natural language processing and machine learning techniques to analyze the stored data. For example, it extracts relevant keywords from "organizational goals" and "assigned tasks" and organizes the information necessary to generate individual goals.

[1535] Step 5:

[1536] The server generates personal goals.

[1537] Based on the analysis results, the server generates appropriate individual goals for each employee. For example, a specific goal such as "acquire 5 new customers per month" is generated.

[1538] Step 6:

[1539] The server verifies the user's grade information.

[1540] The server retrieves user grade information from the employee database. It then compares the retrieved grade information with the generated individual goals to determine if the goals are appropriate for the user's grade.

[1541] Step 7:

[1542] The server modifies the target.

[1543] If the generated individual goals do not match the user's grade, the server will revise the goals. For example, if the goal of "acquiring 5 new customers per month" is too high for a new employee, it will be revised to "acquiring 3 new customers per month."

[1544] Step 8:

[1545] The server uses an emotion engine to recognize the user's emotions.

[1546] The server receives data related to the emotion engine from the terminal (e.g., facial recognition camera or voice input) and analyzes the user's emotional state. It evaluates stress and motivation based on the user's facial expressions and tone of voice when they input data.

[1547] Step 9:

[1548] The server adjusts its goals based on its emotional state.

[1549] Based on the emotional data obtained by the emotion engine, the server further optimizes the goals. For example, if the user is experiencing high stress, the goals are eased; conversely, if high motivation is observed, the goals are made more challenging.

[1550] Step 10:

[1551] The server sends the modified target to the terminal.

[1552] The revised and optimized final personal goals are sent from the server to the terminal and presented to the user.

[1553] Step 11:

[1554] The device receives the target and the user confirms it.

[1555] The user reviews the personal goals presented through their device. They can further modify their goals as needed and then perform a final review.

[1556] Step 12:

[1557] The user confirms the goal, and the device sends it to the server.

[1558] After the user confirms their final goal, the device sends that information to the server.

[1559] Step 13:

[1560] The server saves the deterministic target.

[1561] The server saves the received final goals to a database. The saved goals are then used for progress management and evaluation of goal achievement.

[1562] Through these steps, not only are consistent and appropriate individual goals automatically generated, modified, and saved, but user emotional states are also taken into consideration, enabling efficient management toward achieving the organization's overall goals.

[1563] (Example 2)

[1564] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1565] When setting individual goals for employees, it is necessary to consider each employee's emotional state while maintaining consistency and appropriateness. Traditional systems lacked sufficient coordination between organizational and individual goals, making it difficult to set goals that matched employees' motivation and stress levels. As a result, employee goal achievement may decline, potentially leading to a decrease in overall organizational performance.

[1566] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1567] In this invention, the server includes means for inputting organizational goals, means for inputting assigned tasks, means for inputting work history, means for analyzing the inputted organizational goals, assigned tasks, and work history to generate individual goals, means for determining whether the generated individual goals are appropriate for the user's rank, means for modifying the generated individual goals based on the determination result, means for recognizing the user's emotional state using an emotion engine, means for further modifying the individual goals based on the recognized emotional state, means for transmitting the modified individual goals to the user's terminal, and means for saving the final individual goals modified by the user. This makes it possible to set appropriate goals based on the work content and emotional state of each individual employee.

[1568] "Organizational goals" are specific objectives and indicators that the entire organization should strive to achieve.

[1569] "Assigned duties" refer to the specific tasks and responsibilities that employees are responsible for on a daily basis.

[1570] "Work history" refers to the records and achievements of an employee's past work.

[1571] "Individual goals" are specific objectives that each employee should aim to achieve, and they are set in conjunction with organizational goals.

[1572] "Job rank" refers to a positional rank determined based on an employee's job title and experience, and serves as a basis for evaluation and goal setting.

[1573] The "emotion engine" is a technology for recognizing and analyzing the emotional state of employees, and it collects emotional data using voice analysis and facial recognition.

[1574] "Server" refers to the central processing unit and related software used to analyze input data and generate and modify individual goals.

[1575] A "terminal" is a device used by employees to input data, check goals, and make adjustments.

[1576] A "machine learning model" is an algorithm and framework that learns from data and automatically performs tasks such as goal generation and sentiment analysis.

[1577] This invention relates to a system that automatically generates individual goals for each employee based on organizational goals, assigned tasks, and work history, determines whether these goals are appropriate for the employee's rank, and modifies them as necessary. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, this system can modify goals based on the user's emotional state.

