system

Abstract

We provide the system. [Solution] A data collection means for collecting and processing target data, A learning method that generates a learning model using artificial intelligence based on collected data, An evaluation means that receives target information entered via a user terminal and evaluates said target information, A progress management system that tracks user progress in real time and provides feedback based on evaluation results, A recovery plan generation method that proposes a recovery plan to the user when goal achievement is delayed, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] Existing management systems that effectively support personal goal achievement are often insufficient in terms of goal quantification, progress management, and immediacy of feedback. For this reason, it is difficult to evaluate the validity of goals, and there is a problem that it is difficult to present appropriate countermeasures when progress is delayed. Furthermore, due to insufficient personalization according to the abilities and characteristics of each individual, there is a problem that an environment that effectively promotes self-growth cannot be provided.

Means for Solving the Problems

[0005] The present invention relates to a system that supports individual goal achievement. It includes a data collection means for collecting goal data, a learning means for generating a learning model using artificial intelligence, an evaluation means for evaluating goal information received from a user terminal, and a progress management means for tracking progress in real time, thereby providing feedback based on the evaluation results. Furthermore, a recovery plan generation means enables personalized goal achievement support by proposing a recovery plan to the user based on past data and the artificial intelligence model when goal achievement is delayed.

[0006] "Target data" refers to information related to specific results or numerical targets that an individual or organization aims to achieve.

[0007] "Data collection means" refers to a device or process that has the function of systematically collecting information related to achieving a goal.

[0008] "Artificial intelligence" refers to technology that learns from data, identifies patterns, and has the ability to solve problems.

[0009] A "learning model" refers to a mathematical or algorithmic framework created based on collected data for making predictions and evaluations.

[0010] "Evaluation methods" refer to functions that analyze user input information and determine its appropriateness and feasibility.

[0011] A "user terminal" refers to an electronic device used by a user to input information or receive feedback from a system.

[0012] "Progress management means" refers to a device or program that has the function of recording and managing the progress toward a set goal.

[0013] A "recovery plan" refers to a specific action plan to improve progress when the achievement of goals does not proceed as planned.

[0014] "Past data" refers to the history of information related to goal achievement that has been collected and recorded so far.

[0015] "Personalization" refers to the process of providing customized services according to the characteristics and situations of each user.

Brief Description of the Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a numbered 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.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention provides an AI system for individuals to efficiently manage their goal achievement. The system mainly consists of a server, a user terminal, and an AI model.

[0038] The server collects historical goal data from the company's database and uses this to train an AI model. This model plays a central role in personalizing and supporting users' goal setting and progress. Specifically, the server processes the collected data, supplementing missing data and removing noise to create a reliable training dataset. After the model is trained, the server enables the generation of predictions and recovery plans for achieving new goals.

[0039] The user terminal functions as an interface with the user, allowing for the input of goal information and updates of progress. Users directly input their goals from the terminal screen, and this information is sent to the server. The server uses this information to perform evaluations and provide feedback using an AI model. Users can then use the feedback they receive to revise their goals and develop achievement strategies.

[0040] A distinctive feature of this system is that, if delays are observed in achieving goals, the server automatically generates a recovery plan and proposes it through the user's terminal. For example, if a user is having difficulty achieving their sales targets, the server will propose a new approach based on past success patterns. This process allows users to always take an active stance towards their goals.

[0041] As an example, a sales representative at a certain company sets a goal of "acquiring 15 new customers by the end of the quarter." The server provides the user terminal with successful patterns from similar goals achieved in the past and monitors the progress. If progress is not on schedule, the server proposes a recovery plan, such as "intensive sales activities in a specific region."

[0042] This invention is expected to enable individuals to more reliably achieve their personal goals and contribute to improved productivity across the entire organization.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server collects historical goal data from the company's database. This includes the details of individual goal settings, progress, and achievement levels. The server preprocesses this data and organizes it into a format applicable to AI models.

[0046] Step 2:

[0047] The server trains an AI model using pre-processed data. This model learns the conditions and success patterns necessary to achieve the goal, building a foundation for setting new goals and evaluating progress.

[0048] Step 3:

[0049] Users use a device to input their personal goals, including the goal content, deadline, and progress metrics. The device then sends this information to the server.

[0050] Step 4:

[0051] The server evaluates the received target information. Using an AI model, it compares it with past data to determine the validity and feasibility of the target, and generates feedback based on the results.

[0052] Step 5:

[0053] The terminal displays feedback received from the server to the user. This feedback includes expectations regarding the set goals and suggestions for improvement. The user then modifies and confirms the goals based on this feedback.

[0054] Step 6:

[0055] Users periodically update their progress via their devices. The devices send progress data to a server, which records the degree of achievement towards the goal.

[0056] Step 7:

[0057] The server analyzes progress data and evaluates whether progress toward the goal is on track. Based on the evaluation results, it provides feedback to the user.

[0058] Step 8:

[0059] If the server determines that the goal is not being achieved, it generates a recovery plan. Using an AI model, it analyzes the cause of the delay and creates a specific action plan for improvement.

[0060] Step 9:

[0061] The server sends the generated recovery plan to the terminal and prompts the user to execute it. The terminal visually displays the received recovery plan to the user and clearly explains the execution procedure.

[0062] (Example 1)

[0063] 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."

[0064] To effectively achieve goals set by individuals and organizations, it is necessary to track progress in real time and take swift action when delays occur. However, traditional management methods often rely on experience and intuition, making it difficult to obtain quantitative and objective feedback. This can lead to decreased efficiency in achieving goals and, consequently, a decline in productivity.

[0065] 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.

[0066] In this invention, the server includes data acquisition means that retrieve past target information from a data repository and perform data transformation; learning means that form a learning algorithm using machine learning based on the acquired information; and analysis means that receive achievement targets input via user equipment and analyze those achievement targets. This enables real-time tracking of progress and provision of appropriate feedback.

[0067] "Data acquisition means" refers to the function of collecting past target information from a data repository and performing data transformation.

[0068] "Learning method" refers to the function that generates a learning algorithm using machine learning based on acquired information.

[0069] "Analysis means" refers to a function that receives achievement goals entered via user equipment and analyzes them.

[0070] "Progress management means" refers to a function that tracks the user's progress over time and provides information based on the analysis results.

[0071] "Plan generation method" refers to a function that presents alternative plans to the user when goal achievement is delayed.

[0072] "Machine learning" refers to the technology that uses data to enable algorithms to automatically learn and perform predictions and classifications.

[0073] An "alternative plan" refers to an alternative action plan or strategy presented to the user when the goal is not being achieved.

[0074] This invention relates to an automated system for effectively managing and achieving individual and organizational goals. The system mainly consists of a server, user terminals, and a generative AI model.

[0075] The server uses basic computing resources to retrieve historical target information from a data repository. It runs on a common cloud platform and uses database access software to collect historical data. Data cleansing tools are then used to denoise and impute missing data in the collected data, creating a reliable dataset for machine learning.

[0076] Next, the server uses machine learning libraries such as TENSORFLOW® and PyTorch to form a learning algorithm based on the collected data. This creates a generative AI model, enabling predictions and analyses to help users achieve their goals.

[0077] The user terminal receives goal information from the user via a dedicated application. The terminal operates on common mobile devices and personal computers, and users can input their goals through an intuitive interface. This information is sent to a server, which uses a generative AI model to track and analyze the progress of the goals in real time.

[0078] If the user's set goals are behind schedule, the server uses a generative AI model to analyze past success patterns and propose alternative solutions. These alternatives are then communicated to the user via their device. For example, an alternative suggestion might be to "strengthen approaches to new markets."

[0079] An example of a prompt for a generative AI model is, "Provide alternative plans to improve sales performance." This prompt functions as input instructions for the generative AI model to make specific suggestions.

[0080] This system enables individuals and organizations to efficiently manage and achieve their goals, which is expected to lead to increased overall productivity.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The server retrieves historical target information from a data repository. Input data is obtained through raw database queries and may contain noise and missing values. The server uses data cleansing tools to remove noise and impute missing data. Specifically, it applies filtering algorithms to maintain data integrity and obtains a clean dataset as output.

[0084] Step 2:

[0085] The server uses a clean dataset and leverages machine learning libraries (e.g., TensorFlow) to train a generative AI model. The input is a formatted dataset, and the model learns patterns between data points. Throughout this process, the model's hyperparameters are adjusted, and the optimal model is output after several iterations.

[0086] Step 3:

[0087] The user inputs goal information through their user terminal. This input includes goals to be achieved and deadlines. The user terminal sends this data to the server, where it is processed by the server's analysis tools. This processing outputs basic analysis results regarding the input goals.

[0088] Step 4:

[0089] The server utilizes an AI model generated using analytical tools to track the user's progress toward their goals in real time. Input includes goal information received from the user and real-time activity data, and progress is evaluated through the model. Output includes the progress evaluation results and feedback based on those results.

[0090] Step 5:

[0091] If there is a delay in achieving the goal, the server generates an alternative plan based on predictive data from the generated AI model. The input is past success patterns and current progress data, and the generated alternative plan includes a specific action plan to achieve the goal. As output, the generated alternative plan is sent to the user's terminal and presented to the user.

[0092] Step 6:

[0093] The user reviews the alternatives received on their device and selects an action they can take. The user device sends the selected action back to the server as feedback, and this feedback is used to improve future suggestions. This process allows users to dynamically adapt their strategies for achieving their goals.

[0094] (Application Example 1)

[0095] 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."

[0096] In modern manufacturing, efficient production management is crucial for improving an organization's competitiveness. However, conventional systems are inadequate in managing the progress of production targets and responding to delays. Furthermore, they cannot provide effective recovery plans based on the characteristics of each manufacturing facility, and further advancements are needed to achieve overall improvements in production efficiency.

[0097] 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.

[0098] In this invention, the server includes information gathering means for collecting and processing target data, learning means for generating a learning model using machine learning based on the collected information, evaluation means for receiving target information input via a user terminal and evaluating said target information, and target adjustment means for managing production targets of manufacturing equipment and presenting a recovery plan to optimize the production process when delays in progress are expected. This enables the achievement management of production targets at each manufacturing facility and a quick and effective response to delays.

[0099] "Information gathering means" refers to a device or system that has the necessary functions to collect target data and process that data.

[0100] "Machine learning" is an artificial intelligence technology that generates learning models based on collected data and uses them to identify various patterns and make predictions.

[0101] A "learning tool" is a device or system that has the function of generating a learning model based on collected information.

[0102] "Evaluation means" refers to a device or system that has the function of receiving target information entered via a user terminal and evaluating the target based on that information.

[0103] A "target adjustment mechanism" is a device or system that has the function of managing the production targets set for manufacturing equipment and presenting an optimal recovery plan if progress is delayed.

[0104] A "recovery plan" is a plan devised to optimize the production process and make up for lost time when the achievement of goals is delayed.

[0105] This system consists of information gathering means, learning means, evaluation means, and goal adjustment means. First, the server uses the information gathering means to organize the large amount of goal data collected from each manufacturing facility and uses this to generate a learning model for machine learning. The learning model can sequentially learn new data by utilizing a generative AI model. Based on this model, the server evaluates the goal information entered by the user through the terminal in real time.

[0106] The evaluation system allows the server to monitor the user's progress and provide appropriate feedback based on the results. In particular, if progress toward the goal is behind schedule, the server uses the goal adjustment system to generate an appropriate recovery plan and provide guidance for optimizing the manufacturing process. This enables the user to take proactive steps toward production goals.

[0107] For example, if a manufacturing facility is set to produce 100 units of a specific product per hour, the system will track progress in real time and provide recovery plans such as "increasing speed" or "adding shifts" if delays occur. Through real-time production management, both product quality and quantity can be optimized.

[0108] A concrete example of a prompt for a generating AI model is, "Please suggest the optimal method for producing 100 parts per hour in a factory." Based on this prompt, the server can generate and provide an efficient production method.

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

[0110] Step 1:

[0111] The server utilizes information gathering tools to collect target data from each manufacturing facility. Inputs are raw data from sensors and production management systems, while outputs are structured data that has been organized and stored in a database. Specific operations at this stage include data format conversion and processing of missing data.

