system

Abstract

We provide the system. [Solution] A means of learning from past information data and generating guidance guidelines tailored to the organization and position, A means for receiving information settings entered by the user and automatically correcting them based on the aforementioned guidelines, A means for receiving progress information toward a goal, analyzing the progress status, and generating warnings, A means of calculating evaluation feedback and providing that feedback to the user, A means of presenting short-term goals and improvement measures based on progress analysis, A means of jointly managing goals set individually by residents and administrative departments, and providing integrated information, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional target management system, target setting was centered on communication between superiors and subordinates, so there was a problem that there were variations in the quality of targets and evaluation criteria. As a result, employees may not be convinced of the evaluation they receive after achieving the targets, which may cause a decrease in motivation and engagement. In addition, there is also a problem that the individually set targets may not be aligned with the overall strategy of the organization, making it difficult to optimize the whole.

Means for Solving the Problems

[0005] This invention provides a system that generates standardized goal-setting guidelines tailored to job type and position by using an AI agent to learn from past goal-setting data. Furthermore, it automatically reviews goal settings entered by users based on these guidelines, supporting appropriate and consistent goal setting. In addition, it receives goal progress data, provides alerts and feedback based on the analysis results, and offers concrete action guidelines for achieving goals, thereby providing a means to improve satisfaction with evaluations.

[0006] "Goal-setting data" refers to a collection of information related to goals set within an organization in the past and their achievement.

[0007] "Job title and position" refers to a classification used to identify an employee's work content and role within the organization, and is an element considered when setting goals.

[0008] A "guideline" is a manual that provides standardized guidelines and methods for setting goals, and is provided with the aim of standardizing the quality of goals set by employees.

[0009] "Automatic editing methods" refer to a process that evaluates the goal settings entered by the user and modifies or improves them to comply with guidelines.

[0010] "Progress data" is a collection of information that shows the current results and progress toward set goals, and is used to evaluate the degree of goal achievement.

[0011] "Means of generating alerts" refers to the process of creating notifications to alert users or encourage action based on analysis results regarding the progress of goals.

[0012] "Performance feedback" refers to information that evaluates an individual's performance and indicates areas for improvement based on the results of achieving goals, and serves as a reference for users to decide on their future actions. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled 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.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system using an AI agent to support the standardization and efficiency of goal management systems in companies. This system mainly consists of a server, terminals, and users.

[0035] Server Role

[0036] The server aggregates goal-setting data accumulated throughout the entire company and uses an AI model to learn from that data. Based on the learned model, it generates standardized goal-setting guidelines tailored to job type and position. The server sends these guidelines to each employee's terminal to support goal setting. Furthermore, the server receives progress data and monitors users' goal achievement status. Based on progress, it generates appropriate alerts and evaluation feedback and provides them to each user.

[0037] Terminal role

[0038] The terminal displays goal-setting guidelines sent from the server to the user. Once the user sets a goal, that information is sent from the terminal to the server. The server receives the processing results and presents them to the user as feedback. Furthermore, the terminal provides an input interface for progress data, assisting the user in updating necessary information accordingly.

[0039] User actions

[0040] Users set their own goals and report their progress through their devices. Guidelines and recommended goals provided by the server ensure the quality and consistency of goal setting. Progress is reported regularly, and the results are supplemented by alerts and feedback from the server. Users use this information to adjust their actions toward achieving their goals and receive a final evaluation.

[0041] Specific example

[0042] For example, suppose an employee in the sales department sets a goal of "acquiring 10 new customers in six months." In this case, the server automatically evaluates the feasibility of the goal based on similar past data and, if necessary, suggests a shorter-term goal such as "visiting one new company per week." The user reviews this and reports their progress via their terminal, allowing the server to analyze the progress and issue alerts based on the level of achievement. Finally, the server performs a final evaluation and notifies the user of the results as feedback via their terminal.

[0043] Thus, this invention streamlines goal setting and evaluation, providing a company-wide, uniform, and convincing process that improves employee motivation and performance.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects past goal setting and evaluation data from a database and inputs it into an AI model for training. This prepares it to generate goal setting guidelines for each job type and position.

[0047] Step 2:

[0048] Based on the learning results, the server generates standardized goal-setting guidelines tailored to each job type and position. The generated guidelines are then sent to the user's device.

[0049] Step 3:

[0050] The device receives guidelines sent from the server and presents them to the user. The user then uses the guidelines as a reference to input their own goals.

[0051] Step 4:

[0052] When a user enters a goal into their device, the device sends that goal setting information to the server.

[0053] Step 5:

[0054] The server evaluates the received goal setting information using an automated correction algorithm and generates necessary revisions. This result is then sent to the terminal.

[0055] Step 6:

[0056] The terminal displays the corrected target information returned from the server to the user and prompts them to review the revised version. Once the user reviews and confirms the revised version, the information is stored on the server again.

[0057] Step 7:

[0058] Users periodically input progress data towards their goals via their device. The device then sends this information to the server.

[0059] Step 8:

[0060] The server analyzes the received progress data and evaluates the degree of goal achievement. Based on the evaluation results, it generates necessary alerts and sends them to the terminal.

[0061] Step 9:

[0062] The device receives alerts and evaluation feedback from the server and presents them to the user. This allows the user to obtain information to decide on their next course of action.

[0063] Step 10:

[0064] At the end of the target period, the server comprehensively analyzes all progress data and evaluation feedback to generate a final evaluation result. This result is then notified to the user via their terminal.

[0065] (Example 1)

[0066] 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."

[0067] In traditional goal management systems, each employee sets their own individual goals, making it difficult to maintain consistency and quality across company-wide objectives. Furthermore, insufficient progress monitoring and adequate feedback can lead to decreased efficiency in achieving goals.

[0068] 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.

[0069] In this invention, the server includes means for learning information about past goals and generating guidelines for goals according to the type of work and role; means for receiving information about goals entered by users and automatically adjusting them based on the guidelines; and means for receiving information about progress toward goals, analyzing the progress status and generating notifications. This enables standardized goal management across the entire company, realizing effective goal setting and progress management.

[0070] "Information regarding past goals" refers to data on past goal setting, achievement status, evaluation, and related information within a company, and serves as the foundation for pattern recognition related to goal management.

[0071] "Types of work" refers to categorization based on roles and job responsibilities within a company, and is used to express the specific types of work that each employee is expected to perform.

[0072] "Role" refers to one's position and responsibilities within an organization, and signifies the role a person plays in fulfilling the functions and objectives they are expected to achieve within that organization.

[0073] "Guidelines for setting goals" refer to specific guidelines for setting goals appropriate for each job type and position, generated based on past data, and provide a standard for employees to effectively set goals.

[0074] "Users" refers to those who utilize this goal management system to set goals and report on their progress, and generally includes employees and those engaged in business operations.

[0075] "Progress information" refers to data that shows the current degree of achievement and the progress of work toward the set goals, and is necessary information to clarify the status of goal achievement.

[0076] "Notifications" refer to messages sent to inform users about their progress toward achieving goals, necessary points to note, evaluation results, etc., and are intended to provide feedback to users.

[0077] This invention is a system for standardizing and streamlining goal management systems within companies. This system primarily consists of a server, terminals, and users, with each element working together to effectively manage goals.

[0078] Server Role

[0079] The server collects information on a company's past goals and learns from that information using a generative AI model. The generative AI model has the ability to generate goal-related guidelines tailored to the type of work and role based on that information. This process uses data processing software such as Python or Tensorflow®. The server sends the generated guidelines to each user's terminal to assist with goal setting. Furthermore, the server receives progress information from users and analyzes that data to generate notifications and feedback.

[0080] Terminal role

[0081] The terminal displays guidelines sent from the server to the user and assists the user in setting goals. The goals set by the user are sent to the server via the terminal. The terminal also provides an interface for the user to periodically input information about their progress and sends that information to the server.

[0082] User actions

[0083] Users set goals and report their progress using a terminal. Guidelines provided by the server ensure consistency and quality in goal setting. Users input their progress into the terminal, which is then analyzed by the server, and necessary feedback is provided back to the user via the terminal. This allows users to adjust their actions towards achieving their goals in a timely manner and evaluate their final results.

[0084] Specific example

[0085] For example, if a sales employee sets a goal of acquiring 10 new customers in six months, the server can evaluate the feasibility of that goal based on similar past data and suggest "one new customer visit per week" as a short-term goal. The generative AI model could be input with prompts such as the following:

[0086] "Based on sales target achievement data from the past five years, please generate effective target setting guidelines for new customer acquisition within the sales department."

[0087] In this way, this invention streamlines the goal setting and evaluation process, supporting the achievement of goals for the entire company.

[0088] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0089] Step 1:

[0090] The server collects information on past goals accumulated across the entire company. This includes data on past goal setting, achievement status, and evaluation for each task and role. This data is standardized and formatted so that it can be used by a generative AI model. The input here is data on past goals, and the output is the formatted dataset.

[0091] Step 2:

[0092] The server inputs the formatted dataset into a generative AI model to generate goal-related guidelines tailored to the type and role of the task. In this process, the generative AI model recognizes past patterns and extracts the optimal goal-related guidelines. The input is the formatted dataset, and the output is the generated goal-related guidelines.

[0093] Step 3:

[0094] The server sends guidelines regarding the generated goals to each user's terminal. Through this process, users receive specific goal guidelines tailored to their role. The input is the generated guidelines, and the output is the guidelines displayed on the terminal.

[0095] Step 4:

[0096] Users set individual goals based on guidelines generated from their terminals. The goal information set by the user is sent to the server via the terminal. Here, the input is the generated guidelines and the goals set by the user, and the output is the goal information sent to the server.

[0097] Step 5:

[0098] Users periodically input their progress toward their goals into a terminal and send that data to the server. The input is the progress information reported by the user, and the output is the progress information sent to the server.

[0099] Step 6:

[0100] The server analyzes the received progress information and evaluates the user's progress toward achieving their goals. Based on this analysis, it generates and provides necessary notifications and feedback to the user. The input here is the user's progress information, and the output is the analysis results and the generated notifications and feedback.

[0101] Step 7:

[0102] Users, upon receiving notifications and feedback from the server, modify their actions toward their goals and readjust their strategies as needed. The input here is the feedback and notifications provided by the server, while the output is the user's adjusted actions.

[0103] (Application Example 1)

[0104] 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."

[0105] In modern society, organizations and individuals need to effectively set goals and manage progress, but traditional methods have been ineffective in utilizing past data and providing standardized guidelines. Furthermore, there was a lack of methods for integrating and managing goals individually set by residents and administrative departments, creating a need for improved overall efficiency and transparency.

[0106] 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.

[0107] In this invention, the server includes means for learning past information data and generating guidance guidelines appropriate to the organization and position; means for receiving information settings entered by the user and automatically correcting them based on the guidelines; means for receiving progress information toward goals, analyzing the progress status and generating warnings; means for calculating evaluation feedback and providing that feedback to the user; means for presenting short-term goals and improvement measures based on progress analysis; and means for jointly managing goals individually set by residents and administrative departments and providing integrated information. This enables standardized, efficient, and transparent goal management both within and outside the organization.

[0108] "Information data" is a general term for records of various histories and activities generated both inside and outside an organization.

[0109] "Guidance guidelines" are documents that provide standard guidelines and policies to be used as reference when setting goals.

[0110] "Information settings" is the act of entering the goals that the user wants to achieve and the specific plans to achieve them.

[0111] A "warning" is a cautionary message issued when progress toward a set goal falls short of expectations.

[0112] "Evaluation feedback" is feedback information calculated based on the user's goal achievement status, and is an analytical result provided for improvement and growth.

[0113] "Short-term goals" are specific, short-term objectives set to achieve long-term goals.

[0114] "Improvement measures" refer to specific actions or policies proposed to improve the process of achieving a goal or the current situation.

[0115] "Integrated information" refers to a general term for information that has been compiled from data obtained from different residents and administrative departments and organized in a way that makes it mutually usable.

[0116] To implement this invention, first, historical information data is collected using a cloud server, and a generative AI model is built based on this data. The server uses this AI model to generate guidance guidelines based on the organization and position. These guidelines are delivered to each user's terminal via the cloud. The terminal receives and presents them to the user, who then inputs their own information settings. The user's input is sent from the terminal to the server and automatically corrected based on the guidelines. The server periodically monitors the progress toward the goals based on the corrected information. If progress information is insufficient, a warning message is sent to the user's terminal. In addition, evaluation feedback is calculated and provided to the user. If necessary, the AI ​​model can suggest short-term goals and improvement measures based on progress analysis, and can even manage goals set by residents and administrative departments to provide the user with integrated information.

[0117] As a concrete example, consider a case where a city's environmental protection department sets a goal of "increasing the monthly recycling rate by 20%." This system analyzes past recycling data and proposes a short-term goal of "conducting weekly recycling awareness campaigns." Furthermore, by having users report their progress through the app, it is possible to identify problems and provide appropriate improvement plans as needed.

[0118] An example of a prompt for a generated AI model is, "Based on the recycling data from the past three years, please suggest possible improvements for the next six months." This prompt serves as a guideline to encourage specific actions from the AI ​​system.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The server receives historical data collected from companies and organizations. This data includes the history and achievement status related to each user's goal setting. Based on this input data, the server uses a generative AI model to learn from the data. Here, data processing such as principal component analysis and feature extraction is performed to identify patterns necessary for guideline generation.

[0122] Step 2:

[0123] The server uses the trained model to generate leadership guidelines for organizations and positions. This process creates guidelines that incorporate patterns generated using the AI ​​model. The output is customized guidelines tailored to each job type and position.