[1578] This system incorporates several key mechanisms to input organizational goals, assigned tasks, and work history, and generates individual goals. It also evaluates these goals based on performance ratings, sends revised goals to the user, and saves the final goals, providing a method for consistently achieving overall organizational objectives. Furthermore, by utilizing an emotion engine, it recognizes the user's emotional state (e.g., stress and motivation) and further optimizes individual goals accordingly.

[1579] Specifically, the invention is implemented using the following procedure.

[1580] Users input organizational goals, assigned tasks, and work history from their terminals. The terminals then send this data to a server. The server analyzes the received data using natural language processing (NLP) techniques, specifically utilizing Python's "spaCy" library and "TensorFlow." From the analyzed data, the server generates appropriate personal goals, using the machine learning library "scikit-learn."

[1581] For each generated individual goal, the server retrieves the user's rank information from the employee database and evaluates whether the generated goal is appropriate for that rank. For example, in the case of a new employee, it evaluates whether the generated goal is achievable and modifies the goal as necessary. After this modification process, the server uses an emotion engine to collect and analyze the user's emotional state from voice analysis and facial recognition technology. IBM Watson and Microsoft Azure Cognitive Services are used for this process.

[1582] Based on the data obtained from the emotion engine, the server further optimizes the goals. For example, it eases goals for users with high stress levels, while changing them to more challenging goals for highly motivated users. The final revised personal goals are sent from the server to the user's device, and after the user reviews and confirms them, they are sent back to the server for final confirmation. The server stores the final confirmed goals in a database, which is then used for progress management and evaluation of goal achievement.

[1583] For example, consider the case of Tanaka, a new employee in the sales department, using the system. Tanaka's terminal is populated with the sales department's organizational goal of "achieving 100 million yen in annual sales," as well as his assigned tasks of "acquiring new customers" and "managing existing customers." Since he is a new employee, there is no work history, so the server generates initial individual goals based on this information and checks if they are appropriate for Tanaka's rank. Then, based on the emotion analysis of the emotion engine, the goals are optimized according to Tanaka's stress and motivation. The revised goals are sent to Tanaka's terminal, and after Tanaka reviews them, they are registered as final goals.

[1584] The following are examples of specific prompts for a generative AI model.

[1585] prompt:

[1586] "Please generate personal goals for Ms. Tanaka, a new employee in the sales department. The current organizational goal is to achieve 100 million yen in annual sales, and her responsibilities include acquiring new customers and managing existing ones. As she is a new employee, she has no work history. Please set optimal goals considering Ms. Tanaka's current stress levels and motivation."

[1587] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1588] Step 1:

[1589] The user enters data into the device.

[1590] Description: Users use a terminal to input "organizational goals," "assigned tasks," and "work history." This data is entered from the user's terminal. For example, if a user inputs the organizational goal of "achieving 100 million yen in annual sales" for the sales department, and also inputs assigned tasks such as "acquiring new customers" and "managing existing customers," this data will be entered into the terminal. The input data is sent from the terminal to the server for use in subsequent processing.

[1591] Step 2:

[1592] The device sends data to the server.

[1593] Description: The terminal sends the entered "organizational goals," "assigned tasks," and "work history" data to the server. Specifically, the terminal makes an API call, and the entered data is sent to the server as a POST request. The server temporarily stores the received data and uses it for later analysis. The input data (organizational goals, assigned tasks, work history) is sent to and stored on the server.

[1594] Step 3:

[1595] The server analyzes the data.

[1596] Description: The server analyzes the received data. This analysis utilizes natural language processing (NLP) and machine learning techniques. Specifically, it uses the Python library "spaCy" and "TensorFlow" to analyze text data and evaluate the relationship between organizational goals and assigned tasks. Based on the input data (organizational goals, assigned tasks, and work history), the analysis results (relevance evaluation and extraction of necessary information) are obtained.

[1597] Step 4:

[1598] The server generates personal goals.

[1599] Description: The server generates appropriate personal goals for each employee based on the results of data analysis. The machine learning library "scikit-learn" is used for this generation. For example, a personal goal such as "acquire 5 new customers per month" might be generated. Based on the input (analysis results), the generated personal goals are obtained as output.

[1600] Step 5:

[1601] The server retrieves the user's rank information.

[1602] Description: The server accesses the employee database and retrieves employee rank information. Specifically, it uses SQL queries to retrieve the required rank information from the database. Based on the input (user ID), the output (rank information) is obtained.

[1603] Step 6:

[1604] The server evaluates and modifies the generated individual goals based on their grade level.

[1605] Description: Based on the acquired grade information, the server evaluates whether the generated individual goals are appropriate for the employee's grade. For example, if the goal of "acquiring 5 new customers per month" is too high for new employee Tanaka, it will be revised to "acquiring 3 new customers per month." Based on the input (generated individual goals, grade information), the revised individual goals are output.