[0112] Step 2:

[0113] The server uses learning tools to generate a machine learning model based on the collected data. The input is the structured data obtained in step 1, and the output is the trained model generated by the AI ​​model. Data processing includes feature extraction and preprocessing, and calculations involve tuning the model parameters.

[0114] Step 3:

[0115] The user inputs current goal information from their device, and the server receives this information via an evaluation system to assess progress. The input is specific goal setting data provided by the user, and the output is the evaluation result regarding the degree of goal achievement. Specifically, data validation and consistency checks are performed on the user input form.

[0116] Step 4:

[0117] The server manages progress and monitors the progress in real time. The input is the degree of goal achievement evaluated in step 3, and the output is feedback information. The feedback includes visualization of goal progress and provision of achievement metrics.

[0118] Step 5:

[0119] If goal achievement is delayed, the server utilizes goal adjustment mechanisms to generate and present an appropriate recovery plan to the user. The input is information about the type and extent of the delay, and the output is the proposed recovery plan. Specific actions include referencing data from past success patterns and applying a generative AI model using prompt statements.

[0120] 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.

[0121] This invention provides more adaptive and effective support to an AI system that assists individuals in achieving their goals by incorporating an emotion engine that analyzes the user's emotions. The system mainly consists of a server, a user terminal, an AI model, and an emotion recognition engine.

[0122] The server first collects historical goal data from the company's database and uses this data to train an AI model. This model processes information about the user's goal setting and progress evaluation, and generates feedback. The server also uses the collected data to train an emotion engine. This allows it to more accurately recognize the user's emotional state and provide appropriate responses.

[0123] The user terminal serves as the interface with the user, handling goal information input, progress updates, and data collection for emotion recognition. The user inputs their goals through the terminal and sends them to the server. The terminal is equipped with a camera and microphone, which are used to provide the user's facial expressions and voice data to the emotion engine.

[0124] The emotion engine analyzes facial expression and voice data acquired from the device to identify the user's emotional state. The server takes this emotional data into consideration and uses an AI model to appropriately adjust the feedback provided to the user. For example, if the user is feeling anxious about achieving their goal, the server generates encouraging and supportive messages and presents them to the user through the device.

[0125] As a concrete example, suppose a sales representative sets a goal of acquiring 20 new customers by the end of the month. When the user reports their progress toward the goal, if the emotion recognition function detects their anxiety or impatience, the server will provide a specific and actionable recovery plan, such as "revise your plan and we recommend adding two more approaches next week." Furthermore, if positive feedback is received regarding this recovery plan, it will contribute to increased motivation during its implementation.

[0126] This invention is expected to contribute to improving the overall productivity of the organization by providing more flexible support for individual goal achievement and offering feedback that takes into account the user's mental health.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users enter their personal goals using a device. This includes goal details, deadlines, and progress evaluation criteria. The device then sends this goal information to the server.

[0130] Step 2:

[0131] Based on the received target information, the server uses an AI model to evaluate the validity of the target. In this process, the server refers to past data and analyzes how similar targets were achieved. The results are sent back from the server to the terminal, providing feedback to the user.

[0132] Step 3:

[0133] Users periodically submit progress reports via their devices. These devices send progress status, problems, and performance data to the server. This ensures the server always has the latest progress information.

[0134] Step 4:

[0135] The device's camera and microphone transmit the user's facial expressions and voice to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state.

[0136] Step 5:

[0137] The server integrates and evaluates progress data and emotional state. An AI model generates feedback to alleviate the situation if the user is experiencing difficulties or stress.

[0138] Step 6:

[0139] The server customizes feedback based on the user's emotional state. For example, if the user is feeling stressed, it will generate feedback that includes encouraging messages and relaxation suggestions.

[0140] Step 7:

[0141] If the server detects a delay in progress, AI is used to generate an appropriate recovery plan. This plan is customized to take into account emotional states and past performance data.

[0142] Step 8:

[0143] The device presents the user with feedback and recovery plans sent from the server. Based on this information, the user adjusts their actions to achieve their goals.

[0144] Step 9:

[0145] After users provide feedback and progress is reviewed, the server evaluates its impact and begins collecting new progress and sentiment data. This cycle continues until the goal is achieved.

[0146] (Example 2)

[0147] 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".

[0148] Conventional goal-achievement support systems typically evaluate only the user's progress and provide uniform feedback. However, because they do not consider the user's emotional state, it is difficult to provide appropriate support tailored to individual circumstances, resulting in a lack of flexibility in maintaining user motivation and achieving goals. Therefore, there is a need for a system that can capture changes in the user's emotions in real time and adjust feedback based on those changes to provide more effective support.

[0149] 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.

[0150] In this invention, the server includes data collection means for collecting and processing target data, learning means for generating a learning model using artificial intelligence based on the collected data, progress management and sentiment analysis means for tracking the user's progress and emotional state in real time and providing feedback based on evaluation results and sentiment data, and recovery plan generation means for proposing a recovery plan that takes the user's emotional state into consideration when goal achievement is delayed. This enables flexible and effective feedback and support tailored to the user's individual emotional state.

[0151] A "data collection method" is a mechanism that receives target information from the user and effectively collects various other information, including the user's facial expression data and voice data.

[0152] A "learning tool" is a mechanism that uses collected data to train artificial intelligence and form a learning model to generate more effective feedback.

[0153] An "evaluation tool" is a mechanism that analyzes goal information entered via a user terminal and evaluates the user's progress and status based on that information.

[0154] The "progress management and sentiment analysis means" is a mechanism that monitors the user's progress, analyzes sentiment data, and adjusts feedback based on the evaluation results and sentiment state.

[0155] A "recovery plan generation mechanism" is a system that, when goal achievement is delayed, proposes alternative plans or recovery measures to support achievement, taking into account the user's emotional state.

[0156] This invention is a system that supports users in achieving their goals, and its specific form is as follows: The system mainly consists of a server, a user terminal, a generative AI model, and an emotion recognition engine.

[0157] The server first collects historical goal data from the company's database. This data is used to train a generative AI model. The generative AI model can be built using machine learning frameworks such as Python or TensorFlow. The model has the ability to evaluate the user's set goals and progress and generate appropriate feedback. The server also trains an emotion recognition engine, which allows it to recognize the user's emotional state more accurately.

[0158] The user terminal acts as an interface, allowing for the input of goal information and updates of progress. Users input data via the terminal and send it to the server. This terminal is equipped with a camera and microphone, which are used to capture the user's facial expressions and voice data. This data is then provided to the server for analysis by an emotion recognition engine.

[0159] The emotion recognition engine receives and analyzes facial expression and voice data to identify the user's current emotional state. For example, if a user is feeling anxious about achieving their goal, the server uses that emotional data to generate more encouraging feedback for the user. This kind of feedback can boost the user's motivation and help them achieve their goal.

[0160] As a concrete example, consider a scenario where a sales representative sets a goal of acquiring 20 new customers by the end of the month. When the user reports their progress, if the emotion recognition function detects anxiety, the server provides a viable recovery plan, such as "revise your plan and recommend adding two more approaches next week." An example of this prompt might be, "Please enter your set goal and progress, and send your current emotional state data to the server." This allows the system to provide more effective support and flexible assistance in achieving goals.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The user enters goal information into their terminal. The terminal sends this information to the server as a data collection tool. The entered information includes the goal to be achieved and its deadline. This data is stored on the server for use in subsequent analysis and feedback generation.

[0164] Step 2:

[0165] The device uses a camera and microphone to collect user facial and audio data. This data is transmitted in real time to an emotion recognition engine. The engine analyzes the facial and audio samples obtained as input to identify the user's emotional state. The emotion recognition engine uses facial expression analysis algorithms and audio analysis technology to determine what emotions the user is experiencing and outputs the results to a server.

[0166] Step 3:

[0167] The server receives emotional data from the emotion recognition engine and previously collected goal information as input, and generates feedback using a generative AI model. The AI ​​model comprehensively analyzes past goal achievement data and current user information, and determines the optimal support content while taking into account the user's emotional state. This process outputs feedback on the user's progress and emotions.

[0168] Step 4:

[0169] The server sends the generated feedback to the user's terminal. The terminal displays the feedback on the user's screen or presents it through audio output or other means. For example, the user may receive specific advice such as, "Good progress. For the next step, try XX." This output serves as important guidance for the user when planning actions toward their goals.

[0170] Step 5:

[0171] Users adjust their actions based on feedback received through their devices. For example, they can take actions such as adding new tasks according to suggestions from the server. This allows the entire system to flexibly support and efficiently guide users toward achieving their goals.

[0172] (Application Example 2)

[0173] 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 device 14 will be referred to as the "terminal."

[0174] In systems that support individual goal achievement, a challenge is to provide effective support while considering mental health by offering feedback that takes user emotions into account. Furthermore, in situations where emotions such as stress and anxiety affect work efficiency, it is necessary to take appropriate measures without delay.

[0175] 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.

[0176] In this invention, the server includes data collection means for collecting and processing target data, learning means for generating a learning model using artificial intelligence based on the collected data, and emotion analysis means for analyzing the user's emotional state and generating emotion-conscious feedback. This makes it possible to provide personalized and effective feedback and recovery plans.

[0177] "Data collection means" refers to a device or technology that collects information related to goals and progress from users within a system and organizes and stores it for subsequent processing and analysis.

[0178] A "learning tool" is a device or program that uses artificial intelligence technology to generate a learning model based on collected data.

[0179] "Evaluation means" refers to a device or technology that receives target information entered via a user terminal and has the function of judging and evaluating the progress and degree of achievement toward that target.

[0180] A "progress management tool" is a device or software that has the function of tracking the user's progress toward their goals in real time and providing feedback based on the evaluation results.

[0181] "Emotional analysis means" refers to a technology or program that has the function of detecting and analyzing a user's emotional state and adjusting and providing feedback based on the results.

[0182] A "recovery plan generation means" is a device or technology that proposes an appropriate recovery plan based on past data and artificial intelligence models when the achievement of a goal is delayed.

[0183] The system implementing this invention mainly consists of a server, a user terminal, and related software components. The server plays the role of collecting target data, generating a learning model using artificial intelligence, and providing feedback that takes into account the sentiment analysis results. This system utilizes existing APIs such as Google® Cloud Vision API as a means of sentiment analysis to detect emotions from the user's facial expressions and voice.

[0184] Users use a terminal to input their daily work goals into the system and report their progress. Data is collected in real time through the terminal's camera and microphone and sent to a server, where the user's emotional state is analyzed. Based on the results of the emotional analysis, the server uses an AI model to generate personalized feedback and sends it back to the terminal. This helps users progress towards achieving their goals in an efficient and mentally healthy way.

[0185] As a concrete example, consider its use in a security setting. Security guards use their smartphones to report on their work progress in order to achieve their goals. The system detects signs of tension and anxiety in the security guards by analyzing their facial expressions and provides feedback such as "do some stretching" or "take a break." This supports stress management during work and can improve the security guards' performance.

[0186] An example of a prompt message could be: "Analyze whether the security guard is expressing anxiety about their work objectives, and if anxiety is detected, generate feedback suggesting ways to relax." This instruction could then be given to the generating AI model.

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The user enters goal information using a terminal. Through the terminal's interface, the user enters work-related goals and sends this data to the server. The entered goal information might be specific, such as "acquire 3 new customers each week." This data is received by the server's data collection system and prepared for processing within the system.

[0190] Step 2:

[0191] The device uses a camera and microphone to collect user emotion data. It records facial expressions with the camera and captures voice with the microphone, gathering information about facial expressions and voice tone. This collected data is sent to a server and used as raw material for emotion analysis.

[0192] Step 3:

[0193] The server analyzes the received emotion data. It utilizes the Google Cloud Vision API to analyze facial expression data and identify the emotions the user is expressing. Audio data is also analyzed using a voice tone analysis tool. This analysis process identifies emotions such as "increased tension" and passes the results to the next step.

[0194] Step 4:

[0195] The server uses a generative AI model to generate appropriate feedback based on the analysis results. Taking the analyzed emotional information as input, the AI ​​model follows prompts and generates situation-appropriate feedback messages. For example, the model might generate feedback such as, "Try relaxation techniques to reduce stress."