[0124] Step 3:

[0125] The server sends the generated guidance guidelines to each user's terminal. The terminal receives these guidelines and presents them to the user. At this stage, the data format is converted for presentation to the user.

[0126] Step 4:

[0127] The user reviews the guidelines displayed on the device and enters their personal information settings. These settings include goals and plans they wish to achieve. The entered information is then sent from the device to the server.

[0128] Step 5:

[0129] The server receives the information settings submitted by the user and automatically corrects them based on pre-generated guidelines. In this process, an AI model evaluates whether the input settings are realistic and outputs the corrected information.

[0130] Step 6:

[0131] The server periodically monitors the user's progress toward their goals based on the updated information. Progress data is periodically entered by the user via their terminal and sent to the server. This data is analyzed, and if progress is behind schedule, a warning is issued.

[0132] Step 7:

[0133] The server calculates evaluation feedback and improvement measures based on progress data and sends them to the user's terminal. This process includes analysis and simulation using AI models. The user can then adjust their action plan based on this feedback.

[0134] Step 8:

[0135] The server proposes short-term goals and improvement measures, and manages goals set by residents and administrative departments. In doing so, it generates integrated information and provides it to users. The output information includes recommendations for further actions to achieve the goals.

[0136] 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.

[0137] This invention combines an emotion engine with a goal management system to achieve flexible goal setting and evaluation feedback that takes user emotions into account. The system mainly consists of a server, terminals, users, and the emotion engine.

[0138] Server Role

[0139] The server generates standardized goal-setting guidelines based on existing goal management data. Furthermore, an emotion engine processes the user's emotional data and adjusts the guidelines and feedback to best suit that emotion. The server analyzes goal-setting and progress information received from the user, generates alerts and evaluation feedback based on goal achievement, and makes any necessary adjustments.

[0140] Terminal role

[0141] The device displays goal-setting guidelines and emotion-based feedback sent from the server to the user. It collects user input and progress data and transfers it to the server. In addition, by integrating with an emotion engine, it detects the user's emotional state in real time and sends that information to the server.

[0142] User actions

[0143] Users set and review goals via their device and record their progress as needed. The emotion engine determines emotions in real time based on the user's input patterns and physiological indicators, and provides emotion-appropriate feedback. This allows users to easily receive personalized goal adjustments and motivational support.

[0144] The role of the emotional engine

[0145] The emotion engine utilizes technologies such as text analysis, voice analysis, and biosignal analysis to analyze the user's emotions. This allows the system to respond flexibly according to the user's emotions. Specifically, if a decrease in motivation is detected, the emotion engine will provide further support or suggest short-term goals.

[0146] Specific example

[0147] For example, if the emotion engine determines that a user is experiencing anxiety due to failing to meet sales targets, the server will send an alert based on this, prompting the user to re-evaluate their targets and set specific short-term goals. At the same time, it will suggest recommended content and training plans on the device to boost motivation. This allows the user to reduce anxiety and gain guidance for their next steps.

[0148] This system supports goal achievement by working in conjunction with the user's emotions, thereby promoting optimal performance in a variety of situations.

[0149] The following describes the processing flow.

[0150] Step 1:

[0151] The server collects historical goal-setting data and learns from it using an AI model. This generates and prepares standardized goal-setting guidelines for each job type and position.

[0152] Step 2:

[0153] The server sends the generated guidelines to the user's device. These guidelines also take into account the results of the sentiment engine's analysis of the user's emotions.

[0154] Step 3:

[0155] The terminal displays guidelines provided by the server to the user, and the user enters their goals based on those guidelines.

[0156] Step 4:

[0157] When a user enters a goal, that information is sent from the device to the server.

[0158] Step 5:

[0159] The server analyzes the received goal information and automatically adjusts the goals based on the analysis results of the emotion engine. Appropriate feedback and suggestions for goal adjustment are generated.

[0160] Step 6:

[0161] The terminal presents the user with the adjusted target information returned from the server for confirmation. If necessary, the user resets the target.

[0162] Step 7:

[0163] Users regularly input progress data toward achieving their goals into their device. If a change in the user's psychological state is detected, the emotion engine analyzes that information.

[0164] Step 8:

[0165] The server evaluates the progress based on progress data and the results of the sentiment engine's analysis, and generates alerts or feedback.

[0166] Step 9:

[0167] The device receives alerts and feedback from the server and presents them to the user. Based on the user's emotions, necessary support and short-term goals are suggested.

[0168] Step 10:

[0169] The server works in conjunction with the emotion engine to develop motivation-boosting strategies and provide content tailored to the user. As a result, users can continuously engage in the activities necessary to achieve their goals.

[0170] (Example 2)

[0171] 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".

[0172] Traditional goal management systems lack the flexibility to set goals and provide feedback that respond to users' emotions, resulting in a problem where fixed goals and evaluations lead to decreased user motivation. In particular, it has been difficult to grasp changes in users' psychological state and emotions in real time and provide system responses that adapt to those states.

[0173] 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.

[0174] In this invention, the server includes means for learning past goal-setting data and generating policy guidelines tailored to the industry and position; means for processing data obtained from the user using data analysis techniques to analyze the user's emotional state; and means for adjusting and providing goal-setting guidelines and evaluation feedback based on the user's emotional state. This enables flexible and adaptive goal setting and feedback provision that responds to the user's emotions.

[0175] "Past goal-setting data" refers to information about goals set by users or organizations in the past, including their achievement status and related data.

[0176] "Policy guidelines" are guidelines formulated to guide users in achieving their goals and the processes involved, and they include specific content tailored to the industry and position.

[0177] "Data analysis technology" refers to techniques for processing collected data using statistical or machine learning methods to extract meaningful information and trends.

[0178] "Emotional state" refers to the user's psychological and emotional condition, and is determined based on physiological indicators and behavioral data.

[0179] The term "goal-setting guidelines" refers to a set of guidelines provided in a standardized format to help users clarify the goals they should achieve in the long term or short term.

[0180] "Evaluation feedback" is information generated based on the user's goal achievement and progress, providing indicators and guidelines to help the user decide on their next course of action.

[0181] This invention provides a flexible goal management system that incorporates sentiment analysis to effectively support users in setting and achieving their goals. The system mainly consists of a server, terminals, users, and a sentiment engine.

[0182] The server utilizes a generative AI model integrated with an advanced database management system to collect and learn from past goal-setting data. This generates standard goal-setting guidelines tailored to the industry and position. Furthermore, it uses data analysis techniques to analyze the user's emotional state in real time, tailoring feedback and guidelines individually and delivering them in a way that is appropriate for the user.

[0183] The terminal functions as an interface with the user. It collects user input data and progress information and sends it to the server. This process requires appropriate hardware to collect emotional data through biosensors and text input interfaces. For example, a smartwatch could be used to monitor heart rate and analyze the user's stress level.

[0184] The emotion engine analyzes text, voice, and biosignals to determine the user's emotional state. If it detects a decrease in motivation, it provides emotion-based support and short-term goal suggestions via the server. This allows users to receive goal adjustments and motivation maintenance strategies tailored to their own emotions.

[0185] For example, if the emotion engine determines that a user is experiencing anxiety due to failing to meet sales targets, the server will automatically prompt the user to re-evaluate their goals and provide guidelines for resetting short-term targets. The device will then present recommended content and training plans to boost motivation.

[0186] For example, when a user enters "How should I improve sales for the next quarter?" into the system, the system will return optimal feedback that takes the user's emotional state into account.

[0187] This format allows users to set flexible goals and manage their progress in a way that suits their own circumstances, ultimately leading to goal achievement.

[0188] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0189] Step 1:

[0190] The server collects and learns from past goal-setting data. This data includes goal achievement status and various related information. Historical datasets are used as input, and a generative AI model is used to generate basic guidelines for goal setting. These guidelines are then processed to be optimized according to industry and position before being output. Specifically, the generative AI model extracts patterns from historical data using its algorithms and formats them into guidelines.

[0191] Step 2:

[0192] The terminal collects input data from the user. This input includes goal setting, progress, and user feedback. This data is sent to the server for use in data analysis. Specifically, the terminal receives information via the touchscreen or keyboard, converts it to an appropriate format, and uploads it to the server.

[0193] Step 3:

[0194] The emotion engine analyzes user data transmitted from the device. Specifically, it performs text analysis, voice analysis, and biosignal analysis to determine the emotional state. This process yields output regarding the user's emotions, which is then transmitted to the server. In practice, the emotion engine uses audio sensors and text parsers to analyze data in real time.

[0195] Step 4:

[0196] The server adjusts the goal-setting guidelines based on the emotional state obtained from the emotion engine. It receives the results of the emotion analysis as input and performs data processing to individually optimize the guidelines and feedback. As a result, the adjusted guidelines and feedback are output and sent to the terminal. In its specific operation, the server uses an algorithm to dynamically change the guidelines based on the emotional state.

[0197] Step 5:

[0198] The terminal displays the user the adjusted guidelines and feedback sent from the server. Through this, the user can check their progress and reset their goals as needed. User input and progress data are collected again and sent to the server as input for the next cycle. In concrete terms, the terminal presents information to the user via a GUI and supports more detailed interactions.

[0199] (Application Example 2)

[0200] 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".

[0201] Traditional goal management systems focus on objectively evaluating users' progress and achievement of their work objectives. However, they lack flexibility in achievement and management because they do not take into account individual emotional states and motivations. In particular, they fail to adequately mitigate the impact of individual emotional factors on goal achievement, which can ultimately hinder performance improvement. Therefore, there is a need for a system that takes users' emotional states into account and provides more individualized goal management.

[0202] 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.

[0203] In this invention, the server includes a device that learns past goal-setting information and generates goal-setting guidelines according to the work and role; a device that receives goal-setting information input from the user and automatically adjusts it based on the guidelines; and a device that receives progress data toward the goal, analyzes the progress, and generates a warning. This makes it possible to automatically adjust goals and provide feedback while taking into account the user's emotions.

[0204] "Goal setting information" refers to data that shows the specific goals that users aim to achieve and their content.

[0205] "Work" refers to activities and responsibilities related to a specific job or role.

[0206] A "role" refers to the responsibilities and tasks that an individual within an organization is expected to perform.

[0207] "Guidelines" are information that provides a set of procedures and policies to serve as a basis for setting goals.

[0208] A "device" is a combination of hardware and software designed to perform a specific function.

[0209] "Automatic adjustment" means that the system autonomously changes settings and configurations without human intervention.

[0210] "Progress data" refers to information and indicators that show the extent to which a goal has been achieved.

[0211] A "warning" is a notification or alert that alerts the user to a problem in the progress.

[0212] "Users" refer to individuals or organizations that operate the system and perform goal management and adjustments.

[0213] The system implementing this invention mainly consists of a server, terminals, users, and an emotion engine. The server uses a machine learning algorithm based on past goal-setting information to generate goal-setting guidelines tailored to tasks and roles. These guidelines, based on data collected from individual users, play a role in supporting optimal goal setting. The server also receives progress data in real time and evaluates this data with statistical analysis tools to generate warnings and feedback according to the progress.

[0214] The terminal functions as an interface between the user and the server, visually displaying posted goal-setting guidelines and feedback from the sentiment engine. This utilizes a graphical user interface (GUI) dashboard, allowing for easy manipulation and review of data.

[0215] Users can set goals through their device and record their progress in a timely manner. Furthermore, the emotion engine analyzes audio and video data using software such as Python's TensorFlow library and OpenCV to analyze the user's emotional state. This analysis allows the system to dynamically adjust goals according to the user's emotional state.

[0216] As a concrete example, when a user sets a new health goal and is behind schedule, a motivating message such as, "You've slowed down a bit today, but you're almost there! Keep up the good work!" is displayed on the device. An example of a prompt message generated using a generative AI model would be, "Based on the user's emotional data, please provide recommendations to help maintain motivation toward the current goal."

[0217] Thus, this system enables highly individualized goal management that takes into account the user's emotions and provides flexible support for achieving goals.

[0218] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0219] Step 1:

[0220] The server retrieves past goal-setting information from a database and generates goal-setting guidelines suitable for the job and role. Using user role information and historical data as input, it outputs goal guidelines using a machine learning algorithm. In this process, it utilizes a learning model to detect patterns and adjusts the guidelines based on predicted outcomes.

[0221] Step 2:

[0222] The terminal presents the user with goal-setting guidelines received from the server. Through the terminal's GUI, the user can select and fine-tune goals. It receives guideline data from the server as input and displays it visually to the user as output. Specifically, it provides infographics on the dashboard that show the details of each goal.

[0223] Step 3:

[0224] Users set goals and record their progress via a terminal. Progress data manually entered by the user is used as input, and progress data is generated as output, which is then stored in a database. The terminal provides pull-down menus and templates to facilitate input.

[0225] Step 4:

[0226] The server analyzes progress data collected from users and generates warnings and feedback based on the progress. Progress data and pre-configured criteria are used as input, and warning messages and feedback reports based on the analysis results are generated as output. This process utilizes statistical analysis tools to issue warnings if progress falls below the standard.

[0227] Step 5:

[0228] The emotion engine collects user voice and video data and analyzes their emotions. Using response audio and image data as input, it generates quantitative data on emotional states as output. Specifically, it utilizes speech recognition and facial recognition technologies to determine emotions such as stress and joy.

[0229] Step 6:

[0230] The server uses a generative AI model based on the output of the emotion engine to output prompt messages suggesting goal adjustments and motivational methods that correspond to the user's emotions. The input consists of emotion data and goal information, and the output is a prompt message presented to the user. For example, it might say, "Based on the emotion data, the user appears somewhat depressed, so we suggest relaxation along with short-term goals."