[1606] Step 7:

[1607] The server uses an emotion engine to recognize the user's emotional state.

[1608] Description: The server uses an emotion engine to recognize the user's emotional state through voice analysis and facial recognition technology. IBM Watson and Microsoft Azure Cognitive Services are used to collect and analyze the user's emotional data. Based on the input (voice data, facial data), the emotional state is output.

[1609] Step 8:

[1610] The server further modifies personal goals based on emotional state.

[1611] Description: The server further modifies personal goals based on the recognized emotional state. For example, it eases goals for users with high stress levels and changes them to more challenging goals for highly motivated users. Based on the input (modified personal goals, emotional state), further modified personal goals are output.

[1612] Step 9:

[1613] The server sends the revised personal goals to the device.

[1614] Description: The server sends the final, revised personal goals to the user's device. Specifically, it makes an API call and sends the revised personal goals to the user's device in JSON format. Based on the input (further revised personal goals), the output sent to the user's device is obtained.

[1615] Step 10:

[1616] The device receives the target and the user confirms it.

[1617] Description: The user receives and confirms the revised personal goals via their device. The user provides feedback on the goals and further revises them if necessary. Based on the input (revised personal goals), the user's confirmation and feedback are output.

[1618] Step 11:

[1619] The user confirms the goal, and the device sends it to the server.

[1620] Description: The user confirms the final goal, and the device sends that information back to the server. Specifically, when the confirm button is pressed, an API call is made, and the confirmed goal is sent to the server. Based on the input (user confirmation information), the confirmed goal is sent to the server.

[1621] Step 12:

[1622] The server saves the deterministic target.

[1623] Description: The server saves the finalized goals to the database. This data is used later for progress management and evaluation of goal achievement. Specifically, it uses SQL queries to insert the finalized goals into the database. Based on the input (finalized goals), the output stored in the database is obtained.

[1624] (Application Example 2)

[1625] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1626] Conventional individual goal-setting systems were limited to automatically generating individual goals based on organizational goals, assigned tasks, and work history, and could not take into account the user's emotional state. Therefore, flexible goal setting tailored to the user's stress and motivation levels was difficult, hindering the improvement of individual and organizational efficiency and performance. Furthermore, these systems were dependent on specific hardware and software, limiting their applicability.

[1627] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1628] In this invention, the server includes means for inputting organizational goals, means for inputting assigned tasks, means for inputting work history, means for analyzing the inputted organizational goals, assigned tasks, and work history to generate individual goals, means for determining whether the generated individual goals are appropriate for the user's grade, means for modifying the generated individual goals based on the determination result, means for analyzing emotional data, means for further optimizing the individual goals based on the analyzed emotional data, means for transmitting the modified individual goals to the user's terminal, and means for saving the final individual goals modified by the user. This enables flexible setting of individual goals that take into account the user's emotional state, and allows for efficient management toward achieving goals for both individuals and the organization as a whole.

[1629] "Organizational goals" are specific outcomes or achievement criteria that a particular organization aims to accomplish within a certain period of time.

[1630] "Assigned duties" refer to specific tasks or responsibilities that are individually assigned to each employee.

[1631] "Work history" refers to the records of various tasks and duties performed by an employee in the past, as well as the results thereof.

[1632] "Individual goals" refer to specific and achievable objectives set for individual employees based on organizational goals, assigned duties, and work history.

[1633] "User grade" refers to an indicator that shows an employee's position and experience level within an organization.

[1634] "Emotional data" refers to information about a user's feelings and emotional state collected through voice analysis and facial recognition technology.

[1635] "User devices" refer to electronic devices such as tablets, smartphones, and computers used by employees.

[1636] A "server" is a central management system that processes data and provides information via a network.

[1637] This invention relates to a system that generates and modifies personal goals based on organizational goals, assigned tasks, work history, and emotional data, and transmits them to the user's terminal. The operation of this system is described in detail below.

[1638] First, users input data regarding organizational goals, assigned duties, and work history using a device. For example, tablets, smartphones, or computers can be used as devices. This information is transmitted from the device to a server, which receives and appropriately stores the data.

[1639] The server uses natural language processing and machine learning models to analyze the submitted data and generate individual goals. At this time, the server retrieves the user's grade information from the employee database and determines whether the generated individual goals are appropriate for that grade. It then modifies the goals as needed.

[1640] Furthermore, the server utilizes an emotion engine to analyze emotional data. The emotion engine analyzes the user's emotional state through voice analysis and facial recognition technology, evaluating factors such as stress and motivation. The EmotionEngine library is used for this purpose.