[0196] Step 5:

[0197] The server sends the generated feedback to the terminal. The terminal notifies and displays the feedback to the user. This allows the user to receive advice tailored to their current situation in real time. For example, a security guard could see the displayed message and practice the recommended relaxation technique.

[0198] 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.

[0199] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] This invention provides an AI system for individuals to efficiently manage their goal achievement. The system mainly consists of a server, a user terminal, and an AI model.

[0215] The server collects historical goal data from the company's database and uses this to train an AI model. This model plays a central role in personalizing and supporting users' goal setting and progress. Specifically, the server processes the collected data, supplementing missing data and removing noise to create a reliable training dataset. After the model is trained, the server enables the generation of predictions and recovery plans for achieving new goals.

[0216] The user terminal functions as an interface with the user, allowing for the input of goal information and updates of progress. Users directly input their goals from the terminal screen, and this information is sent to the server. The server uses this information to perform evaluations and provide feedback using an AI model. Users can then use the feedback they receive to revise their goals and develop achievement strategies.

[0217] A distinctive feature of this system is that, if delays are observed in achieving goals, the server automatically generates a recovery plan and proposes it through the user's terminal. For example, if a user is having difficulty achieving their sales targets, the server will propose a new approach based on past success patterns. This process allows users to always take an active stance towards their goals.

[0218] As an example, a sales representative at a certain company sets a goal of "acquiring 15 new customers by the end of the quarter." The server provides the user terminal with successful patterns from similar goals achieved in the past and monitors the progress. If progress is not on schedule, the server proposes a recovery plan, such as "intensive sales activities in a specific region."

[0219] This invention is expected to enable individuals to more reliably achieve their personal goals and contribute to improved productivity across the entire organization.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The server collects historical goal data from the company's database. This includes the details of individual goal settings, progress, and achievement levels. The server preprocesses this data and organizes it into a format applicable to AI models.

[0223] Step 2:

[0224] The server trains an AI model using pre-processed data. This model learns the conditions and success patterns necessary to achieve the goal, building a foundation for setting new goals and evaluating progress.

[0225] Step 3:

[0226] Users use a device to input their personal goals, including the goal content, deadline, and progress metrics. The device then sends this information to the server.

[0227] Step 4:

[0228] The server evaluates the received target information. Using an AI model, it compares it with past data to determine the validity and feasibility of the target, and generates feedback based on the results.

[0229] Step 5:

[0230] The terminal displays feedback received from the server to the user. This feedback includes expectations regarding the set goals and suggestions for improvement. The user then modifies and confirms the goals based on this feedback.

[0231] Step 6:

[0232] Users periodically update their progress via their devices. The devices send progress data to a server, which records the degree of achievement towards the goal.

[0233] Step 7:

[0234] The server analyzes progress data and evaluates whether progress toward the goal is on track. Based on the evaluation results, it provides feedback to the user.

[0235] Step 8:

[0236] If the server determines that the goal is not being achieved, it generates a recovery plan. Using an AI model, it analyzes the cause of the delay and creates a specific action plan for improvement.

[0237] Step 9:

[0238] The server sends the generated recovery plan to the terminal and prompts the user to execute it. The terminal visually displays the received recovery plan to the user and clearly explains the execution procedure.

[0239] (Example 1)

[0240] 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."

[0241] To effectively achieve goals set by individuals and organizations, it is necessary to track progress in real time and take swift action when delays occur. However, traditional management methods often rely on experience and intuition, making it difficult to obtain quantitative and objective feedback. This can lead to decreased efficiency in achieving goals and, consequently, a decline in productivity.

[0242] 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.

[0243] In this invention, the server includes data acquisition means that retrieve past target information from a data repository and perform data transformation; learning means that form a learning algorithm using machine learning based on the acquired information; and analysis means that receive achievement targets input via user equipment and analyze those achievement targets. This enables real-time tracking of progress and provision of appropriate feedback.

[0244] "Data acquisition means" refers to the function of collecting past target information from a data repository and performing data transformation.

[0245] "Learning method" refers to the function that generates a learning algorithm using machine learning based on acquired information.

[0246] "Analysis means" refers to a function that receives achievement goals entered via user equipment and analyzes them.

[0247] "Progress management means" refers to a function that tracks the user's progress over time and provides information based on the analysis results.

[0248] "Plan generation method" refers to a function that presents alternative plans to the user when goal achievement is delayed.

[0249] "Machine learning" refers to the technology that uses data to enable algorithms to automatically learn and perform predictions and classifications.

[0250] An "alternative plan" refers to an alternative action plan or strategy presented to the user when the goal is not being achieved.

[0251] This invention relates to an automated system for effectively managing and achieving individual and organizational goals. The system mainly consists of a server, user terminals, and a generative AI model.

[0252] The server uses basic computing resources to retrieve historical target information from a data repository. It runs on a common cloud platform and uses database access software to collect historical data. Data cleansing tools are then used to denoise and impute missing data in the collected data, creating a reliable dataset for machine learning.

[0253] Next, the server uses machine learning libraries such as TensorFlow and PyTorch to form a learning algorithm based on the collected data. This creates a generative AI model, enabling predictions and analyses to help users achieve their goals.

[0254] The user terminal receives goal information from the user via a dedicated application. The terminal operates on common mobile devices and personal computers, and users can input their goals through an intuitive interface. This information is sent to a server, which uses a generative AI model to track and analyze the progress of the goals in real time.

[0255] If the user's set goals are behind schedule, the server uses a generative AI model to analyze past success patterns and propose alternative solutions. These alternatives are then communicated to the user via their device. For example, an alternative suggestion might be to "strengthen approaches to new markets."

[0256] An example of a prompt for a generative AI model is, "Provide alternative plans to improve sales performance." This prompt functions as input instructions for the generative AI model to make specific suggestions.

[0257] This system enables individuals and organizations to efficiently manage and achieve their goals, which is expected to lead to increased overall productivity.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The server retrieves historical target information from a data repository. Input data is obtained through raw database queries and may contain noise and missing values. The server uses data cleansing tools to remove noise and impute missing data. Specifically, it applies filtering algorithms to maintain data integrity and obtains a clean dataset as output.

[0261] Step 2:

[0262] The server uses a clean dataset and leverages machine learning libraries (e.g., TensorFlow) to train a generative AI model. The input is a formatted dataset, and the model learns patterns between data points. Throughout this process, the model's hyperparameters are adjusted, and the optimal model is output after several iterations.

[0263] Step 3:

[0264] The user inputs goal information through their user terminal. This input includes goals to be achieved and deadlines. The user terminal sends this data to the server, where it is processed by the server's analysis tools. This processing outputs basic analysis results regarding the input goals.

[0265] Step 4:

[0266] The server utilizes an AI model generated using analytical tools to track the user's progress toward their goals in real time. Input includes goal information received from the user and real-time activity data, and progress is evaluated through the model. Output includes the progress evaluation results and feedback based on those results.

[0267] Step 5:

[0268] If there is a delay in achieving the goal, the server generates an alternative plan based on predictive data from the generated AI model. The input is past success patterns and current progress data, and the generated alternative plan includes a specific action plan to achieve the goal. As output, the generated alternative plan is sent to the user's terminal and presented to the user.

[0269] Step 6:

[0270] The user reviews the alternatives received on their device and selects an action they can take. The user device sends the selected action back to the server as feedback, and this feedback is used to improve future suggestions. This process allows users to dynamically adapt their strategies for achieving their goals.

[0271] (Application Example 1)

[0272] 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."

[0273] In modern manufacturing, efficient production management is crucial for improving an organization's competitiveness. However, conventional systems are inadequate in managing the progress of production targets and responding to delays. Furthermore, they cannot provide effective recovery plans based on the characteristics of each manufacturing facility, and further advancements are needed to achieve overall improvements in production efficiency.

[0274] 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.

[0275] In this invention, the server includes information gathering means for collecting and processing target data, learning means for generating a learning model using machine learning based on the collected information, evaluation means for receiving target information input via a user terminal and evaluating said target information, and target adjustment means for managing production targets of manufacturing equipment and presenting a recovery plan to optimize the production process when delays in progress are expected. This enables the achievement management of production targets at each manufacturing facility and a quick and effective response to delays.

[0276] "Information gathering means" refers to a device or system that has the necessary functions to collect target data and process that data.

[0277] "Machine learning" is an artificial intelligence technology that generates learning models based on collected data and uses them to identify various patterns and make predictions.

[0278] A "learning tool" is a device or system that has the function of generating a learning model based on collected information.

[0279] "Evaluation means" refers to a device or system that has the function of receiving target information entered via a user terminal and evaluating the target based on that information.

[0280] A "target adjustment mechanism" is a device or system that has the function of managing the production targets set for manufacturing equipment and presenting an optimal recovery plan if progress is delayed.

[0281] A "recovery plan" is a plan devised to optimize the production process and make up for lost time when the achievement of goals is delayed.

[0282] This system consists of information gathering means, learning means, evaluation means, and goal adjustment means. First, the server uses the information gathering means to organize the large amount of goal data collected from each manufacturing facility and uses this to generate a learning model for machine learning. The learning model can sequentially learn new data by utilizing a generative AI model. Based on this model, the server evaluates the goal information entered by the user through the terminal in real time.

[0283] The evaluation system allows the server to monitor the user's progress and provide appropriate feedback based on the results. In particular, if progress toward the goal is behind schedule, the server uses the goal adjustment system to generate an appropriate recovery plan and provide guidance for optimizing the manufacturing process. This enables the user to take proactive steps toward production goals.

[0284] For example, when a goal is set to produce 100 specific products per hour in a certain manufacturing facility, the system tracks the progress in real time and provides recovery plans such as "speed increase" or "additional shift" when a delay occurs. Through real-time production management, both the quality and quantity of products can be optimized.

[0285] A specific example of a prompt sentence for the generation AI model is "Please propose an optimal method for producing 100 parts per hour in a factory." Based on this prompt sentence, the server can generate and provide an efficient production method.

[0286] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] The server utilizes information collection means to collect target data from each manufacturing facility. The input is raw data from sensors or production management systems, and the output is structured data that has been organized and stored in a database. Specific operations at this stage include data format conversion and missing data supplementation processing.

[0289] Step 2:

[0290] The server uses learning means to generate a machine learning model based on the collected data. The input is the structured data obtained in Step 1, and the output is a learned model by the generation AI model. Data processing includes feature extraction and preprocessing, and model parameter adjustment is performed for the calculation.

[0291] Step 3:

[0292] The user inputs current goal information from their device, and the server receives this information via an evaluation system to assess progress. The input is specific goal setting data provided by the user, and the output is the evaluation result regarding the degree of goal achievement. Specifically, data validation and consistency checks are performed on the user input form.

[0293] Step 4:

[0294] The server manages progress and monitors the progress in real time. The input is the degree of goal achievement evaluated in step 3, and the output is feedback information. The feedback includes visualization of goal progress and provision of achievement metrics.

[0295] Step 5:

[0296] If goal achievement is delayed, the server utilizes goal adjustment mechanisms to generate and present an appropriate recovery plan to the user. The input is information about the type and extent of the delay, and the output is the proposed recovery plan. Specific actions include referencing data from past success patterns and applying a generative AI model using prompt statements.

[0297] 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.

[0298] This invention provides more adaptive and effective support to an AI system that assists individuals in achieving their goals by incorporating an emotion engine that analyzes the user's emotions. The system mainly consists of a server, a user terminal, an AI model, and an emotion recognition engine.

[0299] The server first collects historical goal data from the company's database and uses this data to train an AI model. This model processes information about the user's goal setting and progress evaluation, and generates feedback. The server also uses the collected data to train an emotion engine. This allows it to more accurately recognize the user's emotional state and provide appropriate responses.

[0300] The user terminal serves as the interface with the user, handling goal information input, progress updates, and data collection for emotion recognition. The user inputs their goals through the terminal and sends them to the server. The terminal is equipped with a camera and microphone, which are used to provide the user's facial expressions and voice data to the emotion engine.

[0301] The emotion engine analyzes facial expression and voice data acquired from the device to identify the user's emotional state. The server takes this emotional data into consideration and uses an AI model to appropriately adjust the feedback provided to the user. For example, if the user is feeling anxious about achieving their goal, the server generates encouraging and supportive messages and presents them to the user through the device.