[0231] 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.

[0232] Data generation model 58 is a type of 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.

[0233] 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.

[0234] [Second Embodiment]

[0235] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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).

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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".

[0247] This invention is a system using an AI agent to support the standardization and efficiency of goal management systems in companies. This system mainly consists of a server, terminals, and users.

[0248] Server Role

[0249] The server aggregates goal-setting data accumulated throughout the entire company and uses an AI model to learn from that data. Based on the learned model, it generates standardized goal-setting guidelines tailored to job type and position. The server sends these guidelines to each employee's terminal to support goal setting. Furthermore, the server receives progress data and monitors users' goal achievement status. Based on progress, it generates appropriate alerts and evaluation feedback and provides them to each user.

[0250] Terminal role

[0251] The terminal displays goal-setting guidelines sent from the server to the user. Once the user sets a goal, that information is sent from the terminal to the server. The server receives the processing results and presents them to the user as feedback. Furthermore, the terminal provides an input interface for progress data, assisting the user in updating necessary information accordingly.

[0252] User actions

[0253] Users set their own goals and report their progress through their devices. Guidelines and recommended goals provided by the server ensure the quality and consistency of goal setting. Progress is reported regularly, and the results are supplemented by alerts and feedback from the server. Users use this information to adjust their actions toward achieving their goals and receive a final evaluation.

[0254] Specific example

[0255] For example, suppose an employee in the sales department sets a goal of "acquiring 10 new customers in six months." In this case, the server automatically evaluates the feasibility of the goal based on similar past data and, if necessary, suggests a shorter-term goal such as "visiting one new company per week." The user reviews this and reports their progress via their terminal, allowing the server to analyze the progress and issue alerts based on the level of achievement. Finally, the server performs a final evaluation and notifies the user of the results as feedback via their terminal.

[0256] Thus, this invention streamlines goal setting and evaluation, providing a company-wide, uniform, and convincing process that improves employee motivation and performance.

[0257] The following describes the processing flow.

[0258] Step 1:

[0259] The server collects past goal setting and evaluation data from a database and inputs it into an AI model for training. This prepares it to generate goal setting guidelines for each job type and position.

[0260] Step 2:

[0261] Based on the learning results, the server generates standardized goal-setting guidelines tailored to each job type and position. The generated guidelines are then sent to the user's device.

[0262] Step 3:

[0263] The device receives guidelines sent from the server and presents them to the user. The user then uses the guidelines as a reference to input their own goals.

[0264] Step 4:

[0265] When a user enters a goal into their device, the device sends that goal setting information to the server.

[0266] Step 5:

[0267] The server evaluates the received goal setting information using an automated correction algorithm and generates necessary revisions. This result is then sent to the terminal.

[0268] Step 6:

[0269] The terminal displays the corrected target information returned from the server to the user and prompts them to review the revised version. Once the user reviews and confirms the revised version, the information is stored on the server again.

[0270] Step 7:

[0271] Users periodically input progress data towards their goals via their device. The device then sends this information to the server.

[0272] Step 8:

[0273] The server analyzes the received progress data and evaluates the degree of goal achievement. Based on the evaluation results, it generates necessary alerts and sends them to the terminal.

[0274] Step 9:

[0275] The device receives alerts and evaluation feedback from the server and presents them to the user. This allows the user to obtain information to decide on their next course of action.

[0276] Step 10:

[0277] At the end of the target period, the server comprehensively analyzes all progress data and evaluation feedback to generate a final evaluation result. This result is then notified to the user via their terminal.

[0278] (Example 1)

[0279] 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."

[0280] In traditional goal management systems, each employee sets their own individual goals, making it difficult to maintain consistency and quality across company-wide objectives. Furthermore, insufficient progress monitoring and adequate feedback can lead to decreased efficiency in achieving goals.

[0281] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in the first embodiment is realized by the following means.

[0282] In this invention, the server includes means for learning information on past goals, generating guidelines on goals according to the type and role of business, receiving information on goals input by a user, and automatically adjusting based on the guidelines, and means for receiving information on the progress of goals, analyzing the progress situation, and generating a notification. Thereby, standardized goal management across the entire enterprise becomes possible, and effective goal setting and progress management can be realized.

[0283] The "information on past goals" refers to past goal settings, achievement status, evaluations, and related data in an enterprise, and is the information serving as the basis for pattern recognition regarding goal management.

[0284] The "type of business" refers to the categorization according to the role and job content within an enterprise, and is for expressing the types of specific jobs that each employee should perform.

[0285] The "role" refers to the status and responsibilities within an organization, and means the role for fulfilling the functions and purposes that the person should assume within the organization.

[0286] The "guidelines on goals" refers to the specific guidelines on goal setting suitable for each job type and position, generated based on past data, and provides the criteria for employees to effectively set goals.

[0287] The "user" refers to a person who utilizes this goal management system to set goals and report progress, and generally includes employees and those engaged in business.

[0288] The "information on progress" refers to data indicating the current achievement level and the progress of work for the set goals, and is the information necessary for clarifying the goal achievement status.

[0289] "Notifications" refer to messages sent to inform users about their progress toward achieving goals, necessary points to note, evaluation results, etc., and are intended to provide feedback to users.

[0290] This invention is a system for standardizing and streamlining goal management systems within companies. This system primarily consists of a server, terminals, and users, with each element working together to effectively manage goals.

[0291] Server Role

[0292] The server collects information on a company's past goals and learns from that information using a generative AI model. The generative AI model has the ability to generate goal-setting guidelines tailored to the type of work and role based on that information. This process uses data processing software such as Python or TensorFlow. The server sends the generated guidelines to each user's terminal to assist with goal setting. Furthermore, the server receives progress information from users and analyzes that data to generate notifications and feedback.

[0293] Terminal role

[0294] The terminal displays guidelines sent from the server to the user and assists the user in setting goals. The goals set by the user are sent to the server via the terminal. The terminal also provides an interface for the user to periodically input information about their progress and sends that information to the server.

[0295] User actions

[0296] Users set goals and report their progress using a terminal. Guidelines provided by the server ensure consistency and quality in goal setting. Users input their progress into the terminal, which is then analyzed by the server, and necessary feedback is provided back to the user via the terminal. This allows users to adjust their actions towards achieving their goals in a timely manner and evaluate their final results.

[0297] Specific example

[0298] For example, if a sales employee sets a goal of acquiring 10 new customers in six months, the server can evaluate the feasibility of that goal based on similar past data and suggest "one new customer visit per week" as a short-term goal. The generative AI model could be input with prompts such as the following:

[0299] "Based on sales target achievement data from the past five years, please generate effective target setting guidelines for new customer acquisition within the sales department."

[0300] In this way, this invention streamlines the goal setting and evaluation process, supporting the achievement of goals for the entire company.

[0301] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0302] Step 1:

[0303] The server collects information on past goals accumulated across the entire company. This includes data on past goal setting, achievement status, and evaluation for each task and role. This data is standardized and formatted so that it can be used by a generative AI model. The input here is data on past goals, and the output is the formatted dataset.

[0304] Step 2:

[0305] The server inputs the formatted dataset into a generative AI model to generate goal-related guidelines tailored to the type and role of the task. In this process, the generative AI model recognizes past patterns and extracts the optimal goal-related guidelines. The input is the formatted dataset, and the output is the generated goal-related guidelines.

[0306] Step 3:

[0307] The server sends the guidelines regarding the generated goals to the terminals of each user. Through this process, the user can receive specific goal guidelines according to their own roles. The input is the generated guidelines, and the output is the guidelines displayed on the terminal.

[0308] Step 4:

[0309] Based on the guidelines generated from the terminal, the user sets individual goals. The goal information set by the user is sent to the server via the terminal. Here, the input is the generated guidelines and the goals set by the user, and the output is the goal information sent to the server.

[0310] Step 5:

[0311] The user regularly enters the progress of the goal into the terminal and sends the data to the server. The input is the progress information reported by the user, and the output is the progress information sent to the server.

[0312] Step 6:

[0313] The server analyzes the received progress information and evaluates the user's goal achievement status. Based on this analysis result, necessary notifications and feedback are generated and provided to the user. Here, the input is the user's progress information, and the output is the analysis result and the generated notifications and feedback.

[0314] Step 7:

[0315] The user who receives the notifications and feedback from the server modifies their actions towards the goal based on them and readjusts the strategy if necessary. Here, the input is the feedback and notifications provided by the server, and the output is the adjustment of the user's actions.

[0316] (Application Example 1)

[0317] 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."

[0318] In modern society, organizations and individuals need to effectively set goals and manage progress, but traditional methods have been ineffective in utilizing past data and providing standardized guidelines. Furthermore, there was a lack of methods for integrating and managing goals individually set by residents and administrative departments, creating a need for improved overall efficiency and transparency.

[0319] 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.

[0320] In this invention, the server includes means for learning past information data and generating guidance guidelines appropriate to the organization and position; means for receiving information settings entered by the user and automatically correcting them based on the guidelines; means for receiving progress information toward goals, analyzing the progress status and generating warnings; means for calculating evaluation feedback and providing that feedback to the user; means for presenting short-term goals and improvement measures based on progress analysis; and means for jointly managing goals individually set by residents and administrative departments and providing integrated information. This enables standardized, efficient, and transparent goal management both within and outside the organization.

[0321] "Information data" is a general term for records of various histories and activities generated both inside and outside an organization.

[0322] "Guidance guidelines" are documents that provide standard guidelines and policies to be used as reference when setting goals.

[0323] "Information settings" is the act of entering the goals that the user wants to achieve and the specific plans to achieve them.

[0324] A "warning" is a cautionary message issued when progress toward a set goal falls short of expectations.

[0325] "Evaluation feedback" is feedback information calculated based on the user's goal achievement status, and is an analytical result provided for improvement and growth.

[0326] "Short-term goals" are specific, short-term objectives set to achieve long-term goals.

[0327] "Improvement measures" refer to specific actions or policies proposed to improve the process of achieving a goal or the current situation.

[0328] "Integrated information" refers to a general term for information that has been compiled from data obtained from different residents and administrative departments and organized in a way that makes it mutually usable.

[0329] To implement this invention, first, historical information data is collected using a cloud server, and a generative AI model is built based on this data. The server uses this AI model to generate guidance guidelines based on the organization and position. These guidelines are delivered to each user's terminal via the cloud. The terminal receives and presents them to the user, who then inputs their own information settings. The user's input is sent from the terminal to the server and automatically corrected based on the guidelines. The server periodically monitors the progress toward the goals based on the corrected information. If progress information is insufficient, a warning message is sent to the user's terminal. In addition, evaluation feedback is calculated and provided to the user. If necessary, the AI ​​model can suggest short-term goals and improvement measures based on progress analysis, and can even manage goals set by residents and administrative departments to provide the user with integrated information.

[0330] As a concrete example, consider a case where a city's environmental protection department sets a goal of "increasing the monthly recycling rate by 20%." This system analyzes past recycling data and proposes a short-term goal of "conducting weekly recycling awareness campaigns." Furthermore, by having users report their progress through the app, it is possible to identify problems and provide appropriate improvement plans as needed.

[0331] An example of a prompt for a generated AI model is, "Based on the recycling data from the past three years, please suggest possible improvements for the next six months." This prompt serves as a guideline to encourage specific actions from the AI ​​system.

[0332] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0333] Step 1:

[0334] The server receives historical data collected from companies and organizations. This data includes the history and achievement status related to each user's goal setting. Based on this input data, the server uses a generative AI model to learn from the data. Here, data processing such as principal component analysis and feature extraction is performed to identify patterns necessary for guideline generation.

[0335] Step 2:

[0336] The server uses the trained model to generate leadership guidelines for organizations and positions. This process creates guidelines that incorporate patterns generated using the AI ​​model. The output is customized guidelines tailored to each job type and position.

[0337] Step 3:

[0338] The server sends the generated guidance guidelines to each user's terminal. The terminal receives these guidelines and presents them to the user. At this stage, the data format is converted for presentation to the user.

[0339] Step 4:

[0340] The user reviews the guidelines displayed on the device and enters their personal information settings. These settings include goals and plans they wish to achieve. The entered information is then sent from the device to the server.

[0341] Step 5:

[0342] The server receives the information settings submitted by the user and automatically corrects them based on pre-generated guidelines. In this process, an AI model evaluates whether the input settings are realistic and outputs the corrected information.

[0343] Step 6:

[0344] The server periodically monitors the user's progress toward their goals based on the updated information. Progress data is periodically entered by the user via their terminal and sent to the server. This data is analyzed, and if progress is behind schedule, a warning is issued.

[0345] Step 7:

[0346] The server calculates evaluation feedback and improvement measures based on progress data and sends them to the user's terminal. This process includes analysis and simulation using AI models. The user can then adjust their action plan based on this feedback.

[0347] Step 8:

[0348] The server proposes short-term goals and improvement measures, and manages goals set by residents and administrative departments. In doing so, it generates integrated information and provides it to users. The output information includes recommendations for further actions to achieve the goals.

[0349] 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.

[0350] This invention combines an emotion engine with a goal management system to achieve flexible goal setting and evaluation feedback that takes user emotions into account. The system mainly consists of a server, terminals, users, and the emotion engine.

[0351] Server Role

[0352] The server generates standardized goal-setting guidelines based on existing goal management data. Furthermore, an emotion engine processes the user's emotional data and adjusts the guidelines and feedback to best suit that emotion. The server analyzes goal-setting and progress information received from the user, generates alerts and evaluation feedback based on goal achievement, and makes any necessary adjustments.