[1641] Based on the analyzed emotional data, the server further optimizes personal goals. For example, if a user is experiencing high stress levels, the goals are eased; if they are highly motivated, more challenging goals are set.

[1642] The final revised personal goals are sent from the server to the user's terminal. The user reviews them, makes further revisions as needed, and finalizes them as the final goals. The finalized goals are then sent back to the server and stored in the database.

[1643] As a concrete example, consider the case of a new employee in the manufacturing department using the system. Data such as "production volume 10,000 units / month," "quality inspection," and "no maintenance history for manufacturing line A" are entered into the terminal. The server analyzes this data and generates initial personal goals. If the emotion engine determines that the user's stress level is high, the goal is revised to "perform quality inspections 50 times a day." The revised goal is sent to the user, and after the user confirms it, it is registered as the final goal.

[1644] Examples of prompts to input into a generative AI model include the following:

[1645] "Please generate individual employee goals based on the following data: Organizational goal: Production volume of 10,000 units / month, Job responsibilities: Quality inspection, Maintenance history for production line A, Work history: New. Additionally, for users with a stress level of 80 based on emotional data, please reduce their goals."

[1646] This allows the server to optimize individual goals based on the user's emotional state, enabling efficient management toward achieving the organization's overall goals.

[1647] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1648] Step 1:

[1649] Users input organizational goals, assigned tasks, and work history on their devices. The devices receive this input data, process it into an appropriate format, and send it to the server. Specifically, users use tablets or smartphones to input data such as "production volume 10,000 units / month," "quality inspection," and "no maintenance history for manufacturing line A," and then convert and process this data in JSON format or similar.

[1650] Step 2:

[1651] The server receives data sent from the terminal and stores it in a database. The server first checks the integrity and completeness of the transmitted data, and then stores it in an appropriate database. For example, it might use database software such as MongoDB or MySQL.

[1652] Step 3:

[1653] The server analyzes the received data and generates individual goals. Using natural language processing and machine learning models, the server evaluates the relevance of organizational goals, assigned tasks, and work history to generate initial individual goals. These generated individual goals are expressed as specific objectives, such as "Perform quality checks 100 times a day."

[1654] Step 4:

[1655] The server retrieves user grade information from the employee database and determines whether the generated individual goals are appropriate for that grade. The server executes database queries to retrieve user grade information and evaluates the appropriateness of the goals. For example, it checks whether goals that are too high have been set for new employees.

[1656] Step 5:

[1657] The server will revise individual goals as needed. Based on the grade compliance check results, the server may revise goals. For example, it might change "perform 100 quality checks per day" to "perform 50 quality checks per day."

[1658] Step 6:

[1659] The server analyzes emotional data. The server uses an emotion engine (such as the EmotionEngine library) to collect and analyze emotional data from the user's voice and facial expressions. For example, it can assess stress levels and motivation based on voice data of the user talking about their goals and facial expression data.

[1660] Step 7:

[1661] The server further optimizes individual goals based on the analyzed emotional data. The server readjusts individual goals according to the emotional state. For example, it may further ease goals for users with high stress levels and set challenging goals for users with high motivation levels.

[1662] Step 8:

[1663] The server sends the final revised personal goals to the user's device. The server converts the final goals into the appropriate format and sends them to the user's device. The user's device receives them and displays them to the user.

[1664] Step 9:

[1665] The user reviews the final goal on their device and makes modifications as needed. The user may then make further modifications based on their own judgment. The modified goal is then sent back to the server.

[1666] Step 10:

[1667] The server stores the final individual goals modified by the user. The server then stores these final goals in a database for later progress tracking and evaluation. This results in an effective goal management system.

[1668] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1669] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1670] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1671] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1672] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1673] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1674] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1675] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1676] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1677] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1678] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1679] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1680] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1681] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1682] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1683] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1684] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1685] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1686] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1687] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1688] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1689] The following is further disclosed regarding the embodiments described above.

[1690] (Claim 1)

[1691] A means of inputting organizational goals,

[1692] A means of entering assigned tasks,

[1693] Methods for entering work history,

[1694] A means of generating individual goals by analyzing entered organizational goals, assigned tasks, and work history,

[1695] A means for determining whether the generated personal goals are appropriate for the user's grade,

[1696] A means of modifying personal goals generated based on the assessment results,

[1697] A means of sending the revised personal goals to the user's device,

[1698] A means of saving the final personal goals modified by the user,

[1699] A system that includes this.