[0302] As a concrete example, suppose a sales representative sets a goal of acquiring 20 new customers by the end of the month. When the user reports their progress toward the goal, if the emotion recognition function detects their anxiety or impatience, the server will provide a specific and actionable recovery plan, such as "revise your plan and we recommend adding two more approaches next week." Furthermore, if positive feedback is received regarding this recovery plan, it will contribute to increased motivation during its implementation.

[0303] This invention is expected to contribute to improving the overall productivity of the organization by providing more flexible support for individual goal achievement and offering feedback that takes into account the user's mental health.

[0304] The process flow will be described below.

[0305] Step 1:

[0306] The user inputs personal goals using the terminal. The input content includes goal details, deadlines, and progress evaluation criteria. The terminal sends this goal information to the server.

[0307] Step 2:

[0308] Based on the received goal information, the server uses an AI model to evaluate the validity of the goal. In this process, the server refers to past data and analyzes how similar goals were achieved. The results are sent back from the server to the terminal, providing feedback to the user.

[0309] Step 3:

[0310] The user regularly provides progress reports through the terminal. The terminal sends progress status, problems, and performance data to the server. This enables the server to always obtain the latest progress information.

[0311] Step 4:

[0312] The camera and microphone of the terminal pass the user's expression and voice to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state.

[0313] [[ID=�5]] Step 5:

[0314] The server integrates and evaluates the progress data and emotional state. The AI model generates feedback to alleviate the situation if the user is facing difficulties or feeling stressed.

[0315] Step 6:

[0316] The server customizes feedback based on the user's emotional state. For example, if the user is feeling stressed, it will generate feedback that includes encouraging messages and relaxation suggestions.

[0317] Step 7:

[0318] If the server detects a delay in progress, AI is used to generate an appropriate recovery plan. This plan is customized to take into account emotional states and past performance data.

[0319] Step 8:

[0320] The device presents the user with feedback and recovery plans sent from the server. Based on this information, the user adjusts their actions to achieve their goals.

[0321] Step 9:

[0322] After users provide feedback and progress is reviewed, the server evaluates its impact and begins collecting new progress and sentiment data. This cycle continues until the goal is achieved.

[0323] (Example 2)

[0324] 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".

[0325] Conventional goal-achievement support systems typically evaluate only the user's progress and provide uniform feedback. However, because they do not consider the user's emotional state, it is difficult to provide appropriate support tailored to individual circumstances, resulting in a lack of flexibility in maintaining user motivation and achieving goals. Therefore, there is a need for a system that can capture changes in the user's emotions in real time and adjust feedback based on those changes to provide more effective support.

[0326] 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.

[0327] In this invention, the server includes data collection means for collecting and processing target data, learning means for generating a learning model using artificial intelligence based on the collected data, progress management and sentiment analysis means for tracking the user's progress and emotional state in real time and providing feedback based on evaluation results and sentiment data, and recovery plan generation means for proposing a recovery plan that takes the user's emotional state into consideration when goal achievement is delayed. This enables flexible and effective feedback and support tailored to the user's individual emotional state.

[0328] A "data collection method" is a mechanism that receives target information from the user and effectively collects various other information, including the user's facial expression data and voice data.

[0329] A "learning tool" is a mechanism that uses collected data to train artificial intelligence and form a learning model to generate more effective feedback.

[0330] An "evaluation tool" is a mechanism that analyzes goal information entered via a user terminal and evaluates the user's progress and status based on that information.

[0331] The "progress management and sentiment analysis means" is a mechanism that monitors the user's progress, analyzes sentiment data, and adjusts feedback based on the evaluation results and sentiment state.

[0332] A "recovery plan generation mechanism" is a system that, when goal achievement is delayed, proposes alternative plans or recovery measures to support achievement, taking into account the user's emotional state.

[0333] This invention is a system that supports users in achieving their goals, and its specific form is as follows: The system mainly consists of a server, a user terminal, a generative AI model, and an emotion recognition engine.

[0334] The server first collects historical goal data from the company's database. This data is used to train a generative AI model. The generative AI model can be built using machine learning frameworks such as Python or TensorFlow. The model has the ability to evaluate the user's set goals and progress and generate appropriate feedback. The server also trains an emotion recognition engine, which allows it to recognize the user's emotional state more accurately.

[0335] The user terminal acts as an interface, allowing for the input of goal information and updates of progress. Users input data via the terminal and send it to the server. This terminal is equipped with a camera and microphone, which are used to capture the user's facial expressions and voice data. This data is then provided to the server for analysis by an emotion recognition engine.

[0336] The emotion recognition engine receives and analyzes facial expression and voice data to identify the user's current emotional state. For example, if a user is feeling anxious about achieving their goal, the server uses that emotional data to generate more encouraging feedback for the user. This kind of feedback can boost the user's motivation and help them achieve their goal.

[0337] As a concrete example, consider a scenario where a sales representative sets a goal of acquiring 20 new customers by the end of the month. When the user reports their progress, if the emotion recognition function detects anxiety, the server provides a viable recovery plan, such as "revise your plan and recommend adding two more approaches next week." An example of this prompt might be, "Please enter your set goal and progress, and send your current emotional state data to the server." This allows the system to provide more effective support and flexible assistance in achieving goals.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The user enters goal information into their terminal. The terminal sends this information to the server as a data collection tool. The entered information includes the goal to be achieved and its deadline. This data is stored on the server for use in subsequent analysis and feedback generation.

[0341] Step 2:

[0342] The device uses a camera and microphone to collect user facial and audio data. This data is transmitted in real time to an emotion recognition engine. The engine analyzes the facial and audio samples obtained as input to identify the user's emotional state. The emotion recognition engine uses facial expression analysis algorithms and audio analysis technology to determine what emotions the user is experiencing and outputs the results to a server.

[0343] Step 3:

[0344] The server receives emotional data from the emotion recognition engine and previously collected goal information as input, and generates feedback using a generative AI model. The AI ​​model comprehensively analyzes past goal achievement data and current user information, and determines the optimal support content while taking into account the user's emotional state. This process outputs feedback on the user's progress and emotions.

[0345] Step 4:

[0346] The server sends the generated feedback to the user's terminal. The terminal displays the feedback on the user's screen or presents it through audio output or other means. For example, the user may receive specific advice such as, "Good progress. For the next step, try XX." This output serves as important guidance for the user when planning actions toward their goals.

[0347] Step 5:

[0348] Users adjust their actions based on feedback received through their devices. For example, they can take actions such as adding new tasks according to suggestions from the server. This allows the entire system to flexibly support and efficiently guide users toward achieving their goals.

[0349] (Application Example 2)

[0350] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0351] In systems that support individual goal achievement, a challenge is to provide effective support while considering mental health by offering feedback that takes user emotions into account. Furthermore, in situations where emotions such as stress and anxiety affect work efficiency, it is necessary to take appropriate measures without delay.

[0352] 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.

[0353] In this invention, the server includes data collection means for collecting and processing target data, learning means for generating a learning model using artificial intelligence based on the collected data, and emotion analysis means for analyzing the user's emotional state and generating emotion-conscious feedback. This makes it possible to provide personalized and effective feedback and recovery plans.

[0354] "Data collection means" refers to a device or technology that collects information related to goals and progress from users within a system and organizes and stores it for subsequent processing and analysis.

[0355] A "learning tool" is a device or program that uses artificial intelligence technology to generate a learning model based on collected data.

[0356] "Evaluation means" refers to a device or technology that receives target information entered via a user terminal and has the function of judging and evaluating the progress and degree of achievement toward that target.

[0357] A "progress management tool" is a device or software that has the function of tracking the user's progress toward their goals in real time and providing feedback based on the evaluation results.

[0358] "Emotional analysis means" refers to a technology or program that has the function of detecting and analyzing a user's emotional state and adjusting and providing feedback based on the results.

[0359] A "recovery plan generation means" is a device or technology that proposes an appropriate recovery plan based on past data and artificial intelligence models when the achievement of a goal is delayed.

[0360] The system implementing this invention mainly consists of a server, a user terminal, and related software components. The server plays the role of collecting target data, generating a learning model using artificial intelligence, and providing feedback that takes into account the sentiment analysis results. This system utilizes existing APIs such as the Google Cloud Vision API as a means of sentiment analysis to detect emotions from the user's facial expressions and voice.

[0361] Users use a terminal to input their daily work goals into the system and report their progress. Data is collected in real time through the terminal's camera and microphone and sent to a server, where the user's emotional state is analyzed. Based on the results of the emotional analysis, the server uses an AI model to generate personalized feedback and sends it back to the terminal. This helps users progress towards achieving their goals in an efficient and mentally healthy way.

[0362] As a concrete example, consider its use in a security setting. Security guards use their smartphones to report on their work progress in order to achieve their goals. The system detects signs of tension and anxiety in the security guards by analyzing their facial expressions and provides feedback such as "do some stretching" or "take a break." This supports stress management during work and can improve the security guards' performance.

[0363] An example of a prompt message could be: "Analyze whether the security guard is expressing anxiety about their work objectives, and if anxiety is detected, generate feedback suggesting ways to relax." This instruction could then be given to the generating AI model.

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] The user enters goal information using a terminal. Through the terminal's interface, the user enters work-related goals and sends this data to the server. The entered goal information might be specific, such as "acquire 3 new customers each week." This data is received by the server's data collection system and prepared for processing within the system.

[0367] Step 2:

[0368] The device uses a camera and microphone to collect user emotion data. It records facial expressions with the camera and captures voice with the microphone, gathering information about facial expressions and voice tone. This collected data is sent to a server and used as raw material for emotion analysis.

[0369] Step 3:

[0370] The server analyzes the received emotion data. It utilizes the Google Cloud Vision API to analyze facial expression data and identify the emotions the user is expressing. Audio data is also analyzed using a voice tone analysis tool. This analysis process identifies emotions such as "increased tension" and passes the results to the next step.

[0371] Step 4:

[0372] The server uses a generative AI model to generate appropriate feedback based on the analysis results. Taking the analyzed emotional information as input, the AI ​​model follows prompts and generates situation-appropriate feedback messages. For example, the model might generate feedback such as, "Try relaxation techniques to reduce stress."

[0373] Step 5:

[0374] The server sends the generated feedback to the terminal. The terminal notifies and displays the feedback to the user. This allows the user to receive advice tailored to their current situation in real time. For example, a security guard could see the displayed message and practice the recommended relaxation technique.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] [Third Embodiment]

[0379] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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.

[0388] 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.

[0389] 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.

[0390] 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".

[0391] This invention provides an AI system for individuals to efficiently manage their goal achievement. The system mainly consists of a server, a user terminal, and an AI model.

[0392] The server collects historical goal data from the company's database and uses this to train an AI model. This model plays a central role in personalizing and supporting users' goal setting and progress. Specifically, the server processes the collected data, supplementing missing data and removing noise to create a reliable training dataset. After the model is trained, the server enables the generation of predictions and recovery plans for achieving new goals.

[0393] The user terminal functions as an interface with the user, allowing for the input of goal information and updates of progress. Users directly input their goals from the terminal screen, and this information is sent to the server. The server uses this information to perform evaluations and provide feedback using an AI model. Users can then use the feedback they receive to revise their goals and develop achievement strategies.

[0394] A distinctive feature of this system is that, if delays are observed in achieving goals, the server automatically generates a recovery plan and proposes it through the user's terminal. For example, if a user is having difficulty achieving their sales targets, the server will propose a new approach based on past success patterns. This process allows users to always take an active stance towards their goals.

[0395] As an example, a sales representative at a certain company sets a goal of "acquiring 15 new customers by the end of the quarter." The server provides the user terminal with successful patterns from similar goals achieved in the past and monitors the progress. If progress is not on schedule, the server proposes a recovery plan, such as "intensive sales activities in a specific region."

[0396] This invention is expected to enable individuals to more reliably achieve their personal goals and contribute to improved productivity across the entire organization.

[0397] The following describes the processing flow.

[0398] Step 1:

[0399] The server collects historical goal data from the company's database. This includes the details of individual goal settings, progress, and achievement levels. The server preprocesses this data and organizes it into a format applicable to AI models.