[0353] Terminal role

[0354] The device displays goal-setting guidelines and emotion-based feedback sent from the server to the user. It collects user input and progress data and transfers it to the server. In addition, by integrating with an emotion engine, it detects the user's emotional state in real time and sends that information to the server.

[0355] User actions

[0356] Users set and review goals via their device and record their progress as needed. The emotion engine determines emotions in real time based on the user's input patterns and physiological indicators, and provides emotion-appropriate feedback. This allows users to easily receive personalized goal adjustments and motivational support.

[0357] The role of the emotional engine

[0358] The emotion engine utilizes technologies such as text analysis, voice analysis, and biosignal analysis to analyze the user's emotions. This allows the system to respond flexibly according to the user's emotions. Specifically, if a decrease in motivation is detected, the emotion engine will provide further support or suggest short-term goals.

[0359] Specific example

[0360] For example, if the emotion engine determines that a user is experiencing anxiety due to failing to meet sales targets, the server will send an alert based on this, prompting the user to re-evaluate their targets and set specific short-term goals. At the same time, it will suggest recommended content and training plans on the device to boost motivation. This allows the user to reduce anxiety and gain guidance for their next steps.

[0361] This system supports goal achievement by working in conjunction with the user's emotions, thereby promoting optimal performance in a variety of situations.

[0362] The following describes the processing flow.

[0363] Step 1:

[0364] The server collects historical goal-setting data and learns from it using an AI model. This generates and prepares standardized goal-setting guidelines for each job type and position.

[0365] Step 2:

[0366] The server sends the generated guidelines to the user's device. These guidelines also take into account the results of the sentiment engine's analysis of the user's emotions.

[0367] Step 3:

[0368] The terminal displays guidelines provided by the server to the user, and the user enters their goals based on those guidelines.

[0369] Step 4:

[0370] When a user enters a goal, that information is sent from the device to the server.

[0371] Step 5:

[0372] The server analyzes the received goal information and automatically adjusts the goals based on the analysis results of the emotion engine. Appropriate feedback and suggestions for goal adjustment are generated.

[0373] Step 6:

[0374] The terminal presents the user with the adjusted target information returned from the server for confirmation. If necessary, the user resets the target.

[0375] Step 7:

[0376] Users regularly input progress data toward achieving their goals into their device. If a change in the user's psychological state is detected, the emotion engine analyzes that information.

[0377] Step 8:

[0378] The server evaluates the progress based on progress data and the results of the sentiment engine's analysis, and generates alerts or feedback.

[0379] Step 9:

[0380] The device receives alerts and feedback from the server and presents them to the user. Based on the user's emotions, necessary support and short-term goals are suggested.

[0381] Step 10:

[0382] The server works in conjunction with the emotion engine to develop motivation-boosting strategies and provide content tailored to the user. As a result, users can continuously engage in the activities necessary to achieve their goals.

[0383] (Example 2)

[0384] 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".

[0385] Traditional goal management systems lack the flexibility to set goals and provide feedback that respond to users' emotions, resulting in a problem where fixed goals and evaluations lead to decreased user motivation. In particular, it has been difficult to grasp changes in users' psychological state and emotions in real time and provide system responses that adapt to those states.

[0386] 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.

[0387] In this invention, the server includes means for learning past goal-setting data and generating policy guidelines tailored to the industry and position; means for processing data obtained from the user using data analysis techniques to analyze the user's emotional state; and means for adjusting and providing goal-setting guidelines and evaluation feedback based on the user's emotional state. This enables flexible and adaptive goal setting and feedback provision that responds to the user's emotions.

[0388] "Past goal-setting data" refers to information about goals set by users or organizations in the past, including their achievement status and related data.

[0389] "Policy guidelines" are guidelines formulated to guide users in achieving their goals and the processes involved, and they include specific content tailored to the industry and position.

[0390] "Data analysis technology" refers to techniques for processing collected data using statistical or machine learning methods to extract meaningful information and trends.

[0391] "Emotional state" refers to the user's psychological and emotional condition, and is determined based on physiological indicators and behavioral data.

[0392] The term "goal-setting guidelines" refers to a set of guidelines provided in a standardized format to help users clarify the goals they should achieve in the long term or short term.

[0393] "Evaluation feedback" is information generated based on the user's goal achievement and progress, providing indicators and guidelines to help the user decide on their next course of action.

[0394] This invention provides a flexible goal management system that incorporates sentiment analysis to effectively support users in setting and achieving their goals. The system mainly consists of a server, terminals, users, and a sentiment engine.

[0395] The server utilizes a generative AI model integrated with an advanced database management system to collect and learn from past goal-setting data. This generates standard goal-setting guidelines tailored to the industry and position. Furthermore, it uses data analysis techniques to analyze the user's emotional state in real time, tailoring feedback and guidelines individually and delivering them in a way that is appropriate for the user.

[0396] The terminal functions as an interface with the user. It collects user input data and progress information and sends it to the server. This process requires appropriate hardware to collect emotional data through biosensors and text input interfaces. For example, a smartwatch could be used to monitor heart rate and analyze the user's stress level.

[0397] The emotion engine analyzes text, voice, and biosignals to determine the user's emotional state. If it detects a decrease in motivation, it provides emotion-based support and short-term goal suggestions via the server. This allows users to receive goal adjustments and motivation maintenance strategies tailored to their own emotions.

[0398] For example, if the emotion engine determines that a user is experiencing anxiety due to failing to meet sales targets, the server will automatically prompt the user to re-evaluate their goals and provide guidelines for resetting short-term targets. The device will then present recommended content and training plans to boost motivation.

[0399] For example, when a user enters "How should I improve sales for the next quarter?" into the system, the system will return optimal feedback that takes the user's emotional state into account.

[0400] This format allows users to set flexible goals and manage their progress in a way that suits their own circumstances, ultimately leading to goal achievement.

[0401] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0402] Step 1:

[0403] The server collects and learns from past goal-setting data. This data includes goal achievement status and various related information. Historical datasets are used as input, and a generative AI model is used to generate basic guidelines for goal setting. These guidelines are then processed to be optimized according to industry and position before being output. Specifically, the generative AI model extracts patterns from historical data using its algorithms and formats them into guidelines.

[0404] Step 2:

[0405] The terminal collects input data from the user. This input includes goal setting, progress, and user feedback. This data is sent to the server for use in data analysis. Specifically, the terminal receives information via the touchscreen or keyboard, converts it to an appropriate format, and uploads it to the server.

[0406] Step 3:

[0407] The emotion engine analyzes user data transmitted from the device. Specifically, it performs text analysis, voice analysis, and biosignal analysis to determine the emotional state. This process yields output regarding the user's emotions, which is then transmitted to the server. In practice, the emotion engine uses audio sensors and text parsers to analyze data in real time.

[0408] Step 4:

[0409] The server adjusts the goal-setting guidelines based on the emotional state obtained from the emotion engine. It receives the results of the emotion analysis as input and performs data processing to individually optimize the guidelines and feedback. As a result, the adjusted guidelines and feedback are output and sent to the terminal. In its specific operation, the server uses an algorithm to dynamically change the guidelines based on the emotional state.

[0410] Step 5:

[0411] The terminal displays the user the adjusted guidelines and feedback sent from the server. Through this, the user can check their progress and reset their goals as needed. User input and progress data are collected again and sent to the server as input for the next cycle. In concrete terms, the terminal presents information to the user via a GUI and supports more detailed interactions.

[0412] (Application Example 2)

[0413] 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."

[0414] Traditional goal management systems focus on objectively evaluating users' progress and achievement of their work objectives. However, they lack flexibility in achievement and management because they do not take into account individual emotional states and motivations. In particular, they fail to adequately mitigate the impact of individual emotional factors on goal achievement, which can ultimately hinder performance improvement. Therefore, there is a need for a system that takes users' emotional states into account and provides more individualized goal management.

[0415] 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.

[0416] In this invention, the server includes a device that learns past goal-setting information and generates goal-setting guidelines according to the work and role; a device that receives goal-setting information input from the user and automatically adjusts it based on the guidelines; and a device that receives progress data toward the goal, analyzes the progress, and generates a warning. This makes it possible to automatically adjust goals and provide feedback while taking into account the user's emotions.

[0417] "Goal setting information" refers to data that shows the specific goals that users aim to achieve and their content.

[0418] "Work" refers to activities and responsibilities related to a specific job or role.

[0419] A "role" refers to the responsibilities and tasks that an individual within an organization is expected to perform.

[0420] "Guidelines" are information that provides a set of procedures and policies to serve as a basis for setting goals.

[0421] A "device" is a combination of hardware and software designed to perform a specific function.

[0422] "Automatic adjustment" means that the system autonomously changes settings and configurations without human intervention.

[0423] "Progress data" refers to information and indicators that show the extent to which a goal has been achieved.

[0424] A "warning" is a notification or alert that alerts the user to a problem in the progress.

[0425] "Users" refer to individuals or organizations that operate the system and perform goal management and adjustments.

[0426] The system implementing this invention mainly consists of a server, terminals, users, and an emotion engine. The server uses a machine learning algorithm based on past goal-setting information to generate goal-setting guidelines tailored to tasks and roles. These guidelines, based on data collected from individual users, play a role in supporting optimal goal setting. The server also receives progress data in real time and evaluates this data with statistical analysis tools to generate warnings and feedback according to the progress.

[0427] The terminal functions as an interface between the user and the server, visually displaying posted goal-setting guidelines and feedback from the sentiment engine. This utilizes a graphical user interface (GUI) dashboard, allowing for easy manipulation and review of data.

[0428] Users can set goals through their device and record their progress in a timely manner. Furthermore, the emotion engine analyzes audio and video data using software such as Python's TensorFlow library and OpenCV to analyze the user's emotional state. This analysis allows the system to dynamically adjust goals according to the user's emotional state.

[0429] As a concrete example, when a user sets a new health goal and is behind schedule, a motivating message such as, "You've slowed down a bit today, but you're almost there! Keep up the good work!" is displayed on the device. An example of a prompt message generated using a generative AI model would be, "Based on the user's emotional data, please provide recommendations to help maintain motivation toward the current goal."

[0430] Thus, this system enables highly individualized goal management that takes into account the user's emotions and provides flexible support for achieving goals.

[0431] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0432] Step 1:

[0433] The server retrieves past goal-setting information from a database and generates goal-setting guidelines suitable for the job and role. Using user role information and historical data as input, it outputs goal guidelines using a machine learning algorithm. In this process, it utilizes a learning model to detect patterns and adjusts the guidelines based on predicted outcomes.

[0434] Step 2:

[0435] The terminal presents the user with goal-setting guidelines received from the server. Through the terminal's GUI, the user can select and fine-tune goals. It receives guideline data from the server as input and displays it visually to the user as output. Specifically, it provides infographics on the dashboard that show the details of each goal.

[0436] Step 3:

[0437] Users set goals and record their progress via a terminal. Progress data manually entered by the user is used as input, and progress data is generated as output, which is then stored in a database. The terminal provides pull-down menus and templates to facilitate input.

[0438] Step 4:

[0439] The server analyzes progress data collected from users and generates warnings and feedback based on the progress. Progress data and pre-configured criteria are used as input, and warning messages and feedback reports based on the analysis results are generated as output. This process utilizes statistical analysis tools to issue warnings if progress falls below the standard.

[0440] Step 5:

[0441] The emotion engine collects user voice and video data and analyzes their emotions. Using response audio and image data as input, it generates quantitative data on emotional states as output. Specifically, it utilizes speech recognition and facial recognition technologies to determine emotions such as stress and joy.

[0442] Step 6:

[0443] The server uses a generative AI model based on the output of the emotion engine to output prompt messages suggesting goal adjustments and motivational methods that correspond to the user's emotions. The input consists of emotion data and goal information, and the output is a prompt message presented to the user. For example, it might say, "Based on the emotion data, the user appears somewhat depressed, so we suggest relaxation along with short-term goals."

[0444] 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.

[0445] 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.

[0446] 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.

[0447] [Third Embodiment]

[0448] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0449] 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.

[0450] 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).

[0451] 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.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] 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".

[0460] This invention is a system using an AI agent to support the standardization and efficiency of goal management systems in companies. This system mainly consists of a server, terminals, and users.

[0461] Server Role

[0462] The server aggregates goal-setting data accumulated throughout the entire company and uses an AI model to learn from that data. Based on the learned model, it generates standardized goal-setting guidelines tailored to job type and position. The server sends these guidelines to each employee's terminal to support goal setting. Furthermore, the server receives progress data and monitors users' goal achievement status. Based on progress, it generates appropriate alerts and evaluation feedback and provides them to each user.

[0463] Terminal role

[0464] The terminal displays goal-setting guidelines sent from the server to the user. Once the user sets a goal, that information is sent from the terminal to the server. The server receives the processing results and presents them to the user as feedback. Furthermore, the terminal provides an input interface for progress data, assisting the user in updating necessary information accordingly.

[0465] User actions

[0466] Users set their own goals and report their progress through their devices. Guidelines and recommended goals provided by the server ensure the quality and consistency of goal setting. Progress is reported regularly, and the results are supplemented by alerts and feedback from the server. Users use this information to adjust their actions toward achieving their goals and receive a final evaluation.

[0467] Specific example

[0468] For example, suppose an employee in the sales department sets a goal of "acquiring 10 new customers in six months." In this case, the server automatically evaluates the feasibility of the goal based on similar past data and, if necessary, suggests a shorter-term goal such as "visiting one new company per week." The user reviews this and reports their progress via their terminal, allowing the server to analyze the progress and issue alerts based on the level of achievement. Finally, the server performs a final evaluation and notifies the user of the results as feedback via their terminal.