[1700] (Claim 2)

[1701] The system according to claim 1, further comprising means for obtaining user grade information.

[1702] (Claim 3)

[1703] The system according to claim 1, which generates personal goals using a machine learning model.

[1704] "Example 1"

[1705] (Claim 1)

[1706] A means of inputting organizational goals,

[1707] A means of entering assigned tasks,

[1708] Methods for entering work history,

[1709] A means of generating individual goals by analyzing entered organizational goals, assigned tasks, and work history,

[1710] A means for determining whether the generated personal goals are appropriate for the user's grade,

[1711] A means of modifying personal goals generated based on the assessment results,

[1712] A means of sending the revised personal goals to the user's device,

[1713] A means of saving the final personal goals modified by the user,

[1714] A means of sending data on organizational goals, assigned tasks, and work history to a server,

[1715] A means of analyzing received data using natural language processing and machine learning algorithms,

[1716] A means of obtaining grade information from an employee database and automatically determining the suitability of the generated objectives,

[1717] A means to finalize the revised target and send it to the server,

[1718] A system that includes this.

[1719] (Claim 2)

[1720] The system according to claim 1, further comprising means for obtaining user grade information.

[1721] (Claim 3)

[1722] The system according to claim 1, which generates personal goals using a machine learning model.

[1723] "Application Example 1"

[1724] (Claim 1)

[1725] A means of inputting organizational goals,

[1726] A means of entering assigned tasks,

[1727] Methods for entering work history,

[1728] A means of generating individual goals by analyzing entered organizational goals, assigned tasks, and work history,

[1729] A means for determining whether the generated personal goals are appropriate for the user's grade,

[1730] A means of modifying personal goals generated based on the assessment results,

[1731] A means of sending the revised personal goals to the user's device,

[1732] A means of saving the final personal goals modified by the user,

[1733] A means to optimize individual goals based on performance history, determine whether those goals are appropriate for work capabilities, and modify them as necessary,

[1734] A means of sending the generated target to another system and verifying its execution,

[1735] A system that includes this.

[1736] (Claim 2)

[1737] The system according to claim 1, further comprising means for obtaining user grade information.

[1738] (Claim 3)

[1739] The system according to claim 1, which generates personal goals using a machine learning model.

[1740] "Example 2 of combining an emotion engine"

[1741] (Claim 1)

[1742] A means of inputting organizational goals,

[1743] A means of entering assigned tasks,

[1744] Methods for entering work history,

[1745] A means of generating individual goals by analyzing entered organizational goals, assigned tasks, and work history,

[1746] A means for determining whether the generated personal goals are appropriate for the user's rank,

[1747] A means of modifying personal goals generated based on the assessment results,

[1748] A means of recognizing the user's emotional state using an emotion engine,

[1749] A means of further modifying personal goals based on recognized emotional states,

[1750] A means of sending the revised personal goals to the user's device,

[1751] A means of saving the final personal goals modified by the user,

[1752] A system that includes this.

[1753] (Claim 2)

[1754] The system according to claim 1, further comprising means for obtaining user rank information.

[1755] (Claim 3)

[1756] The system according to claim 1, which generates personal goals using a machine learning model.

[1757] "Application example 2 when combining with an emotional engine"

[1758] (Claim 1)

[1759] A means of inputting organizational goals,

[1760] A means of entering assigned tasks,

[1761] Methods for entering work history,

[1762] A means of generating individual goals by analyzing entered organizational goals, assigned tasks, and work history,

[1763] A means for determining whether the generated personal goals are appropriate for the user's grade,

[1764] A means of modifying personal goals generated based on the assessment results,

[1765] Methods for analyzing emotional data,

[1766] A means to further optimize personal goals based on analyzed emotional data,

[1767] A means of sending the revised personal goals to the user's device,

[1768] A means of saving the final personal goals modified by the user,

[1769] A system that includes this.

[1770] (Claim 2)

[1771] The system according to claim 1, further comprising means for obtaining user grade information.

[1772] (Claim 3)

[1773] The system according to claim 1, which generates personal goals using a machine learning model. [Explanation of Symbols]

[1774] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of inputting organizational goals, A means of entering assigned tasks, Methods for entering work history, A means of generating individual goals by analyzing entered organizational goals, assigned tasks, and work history, A means for determining whether the generated personal goals are appropriate for the user's grade, A means of modifying personal goals generated based on the assessment results, A means of sending the revised personal goals to the user's device, A means of saving the final personal goals modified by the user, A system that includes this.

2. The system according to claim 1, further comprising means for obtaining user grade information.

3. The system according to claim 1, which generates personal goals using a machine learning model.

Images