[0400] Step 2:

[0401] The server trains an AI model using pre-processed data. This model learns the conditions and success patterns necessary to achieve the goal, building a foundation for setting new goals and evaluating progress.

[0402] Step 3:

[0403] Users use a device to input their personal goals, including the goal content, deadline, and progress metrics. The device then sends this information to the server.

[0404] Step 4:

[0405] The server evaluates the received target information. Using an AI model, it compares it with past data to determine the validity and feasibility of the target, and generates feedback based on the results.

[0406] Step 5:

[0407] The terminal displays feedback received from the server to the user. This feedback includes expectations regarding the set goals and suggestions for improvement. The user then modifies and confirms the goals based on this feedback.

[0408] Step 6:

[0409] Users periodically update their progress via their devices. The devices send progress data to a server, which records the degree of achievement towards the goal.

[0410] Step 7:

[0411] The server analyzes progress data and evaluates whether progress toward the goal is on track. Based on the evaluation results, it provides feedback to the user.

[0412] Step 8:

[0413] If the server determines that the goal is not being achieved, it generates a recovery plan. Using an AI model, it analyzes the cause of the delay and creates a specific action plan for improvement.

[0414] Step 9:

[0415] The server sends the generated recovery plan to the terminal and prompts the user to execute it. The terminal visually displays the received recovery plan to the user and clearly explains the execution procedure.

[0416] (Example 1)

[0417] 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."

[0418] To effectively achieve goals set by individuals and organizations, it is necessary to track progress in real time and take swift action when delays occur. However, traditional management methods often rely on experience and intuition, making it difficult to obtain quantitative and objective feedback. This can lead to decreased efficiency in achieving goals and, consequently, a decline in productivity.

[0419] 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.

[0420] In this invention, the server includes data acquisition means that retrieve past target information from a data repository and perform data transformation; learning means that form a learning algorithm using machine learning based on the acquired information; and analysis means that receive achievement targets input via user equipment and analyze those achievement targets. This enables real-time tracking of progress and provision of appropriate feedback.

[0421] "Data acquisition means" refers to the function of collecting past target information from a data repository and performing data transformation.

[0422] "Learning method" refers to the function that generates a learning algorithm using machine learning based on acquired information.

[0423] "Analysis means" refers to a function that receives achievement goals entered via user equipment and analyzes them.

[0424] "Progress management means" refers to a function that tracks the user's progress over time and provides information based on the analysis results.

[0425] "Plan generation method" refers to a function that presents alternative plans to the user when goal achievement is delayed.

[0426] "Machine learning" refers to the technology that uses data to enable algorithms to automatically learn and perform predictions and classifications.

[0427] An "alternative plan" refers to an alternative action plan or strategy presented to the user when the goal is not being achieved.

[0428] This invention relates to an automated system for effectively managing and achieving individual and organizational goals. The system mainly consists of a server, user terminals, and a generative AI model.

[0429] The server uses basic computing resources to retrieve historical target information from a data repository. It runs on a common cloud platform and uses database access software to collect historical data. Data cleansing tools are then used to denoise and impute missing data in the collected data, creating a reliable dataset for machine learning.

[0430] Next, the server uses machine learning libraries such as TensorFlow and PyTorch to form a learning algorithm based on the collected data. This creates a generative AI model, enabling predictions and analyses to help users achieve their goals.

[0431] The user terminal receives goal information from the user via a dedicated application. The terminal operates on common mobile devices and personal computers, and users can input their goals through an intuitive interface. This information is sent to a server, which uses a generative AI model to track and analyze the progress of the goals in real time.

[0432] If the user's set goals are behind schedule, the server uses a generative AI model to analyze past success patterns and propose alternative solutions. These alternatives are then communicated to the user via their device. For example, an alternative suggestion might be to "strengthen approaches to new markets."

[0433] An example of a prompt for a generative AI model is, "Provide alternative plans to improve sales performance." This prompt functions as input instructions for the generative AI model to make specific suggestions.

[0434] This system enables individuals and organizations to efficiently manage and achieve their goals, which is expected to lead to increased overall productivity.

[0435] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0436] Step 1:

[0437] The server retrieves historical target information from a data repository. Input data is obtained through raw database queries and may contain noise and missing values. The server uses data cleansing tools to remove noise and impute missing data. Specifically, it applies filtering algorithms to maintain data integrity and obtains a clean dataset as output.

[0438] Step 2:

[0439] The server uses a clean dataset and leverages machine learning libraries (e.g., TensorFlow) to train a generative AI model. The input is a formatted dataset, and the model learns patterns between data points. Throughout this process, the model's hyperparameters are adjusted, and the optimal model is output after several iterations.

[0440] Step 3:

[0441] The user inputs goal information through their user terminal. This input includes goals to be achieved and deadlines. The user terminal sends this data to the server, where it is processed by the server's analysis tools. This processing outputs basic analysis results regarding the input goals.

[0442] Step 4:

[0443] The server utilizes an AI model generated using analytical tools to track the user's progress toward their goals in real time. Input includes goal information received from the user and real-time activity data, and progress is evaluated through the model. Output includes the progress evaluation results and feedback based on those results.

[0444] Step 5:

[0445] If there is a delay in achieving the goal, the server generates an alternative plan based on predictive data from the generated AI model. The input is past success patterns and current progress data, and the generated alternative plan includes a specific action plan to achieve the goal. As output, the generated alternative plan is sent to the user's terminal and presented to the user.

[0446] Step 6:

[0447] The user reviews the alternatives received on their device and selects an action they can take. The user device sends the selected action back to the server as feedback, and this feedback is used to improve future suggestions. This process allows users to dynamically adapt their strategies for achieving their goals.

[0448] (Application Example 1)

[0449] 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."

[0450] In modern manufacturing, efficient production management is crucial for improving an organization's competitiveness. However, conventional systems are inadequate in managing the progress of production targets and responding to delays. Furthermore, they cannot provide effective recovery plans based on the characteristics of each manufacturing facility, and further advancements are needed to achieve overall improvements in production efficiency.

[0451] 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.

[0452] In this invention, the server includes information gathering means for collecting and processing target data, learning means for generating a learning model using machine learning based on the collected information, evaluation means for receiving target information input via a user terminal and evaluating said target information, and target adjustment means for managing production targets of manufacturing equipment and presenting a recovery plan to optimize the production process when delays in progress are expected. This enables the achievement management of production targets at each manufacturing facility and a quick and effective response to delays.

[0453] "Information gathering means" refers to a device or system that has the necessary functions to collect target data and process that data.

[0454] "Machine learning" is an artificial intelligence technology that generates learning models based on collected data and uses them to identify various patterns and make predictions.

[0455] A "learning tool" is a device or system that has the function of generating a learning model based on collected information.

[0456] "Evaluation means" refers to a device or system that has the function of receiving target information entered via a user terminal and evaluating the target based on that information.

[0457] A "target adjustment mechanism" is a device or system that has the function of managing the production targets set for manufacturing equipment and presenting an optimal recovery plan if progress is delayed.

[0458] A "recovery plan" is a plan devised to optimize the production process and make up for lost time when the achievement of goals is delayed.

[0459] This system consists of information gathering means, learning means, evaluation means, and goal adjustment means. First, the server uses the information gathering means to organize the large amount of goal data collected from each manufacturing facility and uses this to generate a learning model for machine learning. The learning model can sequentially learn new data by utilizing a generative AI model. Based on this model, the server evaluates the goal information entered by the user through the terminal in real time.

[0460] The evaluation system allows the server to monitor the user's progress and provide appropriate feedback based on the results. In particular, if progress toward the goal is behind schedule, the server uses the goal adjustment system to generate an appropriate recovery plan and provide guidance for optimizing the manufacturing process. This enables the user to take proactive steps toward production goals.

[0461] For example, if a manufacturing facility is set to produce 100 units of a specific product per hour, the system will track progress in real time and provide recovery plans such as "increasing speed" or "adding shifts" if delays occur. Through real-time production management, both product quality and quantity can be optimized.

[0462] A concrete example of a prompt for a generating AI model is, "Please suggest the optimal method for producing 100 parts per hour in a factory." Based on this prompt, the server can generate and provide an efficient production method.

[0463] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0464] Step 1:

[0465] The server utilizes information gathering tools to collect target data from each manufacturing facility. Inputs are raw data from sensors and production management systems, while outputs are structured data that has been organized and stored in a database. Specific operations at this stage include data format conversion and processing of missing data.

[0466] Step 2:

[0467] The server uses learning tools to generate a machine learning model based on the collected data. The input is the structured data obtained in step 1, and the output is the trained model generated by the AI ​​model. Data processing includes feature extraction and preprocessing, and calculations involve tuning the model parameters.

[0468] Step 3:

[0469] The user inputs current goal information from their device, and the server receives this information via an evaluation system to assess progress. The input is specific goal setting data provided by the user, and the output is the evaluation result regarding the degree of goal achievement. Specifically, data validation and consistency checks are performed on the user input form.

[0470] Step 4:

[0471] The server manages progress and monitors the progress in real time. The input is the degree of goal achievement evaluated in step 3, and the output is feedback information. The feedback includes visualization of goal progress and provision of achievement metrics.

[0472] Step 5:

[0473] If goal achievement is delayed, the server utilizes goal adjustment mechanisms to generate and present an appropriate recovery plan to the user. The input is information about the type and extent of the delay, and the output is the proposed recovery plan. Specific actions include referencing data from past success patterns and applying a generative AI model using prompt statements.

[0474] 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.

[0475] This invention provides more adaptive and effective support to an AI system that assists individuals in achieving their goals by incorporating an emotion engine that analyzes the user's emotions. The system mainly consists of a server, a user terminal, an AI model, and an emotion recognition engine.

[0476] The server first collects historical goal data from the company's database and uses this data to train an AI model. This model processes information about the user's goal setting and progress evaluation, and generates feedback. The server also uses the collected data to train an emotion engine. This allows it to more accurately recognize the user's emotional state and provide appropriate responses.

[0477] The user terminal serves as the interface with the user, handling goal information input, progress updates, and data collection for emotion recognition. The user inputs their goals through the terminal and sends them to the server. The terminal is equipped with a camera and microphone, which are used to provide the user's facial expressions and voice data to the emotion engine.

[0478] The emotion engine analyzes facial expression and voice data acquired from the device to identify the user's emotional state. The server takes this emotional data into consideration and uses an AI model to appropriately adjust the feedback provided to the user. For example, if the user is feeling anxious about achieving their goal, the server generates encouraging and supportive messages and presents them to the user through the device.

[0479] As a concrete example, suppose a sales representative sets a goal of acquiring 20 new customers by the end of the month. When the user reports their progress toward the goal, if the emotion recognition function detects their anxiety or impatience, the server will provide a specific and actionable recovery plan, such as "revise your plan and we recommend adding two more approaches next week." Furthermore, if positive feedback is received regarding this recovery plan, it will contribute to increased motivation during its implementation.

[0480] This invention is expected to contribute to improving the overall productivity of the organization by providing more flexible support for individual goal achievement and offering feedback that takes into account the user's mental health.

[0481] The following describes the processing flow.

[0482] Step 1:

[0483] Users enter their personal goals using a device. This includes goal details, deadlines, and progress evaluation criteria. The device then sends this goal information to the server.

[0484] Step 2:

[0485] Based on the received target information, the server uses an AI model to evaluate the validity of the target. In this process, the server refers to past data and analyzes how similar targets were achieved. The results are sent back from the server to the terminal, providing feedback to the user.

[0486] Step 3:

[0487] Users periodically submit progress reports via their devices. These devices send progress status, problems, and performance data to the server. This ensures the server always has the latest progress information.

[0488] Step 4:

[0489] The device's camera and microphone transmit the user's facial expressions and voice to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state.

[0490] Step 5:

[0491] The server integrates and evaluates progress data and emotional state. An AI model generates feedback to alleviate the situation if the user is experiencing difficulties or stress.

[0492] Step 6:

[0493] The server customizes feedback based on the user's emotional state. For example, if the user is feeling stressed, it will generate feedback that includes encouraging messages and relaxation suggestions.