[0469] Thus, this invention streamlines goal setting and evaluation, providing a company-wide, uniform, and convincing process that improves employee motivation and performance.

[0470] The following describes the processing flow.

[0471] Step 1:

[0472] The server collects past goal setting and evaluation data from a database and inputs it into an AI model for training. This prepares it to generate goal setting guidelines for each job type and position.

[0473] Step 2:

[0474] Based on the learning results, the server generates standardized goal-setting guidelines tailored to each job type and position. The generated guidelines are then sent to the user's device.

[0475] Step 3:

[0476] The device receives guidelines sent from the server and presents them to the user. The user then uses the guidelines as a reference to input their own goals.

[0477] Step 4:

[0478] When a user enters a goal into their device, the device sends that goal setting information to the server.

[0479] Step 5:

[0480] The server evaluates the received goal setting information using an automated correction algorithm and generates necessary revisions. This result is then sent to the terminal.

[0481] Step 6:

[0482] The terminal displays the corrected target information returned from the server to the user and prompts them to review the revised version. Once the user reviews and confirms the revised version, the information is stored on the server again.

[0483] Step 7:

[0484] Users periodically input progress data towards their goals via their device. The device then sends this information to the server.

[0485] Step 8:

[0486] The server analyzes the received progress data and evaluates the degree of goal achievement. Based on the evaluation results, it generates necessary alerts and sends them to the terminal.

[0487] Step 9:

[0488] The device receives alerts and evaluation feedback from the server and presents them to the user. This allows the user to obtain information to decide on their next course of action.

[0489] Step 10:

[0490] At the end of the target period, the server comprehensively analyzes all progress data and evaluation feedback to generate a final evaluation result. This result is then notified to the user via their terminal.

[0491] (Example 1)

[0492] 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."

[0493] In traditional goal management systems, each employee sets their own individual goals, making it difficult to maintain consistency and quality across company-wide objectives. Furthermore, insufficient progress monitoring and adequate feedback can lead to decreased efficiency in achieving goals.

[0494] 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.

[0495] In this invention, the server includes means for learning information about past goals and generating guidelines for goals according to the type of work and role; means for receiving information about goals entered by users and automatically adjusting them based on the guidelines; and means for receiving information about progress toward goals, analyzing the progress status and generating notifications. This enables standardized goal management across the entire company, realizing effective goal setting and progress management.

[0496] "Information regarding past goals" refers to data on past goal setting, achievement status, evaluation, and related information within a company, and serves as the foundation for pattern recognition related to goal management.

[0497] "Types of work" refers to categorization based on roles and job responsibilities within a company, and is used to express the specific types of work that each employee is expected to perform.

[0498] "Role" refers to one's position and responsibilities within an organization, and signifies the role a person plays in fulfilling the functions and objectives they are expected to achieve within that organization.

[0499] "Guidelines for setting goals" refer to specific guidelines for setting goals appropriate for each job type and position, generated based on past data, and provide a standard for employees to effectively set goals.

[0500] "Users" refers to those who utilize this goal management system to set goals and report on their progress, and generally includes employees and those engaged in business operations.

[0501] "Progress information" refers to data that shows the current degree of achievement and the progress of work toward the set goals, and is necessary information to clarify the status of goal achievement.

[0502] "Notifications" refer to messages sent to inform users about their progress toward achieving goals, necessary points to note, evaluation results, etc., and are intended to provide feedback to users.

[0503] This invention is a system for standardizing and streamlining goal management systems within companies. This system primarily consists of a server, terminals, and users, with each element working together to effectively manage goals.

[0504] Server Role

[0505] The server collects information on a company's past goals and learns from that information using a generative AI model. The generative AI model has the ability to generate goal-setting guidelines tailored to the type of work and role based on that information. This process uses data processing software such as Python or TensorFlow. The server sends the generated guidelines to each user's terminal to assist with goal setting. Furthermore, the server receives progress information from users and analyzes that data to generate notifications and feedback.

[0506] Terminal role

[0507] The terminal displays guidelines sent from the server to the user and assists the user in setting goals. The goals set by the user are sent to the server via the terminal. The terminal also provides an interface for the user to periodically input information about their progress and sends that information to the server.

[0508] User actions

[0509] Users set goals and report their progress using a terminal. Guidelines provided by the server ensure consistency and quality in goal setting. Users input their progress into the terminal, which is then analyzed by the server, and necessary feedback is provided back to the user via the terminal. This allows users to adjust their actions towards achieving their goals in a timely manner and evaluate their final results.

[0510] Specific example

[0511] For example, if a sales employee sets a goal of acquiring 10 new customers in six months, the server can evaluate the feasibility of that goal based on similar past data and suggest "one new customer visit per week" as a short-term goal. The generative AI model could be input with prompts such as the following:

[0512] "Based on sales target achievement data from the past five years, please generate effective target setting guidelines for new customer acquisition within the sales department."

[0513] In this way, this invention streamlines the goal setting and evaluation process, supporting the achievement of goals for the entire company.

[0514] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0515] Step 1:

[0516] The server collects information on past goals accumulated across the entire company. This includes data on past goal setting, achievement status, and evaluation for each task and role. This data is standardized and formatted so that it can be used by a generative AI model. The input here is data on past goals, and the output is the formatted dataset.

[0517] Step 2:

[0518] The server inputs the formatted dataset into a generative AI model to generate goal-related guidelines tailored to the type and role of the task. In this process, the generative AI model recognizes past patterns and extracts the optimal goal-related guidelines. The input is the formatted dataset, and the output is the generated goal-related guidelines.

[0519] Step 3:

[0520] The server sends guidelines regarding the generated goals to each user's terminal. Through this process, users receive specific goal guidelines tailored to their role. The input is the generated guidelines, and the output is the guidelines displayed on the terminal.

[0521] Step 4:

[0522] Users set individual goals based on guidelines generated from their terminals. The goal information set by the user is sent to the server via the terminal. Here, the input is the generated guidelines and the goals set by the user, and the output is the goal information sent to the server.

[0523] Step 5:

[0524] Users periodically input their progress toward their goals into a terminal and send that data to the server. The input is the progress information reported by the user, and the output is the progress information sent to the server.

[0525] Step 6:

[0526] The server analyzes the received progress information and evaluates the user's progress toward achieving their goals. Based on this analysis, it generates and provides necessary notifications and feedback to the user. The input here is the user's progress information, and the output is the analysis results and the generated notifications and feedback.

[0527] Step 7:

[0528] Users, upon receiving notifications and feedback from the server, modify their actions toward their goals and readjust their strategies as needed. The input here is the feedback and notifications provided by the server, while the output is the user's adjusted actions.

[0529] (Application Example 1)

[0530] 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."

[0531] In modern society, organizations and individuals need to effectively set goals and manage progress, but traditional methods have been ineffective in utilizing past data and providing standardized guidelines. Furthermore, there was a lack of methods for integrating and managing goals individually set by residents and administrative departments, creating a need for improved overall efficiency and transparency.

[0532] 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.

[0533] In this invention, the server includes means for learning past information data and generating guidance guidelines appropriate to the organization and position; means for receiving information settings entered by the user and automatically correcting them based on the guidelines; means for receiving progress information toward goals, analyzing the progress status and generating warnings; means for calculating evaluation feedback and providing that feedback to the user; means for presenting short-term goals and improvement measures based on progress analysis; and means for jointly managing goals individually set by residents and administrative departments and providing integrated information. This enables standardized, efficient, and transparent goal management both within and outside the organization.

[0534] "Information data" is a general term for records of various histories and activities generated both inside and outside an organization.

[0535] "Guidance guidelines" are documents that provide standard guidelines and policies to be used as reference when setting goals.

[0536] "Information settings" is the act of entering the goals that the user wants to achieve and the specific plans to achieve them.

[0537] A "warning" is a cautionary message issued when progress toward a set goal falls short of expectations.

[0538] "Evaluation feedback" is feedback information calculated based on the user's goal achievement status, and is an analytical result provided for improvement and growth.

[0539] "Short-term goals" are specific, short-term objectives set to achieve long-term goals.

[0540] "Improvement measures" refer to specific actions or policies proposed to improve the process of achieving a goal or the current situation.

[0541] "Integrated information" refers to a general term for information that has been compiled from data obtained from different residents and administrative departments and organized in a way that makes it mutually usable.

[0542] To implement this invention, first, historical information data is collected using a cloud server, and a generative AI model is built based on this data. The server uses this AI model to generate guidance guidelines based on the organization and position. These guidelines are delivered to each user's terminal via the cloud. The terminal receives and presents them to the user, who then inputs their own information settings. The user's input is sent from the terminal to the server and automatically corrected based on the guidelines. The server periodically monitors the progress toward the goals based on the corrected information. If progress information is insufficient, a warning message is sent to the user's terminal. In addition, evaluation feedback is calculated and provided to the user. If necessary, the AI ​​model can suggest short-term goals and improvement measures based on progress analysis, and can even manage goals set by residents and administrative departments to provide the user with integrated information.

[0543] As a concrete example, consider a case where a city's environmental protection department sets a goal of "increasing the monthly recycling rate by 20%." This system analyzes past recycling data and proposes a short-term goal of "conducting weekly recycling awareness campaigns." Furthermore, by having users report their progress through the app, it is possible to identify problems and provide appropriate improvement plans as needed.

[0544] An example of a prompt for a generated AI model is, "Based on the recycling data from the past three years, please suggest possible improvements for the next six months." This prompt serves as a guideline to encourage specific actions from the AI ​​system.

[0545] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0546] Step 1:

[0547] The server receives historical data collected from companies and organizations. This data includes the history and achievement status related to each user's goal setting. Based on this input data, the server uses a generative AI model to learn from the data. Here, data processing such as principal component analysis and feature extraction is performed to identify patterns necessary for guideline generation.

[0548] Step 2:

[0549] The server uses the trained model to generate leadership guidelines for organizations and positions. This process creates guidelines that incorporate patterns generated using the AI ​​model. The output is customized guidelines tailored to each job type and position.

[0550] Step 3:

[0551] The server sends the generated guidance guidelines to each user's terminal. The terminal receives these guidelines and presents them to the user. At this stage, the data format is converted for presentation to the user.

[0552] Step 4:

[0553] The user reviews the guidelines displayed on the device and enters their personal information settings. These settings include goals and plans they wish to achieve. The entered information is then sent from the device to the server.

[0554] Step 5:

[0555] The server receives the information settings submitted by the user and automatically corrects them based on pre-generated guidelines. In this process, an AI model evaluates whether the input settings are realistic and outputs the corrected information.

[0556] Step 6:

[0557] The server periodically monitors the user's progress toward their goals based on the updated information. Progress data is periodically entered by the user via their terminal and sent to the server. This data is analyzed, and if progress is behind schedule, a warning is issued.

[0558] Step 7:

[0559] The server calculates evaluation feedback and improvement measures based on progress data and sends them to the user's terminal. This process includes analysis and simulation using AI models. The user can then adjust their action plan based on this feedback.

[0560] Step 8:

[0561] The server proposes short-term goals and improvement measures, and manages goals set by residents and administrative departments. In doing so, it generates integrated information and provides it to users. The output information includes recommendations for further actions to achieve the goals.

[0562] 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.

[0563] This invention combines an emotion engine with a goal management system to achieve flexible goal setting and evaluation feedback that takes user emotions into account. The system mainly consists of a server, terminals, users, and the emotion engine.

[0564] Server Role

[0565] The server generates standardized goal-setting guidelines based on existing goal management data. Furthermore, an emotion engine processes the user's emotional data and adjusts the guidelines and feedback to best suit that emotion. The server analyzes goal-setting and progress information received from the user, generates alerts and evaluation feedback based on goal achievement, and makes any necessary adjustments.

[0566] Terminal role

[0567] The device displays goal-setting guidelines and emotion-based feedback sent from the server to the user. It collects user input and progress data and transfers it to the server. In addition, by integrating with an emotion engine, it detects the user's emotional state in real time and sends that information to the server.

[0568] User actions

[0569] Users set and review goals via their device and record their progress as needed. The emotion engine determines emotions in real time based on the user's input patterns and physiological indicators, and provides emotion-appropriate feedback. This allows users to easily receive personalized goal adjustments and motivational support.

[0570] The role of the emotional engine

[0571] The emotion engine utilizes technologies such as text analysis, voice analysis, and biosignal analysis to analyze the user's emotions. This allows the system to respond flexibly according to the user's emotions. Specifically, if a decrease in motivation is detected, the emotion engine will provide further support or suggest short-term goals.

[0572] Specific example

[0573] For example, if the emotion engine determines that a user is experiencing anxiety due to failing to meet sales targets, the server will send an alert based on this, prompting the user to re-evaluate their targets and set specific short-term goals. At the same time, it will suggest recommended content and training plans on the device to boost motivation. This allows the user to reduce anxiety and gain guidance for their next steps.

[0574] This system supports goal achievement by working in conjunction with the user's emotions, thereby promoting optimal performance in a variety of situations.

[0575] The following describes the processing flow.

[0576] Step 1:

[0577] The server collects historical goal-setting data and learns from it using an AI model. This generates and prepares standardized goal-setting guidelines for each job type and position.

[0578] Step 2:

[0579] The server sends the generated guidelines to the user's device. These guidelines also take into account the results of the sentiment engine's analysis of the user's emotions.

[0580] Step 3:

[0581] The terminal displays guidelines provided by the server to the user, and the user enters their goals based on those guidelines.