[0494] Step 7:

[0495] If the server detects a delay in progress, AI is used to generate an appropriate recovery plan. This plan is customized to take into account emotional states and past performance data.

[0496] Step 8:

[0497] The device presents the user with feedback and recovery plans sent from the server. Based on this information, the user adjusts their actions to achieve their goals.

[0498] Step 9:

[0499] After users provide feedback and progress is reviewed, the server evaluates its impact and begins collecting new progress and sentiment data. This cycle continues until the goal is achieved.

[0500] (Example 2)

[0501] 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."

[0502] Conventional goal-achievement support systems typically evaluate only the user's progress and provide uniform feedback. However, because they do not consider the user's emotional state, it is difficult to provide appropriate support tailored to individual circumstances, resulting in a lack of flexibility in maintaining user motivation and achieving goals. Therefore, there is a need for a system that can capture changes in the user's emotions in real time and adjust feedback based on those changes to provide more effective support.

[0503] 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.

[0504] In this invention, the server includes data collection means for collecting and processing target data, learning means for generating a learning model using artificial intelligence based on the collected data, progress management and sentiment analysis means for tracking the user's progress and emotional state in real time and providing feedback based on evaluation results and sentiment data, and recovery plan generation means for proposing a recovery plan that takes the user's emotional state into consideration when goal achievement is delayed. This enables flexible and effective feedback and support tailored to the user's individual emotional state.

[0505] A "data collection method" is a mechanism that receives target information from the user and effectively collects various other information, including the user's facial expression data and voice data.

[0506] A "learning tool" is a mechanism that uses collected data to train artificial intelligence and form a learning model to generate more effective feedback.

[0507] An "evaluation tool" is a mechanism that analyzes goal information entered via a user terminal and evaluates the user's progress and status based on that information.

[0508] The "progress management and sentiment analysis means" is a mechanism that monitors the user's progress, analyzes sentiment data, and adjusts feedback based on the evaluation results and sentiment state.

[0509] A "recovery plan generation mechanism" is a system that, when goal achievement is delayed, proposes alternative plans or recovery measures to support achievement, taking into account the user's emotional state.

[0510] This invention is a system that supports users in achieving their goals, and its specific form is as follows: The system mainly consists of a server, a user terminal, a generative AI model, and an emotion recognition engine.

[0511] The server first collects historical goal data from the company's database. This data is used to train a generative AI model. The generative AI model can be built using machine learning frameworks such as Python or TensorFlow. The model has the ability to evaluate the user's set goals and progress and generate appropriate feedback. The server also trains an emotion recognition engine, which allows it to recognize the user's emotional state more accurately.

[0512] The user terminal acts as an interface, allowing for the input of goal information and updates of progress. Users input data via the terminal and send it to the server. This terminal is equipped with a camera and microphone, which are used to capture the user's facial expressions and voice data. This data is then provided to the server for analysis by an emotion recognition engine.

[0513] The emotion recognition engine receives and analyzes facial expression and voice data to identify the user's current emotional state. For example, if a user is feeling anxious about achieving their goal, the server uses that emotional data to generate more encouraging feedback for the user. This kind of feedback can boost the user's motivation and help them achieve their goal.

[0514] As a concrete example, consider a scenario where a sales representative sets a goal of acquiring 20 new customers by the end of the month. When the user reports their progress, if the emotion recognition function detects anxiety, the server provides a viable recovery plan, such as "revise your plan and recommend adding two more approaches next week." An example of this prompt might be, "Please enter your set goal and progress, and send your current emotional state data to the server." This allows the system to provide more effective support and flexible assistance in achieving goals.

[0515] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0516] Step 1:

[0517] The user enters goal information into their terminal. The terminal sends this information to the server as a data collection tool. The entered information includes the goal to be achieved and its deadline. This data is stored on the server for use in subsequent analysis and feedback generation.

[0518] Step 2:

[0519] The device uses a camera and microphone to collect user facial and audio data. This data is transmitted in real time to an emotion recognition engine. The engine analyzes the facial and audio samples obtained as input to identify the user's emotional state. The emotion recognition engine uses facial expression analysis algorithms and audio analysis technology to determine what emotions the user is experiencing and outputs the results to a server.

[0520] Step 3:

[0521] The server receives emotional data from the emotion recognition engine and previously collected goal information as input, and generates feedback using a generative AI model. The AI ​​model comprehensively analyzes past goal achievement data and current user information, and determines the optimal support content while taking into account the user's emotional state. This process outputs feedback on the user's progress and emotions.

[0522] Step 4:

[0523] The server sends the generated feedback to the user's terminal. The terminal displays the feedback on the user's screen or presents it through audio output or other means. For example, the user may receive specific advice such as, "Good progress. For the next step, try XX." This output serves as important guidance for the user when planning actions toward their goals.

[0524] Step 5:

[0525] Users adjust their actions based on feedback received through their devices. For example, they can take actions such as adding new tasks according to suggestions from the server. This allows the entire system to flexibly support and efficiently guide users toward achieving their goals.

[0526] (Application Example 2)

[0527] 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."

[0528] In systems that support individual goal achievement, a challenge is to provide effective support while considering mental health by offering feedback that takes user emotions into account. Furthermore, in situations where emotions such as stress and anxiety affect work efficiency, it is necessary to take appropriate measures without delay.

[0529] 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.

[0530] In this invention, the server includes data collection means for collecting and processing target data, learning means for generating a learning model using artificial intelligence based on the collected data, and emotion analysis means for analyzing the user's emotional state and generating emotion-conscious feedback. This makes it possible to provide personalized and effective feedback and recovery plans.

[0531] "Data collection means" refers to a device or technology that collects information related to goals and progress from users within a system and organizes and stores it for subsequent processing and analysis.

[0532] A "learning tool" is a device or program that uses artificial intelligence technology to generate a learning model based on collected data.

[0533] "Evaluation means" refers to a device or technology that receives target information entered via a user terminal and has the function of judging and evaluating the progress and degree of achievement toward that target.

[0534] A "progress management tool" is a device or software that has the function of tracking the user's progress toward their goals in real time and providing feedback based on the evaluation results.

[0535] "Emotional analysis means" refers to a technology or program that has the function of detecting and analyzing a user's emotional state and adjusting and providing feedback based on the results.

[0536] A "recovery plan generation means" is a device or technology that proposes an appropriate recovery plan based on past data and artificial intelligence models when the achievement of a goal is delayed.

[0537] The system implementing this invention mainly consists of a server, a user terminal, and related software components. The server plays the role of collecting target data, generating a learning model using artificial intelligence, and providing feedback that takes into account the sentiment analysis results. This system utilizes existing APIs such as the Google Cloud Vision API as a means of sentiment analysis to detect emotions from the user's facial expressions and voice.

[0538] Users use a terminal to input their daily work goals into the system and report their progress. Data is collected in real time through the terminal's camera and microphone and sent to a server, where the user's emotional state is analyzed. Based on the results of the emotional analysis, the server uses an AI model to generate personalized feedback and sends it back to the terminal. This helps users progress towards achieving their goals in an efficient and mentally healthy way.

[0539] As a concrete example, consider its use in a security setting. Security guards use their smartphones to report on their work progress in order to achieve their goals. The system detects signs of tension and anxiety in the security guards by analyzing their facial expressions and provides feedback such as "do some stretching" or "take a break." This supports stress management during work and can improve the security guards' performance.

[0540] An example of a prompt message could be: "Analyze whether the security guard is expressing anxiety about their work objectives, and if anxiety is detected, generate feedback suggesting ways to relax." This instruction could then be given to the generating AI model.

[0541] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0542] Step 1:

[0543] The user enters goal information using a terminal. Through the terminal's interface, the user enters work-related goals and sends this data to the server. The entered goal information might be specific, such as "acquire 3 new customers each week." This data is received by the server's data collection system and prepared for processing within the system.

[0544] Step 2:

[0545] The device uses a camera and microphone to collect user emotion data. It records facial expressions with the camera and captures voice with the microphone, gathering information about facial expressions and voice tone. This collected data is sent to a server and used as raw material for emotion analysis.

[0546] Step 3:

[0547] The server analyzes the received emotion data. It utilizes the Google Cloud Vision API to analyze facial expression data and identify the emotions the user is expressing. Audio data is also analyzed using a voice tone analysis tool. This analysis process identifies emotions such as "increased tension" and passes the results to the next step.

[0548] Step 4:

[0549] The server uses a generative AI model to generate appropriate feedback based on the analysis results. Taking the analyzed emotional information as input, the AI ​​model follows prompts and generates situation-appropriate feedback messages. For example, the model might generate feedback such as, "Try relaxation techniques to reduce stress."

[0550] Step 5:

[0551] The server sends the generated feedback to the terminal. The terminal notifies and displays the feedback to the user. This allows the user to receive advice tailored to their current situation in real time. For example, a security guard could see the displayed message and practice the recommended relaxation technique.

[0552] 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.

[0553] 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.

[0554] 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.

[0555] [Fourth Embodiment]

[0556] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0557] 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.

[0558] 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).

[0559] 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.

[0560] 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.

[0561] 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).

[0562] 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.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] 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".

[0569] This invention provides an AI system for individuals to efficiently manage their goal achievement. The system mainly consists of a server, a user terminal, and an AI model.

[0570] The server collects historical goal data from the company's database and uses this to train an AI model. This model plays a central role in personalizing and supporting users' goal setting and progress. Specifically, the server processes the collected data, supplementing missing data and removing noise to create a reliable training dataset. After the model is trained, the server enables the generation of predictions and recovery plans for achieving new goals.

[0571] The user terminal functions as an interface with the user, allowing for the input of goal information and updates of progress. Users directly input their goals from the terminal screen, and this information is sent to the server. The server uses this information to perform evaluations and provide feedback using an AI model. Users can then use the feedback they receive to revise their goals and develop achievement strategies.

[0572] A distinctive feature of this system is that, if delays are observed in achieving goals, the server automatically generates a recovery plan and proposes it through the user's terminal. For example, if a user is having difficulty achieving their sales targets, the server will propose a new approach based on past success patterns. This process allows users to always take an active stance towards their goals.

[0573] As an example, a sales representative at a certain company sets a goal of "acquiring 15 new customers by the end of the quarter." The server provides the user terminal with successful patterns from similar goals achieved in the past and monitors the progress. If progress is not on schedule, the server proposes a recovery plan, such as "intensive sales activities in a specific region."

[0574] This invention is expected to enable individuals to more reliably achieve their personal goals and contribute to improved productivity across the entire organization.

[0575] The following describes the processing flow.

[0576] Step 1:

[0577] The server collects historical goal data from the company's database. This includes the details of individual goal settings, progress, and achievement levels. The server preprocesses this data and organizes it into a format applicable to AI models.

[0578] Step 2:

[0579] The server trains an AI model using pre-processed data. This model learns the conditions and success patterns necessary to achieve the goal, building a foundation for setting new goals and evaluating progress.

[0580] Step 3:

[0581] Users use a device to input their personal goals, including the goal content, deadline, and progress metrics. The device then sends this information to the server.

[0582] Step 4:

[0583] The server evaluates the received target information. Using an AI model, it compares it with past data to determine the validity and feasibility of the target, and generates feedback based on the results.

[0584] Step 5:

[0585] The terminal displays feedback received from the server to the user. This feedback includes expectations regarding the set goals and suggestions for improvement. The user then modifies and confirms the goals based on this feedback.

[0586] Step 6:

[0587] Users periodically update their progress via their devices. The devices send progress data to a server, which records the degree of achievement towards the goal.

[0588] Step 7:

[0589] The server analyzes progress data and evaluates whether progress toward the goal is on track. Based on the evaluation results, it provides feedback to the user.

[0590] Step 8:

[0591] If the server determines that the goal is not being achieved, it generates a recovery plan. Using an AI model, it analyzes the cause of the delay and creates a specific action plan for improvement.

[0592] Step 9:

[0593] The server sends the generated recovery plan to the terminal and prompts the user to execute it. The terminal visually displays the received recovery plan to the user and clearly explains the execution procedure.

[0594] (Example 1)

[0595] 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".