[0582] Step 4:

[0583] When a user enters a goal, that information is sent from the device to the server.

[0584] Step 5:

[0585] The server analyzes the received goal information and automatically adjusts the goals based on the analysis results of the emotion engine. Appropriate feedback and suggestions for goal adjustment are generated.

[0586] Step 6:

[0587] The terminal presents the user with the adjusted target information returned from the server for confirmation. If necessary, the user resets the target.

[0588] Step 7:

[0589] Users regularly input progress data toward achieving their goals into their device. If a change in the user's psychological state is detected, the emotion engine analyzes that information.

[0590] Step 8:

[0591] The server evaluates the progress based on progress data and the results of the sentiment engine's analysis, and generates alerts or feedback.

[0592] Step 9:

[0593] The device receives alerts and feedback from the server and presents them to the user. Based on the user's emotions, necessary support and short-term goals are suggested.

[0594] Step 10:

[0595] The server works in conjunction with the emotion engine to develop motivation-boosting strategies and provide content tailored to the user. As a result, users can continuously engage in the activities necessary to achieve their goals.

[0596] (Example 2)

[0597] 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."

[0598] Traditional goal management systems lack the flexibility to set goals and provide feedback that respond to users' emotions, resulting in a problem where fixed goals and evaluations lead to decreased user motivation. In particular, it has been difficult to grasp changes in users' psychological state and emotions in real time and provide system responses that adapt to those states.

[0599] 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.

[0600] In this invention, the server includes means for learning past goal-setting data and generating policy guidelines tailored to the industry and position; means for processing data obtained from the user using data analysis techniques to analyze the user's emotional state; and means for adjusting and providing goal-setting guidelines and evaluation feedback based on the user's emotional state. This enables flexible and adaptive goal setting and feedback provision that responds to the user's emotions.

[0601] "Past goal-setting data" refers to information about goals set by users or organizations in the past, including their achievement status and related data.

[0602] "Policy guidelines" are guidelines formulated to guide users in achieving their goals and the processes involved, and they include specific content tailored to the industry and position.

[0603] "Data analysis technology" refers to techniques for processing collected data using statistical or machine learning methods to extract meaningful information and trends.

[0604] "Emotional state" refers to the user's psychological and emotional condition, and is determined based on physiological indicators and behavioral data.

[0605] The term "goal-setting guidelines" refers to a set of guidelines provided in a standardized format to help users clarify the goals they should achieve in the long term or short term.

[0606] "Evaluation feedback" is information generated based on the user's goal achievement and progress, providing indicators and guidelines to help the user decide on their next course of action.

[0607] This invention provides a flexible goal management system that incorporates sentiment analysis to effectively support users in setting and achieving their goals. The system mainly consists of a server, terminals, users, and a sentiment engine.

[0608] The server utilizes a generative AI model integrated with an advanced database management system to collect and learn from past goal-setting data. This generates standard goal-setting guidelines tailored to the industry and position. Furthermore, it uses data analysis techniques to analyze the user's emotional state in real time, tailoring feedback and guidelines individually and delivering them in a way that is appropriate for the user.

[0609] The terminal functions as an interface with the user. It collects user input data and progress information and sends it to the server. This process requires appropriate hardware to collect emotional data through biosensors and text input interfaces. For example, a smartwatch could be used to monitor heart rate and analyze the user's stress level.

[0610] The emotion engine analyzes text, voice, and biosignals to determine the user's emotional state. If it detects a decrease in motivation, it provides emotion-based support and short-term goal suggestions via the server. This allows users to receive goal adjustments and motivation maintenance strategies tailored to their own emotions.

[0611] For example, if the emotion engine determines that a user is experiencing anxiety due to failing to meet sales targets, the server will automatically prompt the user to re-evaluate their goals and provide guidelines for resetting short-term targets. The device will then present recommended content and training plans to boost motivation.

[0612] For example, when a user enters "How should I improve sales for the next quarter?" into the system, the system will return optimal feedback that takes the user's emotional state into account.

[0613] This format allows users to set flexible goals and manage their progress in a way that suits their own circumstances, ultimately leading to goal achievement.

[0614] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0615] Step 1:

[0616] The server collects and learns from past goal-setting data. This data includes goal achievement status and various related information. Historical datasets are used as input, and a generative AI model is used to generate basic guidelines for goal setting. These guidelines are then processed to be optimized according to industry and position before being output. Specifically, the generative AI model extracts patterns from historical data using its algorithms and formats them into guidelines.

[0617] Step 2:

[0618] The terminal collects input data from the user. This input includes goal setting, progress, and user feedback. This data is sent to the server for use in data analysis. Specifically, the terminal receives information via the touchscreen or keyboard, converts it to an appropriate format, and uploads it to the server.

[0619] Step 3:

[0620] The emotion engine analyzes user data transmitted from the device. Specifically, it performs text analysis, voice analysis, and biosignal analysis to determine the emotional state. This process yields output regarding the user's emotions, which is then transmitted to the server. In practice, the emotion engine uses audio sensors and text parsers to analyze data in real time.

[0621] Step 4:

[0622] The server adjusts the goal-setting guidelines based on the emotional state obtained from the emotion engine. It receives the results of the emotion analysis as input and performs data processing to individually optimize the guidelines and feedback. As a result, the adjusted guidelines and feedback are output and sent to the terminal. In its specific operation, the server uses an algorithm to dynamically change the guidelines based on the emotional state.

[0623] Step 5:

[0624] The terminal displays the user the adjusted guidelines and feedback sent from the server. Through this, the user can check their progress and reset their goals as needed. User input and progress data are collected again and sent to the server as input for the next cycle. In concrete terms, the terminal presents information to the user via a GUI and supports more detailed interactions.

[0625] (Application Example 2)

[0626] 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."

[0627] Traditional goal management systems focus on objectively evaluating users' progress and achievement of their work objectives. However, they lack flexibility in achievement and management because they do not take into account individual emotional states and motivations. In particular, they fail to adequately mitigate the impact of individual emotional factors on goal achievement, which can ultimately hinder performance improvement. Therefore, there is a need for a system that takes users' emotional states into account and provides more individualized goal management.

[0628] 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.

[0629] In this invention, the server includes a device that learns past goal-setting information and generates goal-setting guidelines according to the work and role; a device that receives goal-setting information input from the user and automatically adjusts it based on the guidelines; and a device that receives progress data toward the goal, analyzes the progress, and generates a warning. This makes it possible to automatically adjust goals and provide feedback while taking into account the user's emotions.

[0630] "Goal setting information" refers to data that shows the specific goals that users aim to achieve and their content.

[0631] "Work" refers to activities and responsibilities related to a specific job or role.

[0632] A "role" refers to the responsibilities and tasks that an individual within an organization is expected to perform.

[0633] "Guidelines" are information that provides a set of procedures and policies to serve as a basis for setting goals.

[0634] A "device" is a combination of hardware and software designed to perform a specific function.

[0635] "Automatic adjustment" means that the system autonomously changes settings and configurations without human intervention.

[0636] "Progress data" refers to information and indicators that show the extent to which a goal has been achieved.

[0637] A "warning" is a notification or alert that alerts the user to a problem in the progress.

[0638] "Users" refer to individuals or organizations that operate the system and perform goal management and adjustments.

[0639] The system implementing this invention mainly consists of a server, terminals, users, and an emotion engine. The server uses a machine learning algorithm based on past goal-setting information to generate goal-setting guidelines tailored to tasks and roles. These guidelines, based on data collected from individual users, play a role in supporting optimal goal setting. The server also receives progress data in real time and evaluates this data with statistical analysis tools to generate warnings and feedback according to the progress.

[0640] The terminal functions as an interface between the user and the server, visually displaying posted goal-setting guidelines and feedback from the sentiment engine. This utilizes a graphical user interface (GUI) dashboard, allowing for easy manipulation and review of data.

[0641] Users can set goals through their device and record their progress in a timely manner. Furthermore, the emotion engine analyzes audio and video data using software such as Python's TensorFlow library and OpenCV to analyze the user's emotional state. This analysis allows the system to dynamically adjust goals according to the user's emotional state.

[0642] As a concrete example, when a user sets a new health goal and is behind schedule, a motivating message such as, "You've slowed down a bit today, but you're almost there! Keep up the good work!" is displayed on the device. An example of a prompt message generated using a generative AI model would be, "Based on the user's emotional data, please provide recommendations to help maintain motivation toward the current goal."

[0643] Thus, this system enables highly individualized goal management that takes into account the user's emotions and provides flexible support for achieving goals.

[0644] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0645] Step 1:

[0646] The server retrieves past goal-setting information from a database and generates goal-setting guidelines suitable for the job and role. Using user role information and historical data as input, it outputs goal guidelines using a machine learning algorithm. In this process, it utilizes a learning model to detect patterns and adjusts the guidelines based on predicted outcomes.

[0647] Step 2:

[0648] The terminal presents the user with goal-setting guidelines received from the server. Through the terminal's GUI, the user can select and fine-tune goals. It receives guideline data from the server as input and displays it visually to the user as output. Specifically, it provides infographics on the dashboard that show the details of each goal.

[0649] Step 3:

[0650] Users set goals and record their progress via a terminal. Progress data manually entered by the user is used as input, and progress data is generated as output, which is then stored in a database. The terminal provides pull-down menus and templates to facilitate input.

[0651] Step 4:

[0652] The server analyzes progress data collected from users and generates warnings and feedback based on the progress. Progress data and pre-configured criteria are used as input, and warning messages and feedback reports based on the analysis results are generated as output. This process utilizes statistical analysis tools to issue warnings if progress falls below the standard.

[0653] Step 5:

[0654] The emotion engine collects user voice and video data and analyzes their emotions. Using response audio and image data as input, it generates quantitative data on emotional states as output. Specifically, it utilizes speech recognition and facial recognition technologies to determine emotions such as stress and joy.

[0655] Step 6:

[0656] The server uses a generative AI model based on the output of the emotion engine to output prompt messages suggesting goal adjustments and motivational methods that correspond to the user's emotions. The input consists of emotion data and goal information, and the output is a prompt message presented to the user. For example, it might say, "Based on the emotion data, the user appears somewhat depressed, so we suggest relaxation along with short-term goals."

[0657] 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.

[0658] 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.

[0659] 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.

[0660] [Fourth Embodiment]

[0661] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0662] 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.

[0663] 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).

[0664] 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.

[0665] 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.

[0666] 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).

[0667] 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.

[0668] 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.

[0669] 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.

[0670] 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.

[0671] 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.

[0672] 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.

[0673] 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".

[0674] This invention is a system using an AI agent to support the standardization and efficiency of goal management systems in companies. This system mainly consists of a server, terminals, and users.

[0675] Server Role

[0676] The server aggregates goal-setting data accumulated throughout the entire company and uses an AI model to learn from that data. Based on the learned model, it generates standardized goal-setting guidelines tailored to job type and position. The server sends these guidelines to each employee's terminal to support goal setting. Furthermore, the server receives progress data and monitors users' goal achievement status. Based on progress, it generates appropriate alerts and evaluation feedback and provides them to each user.

[0677] Terminal role

[0678] The terminal displays goal-setting guidelines sent from the server to the user. Once the user sets a goal, that information is sent from the terminal to the server. The server receives the processing results and presents them to the user as feedback. Furthermore, the terminal provides an input interface for progress data, assisting the user in updating necessary information accordingly.

[0679] User actions

[0680] Users set their own goals and report their progress through their devices. Guidelines and recommended goals provided by the server ensure the quality and consistency of goal setting. Progress is reported regularly, and the results are supplemented by alerts and feedback from the server. Users use this information to adjust their actions toward achieving their goals and receive a final evaluation.

[0681] Specific example

[0682] For example, suppose an employee in the sales department sets a goal of "acquiring 10 new customers in six months." In this case, the server automatically evaluates the feasibility of the goal based on similar past data and, if necessary, suggests a shorter-term goal such as "visiting one new company per week." The user reviews this and reports their progress via their terminal, allowing the server to analyze the progress and issue alerts based on the level of achievement. Finally, the server performs a final evaluation and notifies the user of the results as feedback via their terminal.

[0683] Thus, this invention streamlines goal setting and evaluation, providing a company-wide, uniform, and convincing process that improves employee motivation and performance.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] The server collects past goal setting and evaluation data from a database and inputs it into an AI model for training. This prepares it to generate goal setting guidelines for each job type and position.

[0687] Step 2:

[0688] Based on the learning results, the server generates standardized goal-setting guidelines tailored to each job type and position. The generated guidelines are then sent to the user's device.

[0689] Step 3:

[0690] The device receives guidelines sent from the server and presents them to the user. The user then uses the guidelines as a reference to input their own goals.

[0691] Step 4:

[0692] When a user enters a goal into their device, the device sends that goal setting information to the server.

[0693] Step 5:

[0694] The server evaluates the received goal setting information using an automated correction algorithm and generates necessary revisions. This result is then sent to the terminal.

[0695] Step 6:

[0696] The terminal displays the corrected target information returned from the server to the user and prompts them to review the revised version. Once the user reviews and confirms the revised version, the information is stored on the server again.

[0697] Step 7:

[0698] Users periodically input progress data towards their goals via their device. The device then sends this information to the server.

[0699] Step 8:

[0700] The server analyzes the received progress data and evaluates the degree of goal achievement. Based on the evaluation results, it generates necessary alerts and sends them to the terminal.

[0701] Step 9:

[0702] The device receives alerts and evaluation feedback from the server and presents them to the user. This allows the user to obtain information to decide on their next course of action.

[0703] Step 10:

[0704] At the end of the target period, the server comprehensively analyzes all progress data and evaluation feedback to generate a final evaluation result. This result is then notified to the user via their terminal.

[0705] (Example 1)

[0706] 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".

[0707] In traditional goal management systems, each employee sets their own individual goals, making it difficult to maintain consistency and quality across company-wide objectives. Furthermore, insufficient progress monitoring and adequate feedback can lead to decreased efficiency in achieving goals.

[0708] 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.

[0709] In this invention, the server includes means for learning information about past goals and generating guidelines for goals according to the type of work and role; means for receiving information about goals entered by users and automatically adjusting them based on the guidelines; and means for receiving information about progress toward goals, analyzing the progress status and generating notifications. This enables standardized goal management across the entire company, realizing effective goal setting and progress management.

[0710] "Information regarding past goals" refers to data on past goal setting, achievement status, evaluation, and related information within a company, and serves as the foundation for pattern recognition related to goal management.

[0711] "Types of work" refers to categorization based on roles and job responsibilities within a company, and is used to express the specific types of work that each employee is expected to perform.

[0712] "Role" refers to one's position and responsibilities within an organization, and signifies the role a person plays in fulfilling the functions and objectives they are expected to achieve within that organization.

[0713] "Guidelines for setting goals" refer to specific guidelines for setting goals appropriate for each job type and position, generated based on past data, and provide a standard for employees to effectively set goals.

[0714] "Users" refers to those who utilize this goal management system to set goals and report on their progress, and generally includes employees and those engaged in business operations.

[0715] "Progress information" refers to data that shows the current degree of achievement and the progress of work toward the set goals, and is necessary information to clarify the status of goal achievement.

[0716] "Notifications" refer to messages sent to inform users about their progress toward achieving goals, necessary points to note, evaluation results, etc., and are intended to provide feedback to users.

[0717] This invention is a system for standardizing and streamlining goal management systems within companies. This system primarily consists of a server, terminals, and users, with each element working together to effectively manage goals.

[0718] Server Role

[0719] The server collects information on a company's past goals and learns from that information using a generative AI model. The generative AI model has the ability to generate goal-setting guidelines tailored to the type of work and role based on that information. This process uses data processing software such as Python or TensorFlow. The server sends the generated guidelines to each user's terminal to assist with goal setting. Furthermore, the server receives progress information from users and analyzes that data to generate notifications and feedback.

[0720] Terminal role

[0721] The terminal displays guidelines sent from the server to the user and assists the user in setting goals. The goals set by the user are sent to the server via the terminal. The terminal also provides an interface for the user to periodically input information about their progress and sends that information to the server.

[0722] User actions

[0723] Users set goals and report their progress using a terminal. Guidelines provided by the server ensure consistency and quality in goal setting. Users input their progress into the terminal, which is then analyzed by the server, and necessary feedback is provided back to the user via the terminal. This allows users to adjust their actions towards achieving their goals in a timely manner and evaluate their final results.

[0724] Specific example

[0725] For example, if a sales employee sets a goal of acquiring 10 new customers in six months, the server can evaluate the feasibility of that goal based on similar past data and suggest "one new customer visit per week" as a short-term goal. The generative AI model could be input with prompts such as the following:

[0726] "Based on sales target achievement data from the past five years, please generate effective target setting guidelines for new customer acquisition within the sales department."

[0727] In this way, this invention streamlines the goal setting and evaluation process, supporting the achievement of goals for the entire company.

[0728] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0729] Step 1:

[0730] The server collects information on past goals accumulated across the entire company. This includes data on past goal setting, achievement status, and evaluation for each task and role. This data is standardized and formatted so that it can be used by a generative AI model. The input here is data on past goals, and the output is the formatted dataset.

[0731] Step 2:

[0732] The server inputs the formatted dataset into a generative AI model to generate goal-related guidelines tailored to the type and role of the task. In this process, the generative AI model recognizes past patterns and extracts the optimal goal-related guidelines. The input is the formatted dataset, and the output is the generated goal-related guidelines.

[0733] Step 3:

[0734] The server sends guidelines regarding the generated goals to each user's terminal. Through this process, users receive specific goal guidelines tailored to their role. The input is the generated guidelines, and the output is the guidelines displayed on the terminal.

[0735] Step 4:

[0736] Users set individual goals based on guidelines generated from their terminals. The goal information set by the user is sent to the server via the terminal. Here, the input is the generated guidelines and the goals set by the user, and the output is the goal information sent to the server.

[0737] Step 5:

[0738] Users periodically input their progress toward their goals into a terminal and send that data to the server. The input is the progress information reported by the user, and the output is the progress information sent to the server.

[0739] Step 6:

[0740] The server analyzes the received progress information and evaluates the user's progress toward achieving their goals. Based on this analysis, it generates and provides necessary notifications and feedback to the user. The input here is the user's progress information, and the output is the analysis results and the generated notifications and feedback.

[0741] Step 7:

[0742] Users, upon receiving notifications and feedback from the server, modify their actions toward their goals and readjust their strategies as needed. The input here is the feedback and notifications provided by the server, while the output is the user's adjusted actions.

[0743] (Application Example 1)

[0744] 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".

[0745] In modern society, organizations and individuals need to effectively set goals and manage progress, but traditional methods have been ineffective in utilizing past data and providing standardized guidelines. Furthermore, there was a lack of methods for integrating and managing goals individually set by residents and administrative departments, creating a need for improved overall efficiency and transparency.

[0746] 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.

[0747] In this invention, the server includes means for learning past information data and generating guidance guidelines appropriate to the organization and position; means for receiving information settings entered by the user and automatically correcting them based on the guidelines; means for receiving progress information toward goals, analyzing the progress status and generating warnings; means for calculating evaluation feedback and providing that feedback to the user; means for presenting short-term goals and improvement measures based on progress analysis; and means for jointly managing goals individually set by residents and administrative departments and providing integrated information. This enables standardized, efficient, and transparent goal management both within and outside the organization.

[0748] "Information data" is a general term for records of various histories and activities generated both inside and outside an organization.

[0749] "Guidance guidelines" are documents that provide standard guidelines and policies to be used as reference when setting goals.

[0750] "Information settings" is the act of entering the goals that the user wants to achieve and the specific plans to achieve them.

[0751] A "warning" is a cautionary message issued when progress toward a set goal falls short of expectations.

[0752] "Evaluation feedback" is feedback information calculated based on the user's goal achievement status, and is an analytical result provided for improvement and growth.

[0753] "Short-term goals" are specific, short-term objectives set to achieve long-term goals.

[0754] "Improvement measures" refer to specific actions or policies proposed to improve the process of achieving a goal or the current situation.

[0755] "Integrated information" refers to a general term for information that has been compiled from data obtained from different residents and administrative departments and organized in a way that makes it mutually usable.

[0756] To implement this invention, first, historical information data is collected using a cloud server, and a generative AI model is built based on this data. The server uses this AI model to generate guidance guidelines based on the organization and position. These guidelines are delivered to each user's terminal via the cloud. The terminal receives and presents them to the user, who then inputs their own information settings. The user's input is sent from the terminal to the server and automatically corrected based on the guidelines. The server periodically monitors the progress toward the goals based on the corrected information. If progress information is insufficient, a warning message is sent to the user's terminal. In addition, evaluation feedback is calculated and provided to the user. If necessary, the AI ​​model can suggest short-term goals and improvement measures based on progress analysis, and can even manage goals set by residents and administrative departments to provide the user with integrated information.

[0757] As a concrete example, consider a case where a city's environmental protection department sets a goal of "increasing the monthly recycling rate by 20%." This system analyzes past recycling data and proposes a short-term goal of "conducting weekly recycling awareness campaigns." Furthermore, by having users report their progress through the app, it is possible to identify problems and provide appropriate improvement plans as needed.

[0758] An example of a prompt for a generated AI model is, "Based on the recycling data from the past three years, please suggest possible improvements for the next six months." This prompt serves as a guideline to encourage specific actions from the AI ​​system.

[0759] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0760] Step 1:

[0761] The server receives historical data collected from companies and organizations. This data includes the history and achievement status related to each user's goal setting. Based on this input data, the server uses a generative AI model to learn from the data. Here, data processing such as principal component analysis and feature extraction is performed to identify patterns necessary for guideline generation.

[0762] Step 2:

[0763] The server uses the trained model to generate leadership guidelines for organizations and positions. This process creates guidelines that incorporate patterns generated using the AI ​​model. The output is customized guidelines tailored to each job type and position.

[0764] Step 3:

[0765] The server sends the generated guidance guidelines to each user's terminal. The terminal receives these guidelines and presents them to the user. At this stage, the data format is converted for presentation to the user.

[0766] Step 4:

[0767] The user reviews the guidelines displayed on the device and enters their personal information settings. These settings include goals and plans they wish to achieve. The entered information is then sent from the device to the server.

[0768] Step 5:

[0769] The server receives the information settings submitted by the user and automatically corrects them based on pre-generated guidelines. In this process, an AI model evaluates whether the input settings are realistic and outputs the corrected information.

[0770] Step 6:

[0771] The server periodically monitors the user's progress toward their goals based on the updated information. Progress data is periodically entered by the user via their terminal and sent to the server. This data is analyzed, and if progress is behind schedule, a warning is issued.

[0772] Step 7:

[0773] The server calculates evaluation feedback and improvement measures based on progress data and sends them to the user's terminal. This process includes analysis and simulation using AI models. The user can then adjust their action plan based on this feedback.

[0774] Step 8:

[0775] The server proposes short-term goals and improvement measures, and manages goals set by residents and administrative departments. In doing so, it generates integrated information and provides it to users. The output information includes recommendations for further actions to achieve the goals.

[0776] 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.

[0777] This invention combines an emotion engine with a goal management system to achieve flexible goal setting and evaluation feedback that takes user emotions into account. The system mainly consists of a server, terminals, users, and the emotion engine.

[0778] Server Role

[0779] The server generates standardized goal-setting guidelines based on existing goal management data. Furthermore, an emotion engine processes the user's emotional data and adjusts the guidelines and feedback to best suit that emotion. The server analyzes goal-setting and progress information received from the user, generates alerts and evaluation feedback based on goal achievement, and makes any necessary adjustments.

[0780] Terminal role

[0781] The device displays goal-setting guidelines and emotion-based feedback sent from the server to the user. It collects user input and progress data and transfers it to the server. In addition, by integrating with an emotion engine, it detects the user's emotional state in real time and sends that information to the server.

[0782] User actions

[0783] Users set and review goals via their device and record their progress as needed. The emotion engine determines emotions in real time based on the user's input patterns and physiological indicators, and provides emotion-appropriate feedback. This allows users to easily receive personalized goal adjustments and motivational support.

[0784] The role of the emotional engine

[0785] The emotion engine utilizes technologies such as text analysis, voice analysis, and biosignal analysis to analyze the user's emotions. This allows the system to respond flexibly according to the user's emotions. Specifically, if a decrease in motivation is detected, the emotion engine will provide further support or suggest short-term goals.

[0786] Specific example

[0787] For example, if the emotion engine determines that a user is experiencing anxiety due to failing to meet sales targets, the server will send an alert based on this, prompting the user to re-evaluate their targets and set specific short-term goals. At the same time, it will suggest recommended content and training plans on the device to boost motivation. This allows the user to reduce anxiety and gain guidance for their next steps.

[0788] This system supports goal achievement by working in conjunction with the user's emotions, thereby promoting optimal performance in a variety of situations.

[0789] The following describes the processing flow.

[0790] Step 1:

[0791] The server collects historical goal-setting data and learns from it using an AI model. This generates and prepares standardized goal-setting guidelines for each job type and position.

[0792] Step 2:

[0793] The server sends the generated guidelines to the user's device. These guidelines also take into account the results of the sentiment engine's analysis of the user's emotions.

[0794] Step 3:

[0795] The terminal displays guidelines provided by the server to the user, and the user enters their goals based on those guidelines.

[0796] Step 4:

[0797] When a user enters a goal, that information is sent from the device to the server.

[0798] Step 5:

[0799] The server analyzes the received goal information and automatically adjusts the goals based on the analysis results of the emotion engine. Appropriate feedback and suggestions for goal adjustment are generated.

[0800] Step 6:

[0801] The terminal presents the user with the adjusted target information returned from the server for confirmation. If necessary, the user resets the target.

[0802] Step 7:

[0803] Users regularly input progress data toward achieving their goals into their device. If a change in the user's psychological state is detected, the emotion engine analyzes that information.

[0804] Step 8:

[0805] The server evaluates the progress based on progress data and the results of the sentiment engine's analysis, and generates alerts or feedback.

[0806] Step 9:

[0807] The device receives alerts and feedback from the server and presents them to the user. Based on the user's emotions, necessary support and short-term goals are suggested.

[0808] Step 10:

[0809] The server works in conjunction with the emotion engine to develop motivation-boosting strategies and provide content tailored to the user. As a result, users can continuously engage in the activities necessary to achieve their goals.

[0810] (Example 2)

[0811] 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".

[0812] Traditional goal management systems lack the flexibility to set goals and provide feedback that respond to users' emotions, resulting in a problem where fixed goals and evaluations lead to decreased user motivation. In particular, it has been difficult to grasp changes in users' psychological state and emotions in real time and provide system responses that adapt to those states.

[0813] 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.

[0814] In this invention, the server includes means for learning past goal-setting data and generating policy guidelines tailored to the industry and position; means for processing data obtained from the user using data analysis techniques to analyze the user's emotional state; and means for adjusting and providing goal-setting guidelines and evaluation feedback based on the user's emotional state. This enables flexible and adaptive goal setting and feedback provision that responds to the user's emotions.