[0596] To effectively achieve goals set by individuals and organizations, it is necessary to track progress in real time and take swift action when delays occur. However, traditional management methods often rely on experience and intuition, making it difficult to obtain quantitative and objective feedback. This can lead to decreased efficiency in achieving goals and, consequently, a decline in productivity.

[0597] 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.

[0598] In this invention, the server includes data acquisition means that retrieve past target information from a data repository and perform data transformation; learning means that form a learning algorithm using machine learning based on the acquired information; and analysis means that receive achievement targets input via user equipment and analyze those achievement targets. This enables real-time tracking of progress and provision of appropriate feedback.

[0599] "Data acquisition means" refers to the function of collecting past target information from a data repository and performing data transformation.

[0600] "Learning method" refers to the function that generates a learning algorithm using machine learning based on acquired information.

[0601] "Analysis means" refers to a function that receives achievement goals entered via user equipment and analyzes them.

[0602] "Progress management means" refers to a function that tracks the user's progress over time and provides information based on the analysis results.

[0603] "Plan generation method" refers to a function that presents alternative plans to the user when goal achievement is delayed.

[0604] "Machine learning" refers to the technology that uses data to enable algorithms to automatically learn and perform predictions and classifications.

[0605] An "alternative plan" refers to an alternative action plan or strategy presented to the user when the goal is not being achieved.

[0606] This invention relates to an automated system for effectively managing and achieving individual and organizational goals. The system mainly consists of a server, user terminals, and a generative AI model.

[0607] The server uses basic computing resources to retrieve historical target information from a data repository. It runs on a common cloud platform and uses database access software to collect historical data. Data cleansing tools are then used to denoise and impute missing data in the collected data, creating a reliable dataset for machine learning.

[0608] Next, the server uses machine learning libraries such as TensorFlow and PyTorch to form a learning algorithm based on the collected data. This creates a generative AI model, enabling predictions and analyses to help users achieve their goals.

[0609] The user terminal receives goal information from the user via a dedicated application. The terminal operates on common mobile devices and personal computers, and users can input their goals through an intuitive interface. This information is sent to a server, which uses a generative AI model to track and analyze the progress of the goals in real time.

[0610] If the user's set goals are behind schedule, the server uses a generative AI model to analyze past success patterns and propose alternative solutions. These alternatives are then communicated to the user via their device. For example, an alternative suggestion might be to "strengthen approaches to new markets."

[0611] An example of a prompt for a generative AI model is, "Provide alternative plans to improve sales performance." This prompt functions as input instructions for the generative AI model to make specific suggestions.

[0612] This system enables individuals and organizations to efficiently manage and achieve their goals, which is expected to lead to increased overall productivity.

[0613] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0614] Step 1:

[0615] The server retrieves historical target information from a data repository. Input data is obtained through raw database queries and may contain noise and missing values. The server uses data cleansing tools to remove noise and impute missing data. Specifically, it applies filtering algorithms to maintain data integrity and obtains a clean dataset as output.

[0616] Step 2:

[0617] The server uses a clean dataset and leverages machine learning libraries (e.g., TensorFlow) to train a generative AI model. The input is a formatted dataset, and the model learns patterns between data points. Throughout this process, the model's hyperparameters are adjusted, and the optimal model is output after several iterations.

[0618] Step 3:

[0619] The user inputs goal information through their user terminal. This input includes goals to be achieved and deadlines. The user terminal sends this data to the server, where it is processed by the server's analysis tools. This processing outputs basic analysis results regarding the input goals.

[0620] Step 4:

[0621] The server utilizes an AI model generated using analytical tools to track the user's progress toward their goals in real time. Input includes goal information received from the user and real-time activity data, and progress is evaluated through the model. Output includes the progress evaluation results and feedback based on those results.

[0622] Step 5:

[0623] If there is a delay in achieving the goal, the server generates an alternative plan based on predictive data from the generated AI model. The input is past success patterns and current progress data, and the generated alternative plan includes a specific action plan to achieve the goal. As output, the generated alternative plan is sent to the user's terminal and presented to the user.

[0624] Step 6:

[0625] The user reviews the alternatives received on their device and selects an action they can take. The user device sends the selected action back to the server as feedback, and this feedback is used to improve future suggestions. This process allows users to dynamically adapt their strategies for achieving their goals.

[0626] (Application Example 1)

[0627] 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".

[0628] In modern manufacturing, efficient production management is crucial for improving an organization's competitiveness. However, conventional systems are inadequate in managing the progress of production targets and responding to delays. Furthermore, they cannot provide effective recovery plans based on the characteristics of each manufacturing facility, and further advancements are needed to achieve overall improvements in production efficiency.

[0629] 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.

[0630] In this invention, the server includes information gathering means for collecting and processing target data, learning means for generating a learning model using machine learning based on the collected information, evaluation means for receiving target information input via a user terminal and evaluating said target information, and target adjustment means for managing production targets of manufacturing equipment and presenting a recovery plan to optimize the production process when delays in progress are expected. This enables the achievement management of production targets at each manufacturing facility and a quick and effective response to delays.

[0631] "Information gathering means" refers to a device or system that has the necessary functions to collect target data and process that data.

[0632] "Machine learning" is an artificial intelligence technology that generates learning models based on collected data and uses them to identify various patterns and make predictions.

[0633] A "learning tool" is a device or system that has the function of generating a learning model based on collected information.

[0634] "Evaluation means" refers to a device or system that has the function of receiving target information entered via a user terminal and evaluating the target based on that information.

[0635] A "target adjustment mechanism" is a device or system that has the function of managing the production targets set for manufacturing equipment and presenting an optimal recovery plan if progress is delayed.

[0636] A "recovery plan" is a plan devised to optimize the production process and make up for lost time when the achievement of goals is delayed.

[0637] This system consists of information gathering means, learning means, evaluation means, and goal adjustment means. First, the server uses the information gathering means to organize the large amount of goal data collected from each manufacturing facility and uses this to generate a learning model for machine learning. The learning model can sequentially learn new data by utilizing a generative AI model. Based on this model, the server evaluates the goal information entered by the user through the terminal in real time.

[0638] The evaluation system allows the server to monitor the user's progress and provide appropriate feedback based on the results. In particular, if progress toward the goal is behind schedule, the server uses the goal adjustment system to generate an appropriate recovery plan and provide guidance for optimizing the manufacturing process. This enables the user to take proactive steps toward production goals.

[0639] For example, if a manufacturing facility is set to produce 100 units of a specific product per hour, the system will track progress in real time and provide recovery plans such as "increasing speed" or "adding shifts" if delays occur. Through real-time production management, both product quality and quantity can be optimized.

[0640] A concrete example of a prompt for a generating AI model is, "Please suggest the optimal method for producing 100 parts per hour in a factory." Based on this prompt, the server can generate and provide an efficient production method.

[0641] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0642] Step 1:

[0643] The server utilizes information gathering tools to collect target data from each manufacturing facility. Inputs are raw data from sensors and production management systems, while outputs are structured data that has been organized and stored in a database. Specific operations at this stage include data format conversion and processing of missing data.

[0644] Step 2:

[0645] The server uses learning tools to generate a machine learning model based on the collected data. The input is the structured data obtained in step 1, and the output is the trained model generated by the AI ​​model. Data processing includes feature extraction and preprocessing, and calculations involve tuning the model parameters.

[0646] Step 3:

[0647] The user inputs current goal information from their device, and the server receives this information via an evaluation system to assess progress. The input is specific goal setting data provided by the user, and the output is the evaluation result regarding the degree of goal achievement. Specifically, data validation and consistency checks are performed on the user input form.

[0648] Step 4:

[0649] The server manages progress and monitors the progress in real time. The input is the degree of goal achievement evaluated in step 3, and the output is feedback information. The feedback includes visualization of goal progress and provision of achievement metrics.

[0650] Step 5:

[0651] If goal achievement is delayed, the server utilizes goal adjustment mechanisms to generate and present an appropriate recovery plan to the user. The input is information about the type and extent of the delay, and the output is the proposed recovery plan. Specific actions include referencing data from past success patterns and applying a generative AI model using prompt statements.

[0652] 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.

[0653] This invention provides more adaptive and effective support to an AI system that assists individuals in achieving their goals by incorporating an emotion engine that analyzes the user's emotions. The system mainly consists of a server, a user terminal, an AI model, and an emotion recognition engine.

[0654] The server first collects historical goal data from the company's database and uses this data to train an AI model. This model processes information about the user's goal setting and progress evaluation, and generates feedback. The server also uses the collected data to train an emotion engine. This allows it to more accurately recognize the user's emotional state and provide appropriate responses.

[0655] The user terminal serves as the interface with the user, handling goal information input, progress updates, and data collection for emotion recognition. The user inputs their goals through the terminal and sends them to the server. The terminal is equipped with a camera and microphone, which are used to provide the user's facial expressions and voice data to the emotion engine.

[0656] The emotion engine analyzes facial expression and voice data acquired from the device to identify the user's emotional state. The server takes this emotional data into consideration and uses an AI model to appropriately adjust the feedback provided to the user. For example, if the user is feeling anxious about achieving their goal, the server generates encouraging and supportive messages and presents them to the user through the device.

[0657] As a concrete example, suppose a sales representative sets a goal of acquiring 20 new customers by the end of the month. When the user reports their progress toward the goal, if the emotion recognition function detects their anxiety or impatience, the server will provide a specific and actionable recovery plan, such as "revise your plan and we recommend adding two more approaches next week." Furthermore, if positive feedback is received regarding this recovery plan, it will contribute to increased motivation during its implementation.

[0658] This invention is expected to contribute to improving the overall productivity of the organization by providing more flexible support for individual goal achievement and offering feedback that takes into account the user's mental health.

[0659] The following describes the processing flow.

[0660] Step 1:

[0661] Users enter their personal goals using a device. This includes goal details, deadlines, and progress evaluation criteria. The device then sends this goal information to the server.

[0662] Step 2:

[0663] Based on the received target information, the server uses an AI model to evaluate the validity of the target. In this process, the server refers to past data and analyzes how similar targets were achieved. The results are sent back from the server to the terminal, providing feedback to the user.

[0664] Step 3:

[0665] Users periodically submit progress reports via their devices. These devices send progress status, problems, and performance data to the server. This ensures the server always has the latest progress information.

[0666] Step 4:

[0667] The device's camera and microphone transmit the user's facial expressions and voice to the emotion engine. The emotion engine analyzes this data to identify the user's emotional state.

[0668] Step 5:

[0669] The server integrates and evaluates progress data and emotional state. An AI model generates feedback to alleviate the situation if the user is experiencing difficulties or stress.

[0670] Step 6:

[0671] The server customizes feedback based on the user's emotional state. For example, if the user is feeling stressed, it will generate feedback that includes encouraging messages and relaxation suggestions.

[0672] Step 7:

[0673] If the server detects a delay in progress, AI is used to generate an appropriate recovery plan. This plan is customized to take into account emotional states and past performance data.

[0674] Step 8:

[0675] The device presents the user with feedback and recovery plans sent from the server. Based on this information, the user adjusts their actions to achieve their goals.

[0676] Step 9:

[0677] After users provide feedback and progress is reviewed, the server evaluates its impact and begins collecting new progress and sentiment data. This cycle continues until the goal is achieved.

[0678] (Example 2)

[0679] 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".

[0680] Conventional goal-achievement support systems typically evaluate only the user's progress and provide uniform feedback. However, because they do not consider the user's emotional state, it is difficult to provide appropriate support tailored to individual circumstances, resulting in a lack of flexibility in maintaining user motivation and achieving goals. Therefore, there is a need for a system that can capture changes in the user's emotions in real time and adjust feedback based on those changes to provide more effective support.

[0681] 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.

[0682] In this invention, the server includes data collection means for collecting and processing target data, learning means for generating a learning model using artificial intelligence based on the collected data, progress management and sentiment analysis means for tracking the user's progress and emotional state in real time and providing feedback based on evaluation results and sentiment data, and recovery plan generation means for proposing a recovery plan that takes the user's emotional state into consideration when goal achievement is delayed. This enables flexible and effective feedback and support tailored to the user's individual emotional state.

[0683] A "data collection method" is a mechanism that receives target information from the user and effectively collects various other information, including the user's facial expression data and voice data.

[0684] A "learning tool" is a mechanism that uses collected data to train artificial intelligence and form a learning model to generate more effective feedback.

[0685] An "evaluation tool" is a mechanism that analyzes goal information entered via a user terminal and evaluates the user's progress and status based on that information.

[0686] The "progress management and sentiment analysis means" is a mechanism that monitors the user's progress, analyzes sentiment data, and adjusts feedback based on the evaluation results and sentiment state.

[0687] A "recovery plan generation mechanism" is a system that, when goal achievement is delayed, proposes alternative plans or recovery measures to support achievement, taking into account the user's emotional state.

[0688] This invention is a system that supports users in achieving their goals, and its specific form is as follows: The system mainly consists of a server, a user terminal, a generative AI model, and an emotion recognition engine.

[0689] The server first collects historical goal data from the company's database. This data is used to train a generative AI model. The generative AI model can be built using machine learning frameworks such as Python or TensorFlow. The model has the ability to evaluate the user's set goals and progress and generate appropriate feedback. The server also trains an emotion recognition engine, which allows it to recognize the user's emotional state more accurately.

[0690] The user terminal acts as an interface, allowing for the input of goal information and updates of progress. Users input data via the terminal and send it to the server. This terminal is equipped with a camera and microphone, which are used to capture the user's facial expressions and voice data. This data is then provided to the server for analysis by an emotion recognition engine.

[0691] The emotion recognition engine receives and analyzes facial expression and voice data to identify the user's current emotional state. For example, if a user is feeling anxious about achieving their goal, the server uses that emotional data to generate more encouraging feedback for the user. This kind of feedback can boost the user's motivation and help them achieve their goal.

[0692] As a concrete example, consider a scenario where a sales representative sets a goal of acquiring 20 new customers by the end of the month. When the user reports their progress, if the emotion recognition function detects anxiety, the server provides a viable recovery plan, such as "revise your plan and recommend adding two more approaches next week." An example of this prompt might be, "Please enter your set goal and progress, and send your current emotional state data to the server." This allows the system to provide more effective support and flexible assistance in achieving goals.

[0693] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0694] Step 1:

[0695] The user enters goal information into their terminal. The terminal sends this information to the server as a data collection tool. The entered information includes the goal to be achieved and its deadline. This data is stored on the server for use in subsequent analysis and feedback generation.

[0696] Step 2:

[0697] The device uses a camera and microphone to collect user facial and audio data. This data is transmitted in real time to an emotion recognition engine. The engine analyzes the facial and audio samples obtained as input to identify the user's emotional state. The emotion recognition engine uses facial expression analysis algorithms and audio analysis technology to determine what emotions the user is experiencing and outputs the results to a server.

[0698] Step 3:

[0699] The server receives emotional data from the emotion recognition engine and previously collected goal information as input, and generates feedback using a generative AI model. The AI ​​model comprehensively analyzes past goal achievement data and current user information, and determines the optimal support content while taking into account the user's emotional state. This process outputs feedback on the user's progress and emotions.

[0700] Step 4:

[0701] The server sends the generated feedback to the user's terminal. The terminal displays the feedback on the user's screen or presents it through audio output or other means. For example, the user may receive specific advice such as, "Good progress. For the next step, try XX." This output serves as important guidance for the user when planning actions toward their goals.

[0702] Step 5:

[0703] Users adjust their actions based on feedback received through their devices. For example, they can take actions such as adding new tasks according to suggestions from the server. This allows the entire system to flexibly support and efficiently guide users toward achieving their goals.

[0704] (Application Example 2)

[0705] 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".

[0706] In systems that support individual goal achievement, a challenge is to provide effective support while considering mental health by offering feedback that takes user emotions into account. Furthermore, in situations where emotions such as stress and anxiety affect work efficiency, it is necessary to take appropriate measures without delay.

[0707] 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.

[0708] In this invention, the server includes data collection means for collecting and processing target data, learning means for generating a learning model using artificial intelligence based on the collected data, and emotion analysis means for analyzing the user's emotional state and generating emotion-conscious feedback. This makes it possible to provide personalized and effective feedback and recovery plans.

[0709] "Data collection means" refers to a device or technology that collects information related to goals and progress from users within a system and organizes and stores it for subsequent processing and analysis.

[0710] A "learning tool" is a device or program that uses artificial intelligence technology to generate a learning model based on collected data.

[0711] "Evaluation means" refers to a device or technology that receives target information entered via a user terminal and has the function of judging and evaluating the progress and degree of achievement toward that target.

[0712] A "progress management tool" is a device or software that has the function of tracking the user's progress toward their goals in real time and providing feedback based on the evaluation results.

[0713] "Emotional analysis means" refers to a technology or program that has the function of detecting and analyzing a user's emotional state and adjusting and providing feedback based on the results.

[0714] A "recovery plan generation means" is a device or technology that proposes an appropriate recovery plan based on past data and artificial intelligence models when the achievement of a goal is delayed.

[0715] The system implementing this invention mainly consists of a server, a user terminal, and related software components. The server plays the role of collecting target data, generating a learning model using artificial intelligence, and providing feedback that takes into account the sentiment analysis results. This system utilizes existing APIs such as the Google Cloud Vision API as a means of sentiment analysis to detect emotions from the user's facial expressions and voice.

[0716] Users use a terminal to input their daily work goals into the system and report their progress. Data is collected in real time through the terminal's camera and microphone and sent to a server, where the user's emotional state is analyzed. Based on the results of the emotional analysis, the server uses an AI model to generate personalized feedback and sends it back to the terminal. This helps users progress towards achieving their goals in an efficient and mentally healthy way.

[0717] As a concrete example, consider its use in a security setting. Security guards use their smartphones to report on their work progress in order to achieve their goals. The system detects signs of tension and anxiety in the security guards by analyzing their facial expressions and provides feedback such as "do some stretching" or "take a break." This supports stress management during work and can improve the security guards' performance.

[0718] An example of a prompt message could be: "Analyze whether the security guard is expressing anxiety about their work objectives, and if anxiety is detected, generate feedback suggesting ways to relax." This instruction could then be given to the generating AI model.

[0719] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0720] Step 1:

[0721] The user enters goal information using a terminal. Through the terminal's interface, the user enters work-related goals and sends this data to the server. The entered goal information might be specific, such as "acquire 3 new customers each week." This data is received by the server's data collection system and prepared for processing within the system.

[0722] Step 2:

[0723] The device uses a camera and microphone to collect user emotion data. It records facial expressions with the camera and captures voice with the microphone, gathering information about facial expressions and voice tone. This collected data is sent to a server and used as raw material for emotion analysis.

[0724] Step 3:

[0725] The server analyzes the received emotion data. It utilizes the Google Cloud Vision API to analyze facial expression data and identify the emotions the user is expressing. Audio data is also analyzed using a voice tone analysis tool. This analysis process identifies emotions such as "increased tension" and passes the results to the next step.

[0726] Step 4:

[0727] The server uses a generative AI model to generate appropriate feedback based on the analysis results. Taking the analyzed emotional information as input, the AI ​​model follows prompts and generates situation-appropriate feedback messages. For example, the model might generate feedback such as, "Try relaxation techniques to reduce stress."

[0728] Step 5:

[0729] The server sends the generated feedback to the terminal. The terminal notifies and displays the feedback to the user. This allows the user to receive advice tailored to their current situation in real time. For example, a security guard could see the displayed message and practice the recommended relaxation technique.

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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."

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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 as being incorporated by reference.

[0751] The following is further disclosed regarding the embodiments described above.

[0752] (Claim 1)

[0753] A data collection means for collecting and processing target data,

[0754] A learning method that generates a learning model using artificial intelligence based on collected data,

[0755] An evaluation means that receives target information entered via a user terminal and evaluates said target information,

[0756] A progress management system that tracks user progress in real time and provides feedback based on evaluation results,

[0757] A recovery plan generation method that proposes a recovery plan to the user when goal achievement is delayed,

[0758] A system that includes this.

[0759] (Claim 2)

[0760] The system according to claim 1, wherein the recovery plan generation means customizes the recovery plan based on past data and an artificial intelligence model.

[0761] (Claim 3)

[0762] The system according to claim 1, wherein the learning means provides quantified goals and personalized goal suggestions according to the user's characteristics.

[0763] "Example 1"

[0764] (Claim 1)

[0765] A data acquisition method that retrieves past target information from a data repository and performs data transformation,

[0766] A learning method that uses machine learning to form a learning algorithm based on acquired information,

[0767] An analysis means that receives achievement goals entered via user equipment and analyzes those achievement goals,

[0768] A progress management system that tracks the user's progress over time and provides information based on the analysis results,

[0769] A plan generation method that presents alternative solutions to the user when goal achievement is delayed,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, wherein the plan generation means personalizes alternatives based on past information and a machine learning model.

[0773] (Claim 3)

[0774] The system according to claim 1, wherein the learning means generates quantified goals and personalized goal setting proposals according to the user's characteristics.

[0775] "Application Example 1"

[0776] (Claim 1)

[0777] Information gathering means for collecting and processing target data,

[0778] A learning method that generates a learning model using machine learning based on collected information,

[0779] An evaluation means that receives target information entered via a user terminal and evaluates said target information,

[0780] A progress management system that tracks user progress in real time and provides feedback based on evaluation results,

[0781] A target adjustment mechanism that manages production targets for manufacturing equipment and presents a recovery plan to optimize the production process when delays in progress are anticipated,

[0782] A system that includes this.

[0783] (Claim 2)

[0784] The system according to claim 1, wherein the recovery plan generation means customizes a recovery plan based on past information and an artificial intelligence model and applies it to the manufacturing equipment.

[0785] (Claim 3)

[0786] The system according to claim 1, wherein the learning means provides quantified goals and personalized goal suggestions according to the characteristics of the user, and also provides measures to improve efficiency in the manufacturing process.

[0787] "Example 2 of combining an emotion engine"

[0788] (Claim 1)

[0789] A data collection means for collecting and processing target data,

[0790] A learning method that generates a learning model using artificial intelligence based on collected data,

[0791] An evaluation means that receives target information entered via a user terminal and evaluates said target information,

[0792] A progress management and sentiment analysis system that tracks the user's progress and emotional state in real time and provides feedback based on evaluation results and sentiment data,

[0793] A recovery plan generation method that proposes a recovery plan considering the user's emotional state when goal achievement is delayed,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, wherein the recovery plan generation means customizes a recovery plan according to the user's emotional state based on past data and an artificial intelligence model.

[0797] (Claim 3)

[0798] The system according to claim 1, wherein the learning means provides quantified goals and personalized goal suggestions according to the user's characteristics and emotions.

[0799] "Application example 2 of combining emotional engines"

[0800] (Claim 1)

[0801] A data collection means for collecting and processing target data,

[0802] A learning method that generates a learning model using artificial intelligence based on collected data,

[0803] An evaluation means that receives target information entered via a user terminal and evaluates said target information,

[0804] A progress management system that tracks user progress in real time and provides feedback based on evaluation results,

[0805] An emotion analysis means that analyzes the user's emotional state and generates feedback that takes emotions into account,

[0806] A recovery plan generation method that proposes a recovery plan to the user when goal achievement is delayed,

[0807] A system that includes this.

[0808] (Claim 2)

[0809] The system according to claim 1, wherein the recovery plan generation means customizes the recovery plan based on past data and an artificial intelligence model, and adjusts the recovery plan by combining it with the results of sentiment analysis.

[0810] (Claim 3)

[0811] The system according to claim 1, wherein the learning means provides quantified goals and personalized goal suggestions according to the user's characteristics, and further modifies the suggested content according to the user's emotional state. [Explanation of Symbols]

[0812] 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 data collection means for collecting and processing target data, A learning method that generates a learning model using artificial intelligence based on collected data, An evaluation means that receives target information entered via a user terminal and evaluates said target information, A progress management system that tracks user progress in real time and provides feedback based on evaluation results, A recovery plan generation method that proposes a recovery plan to the user when goal achievement is delayed, A system that includes this.

2. The system according to claim 1, wherein the recovery plan generation means customizes the recovery plan based on past data and an artificial intelligence model.

3. The system according to claim 1, wherein the learning means provides quantified goals and personalized goal suggestions according to the user's characteristics.

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