[0815] "Past goal-setting data" refers to information about goals set by users or organizations in the past, including their achievement status and related data.

[0816] "Policy guidelines" are guidelines formulated to guide users in achieving their goals and the processes involved, and they include specific content tailored to the industry and position.

[0817] "Data analysis technology" refers to techniques for processing collected data using statistical or machine learning methods to extract meaningful information and trends.

[0818] "Emotional state" refers to the user's psychological and emotional condition, and is determined based on physiological indicators and behavioral data.

[0819] The term "goal-setting guidelines" refers to a set of guidelines provided in a standardized format to help users clarify the goals they should achieve in the long term or short term.

[0820] "Evaluation feedback" is information generated based on the user's goal achievement and progress, providing indicators and guidelines to help the user decide on their next course of action.

[0821] This invention provides a flexible goal management system that incorporates sentiment analysis to effectively support users in setting and achieving their goals. The system mainly consists of a server, terminals, users, and a sentiment engine.

[0822] The server utilizes a generative AI model integrated with an advanced database management system to collect and learn from past goal-setting data. This generates standard goal-setting guidelines tailored to the industry and position. Furthermore, it uses data analysis techniques to analyze the user's emotional state in real time, tailoring feedback and guidelines individually and delivering them in a way that is appropriate for the user.

[0823] The terminal functions as an interface with the user. It collects user input data and progress information and sends it to the server. This process requires appropriate hardware to collect emotional data through biosensors and text input interfaces. For example, a smartwatch could be used to monitor heart rate and analyze the user's stress level.

[0824] The emotion engine analyzes text, voice, and biosignals to determine the user's emotional state. If it detects a decrease in motivation, it provides emotion-based support and short-term goal suggestions via the server. This allows users to receive goal adjustments and motivation maintenance strategies tailored to their own emotions.

[0825] For example, if the emotion engine determines that a user is experiencing anxiety due to failing to meet sales targets, the server will automatically prompt the user to re-evaluate their goals and provide guidelines for resetting short-term targets. The device will then present recommended content and training plans to boost motivation.

[0826] For example, when a user enters "How should I improve sales for the next quarter?" into the system, the system will return optimal feedback that takes the user's emotional state into account.

[0827] This format allows users to set flexible goals and manage their progress in a way that suits their own circumstances, ultimately leading to goal achievement.

[0828] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0829] Step 1:

[0830] The server collects and learns from past goal-setting data. This data includes goal achievement status and various related information. Historical datasets are used as input, and a generative AI model is used to generate basic guidelines for goal setting. These guidelines are then processed to be optimized according to industry and position before being output. Specifically, the generative AI model extracts patterns from historical data using its algorithms and formats them into guidelines.

[0831] Step 2:

[0832] The terminal collects input data from the user. This input includes goal setting, progress, and user feedback. This data is sent to the server for use in data analysis. Specifically, the terminal receives information via the touchscreen or keyboard, converts it to an appropriate format, and uploads it to the server.

[0833] Step 3:

[0834] The emotion engine analyzes user data transmitted from the device. Specifically, it performs text analysis, voice analysis, and biosignal analysis to determine the emotional state. This process yields output regarding the user's emotions, which is then transmitted to the server. In practice, the emotion engine uses audio sensors and text parsers to analyze data in real time.

[0835] Step 4:

[0836] The server adjusts the goal-setting guidelines based on the emotional state obtained from the emotion engine. It receives the results of the emotion analysis as input and performs data processing to individually optimize the guidelines and feedback. As a result, the adjusted guidelines and feedback are output and sent to the terminal. In its specific operation, the server uses an algorithm to dynamically change the guidelines based on the emotional state.

[0837] Step 5:

[0838] The terminal displays the user the adjusted guidelines and feedback sent from the server. Through this, the user can check their progress and reset their goals as needed. User input and progress data are collected again and sent to the server as input for the next cycle. In concrete terms, the terminal presents information to the user via a GUI and supports more detailed interactions.

[0839] (Application Example 2)

[0840] 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".

[0841] Traditional goal management systems focus on objectively evaluating users' progress and achievement of their work objectives. However, they lack flexibility in achievement and management because they do not take into account individual emotional states and motivations. In particular, they fail to adequately mitigate the impact of individual emotional factors on goal achievement, which can ultimately hinder performance improvement. Therefore, there is a need for a system that takes users' emotional states into account and provides more individualized goal management.

[0842] 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.

[0843] In this invention, the server includes a device that learns past goal-setting information and generates goal-setting guidelines according to the work and role; a device that receives goal-setting information input from the user and automatically adjusts it based on the guidelines; and a device that receives progress data toward the goal, analyzes the progress, and generates a warning. This makes it possible to automatically adjust goals and provide feedback while taking into account the user's emotions.

[0844] "Goal setting information" refers to data that shows the specific goals that users aim to achieve and their content.

[0845] "Work" refers to activities and responsibilities related to a specific job or role.

[0846] A "role" refers to the responsibilities and tasks that an individual within an organization is expected to perform.

[0847] "Guidelines" are information that provides a set of procedures and policies to serve as a basis for setting goals.

[0848] A "device" is a combination of hardware and software designed to perform a specific function.

[0849] "Automatic adjustment" means that the system autonomously changes settings and configurations without human intervention.

[0850] "Progress data" refers to information and indicators that show the extent to which a goal has been achieved.

[0851] A "warning" is a notification or alert that alerts the user to a problem in the progress.

[0852] "Users" refer to individuals or organizations that operate the system and perform goal management and adjustments.

[0853] The system implementing this invention mainly consists of a server, terminals, users, and an emotion engine. The server uses a machine learning algorithm based on past goal-setting information to generate goal-setting guidelines tailored to tasks and roles. These guidelines, based on data collected from individual users, play a role in supporting optimal goal setting. The server also receives progress data in real time and evaluates this data with statistical analysis tools to generate warnings and feedback according to the progress.

[0854] The terminal functions as an interface between the user and the server, visually displaying posted goal-setting guidelines and feedback from the sentiment engine. This utilizes a graphical user interface (GUI) dashboard, allowing for easy manipulation and review of data.

[0855] Users can set goals through their device and record their progress in a timely manner. Furthermore, the emotion engine analyzes audio and video data using software such as Python's TensorFlow library and OpenCV to analyze the user's emotional state. This analysis allows the system to dynamically adjust goals according to the user's emotional state.

[0856] As a concrete example, when a user sets a new health goal and is behind schedule, a motivating message such as, "You've slowed down a bit today, but you're almost there! Keep up the good work!" is displayed on the device. An example of a prompt message generated using a generative AI model would be, "Based on the user's emotional data, please provide recommendations to help maintain motivation toward the current goal."

[0857] Thus, this system enables highly individualized goal management that takes into account the user's emotions and provides flexible support for achieving goals.

[0858] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0859] Step 1:

[0860] The server retrieves past goal-setting information from a database and generates goal-setting guidelines suitable for the job and role. Using user role information and historical data as input, it outputs goal guidelines using a machine learning algorithm. In this process, it utilizes a learning model to detect patterns and adjusts the guidelines based on predicted outcomes.

[0861] Step 2:

[0862] The terminal presents the user with goal-setting guidelines received from the server. Through the terminal's GUI, the user can select and fine-tune goals. It receives guideline data from the server as input and displays it visually to the user as output. Specifically, it provides infographics on the dashboard that show the details of each goal.

[0863] Step 3:

[0864] Users set goals and record their progress via a terminal. Progress data manually entered by the user is used as input, and progress data is generated as output, which is then stored in a database. The terminal provides pull-down menus and templates to facilitate input.

[0865] Step 4:

[0866] The server analyzes progress data collected from users and generates warnings and feedback based on the progress. Progress data and pre-configured criteria are used as input, and warning messages and feedback reports based on the analysis results are generated as output. This process utilizes statistical analysis tools to issue warnings if progress falls below the standard.

[0867] Step 5:

[0868] The emotion engine collects user voice and video data and analyzes their emotions. Using response audio and image data as input, it generates quantitative data on emotional states as output. Specifically, it utilizes speech recognition and facial recognition technologies to determine emotions such as stress and joy.

[0869] Step 6:

[0870] The server uses a generative AI model based on the output of the emotion engine to output prompt messages suggesting goal adjustments and motivational methods that correspond to the user's emotions. The input consists of emotion data and goal information, and the output is a prompt message presented to the user. For example, it might say, "Based on the emotion data, the user appears somewhat depressed, so we suggest relaxation along with short-term goals."

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] 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.

[0877] 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.

[0878] 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.

[0879] 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."

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] The following is further disclosed regarding the embodiments described above.

[0893] (Claim 1)

[0894] A means of learning from past goal-setting data and generating goal-setting guidelines tailored to job type and position,

[0895] A means for receiving goal setting information entered by the user and automatically editing it based on the aforementioned guidelines,

[0896] A means of receiving progress data toward a goal, analyzing the progress status, and generating alerts,

[0897] A means of calculating evaluation feedback and providing that feedback to the user,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, comprising means for providing goal-setting guidelines, which refers to data on similar job titles and positions from the past and displays recommended goals based on that data.

[0901] (Claim 3)

[0902] The system according to claim 1, comprising means for presenting the user with short-term goals and improvement measures to achieve the goal based on the analysis of progress data.

[0903] "Example 1"

[0904] (Claim 1)

[0905] A means of learning information about past goals and generating guidelines for goals that are appropriate to the type of work and role,

[0906] A means for receiving information about goals entered by the user and automatically adjusting it based on the aforementioned guidelines,

[0907] A means for receiving information on progress toward a goal, analyzing the progress status, and generating notifications,

[0908] A means of calculating opinions regarding evaluations and providing those opinions to users,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, comprising means for providing guidance on objectives, which refers to information on the types and roles of similar past tasks, and displays recommended objectives based on that information.

[0912] (Claim 3)

[0913] The system according to claim 1, comprising means for presenting users with short-term goals and improvement measures for achieving the objective based on an analysis of progress information.

[0914] "Application Example 1"

[0915] (Claim 1)

[0916] A means of learning from past information data and generating guidance guidelines tailored to the organization and position,

[0917] A means for receiving information settings entered by the user and automatically correcting them based on the aforementioned guidelines,

[0918] A means for receiving progress information toward a goal, analyzing the progress status, and generating warnings,

[0919] A means of calculating evaluation feedback and providing that feedback to the user,

[0920] A means of presenting short-term goals and improvement measures based on progress analysis,

[0921] A means of jointly managing goals set individually by residents and administrative departments, and providing integrated information,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, comprising means for providing goal-setting guidelines, which refer to data from similar organizations or positions in the past and display recommended goals based on that data.

[0925] (Claim 3)

[0926] The system according to claim 1, comprising means for presenting the user with short-term plans and improvement measures for achieving goals based on the analysis of progress information.

[0927] "Example 2 of combining an emotion engine"

[0928] (Claim 1)

[0929] A means of learning from past goal-setting data and generating policy guidelines tailored to the industry and position,

[0930] A means of processing data obtained from users using data analysis techniques in order to analyze the emotional state of users,

[0931] A means of adjusting and providing goal-setting guidelines and evaluation feedback based on the user's emotional state,

[0932] A means of receiving and analyzing progress data toward a goal to generate alerts and emotion-responsive feedback,

[0933] A means for calculating evaluation feedback and providing that adjusted feedback to the user,

[0934] A system that includes this.

[0935] (Claim 2)

[0936] The system according to claim 1, comprising means for providing goal-setting guidelines by referring to past data of similar industries and positions, adjusting recommended goals based on that data, and providing guidelines that respond to the user's emotions.

[0937] (Claim 3)

[0938] The system according to claim 1, comprising means for presenting the user with short-term goals and improvement measures to achieve the goal based on the analysis of progress data and emotional state, and for providing motivational measures that correspond to the user's emotions.

[0939] "Application example 2 when combining with an emotional engine"

[0940] (Claim 1)

[0941] A device that learns past goal-setting information and generates goal-setting guidelines tailored to the job and role,

[0942] A device that receives goal setting information entered by the user and automatically adjusts it based on the aforementioned guidelines,

[0943] A device that receives progress data toward a target, analyzes the progress, and generates a warning,

[0944] A device that calculates evaluation feedback and provides that feedback to the user,

[0945] A device that analyzes users' emotional data and provides goal adjustments and short-term goals based on those emotions,

[0946] A system that includes this.

[0947] (Claim 2)

[0948] The system according to claim 1, comprising a device that, when providing goal-setting guidelines, refers to information on similar past tasks and roles and displays recommended goals based on that information.

[0949] (Claim 3)

[0950] The system according to claim 1, comprising a device that presents users with short-term goals and improvement measures to achieve the target based on the analysis of progress data. [Explanation of Symbols]

[0951] 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 learning from past information data and generating guidance guidelines tailored to the organization and position, A means for receiving information settings entered by the user and automatically correcting them based on the aforementioned guidelines, A means for receiving progress information toward a goal, analyzing the progress status, and generating warnings, A means of calculating evaluation feedback and providing that feedback to the user, A means of presenting short-term goals and improvement measures based on progress analysis, A means of jointly managing goals set individually by residents and administrative departments, and providing integrated information, A system that includes this.

2. The system according to claim 1, comprising means for providing goal-setting guidelines, which refers to data from similar organizations and positions in the past and displays recommended goals based on that data.

3. The system according to claim 1, comprising means for presenting the user with short-term plans and improvement measures for achieving goals based on the analysis of progress information.

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